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Open Access Publications from the University of California


The annual meeting of the Cognitive Science Society is aimed at basic and applied cognitive science research. The conference hosts the latest theories and data from the world's best cognitive science researchers. Each year, in addition to submitted papers, researchers are invited to highlight some aspect of cognitive science.

1. Workshops/Tutorials

How does the mind discover useful abstractions?

Abstraction enables humans to distill a cascade of sensory experiences into a useful format for making sense of the world and generalizing to new contexts. In developing theories of how these various forms of abstraction are discovered and used, cognitive scientists have proposed a multitude of representational formats with different properties to capture behavioral data and neural activity. However, because abstraction manifests in human cognition and behavior in so many ways, individual communities within cognitive science have generally studied these specific forms of abstraction under domain-specific representational assumptions. The goal of this workshop is to facilitate the search for unifying principles governing how humans learn, discover, and use abstractions in different domains, by providing a venue for the exchange of theoretical and empirical insights between research communities.

Advancing Cognitive Science and AI with Cognitive-AI Benchmarking

What are the current limits of AI models in explaining human cognition and behavior? How might approaches from the cognitive sciences drive the development of more robust and reliable AI systems? The goal of this workshop is bring together researchers across cognitive science and artificial intelligence (AI) to engage with these questions and identify opportunities to work together to advance progress in both fields. In particular, we propose Cognitive-AI Benchmarking as a particularly promising strategy --- that is, the community-coordinated establishment of common benchmarks, tools, and best practices for model-human comparisons across diverse and ecologically relevant domains and tasks. We will host a combination of talks, panel discussion, and breakout activities to: highlight past successes in Cognitive-AI Benchmarking and limitations of current approaches, share tools and best practices, and outline future challenges and goals for the field.

Video games as a path to a contextualized cognitive science, or How to move beyond 20 questions with nature

In his famous address “You can’t play 20 questions with nature and win”, Newell (1973) predicted that experimental psychology’s focus on falsifying theories of individual phenomena (e.g., subitizing, directed forgetting, etc.) would not lead to a unifying theory subsuming several (let alone all) of the phenomena. Instead of seeking to detect effects, and binary searching our way to the explananda of these phenomena, Newell argued psychologists should build predictive theories of behavior by modeling 1) the structure of the task environment (i.e., context), 2) the subjects’ own goals, and 3) the invariant structure of the subjects’ processing mechanisms. Newell concludes that psychology will only make real progress in understanding the mind by 1) targeting tasks that are complex enough to cover the space of naturalistic behavior, 2) developing models that can competently perform the task, and 3) generalizing such models to perform multiple complex tasks. For 1), Newell chose to study chess; we believe he would find video games an even more compelling paradigm (see Gobet, 2017).

Large language models meet cognitive science: LLMs as tools, models, and participants

Large language models (LLMs) like GPT-3 are revolutionizing artificial intelligence, leading to breakthroughs in question answering, natural language understanding, and machine translation. Recent work in a variety of social science disciplines, including psychology, economics, and political science has demonstrated remarkable similarity between the behavior of LLMs and human decision makers. At the same time, AI researchers and engineers struggle to understand these systems, leading to practical challenges and ethical questions about fair and safe deployment. This workshop aims to bring together researchers to discuss work on using psychological methods to understand LLMs and LLMs as tools for understanding humans. Along these lines, we have invited leading researchers from cognitive science, psychology, and machine learning to present their work on topics that include: When and why do LLMs exhibit biased behavior? How do these compare to human biases? What sorts of psychological tasks do LLMs struggle with? Can we use psychological theory to structure this search? And how does the knowledge encoded in LLMs differ from human knowledge?

Putting interaction center-stage for the study of knowledge structures and processes

Humans are social animals. Human cognition evolved in a social context. Human cognition develops in a social context. Thus, both the internal mechanisms of cognition and the information we use are social. In this workshop, we aim to extend the boundaries of cognitive sciences beyond individual minds. Following the lead of Dingemanse et al. (2023), we put interaction in focus as a complementary starting point for the study of human cognition.

Towards Broader Adoption of Massive Online Experiments

Since the Workshop on Scaling Cognitive Science was held at CogSci 2020, the methodological landscape of the field has undergone a significant shift toward online experiments. The COVID-19 pandemic forced greater numbers of cognitive scientists to collect data over the internet and to explore online alternatives to methods which they had previously used only in the lab. For example, since this time, eye-tracking capabilities have been introduced to a number of software packages for online experiments (Ba ́nki, de Eccher, Falschlehner, Hoehl, & Markova, 2022; Slim & Hartsuiker, 2022; Yang & Krajbich, 2021).

2. Symposia

Space in Context: Communicative factors shape spatial language

Decades of research have revealed that spatial language is the result of a complex interplay between language-independent, conceptual factors and language-specific forces (e.g., Bowerman, 1996; Johnston & Slobin, 1979; Landau & Jackendoff, 1993; Levinson & Meira 2003; Levinson & Wilkins, 2006). However, currently, a growing body of research acknowledges the importance of a further, much less explored but highly important factor having to do with pragmatic pressures – the general communicative need to convey informative meanings with appropriate levels of required effort (Grice, 1975; Zipf, 1949). This symposium aims to present research documenting the effect of communicative/pragmatic pressures on how spatial language systems are organized, used by speakers of different languages and acquired during development. The contributed papers explore a variety of spatial language phenomena, across many different languages, using a variety of empirical methods and diverse populations of participants.

Embodied Cognition in Context

That cognition is embodied is a claim that virtually no cognitive scientist today will deny: after all, even the researcher who models cognition in terms of entirely abstract, “medium-independent” states and processes will concede that particular instances are always necessarily realized in some body (of some kind) or other. The same is true for the theme of this year’s CogSci meeting, “Cognition in Context”: even if you think that there are cases in which the context plays merely a peripheral role in cognitive processing, you cannot deny that cognition always occurs in some context or other. This symposium is motivated by the realization, on the one hand, that the concept of embodiment means different things to different researchers in different contexts (see, e.g., Wilson 2002; Wilson and Golonka 2013; Crippen and Schulkin 2020), just as, on the other hand, the concept of context means different things to different researchers with different views on body and mind (see, e.g., Clancey 1997; Mesquita, Barrett and Smith 2010; Ibáñez and García 2018).

Methodological issues in the cross-disciplinary study of numerical cognition

Advances in a wide variety of disciplines have begun to tackle the challenge of finding out what happens to which cognitive systems to allow us to develop numerical abilities, yielding mountains of data ripe with explanatory promise (Cohen Kadosh & Dowker 2015). The development of novel experimental methods have allowed ethologists and developmental psychologists to probe into the cognitive abilities and systems operating in preverbal infants and nonhuman animals, hinting at the boundary between our innate cognitive machinery and those formal abilities we develop by interacting with our cultural niche (Carey 2009). Uniting behavioral and brain data has produced strong support for the existence of cognitive systems recruited in the development of our formal numerical abilities (Feigenson, L., Dehaene, S., & Spelke, E. 2004).

Building a consensus about consensus: psychological, computational, and philosophical approaches

People often have to decide if a claim is true (for example, that climate change is caused by human activity) even if they do not have the necessary knowledge or experience. It is common for people to turn to others to determine if a claim is true. When multiple sources agree, it can be a strong indicator of who to trust and what to believe (Mercier & Morin, 2019). In cognitive science, there is debate about what makes a “consensus” and how it should influence our decisions. Some models suggest that agreement among independent sources is more reliable than among sources who have communicated with each other (Whalen, Griffiths, & Buchsbaum, 2018; Bovens & Hartmann, 2004; Dietrich & Spiekermann, 2013). Recent empirical and modelling work (Pilditch, Hahn, Fenton, & Lagnado, 2020), however, suggests that having sources that are connected can sometimes provide more reliable information than having sources that are independent. This symposium brings together researchers from a range of disciplines (cognitive modelling, social network modelling, cognitive psychology, philosophy) to examine what makes a consensus persuasive, and it should guide our judgments and inferences.

Spatial Reasoning: The Role of Context in Learning

Spatial reasoning is an inherent aspect of everyday life. For example, spatial reasoning supports young children playing with blocks, older children learning about positioning on a sports field, and adults navigating where they need to go. Spatial reasoning is also a critical aspect of STEM (science, technology, engineering, mathematics, e.g., Wai, Lubinski, & Benbow, 2009). For example, chemists imagine molecules rotating to compare structure, and physicists reason about the influence of invisible forces on magnetic fields.

Marks and Meanings: new perspectives on the evolution of human symbolic behavior

Understanding the unique evolutionary trajectory taken by humanity is impossible without an appreciation of our singular capacities for symbolic cognition and behaviour, which have evolved into the diverse practices of communication, art, reasoning, and ritual we encounter today (Deacon, 1998; Donald, 1991). But how did these capacities evolve during the Late Pleistocene?

Advances in the Study of Visual and Multisensory Objects

The symposium ‘Advances in the Study of Visual and Multisensory Objects’ provides convergent focus of research from a broad range of subject areas. Using both nuanced philosophical analysis and informed empirical work, the symposium offers an interdisciplinary look at different aspects of visual and multisensory objects. It integrates three methodological approaches in a well-balanced way: philosophy of perception, experimental psychology, and cognitive neuroscience. Presenting a crucial part of the forthcoming collection (A. Mroczko-Wąsowicz & R. Grush (Eds.) Sensory Individuals: Unimodal and Multimodal Perspectives. Oxford: OUP), the symposium focuses on specific and closely related questions.

Mushrooms as ‘food for thought’: Cognitive science perspectives on fungi

The predominance of English and English-speaking research over the topics and findings in cognitive science is increasingly recognized as adversely affecting the field. One of the blind spots owing to this bias is a sweeping disinterest in the fungi world: A simple search of keywords in APA PsycInfo (on Jan 24, 2023) combining cognit* with each of the three major kingdoms of life returned the following number of hits: 44,257 for animals, 1,888 for plants, and 111 for fungi. And yet, fungi are the reason why we are here today, why we thrive, and why some of us lose ourselves in a different world altogether comes fall. Fungi have been critical for life on earth, for cultural achievements from baking and brewing to antibiotics, and as a source of food throughout human history. At the same time, they pose unique challenges to key cognitive processes involved in categorization as when distinguishing highly similar and variable species, in predictive processing during foraging, in causal reasoning and risk appraisal for diagnosing whether a species is poisonous, or in the acquisition and storing of information by way of learning, memorization, and teaching ...

Cognition in Context? What Role should Behavioural and Cognitive Science play in Public Policy?

It is now commonplace to hear that addressing the grand challenges of today’s society – climate change, obesity, ageing populations, pandemics – requires substantial changes in individuals’ behaviour (e.g., Newell & Moss, 2021). This call to arms places cognitive and behavioral science at the forefront of understanding how such widespread change can be achieved. Answering that call has led many researchers to make bold claims for the potential of simple techniques that facilitate positive behaviour change without impinging on people’s freedom of choice (e.g., Thaler & Sunstein, 2008). These techniques, collectively and colloquially known as ‘nudges’ capitalize on promoting ‘desirable’ options by making changes to the choice architectures (physical, social and psychological) in which decisions are made.

Harnessing Linguistic Diversity for Theories of Language and Mind

A comprehensive theory of human language requires a data set that is representative of the typological diversity present in the world’s 7,000+ languages (Evans & Levinson, 2009). However, the treatment of linguistic diversity in cognitive science has been historically uneven. While many subfields of linguistics explicitly aim to describe and explain variation (e.g., linguistic typology, phonology), adjacent fields like psycholinguistics have suffered from an overreliance on English (Blasi et al., 2022; Christiansen et al., 2022a; Kidd & Garcia, 2022), mimicking cognitive science in general (Henrich et al., 2010). This large data skew prevents the field from fully explaining how humans acquire, represent, and process language, one of the core defining features of our species.

Relational Learning: Common Signatures Across Four Different Contexts

What makes humans smart? We think it has to do with our analogical ability—our ability to perceive common relational patterns between different objects, events, or ideas. It is a cornerstone of our higher-order reasoning ability and may have its origins in a relational processing mechanism that allows us to abstract relations using comparison. In this symposium, we present research on the nature of our relational learning ability. We show that findings from infants, preschoolers, elementary school students, and adults reveal common signatures across a wide variety of contexts. These studies provide evidence about how to improve learning of abstract relational concepts and provide insight about how to build better AI models.

Music Cognition between Theory and Experiment

Music is a fascinating and complex aspect of cognition. When listening to music, listeners may experience complex structural relations, many of which are characterized in great detail by music-theoretical accounts. Such cognitive experience of structure in music, however, presents many challenges for empirical investigation: structural interpretations are the result of an inference on the part of the listener, resulting in ambiguous interpretations that listeners cannot often report explicitly. As a comparison, language comprehenders are also sensitive to the syntactic structure of sentences, and psycholinguistic research has an extensive track-record in investigating what representations of syntactic structure look like, and how they are formed in real-time processing. Could we achieve a similar understanding for music too? The goal of the symposium is to reflect on the challenges and prospects of such an endeavour: What can we learn from behavioural and brain-imaging experiments about the cognitive reality of musical structures? How do empirical observations pertaining to prediction and processing complexity relate to theoretical frameworks of musical structure?

3. Papers with Oral Presentation

Predictive and Interpretable: Combining Artificial Neural Networks and Classic Cognitive Models to Understand Human Learning and Decision Making

Quantitative models of behavior are a fundamental tool in cognitive science. Typically, models are hand-crafted to implement specific cognitive mechanisms. Such "classic" models are interpretable by design, but may provide poor fit to experimental data. Artificial neural networks (ANNs), on the contrary, can fit arbitrary datasets at the cost of opaque mechanisms. Here, we adopt a hybrid approach, combining the predictive power of ANNs with the interpretability of classic models. We apply this approach to Reinforcement Learning (RL), beginning with classic RL models and replacing their components one-by-one with ANNs. We find that hybrid models can provide similar fit to fully-general ANNs, while retaining the interpretability of classic cognitive models: They reveal reward-based learning mechanisms in humans that are strikingly similar to classic RL. They also reveal mechanisms not contained in classic models, including separate reward-blind mechanisms, and the specific memory contents relevant to reward-based and reward-blind mechanisms.

Predicting consensus in legal document interpretation

We present a large-scale conceptual replication of an experiment that provided evidence of false consensus biases in legal interpretation: when reading a legal contract, individuals tend to over-estimate the extent to which others would agree with their interpretation of that contract (Solan, Rosenblatt, & Osherson 2008). Our results are consistent with this previous finding. We also observe substantial unexplained item-level variation in the extent to which individuals agree on contract interpretation, as well as unexplained variation in the extent to which the false consensus bias holds across different contexts. In a first step towards understanding the source(s) of this variability, we show that a state-of-the-art large language model (LLM) with zero-shot prompting does not robustly predict the degree to which interpreters will exhibit consensus in a given context. However, performance improves when the model is exposed to data of the form collected in our experiment, suggesting a path forward for modeling and predicting variability in the interpretation of legally-relevant natural language.

Psychological distance and children's innovative problem solving

Children find tool innovation difficult until they are around 8-years-old, despite being prolific tool users and imaginative. We drew on Construal Level Theory to test whether a spatial distance prime could improve children’s performance. In three experiments we found evidence that performance did improve when children were primed to think in an abstract way (Experiments 1 and 2). We examined an alternative explanation that the effect arose through positive mood induction, but found no evidence for this (Experiment 3). Overall, our findings inform us about how children’s innovation might be supported, and are one of few findings showing that psychological distance has impact on children’s thinking.

Mental Simulation in L2 Processing of English Prepositional Phrases

Embodied simulation hypothesis supposes that language processing involves the activation of perceptual-motor systems to recreate the described scene (Bergen, 2012, 2019). The paper investigates whether and how adolescent second language (L2) learners’ online processing of prepositions engages mental simulation. Specifically, the study examines whether any observed mental simulation effect was modulated by prepositions, abstractness of senses, and Stimulus Onset Asynchrony (SOA). 40 Chinese adolescents completed a diagram-picture matching task followed by a semantic priming task in English, where participants saw a diagrammatic prime and made phrasal acceptability judgement. Results showed a compatibility effect of schematic diagrams on adolescent L2 English learners’ accuracy rates (ARs) of processing prepositional phrases (PPs), while response times (RTs) results did not reveal mental simulation effects. The findings suggest that schematic diagrams could serve as effective perceptual cues to prime adolescent L2 learners’ processing of schema-compatible English PPs by improving judgement accuracy but not processing speed.

Inferring the truth from deception: What can people learn from helpful and unhelpful information providers?

Sampling assumptions — the assumptions people make about how an example of a category or concept has been chosen — help us learn from examples efficiently. One context where sampling assumptions are particularly important is in social contexts, where a learner needs to infer the knowledge and intentions of the information provider and vice-versa. The pedagogical sampling assumptions model describes a Bayesian account of how learners and providers should behave given different assumptions they have about the other (e.g., is the provider trying to deceive or help me? Does the learner trust me?). In this study, we tested how well this model could describe learning behaviour in the rectangle game, where a fictional information provider revealed clues about the structure of a rectangle that the learner (a participant) needed to guess. Participants received clues from either a helpful information provider, a provider who was randomly sampling clues, or one of two kinds of unhelpful providers (who could mislead but could not lie). We found that people learned efficiently and in line with model predictions when the provider was helpful and that this was the case even when no cover story was provided. However, although participants could identify that unhelpful providers were not being helpful, they struggled to learn the strategy those providers were using.

Linguistic meanings interpreted

A prominent strand of theorizing in linguistics models mean- ing in language by specifying an "interpretation function" which relates morphosyntactic objects (i.e., those representa- tions whose properties are uncovered by research in morphology and syntax) to elements of non-linguistic experience. Such theorizing has, for the most part, proceeded in relative isolation from developments in the other cognitive sciences. A recent body of experimental work growing out of this tradition has, however, pressed the question of precisely how linguistic representations relate to other faculties of mind. We present the beginnings of a two-step formal proposal for how to do this, specifying: (i) the co-domain of the linguistic interpretation function as the Language of Thought (LoT); (ii) what this mental language is like, (iii) which expression of this language is the semantic value of a sentence like 'Most of the dots are yellow', and (iv) how that LoT expression is interpreted by other cognitive faculties, in ways that produce the choices of verification procedure that have been empirically observed.

Not Everyone Has an Inner Voice: Behavioral Consequences of Anendophasia

It is commonly assumed that inner speech – the experience of thought as occurring in a natural language – is both universal and ubiquitous. Recent evidence, however, suggests that similar to other phenomenal experiences like visual imagery, the experience of inner speech varies between people, ranging from constant to non-existent. We propose a name for a lack of the experience of inner speech – anendophasia – and report four studies examining some of its behavioral consequences. We found that people who report low levels of inner speech have lower performance on a verbal working memory task and have more difficulty performing rhyme judgments based on images. Task switching performance, previously linked to endogenous verbal cueing, was unaffected by differences in inner speech. Studies of anendophasia, together with aphantasia, synesthesia, and differences in autobiographical memory are providing glimpses into what may be a large space of hitherto unexplored differences in people’s phenomenal experience.

How to handle the truth: A model of politeness as strategic truth-stretching

While the literature has mostly focused on the goal of information transfer, many linguistic phenomena only make sense in the light of further goals pursued by the agent. One such phenomenon is polite language use. In this paper, we propose a new model of polite language production. We suggest that patterns characteristic of polite language, e.g., indirectness, emerge from a tension between two goals: on the one hand, being sufficiently truthful and informative, and on the other hand, being kind to the listener. To capture these pressures, we introduce a novel model of probabilistic language production which combines a strategic choice of content selection with the usual pragmatic choice of content expression. We fit our model to empirical data from a previous experiment using a bespoke Bayesian model. We quantitatively compare our model to a previous model of politeness and discuss some ways in which our account is simpler, more general and better accounts for empirical data and theoretical considerations.

Convolutional Neural Generative Coding: Scaling Predictive Coding to Natural Images

In this work, we develop convolutional neural generative coding (Conv-NGC), a generalization of predictive coding to the case of convolution/deconvolution-based computation. Specifically, we concretely implement a flexible neurobiologically-motivated algorithm that progressively refines latent state feature maps in order to dynamically form a more accurate internal representation/reconstruction model of natural images. The performance of the resulting sensory processing system is evaluated on complex datasets such as Color-MNIST, CIFAR-10, and SVHN. We study the effectiveness of our brain-inspired model on the tasks of reconstruction and image denoising and find that it is competitive with convolutional auto-encoding systems trained by backpropagation of errors and outperforms them with respect to out-of-distribution reconstruction (including the full 90k CINIC-10 test set).

Training Sensitivity to Biased Samples in Inductive Reasoning

Environmental restrictions often permit the sampling of some items while excluding others. Such restrictions are termed sampling frames, whereby items can be selected based on their category membership (category frame), or possession of a target property (property frame). According to Bayesian principles, narrower property generalization is expected when a sample is subject to property sampling than category sampling. The current work examined whether sensitivity to such sampling frames could be increased through training with worked examples and practice. Experiment 1 found that training in property or category sampling enhanced sensitivity to that frame relative to a no-training control. Experiment 2 employed a pre-post design where all participants received training in both frames. A positive training effect was found, but only for those with a poor understanding of sampling frames on the pre-test. This work indicates the viability of appropriate training for increasing understanding of the implications of sample selection mechanisms.

Exploring human learning and planning inn grid navigation with arbitrary mappings.

From learning to play video games to using novel tools, humans are able to acquire a variety of complex mappings between their actions and arbitrary outcomes. In addition, once they have learned such mappings, they often have to use them sequentially to achieve goals, i.e., planning. In this work we study how the learning of a novel mapping interacts with planning in the context of grid navigation. In order to do so, we developed a computer-based game where subjects have to move a cursor from start to target locations using the keys of their keyboard. Importantly, to more closely resemble the complexity of the mappings that people acquire in their lives, the cursor movement was determined by a non-trivial rule inspired by the movement of the piece of chess known as the Knight. In Experiment 1, we show that participants were able to improve their performance in our task, though not arriving optimally to the targets in the majority of trials. Additionally, we assessed three cognitive models and found that a model that includes Bayesian mapping-learning, path search and habit formation components described participants' data better. Finally, in Experiment 2, we showed that exposing participants to the mapping component of the task without having to plan, provides a performance improvement when exposed to the full task later. Crucially, this improvement does not occur if subjects are exposed to the planning component of the task prior to doing it fully. Overall, these results suggest that in order for planning processes to be effectively deployed, the mapping of actions should be learned first.

Perceptions of Explanation Completeness Help Decrease Knowledge Overestimation

The tendency to overestimate one’s knowledge has been shown in many domains including the innerworkings of everyday objects. This Illusion of Explanatory Depth (IOED) can be broken through the act of generating a causal explanation, although the reason as to why has yet to be explored. In this study, we investigate what characteristics of a generated explanation result in people recognizing their perceived lack of knowledge. Participants completed a typical IOED paradigm for devices, followed by rating their perceived completeness and accuracy for the explanations they generated. We also coded the explanations to determine their causal complexity. We found that lower ratings of overall perceived completeness and a sense of incomplete big explanatory components were predictive of a larger decrease in perceived understanding for that device post-explanation. Fewer causal links within an explanation also predicted a larger decrease in understanding ratings, suggesting that producing an explanation with a lower causal complexity led to a decrease in perceived understanding of that device. We discuss the implications of these results in relation to explanation characteristics that may cause a person’s illusion of understanding to break and proposed origins of the IOED phenomenon.

Bayesian confirmation and commonsense notions of evidential strength

How can we quantify the degree to which a piece of evidence affects a person’s belief? Philosophers investigating theories of Bayesian Confirmation have identified a plurality of potential measures, each with their own virtues and shortcomings. Psychologists meanwhile have largely neglected this question, which has limited their ability to understand differential belief updating, cases where certain individuals or groups respond to the same evidence in different ways. In this study, we examine how competing Bayesian confirmation measures track commonsense notions of evidential strength. We demonstrate how these measures can be computed from participants’ belief reports, and identify cases where the measures come apart in their characterization of participants’ belief updating. In so doing, this project seeks to build connections between investigations of psychological belief updating processes and formal epistemic theories of confirmation.

Breaking New Ground in Computational Psychiatry: Model-Based Characterization of Forgetting in Healthy Aging and Mild Cognitive Impairment

Computational models of memory used in adaptive learning settings trace a learner's memory capacities. However, less work has been done on the implementation of these models in the clinical realm. Current assessment tools lack the reliable, convenient, and repeatable qualities needed to capture the individualized and evolving nature of memory decline. The goal of this project was to predict and track memory decline in subjectively- or mildly cognitively impaired (MCI) individuals by using a model-based, adaptive fact-learning system. Here, we present data demonstrating that these tools can diagnose mild memory impairment with over 80% accuracy after a single 8-minute learning session. These findings provide new insights into the nature and progression of memory decline and may have implications for the early detection and management of Alzheimer's disease and other forms of dementia.

Towards Understanding How Machines Can Learn Causal Overhypotheses

Recent work in machine learning and cognitive science has suggested that understanding causal information is essential to the development of intelligence. One of the key challenges for current machine learning algorithms is modeling and understanding causal overhypotheses: transferable abstract hypotheses about sets of causal relationships. In contrast, even young children spontaneously learn causal overhypotheses, and use these to guide their exploration or to generalize to new situations. This has been demonstrated in a variety of cognitive science experiments using the “blicket detector” environment. We present a causal learning benchmark adapting the “blicket" environment for machine learning agents and evaluate a range of state-of-the-art methods in this environment. We find that although most agents have no problem learning causal structures seen during training, they are unable to learn causal overhypotheses from these experiences, and thus cannot generalize to new settings.

Young children can identify knowledgeable speakers from their causal influence over listeners

Prior work demonstrates an early-emerging understanding of how speakers can alter listeners’ minds and actions. Yet, an abstract understanding of communication entails more than forward inferences about its influence on the listener; it also supports inverse inferences about the speaker based on its causal influence over the listener. Can children reason about the minds of speakers based on their causal influence over listeners? Across three studies, children viewed two communicative exchanges where a listener attempted to activate a toy; we manipulated when speakers communicated (Exp.1), how listeners’ subsequent actions changed (Exp.2), and whether speakers spoke or sneezed (Exp.3). By 5 years of age, children inferred the speaker who appeared to cause the listener to succeed was more knowledgeable, but only when they produced speech. These results suggest children can reason causally about the sources of communication, identifying knowledgeable speakers based on their influence over a listener’s actions and their outcomes.

Confident Slot Iterative Learning for Multi-Domain Dialogue State Tracking

Dialogue State Tracking (DST), a key component of task-oriented dialogue systems, tracks user intentions by predicting the values of pre-defined slots in a dialogue. Existing works on DST treat all slots indiscriminately and independently, which ignores the relationships across slots and limits the learning of hard slots (those slots are hard to be predicted correctly), eventually hurting overall performance. In this paper, we propose an iterative learning framework, i.e. iteratively updates the dialogue state with confident slots, to alleviate the aforementioned problem. Specifically, we first employ a scorer to estimate slot confidence. Then, those slots with high confidence are utilized to update the previous state, and the updated state will be fed into the scorer again to recalculate the confidence. In the last iteration, we apply an objective with the confidence penalty to focus on the hard slots. The experiments show that our approach outperforms existing methods on popular datasets.

Teaching and Learning Through Pedagogical Environment Design

People often rely on knowledgeable teachers to help them learn. Sometimes, this teaching is direct: teachers provide instructions, examples, demonstrations, or feedback. But other times, teaching is more subtle: teachers construct the physical environment in which a learner explores. In the present research, we investigate this more subtle form of teaching in an artificial grid-based learning environment. How do people construct the physical environment to teach, and how does the (pedagogical) design of the physical environment affect people's learning? Study 1 shows that people pursue multiple approaches to pedagogical environment design. Study 2 shows that learners make systematic, often accurate inferences from pedagogically designed environments, even in the absence of exploration. Together, these studies add to our understanding of the myriad ways in which experts communicate their knowledge to novices—a capacity that is part of what makes human intelligence unique.

Verb Metaphors are Processed as Analogies

We propose a novel process account for how verb metaphors (e.g., The boat waddled) are understood: they are processed as analogical comparisons between the event denoted by the verb and an event schema activated by the noun. We first review evidence that this account is consistent with findings in analogical reasoning and both literal and metaphoric sentence comprehension. Next, we report the results of an online study of verb metaphor comprehension that supports our claims. We conclude with a discussion of the implications of our findings for theories of metaphor processing and language change over time.

Social connection on the dance floor: Movement coordination in small group silent disco leads to greater self-other overlap

Social motor coordination (SMC), defined as the intentional or unintentional coordination of movement between individuals in a social setting, has been linked to greater feelings of rapport and social connectedness. Here, we investigated this relationship using a silent disco paradigm where groups of 3 or 4 individuals danced to either the same music or different music. Visual information was manipulated by initially separating the participants with curtains (2 minutes), after which the curtains were opened (10 minutes). Head movements were recorded with a wireless motion tracking system attached to the silent disco headphones. Rapport and social connectedness measures were obtained using questionnaires completed after participation in the silent disco. Results showed that groups who listened to the same music exhibited a greater degree of SMC than groups that listened to different music. Greater degrees of SMC were also observed when group members were able to see one another. Finally, greater SMC was associated with perceptions of increased self-other overlap and interaction quality.

Computational Insights to Acquisition of Phonemes, Words, and Word Meanings in Early Language: Sequential or Parallel Acquisition?

Previous computational models of early language acquisition have shown how linguistic structure of speech can be acquired using auditory or audiovisual learning mechanisms. However, real infants have sustained access to both uni- and multimodal sensory experiences. Therefore, it is of interest how the uni- and multimodal learning mechanisms could operate in concert, and how their interplay might affect the acquisition dynamics of different linguistic representations. This paper explores these questions with a computational model capable of simultaneous auditory and audiovisual learning from speech and images. We study how the model’s latent representations reflect phonemic, lexical, and semantic knowledge as a function of language experience. We also test how the findings vary with differential emphasis on the two learning mechanisms. As a result, we find phonemic learning always starting to emerge before lexical learning, followed by semantics. However, there is also notable overlap in their development. The same pattern emerges irrespectively of the emphasis on auditory or audiovisual learning. The result illustrates how the acquisition dynamics of linguistic representations are decoupled from the primary learning objectives (mechanisms) of the learner, and how the emergence of phonemes and words can be facilitated by both auditory and audiovisual learning in a synergetic manner.

Iconic Artificial Language Learning: A Conceptual Replication with English Speakers

We introduce an iconic approach to artificial language learning, one that replaces traditional phonologically-grounded stimuli with pictographic writing systems. Conducting an experiment with English speakers, we demonstrate the viability of this approach by reproducing word order effects observed in multiple studies (e.g., Culbertson & Adger, 2014; Martin, Holtz, Abels, Adger, & Culbertson, 2020). Importantly, iconic artificial languages make it possible to re-use the same linguistic stimuli with diverse language populations, facilitating crosslinguistic investigations.

Driven by Information: Children's Exploration Shapes Their Distributed Attention in Category Learning

Categories simplify the vast number of entities we encounter into equivalence classes, serving as a fundamental block of our cognition. This simplification of information supports our ability to reason about and interact with members of each category. In adulthood, selective attention helps us form categories efficiently by focusing on relevant attributes while filtering out irrelevant information. However, young children tend to encode more attributes than necessary, and this developmental difference is partly due to a prolonged development of selective attention. Additionally, the present study proposes that some children have an innate preference for information regardless of its value. In two category-learning experiments, children sampled more information than adults when filtering demands were low. Such exploratory behavior was not due to the motor actions associated with information sampling. The results suggest that children's tendency to explore plays a significant role in shaping their attentional processes in category learning. This work sheds light on the interplay of the developmental courses of exploration and selective attention and highlights the importance of considering children's preference for information in category learning research.

Estimating Sub-categories of Cognitive Load: An Eye-tracking Study

This study explores eye-markers for sub-categories of cognitive load. Experiments were conducted on 63 participants using Image Sliding Puzzle (ISP). NASA-TLX was administered post task completion as a measure of cognitive load. Total scanning duration, total fixation duration, fixation count and total saccadic duration were found to be significant, which is consistent with pre-existing literature. Next, we investigated whether sub-categories of cognitive load (mental demand, temporal demand, perceived performance, effort, and frustration) can be distinguished by characteristic eye-metrics. Our findings reveal signature eye-markers for specific sub-categories of cognitive load. Further, we explored the link between perceived performance and actual performance and established that mean fixation duration, peak velocity, mean saccadic duration, and skewness in saccadic velocity were significant markers for both objective and subjective markers of performance. To our knowledge this is the first study to compare the task-evoked eye measures for sub-categories of cognitive load.

Does Predictability Drive the Holistic Storage of Compound Nouns?

Despite evidence that learners are storing a lot more than simple words, it is still unclear what determines whether a phrase is stored holistically. For example, storage could be driven by either phrasal frequency or by the mutual predictability of a phrase's component parts . Further, the processing consequences of holistic storage are also unclear. Given that sentence processing is incremental, how does recognition of individual words give rise to recognition of holistically stored phrases? The present study examines these questions. Specifically, participants are presented with sentences that contain compound nouns in locally plausible or locally implausible contexts. We examine whether participants are able to overcome local implausibility effects more easily if the compound nouns are highly predictable. We find that predictability does not overcome local implausibility effects, suggesting that either predictability is not driving holistic storage or that holistic storage driven by predictability does not facilitate comprehension in our task.

Where does the flow go? Humans automatically predict liquid pathing with coarse-grained simulation

Bodies of water manifest rich physical interactions via non-linear dynamics. Yet, humans can successfully perceive and negotiate such systems in everyday life. Here, we hypothesize that liquid bodies play such an integral role in human life that the mind automatically computes their approximate flow-paths, with attention dynamically deployed to efficiently predict flow trajectories using coarse mental simulation. When viewing animations of liquids flowing through maze-like scenes, we asked participants to detect temporary slowdowns embedded in these animations. This task, without any overt prompt of path or prediction, reveals that detection rates vary with the moment-to-moment changes in coarse flow-path predictions. Critically, coarse predictions better explain trial-level detection rates than a finer-grained alternative, independently of bottom-up salience of slowdowns. This work suggests liquid flow-path prediction as an implicit task in the mind, and introduces rich attentional dynamics as a new window into intuitive physics computations.

A generalized method for dynamic noise inference in modeling sequential decision-making

Computational cognitive modeling is an important tool for understanding the processes that support human and animal decision-making. Choice data in sequential decision-making tasks are inherently noisy, and separating noise from signal can improve the quality of computational modeling. Currently, most models assume that noise is constant, or static, typically by including a parameter (e.g., uniform ε) to estimate the noise level. However, this assumption is not guaranteed to hold -- for example, an agent can lapse into an inattentive phase for a series of trials in the middle of otherwise low-noise performance. Assuming that noise is static could bias parameter and model identification. Here, we propose a new method to dynamically infer noise in choice behavior, under a model assumption that agents can transition between two discrete latent states (for example, attentive and noisy). Using four empirical datasets with diverse behavioral and modeling features, we demonstrate that our method improves model fit and that it can be easily incorporated into existing fitting procedures, including maximum likelihood estimation and hierarchical Bayesian modeling.

Computational principles underlying the evolution of cultural learning mechanisms

Cumulative culture requires efficient learning mechanisms that can withstand environmental change across generations. We unify two competing theories of the learning mechanism supporting cumulative culture in a common computational framework, distinguishing model-based from model-free social learning. We describe and analyze evolutionary models that explain when and why model-based and model-free social learning are each optimal, and in particular how environmental volatility determines which strategy succeeds. Strikingly, we find that model-based social learning can succeed even in high-volatility environments. These results yield novel predictions concerning cultural variation in social learning mechanisms

Herding cats: children’s intuitive theories of persuasion predict slower collective decisions in larger and more diverse groups, but disregard factional power

Collaboration can make collective judgments more accurate than individual judgments, but it also comes with costs in time, effort, and social cohesion. But how do we estimate these costs? In two experiments, we introduce children and adults to two teams in which the teammates disagree about the optimal solution to a novel problem, and ask which team would need more time to reach a consensus decision. We find that all ages expect slower decisions from teams with more people or factions, and expect the number of factions to matter more than the number of people. But only adults expect decisions initially endorsed by a stronger faction to be faster than those endorsed by a weaker faction. Results are discussed in context of children’s reasoning about dominance, and models of time-rational collective decision-making.

Linguistic Cognitive Load Analysis on Dialogues with an Intelligent Virtual Assistant

Virtual assistants have become fixtures in everyday settings, but most research focuses on their development rather than their use following deployment. To facilitate study of their use in office settings, we introduce OfficeDial, a multimodal dataset containing audio recordings, transcriptions, eye tracking data, and screen recordings from conversations between humans and virtual assistants in office environments. Conversations are paired with physical and behavioral measures of cognitive load. We study the associations between verbal behavior and noise level and reveal key relationships between verbal redundancy, disfluency, and noise level. We make our new dataset available to interested researchers to inspire further exploration.

Just Tell the Truth: Correcting Misconceptions with Simple, Factual Statements

Efforts to correct misconceptions have produced multiple types of complex interventions. However, past research has consistently shown that simple, factual statements may be equally effective in activating the incorrect information and prompting an integration of the correct concept. The current study tests the effectiveness of simple statements on 76 different misconceptions. Findings indicate that for the majority of misconceptions, simple statements are an effective intervention to reduce misconceptions. Limitations and future directions are discussed.

The Edge of Ockham’s Razor: Examining Boundary Conditions on Preferences for Simpler Explanations

People often prefer simpler explanations (that posit the presence of fewer causes), judging these more probable than more complex alternatives. However, simplicity preferences are only mathematically justified under certain conditions. We examine one case where, mathematically, complexity preferences are justified, and test whether this corresponds to a boundary condition on simplicity preferences. Specifically, we focus on cases where causes occur frequently (rather than rarely), and where explanations specify the absence of additional causes (e.g., “Cause A and not B or C”) rather than remaining agnostic about their presence or absence (e.g., “Cause A”). Study 1 showed that, in these cases, simplicity preferences were attenuated, but not reversed. Study 2 suggests that simplicity preferences partly stemmed from failures to explicitly represent absent causes. We suggest that biases towards oversimplification may arise due to over-application of a cognitively simple version of Ockham’s razor, that is insensitive to the probability of absent causes.

The Development in Emotional Content of Children’s Writing: Are Children Getting Less Happy?

Emotion is closely associated with language, but we know little about how children express emotion in their own writing. We used a large-scale data-driven approach to investigate whether emotional expression via writing changes through development, and whether it varies for boys and girls. We first used a lexicon-based bag-of-words approach to identify emotional content in a large corpus of stories written by 7- to 13-year-old children (N>100,000). Generalized Additive Models were then used to model changes in sentiment across age and gender. Two other approaches (BERT and TextBlob) validated and extended these analyses, revealing converging findings that positive sentiments in children’s writing decrease with age. These findings echo previous studies showing lower mood and increased acquisition of negative emotion words across development. We also found stories by girls contained more positive sentiments than boys. Future experimental work should further investigate the complex relationships between written language and emotion across development.

Are words equally surprising in audio and audio-visual comprehension?

We report a controlled study investigating the effect of visual information (i.e., seeing the speaker) on spoken language comprehension. We compare the ERP signature (N400) associated with each word in audio-only and audio-visual presentations of the same verbal stimuli. We assess the extent to which surprisal measures (which quantify the predictability of words in their lexical context) are generated on the basis of different types of language models (specifically n-gram and Transformer models) predict N400 responses for each word. Our results indicate that cognitive effort differs significantly between multimodal and unimodal settings. In addition, our findings suggest that while Transformer-based models, which have access to a larger lexical context, provide a better fit in the audio-only setting, 2-gram language models are more effective in the multimodal setting. This highlights the significant impact of local lexical context on cognitive processing in a multimodal environment.

Situational affordances constrain first impressions from faces

Humans spontaneously attribute a rich variety of traits (e.g., trustworthy, competent) to strangers based on facial appearance. Despite decades of research on these facial first impressions, few studies have investigated how situational affordances relevant to human perceivers impact impression formation. Nearly all existing research comes from participants forming impressions of targets who bear no relevance (real or manipulated) to the participant. Here, we tested whether situational affordances (i.e., opportunities or obstacles to fulfilling one’s goals) related to three fundamental social motives—mate-seeking, self-protection, and disease avoidance—constrain the way that perceivers form impressions from faces. Across 167,951 ratings from 400 Canadian undergraduates, situational affordances caused the structure of facial impressions to change, generally becoming more constrained when targets were rated in goal-relevant contexts versus a goal-neutral context absent any affordances. These changes may arise from participants forming impressions on one central, goal-relevant trait, which influences ratings on other less-relevant traits.

Che-che (‘car-car’) and chi-chi (‘eat-eat’): Reduplication in Mandarin Chinese child-directed speech

Recent studies have demonstrated that reduplication facilitates children’s word segmentation and learning. Although reduplication is often considered a feature of child-directed speech (CDS), it remains unclear if it is indeed more frequent in CDS than in adult-directed speech (ADS). This study examines the production and perception of reduplication in the context of language acquisition by focusing on Chinese, which has a rich system of reduplication. We analyze the frequency of reduplicated types and tokens as a function of speech register (ADS/CDS) and children’s age (18m/24m) in a corpus. Additionally, we conduct a survey to examine adults' perception of reduplications and determine their degree of child-directedness. Results indicate that there are more reduplicated types in CDS than in ADS. However, only the reduplicated tokens that are rated to be child-directed-specific occur more in CDS than in ADS. These findings provide insight into the nature of lexical input in language acquisition.

Structured dynamics of hierarchical action selection

Extensive work has examined how individuals structure information in cognitive representations. However, the dynamics of how these structured representations are implemented in real time and how cognitive and motor processing interact during action selection have received relatively less attention. Here, we use computational modeling to closely examine the dynamics of hierarchical action selection at the scope of individual decisions. We had participants learn eight stimulus-action associations with latent hierarchical structure. They then engaged in a forced response task where we manipulated the amount of time participants had to prepare each response. We find evidence that hierarchical action selection is a top-down, serialized process but appears to occur without bottlenecks between decision-making and movement. Overall, our results highlight a close coupling between cognitive and motor processing during hierarchical action selection.

Exploring to learn: Curiosity, breadth and depth of exploration, and recall in young children

Curiosity relates to learning and recent work shows robust associations between curiosity and recall of information in adults and children over age 10. The current study tested a similar association in younger children and explored children’s information-seeking behaviors. Children (n=90, 4-10-year-olds) played a free-exploration game in which they clicked shapes to learn different facts about different topics. They then recalled what they remembered learning on the task (recall) and asked questions about what they were curious to know more about (curiosity). We observed a positive association between children’s curiosity and recall of information, even when controlling for amount of information seeking and children’s age. This association was seen for recall with and without memory cues. There was no association between children’s curiosity and information seeking behavior, and children showed a strong tendency of breadth exploration over exploring in depth with indication of an association between exploring more depth with age.

Optimizing Random Sampling of Daylong Audio

While naturalistic daylong audio recordings of children’s auditory environments have the potential to reveal key insights about the input children receive and inform our theories of language development, it also presents various methodological hurdles. In the present work, we used three fully transcribed daylong audio recordings to investigate the challenge of manually extrapolating aggregate statistics and quantify the kinds of sampling choices daylong researchers can make. Our findings highlight sampling choices that maximize sampling from the full distribution of the day and potential tradeoffs between human effort and obtaining accuracy.

Young children's curiosity about what others think about the self

Learning about the self is one of the most challenging goals that young children face. Yet, much of the prior work on early learning and curiosity has focused on children’s tendency to attend to and explore the external world. Are children actually curious about themselves? The current study examines this question by investigating whether children actively seek information about what others think of their performance. Three- to five-year-old children participated in a task where an experimenter evaluated the quality of their drawing and of another child’s drawing. Children were then left alone with a folder that contained one of these drawings (Self or Other). Children were more likely to peek inside the folder when it contained their drawing than when it contained the other child's drawing. These preliminary findings suggest that children's curiosity about what others think of them may emerge early in life and manifest as active information-seeking behaviors.

Can Peanuts Fall in Love with Distributional Semantics?

Context changes expectations about upcoming words—following a story involving an anthropomorphic peanut, comprehenders expect the sentence the peanut was in love more than the peanut was salted, as indexed by N400 amplitude (Nieuwland & van Berkum, 2006). This updating of expectations has been explained using Situation Models—mental representations of a described event. However, recent work showing that N400 amplitude is predictable from distributional information alone raises the question whether situation models are necessary for these contextual effects. We model the results of Nieuwland and van Berkum (2006) using six computational language models and three sets of word vectors, none of which have explicit situation models or semantic grounding. We find that a subset of these can fully model the effect found by Nieuwland and van Berkum (2006). Thus, at least some processing effects normally explained through situation models may not in fact require explicit situation models

Compositionality under time pressure

Compositionality is a central component of the human faculty for generalization and flexibility. However, the computations involved are poorly understood, especially in terms of their cognitive costs. On one hand, compositionality requires searching combinatorially large hypothesis spaces, raising issues of tractability. On the other hand, compositional representations afford efficient and compact compression. To shed light on the cognitive resource required for compositionality, we used a within-subject time pressure manipulation to study how participants navigated a series of mazes, generated using recursive operations over spatial primitives. We find evidence that behavior is guided by the use of primitives and abstract operations over them, where the degree of compositional structure increases performance and speeds up decisions. And while time pressure led to more random errors, it did not impair the capacity for compositionality. Rather, participants increased their reliance on reusing and recombining previous computations, suggesting a remarkable robustness of human compositional reasoning.

Simulating Political Polarization as a Function of Uncertain Inference and Signaling of Moral Values

Political polarization is driven by many factors, but the role of moral values as both a signal of political identity and a source of internal conflict is understudied. We report an agent-based computational model of polarization that fills this gap. Agents seek to differentiate in- and outgroup neighbors with a slight preference for the former. However, they must do so by inferring neighbors’ identities from visible but transient moral signals. Moreover, agents experience conflicts within their own values, and if difficult to resolve internally, can copy the values of their ingroup or disengage (i.e., act immorally). Results show that liberals form larger, more homogeneous clusters, are happier, and experience less moral conflict than conservatives. Conservatives experience more and higher levels of conflict and morally disengage significantly more often than liberals.

Violations of physical and psychological expectations in the human adult brain

When adults see one solid object pass through another, or see a person take the long route to a destination when a shortcut was available, we classify those events as surprising. Infants look infants look longer at the same unexpected outcomes, compared with visually similar but expected outcomes, in violation-of-expectation (VOE) experiments. What domain-specific and domain-general cognitive processes support these judgments? In a pre-registered experiment, we scanned 32 adults using functional magnetic resonance imaging (fMRI) while they watched videos designed for infant research. One region implicated in physical reasoning responded selectively to unexpected physical events, providing evidence for domain-specific physical prediction error. Multiple demand regions responded more to unexpected events regardless of domain, providing evidence for domain-general goal-directed attention. Early visual regions responded equally to unexpected and expected events, providing evidence against stimulus-driven prediction error. Thus, in adults, VOE involves domain-specific, and high-level, domain-general computations.

The cross-linguistic order of adjectives and nouns may be the result of iterated pragmatic pressures on referential communication

The world's languages differ in how they order adjectives and nouns relative to each other. We ask whether cross-linguistic variation and systematicity in adjective-noun order can be explained by the iterated pressure for pragmatic referential communication. To this end, we apply the Rational Speech Act framework with an an iterated learning mechanism to study how cooperative pressures may shape typological regularities in referential communication. First, we show that the less informative adjectives are relative to nouns, the more likely they are to occur post-nominally. This is the case when informativeness is manipulated via the composition of the lexical space (i.e., changing the relative number of adjectives vs.~nouns that are available for reference), and via the inherent referential utility of adjectives vs.~nouns. Secondly, we show that under the assumption that nouns are on average more informative than adjectives, the model predicts a cross-linguistic distribution of ordering preferences that qualitatively resembles the empirical one, with these biases becoming further entrenched with iterated language use. Taken together, these results suggest a possible pathway for syntactic preferences to be calcified over time as the result of pragmatic communicative pressures on language.

Errorless irrationality: removing error-driven components from the inverse base-rate effect paradigm

The inverse base-rate effect is a robust irrational bias that arises when people face ambiguity. The most prominent theories of this irrational bias depend on prediction error. In this study, we gradually removed elements of a predictive learning design to test the extent to which error-driven processes underlie this bias. In our first experiment, we removed explicit feedback by implementing the inverse base-rate effect in an observational learning procedure. In our second study, we further removed any causal relationship between stimulus features and category labels by moving towards an unsupervised learning procedure. This removed any information participants could use to identify category labels. In both experiments, the inverse base-rate effect persisted and remained robust. This outcome suggests that this irrational bias is independent of supervised learning procedures. We propose that any theories and models of the inverse base-rate effect must manage information encoding and connection updates without explicit prediction error.

People use Newtonian physics in intuitive sensorimotor decisions under risk

Decisions under risk have been classically studied with tasks involving lotteries with explicit monetary rewards and uncertain gambles. More recently, sensorimotor decisions, specifically single movements to targets yielding rewards and losses, have been conceptualized as decisions under risk. While human choices between gambles have long been known not to maximize expected gains, sensorimotor decisions have been well described by statistical decision theory in many tasks. However, because many naturalistic scenarios of sensorimotor decisions are inescapably governed by the laws of physics, the question arises, how people act under such circumstances. Here, participants slid pucks to target areas, providing gains and losses in a virtual environment so that the uncertainty inherent in motor control interacts with the physical relationships governing objects' motion. Using model comparison with several generative models of participants' sliding actions, we find evidence that human motor decisions in scenarios with prospective economic outcomes take Newtonian physics into account.

A Computational Model of Lexical False Memory based on Semantic Distance from Word Embeddings

Human memory does not simply function like an information storage disk; instead, it flexibly reorganizes information. This flexibility can sometimes produce false memories of items related to those actually encountered–a possible byproduct of an adaptive memory system that enables generalization across related items or experiences. In the Deese/Roediger-McDermott (DRM) task, participants often falsely remember seeing words that are semantically related to presented words. Here, we pro- pose and test a model of lexical (false) memory that predicts these errors, made possible by integrating (i) theories of memory that posit encoding of verbatim and gist-level information with (ii) a computational framework adapted from the perceptual false memory literature, and (iii) semantic relatedness measures from word embeddings analysis of large-scale text corpora. This Lexical Target Confusability Competition (Lexical TCC) model successfully predicts human participants’ false recognition in the DRM task, with implications for understanding how and when the mind produces false semantic memories.

Using manual actions to create visual saliency: an outside-in solution to sustained attention and joint attention

Human cognition is shaped by our bodies and actions. The influence of embodiment on cognition is particularly crucial during early development. Recent evidence shows that infants use actions to accomplish cognitive and social tasks that may later be solved internally. In our study, we propose that a sensorimotor mechanism to hand-eye coordination is through a full path from manual action, to visual saliency, and to visual attention. To provide a rigorous test of this pathway, we analyzed multimodal behavioral data collected from parent-infant toy play. We focused on linking infants’ manual actions with visual properties in the infant’s view and attention. Further, we extended our analyses to quantify the effects of manual actions on one’s own visual attention, infant’s actions on parent attention, and parent’s actions on infant attention. Results suggest that both infants’ and parents’ actions create visual saliency of objects to support visual attention and joint attention.

Are discourse expectations modulated by being linguistically creative? A production and perception study on Implicit Causality

The present study investigates the production and perception of creative language with a particular focus on the discourse level. In particular, it addresses the question whether discourse biases associated with Implicit Causality are altered when we make a contribution that is intended to be original. This issue was addressed in two text production and two offline rating experiments. Our results show that creative contributions to ongoing discourse leave biases such as Implicit Causality largely unchanged but affect other linguistic markers.

Towards a computational account of projection inferences in clause-embedding predicates

Projection inferences are inferences about speaker commitment to a content embedded under an entailment-canceling operator, for example in polar interrogatives with clause-embedding predicates (Does John know that Julian dances salsa?). Speaker commitment to embedded content is modulated by multiple factors, including the predicate, interlocutors' prior beliefs about the content, and its at-issueness. We propose an RSA model of projection inferences in such environments. Crucially, we take the interpretive procedure to involve inferring a speaker's and attitude holder's belief in the content. In a behavioral study, we investigate inferred beliefs about contents embedded under the predicates “think” and “know” that listeners ascribe to the speaker and a potential attitude holder. We use the empirical data to parametrize the model. The resulting predictions mirror some, but not all, of the qualitative empirical patterns. This is a first step towards a systematic analysis of projection inferences using probabilistic pragmatic models.

A common format for representing spatial location in visual and motor working memory

Does the mind rely on similar systems of spatial representation for both perception and action? Here, we assess the format of location representations in two simple spatial localization tasks. In one task, participants simply remembered the location of an item based solely on visual input. In another, participants remembered the location of a point in space based solely on haptic input. A careful analysis of participants’ errors revealed that, in both tasks, participants errors were more consistent with the use of polar coordinates than Cartesian coordinates. Thus, we argue that polar coordinates may be a common format for representing location information across modalities.

Researching Communication in Context: Engaged Epistemology and Ethnographic Fieldwork Transforms Understanding of Interactions after Laryngectomy

This paper presents transdisciplinary research on laryngectomy and a methodological stance to broaden research paradigms for the cognitive sciences. Studying daily experiences of people communicating without biological larynx in an interactive context, we put special emphasis on methodology combining engaged epistemology with ethnographic fieldwork. Our results made evident i) the role of anatomical and physiological adaptations in shaping communication and social relations, ii) the existence of multimodal and context-dependent alternative strategies of conversation, iii) the crucial role of participants’ agency. The dialogue between epistemologically engaged cognitive science and ethnographic fieldwork allowed us to remain open to novel interpretations of the communicative situations and led to unexpected observations. Results of this study point to the importance of integrating qualitative methodologies within research on cognition, and may prove useful for guiding therapeutic interventions and novel technological designs.

Frequency Asymmetries in Vision and Action

According to a large body of research, the left and right cerebral hemispheres are specialized for different frequencies, in vision and audition, but the cause of this specialization is unknown. Here, we tested whether hemispheric asymmetries in visual perception can be explained by asymmetries in people’s tendency to perform high- and low-frequency actions with their dominant and nondominant hands, respectively (the Action Asymmetry Hypothesis [AAH]). In a large, preregistered, online study, participants (N = 1008) judged low- and high-frequency shapes presented in the left and right visual hemifields. Overall, the typical hemispheric asymmetry for high vs. low visual frequencies, which we found in right handers, was significantly reduced in left handers. Hemispheric asymmetries for high-spatial-frequency stimuli were completely reversed between right and left handers. These results provide initial support for the AAH: Frequency asymmetries in perception may be explained by frequency asymmetries in action.

A synthesis of early cognitive and language development using (meta-)meta-analysis

Young children acquire a wide range of linguistic and cognitive skills in the first three years of life. Decades of experimental work have established a solid empirical foundation for our understanding of cognitive development. But most experimental studies are limited in statistical power and focus on specific psychological constructs, thus making them unsuitable for describing developmental growth at scale. Here, we turned to meta-analyses of experimental research. We conducted a meta-meta-analysis to consolidate and integrate 23 meta-analyses compiled on MetaLab, a community-augmented meta-analysis platform. We found that most datasets can not meaningfully distinguish different functional forms for developmental change, but in those that could, there is great diversity in the best-fitting functional forms of the age model. We also evaluated the impact of a range of methodological factors. Overall, our work sheds light on the heterogeneous nature of developmental trajectories and the subtle interactions between research methods and experimental outcomes.

Predicting strategy choice in word formation: A case study of reuse and compounding

Natural language expresses new concepts by reusing existing words or coining new ones. Previous studies have examined these word formation strategies separately through a functional lens, but it is unclear why one strategy might be preferred over another. In this study, we hypothesize that communicative and cognitive efficiency might predict the choice between lexical reuse and compounding for expressing an emerging concept. We test our hypothesis by developing a computational analysis of English word meanings that emerged over the past century. Our results suggest that strategy choice may be explained partly by a pressure for least effort. Our work contributes a novel connection between strategy choice in word formation and functional theories of language.

The Relationship Between Frequency and Irregularity in the Evolution of Linguistic Structure: An Experimental Study

The expressive power of natural languages depends on their regular compositional structure, which allows us to express and understand an infinite set of messages. However, a complete model of language evolution should also account for irregular exceptions to regular rules, common in natural languages. Historical linguistics has established a correlation between irregularity and frequency in language use, which has been attributed to preferential irregularisation of frequent items, or preferential regularisation of infrequent items. In an iterated learning experiment where participants learn and reproduce a miniature language across multiple generations, we show that this correlation can be explained by the relationship between frequency, regularity and learnability, without needing to appeal to frequency-dependent irregularisation. We find that systems of plural marking regularise across generations of transmission, but that high-frequency items remain irregular. Our results further show that the persistence of irregularity is due to high frequency overriding pressures which normally reduce learnability, such as low generalisability of the inflectional strategy (suppletion is disfavoured except in high frequency items) and low type frequency (belonging to a small inflectional class is disfavoured except in high frequency items).

Attention biases in the inverse base-rate effect: Prediction error or novelty?

Attention-based models of categorization and associative learning have received considerable support from human learning phenomena in which multiple predictive cues compete for association with outcomes. Among these, several phenomena (e.g. the highlighting effect and inverse base-rate effect) lend strong support to models that propose attention is driven by the experience of prediction error, and is distributed strategically to minimize prediction error during current and future learning. Here we explore the possibility that attention is determined instead by a relatively simple combination of stimulus novelty and association strength. We apply the model to several key findings in the literature on the inverse base-rate effect and related phenomena. Overall, the model provides a surprisingly good account of complex behavioral biases.

Incidental Coupling of Perceptual-Motor Behaviors Associated with Solution Insight during Physical Collaborative Problem-Solving

Solving problems with others not only reduces the time required to complete a challenge but may also enable the discovery of novel strategies that qualitatively change how a problem is approached. At the dyadic level, the laboratory-based ‘shepherding task’ demonstrated that, when tasked to contain evasive agents to a centralized location, some participants discover a non-obvious but optimal strategy to solve the task. This paper quantified the interactions between participants engaged in the task using Multidimensional Cross-Recurrence Quantification Analysis (MdCRQA), applied to each participant’s gaze and hand movements. The results demonstrated that strategy discoverers exhibited greater amounts of incidental coupling than non-discoverers prior to discovery. Once discovered, the strategy reduced the strength of coupling between participants, indicating that the strategy also reduced coordination demands. Future work will investigate whether differences in problem-solving can be attributable to differences in the perceptual features participants use which scaffold the discovery of task-optimal solutions.

12-month-olds’ reasoning via negation

Abstract combinatorial thought supports adult human reasoning. But it is unknown whether such thought is available to infants who are in the process of acquiring their native language. In a series of three experiments, we ask whether 12-month-old pre-verbal infants have access to a propositional negation operator, as a defining hallmark of abstract combinatorial thought. We examine infants’ understanding of disjunctive reasoning problems, taking the insight that some impossible outcomes are supported by negation, while others may not be. We thus introduce a novel use of the Violation of Expectation paradigm, with visual stimuli which build on the Call (2004) cup task by expanding the range of possibilities and testing different kinds of inconsistent outcomes. Results from three experiments do not provide direct support for the view that infants possess a propositional negation operator, but they are more compatible with it than alternatives.

Double PP Constituent Ordering Preferences in English Early Child Language

What determines children's production of syntactic alternations? This study takes up this question with the double PP construction in English as the test case (e.g., write [PP1 on the paper] [PP2 with this new pen]). Leveraging data of spontaneous child-parent interactions, we investigate the roles of dependency length and parent input frequency, with the latter being operationalized as lexical frequency and contextual predictability. We found that when child and parent data was combined, all three factors turned out to have significant predictive power, with dependency length having the most pronounced role. Results from the developmental trajectory of DLM as well as logistic regression analysis suggest that child production of constituent ordering preferences starts becoming more parent-like after the age of 30 months.

Causal inference shapes counterfactual plausibility

When we reason about what could have been, some possibilities seem plausible, and others far-fetched. According to a recent theory, counterfactual possibilities are plausible if they can be generated by making local, probabilistic adjustments to the causes of what actually happened. We provide evidence that people think about counterfactuals in this way even when they have to infer the causes of what happened. We told participants about the diet of a fictional animal, and then asked them simple counterfactual questions. For example, given that the animal has eaten 1 berry today, how much food could it plausibly have eaten instead? When the amount of food eaten by the animal licensed an inference about a causally upstream variable, participants inferred the state of this variable and used it to guide their counterfactual plausibility judgments. More generally, the distribution over counterfactual values derived from participants' judgments was remarkably similar to the distribution predicted by the model.

A description-experience gap in face stereotyping

Face stereotypes are prevalent and consequential. In this paper, we investigate an experience sampling account of face stereotyping. In two experiments, we show that, in an experience-sampling-based Trust Game, participants in the role of an investor were much more likely to play the game with trustees with trustworthy faces than those with untrustworthy faces (Experiment 1). Crucially, this endogenous experience sampling bias has amplified the behavioral consequences of the facial trustworthiness stereotype. In contrast, when the information was directly described with no sampling constraint for the participants, the face stereotype had a very limited effect on investment decisions (Experiment 2). We conclude by suggesting that the description-experience gap paves a promising avenue for studying sampling-based accounts in social cognition and behavior.

Causation, Foreseeability, and Norms

A growing body of literature has revealed ordinary causal judgement to be sensitive to normative factors, such that a norm-violating agent is regarded more causal than their non-norm-violating counterpart. In this paper, we explore two competing explanations for this phenomenon: the Responsibility View and the Bias View. The Bias View, but not the Responsibility View, predicts features peripheral to the agent’s responsibility to impact causal attributions. In a series of three preregistered experiments (N = 1162), we present new evidence that the Norm Effect arises from such peripheral features, namely from nonpertinent or entirely silly norm violations. Furthermore, we show that this effect cannot be explained by recourse to the agent’s foreknowledge or desire of the outcome, nor by its foreseeability: the Norm Effect arises even when participants judge the norm-violating agent’s doing as equally foreseeable. This, we argue, provides evidence in favour of the Bias View.

An information-theoretic account of availability effects in language production

I present a computational-level model of language production in terms of a combination of information theory and control theory in which words are chosen incrementally in order to maximize communicative value subject to an information-theoretic capacity constraint. The theory generally predicts a tradeoff between ease of production and communicative accuracy. I apply the theory to two cases of apparent availability effects in language production, in which words are selected on the basis of their accessibility to a speaker who has not yet perfectly planned the rest of the utterance. Using corpus data on English relative clause complementizer dropping from Levy & Jaeger (2007) and experimental data on Mandarin noun classifier choice from Zhan & Levy (2019), I show that the theory reproduces the observed phenomena, providing an alternative account to Uniform Information Density (UID) and a promising general model of language production which is tightly linked to emerging theories in computational neuroscience.

Children's understanding of verbal comparatives

English-acquiring children before 4 years of age show a fine-grained understanding of how the meaning of 'more' interacts with the lexical semantics of nouns: if the noun expresses a concept of objects, the comparison is based on number; if it expresses a concept of substance, it is based on volume or area. Is the meaning that children have acquired sufficiently general to support parallel semantic sensitivities when 'more' combines with verbs? We probe this question with 4-5 year olds. Our expectation, based in semantic theory, is that 'more' combined with an event verb like 'jump' should be quantified by number, but with a process verb like 'walk' it should be more flexible. Our Experiment 1 tests this with adults and Experiment 2 with children. We find children's understanding to be broadly consistent with that of adults, providing initial support for an early-acquired, highly general meaning for 'more'.

Children flexibly adapt their evidentiary standards to their informational environment

How do children decide when to fact-check a claim? In two studies and a simulation, we show that children use environmental cues about informational reliability to rationally adapt their information seeking to enable the efficient detection of misinformation. In Study 1, children exposed to an environment containing some misinformation (as opposed to all true information) sampled more evidence before verifying a test claim in a novel domain. In Study 2, children showed a graded sensitivity to environmental reliability: information seeking increased linearly with the proportion of false statements heard during exposure. Additionally, these statements were presented as online search results, which demonstrated that children make sophisticated inferences about their informational environments, beyond speaker-specific cues, to adjust their skepticism toward new information. These results further emphasize the importance of considering the environmental context of learning when developing interventions to promote healthy skepticism and lifelong learning.

Tracking Multiple Objects without Indexes

Computational models of multiple object tracking (MOT) presuppose the existence of non-conceptual indexes in visual perception, and as a result predict that ID (identification) performance on MOT tasks should be no worse than tracking performance for the same stimuli. However, empirical evidence suggests that ID performance is worse than tracking performance in MOT. We propose a computational model of MOT that is able to account for several empirical results related to tracking performance without the use of indexes and thus avoids yoking tracking performance to ID performance. We also test our model empirically, contrasting it with an existing index-based model, and show that an assumption that avoids indexes and instead incorporates an explicit (rather than an implicit) mechanism for identity maintenance accounts well for the variation in ID performance with increasing number of targets in MOT with visually identical objects.

Evidential uncertainty involves both pragmatic and extralinguistic reasoning: a computational account

Using evidential expressions to indicate one’s source of information for an utterance tends to convey uncertainty on the speaker’s part. Previous accounts of this uncertainty inference attribute it to either extralinguistic reasoning about evidence directness, or to pragmatic reasoning about alternative utterances. Here we present a novel hybrid account, and introduce a set of utterances which allows us to tease apart the three accounts’ predictions. We test these predictions in two studies by manipulating the directness of evidence indicated by an evidential expression. Exp. 1 shows that listeners infer more uncertainty with extreme values of directness. Exp. 2 shows that speakers are more likely to indicate evidence in contexts where the evidence is unreliable. We argue that these findings support an account which involves both extralinguistic and pragmatic reasoning, and develop a formal implementation of such an account within the Rational Speech Act framework.

What does learning look like?: Visual recognition of epistemic intent

Can one person tell, just from observing how another person's body moves, what that person is trying to learn? Here, we explore this question through several experiments on "epistemic action recognition". We filmed volunteers playing a box-shaking game in which they attempted to determine either (a) the number of objects hidden in an opaque box, or (b) the shape of the objects hidden in the opaque box. Then, independent subjects watched these videos and were asked to determine which videos came from which task: Who was shaking for number and who was shaking for shape? Across 3 experiments, observers successfully determined what an actor was trying to learn about the contents of the box, based only on their observed actions (i.e., how they shook it). These results demonstrate that humans have the ability to infer epistemic intent from physical behaviors, adding a new dimension to research on action understanding.

Unequal Norms Emerge Under Coordination Uncertainty in Multi-Agent Deep Reinforcement Learning

Successful social coordination requires being able to predict how the other people that one depends on are likely to behave. One solution to this dilemma is to establish social conventions, which constrain individuals' behavior but make prediction easier. Here, we develop a multi-agent deep reinforcement learning environment to investigate the costs associated with these conventions. In our produce-and-trade task, agents have varying production skills, but their actions must be predictable in order to be rewarded. Stronger norms improve the overall success of the group by improving the average rewards of the majority, but also systematically disadvantage agents whose specialization is in the minority of the group. Critically, this outcome is magnified by population size: as larger groups make it potentially more difficult to develop individualized representations of agents, minority agents become more likely to conform to a norm that is disadvantageous to them.

Re-examining cross-cultural similarity judgments using language statistics

Is “cow” more closely related to “grass” or to “chicken”? Speakers of different languages judge similarity in this context differently, but why? One possibility is that cultures covarying with these languages induce differences in conceptualizations of similarity. Specifically, East Asian cultures may promote reasoning about thematic similarity, by which cow and grass are more related, whereasWestern cultures may bias judgments toward taxonomic relations, like cow and chicken. We measure similarity judgments across the US, China, and Vietnam and replicate US-China differences, but do not find that responding in Vietnam patterns with China. Instead, similarity judgments in Vietnam are intermediate between the US and China. We also show that word embedding models (fastText models for each language) are related to judgments within each country, suggesting a possible alternative interpretation of cross cultural differences. Perhaps notions of similarity are similar across contexts, but the statistics of the linguistic environment vary.

Developmental Relations Between Cognitive Reflection and Modal Cognition

Children can be unduly skeptical of events that violate their expectations, claiming these events neither could happen nor should happen, even if the events violate no physical or social laws. Here, we explore whether children’s reasoning about possibility and permissibility—modal cognition—is aided by cognitive reflection, or the disposition to privilege analysis over intuition. Ninety-nine children between the ages of 4 and 11 judged the possibility and permissibility of several hypothetical events, and their judgments were compared to their score on a developmental version of the cognitive reflection test, the CRT-D. Children’s CRT-D scores predicted their ability to differentiate possible events from impossible ones and their ability to differentiate impermissible events from permissible ones, as well as their ability to differentiate possibility from permissibility in general. Such differentiations were predicted by children’s CRT-D scores independent of age and executive function. These findings suggest that mature modal cognition may require the ability to reflect on, and override, the intuition that unexpected events cannot happen.

Confirmation trees: A simple strategy for producing hybrid intelligence

Artificial agents now perform on par with or better than experts on several challenging decision-making tasks. People, however, remain reluctant to allow algorithms to make decisions on their behalf and legal constraints may prevent it altogether. How can we harness artificial intelligence, while maintaining trustworthiness and accountability? We propose confirmation trees, a decision-tree strategy for hybrid intelligence that can improve accuracy while maintaining human control. First, decisions are elicited from a human expert and an artificial agent. If they agree, that decision is adopted. If they disagree, a second human expert is consulted to break the tie. Hence, a human expert always approves the final decision. Our approach outperforms human experts or algorithms alone at diagnosing malign skin lesions. Crucially, it performs better than a strong human baseline, using substantially fewer human ratings. Our results show the potential of this approach for medical diagnostics and beyond.

Reconstructing early human symbolic evolution using transmission experiments

Engraved ochres and ostrich eggshells from the South African Blombos Cave and Diepkloof Rock Shelter are among the earliest expressions of human symbolic behavior. Furthermore, they appear to document a continuous practice of abstract mark-making across ~40.000 years. During this time, the engraved patterns change from simpler unstructured patterns to complex, ordered and symmetric cross-hatchings. To inform discussions of the possible function of the engravings, we conducted a two-part experimental study. Based on the assumption that the pragmatic use of an artifact will motivate incremental adaptive refinements, we used transmission chain experiments to reconstruct the original trajectory of changes. We then conducted five experiments to assess the cognitive implications of changes to the patterns and compared these to the original engravings. Although we observe interesting similarities, our findings suggest the Blombos and Diepkloof engravings are not only a product of human cognitive biases and constraints on working memory.

Measuring Moral Vacillations

Moral decision-making research is currently dominated by experimental studies that employ dilemmas, situations where more than one course of action may be justifiable. Humans almost characteristically vacillate between options before reaching a conclusion while reasoning on such problems. Current experimental designs disregard this vital aspect of moral decisions by only measuring judgments produced at the end of reasoning. We present an experimental paradigm for measuring moral conflict as a function of vacillations experienced by participants while deliberating. We conducted two experiments to correlate our measure with two different definitions of conflict prevalent in the literature. Across both experiments, we found that people vacillate more on conflicting problems and that vacillations correlate with their subjective feeling of conflict and confidence. We also found that the pattern of deliberation uncovered by these vacillations is inconsistent with currently favored models of moral reasoning and more consistent with a single accumulation to threshold process.

Help me help you: A computational model for goal inference and action planning

Helping is an inherently cooperative behavior, but the cognitive mechanisms underlying this behavior remain relatively underexplored. In this paper, we introduce a novel gamified paradigm for understanding a variety of cognitive behaviors associated with helping. Principals are assigned secret goals in a block-based grid (e.g., move all blue blocks to room C), and helpers can either pass their turn or make a move that could help the principal. We show that principals make useful and pragmatic first moves and helpers accumulate evidence over time before initiating a helpful move. We also introduce a preliminary set of computational models based on recursive pragmatic inference and utility maximization that attempt to account for these behavioral findings.

A Structure of basic emotions: A review of basic emotion theories using an emotionally fine-tuned language model

There is growing interest in emotions in textual data. Based on psychological theories, research has been conducted on assigning emotions as labels to text datasets or developing models to detect emotions present in text. However, little research has been done on the appropriateness of using these theories. In this study, we reviewed three commonly used basic emotion theories: Ekman’s Basic Emotions, Plutchik’s Wheel of Emotions, and GoEmotions. By leveraging a research finding that evaluated the emotional values of words, we were able to fine-tune a language model emotionally. Using it, we analyzed the emotional relationship between the names of basic emotions and evaluated the adequacy of the emotional structure each theory presents. Clear patterns of similarity emerged based on the emotional meaning of the words. Ekman’s and GoEmotions were almost in line with our results, while Plutchik’s had some differences. We discussed these matches and mismatches.

Modelling Rumination as a State-Inference Process

Rumination is a kind of repetitive negative thinking that involves prolonged sampling of negative episodes from one's past, typically prompted by a present negative experience. We model rumination as an attempt at hidden-state inference, formalized as a partially-observable Markov decision process (POMDP). Using this allegorical model, we demonstrate conditions under which continuous, prolonged collection of samples from memory is the optimal policy. Consistent with phenomenological observations from clinical and experimental work, we show that prolonged sampling (i.e., chronic rumination), formalized as needing to sample more evidence before selecting an action, is required when possible negative outcomes increase in magnitude, when states of the world with negative outcomes are a priori more likely, and when samples are more variable than expected. By demonstrating that prolonged sampling may allow for optimal action selection under certain environmental conditions, we show how rumination may be an adaptive for solving particular problems.

Probing the Representational Structure of Regular Polysemy in a Contextual Word Embedding Model via Sense Analogy Questions

Regular polysemes are sets of ambiguous words that all share the same relationship between their meanings, such as CHICKEN and LOBSTER both referring to an animal or its meat. To probe how a context embedding model, here exemplified by BERT, represents regular polysemy, we analyzed whether its embeddings support answering sense analogy questions similar to “is the mapping be- tween CHICKEN (as an animal) and CHICKEN (as a meat) the same as that which maps between LOBSTER (as an animal) to LOBSTER (as a meat)?” We found that (1) the model was sensitive to the shared structure within a regularity type; (2) the shared structure varies across regularity types, potentially reflective of a “regularity continuum;” (3) some high-order latent structure may be shared across regularity types, suggestive of a similar la- tent structure across types; and (4) there is equivocal ev- idence that the aforementioned effects are explained by meaning overlap.

Human Relational Concept Learning on the Synthetic Visual Reasoning Test

Humans exhibit a remarkable ability to learn relational concepts from a small number of examples. On the Synthetic Visual Reasoning Test (SVRT), a collection of 23 problems that require learning relational concepts, people typically discover the relational rules from a handful of examples. An important question is what learning mechanisms underlie the human ability to acquire relational concepts so quickly. Previous work has demonstrated that comparison of examples via analogical mapping underlies rapid relational concept acquisition. Here, we examine whether learners switch to learning strategies that do not involve comparison when cognitive load is high. We conducted two experiments that varied the display format and problem order for the SVRT. When problems are presented in an easy-to-hard order, people learn more efficiently when prior examples are displayed in spatially segregated sets, consistent with the use of analogical mapping as a learning strategy. However, when the problems are presented in a random order, the advantage of spatially segregated displays is eliminated. We propose that when hard problems are encountered early in a problem sequence, analogical mapping becomes too demanding, causing people to fall back on a less efficient learning strategy that does not require the comparison of multiple examples.

Underdetermination and Obligation Rules: Adult and Children’s use of Closure Principles in Moral Learning

Moral learners face an underdetermination problem – the rules they are taught cannot account for all novel cases. One way learners solve this problem is through closure principles, through which a learner assumes that anything that isn’t explicitly forbidden is permitted, and vice versa. The current work aims to explore whether closure rules are used when reasoning about obligations. Building on previous work, we ask 1) whether adults and children use obligation rules in a similar manner to rules about permissibility and impermissibility, and 2) how early in life these inference abilities emerge. Across two studies, we explore inferences about obligation from both adults (N = 120, Mage = 33.73 years) and children (N = 103, Mage = 5.52 years). We found that while both adults and children rationally learn closure principles consistent with deontic logic, children and adults make opposite inferences about novel cases when provided with obligation rules.

Unpredictability shortens planning horizons

Recent research has identified intertemporal impulsivity as a critical cognitive variable for explaining the autocatalytic nature of socioeconomic status. However, how exactly this relationship transpires has yet to be clearly identified, with several possible cognitive mechanisms proposed in the literature. We designed an experimental paradigm where participants farmed crops under budgetary constraints and intermittently faced random resource demands. We discovered that, as a result of unpredictable resource shocks, people's preferences shifted from long-term choices to short-term ones. We also found people's self-reported sense-of-control scores to be predictive of the magnitude of their preference shifts. On the basis of these results, we argue that steep inter-temporal discounting arises as a rational adaptation to persistently experiencing long-term planning failures due to unpredictable resource shocks.

Agenda setting and The Emperor’s New Clothes: people infer that letting powerful agents make their opinion known early can trigger information cascades and pluralistic ignorance

Consensus-based social learning strategies often outcompete other strategies in evolutionary models. But while formal proofs suggest that consensus’ reliability is compromised when individual judgments are not independent, this makes for a notoriously implausible assumption in the biological world: the people we learn from are constantly learning from each other as well. How do we avoid being misled by consensus? We present two experiments and a computational model examining commonsense reasoning about how people’s public and private judgments are influenced by the consensus and social status of those around them. Results suggest that while people realize that these two factors can cause others’ public and private judgments to diverge, their own trust in public consensus depends on how accurately they believe it reflects their informants’ true beliefs.

Around the world in 60 words: A generative vocabulary test for online research

Conducting experiments with diverse participants in their native languages can uncover insights into culture, cognition, and language that may not be revealed otherwise. However, conducting these experiments online makes it difficult to validate self-reported language proficiency. Furthermore, existing proficiency tests are small and cover only a few languages. We present an automated pipeline to generate vocabulary tests using text from Wikipedia. Our pipeline samples rare nouns and creates pseudowords with the same low-level statistics. Six behavioral experiments (N=236) in six countries and eight languages show that (a) our test can distinguish between native speakers of closely related languages, (b) the test is reliable (r=0.82), and (c) performance strongly correlates with existing tests (LexTale) and self-reports. We further show that test accuracy is negatively correlated with the linguistic distance between the tested and the native language. Our test, available in eight languages, can easily be extended to other languages.

Generating oscillation activity with Echo State Network to mimic the behaviour of a simple central pattern generator

This paper presents a method for reproducing a simple central pattern generator (CPG) using a modified Echo State Network (ESN). Conventionally, the dynamical reservoir needs to be damped to stabilize and preserve memory. However, we find that a reservoir that develops oscillatory activity without any external excitation can mimic the behaviour of a simple CPG in biological systems. We define the specific neuron ensemble required for generating oscillations in the reservoir and demonstrate how adjustments to the leaking rate, spectral radius, topology, and population size can increase the probability of reproducing these oscillations. The results of the experiments, conducted on the time series simulation tasks, demonstrate that the ESN is able to generate the desired waveform without any input. This approach offers a promising solution for the development of bio-inspired controllers for robotic systems.

Learning what matters: Causal abstraction in human inference

What shape do people's mental models take? We hypothesize that people build causal models that are suited to the task at hand. These models abstract away information to represent what matters. To test this idea empirically, we presented participants with causal learning paradigms where some features were outcome-relevant and others weren't. In Experiment 1, participants had to learn what objects of different shape and color made a machine turn on. In Experiment 2, they had to predict whether blocks sliding down ramps would cross a finish line. In both experiments, participants made systematic errors in a surprise test that asked them to recall what they had seen earlier. The errors people made suggest that they had built mental models of the task that privileged causally relevant information. Our results contribute to recent efforts trying to characterize the important role that causal abstraction plays in human learning and inference.

Storytelling as Inverse Inverse Planning

Great storytelling takes us on a journey the way ordinary reality rarely does. But what exactly do we mean by a "journey"? Recently, literary theorist Kukkonen (2014) proposed that storytelling is "probability design": the art of giving an audience pieces of information bit by bit, to craft the *journey of their changing beliefs* about the fictional world. A good "probability design" choreographs a delicate dance of certainty and surprise in the reader's mind as the story unfolds from beginning to end. In this paper, we computationally model this conception of storytelling. Building on the classic Bayesian inverse planning model of human social cognition, we treat storytelling as *inverse* inverse planning: the task of choosing actions to manipulate an inverse planner's inferences, and therefore a human audience's beliefs. First, we use an inverse inverse planner to depict social and physical situations, and present behavioral studies indicating that inverse inverse planning produces more expressive behavior than ordinary "naïve planning." Then, through a series of examples, we demonstrate how inverse inverse planning captures many storytelling elements from first principles: character, narrative arcs, plot twists, irony, flashbacks, and deus ex machina are all naturally encoded in the flexible language of probability design.

How does the syntax of counting affect learnability? Evidence from artificial language learning.

Generative syntax and a consistently ordered count routine are both understood to have central roles in learning a number system. However, there has been little experimental exploration of how the diversity of each of these features alters the inductive landscape of number learning, as most empirical work has been constrained to correlational studies. We present a causal manipulation both of syntactic structures and counting procedures, using an artificial language paradigm. Our findings suggest that (1) learners have a greater facility with conjunctive over multiplicative rules of composition, (2) counting procedures help learners to recall words independently of syntax, and (3) predictable syntax helps learners to use numerical concepts, independently of - and possibly despite - counting routines.

Human hacks and bugs in the recruitment of reward systems for goal achievement

Human learning is often motivated by self-imposed challenges, which guide behavior even in the absence of external rewards. Previous studies have shown that humans can use personal goals to "hack" the definition of reward, warranting an extension of the classic reinforcement learning framework to account for the flexible attribution of value to outcomes according to current goals. However, learning through goal-derived outcomes is less efficient than learning through more established reinforcers, such as numeric points. At least three possible explanations exist for this sort of impairment, or "bug". First, occasional lapses in executive function, which is required to encode and recognize goals, may result in subsequent failure to update values accordingly. Second, the higher working memory load required to encode novel stimuli as desirable outcomes may impair people's ability to update and remember correct stimulus-reward associations. Third, a weaker commitment to arbitrary goals may result in dimmer appetitive signals. By extending existing experimental paradigms that include learning from both familiar rewards and abstract, goal-contingent outcomes and combining them with computational modeling techniques, we find evidence for each of the proposed accounts. While other factors might also play a role in this process, our results provide an initial indication of the key elements supporting (or impairing) the attribution of rewarding properties to otherwise neutral stimuli, which enable humans to better pursue arbitrarily set goals.

Multitask Learning Via Interleaving: A Neural Network Investigation

The most common settings in machine learning to study multitask learning assume either that a random task is selected on each training trial, or that one task is trained to mastery and then training advances to the next. We study an intermediate setting in which tasks are interleaved, i.e., training proceeds on task A for some period of time, switches to another task B before A is mastered, and continues to alternate. We examine properties of modern neural net learning algorithms and architectures in this setting. The networks exhibit effects of task sequence that are qualitatively similar to established phenomena in human learning and memory, including: forgetting with relearning savings, task switching costs, and better memory consolidation with interleaved training. By improving our understanding of such properties, one can design learning schedules that are suitable given the temporal structure of the environment. We illustrate with a momentum optimizer that resets momentum following a task switch and leads to reliably better online cumulative learning accuracy.

When it's not out of line to get out of line: Principles of universalizability, welfare, and harm

How do we know when it's OK to break moral rules? We propose that — alongside well-studied outcome-based measures of welfare and harm — people sometimes use universalization, asking "What if everyone felt at liberty to ignore the rule?'' We develop a virtual environment where agents stand in line to gather water. Subjects judge agents who get out of line to try to get water more quickly. If subjects use universalization, they would need to imagine all agents getting out of line and going straight for the water in each environment. To test this prediction, we model an action's universalizability by simulating what would happen if every agent tried to follow a path directly to the water, then evaluating the effects. We also investigate the role of several outcome-based measures, including welfare aggregation and harm-based measures. We find that universalizability plays a important role in rule-breaking judgments alongside outcome-based concerns.

The Importance of Non-analytic Models in Decision Making Research: An Empirical Analysis using BEAST

Decision-making models hold a vital role in the field of cognitive science, serving as a means of describing and predicting human behavior. While classical models with similar assumptions are frequently favored, there is no guarantee they provide the best accounts of behavior. Here, we evaluate BEAST, a model that has demonstrated extraordinary predictive capabilities in diverse settings, but was excluded from a recent large-scale comparison of models because it cannot be analytically estimated. Our evaluation of the model's performance on a large collection of experiments of decisions under risk shows it provides excellent predictions in some domains. We further show how BEAST can be adapted to increase its predictive power in contextualized settings. Our results highlight the importance of a more inclusive approach toward models that may be difficult to analytically estimate to deepen our understanding of the psychological mechanisms underlying human decision making behavior.

How Do Syntactic Statistics and Semantic Plausibility Modulate Local Coherence Effects

Local coherence is a phenomenon in human sentence processing whereby word sequences within a sentence incur processing difficulty when they have a plausible reading different from their true syntactic structure as disambiguated by the global context. Prior research (Tabor, Galantucci, & Richardson, 2003) indicates that more plausible substrings incur more processing difficulty than less plausible ones. In the current article, we challenge this view by providing evidence from two experiments which show that local semantic plausibility can actually facilitate processing. We additionally test whether syntactic statistics can modulate local coherence effects, a prediction made by Lossy-Context Surprisal (LCS; Futrell, Levy, & Gibson, 2020; Hahn, Futrell, Levy, & Gibson, 2022). Although we do not find evidence for effects of syntactic statistics, our overall results cannot be fully explained by any existing account of local coherence alone. We discuss implications for theories of sentence processing.

Cultural reinforcement learning: a framework for modeling cumulative culture on a limited channel

Humans' capacity for cumulative culture is remarkable: we can build up vast bodies of knowledge over generations. Communication, particularly via language, is a key component of this process. Previous work has described language as enabling posterior passing, where one Bayesian agent transmits a posterior distribution to the next. In practice, we cannot exactly copy our beliefs into the minds of others--we must communicate over the limited channel language provides. In this paper, we analyze cumulative culture as Bayesian reinforcement learning with communication over a rate-limited channel. We implement an agent that solves a crafting task and communicates to the next agent by approximating the optimal rate-distortion trade-off. Our model produces documented effects, such as the benefits of abstraction and selective social learning. It also suggests a new hypothesis: selective social learning can be harmful in tasks where initial exploration is required.

The Less is More Paradox in Relational Learning

The ability to generalize previous knowledge to new contexts is a key aspect of human cognition and relational learning. A well-known learning maxim is that breadth of training predicts breadth of transfer. When examples vary in their surface features, this provides evidence that only the common relational structure is relevant. However, there is some evidence suggesting that the above maxim may not apply well in early relational learning. Here, we present a further test whether the maxim holds for young infants. We find that 3-month-old infants perform better with a narrow, perceptually similar training set than with a broad, perceptually variable set. We argue that lower-level perceptual similarities can prompt comparison processes that facilitate relational abstraction. These findings cohere with research arguing relational learning depends on relational alignment.

Infants Infer Social Relationships between Individuals who Engage in Imitative Social Interactions

Infants are born into rich social networks and are faced with the challenge of learning about them. Previous research shows that infants learn about individuals when they observe their social interactions, but it is not clear whether they infer their social dispositions, their social relationships to one another, or both. The current studies address this question in 12-month-old infants and 16- to 18-month-old toddlers who observe social interactions involving imitation. In Studies 1 and 3, infants and toddlers expected that imitators, compared to non-imitators, would respond to their social partners’ distress. Likewise, they expected the targets of imitation, compared to non-targets, to respond to their partner’s distress. In Study 2, these expectations did not generalize to interactions with a new partner, providing evidence that infants learned about the relationships between individuals as opposed to their dispositions. In Study 3, infants’ did not make predictions about responses to laughter, suggesting that infants see imitation as indicative of a specific kind of social relationship. Together, these results provide evidence that infants and toddlers learn about the social relationships of unknown individuals by observing interactions involving imitation.

Children’s Cost-Benefit Analysis About Agents who Act for the Greater Good

Acting for the greater good often involves paying a personal cost to benefit the collective. In two studies, we investigate how children (N = 154, Mage = 7.94 years, SD = 1.13, Range = 6.03 – 9.98 years) reason about cost and consequence. Children predicted how many agents would pay a personal cost to prevent a consequence for their entire community and judged agent(s) who refused to pay this cost. In Study 1, children expected more agents to pay a minor cost to prevent a major consequence and judged defection as less permissible than in the opposite case. Study 2 investigated the intermediate cases (Major/Major and Minor/Minor Cost/Consequence). Children expected agents to pay a minor cost regardless of consequence, and only expected agents to pay a major cost when consequence was major. In their judgments, children only considered consequence – defection was more permissible when consequence was minor, regardless of cost.

When to choose: Information seeking in the speed-accuracy tradeoff

Normative accounts of decision-making predict that people attempt to balance the immediate rewards associated with correct responses against the costs of deliberation. However, humans frequently deliberate longer than normative models say they should. We propose that people try to optimize not only their rate of material rewards, but also their rate of information gain. A computational model that implements this idea successfully mimics human decision makers, reproducing key patterns of behavior not predicted by alternative models. Moreover, simulations reveal a normative basis for our model: An agent that exchanges even a small amount of immediate reward for information will improve its decision-making ability through learning, allowing it to earn more reward in the long run than an agent disinterested in information. Maximizing a combination of reward and information rate is a simple yet effective strategy for solving the speed-accuracy tradeoff that may resolve lingering mysteries about human decision-making.

Reward-driven and memory-driven attentional biases automatically modulate rapid choice

In two experiments we examined the influence of ‘history-driven’ attentional biases on choice behavior. In Experiment 1 we used a value-modulated attentional capture procedure to induce an automatic reward-related attentional bias, and found that this bias shaped choice in a subsequent task in which participants were required to pick the highest number from a briefly displayed choice array. In Experiment 2 we investigated the influence of a working memory manipulation, and found that choice in the number-selection task was influenced by the current (and prior) contents of memory, consistent with an influence of memory-driven attentional bias on information encoding. Our findings indicate that history-driven attentional biases can translate to an influence on overt, downstream processes of behavioral choice, and should be incorporated into models of the interaction between attention and choice.

A rational model of spatial neglect

Spatial neglect has been a phenomenon of interest for perceptual and neuropsychological researchers for decades. However, the underlying cognitive processes remain unclear. We provide a Bayesian framework for the classic line bisection task in spatial neglect, regarding it as rational inferences in the face of uncertain information. A Bayesian observer perceives the left and right endpoints of a line with uncertainty, and leverages prior expectations about line lengths to compensate for this uncertainty. This Bayesian model provides a basis for characterizing different patterns of behavior. Our model also captures the paradoxical cross-over effect observed in earlier studies as a natural outcome when uncertainty is high and the observer falls back on priors. It provides measures that correlate well with measures from other neglect tests, and can accurately distinguish stroke patients from healthy controls. It has the potential to facilitate spatial neglect studies and inform clinical decisions.

Speakers' cognitive representations of gender and number morphology shape cross-linguistic tendencies in morpheme order

Languages exhibit a tremendous amount of variation in how they organise and order morphemes within words; however, regularities are also found. For example, gender and number inflectional morphology tend to appear together within a single affix, and when they appear in two separate affixes, gender marking tends to be placed closer to the stem than number. Formal theories of gender and number have been designed (in part) to explain these tendencies. However, determining whether the abstract representations hypothesised by these theories indeed drive the patterns we find cross-linguistically is difficult, if not impossible, based on the natural language data alone. In this study we use an artificial language learning paradigm to test whether the inferences learners make about the order of gender and number affixes—in the absence of any explicit information in the input—accord with formal theories of how they are represented. We test two different populations, English and Italian speakers, with substantially differ- ent gender systems in their first language. Our results suggest a clear preference for placing gender closest to the noun across these populations, across different types of gender systems, and across prefixing and suffixing morphology. These results expand the range of behavioural evidence for the role of cognitive representations in determining morpheme order.

Self-Censorship Appears to be an Effective Way of Reducing the Spread of Misinformation on Social Media

There is increasing pressure on social media companies to reduce the spread of misinformation on their platforms. However, they would prefer not to be the arbiters of truth as the truth can be subjective or otherwise hard to determine. Instead, they would prefer that social media users themselves show better discernment when deciding which information to share. Here we show that allowing people to share only those social media posts that they have indicated are true significantly improves sharing discernment, as measured by the difference in the probability of sharing true information versus the probability of sharing false information. Because it doesn’t require social media companies to be the arbiters of truth, this self-censorship intervention can be employed in situations where social media companies suspect that individuals are propagating misinformation but are not sufficiently confident in their suspicions to directly censor the individuals involved. As such, self-censorship can usefully supplement externally imposed (i.e. traditional) censorship in reducing the propagation of false information on social media platforms.

How Speech and Representational Gestures Align in Child-Directed Language: a Corpus-based Study

Representational gestures are co-speech gestures that carry semantic content related to the content of speech. Previous studies focusing on adult-adult conversation have investigated the temporal alignment of gestures and speech finding that the overwhelming majority of representational gestures are produced right before the lexical content they refer to (their lexical affiliate, LA). However, nothing is yet known about whether caregivers would also time their gestures in the same way in naturalistic interactions. We annotated representational gestures from a large corpus (ECOLANG) of semi-naturalistic conversations between caregivers and their 3-4 year old children (n = 899 gestures from n=36 caregivers). We found that, just as in adult-directed language (ADL), representational gestures in child-directed language (CDL) were more tightly linked to the onset of LAs than the onset of the utterance in which LAs were produced (hence planned when full events are encoded); with the overall majority of the representational gestures starting before their LAs. We further found that age of acquisition (AoA) rating of the LA had a significant effect on the speech-gesture latency. We found that for words acquired earlier, the gesture’s stroke (the meaningful part of a gesture) tended to be produced before the LA’s onset; for the later acquired word, the stroke tended to be produced at the same time or after the onset of the LA in speech. Our findings suggest that: (1) Regardless of their addressee, speakers always time the production of representational gestures to specific conceptual/linguistic units, rather than the full event/utterance. (2) In contrast to ADL, caregivers’ gestures may support addresses’ linguistic processing not only by supporting word prediction (of likely better-known words), but also by supporting the learning of conceptual features (of likely less well-known words).

Social learning with a grain of salt

Humans are remarkably effective social learners, with several recent studies formalizing this capacity using computational models. However, previous research has often been limited to tasks where observer and demonstrator share the same reward function. In contrast, humans can learn from others who have different preferences, skills, or goals. To study social learning under individual differences, we introduce the socially correlated bandit, where participants have personalized rewards, which are correlated with but not identical to those of others. Social information can still be useful, but not when used verbatim. We present a model of social generalization that integrates individual and social information into the generalization process, but assumes social information to be noisier and thus less informative. This model out-competes previous models, with it being the dominant strategy in evolutionary simulations. Our findings expand on previous models of social learning, showing humans can integrate social information more flexibly than previously assumed.

Habits of Mind: Reusing Action Sequences for Efficient Planning

When we exercise sequences of actions, their execution becomes more fluent and precise. Here, we consider the possibility that exercised action sequences can also be used to make planning faster and more accurate by focusing expansion of the search tree on paths that have been frequently used in the past, and by reducing deep planning problems to shallow ones via multi-step jumps in the tree. To capture such sequences, we use a flexible Bayesian action chunking mechanism which finds and exploits statistically reliable structure at different scales. This gives rise to shorter or longer routines that can be embedded into a Monte-Carlo tree search planner. We show the benefits of this scheme using a physical construction task patterned after tangrams.

The Goal Bias Emerges Early in Motion Event Inspection and Speech Planning: Evidence from Eye-Movements

After viewing motion events with a starting-point (Source) and end-point (Goal), people mention the Goal more often and remember it more accurately than the Source. This Goal privilege has been hypothesized to arise from an on-line attentional bias that occurs during event apprehension itself, yet no data exists that: (a) documents this online attentional bias and (b) correlates any online bias with offline memory and linguistic measures. Here we do just that: we recorded participants’ eye movements as they viewed or prepared to describe motion events and later tested their memory of Goals or Sources. We find an online attentional bias for Goals over Sources during initial encoding of events. This bias is stronger during free inspection compared to speech planning, an effect likely to reflect the fact that sentence preparation partially promotes encoding and mentioning Sources. Moreover, the extent of the attentional Goal bias is systematically related to both language production and memory, such that the attentional Goal bias is greatest when the Source is not mentioned later during production or not remembered later at test. Thus, we provide the first evidence that an attentional Goal bias appears as soon as one starts to visually encode motion events.

What people learn from punishment: joint inference of wrongness and punisher's motivations from observation of punitive choices

Punishment is a cost imposed on a target, in response to an un- desirable action. Yet choosing to punish also reveals information about the authority’s own motives and values. We propose that observers jointly infer the wrongness of the action and the authority’s motivations. Using hypothetical scenarios in un- familiar societies, we experimentally manipulated observers’ prior beliefs and measured human observers’ inferences after observing punishment. These inferences were recapitulated in a formal model that inverts an intuitive causal model of authorities who make rational choices about punishment by weighing its costs and benefits (i.e. utilities). An essential component of this model, driving these inferences, is that legitimate authorities consider the utility of a proportional response to harmful actions, which depends on the balance between the wrongness of the act and the severity of the punishment.

Predicting Word Learning in Children from the Performance of Computer Vision Systems

For human children as well as machine learning systems, a key challenge in learning a word is linking the word to the visual phenomena it describes. We explore this aspect of word learning by using the performance of computer vision systems as a proxy for the difficulty of learning a word from visual cues. We show that the age at which children acquire different categories of words is correlated with the performance of visual classification and captioning systems, over and above the expected effects of word frequency. The performance of the computer vision systems is correlated with human judgments of the concreteness of words, which are in turn a predictor of children's word learning, suggesting that these models are capturing the relationship between words and visual phenomena.

Do school-age children learn that 2 x 3 = 3 x 2 relying on previous intuitions?

Commutativity–the fact that changing the order of operands in an operation does not change the result–is a fundamental principle of mathematics. How does its understanding develop in children? While this question has mostly been addressed for addition, here we focus on multiplication. We ask whether children’s formal learning of multiplication commutativity relies on pre-existing non-symbolic intuitions, or whether it is first learnt symbolically and only later elaborated as a general principle that extends to concrete situations. 2nd and 3rd graders received an intervention on commutative multiplication, before and after which they played a number comparison game testing their understanding of commutativity in symbolic and non-symbolic contexts. Our results suggest that the commutative principle of multiplication may not be available to intuition before formal teaching: perceiving that 2 groups of 3 dots and 3 groups of 2 dots contain the same number of dots requires mastering commutativity symbolically first.

Simplicity and Informativeness in the Evolution of Combinatorial Structure

Cultural symbol systems, such as language, music, or pictorial diagrams, are crucial for the storage and transmission of knowledge, and ultimately underpin our capacity for culture. One important feature of these systems is their combinatorial structure: the reuse of building blocks to compose new concepts or ideas. Here, we conduct a study that combines iterated learning with a communication game to show that combinatorial structure is not inevitable, but rather arises as a trade-off between the simplicity of signals and the amount of information they convey. Our results provide additional insights into the role of communication in the emergence of signal structure, as a force that maintains complexity and creates alignment. These results validate a key theoretical prediction about how combinatorial structure arises in the interplay of learning and use, and shed light on how signaling systems such as language have become such powerful and flexible tools in human cognition.

Evidence From Computational Linguistics for the Concept of Biconsonantal Etymons in Hebrew

This paper explores the hypothesis of the historical evolution of Semitic morphology from biconsonantal (2C) etymons, to triconsonantal (3C) roots, which make up the majority of words in Biblical Hebrew as well as in other Semitic languages such as modern Hebrew, Arabic, etc. The rules for reducing the 3C roots to their 2C etymons are provided in detail. We use BHSA, a manually annotated corpus of the Hebrew Bible, and Word2Vec, a method for converting words to a vector representing their semantic meaning, to study the hypothesis of evolution from 2C etymons to 3C roots in biblical Hebrew. Namely, we show that words in Hebrew with different roots, that might have originated from the same 2C etymons form a denser cluster than random sets of words of the same size. These differences are statistically significant and strongly support the hypothesis of evolution from 2C etymons to 3C roots.

Father, don't forgive them, for they could have known what they're doing

What someone knew matters for how we hold them responsible. In three studies, we explore people’s responsibility judgments for negative outcomes to knowledgeable versus ignorant agents. We manipulate whether agents arrived at their knowledge state unintentionally or willfully. In Experiment 1, agents who knew about the harmful consequences of their actions were judged highly responsible no matter how they came to know. In contrast, willfully ignorant agents were judged more responsible than unintentionally ignorant agents. Participants inferred that willfully ignorant agents were more likely to believe that their action might cause harm. When we explicitly stipulate the agents’ beliefs in Experiment 2, the ‘willful ignorance’ effect reduces but persists. Participants inferred that the willfully ignorant agent was more likely to have acted anyhow even if they had known. Explicitly stating whether the agent’s action depended on their knowledge further reduced the ‘willful ignorance’ effect in Experiment 3.

Asymmetry Effects in Generic and Quantified Generalizations

Generic statements ('Tigers have stripes') are pervasive and early-emerging modes of generalization with a distinctive linguistic profile. Previous experimental work found that generics display a unique asymmetry between their acceptance conditions and the implications that are typically drawn from them. This paper presents evidence against the hypothesis that only generics display an asymmetry. Correcting for limitations of previous designs, we found a generalized asymmetry effect across generics, various kinds of explicitly quantified statements ('most', 'some', 'typically', 'usually'), and variations in types of predicated properties (striking vs. neutral). We discuss implications of these results for our understanding of the source of asymmetry effects and whether and in which ways these effects might introduce biased beliefs into social networks.

Modelling the Integration of Co-Speech Gestures into Sentence Meaning Composition

To investigate how co-speech gestures modulate linguistic understanding, we experimentally test two Bayesian Pragmatic models. We identify the semantic effect of a spoken or gestural utterance with the change it makes in listener’s probabilistic predictions of the speaker’s communicative intentions. We focus on action-expressing gestures and the respective verbs that correspond to action-affording instruments or, respectively, their denoting nouns. Combining Pustejovsky’s’ Generative Lexicon approach with Gibson’s affordance theory, we ask: (1) Does a co-speech gesture make any difference for semantic comprehension and the corresponding probabilistic prediction? (2) Is the semantic effect of a gesture similar or identical to the one of the corresponding verb? (3) To which extent does the gesture’s semantic effect depend on the listener’s recognition of the gesture as an expression of the corresponding verb? (4) Does the comprehended affordance predict the instrument better than the co-occurrence statistics (GloVe) regarding the verb and the noun?

Characterizing Drivers' Peripheral Vision via the Functional Field of View for Intelligent Driving Assistance

Previous work has modeled the combination of foveal and peripheral gaze as the Functional Field of View (FFoV), showing a relationship between FFoV degradation and poor driving outcomes making it an object of interest for intelligent driving assistance algorithms. We study the shape and dynamics of the FFoV using a peripheral detection task in a virtual reality (VR) driving simulator with licensed drivers in urban driving environments. We find that missed targets occurred vertically higher in the driver FoV than hits. This supports a vertically asymmetric (upward-inhibited) shape of the FFoV. Additionally, we show that this asymmetry disappears when the same PDT is conducted in a non-driving setting. Finally, we examined the dynamics of the FFoV, finding that drivers' peripheral target detection ability is inhibited (general interference rather than tunnel vision) right after saccades but recovers once drivers fixate for some time.

How Bizarre: Does the color bizarreness effect extend to long-term memory?

A well-known phenomenon of memory is the bizarreness effect which refers to enhanced memory for objects that are highly incongruent with people’s prior expectations. This phenomenon was recently extended into the visual domain of color, showing enhanced memory for objects paired with expectation-incongruent (or bizarre) colors. Here, we explore whether the enhanced memory for bizarre/expectation-incongruent objects extends to memory for the object-color binding and whether this binding is well-preserved long-term. Using a 4-Alternative forced choice task, we assessed memory for object colors as a function of expectation-congruency on one day and 3 days later. Our results revealed no significant difference in recognition memory for bizarre colors compared to expectation-congruent colors, and no enhanced memory for bizarre colors in long-term memory. These findings highlight conditions where the enhanced memory for expectation-incongruent information is limited, providing an interesting challenge to current mechanistic accounts of memory for expectation-related information.

Minority-group incubators and majority-group reservoirs for promoting the diffusion of climate change and public health adaptations

Current theory suggests that heterogeneous metapopulation structures can help foster the diffusion of innovations to solve pressing issues including climate change adaptation and promoting public health. In this paper, we develop an agent-based model of the spread of adaptations in simulated populations with minority-majority metapopulation structure, where subpopulations have different preferences for social interactions (i.e., homophily) and, consequently, learn deferentially from their own group. In our simulations, minority-majority-structured populations with moderate degrees of in-group preference better spread and maintained an adaptation compared to populations with more equal-sized groups and weak homophily. Minority groups act as incubators for novel adaptations, while majority groups act as reservoirs for the adaptation once it has spread widely. This suggests that population structure with in-group preference could promote the maintenance of novel adaptations.

Visual perception principles in constellation creation in individuals

Many cultures share common constellations and common narratives about the stars in the night sky. Previous research has shown that this overlap in asterisms, minimal star groupings inside constellations, is clearly present across 22 distinct culture groups and can be explained in part by properties of individual stars (brightness) and properties of pairs of stars (proximity) (Kemp, Hamacher, Little and Cropper, 2022). The same work, however, found no evidence that properties of triples (angle) and quadruples (good continuation) predicted constellation formation. We developed a behavioural experiment to explore how individuals form constellations under conditions that reduce cultural learning. We found that participants independently selected and connected similar stars, and that their responses were predicted by two properties of triples (angle and even spacing) in addition to the properties of brightness and proximity supported by previous work. Our findings lend further evidence to the theory that commonality of constellations across cultures is not a result of shared human history but rather stems from shared human nature.

Toward a normative theory of (self-)management by goal-setting

People are often confronted with problems whose complexity exceeds their cognitive capacities. To deal with this complexity, individuals and managers can break complex problems down into a series of subgoals. Which subgoals are most effective depends on people's cognitive constraints and the cognitive mechanisms of goal pursuit. This creates an untapped opportunity to derive practical recommendations for which subgoals managers and individuals should set from cognitive models of bounded rationality. To seize this opportunity, we apply the principle of resource rationality to formulate a mathematically precise normative theory of (self-)management by goal-setting. We leverage this theory to computationally derive optimal subgoals from a resource-rational model of human goal pursuit. Finally, we show that the resulting subgoals improve the problem-solving performance of bounded agents and human participants. This constitutes a first step towards grounding prescriptive theories of management and practical recommendations for goal-setting in computational models of the relevant psychological processes and cognitive limitations.

Communicative need shapes choices to use gendered vs. gender-neutral kinship terms across online communities

Work has shown that greater need to refer to a semantic domain drives greater lexical precision within that domain, both across languages, and in lexical choice in (within-language) dyadic interactions. We complement this, studying the relation between communicative need and precision across communities of speakers of the same language. Taking kinship as our domain, we find evidence that differences in communicative need between communities within a language contribute to variation in lexical precision in use. We show that this variation is partly due to differences in the kinds of pragmatic contexts the communities talk about, but that community variation in lexical precision exists over and above the factors we study, suggesting that more work is needed to elucidate additional pragmatic influences on the simplicity--informativity trade-off.

Exploring Joint Attention in American Sign Language: The Influence of Sign Familiarity

Children’s ability to share attention with another social partner (i.e., joint attention) has been found to support language development. Despite the large amount of research examining the effects of joint attention on language in hearing population, little is known about how deaf children learning sign languages achieve joint attention with their caregivers during natural social interaction and how caregivers provide and scaffold learning opportunities for their children. The present study investigates the properties and timing of joint attention surrounding familiar and novel naming events and their relationship to children’s vocabulary. Naturalistic play sessions of caretaker-child-dyads using American Sign Language were analyzed in regards to naming events of either familiar or novel object labeling events and the surrounding joint attention events. We observed that most naming events took place in the context of a successful joint attention event and that sign familiarity was related to the timing of naming events within the joint attention events. Our results suggest that caregivers are highly sensitive to their child’s visual attention in interactions and modulate joint attention differently in the context of naming events of familiar vs. novel object labels.

Efficient Detectives in the Sandbox: Children Demonstrate Adaptive Information-Search Strategies in a Novel Spatial Search Game

Recent studies suggest that children are \emph{ecological active learners} who recognize and exploit the ecology of their learning environment (Ruggeri, 2022). However, when assessed by verbal tasks such as the 20-questions game, systematic search only matures around age 7. The current study examined if even young children can adapt their information-search strategies in a developmentally-appropriate task requiring minimal verbal or conceptual abstraction skills. Three to 7-year-olds (N = 76, M = 5.7 years) played a search game with a structure analogous to the 20-questions game. We manipulated whether children received predecisional cues about the past location of the solution or not, across two search phases, and further varied whether the cues follow a Uniform or Skewed distribution. Children adapted their information-search strategies as predicted: They followed a constraint-seeking strategy in the absence of cues, and only switched to hypothesis-scanning when exposed to the Skewed cues.

Learning and Information Use in an Intergroup Context

When faced with uncertainty, human observers maximize performance by integrating sensory information with learned task-relevant regularities. Does this behavior similarly occur in social settings? In this paper, we explore how reward-seeking behavior in an intergroup context is affected by readily available but task-irrelevant social information (in the form of group membership) when task-relevant reward information can be learned over time. Across two experiments, we show that participants learned and utilized task-relevant regularities to inform their choices. We also show that human observers are not universally biased towards utilizing social information in all settings––participants learned to disregard social information when not relevant to the task at hand. However, learning about the utility of social information (Experiment 2) had a long-term influence on observers’ ability to subsequently learn and utilize available sources of information. Real-world intergroup contexts typically encompass situations and stimuli that have been previously experienced by the observer. Our findings highlight the powerful influence of learning in such contexts.

People seek easily interpretable information

Research in psychology and artificial intelligence has sought to ground information-seeking behavior in rational terms, typically assuming that people or agents prefer more informative data over less informative data. While this seems reasonable on its surface, it assumes that informativeness is only a property of the data, rather than a joint property of the data and a (potentially bounded) learner. That is, to the extent that it is hard to draw the right inferences from data that are theoretically "high information," the data will not actually be highly informative to the learner. Here, we investigate active learning in humans using the code-breaking game Mastermind, which requires deductive reasoning from evidence. We find that people make queries that are less informative than random guesses, challenging standard rational or resource-rational accounts of information-seeking. We then show that people make queries are informative to them assuming they have a bounded capacity to draw inferences. We also find that participants prefer queries that provide easily-interpretable information over queries that provide more information but are less interpretable. Our results suggest that people are aware of their own cognitive limitations and seek information that they can use.

Comparing Humans and Models on a Similar Scale: Towards Cognitive Gender Bias Evaluation in Coreference Resolution

Spurious correlations were found to be an important factor explaining model performance in various NLP tasks (e.g., gender or racial artifacts), often considered to be “shortcuts” to the actual task. However, humans tend to similarly make quick (and sometimes wrong) predictions based on societal and cognitive presuppositions. In this work we address the question: can we quantify the extent to which model biases reflect human behaviour? Answering this question will help shed light on model performance and provide meaningful comparisons against humans. We approach this question through the lens of the dual-process theory for human decision-making. This theory differentiates between an automatic unconscious (and sometimes biased) “fast system” and a “slow system”, which when triggered may revisit earlier automatic reactions. We make several observations from two crowdsourcing experiments of gender bias in coreference resolution, using self- paced reading to study the “fast” system, and question answering to study the “slow” system under a constrained time setting. On real-world data humans make ∼3% more gender- biased decisions compared to models, while on synthetic data models are ∼12% more biased. We make all our of our code and data publicly available.

Eye Movements in Information-Seeking Reading

In this work, we use question answering as a general framework for studying how eye movements in reading reflect the reader's goals, how they are pursued, and the extent to which they are achieved. We leverage fine-grained annotations of task-critical textual information to perform a detailed comparison of eye movements in information-seeking and ordinary reading regimes. We further examine how eye movements during information seeking relate to question answering behavior. We find that reading times, saccade patterns and sensitivity to the linguistic properties of the text are all strongly and systematically conditioned on the reading task, and further interact with question answering behavior. The observed reading patterns are consistent with a rational account of cognitive resource allocation during task-based reading.

Bridging Declarative, Procedural, and Conditional Metacognitive Knowledge Gap Using Deep Reinforcement Learning

In deductive domains, three metacognitive knowledge types in ascending order are declarative, procedural, and conditional learning. This work leverages Deep Reinforcement Learning (DRL) in providing adaptive metacognitive interventions to bridge the gap between the three knowledge types and prepare students for future learning across Intelligent Tutoring Systems (ITSs). Students received these interventions that taught how and when to use a backward-chaining (BC) strategy on a logic tutor that supports a default forward-chaining strategy. Six weeks later, we trained students on a probability tutor that only supports BC without interventions. Our results show that on both ITSs, DRL bridged the metacognitive knowledge gap between students and significantly improved their learning performance over their control peers. Furthermore, the DRL policy adapted to the metacognitive development on the logic tutor across declarative, procedural, and conditional students, causing their strategic decisions to be more autonomous.

Faulty Memories, Favored Outcomes: How Errors Impact Learning Processes

Recent studies suggest that errors facilitate learning in certain conditions. Despite this, reinforcement paradigms dominate learning methods, subscribing to the narrative that errorless learning is the foundation of an ideal learning environment. If we continue to view learning from this restrictive perspective, we may fail to capture and apply the benefits of errors. In this paper, we investigate two potential mechanisms of post-error learning. Participants (N = 61) learned word pairs in either a study or error trial before taking a final test. Supporting past error learning literature, errors before a study opportunity led to better performance on a final test. Differences in reaction times between conditions support the theory that errors increase learning by acting as a mediator, or secondary cue, to the correct answer on subsequent tests.

Verb vocabularies are shaped by complex meanings from the onset of development

Verbs and nouns vary in many ways – including in how they are used in language and in the timing of their early learning. We compare the distribution of semantic features that comprise early-acquired verb and noun meanings. Given overall semantic and syntactic differences between nouns and verbs, we hypothesized that the preference for directly perceptible features observed for nouns would be attenuated for verbs. Building on prior work using semantic features and semantic networks in nouns, we find that compared to early-learned nouns (N = 359), early-learned verbs (N = 103) have meanings disproportionately built from complex information inaccessible to the senses. Further, children’s early verb vocabularies (N = 3,804) show semantic relationships strongly shaped by this complex information from the beginning of vocabulary development. Complexity is observed in early verb meanings and is reflected in the vocabularies of children even at the outset of verb learning.

Trial history influences the malleability of gender differences in children’s mental rotation performance

Despite accumulating evidence of gender differences in mental rotation performance, much remains unknown about the variables that lead to an advantage among boys compared to girls. Here we examined the role of trial history on children’s performance. To this end, we manipulated the difficulty of trials and implemented drift diffusion modeling (DDM) to assess how prior exposure to easy versus hard trials affects the parameters of drift rate and decision threshold. In Experiment 1, children were presented with either an easy-to-hard or a hard-to-easy block order. On easy trials, there were no gender differences in accuracy or drift rates, regardless of order. On hard trials, girls matched boys in accuracy and drift rates, but only when easy trials were presented first, suggesting that girls’ performance on hard trials benefited from prior exposure to easy trials. In Experiment 2, we ruled out a general practice effect, confirming that improvement in girls’ performance is specific to exposure on easy trials prior to hard trials, not just more trials. Additionally, boys, in general, had larger decision thresholds than girls. Taken together, these findings point to gender differences in mental rotation performance that are dependent on trial history and that may reflect differences in affective and/or motivational factors between boys and girls.

Mapping a Plurality of Explanations with NLP: A Case Study of Mothers and Health Workers in India

Understanding the values, norms, behaviors, and causal beliefs of communities is a central goal of cognitive science, with practical benefits of grasping and improving community factors such as healthcare delivery. These cultural causal beliefs are evident, in part, within narratives, interview transcripts, ethnography, and other textual sources, but analyzing these texts presently involves tedious expert hand-coding or relatively shallow qualitative text analysis or classification. We present a novel approach for extracting graphical causal models from text via NLP, including qualitative causality, intentions, teleology, sentiment, welfare, social influence, and other rationale. The factors (i.e., nodes) of these causal models are tagged with ethnographic attributes and word-senses, allowing aggregation of causal models over thousands of passages to identify correlations and recurring themes. We apply this approach to a corpus of narrative interviews about maternal and child health and healthcare delivery in Bihar, India, corroborating the hand-coded results of human experts and also identifying novel insights about explanatory structure.

Very Young Infants’ Sensitivity to Consonant Mispronunciations in Word Recognition

Before they start to talk, infants learn the form and meaning of many common words. In the present work, we investigated the nature of this word knowledge, testing the specificity of very young infants’ (6-14 months) phonological representations in an internet-based language-guided-looking task using correct pronunciations and initial-consonant mispronunciations of common words. Across the current sample (n=78 out of 96 pre-registered), infants’ proportion looking to the target (named image) versus the distracter was significantly lower when the target word was mispronounced, indicating sensitivity to phonological deviation. Performance patterns varied by age group. The youngest group (6-8 months, n=30) was at chance in both conditions, the middle group (9-11 months, n=21) showed significant recognition of correct pronunciations and a marginal mispronunciation effect, and the oldest age group (12-14 months, n=27) demonstrated the mature pattern: significant recognition and a significant mispronunciation effect. Ongoing work is completing the pre-registered sample size.

Active Inference and Psychology of Goals: A study in Substance and Process Metaphysics

Active Inference and its accompanying Bayesian Mechanics (BM) are important psychological and cognitive science theories. While there is a strong interaction between the theories and the philosophical realm, it needs to be clarified what its metaphysical commitments are. We tease out these commitments while looking from the perspective of the psychology of goals. We find that Active Inference cannot account for the dynamic growth of goals, primarily because of its closed generative model. We trace the reason for this through the extrinsic `ontological constraint' of BM, characteristic of all mechanistic models which follow the `logic of machines.' Finally, we ground our arguments in the necessity of external relations in substance metaphysics and its incompatibility with internal relations and impredicativity. Thus we argue that Active Inference implicitly presupposes a Substance metaphysics, yielding the theory no resource to model novelty, growth, and development observed in human psychology. We briefly sketch a powerful alternative grounded in process metaphysics to model biological and cognitive systems.

Evidence for a language-independent conceptual representation of pronominal referents

Across many semantic domains, cross-linguistic regularities in categorization systems (e.g., color or kinship terms) have been taken to reflect constraints on how humans perceive and conceptualize the world. Such conceptual representations are often assumed to be universal, and independent of an individual's experience with a particular language. However, in most cases, representational constraints have not been observed empirically on language-independent grounds. This study comes to fill in this gap. We use a card sorting task to provide the first empirical evidence for a common, language-independent representation of pronominal referents, shared by speakers of different languages.

Differences between Mimicking and Non-Mimicking laughter in Child-Caregiver Conversation: A Distributional and Acoustic Analysis

Despite general agreement that laughter is crucial in social interactions and cognitive development, there is surprisingly little work looking at its use through childhood. Here we investigate laughter in middle childhood, using a corpus of online calls between child and parent and between the (same parent) and another adult. We focus on laughter mimicry, i.e., laughter shortly following laughter from the partner, and we compare mimicking and non-mimicking laughter in terms of distribution and acoustic properties using spectrotemporal modulation measures. Our results show, despite similar frequencies in laughter production, different laughter mimicry patterns between Parent-Child and Parent-Adult interactions. Overall, in comparison with previous work in infants and toddlers, our results show laughter mimicry is more balanced between parents and school-age children. At the acoustic level, we observe differences between mimicking and non-mimicking laughter in children, but not in adults. Moreover, we observe significant differences in laughter acoustics in parents depending on whether they interact with children or adults, which highlights a strong interlocutor effect on laughter mimicry.

Sustaining Relational Preference in a Repeated Relational Match-to-Sample Task in the Absence of Task Support

Previous work has demonstrated that relational preference in the Relational Match-to-Sample task can be improved compared to baseline by providing people with the opportunity to consider the target item in isolation prior to receiving the full triad. However, it remains unclear whether the benefits of these supports persist in their absence, or can be observed when a prior strategy has already been established to complete the task. To this end, we conducted two experiments using 2 (presentation-type) by 2 (order) mixed designs to examine the efficacy of two previously established task supports: isolated-focus and description. The aims of this work were to gain further insight into the utility of these supports as a means of promoting relational preference both when the supports are present and when they are absent. We discuss the implications for pedagogical practices and extensions of this work to other materials and tasks.

What Language Reveals about Perception: Distilling Psychophysical Knowledge from Large Language Models

Understanding the extent to which the perceptual world can be recovered from language is a fundamental problem in cognitive science. We reformulate this problem as that of distilling psychophysical information from text and show how this can be done by combining large language models (LLMs) with a classic psychophysical method based on similarity judgments. Specifically, we use the prompt completion functionality of GPT3, a state-of-the-art LLM, to elicit similarity scores between stimuli and then apply multidimensional scaling to uncover their underlying psychological space. We test our approach on six perceptual domains and show that the elicited judgments strongly correlate with human data and successfully recover well-known psychophysical structures such as the color wheel and pitch spiral. We also explore meaningful divergences between LLM and human representations. Our work showcases how combining state-of-the-art machine models with well-known cognitive paradigms can shed new light on fundamental questions in perception and language research.

Autonomy-supportive teaching algorithm which fosters independent learners

After being taught by teachers, learners often need to work independently in new situations. However, a teaching strategy that most efficiently fosters independent learning remains elusive. In this study, we developed a novel experimental paradigm to compare various teaching strategies. In addition, we formalized autonomy-supportive teaching and constructed an autonomy-support algorithm that estimates learners' mental states and aims to enhance both learners’ competence and autonomy. In the experiment, participants were taught through different teaching algorithms depending on the experimental conditions, after which they independently worked on a new set of tasks. Our results demonstrate that compared to the all- and no-teach algorithms, the autonomy-support algorithm enhances learners' engagement while being taught and enhances performance when learners independently work on a new set of tasks. Our findings contribute to the existing observational and interventional research on education by providing rigorous evidence in an experimentally controlled setting.

The impact of caregivers' multimodal behaviours on children’s word learning: A corpus-based investigation

Studies have shown the importance of caregivers’ multimodal behaviours (e.g., prosody, gestures, gaze) on children’s word learning. However, most studies focus on only one specific behaviour (e.g., only prosody). Here, we investigate which multimodal behaviours used by caregivers best predict children’s word learning and vocabulary growth. Using data from the ECOLANG corpus, we analysed caregiver behaviour in semi-naturalistic interactions with their child (3 to 4 years old) in which they talked about known and unknown toys. We analysed caregivers’ (n=36) use of multimodal cues while labelling the objects, specifically their use of yes/no questions, pitch, representational gestures, pointing, object manipulations and gaze. Caregivers’ pitch, use of yes/no questions and pointing predicted children’s word learning. In particular, higher pitch when labelling unknown toys predicted immediate word learning. The degree to which caregivers used higher pitch when producing the label for known compared to unknown toys predicted both immediate learning and vocabulary growth. Furthermore, the degree to which caregivers used yes/no questions more for unknown toys predicted immediate learning, while the frequency of yes/no questions when naming unknown toys predicted vocabulary growth. Lastly, caregiver pointing also predicted immediate label learning and vocabulary growth, but in the opposite direction from prosody: the more they pointed towards known toys, the better children’s learning of novel toy labels. Other behaviours did not predict word learning. Overall, these results provide evidence for the important role of multimodal caregiver behaviours, particularly prosody, on children’s lexical development.

The emergence of coordinative dialogue – pragmatic context in multi-agent communication

We introduce a model of emergent communication between agents involved in signalling games inspired by early caregiver--child interactions. In the model, the child agent has to communicate its dynamically changing needs to the caregiver agent, which is able to address them. We demonstrate that the dialogical strategy performs better than one-directional communication. When the child's signalling frequency is limited, a particular structure of signals and actions emerges that separates the child's needs into urgent and quiet. The meaning of emerging communication is better understood in pragmatic terms than in terms of mapping. Our model underscores the relationship between the dynamics of the environment and the dynamics of communication as one of the factors driving the language structure.

Understanding the Frequency of a Word by its Associates: A Network Perspective

Why are some words more frequent than others? Some reasons are self-evident. A word like "eat" is far more communicatively useful than a word like "diagonalize". But robust differences in frequency are also observed for words with seemingly equal communicative usefulness. For example, hot and cold seem equally important for communicating temperature yet hot in English is more frequent than cold. We focused on antonym pairs such as these and sought to predict differences in frequency from the connection patterns of these words in a semantic network while controlling for predictors like the number of word senses. Two network properties predicted word frequency especially well: the number of connections the word and its surrounding words have, and the ability of the word to connect less interconnected words. These two network properties not only predicted present word frequency, but also predicted future frequency changes suggesting a potential causality relationship between network properties and word frequency. Overall, this study offers new insights into the underlying causes of differences in word frequency and highlights the importance of considering a network perspective when examining how word frequency evolves.

Progressive Graph Learning over Pruned Dependency Trees For Relation Extraction

Dependency tree is efficient for relation extraction model to exploit relations between words.Recent approaches have achieved promising performance while still suffering from inherent limitations, such as the computation efficiency and flexible pruning strategies. In this paper, we propose a novel relation extraction framework called Progressive Graph Learning over pruned dependency tree (PGLNet). PGLNet constructs a set of graphs by progressively adapting to input sentence. Specially, we implement the model to construct progressive weighted adjacency matrices by learning the relations among graph nodes with multi-head self-attention mechanism.Then, the model takes the learned weights as reference to prune dependency tree in order to preserve useful relevant sub-structures for the relation extraction while removing irrelevant words. Next, progressive convolution module is designed to encode the relations of entities and followed by relations classification.We evaluate our proposed model using public real-world datasets, experimental results demonstrate that the proposed model achieves state-of-the-art performance with consistency in all datasets. We conclude that the ability of PGLNet to progressively adapt to input data and enable the model with robustness.

Dynamic Predictive Coding Explains Both Prediction and Postdiction in Visual Motion Perception

Due to transmission delays, the perceptual information our brain can access quickly becomes outdated as events unfold in real-time. We suggest our perceptual system learns internal representations that encode sequences (or timelines) rather than single points to compensate for transmission de- lays. Specifically, we investigate the dynamic predictive coding (DPC) model in which high-level states predict the transition dynamics of lower-level states and represent lower-level state sequences. We show that a two-level DPC network trained to predict videos captures several aspects of the well-known flash-lag illusion and exhibits both predictive and postdictive effects resembling those observed in human visual motion processing. Our results support the view that visual perception relies on temporally abstracted representations that encode sequences (or timelines) rather than single time points.

Uncovering the Role of Intention in Active and Passive Perception

According to the embodied approach to cognition, perception and action are tightly intertwined, as perception is for action and is guided by action. To better understand what this view implies behaviorally, we studied how active movement and intentionality during perceptual exploration affect perceptual accuracy. Participants explored two-dimensional objects using a sensory substitution device, then reported their object size estimates. We manipulated 1) their control over exploratory movements as being either Active (control present) or Passive (control absent) and 2) their knowledge of the task goals, being either Specific (task-focused) or Generic. We found no difference between the Active and Passive conditions but significantly higher perceptual accuracy in Specific Intention trials compared to Generic Intention ones. These results clarify the nature of active perception and contribute to the growing body of evidence that higher level cognitive goals shape how we dynamically sample even low level sensory information from the world.

Is It All or Nothing? The Other Accent Effect in Talker Recognition

Listeners often have trouble identifying other-accented talkers. Some suggest this Other Accent Effect (OAE) occurs only for non-native accents (e.g., Canadian English listeners experience it for Mandarin-accented English, but not Australian English). But the line between native and non-native accents can be difficult to distinguish, and past studies have confounded accent strength with accent type. Thus, we hypothesize that accent strength modulates the OAE. We predict a heavy non-native accent will elicit an OAE, whereas a light one will not. To test this, we presented native Canadian English listeners with voice line-ups of native Canadian English accented, non-native heavy Mandarin-accented, and non-native light Mandarin-accented talkers. Unsurprisingly, listeners performed better with Canadian English talkers than Mandarin-accented talkers. Crucially, listeners performed equally poorly with both heavy and light Mandarin-accented talkers. Thus, we found no evidence for our hypothesis; instead, we observe that even a weak non-native accent can elicit a strong OAE.

How do Participants Interpret Trials from Individual Cells in a Causal Illusion Task?

In a causal illusion task, participants rate a cue that has an objectively null contingency with an outcome as causal. Trials are usually organized according to a 2x2 table representing the presence/absence of a binary cue and a binary outcome. Cell A outcomes (cue, outcome) can be attributed to the cue. But how do participants interpret trials from cell C (no cue, outcome), where the cause of the outcome is unspecified? In two experiments we asked participants to provide causal explanations for cell A and C trials in a medicine-recovery causal illusion task. Participants who reported that the cause of cell C outcomes (e.g., strong immunity, spontaneous recovery) did not also apply to cell A outcomes showed the strongest causal illusion. Such a causal reasoning process undermines the logic behind the delta P metric typically used to define a contingency, and thereby provides a potential normative account of causal “illusions”.

A reinforcement learning framework for information-seeking and information-avoidance

Every day, people are exposed to vast amounts of information that can impact how they feel, think about, and act upon the world. Here, we extend the computational reinforcement learning framework to explain how such an impact can shape future decisions to either seek or avoid information. By simulating human behavioral data, we showed that agents are more likely to seek information after exposure to information with a positive net impact on the agent’s affect, cognition, and ability to make good decisions. The more the agent is exposed to this kind of information, the higher the probability that it will seek even more information in the future. On the contrary, decisions to remain ignorant are more likely to occur after repeated exposure to information with a negative net impact. Our model offers a novel computational framework within which maladaptive information-seeking and information-avoidance behaviors can be further investigated.

The Influence of Cues to Consensus Quantity and Quality on Belief in Health Claims

Many people turn to social media for public health information, but such platforms often contain conflicting and inaccurate medical advice. To assess complex health claims online, people may consider the prevailing consensus; however, previous work suggests that people may not be very sensitive to important cues to consensus “quality”. To explore further, across two experiments we tested people’s sensitivity to the consensus-quality cues of source diversity and source expertise. Via a mock Twitter platform, participants rated their belief in a series of health claims both before and after reading various kinds of tweets about the claims. Experiment 1 showed that experts (both individual medical experts and health organisations) were more persuasive than non-experts. Additionally, stances that were supported by a diverse set of sources were more persuasive. Experiment 2 showed that participants continue to favour experts even when outnumbered in tweet quantity by non-experts. When experts were not present, however, participants favoured high tweet quantity. Both experiments suggest that cues to consensus quality (namely, expertise and source diversity) and consensus quantity (tweet quantity) are salient cues in belief revision. These findings are important in understanding how socially acquired health information (and misinformation) shifts opinion, and the role that experts can play.

Humans choose visual subgoals to reduce cognitive cost

Physical assembly is a difficult planning problem. Humans do it efficiently by breaking large problems into smaller, easier to solve portions. What governs which portions are chosen? We present a model that predicts that humans break assembly problems down to minimize cognitive costs. We test this by asking participants to choose which part of a tower they want to build next. Participants reliably choose the easier to solve subgoal out of two otherwise similar options. Beyond the immediate cognitive cost, participants also consider how difficult the rest of the tower will be to solve. A model that takes into account near-future cognitive costs best predicts participants' choices. These findings show that humans can estimate how difficult solving a subgoal will be, and that they choose subgoals to minimize immediate and future cognitive costs. These results help explain how humans make efficient use of cognitive resources to solve complex planning problems.

A decomposition of surprisal tracks the N400 and P600 brain potentials

The functional interpretation of language-related ERP components has been a central debate in psycholinguistics for decades. We advance an information-theoretic model of human language processing in the brain, in which incoming linguistic input is processed at two levels, in terms of a heuristic interpretation and in terms of error correction. We propose that these two kinds of information processing have distinct electroencephalographic signatures, corresponding to the well-documented N400 and P600 components of language-related event-related potentials (ERPs). Formally, we show that the information content (surprisal) of a word in context can be decomposed into two quantities: (A) heuristic surprise, which signals the processing difficulty of word given its inferred context, and corresponds with the N400 signal; and (B) discrepancy signal, which reflects divergence between the true context and the inferred context, and corresponds to the P600 signal. Both of these quantities can be estimated using modern NLP techniques. We validate our theory by successfully simulating ERP patterns elicited by a variety of linguistic manipulations in previously-reported experimental data from four experiments. Our theory is in principle compatible with traditional cognitive theories assuming the existence of a `good-enough' heuristic interpretation, but with a precise information-theoretic formulation.

Comparing predictions from the Elaboration Likelihood Model and a Bayesian model of argumentation

Much of our knowledge comes from other people. In considering how argument quality and source reliability influences message persuasiveness, we conduct a comparison of the Elaboration Likelihood Model of Persuasion and the Bayesian Model of Argumentation, which are based on different assumptions. Participants were asked to judge a fictitious character’s degree of belief in a claim given evidence. To test competing predictions, we manipulate the character’s elaboration level, the argument’s quality, and the source’s reliability. The elaboration did not moderate the main effects of argument quality and source reliability, as they both were integral to the overall message strength in both high and low elaboration conditions. Bayesian predictions have better fit with the observed data, whilst ELM predictions did not align well. Overall, the BM is supported, but we discuss how this model could be further improved while the ELM is contested.

Using an Egocentric Human Simulation Paradigm to quantify referential and semantic ambiguity in early word learning

In order to understand early word learning we need to better understand and quantify properties of the input that young children receive. We extended the human simulation paradigm (HSP) using egocentric videos taken from infant head-mounted cameras. The videos were further annotated with gaze information indicating in-the-moment visual attention from the infant. Our new HSP prompted participants for two types of responses, thus differentiating referential from semantic ambiguity in the learning input. Consistent with findings on visual attention in word learning, we find a strongly bimodal distribution over HSP accuracy. Even in this open-ended task, most videos only lead to a small handful of common responses. What's more, referential ambiguity was the key bottleneck to performance: participants can nearly always recover the exact word that was said if they identify the correct referent. Finally, analysis shows that adult learners relied on particular, multimodal behavioral cues to infer those target referents.

Long-form analogies generated by chatGPT lack human-like psycholinguistic properties

Psycholinguistic analyses provide a means of evaluating large language model (LLM) output and making systematic comparisons to human-generated text. These methods can be used to characterize the psycholinguistic properties of LLM output and illustrate areas where LLMs fall short in comparison to human-generated text. In this work, we apply psycholinguistic methods to evaluate individual sentences from long-form analogies about biochemical concepts. We compare analogies generated by human subjects enrolled in introductory biochemistry courses to analogies generated by chatGPT. We perform a supervised classification analysis using 78 features extracted from Coh-metrix that analyze text cohesion, language, and readability (Graesser, McNamara, Louwerse, & Cai, 2004). Results illustrate high performance for classifying student-generated and chatGPT-generated analogies. To evaluate which features contribute most to model performance, we use a hierarchical clustering approach. Results from this analysis illustrate several linguistic differences between the two sources.

4. Papers with Poster Presentation

Competitive eSports as a New Paradigm for Cognitive Science: Current State and Future Directions

Video games represent a complex, engaging task domain that has shown great promise as an experimental paradigm for the study of cognition and skill acquisition. The methods that have thus far been used to study video games, however, have been sub-optimal, and will likely become more outdated as the video gaming landscape continues to change. In this work, I first give a brief overview of the previous literature surrounding video games and cognition. I next address the critical methodological problems such as measuring expertise based solely through time-on-task as a heuristic of expertise rather than true metrics of skilled performance itself. I then suggest how the burgeoning sub-domain of competitive video gaming, or “eSports,” provides an elegant solution to these problems. Finally, I discuss the current and future directions of eSports cognition research.

Happy faces facilitate inhibitory control under a narrow scope of attention

Response inhibition refers to the ability to suppress a prepotent response. Studies investigating the role of emotional information in response inhibition have yielded inconsistent results; some studies have shown that positive emotion, compared to negative, facilitates inhibitory control, while other studies have shown opposite effects. We resolve this debate by hypothesizing that the scope of attention with which emotional information is processed can explain these mixed results. We combined a stop signal task with a global-local Navon task. Participants were required to detect a target presented at either a global or local perceptual level (letters H, S, and T). Occasionally, they encountered a stop signal face with irrelevant angry, happy, or neutral expressions. We found that irrelevant happy facial expression impaired inhibitory control compared to angry facial expression under global processing; however, this effect got reversed under local processing, i.e., happy faces facilitated inhibitory control compared to angry faces.

Adults tailor their emotional expressions to infants through "emotionese"

In many cultures, adults use simple, slow, and dynamic speech when talking to infants ("parentese," or infant-directed speech) and make expansive, repetitive movements when demonstrating object properties to infants ("motionese," or infant-directed actions). These modifications enhance infants’ attention to and learning about language and goal-directed actions. Adults’ interactions with infants are also full of emotions—do adults also modify their emotional expressions when interacting with infants? Here we showed parents of infants (aged 7 to 14 months; N = 25) emotion-evoking pictures including colorful bubbles, adorable stuffed animals, yummy snacks, broken toys, dangerous fire, and rotten fruits. We asked parents to describe their feelings about these pictures either to their infant or to an adult partner (i.e., an experimenter). While the parents’ use of emotion words did not differ between conditions, their emotional expressions did: Their infant-directed emotional expressions were more positive when they discussed positive pictures and more negative when they discussed negative pictures compared to their adult-directed emotional expressions. These findings suggest that besides "parentese" and "motionese," there is also a unique form of emotional communication in parent-child interaction—"emotionese."

The Impact of Quality and Familiarity on Dogs' Food Preferences

Past work has found that dogs perceive and respond to certain characteristics of items, specifically an object's familiarity and quality when making choices. However, in the real world, these characteristics don't exist in isolation, and understanding the interaction between familiarity and valuation as they relate to object choice can provide insight into how dogs make decisions. We aimed to explore how item familiarity and quality intersect to form dogs’ preferences for one food over another in a two-alternative forced choice task. We found that dogs' choices were driven only by the quality of the food item, and the familiarity of the item did not impact choice behavior. Determining what motivates dogs and contributes to their preferences has implications for understanding decision-making at large, as well as for advancing canine science.

Quantifying informativeness of names in visual space

The human lexicon expresses a wide array of concepts with a limited set of words. Previous work has suggested that semantic categories are structured compactly to enable informative communication. Informativeness is typically quantified with respect to an entire semantic domain and not at the level of individual names. We develop a measure of name informativeness using an information-theoretic framework grounded in visual object representations derived from natural images. Our approach uses computer vision models to characterize informativeness of individual names with respect to large-scale data in a naturalistic setting. We show that our informativeness measure predicts degrees of specificity in lexical categories more precisely than alternative measures based on entropy and frequency. We also show that name informativeness jointly captures within-category similarity and distinctiveness across categories. Our analyses suggest how the variability of names from a broad part of the lexicon may be understood through the lens of information theory.

What Is Art? The Role of Intention, Beauty, and Institutional Recognition

In two experiments (N=888), we explore to what extent the folk concept of art is compatible with the leading philosophical definitions of art, and whether it is an essentialist or a non-essentialist concept. We manipulate three factors: whether an object is created intentionally, whether it has aesthetic value, and whether it is institutionally recognized. In addition, we also manipulate the artistic domain (visual art or music). The results suggest that none of the three properties is seen by the folk as necessary for an object to be considered art, which suggests that the folk concept of art might be a cluster concept.

Human Attention-Guided Explainable AI for Object Detection

Although object detection AI plays an important role in many critical systems, corresponding Explainable AI (XAI) methods remain very limited. Here we first developed FullGrad-CAM and FullGrad-CAM++ by extending traditional gradient-based methods to generate object-specific explanations with higher plausibility. Since human attention may reflect features more in-terpretable to humans, we explored the possibility to use it as guidance to learn how to combine the explanatory information in the detector model to best present as an XAI saliency map that is interpretable (plausible) to humans. Interestingly, we found that human attention maps had higher faithfulness for explaining the detector model than existing saliency-based XAI methods. By using trainable activation functions and smoothing kernels to maximize the XAI saliency map similarity to human attention maps, the generated map had higher faithfulness and plausibility than both existing XAI methods and human atten-tion maps. The learned functions were model-specific, well generalizable to other databases.

Children’s developing understanding of learning as improvement over time

How do children – who are undeniably productive learners – think about their learning? Do children understand, as adults do, that learning is a process of continuous improvement over time? To explore children’s emerging representations of the learning process, we created a non-verbal motor learning paradigm where 4- to 8-year-olds predicted their own learning curve without prior experience. We found that by age 7, children predicted improved performance over time. Younger children, however, were overly optimistic about how well they would do at the game and often predicted near-perfect performance across trials. This work suggests that children’s predictions of their future learning curve become more accurate with age, which may have implications for young children’s learning decisions.

Ideological Differences in Paths to Persistence

From abortion to vaccination, liberals and conservatives may not share the same views, yet they share a tendency to persist in their own beliefs despite knowing that millions of others disagree. In this paper, we investigate whether this shared tendency for persistence is supported by different psychological mechanisms across the two groups. An experiment with a large, nationally-representative sample in the U.S (N = 2,000) focusing on 12 controversies revealed similar rates of persistence across ideologies. However, the drivers of persistence differed across the two groups: Conservatives disproportionately relied on meta-epistemic explanations (i.e., appeals to subjectivity or unknowability) to maintain their controversial scientific views, whereas liberals tended to restrict such explanations to religious and moral controversies. Moreover, reliance on different explanations for persistence predicted differences in affective polarization.

Trust in Human-bot Teaming: Applications of the Judge Advisor System

Recent years have seen remarkable advances in the development and use of Artificial Intelligence (AI) in image classification, driving cars, and writing scientific articles. Although AI can outperform humans in many tasks, there remain domains where humans and AI working together can outperform either working alone. For humans and AI to work together effectively, the human must trust the AI bot to the right degree (calibrated). If the human does not trust the bot sufficiently, or conversely trusts the bot more than is warranted, the human-bot team will not perform as well as they could. We report three experiments examining trust in human-AI teaming. While existing studies typically collect binary responses (to trust, or not to trust), we present a novel paradigm that quantifies trust in a bot-recommendation in a continuous fashion. These data allow better precision, and in the future the development of more refined models of human-bot trust.

Measuring the completeness of race models for perceptual decision-making

Computational models of perceptual decision-making depend heavily on empirical goodness-of-fit measures for model selection. However, it is not possible to improve models' fit to data indefinitely, particularly when the data in question are variable across multiple elicitations. The completeness of a model or a theory assesses the extent to which it can predict observations in comparison with an ideal model. We measure the completeness of contemporary race models on a paradigmatic perceptual decision-making task - random dot motion discrimination - and show that the simple drift diffusion model is already close to complete in describing random dot motion discrimination data, with more complex models being in fact over-fit to datasets. Thus, in this paper, we quantitatively demonstrate limits to the ability of conventional choice fraction and response time data to disambiguate complex models of perceptual decision-making.

A memory for goals model of prospective memory

We present a novel model of prospective memory and fit it to data from a classic experimental paradigm (Einstein & Mc- Daniel, 1990). Our model uses memory for goals (Altmann & Trafton, 2002) and elaboration with spreading activation to show how prospective intentions can be cued by perceptual cues. Our model also suggests how to resolve some of the con- troversies concerning prospective memory and aging.

Continuous and Discrete Transitions during Task-Switching

Decades of research have established that while people’s performance suffers when they need to quickly switch between tasks, they can reduce these performance costs the more time they have to prepare. Two major theories have attempted to explain how people actively prepare for tasks over time, debating whether these task state transitions are discrete or gradual. We attempted to bring clarity to this debate by developing new statistical methods for single-trial modeling of task state transitions, which we use in a task that combines the strengths of cued and predictable task-switching. We found that participants’ behavior was best explained as a hybrid between discrete and gradual transitions. Over the preparation period, participants discretely transitioned from an unprepared state into a dynamic, increasingly prepared state. These findings provide a new account of cognitive flexibility, paving the way for mechanistic models of task-switching.

Why do some words have more meanings than others? A true neutral model for the meaning-frequency correlation.

The lexica of natural languages are ambiguous, but the de- gree of ambiguity is unequal between words. Some words have more meanings than others. However, the exact prop- erties that favor some words over others when acquiring a new meaning are not very well understood. In recent years, several studies suggested that some words gain more meanings than others based on selection for efficient communication, which could explain the correlation between meaning and frequency discovered by Zipf (Piantadosi, Tily, & Gibson, 2012; Gib- son et al., 2019). The object of this study is to assess the role of selection in the meaning-frequency correlation using a neu- tral model that yields a meaning-frequency correlation without selection pressures. We provide a model where words gain additional meanings through reuse. In the neutral model pre- sented in this paper, words are chosen to be reused at random, independently of their frequency, hence there is no selection mechanism favoring efficient communication. Unlike previous attempts to introduce null models of the meaning-frequency correlation (Caplan, Kodner, & Yang, 2020; Trott & Bergen, 2020), it truly does not rely on selection for frequency. We show that statistical regularities related to ambiguity, such as Zipf’s meaning-frequency correlation, can arise in conditions when words are not undergoing any selective pressures. This model has the additional property of matching word frequency distributions of natural languages. It can provide the baseline against which the presence of selection for efficient communi- cation in natural languages can be assessed.

Evaluating Cognitive Status-Informed Referring Form Selection for Human-Robot Interactions

Robots must be able to communicate naturally and efficiently, e.g., using concise referring forms like it, that, and the ⟨N’⟩. Recently researchers have started working on Referring Form Selection (RFS) machine learning algorithms but only evaluating them offline using traditional metrics like accuracy. In this work, we investigated how a cognitive status-informed RFS computational model might fare in actual human-robot interactions in a human-subjects study (N=36). Results showed improvements over a random baseline in task performance, naturalness, understandability, and mental workload. However, the model was not perceived to outperform a simple, naive, non-random baseline (constant use of indefinite noun phrases). We contribute several key research directions for further development of cognitive status-informed RFS models, the inclusion of multi-modality, and further development of testbeds.

Fight Bias with Bias? Two Interventions for Mitigating the Selective Avoidance of Clicking Uncongenial Facts

Selective avoidance of facts that are uncongenial to preexisting false beliefs is a biased click behavior that decreases the effect of correcting misinformation. This study examined the strength of this avoidance tendency and whether interventions could reduce it. In a preregistered experiment with 1,203 participants, we compared two different types of interventions: an intervention with instruction that directly calls for reflection via text (instruction intervention); an intervention with a ranking-biased order that induces people to click on what they easily see and vice versa (ranking-biased intervention). The results showed no significant effect of the instruction intervention. However, ranking-biased intervention showed preventive outcomes regarding participants’ selective avoidance behaviors and promoted clicking on links to uncongenial facts. The ranking-biased intervention was effective for participants with high reflexiveness as well as for participants with low reflexiveness. We discuss the implication of interaction between the interventions and click behavior based on cognitive characteristics.

Augmenting EEG with Generative Adversarial Networks Enhances Brain Decoding Across Classifiers and Sample Sizes

There is major potential for using electroencephalography (EEG) in brain decoding that has been untapped due to the need for large amounts of data. Advances in machine learning have mitigated this need through data augmentation techniques, such as Generative Adversarial Networks (GANs). Here, we gauged the extent to which GANs can augment EEG data to enhance classification performance. Our objectives were to determine which classifiers benefit from GAN-augmented EEG and to estimate the impact of sample sizes on GAN-enhancements. We investigated three classifiers---neural networks, support vector machines, and logistic regressions---across seven sample sizes ranging from 5 to 100 participants. GAN-augmented EEG enhanced classification for neural networks and support vector machines, but not logistic regressions. Further, GAN-enhancements diminished as sample sizes increased---suggesting it is most effective with small samples, which may facilitate research that is unable to collect large amounts of data.

Simulating children’s verb inflection errors in English using an LSTM language model

We present a computational (LSTM) model that learns to produce English (3sg and -bare) verb inflection when trained on English child-directed speech (CDS). The model is trained on input containing morphemized verbs and learns to predict the next token (word/morpheme) given a preceding sequence of tokens. The model produces the type of error (-bare for -3s) made by English-learning children while avoiding errors that children do not often make (-3s for -bare). The model also shows the same type of sensitivity to input statistics that has been reported in English-learning children. Finally, we manipulated the length of the sequences the model is trained on and show that this results in the delayed acquisition of -3sg forms that is characteristic of English-learning children with Developmental Language Disorder (DLD). Taken together these results suggest that input-driven learning is a major determinant of the patterns observed in both typical and impaired acquisition of English verb inflection.

Plural causes in causal judgment

Causal selection is the process whereby people decide which of several events responsible for a realized outcome should be considered as “the cause” of that outcome. A theory of causal selection requires a definition of the relevant candidates to be considered for selection. So far, the psychological literature has operated on the implicit premise that the only relevant candidates for causal selection are individual variables, corresponding to the distinct nodes of a causal network. Instead, we argue that causal judgment can recognize plural causes, featuring more than one variable. We provide evidence for the psychological relevance of plural causes by showing: (a) that plural cause judgments are influenced by the same factors that have been proven to influence causal selection judgments in general; (b) that this influence cannot be explained away by assuming that participants estimate the strength of plural cause simply by combining the strength of its individual constituents.

Emoji-Induced Positivity Bias in Lexical Processing: Evidence from a Priming Lexical Decision Task

Emojis are pervasive in modern communication, highlighting the importance of understanding their influence on language processing. This study used two online primed LDT experiments to examine the lexical effects of emoji-conveyed valence and face status. In Experiment 1, neutral target words were primed by positive face and non-face emojis, compared to neutral control conditions. Experiment 2 involved neutral target words primed by negative face and non-face emojis relative to neutral controls. Experiment 1 revealed a main facilitatory priming effect in the positive condition, regardless of face status. In Experiment 2, no effects were significant. The results suggest that emoji-induced positivity bias occurs independently of the face or non-face status, reflecting emotional processing and interaction between lexical and affective processes rather than the semantic richness of emotional stimuli. These findings have practical implications for emoji-conveyed positivity in reading and stress the need to control emotional content in visual word recognition.

Conceptual and Linguistic Factors Affect Entity Processing and Labeling

How do learners acquire the meanings of nouns? Given the complex linguistic and non-linguistic input present in the learning environment, how do learners identify the concepts denoted by nouns? In other words, how does a learner map the input language to non-linguistic concepts? In the current study, we focus on the case of mass-count language and physical entities (e.g., objects and substances). We conduct novel word extension experiments to investigate whether conceptual and linguistic factors universally affect label extension to previously unseen entities in languages that do and do not have a grammatical mass-count distinction (Experiment 1: English; Experiment 2: Korean, respectively). We find that objecthood and linguistic (count/mass) context both modulate how speakers extend labels to structurally disrupted novel entities.

Comparing Children and Large Language Models in Word Sense Disambiguation: Insights and Challenges

Understanding how children process ambiguous words is a challenge because sense disambiguation depends on sentence context bottom-up and top-down aspects. Here, we seek insight into this phenomenon by investigating how such a competence might arise in large distributional learners (Transformers) that purport to acquire sense representations from language input in a largely unsupervised fashion. We investigated how sense disambiguation might be achieved using model representations derived from naturalistic child-directed speech. We tested a large pool of Transformer models, varying in their pretraining input size/nature as well as the size of their parameter space. Tested across three behavioral experiments from the developmental literature, we found that these models capture some essential properties of child sense disambiguation, although most still struggle in the more challenging tasks with contrastive cues. We discuss implications for both theories of word learning and for using Transformers to capture child language processing.

Prediction, Explanation, and Control Under Free Exploration

Prediction, explanation, and control are basic cognitive abilities. Here we show how they can arise, simultaneously, from underlying mental models built during unstructured, exploration-based learning. Our experimental paradigm, involving interaction with a symbolic "chatbot", allows us to vary the relative difficulty of the tasks, and to measure how participants leverage the Bayesian evidence of their mental models for decision-making. Our experimental manipulation focuses on hidden information and task complexity. With full information, there are significant differences between the three tasks: for example, people are more sensitive to Bayesian evidence in prediction than in control or explanation. When information is hidden, however, performance equalizes. Taken together, our results suggest that, while specific heuristics may lead to different levels of performance in cases with full information, more fundamental forms of reasoning, based on an underlying mental model, and less sensitive to the specific task, come into play when pieces are missing.

Guiding Inference: Signaling intentions using efficient action

People have a remarkable capacity to infer others’ goals and intentions based on how they behave. Yet, humans are also motivated to ensure that others can infer their mental states easily and accurately. Past work has shown that people achieve this by introducing inefficiencies to their behavior, which reveal its underlying goal (e.g., exaggerating one’s movements so as to make their purpose obvious). We hypothesized that inefficiency is not a constitutive feature of signaling, and that people will often signal their goals and intentions solely through efficient action. We test this idea in a signal-design experiment where participants need to reach an instrumental goal while also making that goal as inferable as possible. In line with our hypothesis, people shape their behavior to increase inferability without jeopardizing efficiency (Experiment 1). Using a computational model, we show that these efficient signals are well-designed to guide observers’ inferences about the relevant instrumental goal. Moreover, observers’ intuitions about which paths were produced to signal correlate with the proportion of times that the paths were generated in the signaling condition of our first experiment (Experiment 2). Our results show that humans not only exploit opportunities to reveal their goals without deviating from efficient action, but that these signals allow observers to understand the instrumental and signaling goals underpinning the movement.

Social Behavioral Sensing: An Exploratory Study to Assess Learning Motivation and Perceived Relatedness of University Students using Mobile Sensing

Learning motivation plays a crucial role in student’s daily study life since it greatly affects academic performance and engagement. Perceived relatedness, based on self-determined theory, is an important predictor of learning motivation. Today, assessment for both of them still relies on subjective evaluations and self-reports, which is time-consuming and onerous. Hence, we propose a novel approach blended with mobile sensing by simultaneously collecting psychological measurements and objective mobile sensing data from N=58 undergraduates to explore new methods of assessing learning motivation and perceived relatedness. We identify a variety of social behavioral patterns from mobile sensing data, and investigate associations between psychological measures and these patterns. Our study helps enlighten what the new forms of assessing learning motivation and perceived relatedness in education could be, and paves the way for personalizing intervention in future research.

Availability and Timing of Informativity Inferences

Conversational partners expect each other to communicate rationally and cooperatively and to contribute relevant and informative utterances. Occasionally, however, speakers produce trivial utterances which may violate our expectations of informativity. These utterances can prompt listeners to draw inferences about a speaker’s goals in producing such an utterance. Here we present two studies investigating how and when listeners derive informativity-based inferences. The results demonstrate that speaker knowledge plays an important role in the computation of inferences. Furthermore, the timecourse of the results suggests that these inferences do not always arise automatically and that their computation is costly.

Humans vs. AI in Detecting Vehicles and Humans in Driving Scenarios

To inform Explainable AI (XAI) design for updating users’ beliefs about AI based on their mental models, we examined the similarities and differences between humans and AI in object detection in driving scenarios. In humans, individuals differed in adopting focused or explorative attention strategies, with better performance associated with the focused strategy. AI (Yolo-v5s) had higher similarity in attended features to the focused than the explorative strategy in humans, and achieved human-expert-level performance in vehicle detection even in difficult cases such as occlusion and degradation. In contrast, it performed much poorer than humans in detecting humans with low attended feature similarity due to humans’ attention bias for stimuli with evolutionary significance. Also, higher similarity to humans’ attended features was associated with better AI performance, suggesting that human attention may be used for guiding AI design. These findings have significant implications for both AI and XAI designs.

Adaptivity and optimization under constraints

Problem solving under time and material constraints assumes that humans face both their own internal computational limitations and constraints of the external task environment. To find optimal solutions under these constraints, the agent must adapt their behavior by using various strategies such as offloading (i.e., using external materials to aid their performance). We designed a novel tower building task to investigate adaptive use of strategies under constraints. The task worked as designed: Participants found the optimal solution most often on the least difficult scenario and least often on the most difficult scenario. Surprisingly, offloading led to no significant differences in performance. However, on the most difficult scenario, some participants found the optimal solution using a prospective, concurrent, or retrospective strategy based on experience with the constraints of the task environment. We conclude that optimality can be understood as a trend over time and investigated in studies that allow multiple attempts.

Preferences for descriptiveness and co-explanation in complex explanations

Good explanations can be distinguished from bad ones in different ways, for instance by how much of the available information they can explain (i.e., maximise the likelihood of) the available data. Here, we consider two different components of likelihood: descriptiveness (the likelihood of the individual data points) and co-explanation (the likelihood of the specific subset of data under consideration). We consider whether people prefer explanations that are high in descriptiveness vs. coexplanation. Moreover, we consider whether people who endorse conspiracy theories prefer explanations for either quality. In a medical diagnosis task, participants make binary choices between two fictional disease variants: one higher in descriptiveness versus another higher in co-explanation. Overall, participants displayed a weak preference for descriptiveness. This preference, however, did not vary across increasing levels of descriptiveness. Moreover, such preferences were unrelated to conspiracy mentality. Thus, both explanatory virtues may play a role in the appeal of likely explanations.

Genetic Programming for Developing Simple Cognitive Models

Frequently in psychology, simple tasks that are designed to tap a particular feature of cognition are used without considering the other mechanisms that might be at play. For example, the delayed-match-to-sample (DMTS) task is often used to examine short-term memory; however, a number of cognitive mechanisms interact to produce the observed behaviour, such as decision-making and attention processes. As these simple tasks form the basis of more complex psychological experiments and theories, it is critical to understand what strategies might be producing the recorded behaviour. The current paper uses the GEMS methodology, a system that generates models of cognition using genetic programming, and applies it to differing DMTS experimental conditions. We investigate the strategies that participants might be using, while looking at similarities and differences in strategy depending on task variations; in this case, changes to the interval between study and recall affected the strategies used by the generated models.

Contrast Categories Elicit Illusory Correlations When Learning Social Groups

Assigning category labels to examples varying along a continuous dimension exaggerates perceived differences between members on opposite sides of category boundaries. Using social categories, we investigated how contrast may guide representation of not only features that differentiate between groups but also features that are neither diagnostic nor correlated across trained examples. In a classification task, participants learned which residence hall to correctly assign students varying along three psychological traits (academic, athletic, social). The same target category was learned alongside one of two contrast categories with either higher or lower values along one diagnostic dimension. After learning, participants provided estimates of average trait values for each dorm. Predicted contrast effects were found along diagnostic dimensions but contrast also influenced memory for non-diagnostic and uncorrelated dimensions, presumably based on assumptions about general co-occurrence of features. These findings have implications for how stereotypes are learned and applied and how illusory correlations are perpetuated.

Abstract Concepts and Inner Speech: A Dual-Task Interference Study

Do we need inner speech to understand and process abstract concepts? In two preregistered experiments, we tested these questions using dual-task interference in an odd-one-out paradigm where participants were asked to decide either which image did not represent the same concept as two other images (Experiment 1) or which word was not a synonym for the same concept as two other words (Experiment 2). In Experiment 1, there were large differences in both reaction time and accuracy between concrete and abstract concepts. When abstract concepts were represented through images, visuospatial interference had a detrimental effect on reaction time, but verbal interference did not. When the same abstract concepts were represented through words (Experiment 2), there were facilitatory effects of both interference types. We discuss possible interpretations of these findings in terms of visual and verbal access to abstract concepts and the hypothesized role of inner speech in processing abstract concepts.

Comparing serial reproduction and serial prediction of random walk

Current studies of the serial reproduction paradigm focused on stimuli that were statistically independent of each other. We explored serial reproductions of random walk series and examined whether Bayesian models previously built for independent stimulus could be adapted to autocorrelated stimulus. We found that Bayesian models captured most of the empirical results qualitatively, but could be further improved by incorporating recency effects. Besides, given that the optimal strategy of iterative prediction of random walk was to reproduce the current stimuli, we also compared serial prediction of random walk to serial reproduction. We found that serially reproduced and predicted series both decorrelate as a function of chain position and that the means of the series increase in both tasks, which matched qualitative predictions of the Bayesian models.

Priming Children's Interpretation of Globally Ambiguous Sentences

Language input is potentially ambiguous in a number of ways. In order to process language effectively, language users need to resolve these ambiguities quickly and efficiently. Many sources of information are recruited to complete this process including contextual constraints, prosody, and verb biases. The current work focuses on the development of verb biases given children’s overreliance on them. To explore this issue, I examined the effect of syntactic priming on the interpretation of PP-attachment ambiguities by 5-year-old children. Three priming experiments utilized three different but related prime types: globally ambiguous PP-attachments, unambiguous attachments disambiguated by syntax, and unambiguous attachments disambiguated by pragmatics. Results demonstrated priming in all three experiments, although it was strongest when the primes themselves were ambiguous. This finding provides further evidence for comprehension priming in children, and suggests that their verb biases can be overcome given the appropriate resources.

InferEM: Inferring the Speaker's Intention for Empathetic Dialogue Generation

Current approaches to empathetic response generation typically encode the entire dialogue history directly and put the output into a decoder to generate friendly feedback. These methods focus on modelling contextual information but neglect capturing the direct intention of the speaker. We argue that the last utterance in the dialogue empirically conveys the intention of the speaker. Consequently, we propose a novel model named InferEM for empathetic response generation. We separately encode the last utterance and fuse it with the entire dialogue through the multi-head attention based intention fusion module to capture the speaker's intention. Besides, we utilize previous utterances to predict the last utterance, which simulates human's psychology to guess what the interlocutor may speak in advance. To balance the optimizing rates of the utterance prediction and response generation, a multi-task learning strategy is designed for InferEM. Experimental results demonstrate the plausibility and validity of InferEM in improving empathetic expression.

Divergence in Word Meanings and its Consequence for Communication

How similar are people's meanings of common words? Do differences in word meanings lead to miscommunication? We examined divergence between word meanings using similarity ratings such as "Is a penguin more similar to a chicken or a dolphin". We found that given the identical instructions some people prioritized taxonomic relationships more than other people. Moreover, this taxonomic bias generalized from animals to artifacts suggesting a more general difference in semantic organization. When people with different biases are paired in a matcher-director type task, they were less likely to achieve communicative success.

The Relation Between the Development of Counterfactual Thinking and Piagetian Conservation

Conservation experiments are at the heart of Piaget’s theory that young children are easily misled by appearances and fail to acknowledge the stability of reality. An independent line of research on the development of counterfactual thinking suggests that children struggle to imagine how the world would be if specific events had not occurred. Although seemingly unrelated, we proposed that counterfactual thinking may play a role in mentally reversing changes in conservation tasks. The present study examined whether children’s counterfactual thinking predicts their performance on conservation problems. Forty-eight children between 6 and 8 years old completed conservation and counterfactual thinking tasks online. Results showed that children’s performance on the counterfactual thinking task significantly predicted their performance on the conservation task. Age also significantly predicted children’s performance on both tasks. The findings suggest that there is a so-far undetected involvement of counterfactual thinking in understanding the principle of conservation.

Friendly-Bot: The Impact of Chatbot Appearance and Relationship Style on User Trust

With AIs becoming prevalent in our daily lives, it is still controversial whether they are a reliable conversational assistant. We, therefore, developed a chatbot, a Friendly-Bot, where users can share interpersonal experiences (e.g., friendship and romantic relationships). We then manipulated chatbot appearances into two types: robot and human-looking. We found that chatbot appearance predicted users’ trust. Participants preferred the robot-looking chatbot over the human-looking one. Participants also showed higher trust in robot-looking chatbots when conversing about positive interpersonal relationships. Participants showed a different pattern of trust depending on the appearance condition: positive experience led to higher trust in robot-looking condition, whereas the opposite was observed in human-looking condition. Our findings show how chatbot appearance influences rapport-building in AI-assisted interactions (e.g., counseling).

How communicative efficiency and social biases shape language in autistic and allistic learners

In natural languages and in experimental studies of artificial language learning, case marking of grammatical arguments is more likely to be used in languages with flexible word order due to an efficiency trade-off between production effort and communicative accuracy. However, experimental evidence suggests that language learners are less efficient when there is a social bias in favour of a group whose productions are inefficient. Here, we examine the impact of autistic traits on efficient communication. We find that autistic people's use of case in the absence of a social bias is comparative to their neurotypical peers. However, we also find evidence that autistic people adhere more to social biases; they increase production effort in order to behave more like the group they are biased towards. We argue that some autistic people may be more likely to adhere to a social bias as a result of learnt social behaviours. More generally, these results underscore the importance of studying more diverse populations in language evolution research.

Scare quotes in name-informing constructions: A self-paced reading study on the processing of modalizing quotational constructions

Name-mentioning constructions as in "This person is called a wine connoisseur" can give rise to two distinct meanings: a literal or a non-literal, ironic reading. This investigation presents empirical evidence from a self-paced reading study that analysed the reading times for target nominals in sentential constructions using predicates like 'call' and 'refer to as' in German. The target words were either simplicia or compounds with a high or low lexical frequency. The study revealed that the mean reading time for non-literal target words is longer as opposed to literal conditions and that compounds are preferred in name-informing conditions while simplicia are processed faster in ironic sentences.

Individual Differences in Preferred Thought Formats Predict Features of Narrative Recall

Humans differ in how they experience their own thoughts. Some say they hear sentences in their “mind's ear”, others report seeing images in their “mind’s eye”, and many struggle to describe their inner worlds. Here, we tested whether individual differences in thought formats predict accuracy and properties of verbal recall after listening to short podcasts about science. To assess the accuracy of recall, we measured the semantic similarity between embeddings of participant recall statements and the original podcasts. To characterize the properties of participants’ recall language, we measured the perceptual strength of content words in their responses. Individual differences in thought formats were not associated with differences in the accuracy of verbal recall. By contrast, recall statements high in perceptual strength were more likely among participants who reported vivid visual imagery, while statements low in perceptual strength were more likely among those with higher verbal scores. Results highlight an intriguing connection between subjective reports about thought format and the attributes of naturalistic verbal memory recall.

Typology of topological relations using machine translation

Languages describe spatial relations in different manners. It is however hypothesized that highly frequent ways of categorizing spatial relations across languages correspond to the natural ways humans conceptualize them. In this study, we explore the use of machine translation to gather data in semantic typology to address whether different languages show similarities in how they carve up space. We collected spatial descriptions in English, translated them using machine translation, and subsequently extracted spatial terms automatically. Our results suggest that most spatial descriptions are accurately translated. Despite limitations in our extraction of spatial terms, we obtain meaningful patterns of spatial relation categorization across languages. We discuss translation limits for semantic typology and possible future directions.

Iterated learning and communication jointly explain efficient color naming systems

It has been argued that semantic systems reflect pressure for efficiency, and a current debate concerns the cultural evolutionary process that produces this pattern. We consider efficiency as instantiated in the Information Bottleneck (IB) principle, and a model of cultural evolution that combines iterated learning and communication. We show that this model, instantiated in neural networks, converges to color naming systems that are efficient in the IB sense and similar to human color naming systems. We also show that iterated learning alone, and communication alone, do not yield the same outcome as clearly.

Perceptual colorization of the peripheral retinotopic visual field using adversarially-optimized neural networks

There is an apparent discrepancy between visual perception, which is colorful, complete, and in high resolution, and the saccadic, and spatially heterogeneous retinal input data. In this work, we computationally emulated foveated color maps and intensity channels as well as intra-saccadic motion data using a neuromorphic event camera. We used a convolutional neural network, U-Net, and adversarial optimization to demonstrate how retinal inputs can be used for the reconstruction of colorful images in high resolution. Our model may set the groundwork for the development of biologically plausible neural networks for computational vision perception.

Parents take over less when they think their young child is learning

Over-engaged parenting is on the rise: Parents in the U.S. are increasingly completing developmentally appropriate tasks for young children, which relates to reduced child persistence. What can help parents to step back? Across two pre-registered studies, we test the novel hypothesis that parents step back when they prioritize their child’s learning. In line with this hypothesis, parents of 4-5-year-olds (N = 77) reported taking over less on tasks from which they think their child learns more, which was more often the case for academic (versus non-academic) tasks (Study 1). Study 2 found causal evidence for this hypothesis: Emphasizing children’s learning in a novel, non-academic context reduced parents' (N = 60) taking over actions by half compared to a control condition. This work shows that parents take over less when they think that their young child is learning and opens new avenues for interventions to reduce over-engaged parenting.

To each their own theory: Exploring the limits of individual differences in decisions under risk

Theories in cognitive science are primarily aimed at explaining human behavior in general, appealing to universal constructs such as perception or attention. When it is considered, modeling of individual differences is typically performed by adapting model parameters. The implicit assumption of this standard approach is that people are relatively similar, employing the same basic cognitive processes in a given problem domain. In this work, we consider a broader evaluation of the way in which people may differ. We evaluate 23 models of risky choice on around 300 individuals, and find that most models---spanning various constructs from heuristic rules and attention to regret and subjective perception---explain the behavior of different subpopulations of individuals. These results may account for part of the difficulty in obtaining a single elegant explanation of behavior in some long-studied domains, and suggest a more serious consideration of individual variability in theory comparisons going forward.

Teachers’ attitudes towards AI: what is the difference with non-AI technologies?

Educational technologies with AI are designed to personalize students’ learning, but also to alleviate teacher’s workload. However, acceptability of such technologies among teachers may be impacted by factors such as fear of replacement or ethical concerns. The purpose of the current study is to investigate attitudes of teachers towards educational tools with or without AI. The main hypothesis is that technologies with AI would be more negatively judged than technologies without AI, and thus intention to use would be weaker for technologies with AI. Results show that teachers seem to accurately perceive the potential benefit of AI technologies for reduction of workload, without feeling threatened by a replacement in the future. Ethical concerns are higher for AI technologies, but intention to use is similar. Differences between primary and secondary school teachers are discussed.

Exploring Teaching with Evaluative Feedback

In this experiment, we explore how teachers use evaluative feedback—such as praise and criticism, or reward and punishment—to guide learners’ behavior. Although common in daily life, there has been limited research in this area. Our study combines insights from Bayesian models of pedagogy and prior experimental research on evaluative feedback to address this gap. We defined an objective within a complex conceptual space and observe how teachers use only evaluative feedback to guide naive learners’ choice. Our findings indicate that teachers tend to structure their feedback communicatively, in a way that minimizes uncertainty and prioritizes establishing common ground. Our results offer preliminary but exciting insights into how humans teach with evaluative feedback, providing a more comprehensive understanding of the ease and agility with which we engage in intuitive teaching.

Cognitive Primitives and Bayesian Number Word Learning

We use the computational Bayesian learning model in Piantadosi, Tenenbaum, and Goodman (2012) to explore how different combinations of cognitive primitives and frequency distributions affect the learning of natural numbers. We find that the model converges on the natural numbers through attested developmental stages only under very restricted sets of primitives and frequency distributions. Assuming the size principle familiar from Bayesian approaches to inductive generalization, it would be natural to conclude that there are sharp constraints on the primitives out of which humans build natural numbers, some of which we hope to elucidate below.

Learning traps lead to change blindness in dynamic environments

The ability to selectively attend to stimuli increases the efficiency of learning. However, learning traps can develop when attention prematurely narrows to a subset of the features that predict outcomes, resulting in suboptimal decisions. The current work investigated the potential for learning traps to be particularly damaging in dynamic environments, where the features that predict rewards and losses change during learning. Two experiments (N=316) found that when learners received choice-contingent feedback, they frequently fell into a learning trap, using a suboptimal categorisation rule. Critically, these learners were unlikely to detect a subsequent rule change nor learn the new optimal rule. This change blindness was not attenuated by priming participants to expect change. These results show that the pernicious effects of learning traps are amplified in dynamic environments.

Transformer-Maze: An Accessible Incremental Processing Measurement Tool

The lesser known G(rammatical)-Maze task (Forster, Guerrera, & Elliot, 2009) is arguably a better choice than self-paced reading (Mitchell, 2004) for detecting difficulty from word to word in online sentence processing over crowdsourcing platforms. In G-Maze, a participant must choose between each successive word in a sentence and a distractor word that does not make sense based on the preceding context. If a participant chooses the distractor as opposed to the actual word, then the trial ends and they may not complete the sentence. Thus, G-Maze automatically filters out data from inattentive participants, and more effectively localizes differences in processing difficulty. Still, the effort required to pick contextually inappropriate distractors for hundreds of words might cause an experimenter to hesitate before picking this method. To save experimenters this time and effort, Boyce, Futrell, and Levy (2020) developed A(uto)-Maze, a tool that automatically generates distractors using a computational language model. We now introduce the next generation of A-Maze: T(ransformer)-Maze. Transformer models are the current state of the art in natural language processing, and thousands, pretrained in a variety of languages, are freely available on the internet, specifically through Huggingface’s Transformers package (Wolf et al., 2020). In our validation experiment, T-Maze proves itself to be as effective as G-Maze with handmade materials, run in a lab. This tool thus allows psycholinguists to easily gather high-quality online sentence processing data in many different languages.

Handedness Modulates Spatial Attention – Insights From Individual Variations In Lateralization Of Cognitive Functions

Increased left-handedness and atypical lateralization in individuals with neurodevelopmental and psychiatric disorders point to a deep yet unresolved connection between handedness, hemispheric asymmetry, and normal brain functioning. Most handedness-lateralization research has either excluded left-handers due to their higher variability, or failed to control the degree of handedness. The discrete categorization based on arbitrary criteria or cut-offs has made it challenging to address inter-individual variations in the lateralization of cognitive functions. In this study, capturing responses from across the handedness continuum in tasks of the divided visual half-field paradigm, we explored the lateralization patterns in different stages of visual processing of orientation, global-and-local, faces and words. We found that the degree of handedness significantly affects lateralization in all tasks except orientation. Notably, even though the direction of hemispheric preference did reverse in left-handers for visuospatial attention like in global-local processing, the same was not for word and face processing. Our results substantially evidence that handedness differentially influences the lateralization of visual processes. The observed relationship between the dominant hand and global-local processing significantly points to the action-dependent modulation of visual attention. We conclude that the degree of left-handedness is potentially a critical factor in lateralization, and a continuum approach would be beneficial to control for the individual variations in laterality research.

A Perceptual Front-End for Probability Learning: Object Detection with YOLO

Neural Probabilistic Learner and Sampler (NPLS) is an algorithm that has simulated children’s non-symbolic probability learning from visual stimuli such as collections of different colors of marbles. Although NPLS closely simulates the cognitive process of probability learning, the training of such learning algorithms often uses binary encoding of inputs that represent the perceived visual stimuli, avoiding simulation of the visual perception of the stimuli. Here, the computer vision technique You Only Look Once (YOLO) (Jocher et al., 2021; Redmon et al., 2016), is integrated into the workflow of an NPLS simulation probability learning experiments with children. YOLO is a convolutional neural network (CNN) designed to detect objects. The model’s performance on marble datasets is tested through an analysis of precision and recall. Results indicate that the YOLO model, when trained sufficiently, outputs predictions on marble image datasets with high accuracy and precision. We also analyze YOLO’s suitability as a biologically plausible model of visual processing, interfering with YOLO’s training process by shortening the training time to examine the effects of perceptual errors on simulated probabilistic reasoning.

Learning About Scientists from Climate Consensus Messaging

Informing people of the overwhelming consensus among climate scientists that human-caused climate change is occurring increases belief in the proposition and the importance of policy action. However, consensus may not be interpreted in the same way; it could emerge from skilled experts converging on the truth, or a biased cabal working for their own gain. We show that the weight that an individual places on the skill and bias of experts affects whether they are persuaded by strong consensus. We demonstrate that beliefs about the skill and bias of pro-consensus scientists (those who express that climate change is occurring) and anti-consensus scientists (those who do not) are central components of a belief system about climate change, determining what individuals learn from climate scientists. However, these characteristics are not fixed as individuals also learn about scientists from consensus. In this way, people learn both from and about climate scientists given consensus.

Communicative Feedback in Response to Children’s Grammatical Errors

Children learning their mother tongue engage in interactive communication starting from the early stages of their development. In a large-scale study of transcribed child-caregiver conversations, we investigated the role of Communicative Feedback in response to children's grammatical errors. We found evidence for both positive and negative feedback signals that are useful for learning the grammar of one's native language: Caregivers are more likely to provide acknowledgments if an utterance is grammatical, and they are more likely to ask for clarification if an utterance is ungrammatical. Further, we investigate how children react in response to negative communicative feedback signals and find evidence that grammaticality is improved in direct follow-ups to negative feedback signals. This study provides the largest and most comprehensive evidence supporting the presence and effectiveness of communicative feedback signals in grammar learning, broadening the literature on communicative feedback in language acquisition more generally.

Evaluating statistical language models as pragmatic reasoners

The relationship between communicated language and intended meaning is often probabilistic and sensitive to context. Numerous strategies attempt to estimate such a mapping, often leveraging recursive Bayesian models of communication. In parallel, LLMs have been increasingly applied to semantic parsing applications, tasked with inferring logical representations from natural language. While existing LLM explorations have been largely restricted to literal language use, in this work, we evaluate the capacity of LLMs to infer the meanings of pragmatic utterances. Specifically, we explore the case of threshold estimation on the gradable adjective “strong”, contextually conditioned on a strength prior, then extended to composition with qualification, negation, polarity inversion, and class comparison. We find that LLMs can derive context-grounded, human-like distributions over the interpretations of several complex pragmatic utterances, yet struggle composing with negation. These results inform the inferential capacity of statistical language models, and their use in pragmatic and semantic parsing applications.

Reading Comprehension as Embodied Action: Exploratory Findings on Nonlinear Eye Movement Dynamics and Comprehension of Scientific Texts

Reading comprehension is often conceptualized in terms of the internal processing of linguistic information and construction of accurate mental representations. In contrast, an ecological-enactive approach rejects this internalist focus and instead emphasizes the dynamic process of reader-text coupling in which eye movements play a constitutive role. In this study, we employed recurrence quantification analysis (RQA) to examine the relationship between reading comprehension and eye movement dynamics, based on eye-tracking data from the Potsdam Textbook Corpus recorded from beginners and experts reading scientific texts, followed by comprehension questionnaires. Moreover, we compared the findings from RQA to classical eye movement measures (number of fixations, mean fixation duration, regression fixation proportion). The results indicated that classical eye movement measures did not predict reading comprehension reliably, whereas recurrences in gaze steps were reliably associated with reading comprehension proficiency. Contrary to our original hypothesis, experts showed more irregular, rather than more regular, eye movement dynamics, and these were linked to more proficient reading comprehension. In line with previous research on naturalistic reading using nonlinear methods, the present findings suggest that reading comprehension is best understood as emerging from interaction-dominant coordination processes.

Even those who perceive COVID-19 as low risk engage in infection prevention behavior in Japan: COVID-19 infection prevention behavior as a social norm

The global outbreak of COVID-19 has caused severe physical and economic damage. The extent of damage differs across countries, and is relatively small in Japan. This study investigated the predictors of infection prevention behavior among Japanese people and attempted to understand why the damage in Japan was less compared to other countries. We explored the following predictions: (1) people who perceive higher COVID-19 risks will engage in infection prevention behaviors regardless of their perceived norms; and (2) people who perceive lower risks for COVID-19 will engage in infection prevention only when they perceive infection prevention behavior as a social norm. We conducted two studies by recruiting 1,588 and 339 participants for studies 1 and 2, respectively. In Study 1, as an indicator of the perceived infection risk, we measured whether participants had been vaccinated, assuming that unvaccinated people perceived COVID-19 to be low risk. In Study 2, we directly measured the perceived infection risk. The results were consistent with our predictions, suggesting that social norms promote infection prevention behavior, even among individuals who perceived COVID-19 as low risk. This may be one of the reasons for the relatively small COVID-19-related damage in Japan.

Large-scale Network Analyses Reveal Cross-Language Differences in Semantic Structures: A Comparative Study

English and Mandarin Chinese are two distinct languages in many aspects, such as orthography and morphology. Previous network analyses show strong clustering coefficients (C) on English semantic networks, revealing the interconnectedness of semantic representations between words. However, it is not clear whether such semantic representation properties are language specific or general, and whether the linguistic- feature difference (e.g., subword components such as orthography and morphology) may affect the lexico-semantic structure. Here, we compared Cs of words in English and Mandarin semantic networks based on a) feature norms empirically derived from human subjects and b) distributed semantic information of text retrieved by word embedding models. We consistently observed higher Cs of Mandarin words than English words, especially when the semantic network considers subword features. Linear regressions suggested that the subword components’ semantic properties in Mandarin, but not in English, could significantly and positively predict the C of words in semantic networks. The results indicate an important role of language-specific properties in lexico-semantic structures and imply the diversity of human language processing.

Evidence for Cross-situational Syntactic Bootstrapping: Three-year olds Generalize Verb Meaning across Different Syntactic Frames

Previous research suggests that a verb’s meaning is learned partly through the aggregated profile of syntactic frames associated with it. For example, “turn” occurs with transitive and intransitive frames in causative alternation (“He turned the car”/“The car turned”), indicating it is a causal verb. Some evidence demonstrates that young children combine multiple frames to map verbs to appropriate events. However, previous work always presented these frames together, in a single dialogue. What remains unknown is how verb learning occurs when the frames are separated, uttered in different referential contexts, as is likely in children’s everyday life. In a series of cross-situational word-learning experiments, we show that both adults and three-year-olds generalize verb meanings across different syntactic frames in a cross-situational learning task. These results shed light on the cross-situational mechanisms of syntactic bootstrapping.

A recipient design in multimodal language on TV: A comparison of child-directed and adult-directed broadcasting

Child-directed language is a unique multimodal communication behaviour that differs from adult-directed language. We investigated how broadcasters organize their multimodal language production on an adult and child-directed programme to better understand the recipient design in the broadcasting context. Thirty-six future broadcasters produced live programmes for children and adults, respectively, whose linguistic features (utterance=3888), speech prosody, and gestures (N=8486) were analysed as a function of programme. We found that broadcasters used a higher mean pitch but a smaller pitch range, shorter utterances, high(er) frequency words, more questions, pointing and representational gestures but fewer pragmatic gestures in child-directed broadcasting. Gestures were also more salient and slower when addressing children audiences. However, there were no differences in lexical diversity, speaking rate, pausing, or beat gestures between programmes. In conclusion, broadcasters did engage in recipient design multimodally, but the distinction between the speaker and audience orientation is not binary but should be understood across signal channels according to contexts.

Relational abstraction in early childhood: Three cultures and three trajectories

Abstract reasoning in early childhood is often described as following a "relational shift," over which children become increasingly sensitive to relations. However, recent work has challenged the generality of this account, showing that children in the US and China follow distinct trajectories in a relational match-to-sample task (Carstensen et al., 2019). This difference aligns with multiple cultural and linguistic factors implicated in relational reasoning, in which English speakers in the US and Mandarin speakers in China appear at opposite ends of a continuum spanning from a focus on objects (US) to relations (CN). We explore early relational reasoning in a context that represents a cultural middle ground with a key linguistic similarity (noun spurts) to the US: Korean-learning children in South Korea. In two experiments with 262 Korean children, we document relational reasoning in this novel cultural context, revealing similarities and differences to developmental trajectories in the US and China.

Wiring Cost Minimization: A Dominant Factor in the Evolution of Brain Networks across Five Species

There is substantial evidence to suggest that brain circuits have evolved to be highly efficient and robust while consuming relatively minimal energy. These circuits possess unique structural and functional properties, such as sparsity, complexity, and small-world nature. Studies suggest that brain network development is shaped by a trade-off between minimal wiring cost and efficient communication. However, it is not entirely clear which factors are most influential, and to what extent each factor contributes to this development. Our examination of several potential underlying factors reveals that, with connectivity guaranteed by a fixed degree distribution, minimizing wiring cost has the greatest impact on network structure, compared to factors such as maximizing the clustering coefficient or coefficient of variation for wiring length distribution. While the cost-efficiency balance is capable of optimally reproducing brain networks in five different species without degree constraints, minimizing wiring cost remains the primary determinant.

Effect of combining the direction of three human-like objects on choice between two apertures

Two experiments were conducted to investigate perceptual judgment and choice of apertures in a virtual environment. In both experiments, we showed participants two apertures of the same width consisting of three human-like objects on a computer screen. In Experiment 1, participants were asked to judge which apertures they perceived to be wider. In Experiment 2, participants were asked to choose which apertures they preferred to pass through. We manipulated the face directions of the three human-like objects and analyzed participants’ choice ratios. In Experiment 1, there was a significant difference in width perception in specific condition. In Experiment 2, there were significant differences in choice of aperture in all eight conditions. These results indicate that the combination of three human-like objects' directions affects the participant’s aperture choice. Surprisingly, although two apertures were physically the same width, we found perceptual bias or illusion in choice between them under particular experimental conditions.

Preference for Happy Faces in Emotion-based Attentional Priority in Visual Short Term Memory

Prior literature provides contradictory claims regarding differences in preferential processing among different emotional stimuli. While some studies have indicated that happy faces are processed more effectively than others, Simione et al. (2014) demonstrated that threat superiority emerges when processing resources are constrained in the visual short term memory. Given the contrasting claims about angry vs. happy faces, we did a modified replication of the same study using real instead of schematic faces. We hypothesized that performance would be better with happy faces. We conducted two experiments by manipulating display times in experiment 1 and set sizes in experiment 2. We found a general emotional superiority effect and a “happiness superiority” effect in both experiments, which contrasts with Simione et al. (2014)’s results. These findings suggest that happy faces receive attentional priority for storage in VSTM, highlighting the need to incorporate saliency-based processing differences in theories of consolidation and processing in visual short-term memory.

Common words, uncommon meanings: Evidence for widespread gender differences in word meaning.

Communication relies on a shared understanding of word meaning; however, recent evidence suggests that individual variation in meaning exists even for common nouns. Understanding where and how this variation arises is therefore integral to circumnavigating misunderstandings and facilitating more efficient communication. This study investigated the degree to which men and women ascribe different meanings to the same words. Experiment 1 used a constrained word association task where participants generated three adjectives for each of 42 words. These data were used in Experiment 2, where a separate sample judged the association strength between word pairs. Both experiments investigated the role of gender in word meaning variation and found evidence for gender-specific meaning for a substantial fraction of the 42 words (Experiment 1: 12 or 29\%; Experiment 2: 13 or 31\%). Experiment 2 also investigated whether conceptual diversity can be explained by gender. Using Gaussian mixture modelling, we found evidence for 62 clusters (indicating concepts), with over 30\% of words mapping onto multiple concepts. Evidence for gender-specific concepts was found for nearly half (46\%) of the words with multiple clusters. Moreover, gender differences in meaning were not restricted to gender-connoted words but included apparently neutral words. Altogether, the results demonstrate how male and female speakers of the same language may have slightly different conceptual representations, even of common English nouns.

Perceptual Strength Norms for 510 Japanese Words, Including Ideophones: A Comparative Study with English

Words express various sensory information to various degrees. Norming studies have collected native speakers’ subjective perceptual strength ratings for numerous words in several languages. This paper presents perceptual strength norms for 510 Japanese words, including iconic lexemes called ideophones. The newly collected norms replicated some previous findings, such as visual dominance, olfactory inferiority, and the correlation between overall perceptual strength and iconicity. A systematic comparison between the Japanese and English perceptual strength norms further revealed that Japanese onomatopoeic ideophones tend to be more multisensory than their English equivalents and that Japanese words in general tend to encode more interoceptive information than English words. These findings suggest the usefulness of norming data in typological discussions on lexical semantics.

Towards a model of confidence judgements in concept learning

Confidence is an important concept in cognitive science, as it integrates seamlessly with our beliefs, goals, and decisions. Humans naturally represent and express degrees of confidence in beliefs and predictions that reflect their accuracy. However, the dynamics of how our underlying beliefs about the world relate to explicitly represented confidence over those beliefs is yet not well understood. In this work, we make progress on this question in the domain of \textit{concept learning}. Specifically, we analyze how confidence and beliefs jointly evolve in the absence of explicit feedback. We evaluate some leading computational accounts of confidence in the present literature, and we find that these accounts do not accurately predict confidence in the context of our task. We advocate for caution in making claims about the generalizability of such accounts across tasks or domains and propose some new model-based measurements for predicting humans' confidence judgments. Of these measurements, we find that ones taking individual-level response patterns into account perform the best. We close by suggesting promising future directions for the study of confidence in concept learning.

Intended and Perceived Sarcasm Between Close Friends: What Triggers Sarcasm and What Gets Conveyed?

We conducted two experiments to investigate what triggers sarcasm between close friends and whether the factors prompting sarcastic comments in production are also shared with an external observer. In Experiment 1, participants freely reacted to different types of situations in written form and rated their perception of the given contexts, the level of sarcasm of their responses, and the intentions behind their responses. Results showed that the intentions to say clever things or to mock the addressee in a hilarious or friendly manner triggered a higher number of sarcastic answers. In contrast, the intentions to be direct or to be nice to the addressee triggered less sarcastic answers. In Experiment 2, a new group of participants rated the responses collected in Experiment 1 on the same dimensions. Overall, we observed similar patterns in both experiments. However, the intentions to criticize the addressee softly and to say clever things were stronger predictors of sarcasm for the observers than for the producer of the statement.

Mapping between the Human Visual System and Two-stream DCNNs in Action Representation

Deep convolutional neural networks (DCNNs) have been found to demonstrate hierarchical mapping to human brain regions on tasks such as object recognition. However, it remains unclear if such hierarchical mapping also applies to action recognition, which involves dynamic visual information processing. Here, we compared action representations of two-stream DCNNs to the human visual system. Five visual areas that are associated with object and action processing were selected. Nine human action categories were adopted from three semantic classes to examine the action representations of both DCNNs and human visual areas. In two fMRI experiments, actions were presented in the forms of computer-rendered videos and point-light biological motion videos. Results showed that although two-stream DCNNs demonstrated hierarchical representations of actions as layers grow deeper, DCNNs lack a hierarchical mapping to human visual areas. Consistently across different video displays and DCNN pathways, only the top DCNN layers demonstrated high similarity to representations in the human visual system. The results suggest that the dynamic representations of human actions may be different in DCNNs compared to the human visual system, even after big-data training.

Linguistic Anticipation in Children’s Correction Sentences

The present research explored the development of prediction systems in 24- and 30-month-old toddlers in a visual-world-paradigm. Participants heard a sentence that included a correction (“no”) or a conjunction (“and”) while seeing an array of pictures with an associatively related noun (“cat”) and the erroneous noun (“dog”). The young group showed signs of early prediction of the associative picture on both types of sentences while the older group showed signs of an early prediction of the coordination condition, but a later prediction in the correction sentences. Our results suggest that older toddlers deployed different predictive systems, while the younger group used the same system.

The categorization of intransitive verbs in Mandarin: Evidence from word2vec modeling and behavioral experiment

Intransitive verbs in human language can be subcategorized into at least two linguistic categories, unaccusative and unergative verbs. The categorization of these verbs has been a subject of debate, with Projectional approaches emphasizing the role of verb semantics and Constructional approaches highlighting the importance of the sentence context. We utilize a word2vec model to capture the environmental influence on verb semantics, providing evidence that supports both approaches. Our results demonstrate that the categorization of Unaccusativity for new verbs can be influenced by the sentence context in which they appear, and the frequency of verbs in specific contexts also plays a significant role. Additionally, through a child language acquisition experiment, we show that the sentence environment has a significant impact on the categorization of Unaccusativity when the semantics of new verbs are provided. These findings suggest that both approaches have merit, highlighting the psychological universality of Unaccusativity across languages.

Neural Correlates of Analogical Reasoning of Syntactic Rules

Analogical reasoning is central to thought and learning, with a key focus on similarities where the same relations hold across different sets of elements. The current research investigated analogical reasoning in first-order and second-order relations, represented in word-like symbols following syntactic rules. Since most previous studies in this field have focused on visuospatial and semantic analogy, the primary goal of this paper was to specify the neural substrates for analogical reasoning based on syntactic rules. We found the activation locates in both anterior and posterior MFG, and these two regions correspond to parts of rlPFC and dlPFC respectively. The results reveal that although analogical reasoning with syntactic rules recruits similar brain regions as those in visuospatial and semantic analogy, their activation patterns are not the same. The role of rlPFC, the core area for analogical reasoning, needs to be reconsidered, and the complexity of syntactic-related activation is worth more attention.

Modeling Reliance on XAI Indicating Its Purpose and Attention

This study used XAI, which shows its purposes and attention as explanations of its process, and investigated how these explanations affect human trust in and use of AI. In this study, we generated heatmaps indicating AI attention, conducted Experiment 1 to confirm the validity of the interpretability of the heatmaps, and conducted Experiment 2 to investigate the effects of the purpose and heatmaps in terms of reliance (depending on AI) and compliance (accepting answers of AI). The results of structural equation modeling analyses showed that (1) displaying the purpose of AI positively and negatively influenced trust depending on the types of AI usage, reliance or compliance, and task difficulty, (2) just displaying the heatmaps negatively influenced trust in a more difficult task, and (3) the heatmaps positively influenced trust according to their interpretability in a more difficult task.

Parallel development of social preferences in fish and machines

What are the computational foundations of social grouping? Traditional approaches to this question have focused on verbal reasoning or simple (low-dimensional) quantitative models. In the real world, however, social preferences emerge when high-dimensional learning systems (brains and bodies) interact with high-dimensional sensory inputs during an animal’s embodied interactions with the world. A deep understanding of social grouping will therefore require embodied models that learn directly from sensory inputs using high-dimensional learning mechanisms. To this end, we built artificial neural networks (ANNs), embodied those ANNs in virtual fish bodies, and raised the artificial fish in virtual fish tanks that mimicked the rearing conditions of real fish. We then compared the social preferences that emerged in real fish versus artificial fish. We found that when artificial fish had two core learning mechanisms (reinforcement learning and curiosity-driven learning), artificial fish developed fish-like social preferences. Like real fish, the artificial fish spontaneously learned to prefer members of their own group over members of other groups. The artificial fish also spontaneously learned to self-segregate with their in-group, akin to self-segregation behavior seen in nature. Our results suggest that social grouping can emerge from three ingredients: (1) reinforcement learning, (2) intrinsic motivation, and (3) early social experiences with in-group members. This approach lays a foundation for reverse engineering animal-like social behavior with image-computable models, bridging the divide between high-dimensional sensory inputs and social preferences.

Does “never” implicate a larger reward than “possible”? : Risk–reward correlation in verbal probability phrases

This study aimed to explore whether the risk-reward correlation would hold in verbal probability phrases. The risk-reward correlation refers to a tendency to infer inverse proportionality between probability and outcome magnitude and have been considered as reflecting the statistical structure of the environment. Existing studies have demonstrated this inverse proportionality in numerical probabilities. However, it is well known that verbal probability phrases can convey contextual information about uncertainty without numbers, and express contextual information that are not contained by numerical probabilities. Specifically, verbal probabilities have positive and negative directionality that make listener’s attention to occurrence or non-occurrence of outcome and as a result affect listener’s decision making. A purpose of this study is to examine whether the risk-reward correlation also hold in verbal probability phrases, To accomplish this, two empirical studies that required participants to estimate reward values and winning probability of gambles that were expressed by verbal probability phrases such as “certain” or “impossible” were performed. Results indicate that risk-reward correlation also hold in verbal probability phrases, and the directionality of the verbal probability phrases.

The more, the better? The influence of multilingualism on cross-situational statistical word learning and mutual exclusivity

While there is evidence that bilingualism enhances statistical learning and substantially reduces the degree to which learners use mutual exclusivity (ME) constraints, little is known about the role of multilingualism. In this study, we tested ME in monolinguals, sequential-bilinguals, trilinguals, and quadrilinguals using a cross-situational statistical learning task. Participants were familiarized with a mixed set of one-word-to-one-object mappings and two-objects-to-one-word mappings in three consecutive phases, each of which was followed by a test. Results revealed that all language groups learned both mapping types by the end. They also learned more one-to-one mappings than two-to-one mappings. Inconsistent with previous research, bilinguals and monolinguals showed a similar learning trajectory of two-to-one mappings. However, both trilinguals and quadrilinguals outperformed monolinguals. Trilinguals did not differ from quadrilinguals in accepting multiple referents for the same label. Findings suggest that multilingual language experience might enhance cross-situational statistical word learning and ME relaxing ability more than bilingualism.

Children Are Not Just Noisy Adults: Disentangling Noise and Bias in Numerical Estimation

There are ongoing debates whether logarithmic compression in number-to-space mapping reflects logarithmic encoding of large numbers (bias) or uncertainty about numeric value (noise). We tested these two hypotheses by disentangling the effect of bias and noise. When 80 adults and 80 4- to 7-year-olds were asked to estimate the number of dots on a number line, both children and adults were more logarithmic on 0-100 than 0-30 problems. Internal noise explained some of the variance in logarithmicity, but only for children. We then examined the wisdom of crowds effect by comparing accuracy of children's mean estimate with accuracy of each adult's estimates. As predicted, children as a crowd were not as accurate as individual adults, indicating that noise is not the only source of children's errors. Generally, increasing the size of a crowd also had a smaller effect on 0-100 than 0-30 problems, indicating that inaccuracy on 0-30 problems is likely due to noise. The present study provides evidence that bias and noise have an additive effect on logarithmic compression and that children's logarithmicity reflects bias in number representations, not just noise.

Overinformative Question Answering by Humans and Machines

When faced with a polar question, speakers often provide overinformative answers going beyond a simple “yes” or “no”. But what principles guide the selection of additional information? In this paper, we provide experimental evidence from two studies suggesting that overinformativeness in human answering is driven by considerations of relevance to the questioner’s goals which they flexibly adjust given the functional context in which the question is uttered. We take these human results as a strong benchmark for investigating question-answering performance in state-of-the-art neural language models, conducting an extensive evaluation on items from human experiments. We find that most models fail to adjust their answering behavior in a human-like way and tend to include irrelevant information. We show that GPT-3 is highly sensitive to the form of the prompt and only achieves human-like answer patterns when guided by an example and cognitively-motivated explanation.

Multisensory Integration of Audiovisual Information in Avatar Faces: An Experimental Investigation of McGurk Effect

The McGurk effect is a multisensory phenomenon, an audiovisual illusion that shows how speech sounds may interfere with the visual sense. In case of an incongruency between a speech sound and a human face articulating the speech sound, human interlocutors tend to perceive an audiovisual percept different from the sound percept and the visual percept. For example, if the speech sound is ba and the visual presentation of the articulation is fa, human interlocutors usually perceive a third sound, va. What can we learn about audiovisual integration using an avatar face? To what extent do we observe the McGurk effect? The present study reports an empirical investigation that tested the extent of the effect in an avatar face designed for the purpose of the study. Our findings suggest that systematic patterns may obtained from the analysis of a specific avatar face, designed for the purpose of the study. The results also reveal the potential use of avatar designs for evaluating the impact of avatar designs on human multisensory integration by employing human responses to audiovisual illusions on avatar faces.

Testing the Effectiveness of Augmenting Perceptual Training With Annotations and Steps in a Difficult Visual Discrimination Task

Perceptual training has been shown to be an effective and rapid way of training people to make simple diagnoses using medical images. However, it appears to be less effective at training people to make more complex diagnoses that require non-binary judgements. In the present study, we investigated whether perceptual training could be augmented to make it more effective and what factors limited its effectiveness. In Experiment 1, we created artificial stimuli that were designed to simulate liver ultrasound images to assess perceptual learning for a complex task that involved judgements on a 7-point scale. Whilst performance improved somewhat with training, we found that incorporating annotations into the training provided no benefits. Additionally, contrary to our expectations, training that was structured in a stepped fashion was detrimental to learning. In Experiment 2, we found that perceptual learning in a simple task with shaded disks was most impacted by the extent to which the brightness levels of each disk were discriminable but that attending to multiple locations did not result in a significant cost to performance. Our findings show that augmenting perceptual training does not increase learning and that learning is less when the relevant features are harder to discriminate.

An information theoretic analysis of coherence

The aim of this paper is to import basic concepts from information theory into the epistemological debate about probabilistic measures of coherence. Rather than putting forward and defending a new measure, this paper will sketch an account of `external' coherence, which will be defined as a relation between a target variable of interest, sources containing (more or less) information about the target, and a rule or theoretical hypothesis that postulates the relevant connections between source and target. Relations with and potential insights for standard notions of coherence in formal epistemology are explored. More generaly, the paper explores the potential benefits of applying information theory to the epistemological debate about coherence.

A Neural Model of Number Comparison with Robust Generalization

We propose and implement a relatively simple computational neural-network model of number comparison. Training on paired comparisons of the integers 1-9 enables the model to efficiently and accurately simulate some fundamental empirical phenomena (distance and ratio effects on accuracy and response time). It also generalizes robustly to more advanced tasks involving multidigit integers, negative numbers, and decimal numbers. The work demonstrates that small neural networks can sometimes efficiently learn a powerful system that exhibits extremely robust generalization to untrained items. Some important alternate models of number comparison are considered to establish a broader context. Several predictions and suggestions are made for future empirical and computational research in this area.

CogTrans: A Cognitive Transfer Learning-based Self-Attention Mechanism Architecture for Knowledge Graph Reasoning

Knowledge Graph Reasoning (KGR) is an effective method to solve the incompleteness and sparsity problems of Knowledge Graph (KG), which infers new knowledge based on existing knowledge. Especially, the Graph Convolution Network (GCN)-based approaches can obtain state-of-the-art effectiveness, but there are still some problems such as weak reasoning ability, incomplete local information acquisition, insufficient attention score, and high learning cost, which lead to limited prediction accuracy. This paper proposes a multi-head self-attention mechanism architecture based on cognitive transfer learning, named CogTrans, to make effective improvements in the above problems. Shaped like a cross, CogTrans horizontally includes intuition and reasoning stages, which can achieve a faster convergence rate and obtain prediction results that are more in line with human intuition. Furthermore, CogTrans longitudinally includes source and target domains, and benefit from transfer learning, it can not only obtain the advantages of the horizontal architecture but also can “draw inferences from one instance”, which is more conducive to realizing the human brain-like reasoning effect of the architecture. Extensive experimental results show that our CogTrans architecture can obtain the most advanced accuracy of current GCN-based methods.

A Colorful Formalization of the Typological Prevalence Hypothesis

Languages vary in the way they categorize semantic domains. Incidentally certain semantic systems appear more often than others across the world. Recent research has shown that the attested variability can be explained as the result of languages being a plurality of optimal solutions to efficiency constraints. However, the question of the prevalence remains open. Assuming that languages are a form of culturally transmitted cognitive technology, the Typological Prevalence Hypothesis proposes that the prevalence of a linguistic system is explained by how cognitively natural it is to learn and use. We aim to formalize and test this hypothesis by proposing an information-theoretic measure of communicative and developmental naturalness applied to color typology. While controlling for phylogenetic relatedness, we find that both communicative and developmental naturalness are important predictors of typological prevalence.

Human similarity judgments of emojis support alignment of conceptual systems across modalities

Humans can readily generalize their learning to new visual concepts, and infer their associated meanings. How do people align the different conceptual systems learned from different modalities? In the present paper, we examine emojis— pictographs uniquely situated between visual and linguistic modalities—to explore the role of alignment and multimodality in visual and linguistic semantics. Simulation experiments show that relational structures of emojis captured in visual and linguistic conceptual systems can be aligned, and that the ease of alignment increases as the number of emojis increases. We also found that emojis with subjective impressions of high popularity are easier to align between their visual and linguistic representations. A behavioral experiment was conducted to measure similarity patterns between 48 emojis, and to compare human similarity judgments with three models based on visual, semantic and multimodal-joint representations of emojis. We found that the model trained with multimodal data by aligning visual and semantic spaces best accounts for human judgments.

The role of morphosyntactic cues on anticipatory sentence processing within a rich visual context: Evidence from eye-tracking

Eye-tracking research has revealed that people rely more on a recently enacted action-event than consider a plausible future action-event when hearing a sentence referring to a visual scene. When participants encountered a recently enacted action-event, and then listened to a (NP1-Verb-Adv-NP2) past or futuric present tense sentence in German, they inspected the target of the recently seen event more often than that of the equally plausible future target shown, irrespective of sentence tense. These preferential looks towards the recent target persisted even when future events and futuric present sentences were presented with greater frequency within the experiment. The current experiments assessed whether the preferential looks toward recent targets occur in similarly structured English sentences containing earlier, more localized tense markers (auxiliary verbs: will/has), and in Georgian sentences containing an even earlier case marking at the first noun phrase (nominative and ergative case). Can the early morphosyntactic cues eliminate the preferential inspection of the recent target? Results revealed that when participants processed the tense marker in Experiment 1 (English) and 2 (Georgian), the bias towards looking at the recent target was reduced. However, in Georgian, the morphological marker on its own was not able to eliminate the strength of the recent event. In both experiments, participants rapidly started to decrease their looks towards the recent target when exposed to the clear future tense cues at the verbs and this gaze pattern continued into the later word regions. This shows that participants were able to use the tense cues but only partly use the morphosyntactic cues to anticipate sentence referents to the visual context.

Characterizing Shifts in Strategy in Active Function Learning

We investigate people’s use of strategies for sampling data in an active learning task. In the spirit of resource-rational analysis, we argue that people may often use effective heuristics to guide sampling in lieu of more computationally expensive optimization strategies, but that when they encounter evidence that their heuristics are now ineffective they flexibly shift to new strategies. When the function family changed, participants quickly updated their beliefs about the likely function family on subsequent trials. By clustering participants’ sampling behaviour, we show that people can employ varied sampling strategies, shifting strategies more often when encountering unusual function families that are more adversarial to generic sampling strategies. Not all new strategies improved participants’ performance on a subsequent prediction task; nonetheless, people’s ability to dynamically shift their active learning behaviour may help them understand the abstract features of complex relationships.

The Paired Process of Preschoolers' Speech: Dyadic Variance Predominates Classroom Vocalizations

Theories of cognitive development emphasize the role of dyadic processes in language acquisition. Empirical research has focused on caregiver-child dyads at home, which does not facilitate teasing apart dyadic and individual processes. The preschool classroom is one understudied context in which the same child participates in multiple interactions. Here we disentangle sources of variance in children’s language using objectively measured peer speech in preschool classrooms.

Behavioural changes in the interaction between child with autism spectrum disorder and mother through the Comprehensive Care Humanitude™ intervention

ASD children are characterised by their refusal to change and difficulty in changing their responses to different social situations, which make it difficult for them to communicate with others, including their parents. We applied the comprehensive care technique Humanitude™ to mothers of children with ASD to support them from a multimodal communication perspective. Changes in mother-child communication were analysed in terms of mother-child gaze and the time the mother-child spent playing together. The results indicate that after the intervention with Humanitude™, there were significant differences in eye contact, mothers looking at their children, and children looking at their mothers. There was also a decrease in the amount of time children spent playing alone was found. There was a high correlation between pre and post-Humanitude™ intervention in the amount of time both mothers and children spent looking at each other, which suggests that incorporating multimodal communication can affect communication between parents-children.

Do Children Learn English More Quickly When Their Native Language Is Similar To English?

The impact of an individual's first language (L1) on their acquisition of a second language (L2) is widely recognized in the field of psycholinguistics. However, previous research has focused on limited L1-L2 pairs, leaving questions about how different linguistic structures, such as genetic relationship, phonology, or syntax, can either facilitate or impede the learning of a new language. In our current study, we aimed to address these gaps by analyzing standardized English assessment scores of English Language Learners (ELLs) from 46 international schools across 30 countries. We compared linguistic similarities between more than 40 L1s and examined how different linguistic structures of the L1 influenced the acquisition of L2, specifically in terms of genetic, phonological, and syntactic similarities. Our findings indicate that older ELLs learn English at a faster rate than younger ELLs, and among older ELLs, those who speak an L1 that shares linguistic similarities with English acquire the language faster than ELLs whose L1 is less closely related to English. These results highlight the transfer of linguistic knowledge from L1 to L2 and emphasize the importance of age of acquisition in L2 learning. Our study has important theoretical and pedagogical implications for research on second language acquisition.

Infant Perception of Pitch Contour in Music and Speech

This study examined infants’ ability to perceive changes in the pitch contour of music and speech and its developmental changes. Japanese 6- and 10-month-old infants were habituated to a five-note-melody and a five-syllable Japanese non-word and tested with both the habituated and non-habituated stimuli of different pitch contours. The infants of neither age group detected the pitch changes in the music melodies, whereas both did for speech. Infant performances in music and speech were moderately correlated among the 6-month-olds but not the 10-month-olds. These results suggest that the perception of pitch contour changes in a five-unit sound sequence has developed by 6 months of age for speech, especially in a pitch-accent language, but not music. The perception of pitch contour may be domain-general at the age of 6 months, and it may develop via two different pathways during the latter half of the first year after birth.

How infants learn about people and object causal action: An associative account

Causal perception is a cornerstone of early cognitive development. A large database of research attests to the fact that infant causal perception emerges between 6 and 10 months of age. However, it remains unknown how infants learn about the causal properties of more realistic categories such as people and objects. For example, how do infants learn that people can cause other people to act and behave at a distance, whereas inanimate objects require contact to move and act? One answer to this question is that this knowledge is present from birth or shortly thereafter and is underpinned by core knowledge systems. An alternative perspective maintains that infants acquire this knowledge via domain-general associative learning. The goal of the present paper is to demonstrate that this alternative perspective—implemented in a connectionist computational model—is sufficient to explain infants’ developing knowledge about people and object causal action.

Blame attribution in human-AI and human-only systems: Crowdsourcing judgments from Twitter

We introduce a novel methodology to scrutinize blame attributions in 'Tweets', focusing on Artificial Intelligence (AI) incidents - a contemporary issue that provokes regular discourse. The method identifies the agents that get blamed and the factors that are associated with blame attributions. The proposed methodology replicates and contextualizes findings from experimental settings, revealing AI entities are often held accountable for adverse outcomes, while human agents are judged based on intentions. It also identifies unexplored factors, such as blaming data for perceived biases or AI for replacing humans. This method offers a robust tool for mitigating measurement bias in specific fields, enabling the continual rejuvenation of theoretical frameworks with emerging variables.

Investigating the Influence of Partner Gaze on the Relationship Between Interpersonal Coordination and Social Anxiety

Interpersonal coordination is a key determinant of successful social interaction but is disrupted for people who experience social anxiety. Effective coordination rests on individuals directing their attention toward others, an effect well-documented in previous literature. Yet, little research has considered the concurrent behaviour of interaction partners. Using a novel virtual reality task, we investigated how partner gaze (i.e., direct vs. averted) influenced the emergence of interpersonal coordination. The results revealed two novel effects: (i) spontaneous coordination was diminished in the averted (cf. direct) gaze condition; (ii) spontaneous coordination was positively related to symptoms of social anxiety, but only when partner gaze was averted. This latter finding contrasts the extant literature and points to interaction intensity as a factor governing the social anxiety-coordination association. More broadly, this work provides further evidence that emergent patterns of interpersonal coordination fluctuate as a function of changes in social context and the behaviour of others.

Personalized multi-task attention for multimodal mental health detection and explanation

The unprecedented spread of smartphone usage and its various boarding sensors have been garnering increasing interest in automatic mental health detection. However, there are two major barriers to reliable mental health detection applications that can be adopted in real-life: (a)The outputs of the complex machine learning model are not explainable, which reduces the trust of users and thus hinders the application in real-life scenarios. (b)The sensor signal distribution discrepancy across individuals is a major barrier to accurate detection since each individual has their own characteristics. We propose an explainable mental health detection model. Spatial and temporal features of multiple sensory sequences are extracted and fused with different weights generated by the attention mechanism so that the discrepancy of contribution to classifiers across different modalities can be considered in the model. Through a series of experiments on real-life datasets, results show the effectiveness of our model compared to the existing approaches.

Memory as a computational constraint in cross-situational word learning

A central challenge of cross-situational word learning is retaining word-referent mappings across exposures. We evaluate Memory-Bound Pursuit (MBP), a hypothesis-testing model of cross-situational word-learning which aims to account for learners’ memory constraints via a single parameter targeting the number of words that can be learned concurrently. Here, we show that by varying this parameter with age, MBP can capture both children’s and adults’ cross-situational word-learning success under varying levels of ambiguity. We also present new experimental findings supporting novel predictions made by MBP about the retention of word-referent mappings across intervening exposures. These findings suggest that MBP provides a strong baseline model of cross-situational word learning, capturing both developmental trends and experimental evidence of memory limitations for word learning.

Social Learning from Incomplete Information in a Dynamic Decision-Making Task

The exploration-exploitation dilemma in dynamic decision-making scenarios is a notoriously hard problem to solve. Having a partner to potentially learn from might make it easier to balance exploration and exploitation. In the current study, we investigate the impact of social information (i.e., about others’ exploration behavior vs. their rewards) and partner performance (optimal vs. random) on participants’ behavior in a dynamic decision-making task that contains a learning trap. We find that observing the exploration behavior of an optimally choosing partner was detrimental to participants’ overall performance and reduced participants’ exploratory tendencies. In contrast, observing a random partner’s exploration behavior stimulated participants’ exploration, though this increase in exploration did not help participants to uncover the reward function. Following previous literature, a reinforcement learning model that contained eligibility traces was able to describe human behavior and helped to uncover potential mechanisms that could explain aspects of the findings.

Prediction and learning under unsignalled changing contexts

Predictive inference and error-driven learning are critical to optimal performance across many different contexts. However, the specific context determines the informativeness of errors in updating predictions. In this study, participants experienced two changing, unsignalled contexts with opposite optimal responses to errors; the change-point context, where errors were informative, and the oddball context, where they were not. The changes to the context occurred under two task structures: 1) a fixed task structure, with consecutive blocks of each context, and 2) a random task structure, with the context randomly selected for each new block. We modelled participants' performance using a Hierarchical Gaussian Filter (HGF) model. We found that performance was greater in the oddball than change-point context, with more accurate and precise estimates. The estimates from the fixed task structure were also more precise than those in the random task structure. We showed that consistency in context can improve precision.

When do Children Pass the Relational-Match-To-Sample Task?

Relational ability—the ability to compare situations or ideas and discover common relations – is a key process in higher-order cognition that underlies transfer in learning and creative problem solving. For this reason, it has generated intense interest both among developmentalist and in cross-species comparative studies. The gold standard for evaluating relational ability is the Relational-Match-to-Sample (RMTS) task (Premack, 1983). Current work in cognitive development has produced inconsistent results as to when children are able to pass the RMTS, with Christie and Gentner (2014) finding earlier success than Hochmann et al. (2017) and Kroupin and Carey (2022). In this research, we attempt to resolve this issue. We first describe two studies that bear out and extend Christie and Gentner’s (2014) findings. We then discuss factors that might explain the discrepancy between the findings.

Factors in the Presentation Method of Museum Audio Guides Affecting Human Appreciation Behavior

Audio guides are used for appreciating works in museums. Although factors that influence such appreciation behavior have been studied, little is known about the effect of changing the audio guide presentation method when the viewer uses it. To understand the influence of audio guides on appreciation, this study conducted experiments to identify whether the audio guide presentation method affected viewers’ appreciation behavior. The results demonstrated that changes in speaking speed and presentation timing affected appreciation behavior, whereas priming did not affect appreciation but affected moving behavior. In addition, we examined subjective impressions of appreciation. Consequently, in terms of speaking speed and presentation timing, which affect appreciation time, it became clear that certain conditions made people feel uncomfortable in the subjective evaluation. On the other hand, in priming factors that only affect moving time, no unusual impression was found in the appreciation itself. The findings suggested the possibility of automatically controlling the presentation method without decreasing satisfaction with appreciation.

Scaffolding Deep Reinforcement Learning Agents using Dynamical Perceptual-Motor Primitives

Agents trained using deep reinforcement learning (DRL) are capable of meeting or exceeding human-levels of performance in multi-agent tasks. However, the behaviors exhibited by these agents are not guaranteed to be human-like or human-compatible. This poses a problem if the goal is to design agents capable of collaborating with humans in cooperative or team-based tasks. Previous approaches to encourage the development of human-compatible agents have relied on pre-recorded human data during training. However, such data is not available for the majority of everyday tasks. Importantly, research on human perceptual-motor behavior has found that task-directed behavior is often low-dimensional and can be decomposed into a defined set of dynamical perceptual-motor primitives (DPMPs). Accordingly, we propose a hierarchical approach to simplify DRL training by defining the action dynamics of agents using DPMPs at the lower level, while using DRL to train the decision-making dynamics of agents at the higher level. We evaluate our approach using a multi-agent shepherding task used to study human and multi-agent coordination. Our hierarchical, DRL-DPMP approach resulted in agents which trained faster than vanilla, black-box DRL agents. Further, the hierarchical agents reached higher levels of performance not only when interacting with each other during self-play, but also when completing the task alongside agents embodying models of novice and expert human behavior. Finally, the hierarchical DRL-DPMP agents developed decision-making policies that outperformed heuristic-based agents used in previous research in human-agent coordination.

The Impact of Risk and Prevalence on Foraging Behavior in Hybrid Visual Search

The hybrid foraging paradigm mimics a wide range of real-world searching scenarios. In the hybrid foraging paradigm, foragers search for multiple targets in multiple patches throughout the foraging session. In this study, we incorporate an element of risk in the standard hybrid foraging paradigm, and investigate the effects of risk and prevalence on foraging behavior. The primary finding reveals that human foragers tend to prefer certainty and avoid risk when performing hybrid foraging tasks. Changing the prevalence of the risky targets modulates the aversion to risk, but overall the effect of risk still outweighs the effect of prevalence. Our findings suggest that risk aversion might lead to sub-optimal foraging strategies.

Revisiting Causal Pluralism: Intention, Process, and Dependency in Cases of Double Prevention

Causal pluralism proposes that humans can reason about causes and effects in terms of both dependency and process relations, depending on the scenario. Empirical support for this view is provided by responses to double prevention scenarios in which an actor attempts to bring about an outcome, a preventer attempts to prevent the outcome, and a double preventer intervenes to stop the preventer’s prevention attempt. In contrast to the predictions of the causal pluralism account, two pre-registered experiments (Ns = 400 and 450) indicate (a) that intentional actions are judged to be significantly more causative of an outcome than unintentional actions for both the actor and the double preventer and (b) that reasoners interpret the double preventer’s link to the outcome in terms of a process relation. These results underscore the need to revisit fundamental questions regarding how reasoners form and reason over representations of causal scenarios featuring intentional actions.

In search of proxy measures of heterogeneity in conceptual definitions: A cognitive linguistic perspective

As a prime step in the empirical research cycle, we rely on language to define the constituted concepts. In the plethora of scholarly output, we often find a wide range of discrepant definitions of a given concept manifested by varied linguistic expressions. Capturing the linguistic variability of de facto conceptual definitions helps researchers assess how convergent our understanding of a concept has become across different contexts. To estimate the informativeness of conceptual definitions, we proposed and validated proxy measures of conceptual definition variability using natural language processing (NLP) techniques. As a use case study, we quantified the variability of conceptual definitions of 216 subordinate concepts associated with "open scholarship". We aggregated 2212 conceptual definitions from online dictionaries and scientific texts and explored the validity of the proposed proxies of definition variability. Our study brings new perspectives on reappraising the role of language in constructing knowledge and scientific theories.

M & M: Modifiers and their Effect on Memory in Younger and Older Adults

Modification is often required to differentiate potential referents in discourse context and enhances future memory for those referents. Not yet known is whether the type of modifier produced by younger and older adults differentially affects their object memory. We investigated the use of modifiers and whether it affects memory in younger and older adults. Further, we examined whether the effects vary depending on the type of modifiers produced, namely color versus state. Participants were asked to describe an object that was accompanied by a same-category object of different color or different state, or an unrelated object. A follow-up memory task then assessed their recognition memory. Older adults overspecified more than younger adults. Although modifiers improved memory for both age groups, older adults showed better memory performance. The current finding suggests a link between language production and memory, but we did not observe evidence that specific types of modifiers affected memory.

How does higher education in the social sciences impact social essentialist thinking?

Conceptions of social disparities as natural and biologically-caused, known as “essentialist” conceptions, support the maintenance of social disparities. Surprisingly little research has examined whether formal educational experiences – directly teaching people about the nature and origins of social disparities – can reduce essentialist conceptions. We investigate how social science coursework (e.g., sociology, history, anthropology) impacts (a) essentialist vs. structural explanations for racial disparities, and (b) social essentialism more broadly, in a diverse group of undergraduates (n = 246). Results suggest that students who have completed such coursework show reduced endorsement of essentialist explanations for racial disparities, but enhanced endorsement of other dimensions of social essentialist thought (e.g., the view that social categories are meaningfully distinct from one another). Thus, social science coursework may have a nuanced impact on social essentialism. We discuss the questions these results raise regarding the relations between explanatory thinking and other dimensions of social essentialism.

How Beliefs Influence Perceptions of Choices

People bring many beliefs to everyday decisions. Beliefs, such as those about health, can vary in both degree to which people believe them and degree to which they are correct. While prior work has found that it is difficult to correct mistaken beliefs, it has also shown that causal models may be more effective than other types of information for getting people to adopt correct information. However, it is an open question as to whether such information will change beliefs enough to influence decision-making. Through two experiments in the health domain we investigate (1) how degree of belief influences how people assess options, and (2) whether new information changes people's assessment of how reasonable those options are. Our results demonstrate the impact of incorrect beliefs on decision-making, and the difficulty of using causal models to correct these beliefs.

Analogy and the Evolution of the Cognitive Foundations of Metaphor: A Comparative and Archaeological Perspective

Metaphor is central to human language and cognition. It has also been proposed to play an important role in language evolution. For these reasons, the evolution of metaphor and the cognitive processes supporting it are an important explanatory target for evolutionary accounts of human language. Here, we focus on the evolution of one particular capacity supporting metaphor, that of analogy. We integrate data from comparative psychology and cognitive archaeology to investigate the evolution of analogy as well as its evolutionary foundations. We present evidence that many aspects of analogy display evolutionary continuity between humans and non-human animals. In addition, we propose that analogical capacities can also be inferred from the archaeological record by looking at productional diversity in tool-making. Overall, we argue that analogy as an important cognitive process supporting metaphor has deep evolutionary roots.

The Categorization Task is Insufficient to Distinguish between Strategies: A Case for Partial-XOR-like Tasks

In categorization research, competing theories are typically compared by fitting their predictions to participant’s responses on a set of test items. The theory that best matches each participant's responses is identified as the strategy the participant is most likely employing. Researchers face considerable difficulty in selecting the best-fitting model due to several factors. In this study, we show the frailty of this approach. Due to pervasive model mimicry and across the similarity- and rule-based models, typical categorization task designs fail to reliably distinguish strategies. Some design modifications that might help are counter-indicated on practical grounds (e.g., carry-over effects); other possible means of improving strategy identification are also discussed.

The power of Embodied Learning in an Online Course with Chinese High Schoolers

In recent years, embodied learning has gained significant at- tention as a valuable approach to STEM education. However, previous studies have often focused on highly controlled lab experiments and have failed to consider the unique perspec- tives and backgrounds of learners. The current study aims to replicate the findings of Zhang et al. (2022) by integrat- ing the embodied learning intervention into an online class with students that may differ in important ways from Amer- ican college students (Chinese high school students). Students were introduced to abstract concepts related to randomness and using coding to mimic a shuffling process. Students in sec- tions were randomly assigned to get this introduction through an embodied hands-on video or a less-embodied live-coding video. The learning outcomes were evaluated through authen- tic class assessments (homework and exam). Results showed that students introduced to target concepts with the more em- bodied video outperformed those who watched the less em- bodied video. The benefit of embodiment was observed only on questions related to the topic covered in the intervention videos.

The Entropy of Communication Turn Taking during a Collaborative Problem-Solving Task

Collaboration and teaming are critical for solving complex problems. However, little is known about how group dynamics affect teaming behaviours and, ultimately, problem-solving effectiveness. The present study aimed to validate a novel measure of the dynamics of team communication – here termed turn-taking entropy – and to investigate what aspects of those dynamics affect collaborative-problem-solving performance. Thirty-two teams of 4 were asked to complete a simulated crisis-response task in which they had to rank 15 items in order of their importance to their team’s survival (first individually and then as a team). Group responses were better than the aggregated individual responses of team members (suggesting teaming benefits), and were better when team members had task-relevant skills and knowledge. However, response quality was not significantly related to task completion time. Additionally, the proposed entropy measure appeared to capture group communication dynamics, and appeared to differentiate stable an unstable patterns of communication. Implications and directions for future research are discussed.

Lack of visual experience influences silent gesture productions for concepts across semantic categories

The extent to which experience influences conceptual representations is a matter of ongoing debate. This pre-registered study tested whether lack of visual experience affects how concepts are mapped onto gestures. Theories claim gestures arise from sensorimotor simulations, reflecting gesturers’ experience with objects. This raises the question of whether sensory experience influences gesture forms for concepts. Thirty congenitally blind and 30 sighted Turkish speakers produced silent gestures for individual concepts from three semantic categories that rely on motor or visual experience to different extents. Blind gesturers were less likely than sighted gesturers to produce a gesture for visual concepts, but this was not the case for motor concepts. Their gestures were also quantitatively different than sighted people’s gestures, relying less on strategies depicting visual features—e.g., drawing. Thus, visual experience plays a key role in how concepts are depicted in gestures, in line with embodied theories of gesture and conceptual representation.

Language in the Time of COVID: Sensitivity of Linguistic Alignment to Conversation Type and Communication Modality

The present study investigated the degree to which linguistic alignment is affected by communication medium and conversation type. To do so, we took advantage of the pandemic mitigation changes to reduce the spread of COVID-19 by shifting to engage with others over videoconferencing (VC) platforms or to meet face-to-face (FF) with public health constraints. We asked pairs of participants to conduct three conversations in one of three communication media. Here we analyze conversations from 23 dyads: 8 dyads who conversed FF, 8 who conversed in a laboratory VC set-up, and 7 who conversed in a remote VC set-up. Every dyad had an affiliative, an argumentative, and a task-based cooperative conversation. Results showed differences in lexical and syntactic alignment between conversation types. Interestingly, we also found interaction effects. These results point to changes in alignment based on communication constraints and provide support for the interpersonal synergies approach to conversation.

Semantic uncertainty guides the extension of conventions to new referents

A long tradition of studies in psycholinguistics has examined the formation and generalization of ad hoc conventions in reference games, showing how newly acquired conventions for a given target transfer to new referential contexts. However, another axis of generalization remains understudied: how do conventions formed for one target transfer to completely distinct targets, when specific lexical choices are unlikely to repeat? This paper presents two dyadic studies (N=240) that address this axis of generalization, focusing on the role of nameability --- the a priori likelihood that two individuals will share the same label. We leverage the recently-released KiloGram dataset, a collection of abstract tangram images that is orders of magnitude larger than previously available, exhibiting high diversity of properties like nameability. Our first study asks how nameability shapes convention formation, while the second asks how new conventions generalize to entirely new targets of reference. Our results raise new questions about how ad hoc conventions extend beyond target-specific re-use of specific lexical choices.

Preschool children reason about third-party goals when evaluating acoustic environments

Despite the unpredictable and ubiquitous nature of noise in the natural acoustic environment, most children still manage to ex- tract the linguistic, cognitive, and social information needed to engage with their world. This is no small feat. We examined what strategies children use to navigate different acoustic environments. One possibility we test is that children can select acoustic contexts that are consistent with particular goals. In Experiment 1, we presented preschool children with a set of auditory stimuli, meant to approximate various acoustic environments, and activity goals to complete within those environments. Children integrated auditory information with goals to select the best environment. To assess the flexibility of children’s decision-making, Experiment 2 built on this framework by replacing familiar activity goals with relatively less familiar ones. In preliminary data, adults and preschoolers reliably evaluated acoustic environments that best matched these less familiar activities, providing evidence for flexible reasoning about goal-consistent environments.

The role of temporal predictability in contingency learning

Whether timing affects contingency learning is currently poorly understood. Specifically, does temporal predictability aid the acquisition of contingencies? We asked participants in a music contingency learning task to respond to the target (the name of a note) while ignoring tones. To assess whether temporal predictability influences the acquisition of these contingencies, we manipulated the temporal context of tone presentation in two experiments. In Experiment 1, one group of participants listened to tones presented with a regular temporal interval (1000 ms between each trial), while the other group listened to tones presented in a random manner (random intervals selected between 600 and 1400 ms). In Experiment 2 we manipulated the temporal presentation of the tones within trials: one group listened to tones with a 300 ms fixed interval between the cue and the target, and the other group with an interval randomly selected between 0 and 600 ms. Although participants learned to associate pitch labels in both experiments this occurred independently of the timing manipulations. These results confirm prior evidence on contingency learning but do not show the effects of temporal predictability in this learning context.

Representation of object state-changes of semantically similar objects

Many studies show that language comprehenders track multiple object state-changes during event comprehension. In this research, we investigate if comprehenders are sensitive to object state-changes of semantically similar objects. In Experiments 1 to 3, participants were asked to verify whether the object depicted in a picture was mentioned in the previously read sentence. Crucially, the picture either showed the original/modified state of an object that was mentioned in the sentence (Experiment 1) or not (Experiments 2 and 3). In Experiment 1, participants were faster to verify that an object was mentioned in the sentence if its pictured state matched the state implied by the sentence. In Experiments 2 and 3, participants were slower to verify pictured objects (e.g., cake) after reading a sentence that implied multiple states for a semantically similar (e.g., egg) rather than dissimilar (e.g., thermometer) object. Thus, comprehenders are sensitive to object state-changes of semantically similar objects.

Finding your (child's) voice: Caregiver identification of familiar child voices

Previous research has shown that voices of unfamiliar young children are more difficult to differentiate and identify than the voices of adults. In the present study, we examine whether difficulty identifying child voices extends to cases in which those voices are highly familiar. Caregivers (n = 132) of 3.5- to 10-year-old children were presented with voice recordings of their own child amongst gender- and age-matched peers and asked to identify which voice belonged to their child. Although overall accuracy was high, voices of younger children were misidentified more often than voices of older children. In contrast with existing models of familiar voice identification, results suggest that listeners are sensitive to variability in low-level acoustic cues to speaker identity in familiar as well as unfamiliar voice processing.

Confirmation, Coherence and the Strength of Arguments

Alongside science and law, argumentation is also of central importance in everyday life. But what characterizes a good argument? This question has occupied philosophers and psychologists for centuries. The theory of Bayesian argumentation is particularly suitable for clarifying it, because it allows us to take into account in a natural way the role of uncertainty, which is central to much argumentation. Moreover, it offers the possibility of measuring the strength of an argument in probabilistic terms. One way to do this, implicit in much work, is to identify the strength of an argument with the degree to which the premises of the argument confirm the conclusion. We criticize this prima facie plausible proposal and suggest instead that the strength of an argument has something to do with how much the premises and the conclusion of the argument cohere with each other. This leads to a new probabilistic measure whose properties we examine in more detail.

Groups are Better than Individuals at Solving Optimum Stopping Problems

Many real-life decisions, such as booking a vacation or selecting a partner, involve relatively cost-free sampling of options up to a terminal decision point, beyond which the choice becomes costly to reverse. Such problems can be formulated as optimal stopping problems (OSPs), such as the famous secretary problem. Although human behavior on optimal stopping problems has been studied extensively, much of the literature has focused on the behavior of individual decision-makers operating using a binary payoff function. In this study, we use an OSP with a continuous payoff function to study how individuals’ decisions differ from the collective decision of groups of three members working together. An independent threshold model offered the best explanation for the behavior of both individuals and groups. We found groups performed significantly better than individuals, with individuals consistently waiting too long to make a choice relative to the optimal strategy. Groups are also more decisive in following their internal thresholds, which are also different than a simple average of member thresholds. Finally, we also found a lack of long-term learning in OSPs for groups, a trend previously documented in individuals.

Neural Network Modeling of Pure Reasoning in Preverbal Infants

Recent empirical work has provided evidence for pure reasoning in infancy, a capacity permitting flexible integration of multiple sources of information to form rational expectations about novel events (Teglas et al., 2011). However, the neural underpinnings of this capacity have remained elusive. In this work, we present the first ecologically rational, neural-level account of these findings on pure reasoning in human infants. Our work bridges two dominant approaches in computational developmental psychology, i.e. neural-network models and Bayesian modeling, substantiating the view that intuitive physics in infancy might, at least partly, involve heuristics: a set of simple, fast, resource-efficient, approximation algorithms that yield sufficiently good results.

Psychologically-informed chain-of-thought prompts for metaphor understanding in large language models

Probabilistic models of language understanding are valuable tools for investigating human language use. However, they need to be hand-designed for a particular domain. In contrast, large language models (LLMs) are trained on text that spans a wide array of domains, but they lack the structure and interpretability of probabilistic models. In this paper, we use chain-of-thought prompts to introduce structures from probabilistic models into LLMs. We explore this approach in the case of metaphor understanding. Our chain-of-thought prompts lead language models to infer latent variables and reason about their relationships in order to choose appropriate paraphrases for metaphors. The latent variables and relationships chosen are informed by theories of metaphor understanding from cognitive psychology. We apply these prompts to the two largest versions of GPT-3 and show that they can improve performance in a paraphrase selection task.

A Bayesian account of two visual illusions involving lighthouse beams

Lighthouse beams are often perceived as bent rather than straight, and observers sometimes infer that a rotating lighthouse beam originates from a ``phantom lighthouse'' that lies in the opposite direction from the true source of the beam. We argue that both illusions arise as a result of Bayesian inference based on natural scene statistics and support our argument by implementing a formal computational model. In addition to capturing both illusions, our model makes the novel predictions that a beam viewed from the side is perceived to bend towards the observer, and that the phantom lighthouse illusion should only emerge at a critical point at which the observer is located around 75 metres in front of the true source of a rotating lighthouse beam. Our theory therefore motivates a future line of experimental work, and contributes to a broader body of research that explains perceptual phenomena (including visual illusions) in terms of Bayesian inference.

Quantifying the Digital Phenotype of Loneliness on Twitter

Social media promotes social connectedness, but social media users can still be lonely which is an important preceding condition to various mental health disorders such as anxiety and depression. Here we aim to describe online loneliness in individuals from the linguistic and social features of their platform use. We define a sample of Twitter users who explicitly report being lonely and compare their language to a matching random control sample. For each user, we create a text embedding - a numerical representation of the content of their online posts, excluding terms and expressions related to loneliness. We utilize principal component analysis on the resulting embeddings to condense the data into a smaller number of variables, while still retaining the majority of the variance. By doing so, we are able to position each user within a two-dimensional space, defined by the first two principal components, which capture the most significant amount of variation in the data. Lonely individuals are spatially separated from the control sample, indicating that lonely individuals exhibit distinct language patterns that is often self-referential, e.g. “I should” and “but I”. Indicators of online social relations, such as the number of online friends, favorites, mentions, show that lonely individuals have fewer social relations, while a sentiment analysis demonstrates that their posts have lower valence. Our results provide insights into the lexical, social, and affective markers that characterize loneliness online, providing a starting point for the development of diagnostics and prevention.

Integrating Distributed Semantic Models with an Instance Memory Model to Explain False Recognition

In this paper, we simulated true and false recognition in the Deese/Roediger/McDermott (DRM; Deese, 1959; Roediger & McDermott, 1995) paradigm by incorporating word embeddings derived from distributed semantic models (word2vec) into an instance memory model (MINERVA2). Previously, Arndt and Hirshman (1998) demonstrated that MINERVA2 (Hintzman, 1984) could capture multiple classic false recognition findings with randomly generated word representations. However, as randomized representations deviate systematically from semantic representations learned from the natural language environment, there remains uncertainty about whether MINERVA2 can capture the false memory illusion when scaling up to real-life complexity in word representations. To address this uncertainty, we used word2vec embeddings that are derived from large corpora of natural language instead of randomized representations in MINERVA2. Our results showed that MINERVA2 can still capture the standard true and false recognition, and it can also accommodate the true and false recognition effects of various classic manipulations (e.g., associative strength, number of associates, divided attention, retention interval).

Quantifying the Effect of Visual Impairments on Daily Activities in Virtual, Interactive Environments

We propose a novel approach that utilizes virtual reality (VR) and simulation environments to quantify the impact of visual impairments (VIs) on daily tasks, e.g., to what extent does glaucoma slow people down in wiping a table or chopping vegetables? We utilize clinical data from patients to develop visual field models, allowing VR to mimic the visual perception of VI patients as if it is seen through their eyes. Additionally, we leverage BEHAVIOR, the state-of-the-art simulation environment, to recreate a household environment for six daily activities. By measuring the disparity between the subjects' performance with and without VI, we can accurately quantify the impact of VIs. We further quantify the effects of VIs on body and eye movements, and model how movement strategies affect task performance under the influence of VIs. We hope our results can provide valuable insights into the challenges faced by individuals with VIs.

Typing time PWI: A scalable paradigm for studying lexical production

Lexical production research has relied extensively on in- person naming experiments such as Picture Word Interference (PWI). While the interference effects observed in the standard paradigm have been well-attested, PWI experiments have tra- ditionally been underpowered, owing to limitations associated with large-scale data collection. In this study, we validate a scalable, typing time-based PWI paradigm that can be de- ployed on the internet, and enables large-scale replications of the interference effects observed in the spoken modality. We also propose an automated response coding process that in- corporates production errors such as incorrect responses and restarts, and can be leveraged to illuminate aspects of response conflict and correction processes in incremental production.

Contrastiveness in the context of action demonstration: an eye-tracking study on its effects on action perception and action recall

The study investigates two different ways of guiding the addressee of an explanation - an explainee, through action demonstration: contrastive and non-contrastive. Their effect was tested on attention to specific action elements (goal) as well as on event memory. In an eye-tracking experiment, participants were shown different motion videos that were either contrastive or non-contrastive with respect to the segments of movement presentation. Given that everyday action demonstration is often multimodal, the stimuli were created with respect to their visual and verbal presentation. For visual presentation, a video combined two movements in a contrastive (e.g., Up-motion following a Down-motion) or non-contrastive way (e.g., two Up-motions following each other). For verbal presentation, each video was combined with a sequence of instruction descriptions in the form of negative (i.e., contrastive) or assertive (i.e., non-contrastive) guidance. It was found that a) attention to the event goal increased for this condition in the later time window, and b) participants’ recall of the event was facilitated when a visually contrastive motion was combined with a verbal contrast.

Crowd-Sourcing Human Ratings of Linguistic Production

This study examines the reliability and validity of using two types of crowd-sourced judgments to collect lexical diversity scores. Scaled and pairwise comparison approaches were used to collect data from non-expert Amazon Mechanical Turk workers. The reliability of the lexical diversity ratings for the crowd-sourced raters was assessed along with those from trained raters using a variety of reliability statistics. The validity of the ratings was examined by 1) comparing crowd-sourced and trained ratings, 2) comparing crowd-sourced and trained ratings to ratings of language proficiency, and 3) by using an objective measure of lexical diversity to predict the crowd-sourced and trained ratings. The results indicate that scaled crowd-sourced ratings showed strong reliability in terms of text and rater strata and showed fewer misfitted texts than the trained raters. The scaled crowd-sourced ratings were also strongly predicted by lexical diversity features derived from the texts themselves.

Measures of Semantic Distance from Word Embeddings Predict Neural Responses During Inferences about People and Objects

Recent advances in Natural Language Processing (NLP) make it possible to quantify relationships among different words extracted from large-scale human text corpora. Using a word embeddings model, we quantified the semantic distance between pairs of adjectives that could describe people or objects (e.g., smart, friendly; round, wooden) and scanned participants using fMRI while they had the opportunity to generalize from one known attribute to an unknown attribute across parametrically varying degrees of semantic distance (e.g., given that this person is smart, how likely are they to be friendly?; given that this furniture is round, how likely is it to be wooden?). Across categories, we observed a positive parametric effect of semantic distance on activation in the dorsomedial prefrontal cortex (DMPFC). Results connect to this region’s role in abstraction and inference under reducible uncertainty, with implications for understanding how people generalize beyond what they know to make inferences about novel individuals, items, or experiences.

Discourse structure affects reference resolution to events in English: Evidence from a new paradigm

Reference to events using pronouns like it or demonstratives like that has been difficult to study, because unlike in reference to people or objects, the ground truth of interpretation is harder to establish. In this paper, we introduce a new task to understand the roles of three parameters in event reference resolution: The referential expressions themselves, sentential aspect, and discourse structure. We find that different referring expressions reliably refer to specific parts of a discourse, and confirm previous findings that sentential aspect influences referential accessibility; that the structure of a discourse itself has a major effect on reference resolution, and most notably that, contrary to predictions in the literature, some events that are not on the right-frontier of a discourse are nevertheless available for reference. Our findings contribute to a growing literature on anaphor resolution that finds that the parameter structure that determines reference resolution is multifaceted, but predictable.

Manipulating the face contour reduces overall recognition performance for scrambled faces

This study (n=64) investigated the influence of manipulating the face contour on the face inversion effect (better recognition performance for upright vs inverted upside-down faces) and on face recognition in general. The study used upright and inverted scrambled faces which suffer from disruption of the configural information (spatial relationships among the facial features) typically found in normal faces. This is because we aimed to isolate the face contour information from the configural information. A delayed- matching task was adopted to ensure a high level of recognition performance, especially for inverted faces. The results revealed no significant difference between the inversion effect for scrambled faces with the normal contour vs that for scrambled faces with the manipulated (blurred) contour. Critically, we found an effect on overall recognition performance whereby scrambled faces with the blurred contour suffered greatly from the manipulation. Our results suggest that the face contour affects overall face recognition performance.

Using Pupillometry to Assess Visual Working Memory for Temporal Order

What factors influence our ability to maintain the temporal order of sequences in visual working memory? We used task-evoked pupillometry to investigate how the semantic content and spatial positions of images influence memory for temporal order. We found that memory for temporal order was stronger for sequences consisting of semantically meaningful images compared to abstract images, but there was no memory benefit for presenting sequences from left-to-right compared to centrally or from right-to-left. In addition, we found that pupil dilation was greater for sequences of semantic images compared to abstract images and particularly for items later in the sequence and in sequences presented from left-to-right. These results highlight the utility of using pupillometry to study working memory processes and provide new insights into how the nature of the items to be remembered and the spatial positions of those items influence visual working memory for temporal order.

Do constraints in APM solving affect APM-like puzzle creation?

The current study examines the role of constraints in well-defined problem-solving in ill-defined problem-solving. We chose variants of Raven’s advanced progressive matrices(APM) for well-defined problem-solving and creative reasoning tasks (CRT) for ill-defined problem-solving. Using traditional APM, we created a novel version of APM with comparatively lesser constraints available to solve the puzzle, called creative APM(cAPM). The cAPM task was designed to induce divergent thinking along with convergent thinking. It is assumed that the difference in constraints changes the nature of the problem space in solving APM and cAPM and may differently affect the following creative reasoning task. We randomly assigned 50 participants to perform APM or cAPM, followed by the CRT, in a fixed order. We observed a significant effect of constraints available to solve well-defined problems on ill-defined problem-solving. The current result shows higher CRT scores when CRT preceded cAPM (Median = 79.25) than APM (Median = 53.00). The result suggests that the flexibility in constraints to solve a well-defined problem induces more divergent thinking alongside convergent thinking and facilitates creative thinking required in ill-defined problem-solving.

What does the body do, when it's doing mathematics?

Mathematical concepts are paragons of abstraction. The actual practice of mathematics, though, is decidedly concrete, material, one might even say fleshy. This raises the question, What is the body doing, when the body is doing mathematics? We answer by analyzing a video corpus of mathematicians in their natural habitat: at the blackboard, chalk in hand. Mixing qualitative and quantitative analyses, we describe systematically the ways mathematicians use their bodies to write, move, and gesture. Some surprises arise, such as the observation that mathematicians point nearly constantly but seldom produce representational gestures; and that they spend most of their time away from the blackboard at which they are writing. We discuss implications for creativity, mathematical cognition, and theories of embodied and distributed cognition more broadly.

A Benchmark for Modeling Violation-of-Expectation in Physical Reasoning Across Event Categories

Recent work in computer vision and cognitive reasoning has given rise to an increasing adoption of the Violation-of-Expectation (VoE) paradigm in synthetic datasets. Inspired by infant psychology, researchers are now evaluating a model’s ability to label scenes as either expected or surprising with knowledge of only expected scenes. However, existing VoE-based 3D datasets in physical reasoning provide mainly vision data with little to no heuristics or inductive biases. Cognitive models of physical reasoning reveal infants create high-level abstract representations of objects and interactions. Capitalizing on this knowledge, we established a benchmark to study physical reasoning by curating a novel large-scale synthetic 3D VoE dataset armed with ground-truth heuristic labels of causally relevant features and rules. To validate our dataset in five event categories of physical reasoning, we benchmarked and analyzed human performance. We also proposed the Object File Physical Reasoning Network (OFPR-Net) which exploits the dataset's novel heuristics to outperform our baseline and ablation models. The OFPR-Net is also flexible in learning an alternate physical reality, showcasing its ability to learn universal causal relationships in physical reasoning to create systems with better interpretability.

Do humans visually adapt to number, or just itemhood?

Visual number adaption is a widely accepted phenomenon. This paper advances an alternative explanation for putative cases of the phenomenon. We propose that such cases may simply reflect observers adapting to the items in perceived displays, rather than their numerical quantity. Three experiments motivate consideration of this novel proposal and call into question the evidential basis for received formulations of the number adaptation hypothesis.

You are what you’re for: Essentialist categorization in large language models

How do essentialist beliefs about categories arise? We hypothesize that such beliefs are transmitted via language. We subject large language models (LLMs) to vignettes from the literature on essentialist categorization and find that they align well with people when the studies manipulated teleological information – information about what something is for. We examine whether in a classic test of essentialist categorization – the transformation task – LLMs prioritize teleological properties over information about what something looks like, or is made of. Experiments 1 and 2 find that telos and what something is made of matter more than appearance. Experiment 3 manipulates all three factors and finds that what something is for matters more than what it’s made of. Overall, these studies suggest that language alone may be sufficient to give rise to essentialist beliefs, and that information about what something is for matters more.

People have systematic expectations linking social relationships to patterns of reciprocal altruism

In two-person asymmetric coordination dilemmas, both people are better off if they coordinate, but one person benefits more than the other. When these interactions recur, people can form expectations to balance who is better off over time. What does it mean when asymmetric social interactions recur, and what can we learn from how people solve these dilemmas? We hypothesize that people expect social interactions to recur when two people are in a social relationship, and that knowing about the symmetry of the social relationship influences the stable solution to asymmetric coordination dilemmas over time. We report two experiments where participants read stories and answered questions about social interactions between two people. In Experiment 1, participants infer that two people are in a social relationship when there is a sequence of altruistic interactions between them, and specifically infer an asymmetric relationship when one person always performs the altruistic action, and a symmetric relationship when the two people alternate performing the altruistic action. In Experiment 2, participants equally expect alternating and repeating altruistic actions when the relationship is symmetric, but expect repeating actions (following a precedent) when the relationship is asymmetric. Our results suggest that people are able to use knowledge of relationships to generate shared expectations for coordinating on recurrent altruistic social interactions, and vice versa.

Semantic Retrieval Strategies in Divergent Thinking

In this study we investigate semantic retrieval strategies in order to inform discussions about cognitive mechanisms underlying divergent thinking and creativity. Relying on a verbal fluency task, we map the particular associative strategies participants engage and how they predict their performance. The study starts from the assumption that during divergent thinking processes, participants move along a path through a semantic space, and that each step is prompted by an associative strategy taking them from one word to the next. There are, however, a number of such strategies, and we predict that the outcome of the process is contingent on participants’ engagement of and shift between strategies. The study consists of a two-part elicitation paradigm where participants first conduct a verbal fluency task and then are guided through a meta-cognitive retrieval process to individuate the strategies employed in the task. We report significant correlations between the engagement of associative strategies and outcome measures of divergent thinking in terms of originality, flexibility and fluency.

Who feels more sad? Children reason about sunk costs to infer emotions

People expect that others will be biased by sunk costs in their decisions. However, previous work has shown that children do not hold this same expectation. In our work, we examined whether children make other inferences about sunk costs. Specifically, we wondered whether they would anticipate that sunk costs lead others to experience negative emotions, like sadness. In two experiments, we showed children aged five to seven years (N = 168) agents who expended high- or low-costs to obtain objects that were subsequently lost or broken. We found that from around age five, children predicted that an agent will be sadder if they invested greater effort to obtain an object. We show that this expectation is not based on a simple tendency to attribute greater emotions to agents who overcame larger obstacles. Our work shows that children are not entirely insensitive to sunk costs, as previous work may have suggested.

Online communication to the ingroup and the outgroup: the role of identity in the “what” and “why” of information sharing

How and why do people share opinions online? In research conducted offline, the social identity of the audience is a key factor: whether they are composed of one’s ingroup or outgroup affects what people share and why. Do people behave similarly and for similar reasons online? To test this, we put participants (N = 326) in imaginary forums belonging to their ingroup and outgroup. In each, people selected statements to share, along with reasons for doing so. The results showed a high degree of heterogeneity; people shared nearly all kinds of statements with both audiences, for a variety of reasons. However, there were also consistent patterns. Identity expression was the most common reason for sharing to both audiences, but this led to different things being shared to each. To the ingroup, people preferred to share statements expressing ingroup beliefs, while to the outgroup, they preferred statements expressing universal beliefs.

Charting children's fruit categories with Markov-Chain Monte Carlo with People

Uncovering how categories develop through childhood is crucial for cognitive science. However, even for simple domains, categories can be complex, making it challenging to access them experimentally, especially in developmental studies. Markov-Chain Monte Carlo with People (MCMCp) is a statistically-based procedure that allows us to elicit category members from participants' implicit categories. However, due to the complexity of the paradigm, MCMCp has been limited to experiments with adult populations. Here, we develop and validate a child-friendly method for applying MCMCp, producing the first MCMCp experiment to elicit category examples from children. Comparing fruit category members for five-year-olds and seven-year-olds, we find generally consistent representative fruits and a developmental progression of initially broad and overlapping fruit categories to more differentiated distributions.

"Just In Time" Representations for Mental Simulation in Intuitive Physics

Many models of intuitive physical reasoning posit some kind of mental simulation mechanism, yet everyday environments frequently contain far more objects than people could plau- sibly represent with their limited cognitive capacity. What determines which objects are actually included in our repre- sentations? We asked participants to predict how a ball will bounce through a complex field of obstacles, and probed work- ing memory for objects in the scene that were more and less likely to be relevant to the ball’s trajectory. We evaluate differ- ent accounts of relevance and find that successful object mem- ory is best predicted by how frequently a ball’s trajectory is expected to contact that object under a probabilistic simulation model. This suggests that people construct representations for mental simulation efficiently and dynamically, on the fly, by adding objects “just in time”: only when they are expected to become relevant for the next stage of simulation.

Evaluating Visual Number Discrimination in Deep Neural Networks

The ability to discriminate between large and small quantities is a core aspect of basic numerical competence in both humans and animals. In this work, we examine the extent to which the state-of-the-art neural networks designed for vision exhibit this basic ability. Motivated by studies in animal and infant numerical cognition, we use the numerical bisection procedure to test number discrimination in different families of neural architectures. Our results suggest that vision-specific inductive biases are helpful in numerosity discrimination, as models with such biases have lowest test errors on the task, and often have psychometric curves that qualitatively resemble those of humans and animals performing the task. However, even the strongest models, as measured on standard metrics of performance, fail to discriminate quantities in transfer experiments with differing training and testing conditions, indicating that such inductive biases might not be sufficient.

Do Prospective Confidence Ratings Enhance Problem-Solving?

Previous research has shown that eliciting retrospective confidence ratings during problem-solving enhances performance in high self-efficacy individuals but impairs performance in low self-efficacy individuals. In the current study, we examined whether judgments of solvability, a metacognitive rating made prior to participants’ first-order responses are similarly reactive and whether, like retrospective confidence ratings, this effect is moderated by pre-existing self-efficacy. In two experiments, we showed that judgments of solvability are reactive, but this effect is not moderated by pre-existing self-efficacy. We argue that eliciting metacognitive ratings prior to a participant’s response changes metacognitive control, without necessarily activating self-evaluative thoughts. These results are encouraging for the use of judgments of solvability as an education intervention.

The Effect of Response Suggestion on Dialogue Flow: Analysis Based on Dialogue Act and Initiative

Technology to predict responses is a key element in human-to-human messaging that has increasingly been utilized to enable AI-mediated communication. When response suggestions from AI are incorporated into human messaging, it will have an impact on the flow and content of the dialogue. In this paper, we investigated the effect of AI response suggestion sentences used for chat messaging on dialogue flow from two aspects: dialogue act and initiative. Usage rates of response suggestions for different dialogue acts were measured with BLEU scores, and we found that the response suggestions contributed to the establishment of a proper dialogue flow, such as an answer in response to a question. The results of our case study indicated that users who take the initiative in the dialogue tend to utilize response suggestions less frequently. We also found that some written responses were based on the suggested sentence structure but conveyed different messages.

Common and distinct neural bases for rule- and similarity-based category learning

Category learning is a core competence for minimizing cognitive load and optimizing decision-making. An identical problem can be solved by employing a rule-based or a similarity-based strategy. This work examined whether the use of the two strategies was supported by common or distinct neural substrates. We conducted a category learning experiment with rule-plus-similarity stimuli using EEG-fNIRS fusion methodology. Participants learned two artificial categories using either a rule-based or similarity-based strategy. The results showed a common visual-perceptual-analysis process and distinct decision-making processes between the uses of the two strategies. Larger P300 and N400 amplitudes and Wernicke's area activation indicated that hypotheses testing and verbal rule abstraction processes were critical for rule-based categorization. In contrast, increased frontopolar cortex activity indicated that integration of multiple dimensions was critical for similarity-based categorization. These results were consistent with COVIS theory, implying an explicit system in rule-based category learning whereas an implicit system in similarity-based learning.

The space of words: on the sensorimotor processing of variable affordances in noun-adjective combinations

Evidence suggests that the processing of graspable object nouns elicits specific motor programs related to potential hand-object interactions. Notably, adjectives specifying manipulative features of these objects are integrated into this sensorimotor representation. The present experiment investigated the effect of adjectives denoting the position of the object in space on the sensorimotor representation of graspable object nouns. We used a reach-to-grasp compatibility task, in which participants had to categorize object nouns as artifact or natural, by performing either a power or precision grip matching or not the typical grip associated with the object. On each trial, the object noun was presented with a near or far adjective. While reliable grasp-compatibility effects emerged for object nouns on RTs, this was not modulated by the spatial position denoted by the adjective. Spatial adjectives appear not to be integrated into the noun sensorimotor representation, supporting the distinction between stable and variable affordances.

Multimodal Behavior Analysis: Two Patterns of Collaborative Construction of Embodied Knowledge

This study investigates how individuals collaboratively constructed shared knowledge during a group activity. The dataset was collected from group activities for pre-service teachers in professional development. Participants designed body poses and action sequences that could help their students’ mathematical conceptualization. Using k-means clustering and principal component analysis, patterns of individuals’ contributions based on their verbal and gestural behavior identified two groups of individuals: (1) Those who contributed to the discussion by speaking and gesturing frequently (~ 25% of the participants), and (2) those who mostly listened and focused on design ideas presented by others. Furthermore, epistemic network analysis corroborated significant differences in discourse patterns between the clusters, the results of which has significant implications for collaborative embodied learning and application for teacher education and professional development.

Behavioral dynamics of conversation and (mis)communication in noisy environments

While communication is an essential part of life, it is not always easy and effortless. This is especially true when talking with someone in a noisy environment. Although communicating in noise is often the rule rather than the exception, very little research has investigated the behavioral processes individuals might use to minimize miscommunication when listening becomes challenged. Here we explored synergistic speech and movement processes that 22 pairs of adults used to hear and be heard when (mis)communicating in noise. The results revealed intricate dynamics both with respect to acoustic optimization of the speech produced and heard, as well as how individuals modulate interpersonal distance and behavioral coordination patterns.

How Well Do Humans Learn Conditional Probabilities?

Although there is a great deal of interest in conditional probabilities in Bayesian cognitive science, there is still little understanding of how well human agents can learn them. This paper addresses the issue by theoretical and experimental means. In the theoretical part, we distinguish between cases of vacuous learning, where learned probabilistic information is not new, and belief revision is unwarranted, from cases with truly new information. In the experimental part, we investigate how well participants can distinguish these cases and how well they respect the probabilistic norms, thus adding new insights to the long-standing question of the extent to which the human mind is adapted to probabilistic norms.

Seeing the connection: Manipulating access to visual information facilitates creative insight

Creative people move in ways that seem aimless. Artists and mathematicians wander about, sometimes standing next to their easel or blackboard, other times standing across the room. Why do creatives expend energy on aimless movement? We propose that such movements facilitate insight by changing the information that is visually available. We tested this mechanism in two online studies. Participants attempted to solve an insight puzzle. We manipulated whether participants could only see a diagram representing the puzzle, as though they were standing close to it, or could also see a diagram from an earlier puzzle, as though they had stepped back. Visual access to the second diagram acted as a visual hint, increasing the rate of insight by suggesting an analogous solution. We argue that this mechanism explains the creative benefits of seemingly aimless movement. We discuss implications for understanding creativity as arising from interactions among brain, body, and environment.

The prevalence of multitasking presents challenges for theories of event segmentation

Event cognition research has typically considered events to be contiguous in time, with defined starts and ends. However, people sometimes engage in more than one event at the same time. If this happens frequently, then theories of event cognition may require modification. This research study aims to estimate how often people engage in multitasking in daily life. Ninety-seven participants were asked whether they had been multitasking at four time points during the last 24 hours. Forty-five per cent of responses reported multitasking with a diverse range of event structures. Twenty-one per cent of reports specifically listed multiple overlapping activities. The prevalence of multitasking suggests that theories of event cognition need to be expanded to accommodate non-contiguous and simultaneous events.

Discovering Low-Dimensional Causal Pathways between Multiple Interacting Neuronal Populations

Understanding the nature of neural activity and computations in the brain will help us build better decision-making models to facilitate human-AI collaboration. Recording the neural activity of multiple and large neural populations in the brain is becoming widely available with modern recording techniques. It still remains a challenge, however, to understand how distinct and anatomically different neural populations interact with each other to control behaviour. We propose a new method to discover causal interactions between neural populations based on recurrent switching dynamical systems. We introduce an extended dynamics model that incorporates the current time-step when calculating the latent state variables. We also introduce an acyclicity constraint in learning the parameters of the model. These mechanisms enable rich causal interactions between neural populations to be identified from the learned model. Our model outperforms previous work on discovering interactions between neural populations in simulated datasets, without sacrificing the prediction performance of firing rates. We also apply our method on real neural recordings from two Macaque monkey brains performing a behavioral task, and show that the proposed method is able to detect causal interactions between brain regions related to the different time windows of the task.

Swipe and hold: composing interventions in continuous time causal learning

In this study, we investigated human causal learning in a continuous time and space setting. Our experiments revealed that people are capable causal learners in such contexts, and that standard Bayesian updating of prior beliefs partially explains how priors impact their judgments. Specifically, individuals with strong prior beliefs consistent with the ground truth are less likely to misinterpret indirect effects as direct in structures like causal chains. When priors were inconsistent with the ground truth however, participants performed worse on average, choosing less informative interventions. Computational modelling revealed that to deal with the abundance of data in this setting, participants use interventions to mark informative data and focus on direct outgoing links from the variable they intervene on. This task-decomposition strategy, when paired with participants' interventions, achieves comparable accuracy to using the entirety of the dataset, despite ignoring almost half of the available data. These findings are in line with the resource rational framework, where discarding data outside of interventions saves computational costs and the full inference problem about the graph is broken down into sub-problems of inferring individual links between variables. Overall, our study reinforces the idea that humans are frugal and intuitive active learners who combine actions and inference to optimize learning while minimizing effort.

Effects of Accent Exposure on Monolingual and Bilingual Children’s Early Vocabulary Development

Past studies have revealed that language experience impacts children’s vocabulary development. For example, bilingual children tend to have smaller vocabularies than monolingual children in each of their languages. However, it is unclear whether routine exposure to multiple accents also affects children’s vocabulary growth. Here, using standardized vocabulary assessments, we compared the reported vocabulary sizes of 11- to 34-month-old monolingual and bilingual children (N = 2881) who had various degrees of accent exposure. Our results show that routine exposure to multiple accents, regardless of accent type, does not negatively impact vocabulary development. Our findings suggest that children are well-equipped to handle language variation in their input.

Measuring and Modeling Physical Intrinsic Motivation

Humans are interactive agents driven to seek out situations with interesting physical dynamics. Here we formalize the functional form of physical intrinsic motivation. We first collect ratings of how interesting humans find a variety of physics scenarios. We then model human interestingness responses by implementing various hypotheses of intrinsic motivation including models that rely on simple scene features to models that depend on forward physics prediction. We find that the single best predictor of human responses is adversarial reward, a model derived from physical prediction loss. We also find that simple scene feature models do not generalize their prediction of human responses across all scenarios. Finally, linearly combining the adversarial model with the number of collisions in a scene leads to the greatest improvement in predictivity of human responses, suggesting humans are driven towards scenarios that result in high information gain and physical activity.

Unaccusativity, expectation, and reactivation of the subject in sentence comprehension

Psycholinguistic studies have shown that human sentence comprehension is predictive. The current study investigates if this is also the case in the realm of argument structure. Previous studies using the cross-modal priming (CMP) paradigm show that whether and when the subject is reactivated after the encounter with the verb depends on the type of the verb (unaccusative vs. unergative). We hypothesized that this is because the human parser predicts the verb type in advance. To test this hypothesis, we conducted a CMP experiment in Japanese, a verb-final language, and manipulated the parser's prediction by changing the bias on the verb type in the experimental stimuli. As expected, there was a significant interaction between the reactivation pattern and the bias. This suggests that the human parser makes an early prediction of the argument structure before the verb is revealed, and that prediction is quickly adapted to the current environment.

Statistical learning or phonological universals? Ambient language statistics guide consonant acquisition in four languages

What predicts individual differences in children’s acquisition of consonant production across languages? Considerations of children’s development of early speech production have traditionally emphasized inherent physiological constraints of the vocal apparatus that speakers generally have in common (i.e., articulatory complexity). In contrast, we propose a statistical learning account of phonological development, in which phonological regularities of the ambient language guide children’s learning of those regularities in production. Across four languages (English, Spanish, Japanese, and Korean), we utilized recent meta-analytic dataset of age of consonant acquisition spanning 28 studies. High-density measures of children's ambient language environment from over 8,000 transcripts of speech directed to over 1,000 children were used to assess how well the frequency of consonants in child-directed speech predict the age of consonant acquisition. Our results suggest that both frequency and articulatory complexity are related to age of acquisition, with similar results found for English, Spanish, Japanese, and Korean. Consonants heard frequently by children tended to be incorporated into their production repertoires earlier and consonants heard less frequently are incorporated into production repertoires later in development. We discuss future directions that incorporate a statistical learning pathway towards learning to produce the sound patterns of the ambient language.

Framing Space: Alzheimer’s Disease and Object Location in Brazilian Portuguese

This paper delves into the possible correlations between cognitive impairment due to Alzheimer’s Disease and differences in the ways spatial relations are decoded by groups of individuals with and without AD. We tested participants’ judgments of spatial scenes coded according to intrinsic, relative or undissociated Frames of Reference, ground rotation and use of a locative construction. We found significant differences in the costs of processing spatial information which were considerably higher in elderly and AD groups. On the other hand, judgment ratings reveal that those groups tend to maintain the same Frame of Reference applied by health young individuals to resolve the spatial ambiguity. Furthermore, a general preference for non-relative Frames of Reference was found among participants, all native speakers of Brazilian Portuguese.

Processing Scatterplots: Impact of Outliers on Correlational and Causal Inferences

Scatterplot research has identified factors that impact people’s perception of correlation magnitudes, yet much less is known about how people reason about data represented in scatterplots. We investigated how people make correlational and causal inferences based on scatterplots with and without outliers. In Experiment 1 and 2, participants viewed scatterplots matched in overall correlational magnitude depicted, but half had an outlier. In Experiment 3, the scatterplots in the two conditions were matched in the correlation magnitude depicted by all the dots excluding the outlier. For each scatterplot, participants stated their endorsement for correlational (X and Y change together) and causal statements (X changes Y). Only when outliers further strengthened an already moderate to strong relationship, people endorsed related correlational statements more and showed a stronger causality bias. Altogether we demonstrate that the impact of outliers in scatterplots on visual reasoning depends on the strength of the relationship depicted.

User-independent Emotion Classification based on Domain Adversarial Transfer Learning

EEG-based emotion recognition is one of the hot research directions in the field of human-computer interaction. The traditional user-dependent models have had remarkable success. However, due to the individual differences, the generalization performance of traditional models is poor for user-independent emotion recognition. Therefore, this work proposes a two-step domain adversarial transfer learning based on typical subjects (TS-DATL) framework with pretraining and domain adversarial training. Pre-training is to find out several typical representative subjects in the training dataset and mark the data most similar to the target domain as the source domain. Domain adversarial training is to narrow the mapping gap between the source domain and the target domain on the common feature space. Experiments were conducted on a public dataset DEAP. The results show that TS-DATL framework successfully reduces the difference of EEG signals across subjects, and effectively improves the prediction accuracy of two emotional dimensions.

A newborn embodied Turing test for view-invariant object recognition

Recent progress in artificial intelligence has renewed interest in building machines that learn like animals. Almost all of the work comparing learning across biological and artificial systems comes from studies where animals and machines received different training data, obscuring whether differences between animals and machines emerged from differences in learning mechanisms versus training data. We present an experimental approach—a “newborn embodied Turing Test”—that allows newborn animals and machines to be raised in the same environments and tested with the same tasks, permitting direct comparison of their learning abilities. To make this platform, we first collected controlled-rearing data from newborn chicks, then performed “digital twin” experiments in which machines were raised in virtual environments that mimicked the rearing conditions of the chicks. We found that (1) machines (deep reinforcement learning agents with intrinsic motivation) can spontaneously develop visually guided preference behavior, akin to imprinting in newborn chicks, and (2) machines are still far from newborn-level performance on object recognition tasks. Almost all of the chicks developed view-invariant object recognition, whereas the machines tended to develop view-dependent recognition. The learning outcomes were also far more constrained in the chicks versus machines. Ultimately, we anticipate that this approach will help researchers develop embodied AI systems that learn like newborn animals.

Verbalization Toward Others Facilitates Insight Problem Solving

We examined the effect of verbalization on problem solving using mathematical insight and non-insight problems. A total of 321 participants were randomly assigned to one of three conditions (verbalization toward self, verbalization toward others or control). A one-minute problem solving phase was followed by a one-minute verbalization phase; afterward, the participants were asked to work on the same problem again for two minutes. Each participant worked on three insight and non-insight problems each. A generalized linear mixed model analysis showed that the solution rate was significantly higher in the verbalization toward others condition than the other two conditions. There was no interaction with the problem type. When examining the effect of verbalization on insight problem solving, the type of insight problem (verbal, spatial or mathematical) and the verbalization addressee (self or others) should be considered.

Socio-Affective Traits Mediating Charitable Giving

This study explored the influence of various socio-affective factors on charitable giving, using an online task in which participants could choose to exert time and effort that was translated into monetary donations. Participants had the option of making Public Donations, Anonymous Donations, or No Donations. Moreover, some participants were given Social Information (SI) regarding the percentage of Public vs. Anonymous donations obtained in a pilot study. We found that the proportion of Public Donations increased with greater scores on the Narcissistic Entitlement & Exploitativeness scale (NPI EE), but only in the SI group. Conversely, the proportion of Anonymous Donations decreased with greater NPI EE scores, in the No Social Information group (NSI). In the absence of Social Information, Simulated Compassion scores (SCS), indicative of social approval seeking, decreased the proportion of No Donation decisions as well as the average amount donated. Finally, Social Information modulated the proportion of Public donations.

Evaluating testimony from multiple witnesses: consistent undervaluing and selective devaluing of corroborating reports

This study identified a novel and robust reasoning error. Lay reasoners significantly deviate from the prediction of Bayesian inference by consistently underestimating the added probative value of corroborating testimonial reports. Most surprisingly, however, is that in certain contexts the sum of corroborating evidence is considered to be significantly less valuable than a single report. There is a selective devaluing of corroborating testimony when a highly reliable report is corroborated by less reliable, but credible, reports. This intuitive error is not explained by an inaccurate understanding of individual cues of reliability and number of reports, but specifically when it is required to integrate these both cues. Findings indicate the operation of alternative reasoning strategies, resulting in errors at individual and group level.

Measuring Children's Early Vocabulary in Low-Resource Languages Using a Swadesh-style Word List

Early language skill is predictive of later life outcomes, and is thus of great interest to developmental psychologists and clinicians. The Communicative Development Inventories (CDIs), parent-reported inventories of early-learned vocabulary items, have proven to be valid and reliable instruments for measuring children's early language skill. CDIs have been painstakingly adapted to dozens of languages, and cross-linguistic comparisons thus far show both consistency and variability in language acquisition trajectories. However, thousands of languages do not yet have CDIs, posing a significant barrier to increasing the diversity of languages that are studied. Here, we propose a method for selecting candidate words to include on new CDIs, leveraging analysis of psychometric properties of translation-equivalent concepts that are frequently included on existing CDIs. Leveraging 26 datasets from existing CDIs, we propose a list of 229 concepts that have low variability in their cross-linguistic learning difficulty. This pool of common concepts---analogous to the "Swadesh" lists used in glottochronology---can be used as a starting point for future CDI adaptations. We test how well the proposed list generalizes to data from 8 additional languages.

Novel Noun Generalization in a Free-Choice Design : Investigating Noun Generalization Constraints.

A common result in novel word generalization is that comparison settings (i.e., several stimuli introduced simultaneously) favor taxonomically-based generalization. Most generalization studies on comparison have been done with forced-choice designs. We investigated which type of items five-year-old children would choose as referents in a free-choice novel noun generalization task. Options were items from the same basic level category, from a near superordinate category, a distant superordinate category, and also perceptual lures, thematic lures, and unrelated lures. We manipulated the generalization items availability at test (i.e., generalization stimuli introduced sequentially or simultaneously). Results show that items from the same basic level category were more chosen than other taxonomically related items. Interestingly, perceptual lures and near superordinate items did not differ, suggesting that children did not arbitrate between perception and taxonomy. Results are discussed in terms of the respective role of taxonomic relations and perception but also mode of presentation (availability).

Cognitive Attractors and the Cultural Evolution of Religion

We use data on a cultural fitness landscape, recently inferred from a large-scale cross-cultural survey of religious practices (6000+ years, 407 cultures), to provide new insights into the dynamics of cultural macroevolution. We report three main results. First, we observe an emergent distinction between the long-run fitness of a religious practice, and its short-term stability: in particular, some low-fitness practices are nonetheless highly stable. Second, despite the exponentially large size of the landscape, we find a small number of cultural attractors, and 70% of all observed configurations flow into just four, which we label "monastic", "evangelical", "indigenous", and "pre-Axial". Finally, we find large variation in the evolvability of different traits, with some (such as a belief in punishing gods) strongly fixed by context, and others (such as belief in reincarnation) much more fluid.

Question Framing Modulates the Cause Density and Effect Density Biases in Causal Illusions

A causal illusion occurs when people perceive a causal relationship between two events that are not contingent on each other. This experiment explored how this illusion varies when people reason diagnostically (i.e., in an effect-to-cause direction). Participants learnt about an illusory cause-effect relationship in which the probability of the cause and the probability of the effect were orthogonally manipulated to be either high or low. Participants learnt either predictively (i.e., cause-to-effect) or diagnostically, and at test had to make two causal judgements that encouraged either predictive (cause-to-effect) or diagnostic (effect-to-cause) reasoning. Diagnostic reasoning at test increased the strength of the cause density bias and decreased the strength of the effect density bias. It also decreased causal ratings, but only after predictive learning. Explaining these results requires an understanding of how the process of causal learning can impact later reasoning; something the current literature is yet to provide.

Excess Capacity Learning

Many paradigms in cognitive science posit that human learning is characterized by a limited capacity to represent the information relevant for a given task. We argue that excess capacity -- using more representational resources than needed for a task at hand -- is a plausible alternative paradigm for the study of human learning. Leveraging recent results from machine learning, we show that excess capacity can be consistent with high predictive ability. We also review extant empirical findings from the cognitive science literature, demonstrating that excess capacity learning can account for a range of empirical phenomena, such as humans' simultaneous yet apparently contradictory tendency to both memorize observations and capture higher-level patterns in them. We conclude by discussing promising directions for future inquiry under the excess capacity learning paradigm.

Do people prefer prediction over accommodation? An empirical study

Theories can be designed to predict novel evidence or to accommodate known evidence. Despite the lively debate in philosophy of science whether prediction may hold a superior value over accommodation, people’s intuitions about this issue have not been empirically examined. Within a medical scenario, we assess individuals’ sensitivity to this dilemma. Overall, we find tentative evidence that people favour the predictive account and regard the predicting theorist (i.e., doctor) as more reliable in contrast to their accommodating counterpart. Strikingly, discrepant preference patterns emerged out of their verbal reasoning data echoing the distinct philosophical stances surprisingly well. Possible reasons why people’s reasoning systematically diverges despite the general preference for prediction are discussed.

MVRACE: Multi-view Graph Contrastive Encoding for Graph Neural Network Pre-training

Graph neural networks (GNNs) have become a defacto paradigm for graph representation learning. Generally, GNNs are trained in an end-to-end manner with supervision, requiring considerable task-specific labeled data. To reduce the labeling burden, recent works leverage self-supervised tasks to pre-train an expressive GNN model on abundant unlabeled data and finetune the trained model on downstream datasets with only a few labels. However, existing GNN pre-training approaches only concentrate on a single view for graph self-supervised learning while ignoring the rich semantic information in graphs, leading to the lack of sample utilization efficiency during the pre-training process. To tackle such challenges, we propose a multi-view graph contrastive encoding for graphs during GNN pre-training, called MVRACE. The critical insight is that we construct node and graph-level views to capture local attribute information and global structure in a graph. Concretely, the node-level view utilizes graph centrality and encodes the $r$-ego network to capture the local-whole relationship in a graph. The graph-level view aims to encode graph pairs to explore different graph structures and empower the discrimination ability of the GNN encoder. In addition, we combine multi-views with a joint contrastive loss function to integrate node- and graph-semantic information simultaneously. Comprehensive experiments on multiple domain datasets demonstrate that our approach can significantly yield competitive performance compared to state-of-the-art methods.

Re-Evaluating the Evaluation of Neural Morphological Inflection Models

Computational models of morphology acquisition have played a central role in debates over the nature of morphological representations. The apparent success of recent artificial neural network architectures for morphological inflection in natural language processing has renewed this debate. However, the actual suitability of these advanced neural models as models of human morphology acquisition remains uncertain. We argue that much of this confusion stems from inconsistent methods of training and evaluation. In this work, we demonstrate that more careful data set creation and an evaluation combining quantitative analysis and comparison with human development will put the evaluation of neural models on firmer ground.

Lexical Entrainment in Bilingual Language Use

Lexical entrainment is a phenomenon in which people tend to re-use the words used by conversational partners (Brennan & Clark, 1996). It is explained as either an automatic reaction caused by priming (Pickering & Garrod, 2004), or a strategic behavior where two interlocutors achieve conceptual agreements for communicative purposes (Brennan & Clark, 1996). Past studies suggest that speakers tend to entrain more when interacting with listeners with lower language competence, such as computers (Branigan, Pickering, Pearson, McLean, & Brown, 2011), children (Cai, Sun, & Zhao, 2021), and non-native partners (Cai et al., 2021; Suffill, Kutasi, Pickering, & Branigan, 2021). However, few studies have explored how the features of speakers themselves determine the pattern of entrainment, and the studies that do exist suggest that speaker proficiency (as opposed to listener proficiency) may not affect entrainment behavior. In this study, we target bilingual groups and explore individual differences in lexical entrainment by looking at their entrainment behavior with picture matching and naming tasks. Over the course of two experiments, we investigate English entrainment in English-speaking bilinguals, as well as Mandarin entrainment in Mandarin-English bilinguals. Unlike the previous literature, our results suggest that a speaker’s language dominance/proficiency may have an effect on that speaker’s entrainment: bilinguals who are less dominant/proficient in English tend to entrain more in English, although the effect in Mandarin did not reach significance.

Exploring the Role of Visual Imagery in the Recall of Emotional Autobiographical Memories

A large body of evidence demonstrates that emotion impacts memory. Although visual information dominates emotional memories, previous studies have not examined the role of visual imagery as an individual difference variable in the representation of emotional memories. This study examines the role of visual imagery skills (namely, object and spatial imagery) on emotional memories. Participants (N = 115) recalled positive, negative, and neutral events in response to the cue words and then rated the phenomenological characteristics of each event. Event accounts were coded for episodic detail categories (event, place, perceptual, time, emotion-thought details). The results showed that visual imagery skills contributed to the remembrance of the episodic details of positive memories and the phenomenology of both positive and negative events. Overall, this study emphasizes the importance of considering the individual differences in memory research and highlights the differences between emotional and neutral events.

Belief Attitude and Belief Updating

Despite the importance of risk attitude on decision making, its role on belief updating has been overlooked. In this paper, we aim to answer this fundamental question. We show, using economic theory, that stronger risk aversion drives more conservative actions, decreases the instrumental value of information relative to the importance of belief-based utility. Thus, with utility for clarity, stronger risk attitude yields to more belief change; whereas with updating cost, stronger risk attitude leads to less belief change. We confirm the result experimentally in two settings where subjects update their belief about their IQ and a randomly drawn number, respectively.

The Interplay of Relevance, Sensory Uncertainty and Statistical Learning Influences Auditory Categorization

Auditory perception requires categorizing sound sequences, such as speech, into classes, such as syllables. Such categorization depends not only on the sequences’ acoustic waveform, but also on the listener’s sensory uncertainty, any individual sound’s relevance to the task, and learning the temporal statistics of the acoustic environment. Although previous studies have explored the effects of these perceptual and cognitive factors in separation, whether and how their interplay shapes categorization is unknown. Here, we tested this interplay by measuring human participants’ performance on a multi-tone categorization task. Using a Bayesian framework, we found that task-relevant tones contributed more to category choice than task-irrelevant tones, confirming that participants combined information about sensory features with task relevance. Conversely, poor estimates of tones’ task relevance or high sensory uncertainty adversely impacted category choice. Learning temporal statistics of sound category also affected decisions – the magnitude of this effect correlated inversely with participants’ relevance estimates. These results demonstrate that humans differentially weigh sensory uncertainty, task relevance and statistical learning, providing a novel understanding of sensory decision-making under real-life behavioral demands.

The Effects of Refutative Elements in Others’ Comments on Accepting Health-Related Fallacious Claims

This study investigated the impact of refutative elements in others’ comments on accepting fallacious claims about food nutrition. Four types of comments were used, two of which included refutative elements (challenging evidence and deductive process). A multiple regression analysis was conducted with 506 participants’ agreement with the fallacious claim as the dependent variable and the type of comment and their agreement before exposure to the comments as independent variables. The study also considered individual differences such as media literacy, information literacy, cognitive reflection, and interest in and familiarity with the topic. The results showed that presenting comments that challenged the deductive process significantly decreased agreement when participants had higher agreement before exposure. Further analysis of participants with high initial agreement revealed significant effects of all three comment types. A supplemental survey (n=182) suggested that the perceived negativity and usefulness of the comments influenced the participants' agreement.

Individual differences in explanation strategies for image classification and implications for explainable AI

While saliency-based explainable AI (XAI) methods have been well developed for image classification models, they fall short in comparison with human explanations. Here we examined human explanation strategies for image classification and their relationship with explanation quality to inform better XAI designs. We found that individuals differed in attention strategies during explanation: Participants adopting more explorative strategies used more visual information in their explanations, whereas those adopting more focused strategies included more conceptual information. In addition, visual explanations were rated higher for effectiveness in teaching learners without prior category knowledge, whereas conceptual explanations were more diagnostic for observers with prior knowledge to infer the class label. Thus, individuals differ in the use of visual and conceptual information to explain image classification, which facilitate different aspects of explanation quality and suit learners with different experiences. These findings have important implications for adaptive use of visual and conceptual information in XAI development.

Sampling in Approximate Number Perception

Approximate number perception is noisy, but it is unclear what kind of underlying process the noise reflects. Here we provide evidence that approximate number estimation should be thought of as a sampling procedure. We show that the the average of two approximate number estimates of the same stimulus tends to outperform either estimate alone; additionally, the average difference between the two estimates of a given number linearly increases as a function of number, consistent with Weber’s law. Finally, we provide evidence that people report confidence ranges consistent with Weber’s law. This suggests that they represent a distribution of possible responses even on a single trial.

Human music perception ability is not a sexually dimorphic trait

Since Darwin (1871), researchers have proposed that musicality evolved in a reproductive context in which males produce music to signal their mate quality. The extent to which evidence supports this contention, however, remains unclear. Related traits in many non-human animals are sexually differentiated, and while some sex differences in human auditory perception have been documented, the pattern of results is murky. Here, we study melodic discrimination, mistuning perception, and beat alignment perception in 360,009 men and 194,291 women from 208 countries. We find that, in contrast to other non-music human traits, and in contrast to non-human traits, there was no overall advantage for either sex, and the observed sex differences were minuscule (Cohen’s d: 0.009 - 0.11) and of inconsistent direction. These results do not provide compelling support for human music perception being a sexually dimorphic trait, and therefore it is unlikely to have been shaped by sexual selection.

Within-Individual Variation in Cognitive Performance is Not Noise: A Case for Examining Within-Person Variation on Cognitive Assessments

Despite the long-standing recognition that individuals vary in their cognitive performance across relatively short time periods, little research has integrated an understanding of short-term within-individual variation in cognitive performance into our theories of cognitive ability. We contend that systematic patterns of between-individual differences in within-individual variation are meaningful and should not be viewed merely as measurement error. We argue that predominant cognitive testing methods using between-individual analysis of single-occasion scores are limited in their capacity to develop a process account of why individuals with the same test score differ in practical contexts. We propose that short-term repeated measures paradigms (e.g., the Experience Sampling Method) be used to understand the nature and sources of between-individual differences in within-individual variation. Finally, we outline considerations for researchers when adapting this paradigm for cognitive assessment and present initial findings from our lab on the feasibility of this paradigm.

Degree of heterogeneity in the contexts of language users mediates the cognitive-communicative trade-off in semantic categorization

It has been argued that patterns of cross-linguistic variation in the semantic categories labelled by individual words are a result of a trade-off between cognitive pressures (so as to be simple to learn and use) and communicative pressures (so as to be efficient in communication). However, the question of what exact mechanisms control this trade-off has been left largely unanswered. We argue that one factor could be the extent to which referential contexts at the level of local interactions are similar or different across users of a category system. To test this hypothesis we propose a hierarchical Bayesian model for communication in a multidimensional meaning space, in which agents actively consider spatial similarity relations during interaction. Our models predict that less variability in contexts across interactions induces categories with lower communicative cost, while more variable contexts across partners are more strongly associated with category systems with lower cognitive cost.

Observing Gestures During L2 Word Learning Facilitates Differentiation Between Unfamiliar Speech Sounds and Word Meanings

This study investigated how observing pitch gestures conveying lexical tones and representational gestures conveying word meanings when learning L2 Mandarin words differing in lexical tone affects their subsequent semantic and phonological processing in L1 English speakers using the N400 event-related potential (ERP). Larger N400s for English target words mismatching vs. matching Mandarin prime words in meaning were observed for words learned with pitch and representational gesture, but not no gesture. Additionally, larger N400s for Mandarin target words mismatching vs. matching Mandarin prime words in lexical tone were observed for words learned with pitch gesture, but not representational or no gesture. These findings provide the first ERP evidence that observing gestures conveying phonological and semantic information during L2 word learning enhances subsequent phonological and semantic processing of learned L2 words.

Scent of Poetry: Influence of Olfactory Imagery during Haiku Appreciation on Aesthetic Evaluation

In cognitive science, research about mental imagery is often limited to visual, often overlooking olfactory imagery. In this study, we examined the relationship between beauty and olfactory imagery evoked by haiku. We used an odor priming commonly used in cognitive science to measure olfaction so that we could examine the effects of environmental aromas on the aesthetic experience. 44 participants were asked to evaluate 30 haikus. Half of them were exposed to a cypress aroma while the other half had no aroma exposure. The results showed that olfactory imagery during haiku appreciation positively influenced the beauty of haiku, and higher olfactory imagery ability led to a deeper immersion in the haiku. Odor priming did not affect evaluations, but it did affect gaze bias as measured by eye tracking. This is the first time to demonstrate the influence of olfactory imagery on aesthetic evaluation in the psychology of aesthetics.

Mental Simulation in L2 Processing of English Prepositional Phrases

How does cognition engage with the visual world? I make the case that multiple object tracking tasks isolate an object selection process that also applies to unmoving objects. Among other characteristics, the hemisphere specificity of object selection sets it apart from cognitive processing. Tracking is blind in that cognition generally does not know which tracked object is which. Contrary to a recent suggestion, this means that trackers do not function as the labeled pointers thought to be necessary to comprehend language or compute certain spatial relations. Instead, tracking has more in common with stimulus-driven attention, saliency, and featural attention.

“I forgive you!” — Exploring the Impact of Forgiveness on Negative Emotions and Blame

Forgiveness plays a significant role in our everyday social life, and, because of that, it has received an increasing amount of attention in academic research. However, philosophers and psychologists are equally worried by the fact that we still lack an empirically adequate characterization of forgiveness. In this paper, we present two preregistered studies in which we explore what ordinary people believe a speaker does when he or she performs the speech act of forgiving by uttering the phrase, “I forgive you”. Study 1 uses a vignette-based stimulus to examine what participants believe to change after the victim granted forgiveness to their wrong-doer. In Study 2, we apply a linguistic test, the cancellability test, to determine whether participants consider forgiving the wrong-doer but still blaming them compatible.

Chinese words shorten in more predictive contexts

In Mandarin Chinese, abbreviation happens commonly to compound words across different syntactic categories. What is the motivation behind this shortening of words? This paper presents an investigation of this phenomenon from an information-theoretic point of view. A corpus study was con- ducted to measure the average amount of information contained in the full (long) form and the abbreviated (short) form of words given certain contexts. The amount of information was then compared between the long and short forms of a word, revealing that the short one usually contains less information, and therefore is more likely to be used in more predictive contexts. This result indicates that speakers of Chinese can choose to use shorter words when the context is more predictive, in accordance with considerations of efficiency.

How do communicative goals guide which data visualizations people think are effective?

Data visualizations are powerful tools for communicating quantitative information. While prior work has focused on how experts design informative graphs, little is known about the intuitions non-experts have about what makes a graph effective for communicating a specific message. In the current study, we asked participants (N=398) which of eight graphs would be most useful for answering a particular question, where all graphs were generated from the same dataset but varied in how the data were arranged. We tested the degree to which participants based their decisions on sensitivity to how easily other participants (N=542) would be able to answer that question with that graph. Our results suggest that while people were biased towards graphs that were at least minimally informative (i.e., contained the relevant variables), their decisions did not necessarily reflect sensitivity to more graded but systematic variation in actual graph comprehensibility.

Dynamic Modeling of Visual Search

In 1998/1999 three participants trained for up to 74 hour long sessions to find a target in visual displays of 1, 2, or 4 objects. There were four targets and four foils that never changed. Displays occurred simultaneously, or the objects occurred successively, or the features of each object occurred successively. When successive, the SOAs were short (17, 33, or 50 ms) so the displays appeared simultaneous, making it likely that the search strategy was the same in all conditions. A 2004 publication examined only the simultaneous condition and found evidence for serial search as well as some small amount of automatic attention to targets, and occasional early or late search termination. A 2021 publication examined only the displays with single objects, obtaining evidence for dynamic perception of features. Building on these results we present a simple dynamic model that explains the main processes operating in all the experimental conditions.

Exploring Functions of Relational Monitoring: Relational Integration and Interference Control

Relational integration, the process of integrating stimuli into relations, is often thought as the primary demand of the relation-monitoring task (RMT). The current study investigated the attentional demand to inhibit irrelevant visual stimuli on relational processing in the RMT. The relevance and salience of these stimuli was manipulated while considering the complexity of relations to be integrated. 172 participants also completed Latin Square Task and Anti-saccade task as criterion individual differences in relational integration and inhibition abilities. The results revealed interference from non-target stimuli was partly accounted for by their perceptual similarity with target stimuli. The salience effect was observed but was not moderated by Anti-saccade task performance. Relational complexity was found to interact with all manipulations probing attentional function. The findings advanced our understanding of the interplay between the attentional and relational processes involved in the RMT.

Measuring the time utility of mental effort

The empirical measurement of mental effort is an important problem in the cognitive sciences. Recently, researchers have adopted econometric tools to attempt to characterize mental effort in terms of monetary costs foregone. Such efforts yield a very helpful calculation device - a money utility of mental effort. However, since the opportunity cost of applying mental effort in any given situation is measured with respect to time rather than money in most ecologically reasonable settings, it is even more desirable to obtain a measure of the time utility of mental effort. In the absence of direct measurements of men- tal effort, such a task has proved econometrically challenging. We use a recently developed direct measure of mental effort to characterize its time utility, finding that it is approximately linear in effort. We discuss some implications of this result for current theories of mental effort, as well as for practical applications.

Is Reliability of Cognitive Measures in Children Dependent on Participant Age? A Case Study with Two Large-Scale Datasets

When assessing children in laboratory experiments, the measured responses also contain task-irrelevant participant-level variability (“noise”) and measurement noise. Since experimental data are used to make inferences of development of cognitive capabilities with age, it is important to know if reliability of the used measurements depends on child age. Any systematic age-dependent changes in reliability could result in misleading developmental trajectories, as lower reliability will necessarily result in smaller effect sizes. This paper examines age-dependency of task-independent measurement variability in early childhood (3–40 months) by analyzing two large-scale datasets of participant-level experimental responses: the ManyBabies infant-directed speech preference (MB-IDS) dataset and a saccadic reaction time (SRT) dataset collected from rural South Africa. Analysis of participant- and study-level data reveals that MB-IDS shows comparable reliability across the included age range. In contrast, SRTs reflect systematically increasing measurement consistency with increasing age. Potential reasons and implications of this divergence are briefly discussed.

Learning about Benefits and Side Effects of a Bogus Treatment are Similarly Influenced by the Frequency of the Outcome Occurring

Detecting covariation in sequential events provides us with a powerful means of inferring the causal structure of our world. However, people often overestimate the causal relationship between unrelated events, a phenomenon referred to as illusory causation. This tendency is greatest when the putative effect occurs frequently; the widely replicated outcome density (OD) effect. Most laboratory research on illusory causation and the OD effect has focused on possible causes of a positive outcome, such as a drug that causes patient recovery. Despite its relevance, relatively few studies have examined illusory causation in cases where a cue is hypothesized to generate an unfavorable (negative) outcome, such as a drug that produces unwanted side effects. Here, we directly compared how people develop illusory beliefs about the generation of positive versus negative outcomes. We presented all participants with a drug treatment that was hypothesized to cause high readings of a fictitious cell count (but had no effect on cell count across a series of learning trials). We manipulated whether a high cell count occurred frequently or infrequently and whether a high cell count should be considered a beneficial medical outcome or an undesirable side effect. We found consistent evidence of an OD effect but no effect of the valence of the high cell count outcome. This suggests that illusory beliefs are not controlled by the desirability of the cause-effect relationship. We discuss implications for theories of applied causal reasoning.

Considering Alternative Outcomes of Research: Does Knowing the Actual Outcome Create Bias?

Learning a research outcome in class or the media may bias people towards that outcome (hindsight bias), and receiving an explanation may accentuate bias (explanation bias), both of which could hinder understanding of the necessity of replication. We tested whether providing outcomes and explanations of research findings increased difficulty of explaining alternative outcomes, and, if so, whether people were less surprised by the presented findings, and found them more likely to replicate. Amazon Mechanical Turk (AMT) workers and introductory psychology students were randomly assigned to do one of the following: 1. Read details of four psychological studies without their outcomes, 2. additionally receive the outcomes, 3. additionally receive explanations of outcomes. We did not find reliable effects on difficulty of explaining alternative outcomes, and found little evidence for hindsight or explanation biases. We speculate that explaining alternative outcomes immediately after considering the actual outcomes may have debiased our participants.

Violation of epistemic expectations: Children monitor what others know and recognize unexpected sources of knowledge

Humans have an intuitive sense of what others know and how they learned it. These expectations are often latent, but violating them can elicit surprise and curiosity (e.g., a stranger knowing a lot about you). Here we investigate the development of epistemic expectations by measuring young children's sensitivity to such violations. First, parents reported that children typically respond to violations of epistemic expectations by age 4 (Exp.1). In naturalistic dialogue experiments with 4- and 5-year-olds, children were more likely to display surprised expressions and report being surprised when the experimenter’s parent knew personal information about them than when their own parent did (Exp.2). However, children showed an opposite pattern when these people knew information about the experimenter’s sibling (Exp.3). Together, these results suggest preschool-aged children are sensitive to others' access to information and readily detect violations of their epistemic expectations in casual conversation.

How does knowledge of detainment affect juror reasoning?

Recent work suggests that the decisions to detain defendants before trial increase the likelihood of conviction. One reason may be that knowledge of detainment makes jurors more likely to convict. Previous work has claimed this as an example of ‘bounded rationality’ i.e., due to a simple bias. We argue that this inference represents sophisticated causal reasoning e.g. about information hidden from jurors such as criminal history. We examine whether the effect of detainment knowledge on conviction depends on rational inference by presenting participants with a legal vignette in a 2x2 design: a defendant either has or has not been detained, and this detainment decision is either (1) not explained or (2) explained as due to an iron clad rule always used for this class of crimes. We find an effect of detainment when it is not explained, but either no or a limited effect when explained, providing evidence against the ‘bounded rationality’ view. We provide qualitative extracts of participants’ reasoning, demonstrating sophisticated and nuanced inferences from detainment to hidden information when the decision is not explained.

Coherence of Information: What It Is and Why It Matters

Coherence considerations play an important role in science and in everyday reasoning. However, it is unclear what exactly is meant by coherence of information and why we prefer more coherent information over less coherent information. To answer these questions, we first explore how to explicate the dazzling notion of ``coherence'' and how to measure the coherence of an information set. To do so, we critique prima facie plausible proposals that incorporate normative principles such as ``Agreement'' or ``Dependence'' and then argue that the coherence of an information set is best understood as an indicator of the truth of the set under certain conditions. Using computer simulations, we then show that a new probabilistic measure of coherence that combines aspects of the two principles above, but without strictly satisfying either principle, performs particularly well in this regard.

Beyond Transformers for Function Learning

The ability to learn and predict simple functions is a key aspect of human intelligence. Recent works have started to explore this ability using transformer architectures, however it remains unclear whether this is sufficient to recapitulate the extrapolation abilities of people in this domain. Here, we propose to ad- dress this gap by augmenting the transformer architecture with two simple inductive learning biases, that are directly adapted from recent models of abstract reasoning in cognitive science. The results we report demonstrate that these biases are helpful in the context of large neural network models, as well as shed light on the types of inductive learning biases that may contribute to human abilities in extrapolation.

Categorical Learning and the Cognitive Foundations of Language Evolution and Development

Categorical learning plays a foundational role in language development. By reviewing comparative studies on categorical learning in humans and nonhuman animals, we show that categorical learning displays evolutionary continuity across invertebrates and vertebrates. Great apes and parrots can be trained to produce categories of (proto-)language-like symbols in different modalities. From the neurological perspective, we show that as a conserved brain structure, the basal ganglia are involved in categorical learning across species, and language processing in humans. This raises the possibility that categorical learning is one of the crucial cognitive foundations for language evolution.

Modeling the Category Variability Effect in an Exemplar-Similarity Framework

The category variability effect describes assigning objects to high-variability categories. We show that similarity-based categorization theories can predict the category variability effect and conduct a rigorous empirical test. In an optimized categorization experiment, participants learned to assign geometrical figures to a high-variability and a low-variability category and then categorized transfer stimuli located between the categories. We compared a formal model that ignores category variability (Euclidean model) to one that considers category variability (Mahalanobis model) during similarity computation. The data (N = 43) revealed that most participants did not show the category variability effect, in line with the Euclidean model. Nevertheless, the Mahalanobis model consistently described the participants that selected the high-variability category. This demonstrates that—contrary to previous claims—similarity can explain the category variability effect. However, in our data, most people do not seem to show the effect, maybe because the low-variability category was more coherent than the high-variability category.

Alternation as a Relational Category

Key questions in the study of categorization are how individuals form categories from experience and extend that knowledge to assess membership of novel examples. Popular accounts predict generalization to be based on either similarity to reference points or the application of rules or bounds. However, recent data show that some categorization behavior defies the predictions of leading accounts. Expanding on these findings, in the present study participants learned a one or two dimensional alternating category structure and were then tested on near and far transfer tasks. Findings reveal that individuals can extend a learned alternating category structure across multidimensional spaces and increasingly distant generalization regions. Additionally, subjects readily invoke alternation during the far transfer task (a task which does not involve classification), providing critical evidence that learning of the alternating category structure was driven by relational rather than feature-based similarity.

Expectation, Representation, and Enactivism

This paper presents a challenge to enactivist approaches to cognition (e.g. Ward, D., Silverman, D. & Villalobos, M. 2017) that is based on the theoretical commitments behind forms of looking time studies that have been extensively used to probe into the cognitive abilities of infants and nonhuman animals. I briefly summarize the Violation of Expectation (VoE) paradigm (Ginnobili & Olmos 2021) to illustrate why such methods might pose a problem for enactivists and their conception of cognition as a largely representation-free dynamic coupling between organism and environment. I argue that despite the lack of clarity regarding how the notion of expectation should be applied to the minds of neonates and nonhuman animals, there is an inherently representational aspect to expectation, given that it embodies satisfaction conditions. The challenge is, then: given that many forms of enactivism seem to reject the notion of representation as it is used in the VoE literature, how can enactivists make sense of data and results obtained using such research methods?

Exploring the Effect of Socio-linguistic Competence in Native and Non-native English Speakers on Visual and Auditory Humor Comprehension

Despite the universal phenomenon of humor across societies and communities, humor comprehension in second language (L2) speakers is often overlooked. It is unclear whether L2 speakers rely primarily on sociocultural proficiency or linguistic proficiency when they process humor in a foreign language. We conducted two experiments examining the direct association of sociocultural proficiency and linguistic proficiency in humor comprehension behaviors in the visual and auditory modalities. Across both modalities, results revealed a significant association of social connectedness with humor detection and appreciation in non-native speakers. Furthermore, individual differences in language proficiency and social connectedness were shown to be more relevant for humor ratings in the visual modality. The finding suggests that L2 speakers’ humor comprehension performance was related to sociocultural proficiency and integration with the L2 community.

Comparing Human Predictions from Expert Advice to On-line Optimization Algorithms

On-line decision problems – in which a decision is made based on a sequence of past events without knowledge of the future – have been extensively studied in theoretical computer science. A famous example is the Prediction from Expert Advice problem, in which an agent has to make a decision informed by the predictions of a set of experts. An optimal solution to this problem is the Multiplicative Weights Update Method (MWUM). In this paper, we investigate how humans behave in a Prediction from Expert Advice task. We compare MWUM and several other algorithms proposed in the computer science literature against human behavior. We find that MWUM provides the best fit to people’s choices.

Comparing AI Planning Algorithms with Humans on the Tower of London Task

Understanding problem solving or planning has been a shared challenge for both AI and cognitive science since the birth of both fields. We explore the extent to which modern planners from the field of AI can account for human performance on the Tower of London (TOL) task, a close relative of the Tower of Hanoi problem that has been extensively studied by psychologists. We characterize the task using the Planning Domain Definition Language (PDDL) and evaluate an adaptive online planner and a family of well-known planners, including online planners, optimal planners and satisficing planners. Each planner is evaluated based on its ability to predict the actions and planning times of participants in a new behavioral experiment. Our results suggest that participants use a range of strategies but that an adaptive lookahead planner provides the best overall account of both human actions and human planning times. This finding is consistent with the view that humans differ from standard AI planners by integrating a mechanism for evidence accumulation.

Temporal Context and the Experience and Memory of Art

One aspect of context on cognition and memory that has been understudied is the influence of temporal context. The temporal contexts used in this study were different times of the year. More specifically, given that we were dealing with a largely Roman Catholic population, we operationalized temporal context in terms of two religious seasons, Lent and Ordinary Time. For this study, we assessed experience of, and memory for, representational and abstract art as a function of whether there was temporal congruity or incongruity. Temporal context did influence memory for perceptual details but not the experience of art during viewing, or gist and autobiographical memory. Thus, temporal context can influence cognition, but the scope of this influence is limited.

Spontaneous co-speech gestures with prompt phrases reflect linguistic structures

This study aimed to investigate whether people produce spontaneous co-speech gestures that reflect the underlying linguistic structures and additional information when speech is restricted by prompt phrases. Participants were asked to convey information about an animated movie using a three-word phrase in Japanese that could be interpreted in two different ways depending on their underlying deep structures. The animated movie included or did not include important information that was not described by the prompt phrases. The results showed that most participants produced gestures while uttering the phrase, and the onset of the nouns reflected the underlying linguistic structures. A time-series analysis revealed that the occurrence of object-action gestures that depicted a noun’s movement tended to reflect the associated linguistic structures. People spontaneously produce gestures, which may syntactically and semantically help to disambiguate ambiguous phrases.

How to Leverage Machine Learning Interpretability and Explainability to Generate Hypotheses in Cognitive Psychology

This paper describes the principles of a research programme for cognitive science that exploits recent developments in machine learning (ML) to generate novel hypotheses about the structure of human cognition. Current debate over the interpretability and explainability algorithms usually focuses on the properties of the algorithms themselves in virtue of which they are either interpretable or explainable. However, we argue that there is value in conceptualizing these categories as inherently psychological constructs. Given certain mathematical features of machine learning algorithms – specifically, that many useful ML algorithms are members of Rashomon sets – it is possible to exploit their utility to reason using a principle of parsimony about the inferential structure of certain human cognitive tasks. Algorithms that do something the human mind can do, and are both interpretable and explicable could be, we shall argue, inferential homologues of certain core cognitive processes. We illustrate this proposal with an example drawn from clustering models used in exploratory data analysis, and then conclude with a discussion of some of the philosophical limitations of our proposal.

Evaluating machine comprehension of sketch meaning at different levels of abstraction

People can reliably understand images that vary in visual abstraction---from detailed illustrations to schematic icons. To what degree are current vision algorithms robust to such variation when attributing meaning to abstract images? We first obtained >90K human-generated sketches produced under different time limits (4s, 8s, 16s, 32s; N=5,563 participants) and AI-generated sketches (Vinker et al., 2022) produced under different ink limits (4, 8, 16, 32 strokes) of 2,048 real-world object concepts spanning 128 categories from the THINGS dataset (Hebart et al., 2019). We then evaluated how well 12 state-of-the-art vision algorithms could (1) predict which concept each sketch was intended to convey and (2) match human performance and response patterns when presented with the same sketches.We found that models achieving generally higher recognition accuracy also tracked human error patterns better, although there remains a sizable gap between human and machine sketch understanding. We also found that, on average, different models expressed similar uncertainty about sketches of the same concept across different levels of abstraction. We hope that public release of this dataset and evaluation protocol will lead to algorithms that display more human-like visual abstraction.

Self-relevance Facilitates Attention to Self-associated Targets on Feature-based Selective Attention Tasks

The ‘Self’ has a prioritized cognitive status, attributed to an automatic bottom-up attentional enhancement for self-relevant stimuli. Two predictions follow if self-relevant information is automatically boosted. First, processing should be enhanced for self- compared to other-relevant targets. Second, interference should be greater for self- compared to other-relevant distractors. To investigate these predictions, we adapted a motion reproduction task. Participants first learned to associate a colour (blue, pink) with themselves and a stranger (other), then viewed a label (YOU or OTHER) and two different coloured superimposed random dot kinematograms (RDKs; blue, pink). A response dial recorded participants’ reproduced direction of motion for the coloured RDK associated with the presented label. Facilitation and interference for self- and other-labelled features was assessed by the angular difference between the reported and true direction of motion (signed error magnitude). There was a small, but reliable response bias in direction of distractor motion showing that attentional selection was imperfect. Further regression-based analyses quantified the degree to which self and other-related stimuli influenced responses (decision weights). As predicted, decision weights for target stimuli showed a significant advantage for self- compared with other-relevant motions. By contrast distractor weights did not differ significantly between self- and other-relevant features, suggesting self-relevance did not modulate the degree of interference and self-relevant stimuli did not automatically capture attention. Overall, we show that feature-based attention is enhanced for self-associated sensory input, but only when task-relevant.

The Epistemic Weight of Silence

When people “fail to deny” unflattering claims, it is commonly taken to imply they are true. Yet, the ‘argument from ignorance’ – arguing in favour of something due to a lack of evidence against it – is often deemed a fallacy. Why is abduction from missing evidence permissible in some cases, but not others? We present a framework of factors which disambiguate these cases, using a Bayesian Network model. We suggest that a source’s silence often reflects a latent conflict between their motives for what they want their audience to believe, and complying with external constraints on their speech, like the need to be accurate. In these cases, silence implies that the source does not believe that what they would like to say is true, which licenses a probabilistic inference that it is false. We present data from two studies suggesting people infer from silence like this.

Does Machine Learning Replicate the Uncanny Valley? An Example using FaceNet

Androids that strongly but imperfectly resemble humans in shape can elicit negative emotions in people, a phenomenon known as the "uncanny valley," which has been replicated in laboratory experiments. Recently, the accuracy of face recognition utilizing machine learning has increased, raising the question of whether machine learning can replicate the uncanny valley. Using FaceNet as a representative face recognition algorithm, we examined the similarity of face recognition to human evaluation and its replication of the uncanny valley. The results revealed a strong correlation between machine learning and human evaluation of human-like shapes. However, it is evident that only certain aspects of the uncanny valley were replicated. Furthermore, visualization of the activation maps suggests that localized regions, such as the mouth and chin, acted as the basis for judgment. These findings support the idea that human and machine learning have distinct areas of attention.

A Semantics for Causing, Enabling, and Preventing Verbs Using Structural Causal Models

When choosing how to describe what happened, we have a number of causal verbs at our disposal. In this paper, we develop a model-theoretic formal semantics for nine causal verbs that span the categories of CAUSE, ENABLE, and PREVENT. We use structural causal models (SCMs) to represent participants’ mental construction of a scene when assessing the correctness of causal expressions relative to a presented context. Furthermore, SCMs enable us to model events relating both the physical world as well as agents’ mental states. In experimental evaluations, we find that the proposed semantics exhibits a closer alignment with human evaluations in comparison to prior accounts of the verb families

Limited Neural Capacity and Hyper-Excitability Affect Quantity Processing: A Computational Account

Developmental dyscalculia (DD) is a neurodevelopmental disorder characterized by persistent poor math performance despite normal intelligence and education opportunities. Existing behavioral and neuroimaging studies have demonstrated that quantity processing deficits in DD are accompanied by aberrant brain functions and neurobiological alterations. Although theories have argued that the behavioral impairments observed in DD result from neurobiological deficiency and imbalance of excitatory and inhibitory signals in the brain, these hypotheses are difficult to test in human subjects. Therefore, in the current study, we implemented convolutional neural network models and tested the causal influence of neural capacity (i.e., number of units) and system excitability (i.e., the slope of activation) during the learning of quantity information. For both symbolic and non-symbolic processing, we observed that reducing the number of units did not lead to changes in learning performance. In contrast, increased excitability largely impaired the accuracy of learning, especially for the non-symbolic representations. Therefore, our model simulations provided direct evidence that increased excitability in the brain could result in behavioral impairments in learning quantity information, potentially suggesting a neurobiological basis for DD.

Hybrid Frameworks of Reasoning: Normative-Descriptive Interplay

The investigation of reasoning involves the study of what is rational as well as the empirical study of human reasoning. We are interested in rational answers to reasoning problems but also in the way human reasoning works. While the first aspect is typically covered by logic or formal epistemology, the second is a matter of empirical psychological research. However, many approaches relate both aspects. This paper discusses these hybrid approaches, their motivation, general criticism that has been raised against them as well as the kind of philosophical commitment behind choosing such kind of framework.

What are the mechanisms underlying metacognitive learning in the context of planning?

How is it that humans can solve complex planning tasks so efficiently despite limited cognitive resources? One reason is its ability to know how to use its limited computational resources to make clever choices. We postulate that people learn this ability from trial and error (metacognitive reinforcement learning). In this work, we systematize models of the underlying learning mechanisms and enhance them with more sophisticated additional mechanisms. We fit the resulting 86 models to human data collected in previous experiments where different phenomena of metacognitive learning were demonstrated and performed Bayesian model selection. Our results suggest that a gradient ascent through the space of cognitive strategies can explain most of the observed qualitative phenomena, and is, therefore, a promising candidate for explaining the mechanism underlying metacognitive learning.

Examination of Developmental Changes in Complexity of Memory Structures

Episodic memory proves fragile and undergoes a protracted development, as it often requires a combination of multiple elements, during which interference occurs as different sets of learned information partially overlap. One way of preventing interference is a complex representation that links together multiple components of an experience (i.e., three-way binding between items and context). The present study aimed to examine the developmental trajectory of the ability to form complex memory structures. Seventeen 5-year-olds and 26 adults participated in this study and performed a memory task that required binding two items to a context. The results showed that adults were able to form three-way binding; however, 5-year-olds exhibited difficulties in both three-way binding and binding between two items. Moreover, 5-year-olds did not benefit from extra learning opportunities, indicating that their difficulty in forming complex binding structures did not result from insufficient learning, but from a property of immature episodic memory.

Improving neural saliency prediction with a cognitive model of human visual attention

We present a novel method for deep image saliency prediction that leverages a cognitive model of visual attention as an inductive bias. This is in stark contrast to recent purely data-driven models that have achieved performance improvements mainly by increased model capacity, resulting in high computational costs and the need for large scale, domain specific training data. We demonstrate that by leveraging a cognitive model of visual attention, our method achieves competitive performance to the state-of-the-art across several benchmark natural image datasets while only requiring a third of the parameters. Furthermore, we set the new state of the art for saliency prediction on information visualizations, demonstrating the effectiveness of our approach for cross-domain generalization.We further provide large-scale cognitively plausible synthetic gaze data on corresponding images in the full MSCOCO and FigureQA datasets, which we used for pre-training. These results are highly promising and underline the significant potential of bridging between firstprinciple cognitive and data-driven models for computer vision tasks, potentially also beyond saliency prediction, and even visual attention.

Computation-Limited Bayesian Updating

Effectively updating one’s beliefs requires sufficient empirical evidence (i.e., data) and the computational capacity to process it. Yet both data and computational resources are limited for human minds. Here, we study the problem of belief updating under limited data and limited computation. Using information theory to characterize constraints on computation, we find that the solution to the resulting optimization problem links the data and computational limitations together: when computational resources are tight, agents may not be able to integrate new empirical evidence. The resource-rational belief updating rule we identify offers a novel interpretation of conservative Bayesian updating.

Effect of Response Format on Syllogistic Reasoning

Comprehensive datasets used for modeling endeavours in syllogistic reasoning research usually contain only a single conclusion per task for each subject. However, this means that no information about the other conclusions is provided, preventing the distinction between conclusions that were rejected, conclusions deemed to be valid but not the preferred conclusion and conclusions that were not considered at all. In this work, we present a multiple choice dataset containing all conclusions that participants considers valid. The data is compared to datasets with other response designs, and an extensive evaluation is performed to assess the impact of the response design on the predictive performance of cognitive models. Finally, our results are discussed and put into perspective.

An Evaluation of Experimental Sampling Strategies for Autonomous Empirical Research in Cognitive Science

In light of constraints inherent to empirical research, such as finite time and resources, there has been growing interest in using artificial intelligence to streamline the scientific process. However, despite advancements in automating scientific discovery, the implementation of strategies for sampling useful experiments remains a challenge. This metascientific study evaluates different experimental sampling strategies based on their effectiveness in advancing the discovery of linear models of human cognition based on synthetic data. We investigate the hypothesis put forth by Dubova et al. (2022) that random sampling of experiments is more effective than model-driven sampling. Indeed, the results of this study indicate that random sampling is more effective in a majority of cases, and that the underperformance of model-driven strategies can be attributed to a narrow sampling of the design space. Despite limitations in our approach, the work presented offers a novel framework for the metascientific study of autonomous empirical research.

Semantic access to constituents of compounds and pseudocompounds: Evidence from dichoptic presentation

The early moments of compound and pseudocompound visual word recognition were investigated by probing their “constituent” concepts (e.g., BED in bedroom and FAN in fanfare). This was achieved by concomitantly presenting target words in one visual field (left or right, projected to the right or left hemisphere, respectively) and a picture representing the referent of either the first or second “constituent” in the opposing visual field. The stimuli were presented for 133 ms followed by a backward mask and participants judged whether the word and picture were related to each other. The experimental manipulations consisted of target word type (compound or pseudocompound), word complexity (whole word or “constituent”), probed constituent position (first or second “constituent”), and word projection (left or right hemisphere). Our results suggest that the “constituents” of compounds and pseudocompounds are conceptually accessed. We discuss the implications of our findings for the nature of the visual word recognition system.

Flexible spatial memory in children: Different reference frames on different axes

Spatial cognition is central to human behavior, but the way we conceptualize space varies over development and across cultures. When remembering the locations or movements of nearby objects, educated adults predominantly rely on a body-based spatial reference frame (e.g. to the left), whereas other groups prefer environment-based frames (e.g. toward the road), at least in some contexts. We propose that this variation in spatial thinking partly reflects differences in the ability to reliably discriminate left-right space, an ability that is common only among educated adults. To evaluate this proposal, here we tested US children’s spontaneous use of spatial reference frames on two axes. On the front-back axis, where spatial discrimination was relatively high, participants remembered object locations and movement directions using a body-based reference frame. On the left-right axis, where their spatial discrimination was significantly worse, the same participants preferred environment-based reference frames. This reversal reveals remarkable flexibility in children’s spontaneous use of spatial reference frames, extends findings in indigenous adults, and clarifies the likely mechanisms underlying spatial cognitive diversity.

Cultural Differences in the Effect of Mask Use on Face and Facial Expression Recognition

We examined whether mask use had differential impacts on face and facial expression recognition across cultures, as cultures associated with more eyes-focused face scanning strategies may be less affected. Asian and White participants performed face and facial expression recognition with unmasked and masked Asian and White faces. White participants attended more to the eye region in both tasks; however, their performance was less impaired by mask use only in facial expression recognition. In both tasks, individuals adopting more eyes-focused strategies for unmasked faces were less impaired by mask use. Also, participants had larger performance impairment for judging expressions of Asian than White faces, consistent with the finding that they adopted more nose-focused strategies for Asian than White faces. Thus, although individuals from different cultures or expression recognition of different races may be affected differentially by mask use, these effects may be better explained by individual differences in preferred attention strategies.

The evolution of efficient compression in signaling games

Converging evidence suggests that natural language meaning systems are efficient by jointly maximizing cognitive simplicity and communicative informativeness. Comparatively less is known about how languages might optimize over time for communicative efficiency. Our goal in this paper is to use minimal dynamic models to give a high-level description of the evolution of efficient meaning systems. To do this, we provide a model of emergent communication combining evolutionary game theory with a recent information theoretic account of efficiency in semantic systems. We perform simulations of adaptive dynamics requiring minimal assumptions about agents’ cognitive resources, and observe that emergent languages converge near the achievable bounds of efficient compression. This unifies existing accounts of communicative efficiency with minimalist accounts of how meaning can emerge ex nihilo.

No evidence for familiarity preferences after limited exposure to visual concepts in preschoolers and infants

From birth, humans make decisions about what to look at and for how long. A classic framework proposes encoding as a key driver of looking behavior in development - in early stages of encoding, infants and young children prefer to engage with familiar stimuli, while at later stages of encoding they prefer novel stimuli. Though this framework is often invoked when interpreting looking time studies, it is rarely validated empirically. Here, we test these predictions by explicitly manipulating exposure durations within-subjects. While we found robust evidence for habituation and novelty preferences, limiting exposure to visual concepts did not result in familiarity preferences in any age group. Our findings suggest that limited exposure does not generically lead to familiarity preferences, and that interpretations of observed familiarity preferences should be made with care. We argue for the development of formal frameworks which link the learning problem faced by participants to their attentional preferences.

Relating aesthetic-value judgment to perception: An eye-tracking and computational study of Japanese art Ukiyo-e

Aesthetic value, beauty, is a complex concept in that it has both subjective and objective aspects. However, in previous eye-tracking studies of artworks, the target associated with the gaze has often been only the latter image feature (e.g., symmetry). By contrast, recent developments in computational aesthetics (especially aesthetic classifiers) offers a path that treats these two aspects comprehensively. Along this line, we further develop eye-tracking research. We conducted computational model-based data analyses of eye-tracking behaviour, with the aim of providing more fine-grained insights into the comprehensive concept of beauty. In contrast to previous studies that focused on Western fine-artworks, our study is distinctive in that it focuses on visual art in Eastern culture, namely Japanese ukiyo-e (the well-known works of Katsushika Hokusai and Utagawa Hiroshige). Our empirical results showed that the gaze trajectory and movement area differed significantly between highly-rated and non-highly-rated paintings. This provides positive evidence for the relationship between aesthetic value and perceptual behaviour.

Looking at image schemas: Combinations and modifications

The goal of this study was to revisit the nature and role of image schemata in language use. Image schemas have often been thought to be low-level quasi-primitives that structure much of language and thought (Johnson, 1987; Oakley 2010). Over the years, they have been studied by cognitive scientists who are interested in the semantics of language, both literal and non-literal. In this study, we queried naive participants’ intuitions about various verbs in everyday English sentences. The events they denoted have various visuospatial orientations: horizontal, vertical, or some combination or modification of those orientations. The results of our survey, which follow on earlier work (Richardson, Spivey, Edelman & Naples, 2001), show robust consistency among people’s intuitions and provide further insights into how image schemas work, in particular, how they are dynamic, flexible, and combine to create meaning.

Models of human visual clustering

How humans visually cluster points is relevant for perception, information visualization, and many other domains. However, there are relatively few empirical studies and models of this ability. Here, we propose a new competitive clustering model that uses neurally plausible mechanisms such as hebbian learning and lateral inhibition. We evaluate its fit to the data from a behavioral study of visual clustering, as well as the fits of two categorization models (the Rational model and SUSTAIN) and one statistical learning algorithm (K-Means). We find that people are highly reliable in their clusterings of the same stimulus on two occasions, suggesting they are using a stable strategy. The models were generally successful at predicting human clusterings and were able to replicate the qualitative performance profile of human reliability over different numbers of points and levels of cluster structure. The performance of the competitive clustering model motivates further investigation of its computational properties and empirical validity.

Does he hears a prime I prefer the? Testing a novel chunk-based perspective on structural priming

Repetition of linguistic structure plays a role in both language comprehension and production. Previously encountered structures are processed faster, and speakers tend to reuse them in new utterances—a phenomenon known as structural priming. According to one well-established interpretation of structural priming, linguistic input activates an underlying mental representation based on constituents, a syntactic unit derived from rule-based grammars (e.g., [[he]NP [hears [a sound]NP]VP]S). Here we ask whether structural priming can occur for non-constituent parts-of-speech fragments, such as pronoun verb determiner (e.g., he hears a). Across two preregistered phrasal decision experiments, we show that structural priming can occur at the level of three-word parts-of-speech sequences and in the absence of constituents. Using corpus analysis, we further show that structural priming of non-constituents also occurs in real-life dialogue. These results imply that constituent structure is not a necessary prerequisite for structural priming and provide a challenge to contemporary approaches to grammar.

A computational model of responsibility judgments from counterfactual simulations and intention inferences

How responsible someone is for an outcome depends on what causal role their actions played, and what those actions reveal about their mental states, such as their intentions. In this paper, we develop a computational account of responsibility attribution that integrates these two cognitive processes: causal attribution and mental state inference. Our model makes use of a shared generative planning algorithm assumed to approximate people's intuitive theory of mind about others' behavior. We test our model on a variety of animated social scenarios in two experiments. Experiment 1 features simple cases of helping and hindering. Experiment 2 features more complex interactions that require recursive reasoning, including cases where one agent affects another by merely signaling their intentions without physically acting on the world. Across both experiments, our model accurately captures participants' counterfactual simulations and intention inferences, and establishes that these two factors together explain responsibility judgments.

Does enlarging font size facilitate English word and sentence reading in children as beginning readers?

We examined how enlarging the font size from a regular size that young children were typically exposed to affected their reading performance and eye movement behavior. We showed that making the font size larger than a regular size impaired word pronunciation accuracy. This impairment was associated with non-verbal IQ but not changes in eye movement behavior, suggesting that it may be more related to the development of font-size dependent perceptual representations of words than information extraction strategies. The font size change also decreased children’s gaze transition consistency among words during sentence reading, suggesting that it interfered with eye movement planning for implementing and developing visual routines for reading. These results suggested that young children may have initially developed font-size dependent perceptual representations for words and for eye movement planning during sentence reading before forming font-size independent reading behavior as observed in adults through increasing reading experiences with different font sizes.

Using face averages to measure differential accuracy for demographic groups in facial recognition

Facial recognition Deep Neural Networks (DNNs) and humans both show systematic differences in face recognition accuracy for different demographic groups. Differential accuracy has been shown due to race, age and gender, raising important questions about the impact of facial recognition decisions on the fairness of society. Current methods for measuring bias require curating large databases of face images, which is labor-intensive, producing bespoke tests that cannot be easily shared or standardized due to data privacy. Here we develop a novel solution to this problem inspired by psychological research in face perception. We ask whether face averages can be used to predict face recognition accuracy differentials for DNNs and humans. We generate sets of average images from random samples of exemplar faces from a demographic group to measure the density of these sets in representational space. We find that this approach provides reliable predictions of differential accuracy across demographic groups in both DNNs and human participants. However, we also find evidence that face averages were not represented in the center of face categories. This finding should be addressed in future development of our approach, and also challenges influential cognitive models of face identity representation.

Accentuate the negative: Expectations about sampling procedures determine the impact of negative evidence on children’s inductive judgments

The present study examined the role of negative evidence on children's inductive generalizations. Three-, 4-, and 5-year-olds (N=98) were asked to generalize from samples with negative evidence and samples with positive evidence that were selected either deliberately or incidentally. Children in all three age groups made a higher rate of generalizations from samples that included negative evidence than from samples that included positive evidence, but only when evidence was described as having been selected deliberately by the experimenter. Furthermore, there were developmental differences in the scope of generalizations such that deliberate sampling elicited a higher rate of generalizations among older children compared to younger children. These results are discussed in light of other work on inductive reasoning that emphasizes the role of dyadic factors on generalization, such as the expectation that informants intend to share relevant information.

The effect of implicit theories on help-seeking behavior: Focusing on anticipated evaluation and perceived implicit theories of the peer member.

We investigated the effect of implicit theories (belief about malleability of ability) on help-seeking behavior in peer learning situation. We predicted that entity theorists (those who believe that ability is fixed) are less likely to seek help because they anticipate that peer members would lower their evaluation of competence. We conducted a scenario experiment and required participants to indicate to what extent they would ask questions to the peer member when they encounter an incomprehensive term which was already explained, and others seemed to understand. The results did not support our hypotheses. The results revealed that the interaction between one’s own and perceived others implicit theories predicted intention to ask questions. Specifically, while perceived others’ implicit theory did not affect incremental theorists’ (those who believe that ability is malleable) intention to ask a question, entity theorists were more likely to ask a question when they perceive others as entity theorists.

Conditionals conflict with their denials

What does it mean to deny the conditional statement, "if you steal an apple, you go to jail"? One theory argues that, because conditionals are probabilistic, their denials are too. And so the conditional probability, P(not-jail | steal-apple), best describes a conditional denial. Another theory argues that conditional denials concern possibilities, i.e., they activate imagined situations in which you cheat on your taxes but don’t go to jail. The two accounts make diverging predictions: only the latter predicts that people should assess conditionals and their denials as mutually inconsistent. Two experiments corroborate the possibility-based account: the studies show that both in implicit and explicit evaluations of consistency, conditional denials conflict with the conditionals they deny.

A Quantum Model of Concepts

In this paper we present a new modelling framework for concepts based on quantum theory, and demonstrate how the conceptual representations can be learned from data. Our approach builds upon Gardenfors' classical framework of conceptual spaces, in which cognition is modelled geometrically through the use of convex spaces, which in turn factorise in terms of simpler spaces called domains. We show how concepts from the domains of SHAPE, COLOUR, SIZE and POSITION can be learned from images of simple shapes, where individual images are represented as quantum states and concepts as quantum effects. Concepts are learned by a hybrid classical-quantum network trained to perform concept classification. We also use discarding to produce mixed effects, which can then be used to learn concepts which only apply to a subset of the domains, and show how entanglement (together with discarding) can be used to capture interesting correlations across domains.

The Units of Gating and Access to Lexical Representations During Spoken Word Recognition

Word recognition models such as Cohort have long relied on the gating paradigm to investigate how acoustic-phonetic information maps onto lexical representations. We report on a methodological study investigating (a) whether the recognition point of a spoken word is affected by the speech variables employed in the gating paradigm, and (b) which distributional properties of a words’ linguistic and social usage pattern affect its recognition point. We addressed the first question by contrasting the traditional “brute-force” gating paradigm (i.e., employing incremental segments of 50 ms) to “phonetically-driven” gating paradigms. Three methodologies were employed for determining phonemic segments: (1) articulatory measures, relying on the peak velocity of articulatory gestures, (2) acoustic measures, relying on the acoustic energy of consonants and vowels, and (3) brute-force measures, relying on 50 ms increments. We addressed the second question by relying on four social measures of lexical strength, which were attained from a corpus of 57 billion words from Reddit: word frequency (WF), contextual diversity (CD), discourse contextual diversity (DCD), and user contextual diversity (UCD). Results showed that the traditional brute-force gating method yielded significantly faster word recognition times, in comparison to articulatory and acoustically driven gating methods. Our results also showed that CD is a superior measure of lexical strength than WF, UCD, and DCD. Overall, our results suggest that the traditional gating paradigm is a reliable method for investigating spoken word recognition, given that spoken word recognition may rely on the gradual accumulation of phonetic information over time, rather than relying solely on the recovery of categorical phonetic features that are distributed non-linearly in time. We also suggest that the lexical system may be organized as a function of usage-based contextual measures of lexical items.

Metacognitive skill: how it is acquired

Metacognition can improve with practice, yet the mechanisms underlying metacognitive skill learning remain unclear and lack a robust theoretical framework. We propose that metacognitive skill learning can be largely explained by the skill acquisition model advanced by Fitts (1964) and Anderson (1982). While this model has been successful within the domains of motor skill and cognitive skill, it has not yet been applied to metacognitive skill. This novel framework can help to explain metacognitive skill learning, its cognitive underpinnings, and shed light on otherwise unexplainable empirical data.

Generate your neural signals from mine: individual-to-individual EEG converters

Most models in cognitive and computational neuroscience trained on one subject do not generalize to other subjects due to individual differences. An ideal individual-to-individual neural converter is expected to generate real neural signals of one subject from those of another one, which can overcome the problem of individual differences for cognitive and computational models. In this study, we propose a novel individual-to-individual EEG converter, called EEG2EEG, inspired by generative models in computer vision. We applied THINGS EEG2 dataset to train and test 72 independent EEG2EEG models corresponding to 72 pairs across 9 subjects. Our results demonstrate that EEG2EEG is able to effectively learn the mapping of neural representations in EEG signals from one subject to another and achieve high conversion performance. Additionally, the generated EEG signals contain clearer representations of visual information than that can be obtained from real data. This method establishes a novel and state-of-the-art framework for neural conversion of EEG signals, which can realize a flexible and high-performance mapping from individual to individual and provide insight for both neural engineering and cognitive neuroscience.

Paradigmatic formation through context-mediation

Words that regularly fill the same sentential slots are said to be paradigmatically related. Paradigmatic relations may be retained through a direct association or a latent representation at encoding, or by reinstating context during retrieval. We paired proper names by embedding them into two instances of the same sentence frame, each in a separate list, yielding blocks of two study-cloze sessions. The pairing between proper names was fixed across twelve blocks. In the static condition, the same sentence frames were used across blocks, while in the dynamic condition sentence frames changed for each block. Interference should accrue in both conditions if paradigmatic relations are based on a direct association or overlap in a latent representation, however, if paradigmatic relations are mediated by retrieved context then changing the sentence frame should release interference. Our results are consistent with a context-mediation account of paradigmatic relations.

Preschoolers select the relevant information when looking for a hidden present

Previous research suggests that children’s information search remains largely inefficient until age 4. Here, we investigate the early emergence of children’s information-search competence using a simplified version of Lindow’s (2021) finding-presents game. Children (n = 86, 24- to 59-months old) had to find a present hidden in one of three closed boxes. All boxes were identical but for one feature (e.g., all boxes were blue and had a flower icon on top, but one box was round, one heart-shaped, and one squared). To identify the target box, children received three information cards revealing one feature of the target box (i.e., its color, shape, or icon). As the boxes differed in only one feature (e.g., their shape), only one information card contained the relevant information to the decision (i.e., the information card indicating the correct shape). Children could flip one information card to learn about one particular feature before deciding which box to open. This was our dependent measure. Our findings indicate that children as young as 2 years can efficiently search for information to guide their decisions and underline the importance of using age-appropriate paradigms.

Estimating attitudes toward vaccination: A Bayesian framework

Vaccines are among the best tools to limit the spread of preventable diseases. yet, recent years have seen a rise in anti-vaccination sentiments for vaccines against COVID-19, the MMR, and more. It is critical to understand the factors that influence whether a person will accept or reject a vaccine for a given disease. This paper tests a Bayesian model to predict attitudes toward vaccines. with five factors (subjective beliefs concerning danger of illness, safety of the vaccine, prevalence of the disease, perceived social norm, and governmental recommendation). To parameterize the model, Study 1 elicits the full conditional probability table while Study 2 tests model predictions by eliciting people’s priors for COVID-19 and the common flu. We find a good fit between predictions and observations, accounting for 53% and 44% of the variance. This suggests the usefulness of a formal model to capture people’s beliefs about vaccination.

Embodied attention resolves visual ambiguity to support infants’ real-time word learning

The input for early language learning is often viewed as a landscape of ambiguity with the occasional high-quality naming event providing resources to resolve uncertainty. Word learning from ambiguous naming events is often studied using screen-based cross-situational learning tasks. Little is known, however, on how ambiguity impacts real-time word learning in free-flowing interactions. To explore this question, we asked parent-infant dyads to play in a home-like environment with unfamiliar objects while wearing head-mounted eye trackers. After the play session, we tested whether infants learned any of the object-label mappings and categorized individual words as learned or not learned. Dyadic behaviors and the visual information available to infants during the naming moments of learned and not learned words were analyzed. The results show that infants’ embodied attention during ambiguous naming moments was the key to predicting learning outcomes. Specifically, infants held and looked at the target object longer in ambiguous instances that led to learning. Our results emphasize the importance of studying word learning in naturalistic environments to better understand the cues infants use to resolve ambiguity in everyday learning contexts.

Investigating Conversational Dynamics in Human-Robot Interaction with fMRI

We investigated how verbal communication with a robot differs from talking to a human in terms of brain activity by analysing an open-source fMRI dataset. We focused on modeling conversational dynamics rather than conversation as a whole, by analysing fine-grained events, in particular turn initiation. The results indicate that turn initiation in a conversation with a human involves higher activation in auditory and visual cortex than turn initiation with a robot. Conversely, listening to the robot showed higher engagement of auditory cortex than listening to a human. We suggest that verbal and non-verbal turn-taking cues provided by the human agent engage more cognitive processing for picking up the turn. On the other hand, listening to a robot agent requires more processing than listening to a human. Both findings suggest that the accurate simulation of appropriate turn-taking cues and behaviors will help robots to establish more natural conversation dynamics and that the use of brain imaging can provide valuable objective measurements for assessing user states in human-robot interaction.

Learning planning strategies without feedback

How do humans get better at planning? Previous work postulated that the improvement of cognitive strategies occurs through feedback-based metacognitive reinforcement learning (MCRL). However, it is not clear whether and, if so, how people can learn planning strategies without reinforcement. To answer these questions, we experimentally investigated the effect of frequency of feedback on people's ability to learn adaptive planning strategies. We found that participants receiving feedback only 25\% of the time nonetheless learned about as well as participants receiving constant feedback. Quantitative modelling of the data revealed that state-of-the-art MCRL models cannot explain this finding. However, extending these models by a mechanism generating an additional learning signal through self-evaluation of plan quality can account for people's ability to learn planning strategies without feedback. The findings of this research have implications for the design of learning environments and enabling people and machines to self-sufficiently improve their strategies in naturalistic settings.

Phono-semantic prediction during language comprehension: Effects of working memory

There is strong evidence about the preactivation of semantic information but controversial results about phonological preactivation. This research explored the individual differences in phono-semantic preactivation using the visual world paradigm. Participants looked at four competitors (semantic, phonological, and two unrelated) while hearing highly constraining sentences. Moreover, they were evaluated in verbal and nonverbal speed processing and working memory. Our results showed a strong semantic prediction but an inhibition of the phonological effect. The semantic prediction was related to verbal and nonverbal working memory but not processing speed. The results were discussed in terms of lexical selection and inhibitory top-down influences.

The Relationship Between Teaming Behaviours and Joint Capacity of Hybrid Human-Machine Teams

Collaboration in shared environments requires human agents to coordinate their behaviour according to the machines’ actions. In this study, we compared the performance and behaviour of Human-Machine (HM) and Human-Human (HH) teams. While HH teaming behaviour is sensitive to Collaborative contexts, little is known about HM teaming behaviour. Furthermore, teaming behaviour may impact the team’s Joint Capacity the team’s ability to handle teamwork processes and task demands. To assess teaming behaviour at every moment of a trial we used three distinct spatiotemporal measures (Momentary Distance, Highly Correlated Segments, and Running Correlation). To assess the team's joint performance, we adopted the Capacity Coefficient (Townsend & Nozawa, 1995). For both HH and HM teams, behavioural measures predicted Joint Capacity. HH teams demonstrated greater performance and less synchronous behaviour than HM teams. The reduced synchrony of HH teams likely improved their performance as they could complement each other’s behaviour rather than duplicate inefficiencies.

Modeling Human Sequential Behavior with Deep Neural Networks in Emergent Communication

In this paper, we study human sequential behavior by integrating cognitive, evolutionary, and computational approaches. Our work centers around the emergence of shared vocabularies in the Embodied Communication Game (ECG). Here, participant pairs solve a shared task without access to conventional means of communication, enforcing the emergence of a new communication system. This problem is solved typically by negotiating a shared set of sequential signals that acquire meaning through interactions. Individual differences in Personal Need for Structure (PNS) have been found to influence how this process develops. We trained deep neural networks to mimic the emergence of new communicative systems and used hyperparameter optimization to approximate latent human cognitive variables to explain human behavior. We demonstrate that models based on bidirectional LSTM networks are better at capturing human behavior than unidirectional LSTM networks. This suggests that human sequence processing in the ECG is influenced by expected future states. The approximated variables cannot explain the differences in PNS, but we do provide evidence suggesting that random and uncertainty-directed exploration strategies are combined to develop optimal behavior.

Conceptual Change During Recursive Pattern Learning in Children and Adults

There are two hypotheses about complex pattern processing: one proposes that people are predisposed towards simple, local patterns; the other proposes that prefer more complex hierarchical patterns. Children and adults can both learn to preferentially generate hierarchical sequences. One unanswered question is whether children and adults generate complex patterns spontaneously. We investigated the predispositions or inductive biases of human children and adults in a novel open-ended sequence generation task. We found that children and adults display strong biases to generate simple patterns. Both groups overcome this inductive bias by generating more complex patterns in subsequent learning tasks. However, children find it more difficult to override their biases. Our results suggest that people possess an inductive bias towards simple patterns that are well-organized but minimally hierarchical. With some experience, humans learn complex hierarchical patterns. These findings reveal substantial encoding flexibility for patterns in humans—a flexibility beginning in childhood.

Thematic relations outperform taxonomic relations in a cued recall task

Prior knowledge has long been known to influence retention of newly experienced information. In particular, known semantic associations across items facilitate subsequent memory for these items, and this effect has been shown to increase with measures of semantic relatedness. In the field of categories and concepts, the processing of taxonomic (e.g., cup-fork, dog-bird) versus thematic (e.g., cup-drink, dog-leash) conceptual relations can be differentiated at the behavioral and neural levels. However, the effects of these distinct conceptual relations on memory remain unresolved. The current study used a stimulus set consisting of thematic, taxonomic, and unrelated noun-noun word pairs, to shed light on this issue. Our results indicate that pairs with thematic relations lead to improved cued memory performance, followed by taxonomic relations, and finally unrelated pairs. This study provides evidence that conceptual relations differ in the extent to which they facilitate cued memory performance.

When you say that, do you mean it? Crosslinguistic patterns of anaphor resolution in English, German, and Polish

When hearing a pronoun, people find its referent effortlessly most of the time. However, across languages, pronominal systems vary: While in one language, a pronoun may point to a referent as a function of its accessibility in discourse, in others, pronoun resolution might rely on a range of different processes, specific to each individual pronominal form. In three studies, using an act-out task in English, German and Polish, we found evidence for an overarching tendency, but also crosslinguistic differences: In general, participants were more likely to relate simple pronouns to single, most salient referents and demonstratives to conceptual composites, but cross-linguistic differences reflect the complexity of each language’s pronominal system. Overall, our results extend the empirical basis for anaphora resolution, refining a model of anaphora resolution as a multifaceted interaction of various linguistic and non-linguistic mechanisms at its core.

Agreement affects the interpretation of null arguments in semi-artificial Japanese

The availability of sloppy interpretation for null arguments differs across languages. This difference is a challenge for child learners because they are unlikely to receive sufficient input that provides clear evidence about the available interpretation. Theories suggest that knowing the presence/absence of agreement could help solving this learning problem. While languages like Japanese that lack agreement allow argument ellipsis (hence the sloppy reading is available), languages with a rich agreement system like Spanish do not. This study explores the utility of this correlation as a cue for learners to infer the available interpretation of a null argument. We show that Japanese adults who learned semi-artificial Japanese that has object-verb agreement are more likely to accept the strict reading than the ones who learned only an artificial singular/plural marker attached to an object. Our results provide evidence that agreement may play a role in learning the interpretation of null arguments.

Are there Irrelevant Utilities? What the Folk Think (and Why This is Relevant)

We test the importance people attribute to the realization of small gains in outcome value for cases where the decision-maker must competitively distribute significant harm between separate groups. We find that, in line with recent non-consequentialist moral theories, subjects (i) sometimes rank giving those that stand to suffer harm equal chances above maximizing outcome value and (ii) that whether they opt for equal chance procedures (‘coin flips’) depends on the magnitude of the value that can be secured by not offering them. Our findings vindicate the idea that there can be ‘irrelevant utilities’ in cases of competing claims to avoid harm. Our study thus extends existing work on decision-making in conflict of harm cases along several dimensions, and we demonstrate their import for determining which version of ‘partially aggregative’ accounts in normative ethics aligns best with common sense.

Forget About It: Entity-Level Working Memory Models for Referring Expression Generation in Robot Cognitive Architectures

Working Memory (WM) plays a key role in natural language understanding and generation. To enable a human-like breadth and flexibility of language understanding and generation capabilities, cognitive systems for language-capable robots should feature a human-like WM system in a similarly central role. However, it is still quite unclear how robotic WM should be designed, as a variety of models of human WM have been proposed in cognitive psychology. Moreover, human reliance on WM during language production is sometimes to help the speaker rather than to help hearers. Thus, it is unclear whether different robotic WM systems might harm certain dimensions of interaction for the sake of the robot speaker's ostensible ease of cognitive processing. In this paper we demonstrate how different models of human WM can be implemented into robot cognitive architectures. Our results suggest that these models can be effective in terms of accuracy, perceived naturalness, and perceived human-likeness.

Dependency Locality Influences Word Order During Production in SOV Languages: Evidence from Hindi

When two arguments of a sentence vary in length, speakers of SOV languages prefer to place the longer argument before the shorter one leading to a long-before-short word order. The functional motivation behind such an ordering choice has been provided by two opposing accounts. According to the first account, long-before-short order makes production efficient by keeping syntactic heads and dependents close to each other (dependency locality). The alternate account argues that longbefore-short order is a product of increased conceptual accessibility of long arguments during production. In this work, we test the predictions made by the two accounts by comparing ordering choices in Hindi transitive sentences containing object-modifying post-nominal relative clauses (RCs). Results reveal that it is efficiency and not accessibility that determines word order during production. These findings add to the body of work that argues for an overarching influence of working memory constraints on both comprehension and production.

More means more? Illusory causation between uncorrelated continuous events

Illusions of causality arise when people observe statistically unrelated events and yet form a belief that the events are causally linked. When participants observe a sequence of discrete binary events (e.g., a patient was either administered a treatment or no treatment, and subsequently recovers or does not recover from their illness), the frequency of the putative cause and outcome occurring inflates the illusion of causality. Recently, similar effects have been observed using outcomes of continuous magnitude. Participants are more likely to endorse the causal status of a (completely ineffective) cue if the target outcome (e.g., high magnitude outcomes) occur frequently. Here, we extended these findings by investigating how predictions and causal judgments for a cue of continuous magnitude were affected by the distribution of cue values presented. Participants observed cue values (dose of a fictitious medicine) sourced from either a continuous distribution or from two discrete values, and were followed by outcomes that were either continuous (Experiment 1) or binary in nature (Experiment 2). Our results show that participants were more likely to assume a linear relationship between drug dose and magnitude of recovery when cue dosage were predominantly high than when they were predominantly low.

Investigating age-related changes in adults’ cue-integration: An eye-tracking study

The present study investigated age-related changes in the ability to engage cue integration capacities to understand a speaker’s referential intention. Forty young adults (M = 22.18 years, SD = 1.39) and 40 older adults (M = 67.70 years, SD = 4.86) were tested on a cue-integration task with eye-tracking, where they integrated multiple cues to identify a target object across two conditions. In the three-cue condition, they were presented with contextual, semantic and gaze cues, while the two-cue condition consisted of only the contextual and semantic cues. Behavioral results showed that overall, older adults were less accurate in selecting the target object than young adults in our task. Furthermore, eye-tracking results indicated that older adults were less likely to distinguish between the target and non-target objects than young adults. Our results suggest an age-related decline in the ability to integrate multiple cues when inferring referential intention. These findings provide evidence for communicative challenges in late adulthood.

Physically salient stimuli capture attention despite external motivation to ignore

Stimuli that are physically salient—e.g., brighter or differently colored to others in the visual scene—capture eye gaze and attention. Many studies have shown that color-singleton distractors slow visual search for a target, even when participants are informed beforehand of the features (e.g., color) of the upcoming distractor. In those studies, however, participants may not have been particularly motivated to recruit attentional processes and try to prevent attentional distraction by upcoming stimuli. In the current study we investigated whether participants could use pre-trial information about the color of an upcoming distractor to prevent themselves from getting distracted by it, when a reward was at stake. Results showed that a performance-contingent reward reduced overall distraction by physically salient stimuli. However, reward did not increase the likelihood that participants would use information about the color of the upcoming distractor to further improve visual search performance. This study highlights the fast and reflexive nature of attentional capture by physically salient distractors, which is difficult to control strategically, even when motivated to do so.

Tell Me Your (Cognitive) Budget, and I’ll Tell You What You Value: Evidential Relationships Between Values, Data, and Generic Causal Claims about the Social World

Consider the following two (hypothetical) generic causal claims: “Attending an all-girls school improves girls’ math scores” and “attending an affluent all-girls school improves girls’ math scores.” These claims not only differ in what they suggest about how test scores are distributed across different types of schools (i.e., “the data”), but also have the potential to communicate something about the speakers’ values: namely, the prominence they accord to affluence in representing and making decisions about the social world. Here, we examine the relationship between the level of granularity with which a cause is described in a generic causal claim (e.g., all-girls school vs. affluent all-girls school) and the value of the information contained in the causal model that generates that claim. We argue that listeners who know any two of the following can make reliable inferences about the third: 1) the level of granularity at which a speaker makes a generic causal claim, 2) the speaker’s decision-theoretic values, and 3) the data available to the speaker. We present results of three experiments in the domain of social categories (N=853) that provide evidence in keeping with these predictions.

Examining Mechanisms Underlying the Ability to Form Paradigmatic Associations

Paradigmatic associations are second-order associations where the items share a common context rather than being directly associated. Despite the importance of the structure in knowledge representation, the underlying mechanisms to form paradigmatic associations are not well studied. In the current study, we examined whether explicit attentional control is critical for forming paradigmatic associations. We used an implicit learning task, which limits the use of explicit attentional control, to see whether the associations can be formed without attentional control. Results showed evidence for learning, which implies that explicit attentional control may not be necessary for forming paradigmatic associations. We also used the n-back task to examine whether the ability to maintain information is critical for forming paradigmatic associations. Results did not provide evidence for the relationship between the two. We discuss the results in terms of the core mechanisms that may enable the formation of higher-order associations.

A lurking bias: Representativeness of users across social media and its implications for sampling bias in cognitive science

Within internet there exists the 90-9-1 principle (also called the 1% rule), which dictates that a vast majority of user-generated content in any specific community comes from the top 1% active users, with most people only listening in. When combined with other demographic biases among social media users, this casts doubt as to how well these users represent the wider world, which might be problematic considering how user-generated content is used in psychological research and in the wider media. We conduct three computational studies using pre-existing datasets from Reddit and Twitter; we examine the accuracy of the 1% rule and what effect this might have on how user-generated content is perceived by performing and comparing sentiment analyses between user groups. Our findings support the accuracy of the 1% rule, and we report a bias in sentiments between low- and high-frequency users. Limitations of our analyses will be discussed.

Comparing Intuitions about Agents’ Goals, Preferences and Actions in Human Infants and Video Transformers

Although AI has made large strides in recent years, state-of-the-art models still largely lack core components of social cognition which emerge early on in infant development. The Baby Intuitions Benchmark was explicitly designed to compare these "commonsense psychology" abilities in humans and machines. Recurrent neural network-based models previously applied to this dataset have been shown to not capture the desired knowledge. We here apply a different class of deep learning-based model, namely a video transformer, and show that it quantitatively more closely matches infant intuitions. However, qualitative error analyses show that model is prone to exploiting particularities of the training data for its decisions.

Laying the Foundation: Extracting Partial Meanings of Hard Nouns via Observational Contexts

A key aspect of understanding how children learn the meanings of words involves understanding how they mine different sources of information (e.g., observational, linguistic) in the service of learning. According to one dominant view, there exists a class of words (i.e., “hard words”; Gleitman et al., 2005) for which learning their meaning requires access to information beyond the observational contexts in which those words occur. Building upon previous work on this topic that employed the Human Simulation Paradigm, a paradigm commonly used for investigating vocabulary learning, the current study revisits the role of observational contexts for the acquisition of one class of hard words: nouns that denote non-basic level object categories (or “hard nouns”; see Kako, 2005). These data reveal that although observational contexts may not be sufficient to yield learning of precise hard noun meanings, they allow learners to extract systematic partial knowledge, knowledge that may lay a critical foundation for meaning acquisition.

Active Predictive Coding: A Unified Neural Framework for Learning Hierarchical World Models for Perception and Planning

Predictive coding has emerged as a prominent model of how the brain learns through predictions and prediction errors. Traditional predictive coding focused primarily on sensory coding and perception. Here we propose active predictive coding (APC), a unified framework for perception, action and cognition. By learning hierarchical world models, the APC framework addresses important open problems in cognitive science and AI such as: (1) how do we learn compositional representations, e.g., part-whole hierarchies for equivariant vision? and (2) how do we solve large-scale planning problems, which are hard for traditional reinforcement learning, by composing complex action sequences from primitive policies? APC exploits hypernetworks, self-supervised learning and reinforcement learning to learn hierarchical models that combine task-invariant state transition networks and task-dependent policy networks at multiple abstraction levels. We illustrate the applicability of the APC model to active visual perception and hierarchical planning. Our results represent, to our knowledge, the first proof-of-concept demonstration of a unified approach to addressing the part-whole learning problem in vision, the nested reference frames learning problem in cognition, and the integrated state-action hierarchy learning problem in reinforcement learning.

Detecting social biases using mental state inference

Social biases can negatively impact our sense of belonging, achievement, and social relationships. However, it is unclear what inferential processes underlie how people detect biases. We propose that people infer social biases by positing prior beliefs to account for potential gaps between what someone observed (e.g., seeing you succeed on a challenging task) and how they responded (e.g., recommending you try something easier). We present a computational model formalizing this process, and validate it with two experiments. We find a strong quantitative fit between model predictions and participant judgments across a range of inferences, namely which prior belief the coach held (i.e., which team the coach thought the player was on, or which bias the coach has). This work bridges computational methods with social psychological research on social biases, by showing how mental state inferences contribute to our ability to rapidly detect biases.

What's the matter with 'reasonable'?

The reasonable person standard is key to both Criminal Law and Torts. What does and does not count as reasonable behavior and decision-making is frequently determined by lay jurors. Hence, laypeople's understanding of the term must be considered, especially whether they use it predominately in an evaluative fashion. In this corpus study, we investigate whether laypeople use 'reasonable' mainly as descriptive, evaluative, or merely value-associated term, based on supervised machine learning models. We find that 'reasonable' is predicted to be an evaluative term in a majority of cases. This supports prescriptive accounts, and poses potential problems for descriptive and hybrid accounts of the term. Other terms often used interchangeably in jury instructions (e.g., 'careful,' 'ordinary,' 'prudent,' etc), however, are predicted to be descriptive. This indicates a discrepancy between the intended use of the term and the understanding lay jurors might bring into the court room.

Uncertainty can explain apparent mistakes in causal reasoning

Humans excel at causal reasoning, yet at the same time consistently fail to respect its basic axioms. They seemingly fail to recognize, for instance, that only the direct causes of an event can affect its probability (the Markov condition). How can one explain this paradox? Here we argue that standard normative analyses of causal reasoning mostly apply to the idealized case where the reasoner has perfect confidence in her knowledge of the underlying causal model. Given uncertainty about the correct representation of a causal system, it is not always rational for a reasoner to respect the Markov condition and other ‘normative’ principles. To test whether uncertainty can account for the apparent fallibility of human judgments, we formulate a simple computational model of a rational-but-uncertain causal reasoner. In a re-analysis of a recent causal reasoning study, the model fits the data significantly better than its standard normative counterpart.

An Exploratory Study on the Effect of Contour Types on Decision Making via Optic Brain Imaging Method (fNIRS)

Decision-making is a combination of our positive anticipations from the future with the contribution of our past experiences, emotions, and what we perceive at the moment. Therefore, the cues perceived from the environment play an important role in shaping the decisions. Contours, which are the hidden identity of the objects, are among these cues. Aesthetic evaluation, on the other hand, has been shown to have a profound impact on decision-making, both as a subjective experience of beauty and as having an evolutionary background. The aim of this empirical study is to explain the effect of contour types on preference decisions in the prefrontal cortex through risk-taking and aesthetic appraisal. The obtained findings indicated a relation between preference decision, contour type, and PFC subregion. The results of the current study suggest that contour type is an effective cue in decision-making, furthermore, left OFC and right dlPFC respond differently to contour types.

Development of Multimodal Turn Coordination in Conversations: Evidence for Adult-like behavior in Middle Childhood

The question of how children develop multimodal coordination skills to engage in meaningful face-to-face conversations is crucial for our broader understanding of children's healthy socio-cognitive development. Here we focus on investigating the ability of school-age children to coordinate turns with their interlocutors, especially regarding when to take the floor (i.e., the main channel of the conversation) and when to provide attentive listening signals via the back channel. Using data of child-caregiver naturalistic conversations and data-driven research tools, we found that children aged 6 to 12 years old already show adult-like behavior both in terms of reacting to the relevant channel-specific cues and in terms of providing reliable, multimodal inviting cues to help their interlocutor select the most appropriate channel of the conversation.

Anaphoric Structure Emerges Between Neural Networks

Pragmatics is core to natural language, enabling speakers to communicate efficiently with structures like ellipsis and anaphora that can shorten utterances without loss of meaning. These structures require a listener to interpret an ambiguous form—like a pronoun—and infer the speaker’s intended meaning—who that pronoun refers to. Despite potential to introduce ambiguity, anaphora is ubiquitous across human language. In an effort to better understand the origins of anaphoric structure in natural language, we look to see if analogous structures can emerge between artificial neural networks trained to solve a communicative task. We show that: first, despite the potential for increased ambiguity, languages with anaphoric structures are learnable by neural models. Second, anaphoric structures emerge between models ‘naturally’ without need for additional constraints. Finally, introducing an explicit efficiency pressure on the speaker increases the prevalence of these structures. We conclude that certain pragmatic structures straightforwardly emerge between neural networks, without explicit efficiency pressures, but that the competing needs of speakers and listeners conditions the degree and nature of their emergence.

Expectations of Determinism Underlie Domain Effects on Adult Causal Learning

Prior knowledge can affect causal judgments by inducing expectations in learners. One important type of prior knowledge is about domains; for example, physical systems are typically believed to be more deterministic than psychological ones. We examine the role of these types of determinism expectations in shaping subsequent causal judgments, and we argue that they mediate effects of abstract domain-wide expectations. Study 1 shows that expectations of determinism—in particular, of sufficiency—positively predict judgments of strength and sufficiency ratings, even when holding statistical data constant. Study 2 shows that abstract expectations at the domain level affect causal judgments, but these effects are mediated (differently between conditions) by vignette-level expectations of determinism. These results jointly suggest a productive way to conceptualize domain effects on causal judgments, namely as effects of expectations about the causal relations being investigated.

Moving the Goalposts: The Influence of Context on Behavioral Transitions in a Unilateral Manual Reaching Task

Prior work has demonstrated the presence of hysteresis in the control of affordance-guided behavior, whereby behavioral transitions around a critical action boundary vary with directions of change in said action boundary. To date, research on this topic has overlooked the influence of global context on these phenomena. We employ an affordance-based reaching task, whereby participants were asked to move a target to a goal by passing through one of two apertures (size variable or size constant). It was found that the direction of change in the size variable aperture influenced the point of behavioral transitions, and this effect interacted with the location of a proceeding goal. Further, considering the nature of the behavioral phase transitions, differences in the structure of entropy were found depending on the direction of change in the size variable aperture. These results are discussed considering a dynamical systems approach, and recommendations for future work are made.

Comparison and Explanation in Learning Causal System Categories

The ability to notice relational patterns across situations is crucial to learning. An important question is how people build transferable and generalizable knowledge by learning from examples. The study investigated the conditions that make comparison and explanation beneficial to learning by comparing learning outcomes of engaging in explanation or comparison in a categorization task and examining how varying degrees of instructional support affect these two processes. The results showed an advantage of comparison over explanation; however, this was specific to combination of relational labels and definitions and prompts to compare. These results add to existing research and extend our understanding of how best to support college students learning relatively difficult material. The findings also inform ways educators can support learning by developing instructional designs that support learning through analogical reasoning.

Kinship terminologies reflect culture-specific communicative need: Evidence from Hindi and English

Systems of semantic categories vary across languages, and it has been proposed that this variation is constrained by a need for efficiency in communication. An important element of efficiency is communicative need, or how often a particular object needs to be referenced. Previous work has sometimes assumed for simplicity that the distribution of need over objects in a semantic domain does not vary across languages or cultures. Here, we explore culture-specific need as it relates to the kinship terminologies of Hindi and English. We assess the efficiency of each language's kin naming system under a variety of need distributions, including one based on that language's usage statistics, one based on the other language's usage statistics, and random permutations of each of those two distributions. Our results suggest that kinship terminologies reflect culture-specific communicative need.

Active information-seeking in support of learning extensions of novel words

A key debate in language learning centers on how people successfully learn the extension of a novel word, despite inherent ambiguity in the input. Across two studies, we tested whether learners reduce ambiguity about a word’s extension by actively sampling the environment. Adult participants were first shown ambiguous learning situations in which novel words were presented with a set of exemplars that were drawn from a subordinate-level category (e.g., Dalmatians), a basic-level category (e.g., dogs), or a superordinate-level category (e.g., animals). Learners then had the opportunity to sample the label of additional exemplars. Participants systematically adapted their sampling choices as a function of training. Moreover, participants varied in their sampling strategies, pursuing both confirmatory strategies (selecting exemplars similar to the training set) and constraining strategies (selecting exemplars that constrain the word’s extension). Overall, these findings show that learners spontaneously pursue sampling strategies that support generalizing novel word meanings.

Epistemic language in news headlines affects readers’ perceptions of objectivity

Information from the news undoubtedly shapes what we believe is true, but we argue the language it employs also influences whether we think an assertion has a ground truth at all. Six studies examined how epistemic language in particular influences adults’ inferences of objectivity and truth. When headlines about novel topics (Studies 1a-b) or climate change (Studies 3a-b) presented information as belief (e.g., “Tortoise breeders believe tortoises are becoming more popular pets”), adults rated that information as less objective and less likely to be true compared to information presented as knowledge (e.g., “Tortoise breeders know [...]”). Epistemic language even influenced participants’ objectivity judgments when it had no influence on their truth judgments (Studies 2a-b). Overall, these results show the way epistemic language frames information affects what we perceive as true and, more so, whether we believe an objective truth exists in the first place.

Without even Trying: How Incidental Exposure Shapes Category Learning

Our knowledge about the world is populated with categories like dog, chair, and cup. Yet much of what we understand about how we acquire this knowledge comes from studies of learning in circumstances that little resemble real-world experience. In the lab, category learning typically involves pursuing an explicit goal to learn categories that prompts a search for just one or a few features diagnostic of category membership. In contrast, everyday experience is full of incidental encounters that allow us to observe how features cluster together in categories, such observing the co-occurrence of four legs, tail, and snout in dogs we happen to pass on the street. Here, we investigated how incidental exposure shapes category learning using a combined behavioral, eye tracking, and computational modeling approach. We found that learners picked up on the way features clustered together in categories just from incidental exposure, with pronounced downstream consequences for category learning.

Understanding "Compositionality" in Research on Language Emergence

The goal of this paper is to analyze the notion of “compositionality” and its use in contemporary cognitive science. We argue that the concept has undergone a series of apparently minor definitional shifts since its initial inception within the field of philosophy of language (as indicated by Janssen, 2012). These changes result in a divergent meaning of the term as it is used in the emergent communication and language evolution communities. Hitherto, this fact has been underappreciated, whereas we believe that it has significant implications for understanding the nature of syntax and the sources of linguistic and conceptual structure. We argue that originally, “compositionality” was understood as pertaining primarily to the process of understanding a compound utterance by a hearer. Other scholars, however, take it to be a prerequisite of the structure of languages. In all contexts, investigating compositionality of natural languages requires making a host of idealizing assumptions. For this reason, we propose to understand compositionality as just one idealized principle influencing the construction of compound expressions in language, necessarily complemented by other principles. This allows for appreciating the structural entanglements permeating natural language and opens new avenues for accounting for them.

Modelling Pattern Reproduction in The ACT-R Cognitive Architecture

We present two models of visual location memory developed within the ACT-R cognitive architecture and compare the model’s performance to that of human participants in a pattern reproduction task. The snapshot model has a fovea-peripheral based activation mechanism, which simulates how more attention and processing resources are giv-en to the centre of the visual field for short stimulus expo-sure trials (50ms and 200ms). For long exposure trials (>=1s), a chunking model was developed based on the snap-shot model by adding chunking processes which can encode geometric patterns. Both models can match the task response accuracy and pause data of human participants. The results of the modelling reveal that for the short stimulus exposure trials the accuracy of recall is affected by the distance between the object location and the fovea vision location. For trials with long stimulus exposure times, participants were likely to use salient geometric patterns to encode the configuration of discs

Psychophysical-Score: A Behavioral Measure for Assessing the Biological Plausibility of Visual Recognition Models

For the last decade, convolutional neural networks (CNNs) have vastly superseded their predecessors in nearly all vision tasks in artificial intelligence, including object recognition. However, despite abundant advancements, they continue to pale in comparison to biological vision. This chasm has prompted the development of biologically-inspired models that have attempted to mimic the human visual system, primarily at a neural level, which is evaluated using standard dataset benchmarks. However, more work is needed to understand how these models perceive the visual world. This article proposes a state-of-the-art procedure that generates a new metric, Psychophysical-Score, which is grounded in visual psychophysics and is capable of reliably estimating perceptual responses across numerous models --- representing a large range in complexity and biological inspiration. We perform the procedure on twelve models that vary in degree of biological inspiration and complexity, we compare the results against the aggregated results of 2,390 Amazon Mechanical Turk workers who together provided $\sim2.7$ million perceptual responses. Each model's Psychophysical-Score is compared against the state-of-the-art neural activity-based metric, Brain-Score. Our study indicates that models with a high correlation to human perceptual behavior also have a high correlation with the corresponding neural activity.

Grounded physical language understanding with probabilistic programs and simulated worlds

Human language richly invokes our intuitive physical knowledge. We talk about physical objects, scenes, properties, and events; and we can ask questions and answer them with predictions and inferences about physical worlds described entirely in language. How does language construct meanings that connect to our general physical reasoning? In this paper, we propose PiLoT, a computational model that maps language into a probabilistic language of thought—meanings are constructed as probabilistic programs, which provide a formal basis for probabilistic and physical reasoning. Our model uses a large language model (LLM) to map from language to meanings and a probabilistic physics engine to support inferences over scenes described in language. We conduct a linguistic reasoning experiment based on prior psychophysics studies that requires reasoning about physical outcomes based on linguistic descriptions. We show that PiLoT well predicts human judgments across this experiment and outperforms baseline models which use the LLM to directly perform the same task.

Asymmetry in similarity and difference judgments results from asymmetry in the complexity of the relations same and different

Explicit similarity judgments tend to emphasize relational information more than do difference judgments. We propose and test the hypothesis that this asymmetry arises because human reasoners represent the relation different as the negation of the relation same, so that processing difference is more cognitively demanding than processing similarity. For both verbal comparisons between word pairs, and visual comparisons between sets of geometric shapes, we asked participants to select which of two options was either more similar to or more different from a standard. On unambiguous trials, one option was unambiguously more similar to the standard; on ambiguous trials, one option was more featurally similar to the standard, whereas the other was more relationally similar. Given the higher cognitive complexity of assessing relational similarity, we predicted that detecting relational difference would be particularly demanding. We found that participants (1) had more difficulty accurately detecting relational difference than they did relational similarity on unambiguous trials, and (2) tended to emphasize relational information more when judging similarity than when judging difference on ambiguous trials. The latter finding was captured by a computational model of comparison that weights relational information more heavily for similarity than for difference judgments. Our results provide convergent evidence for a representational asymmetry between the relations same and different.

Exploring the relationships between reading instruction and individual differences in a computational model of reading

Studies have shown that individual differences in word reading can be observed for both skilled and novice readers. Several factors that could cause individual differences including reading experience, reading capacity, and oral language have been investigated. However, little is known about the influence of reading instruction on individual differences in reading. Given that early reading, training is critical to help children become proficient readers, the influence of reading instruction on subsequent reading behaviours should also be well understood. Thus, in this study, we investigated the relationships between reading instruction and individual differences in reading using computational models of reading. The model was exposed to a sound-focused, meaning-focused or balanced training scheme. We quantified the model’s reliance on accessing semantics for reading, as an index of individual differences in semantic reliance (SR). The simulation results demonstrated that the degree of SR depended on reading instruction. Meaning-focused training resulted in higher SR, and that was followed by balanced training and then sound-focused. Moreover, SR was able to predict the model’s word reading performance and interacted with other psycholinguistic reading factors including frequency, consistency, and orthographic neighbourhood size.

Do children think others should avoid wasting resources?

People tend to avoid wasting resources, but little is known about when this emerges in development. Though young children are often wasteful with food and other items, previous work suggests that children consider waste in other judgments. Here, we examined if children anticipate that others should minimize waste. In two experiments (total N = 195), children chose which of two foods someone should eat (Experiment 1; 3-7-year-olds) or two papers someone should make a snowflake with (Experiment 2; 5-year-olds). One of the options would result in minimal waste (i.e., a small food/paper) while the other would result in greater waste (i.e., a large food/paper). Children did not anticipate that others would choose smaller foods, however, at around five years they predicted that others would choose smaller paper. These findings contribute to our knowledge of the development of waste aversion and may extend our understanding of waste aversion as a form of efficiency.

Analysing the Impact of Audio Quality on the Use of Naturalistic Long-Form Recordings for Infant-Directed Speech Research

Modelling of early language acquisition aims to understand how infants bootstrap their language skills. The modelling encompasses properties of the input data used for training the models, the cognitive hypotheses and their algorithmic implementations being tested, and the evaluation methodologies to compare models to human data. Recent developments have enabled the use of more naturalistic training data for computational models. This also motivates development of more naturalistic tests of model behaviour. A crucial step towards such an aim is to develop representative speech datasets consisting of speech heard by infants in their natural environments. However, a major drawback of such recordings is that they are typically noisy, and it is currently unclear how the sound quality could affect analyses and modelling experiments conducted on such data. In this paper, we explore this aspect for the case of infant-directed speech (IDS) and adult-directed speech (ADS) analysis. First, we manually and automatically annotated audio quality of utterances extracted from two corpora of child-centred long-form recordings (in English and French). We then compared acoustic features of IDS and ADS in an in-lab dataset and across different audio quality subsets of naturalistic data. Finally, we assessed how the audio quality and recording environment may change the conclusions of a modelling analysis using a recent self-supervised learning model. Our results show that the use of modest and high audio quality naturalistic speech data result in largely similar conclusions on IDS and ADS in terms of acoustic analyses and modelling experiments. We also found that an automatic sound quality assessment tool can be used to screen out useful parts of long-form recordings for a closer analysis with comparable results to that of manual quality annotation.

Modeling Substitution Errors in Spanish Morphology Learning

In early stages of language acquisition, children often make inflectional errors on regular verbs, e.g., Spanish-speaking children produce –a (present-tense 3rd person singular) when other inflections are expected. Most previous models of morphology learning have focused on later stages of learning relating to the production of irregular verbs. We propose a computational model of Spanish inflection learning to examine the earlier stages of learning and present a novel data set of gold-standard inflectional annotations for Spanish verbs. Our model replicates data from Spanish-learning children, capturing the acquisition order of different inflections and correctly predicting the substitution errors they make. Analyses show that the learning trajectory can be explained as a result of the gradual acquisition of inflection-meaning associations. Ours is the first computational model to provide an explanation for this acquisition trajectory in Spanish, and represents a theoretical advance more generally in explaining substitution errors in early morphology learning.

Teleology and generics

Generic statements, such as ``Bees are striped'' are thought to be a central vehicle by which essentialist beliefs are transmitted. But work on generics and essentialism almost never focuses on the type of properties mentioned in generic statements. We test the hypothesis that teleological properties, what something is for, affect categorization judgments more strongly than behavioral, biological, or social properties. In Experiment 1, participants categorized properties as being either behavioral, biological, social, or teleological. In Experiment 2, we used the top four properties from each group to describe a generic noun or a specific individual. Participants then categorized creatures that had one of their properties transformed. We found that changes to teleological properties had the strongest impact on categorization judgments. In Experiment 3, we also found that teleological properties mattered more in an induction task. We suggest that teleological properties play this privileged role in categorization because they are treated as essential properties.

Brain Encoding using Randomized Recurrent Networks

Seeking plausible models for brain computation has been a continuing effort in brain encoding and decoding. Most prior works have mapped the association between stimulus representation from language models and fMRI brain activity using ridge regression. However, these models are not biologically plausible from the perspective of representing neural dynamics of the brain underlying the fMRI recordings. In this work, our primary motivation is to challenge ridge regression models with simple neural architectures such as echo state network (ESNs) and long short-term memory (LSTMs) on the brain encoding task that requires full-sentence processing in the task of reading short sentences. We explore various pre-trained Transformer language models for computing sentence representations and predict the fMRI brain activity from simple neural architectures that include initial layers with random parameterization and that do not require explicit training. Experiment results show that (i) ESNs with online learning can accurately predict the fMRI brain activity comparable to ridge regression models, (ii) Both cell-state (internal memory representation related to long term memory) and out-gate (related to short term memory) of LSTM display an equal level performance during short sentences in random LSTMs, (iii) left hemisphere language area has higher predictive brain activity versus right hemisphere language area, (iv) ESNs with online learning yield superior performance over offline learning, indicating the biological plausibility of ESNs and the cognitive process of sentence reading, and (v) among all the variants of transformer models, Longformer features facilitate better accuracy when utilized with both ridge regression and ESN online learning models. The proposed framework that combines input featurization, dynamic memory and learning modules offers a flexible, biologically plausible architecture for investigating brain encoding in neuroscience.

Children’s Estimation of Peripheral Information Drives Improvements in Approximate Number Sense

Children rely on their approximate number system (ANS) to guess quantities from a young age. Studies have shown that older children displayed better ANS performance. However, previous research did not provide an explanation for this ANS improvement. We show that children’s development in ANS is primarily driven by improved attentional control and awareness of peripheral information. Children guess the number of dots on a computer screen while being eye-tracked in our experiment. The behavioral and eye-tracking results provide supporting evidence for our account. Our analysis shows that children estimate better under the longer display-time condition and more visual foveation, with the effect of visual foveation mediating that of time. It also shows that older children make fewer underestimations because they are better at directing their attention and gaze toward areas of interest, and they are also more aware of dots in their peripheral vision. Our finding suggests that the development of children’s ANS is significantly impacted by the development of children’s non-numerical cognitive abilities.

Does Evaluative Language Provide Reasons to Act? An Empirical Study of the Action-Guiding Potential of Evaluative Concepts

What is the difference between language that describes the world and language that evaluates it? It has been suggested that an essential, distinguishing feature of evaluative language is its potential to guide actions by providing us with reasons to act. Calling an action “cruel” not only evaluates it negatively, its cruelty also provides us with a reason to refrain from it. Descriptive language, in and by itself, is relatively inert in this respect. In this paper, we examine whether this undisputed assumption is empirically adequate. We present three preregistered studies that demonstrate that evaluative language provides reasons for action when an agent contemplates how she should act, and also in conversational contexts. However, we also demonstrate that the speaker can easily deny the intention to provide such reasons to act.

Can Adults Revise Their Core Beliefs about Agents?

A set of fundamental principles governs our reasoning about agents since infancy. Past research has shown that adults are surprised when they observe apparent violations of these principles, which might prime them to learn from the violations and update their beliefs. In the present experiments, we demonstrate that adults can revise their beliefs about these principles in a specific, virtual world when they observe multiple pieces of counterevidence, and generalize their revised beliefs to new agents in the same environment. We discuss these findings together with the findings of a similar study with preschoolers, and we suggest future directions for this line of research.

Machine learning-based measure of cognitive complexity explains variance in rank-ordered preference

Cognitive complexity can provide insight into how people make decisions, ranging from the most minor to the most impactful. Here, we present a novel approach to inferring the complexity of processes associated with preference and decision making. We measured the complexity of participant-generated descriptive features of consumer products and the relationship to preference rankings. In order to measure cognitive complexity over a sparse set of features, we developed a natural language processing approach that compared the descriptive words generated by participants to those generated by a machine learning model; words that were more distinct from those generated by the model were rated more complex. We show preliminary evidence that cognitive complexity is related to preference for products, explaining unique variance in rankings and also capturing a new facet of the process through which preference is revealed through choice. We also show the value of participant-generated features for understanding choice processes.

Children Use Causality to Guide Question Asking

Gathering information via question asking is an essential and effective tool for learning. However, it also requires learners to select from a near infinite space of possible queries. Here, we investigate a potentially powerful guide for question asking in young learners: the relationship between cause and effect. Children (5- and 7-year-olds) read a storybook about an event with an unknown cause and made several choices between two questions to ask about possible candidate causes. Both questions revealed similar information, but only one had the potential to determine whether a candidate was capable of causing the event described. Participants overwhelmingly selected causally relevant over irrelevant questions, with strong performance in both age-groups and for all types of information. These results suggest that young learners employ their prior knowledge of the causal connections between events to identify relevant queries during information search.

Choosers Adapt Value Coding to the Environment, But Do Not Attain Efficiency

We investigate how human choosers adapt their value encoding strategy to the statistics of the choice environment. Specifically, we ask whether the human value encoding mechanism exhibits divisive normalization only in the Pareto-distributed environments in which it is information-maximizing. To test this theory, we conduct a risky choice experiment in which subjects are presented with blocks of choice stimuli drawn from either a Pareto-distributed environment or a uniform-distributed environment. Our results show that subjects exhibit some degree of normalization regardless of whether it is efficient or not, but do adapt the curvature of their encoding function to the environment. These findings suggest that human value coding mechanisms are flexible but biologically constrained to be perfectly efficient only in specific environments. This study provides new insights into the neural mechanism of human decision-making and the role of environmental statistics in shaping it.

Naive human judges can accurately predict expertise in children's block building. Can embedded motion sensors do just as well?

Motion quality can differentiate experts from novices in fields like surgery (Ershad et al., 2018). We extend approaches used by researchers in that field to examine the relationship between motion and skill in a children’s block-building task. We ask whether the relationship between these two variables is detected equally well by humans and machines—in this case, motion sensors embedded in the blocks. We investigate whether adults’ judgments about motion quality and children’s overall building skill reflect children’s actual construction ability, and whether data from embedded motion sensors predict children’s skill as well as adult judgments do. We find that human raters outperform the motion sensor data. Our findings raise questions about how people form such intuitive judgments of expertise, and how automated judgments of skill can be enhanced to more accurately predict expertise in block building and other similar cognitive tasks.

Emergent Communication with Attention

To develop computational agents that better communicate using their own emergent language, we endow the agents with an ability to focus their attention on particular concepts in the environment. Humans often understand an object or scene as a composite of concepts and those concepts are further mapped onto words. We implement this intuition as cross-modal attention mechanisms in Speaker and Listener agents in a referential game and show attention leads to more compositional and interpretable emergent language. We also demonstrate how attention aids in understanding the learned communication protocol by investigating the attention weights associated with each message symbol and the alignment of attention weights between Speaker and Listener agents. Overall, our results suggest that attention is a promising mechanism for developing more human-like emergent language.

Extremizing Judgements Produces More Inaccurate Individuals but Wiser Crowds

The crowd wisdom effect is a well-established phenomenon that is widely employed for predicting and estimating variables across various domains. Previous research has focused on enhancing the wisdom of crowds by improving individual estimates while maintaining some of the initial opinion diversity. However, it is theoretically possible to increase collective accuracy by largely increasing the diversity in a crowd. In this study, we propose a method that leverages the anchoring effect to extremize individual judgments and thus increase the diversity of opinions in a crowd. This is achieved by dividing the crowd into two groups, anchoring each group to either a low or high value, and aggregating all estimates. We use a mathematical model of anchoring to determine when this strategy is expected to outperform the crowd wisdom effect. Results from three experiments provide converging evidence that the proposed approach outperforms traditional methods in estimating and forecasting unknown quantities.

Show and tell: Learning causal structures from observations and explanations

There are at least three ways of learning how the world works: learning from observations, from interventions, and from explanations. Prior work on causal inference focused on how people learn causal structures through observation and intervention. Our study is the first to look at how explanations support causal structure learning. We develop a normative inference model that learns from observations and explanations, and compare the model's predictions to participants' judgments. The task is to infer the causal connections in 3-node graphs, based on information about their co-activation, and explanations of the kind "B activated because A activated". We find that participants learn better from explanations than from observations. However, while the normative model benefits from having observations in addition to explanations, participants did not.

A Weak Bias Against Strong Synonymy?

Is there a cognitive bias against absolute synonymy? The current work explored this question via a miniature mini-artificial language experiment featuring iterated learning, which can amplify weak cognitive biases, as languages are sifted through multiple adult learners. Participants were taught two novel synonymous verbs in positive or negative sentences. Afterwards, they had to generalize the words to new positive or negative sentences. These sentences then served as the input sentences for the next participant in the diffusion chain and so on. Despite inconsistent input with regard to positive or negative meaning, participants differentiated the verbs. More than half of participants strongly differentiated them, specializing at least one term. However, transmission did not increase differentiation overall, suggesting that a bias against synonymy may encourage a minimally distinctive difference, not necessarily a systematic one, between synonyms.

Not all complex disjunctions are alike: On inclusive and conjunctive interpretations in child Romanian

We investigate the interpretation of disjunction in child and adult Romanian via a replication of Tieu et al. (2017). Specifically, we target the simple disjunction 'sau' ‘or’ (with two intonation patterns: neutral and marked), and the complex disjunction markers 'sau…sau' and 'fie…fie' ‘either…or’. In a predictive Truth Value Judgment Task, participants evaluated a puppet’s disjunctive guesses ('The hen pushed the bus or the plane') after seeing the outcome. Adults assigned predominantly exclusive interpretations to both simple and complex disjunctions ('The hen pushed only one'). Children, however, generally interpreted 'sau' (with both intonational patterns) and 'sau…sau' inclusively (The hen pushed one and possibly both), while they interpreted 'fie…fie' conjunctively ('The hen pushed both'). It would appear that at an initial developmental stage, morphological/prosodic markedness does not affect children’s interpretation of disjunction. We discuss several possible accounts for the observed variation among complex disjunctions in child Romanian.

Communicative efficiency is present in young children and becomes more adult-like with age

Languages seem to be designed for efficient communication. For example, shorter forms are used for more predictable meanings, a tendency argued to stem from speakers’ efficient language use. However, no study to date has systematically tested whether communicative efficiency shapes children’s language use. Investigating whether such a pressure is already present in children will shed light on the development of children’s’ communicative behaviour and the respective roles of adults and children in shaping language structure. Here, we investigate the development of communicative efficiency using a novel experimental paradigm with children ages 4-10. Results show that communicative efficiency is attested already in young children and becomes more adult-like with age: as children grow, they are more likely to shorten messages (minimize effort) when a short message is sufficient for accurate communication. We discuss the implications of our results for cognitive development and for theories of language evolution and change.

Interpretability in Sign Language Animal Signs

The meanings of iconic signs are usually not easily accessible to sign-naïve people. However, most previous studies asked participants to guess the meaning of iconic signs in isolation and without any context or cues. We ask whether signs whose form is based on more cross-linguistically common underlying motivations are easier to interpret than signs based on less common underlying motivations. Since recent research suggests that iconicity is a relationship of resemblance between the signifier and the signified that is instantiated contextually, we also provide participants with a prompt (in the form of a word). We find that interpretability of iconic signs does correlate with cross-linguistic frequency of the underlying motivation of the sign.

Risk Aversion and Demographic Factors Affect Preference Elicitation and Outcomes of a Salary Negotiation

Women and minorities obtain lower salaries when negotiating their employment compensation. Some have suggested that automated negotiation and dispute-resolution technology might address such material inequities. These algorithms elicit the multi-criteria preferences of each side of a dispute and arrive at solutions that are efficient and "provably" fair. In a study that explores the potential benefit of these methods, we highlight cognitive factors that may allow inequities to persist despite these methods. Specifically, risk-averse individuals express lower preferences for salary and as risk-aversion is more common in women and minorities, this translates into a ``provably'' fair lower salary. While this may reflect actual underlying differences in preferences across groups, individuals may be confounding their preferences for salary with their risk preference (i.e., their fear of not reaching an agreement), such that these groups achieve worse outcomes than they should. We further highlight that methodological choices in how negotiation processes are often studied can obscure the magnitude of this effect.

Dynamic Information Sampling via Rapid Sequential Storage and Recurrence

When making category decisions, humans sample features following their dynamic informativeness. Attention optimization models successfully predict these categorization behaviors, but optimization is not the only solution. Alternatively, categorization can be viewed as a Reinforcement Learning (RL) task in which learners sample information based on its expected utility. However, RL models of information sampling have high computational load, even though human learners solve this problem on the millisecond timescale. Therefore, we propose ATHENA-RSS, a model that implements reward-based information search in a more computationally efficient way, via the rapid sequential storage of memories and recurrent retrieval. To test the model, we conducted an experiment where participants (N = 99) learned hierarchically structured categories by uncovering stimulus features. We then conducted a simulation study, where ATHENA-RSS successfully reproduced all search patterns exhibited by participants. We conclude that rapid sequential storage and recurrent memory retrieval were sufficient to achieve human-like information sampling in this task.

Moral Judgments in COVID-19 Triage Dilemmas: Does the Type of Life-Saving Resource Matter?

The present study explores moral judgments in COVID-19 dilemmas involving allocation of two types of resources – ventilators or beds. Utilitarian principles are opposed to random allocation and first-come, first-served. In triage dilemmas there are two patients in a critical state either needing a ventilator or a bed but only one is available. The results show different patterns of moral judgments depending on the type of the resource. If ventilators are allocated, the utilitarian principles are supported. But if the limited resource is a bed, first-come, first-served is preferred thus casting doubt on the egalitarian nature of this principle. Participants also rated their agreement with several triage principles. Four clusters of participants are identified. The first has a uniform distribution of preferences over all principles; the second favors all utilitarian principles; the third - only one utilitarian principle (higher chances of recovery); and the fourth - the first-come, first-served principle.

A Computational Account Of Self-Supervised Visual Learning From Egocentric Object Play

Research in child development has shown that embodied experience handling physical objects contributes to many cognitive abilities, including visual learning. One characteristic of such experience is that the learner sees the same object from several different viewpoints. In this paper, we study how learning signals that equate different viewpoints---e.g., assigning similar representations to different views of a single object---can support robust visual learning. We use the Toybox dataset, which contains egocentric videos of humans manipulating different objects, and conduct experiments using a computer vision framework for self-supervised contrastive learning. We find that representations learned by equating different physical viewpoints of an object benefit downstream image classification accuracy. Further experiments show that this performance improvement is robust to variations in the gaps between viewpoints, and that the benefits transfer to several different image classification tasks.

Promoting Climate Actions: Applying a cognitive constraints approach

The present paper reports an experiment (N = 348) with a two-year-delayed (M = 695 days) follow-up that tests an approach to raising willingness to take climate actions. Here we focus on the longitudinal results. Our experimental materials were designed to harness the power of two cognitive constraints — coherence and causal invariance, which map onto two narrative proclivities that anthropologists have identified as universal — to promote climate action across the political spectrum. Towards that goal, the essential role of these constraints in belief formation predicts that climate-change information would be more persuasive when it is embedded in a personal climate-action narrative, the evocation of which can benefit from exposure to parsimonious scientific explanations of indisputable everyday observations, juxtaposed with reasoners’ own, typically less coherent explanations, occurring in a context that engages their moral stance. Our brief one-time intervention, conducted in U.S. states with the highest level of climate skepticism, showed that across the political spectrum, our materials raised willingness to take climate actions in the immediate assessment. It also raised the likelihood of reports two years later of having taken or would have taken those actions had the opportunity existed, suggesting long-lasting effects. Our approach adopts the framework that conceptions of reality are representations, and adaptive solutions in that infinite space of representations require cognitive constraints to narrow the search.

I Remember Me the best, always? Evidence for Self-Prioritization in Working Memory Binding using a Visuo-Spatial Working Memory Task.

Research has demonstrated an advantage for the processing of self-associated stimuli for various mental functions (Sui & Humphreys, 2017). However, relatively little is known about whether prioritization exists for internal representations (Yin et al., 2019). In the current study, we first asked participants to associate social – labels ('self', ‘friend’, ‘stranger’) with arbitrary geometrical shapes (triangle, quadrilateral, and pentagon) (Sui et al., 2012) and then tested them for the maintenance of one or more features (shape, location, or a combination) of the target stimuli during a delayed match – to – sample task. In line with our expectations, our participants indeed showed a distinct advantage for self-associated stimuli for maintaining single features (identity, location) and a combination (shape & location). Our findings align with the proposal that self-reference may aid in binding information in working memory (Sui & Humphreys, 2015).

Developmental Curiosity and Social Interaction in Virtual Agents

Infants explore their complex physical and social environment in an organized way. To gain insight into what intrinsic motivations may help structure this exploration, we create a virtual infant agent and place it in a developmentally-inspired 3D environment with no external rewards. The environment has a virtual caregiver agent with the capability to interact contingently with the infant agent in ways that resemble play. We test intrinsic reward functions that are similar to motivations that have been proposed to drive exploration in humans: surprise, uncertainty, novelty, and learning progress. These generic reward functions lead the infant agent to explore its environment and discover the contingencies that are embedded into the caregiver agent. The reward functions that are proxies for novelty and uncertainty are the most successful in generating diverse experiences and activating the environment contingencies. We also find that learning a world model in the presence of an attentive caregiver helps the infant agent learn how to predict scenarios with challenging social and physical dynamics. Taken together, our findings provide insight into how curiosity-like intrinsic rewards and contingent social interaction lead to dynamic social behavior and the creation of a robust predictive world model.

Unpacking how Context Reinstatement aids Memory using Virtual Reality

How does our environment impact what we will later remember? Early work in real-world environments suggested that a matching encoding / retrieval context improves memory. However, some laboratory-based studies have not replicated this advantageous context-dependent memory effect. Using virtual reality methods, we find support for context-dependent memory effects: participants remembered more when placed in the same context as during encoding. This advantage has a tradeoff of falsely ‘recognizing’ similar lures, however. In addition, we find that schema-consistency in terms of the object-environment relationship is beneficial for memory recall, but schema-inconsistency helps recognition. Lastly, we find that differences in the presence (or absence) of dynamic background components differentially elicit the benefits of context-dependent memory. These findings not only add to our understanding of when and how context affects our memory, but they also present an exciting and more naturalistic approach to studying such effects.

A common oblique bias in perception and action

A variety of phenomena related to the oblique regions of space have been observed across modality and across domain. For instance, the classic ‘oblique effect’ describes a deficit in visual acuity for oriented lines in the oblique regions of space, and classic ‘prototype effects’ describe a bias to mis-localize objects towards the oblique regions of space. While there has been speculation that some ‘oblique-related effects’ share a common mechanism, many of these effects are explained in very different terms. The visual oblique effect itself is often understood as arising from coding asymmetries in orientation-selective neurons in the brain, whereas motor oblique effects have been described as arising from gravitational cues and/or physical limitations of the arm. Are these really distinct effects? Here, we show that individuals show stable oblique biases across these two modalities, suggesting that these effects may have a common cause.

Quantifying the Utility of Complexity and Feedback Loops in Causal Models for Decision Making

Many methods exist to learn causal models from data, as causal relationships form the basis for successful actions. These methods are frequently evaluated based on the completeness of the models they can infer. Yet, there is a gap between the highly complete and potentially complex models algorithms can learn and the types of information people can use successfully to make decisions. To address this we conduct two experiments to understand how the size and features of causal models influence how well they can be used for decision-making. In Experiment 1 we systematically vary model size for a series of topics, finding that there is a negative and linear relationship between causal model size and decision accuracy. In Experiment 2 we examine how model structure influences decisions, varying whether the models include feedback loops, again finding that smaller models lead to better choices, and that feedback loops are also beneficial.

The Self-Other Distinction in Perceptions of Social Influence: Evidence of Cultural Generalizability and Childhood Emergence

Prior research documents that adults in Western cultures perceive others as more susceptible to social influence than themselves (Pronin et al., 2007). Study 1 (N = 318) investigated the cultural generalizability of this asymmetric perception effect by examining young adults in South Korea where conformity is relatively valued, and a comparison sample of young adults in the United States. The results documented that the self-other distinction was just as strong in South Korea as it was in the United States. Study 2 (N = 102) examined the development of this tendency among 6- to 12-year-old South Korean children and showed that this asymmetry increasingly emerges with age. These findings suggest that asymmetric perceptions of conformity are robust and emerge over the course of development.

Context-sensitive features predict sentence memorability in the absence of memorable words

What makes some sentences more memorable than others? In this work, we treat the problem of recognizing previously seen sentences as a comparison between a target stimulus and noisy memory representations of previously presented stimuli. Building on past work in image and word memorability, we conduct a large-scale memorability experiment with 500 participants and 2,500 target sentences, eliciting variation in how accurately participants recognize repeated sentences. We predict the memorability of sentences from a) empirically established word-level memorability scores, and b) sentence-level distinctiveness and surprisal features that capture the compositional semantics of sentences. We find that the presence of individually memorable words is highly predictive of sentence memorability, but that sentence-level features also predict sentence memorability – especially in the absence of memorable words. This suggests that otherwise forgettable words can together create memorable compositional meanings that remain in memory and facilitate recognition.

Time-pressure Does Not Alter the Bias Towards Canonical Interpretation of Quantifiers

The Interface Transparency Thesis (ITT) proposes that people tend to use a canonical interpretation of linguistic expressions, even when this interpretation is sub-optimal for the task at hand. The current paper sought to investigate this claim further by adding a time-pressure manipulation to a quantified sentence verification task and analyzing the results through a computational model of decision-making. The results indicate that time pressure -while effectively changing behavioral responses- does not alter cognitive processes associated with quantifier verification, thus supporting the ITT.

Effects of bilingualism on inhibition unlikely- Evidence from a Bayesian Inquiry

Recent literature indicates that the effects of bilingualism on executive control functions need to be examined with a more comprehensive characterization of bilingualism, and with the use of multiple measures of executive control (Backer and Bortfeld, 2021,Paap and Greenberg, 2013). In the current study, we operationalize bilingualism using a set of continuous variables related to language knowledge and use. We exam- ine the effects of language proficiency, immersion, language dominance, diversity of language use and language switching on individuals’ performance on tasks measuring inhibition. 66 Hindi-English bilinguals responded to the LHQ3, BSWQ and completed four inhibition tasks online. Inhibition tasks varied on the type of conflict (stimulus-stimulus/ stimulus-response) and the type of stimuli (arrows/ words). Bilingualism related variables failed to predict performance on any of the four tasks when included in linear regression models. We also conducted Bayesian regression analyses to validate the evidence for the lack of an effect. For three out of four tasks, we find BF10 (Bayes Factor indicating evidence for the alternate over null) of less than 1. Our data were most likely for the case where the null is true. Posterior odds for the null increased by fac- tors of 13.1, 10.9, 4.3 and 11.8 for the four tasks, respec- tively. However, for the nonverbal Vertical Stroop Task, the best model contained only multilingual diversity scores as a predictor. We fail to find adequate evidence for the effects of bilingualism on inhibitory performance. We find that the effects of bilingualism do not appear to be task-specific or de- pendent on the type of conflict involved in a task, as previously suggested.(Blumenfeld and Marian, 2014) and conclude that it is unlikely that behavioral effects of bilingualism on inhibition exist.

How do instructions, examples, and testing shape task representations?

People need to generate and test hypotheses in order to create accurate representations of their environments. But how do they know which hypotheses to consider when there are often infinitely many possibilities? Here we explore the idea that evolutionary mental representation generation and selection processes – responsible for the generation of both local (i.e., within a task) and global (i.e., about a task) representations – enable people to address this problem. We investigated this through an active learning experiment, where participants’ task was to discover a hidden rule determining the behavior of a simple physical system. Specifically, we aimed to manipulate factors that constrain this process, particularly through experimental instructions and feedback. We found that providing more opportunities for participants to recognize when their initial task conceptualization was wrong and adjust it helped them create more accurate representations about the task, which in turn led to better accuracy within the task.

Assessing Distributions of Causal Beliefs in the Illusory Causation Task

The illusory causation effect describes the tendency to judge an unrelated cue and outcome to be causally related. The standard procedure for assessing the illusion is based on the implicit assumptions that participants start as naïve observers with no prior beliefs about the likely relationship between the cue and outcome, and that learning can be adequately captured as a point-estimate causal rating after null contingency training. Here, we use a novel distributional measure to assess participants’ beliefs over a range of causal relationships prior to, as well as after, exposure to non-contingent cues and outcomes. Across two experiments with different causal scenarios and 50% cue and outcome density, we show that participants have an initial bias towards expecting a causal relationship between the cue and outcome, and that this bias is mostly corrected after exposure to the null contingency. We conclude that distributional measures of causal beliefs can offer novel insights in understanding the illusory causation effect.

Quantum Sequential Sampler: a dynamical model for human probability reasoning and judgments

Probability judgments appear to violate basic axioms of probability theory, which seems to contradict with the recent successes of Bayesian models of cognition. To explain these violations, we propose the Quantum Sequential Sampler model, which combines quantum probability for explaining conjunction and disjunction fallacies, and a sequential sampling model that maps subjective quantum probabilities into responses. Our model explains probability judgments by a dynamical process, and achieves state-of-the-art performance in the biggest dataset for probability judgments to-date. Comparing with existing Bayesian models, our model predicts both probability judgments and violations of probability identities better.

Connecting Adaptive Perceptual Learning and Signal Detection Theory in Skin Cancer Screening

Combining perceptual learning techniques with adaptive learning algorithms has been shown to accelerate the development of expertise in medical and STEM learning domains (Kellman & Massey, 2013; Kellman, Jacoby, Massey & Krasne, 2022). Virtually all adaptive learning systems have relied on simple accuracy data that does not take into account response bias, a problem that may be especially consequential in multi-category perceptual classifications. We investigated whether adaptive perceptual learning in skin cancer screening can be enhanced by incorporating signal detection theory (SDT) methods that separate sensitivity from criterion. SDT-style concepts were used to alter sequencing, and separately to define mastery (category retirement). SDT retirement used a running d’ estimate calculated from a recent window of trials based on hit and false alarm rates. Undergraduate participants used a Skin Cancer PALM (perceptual adaptive learning module) to learn classification of 10 cancerous and readily-confused non-cancerous skin lesion types. Four adaptive conditions varied either the type of adaptive sequencing (standard vs. SDT) or retirement criteria (standard vs. SDT). A non-adaptive control condition presented didactic instruction on dermatologic screening in video form, including images, classification schemes, and detailed explanations. All adaptive conditions robustly outperformed the non-adaptive control in both learning efficiency and fluency (large effect sizes). Between adaptive conditions, SDT retirement criteria produced greater learning efficiency than standard, accuracy-based mastery criteria at both immediate and delayed posttests (medium effect sizes). SDT sequencing and standard adaptive sequencing did not differ. SDT enhancements to adaptive perceptual learning procedures have potential to enhance learning efficiency.

Predicting judgments of food healthiness with deep latent-construct cultural consensus theory

Deep neural network representations of entities can serve as inputs to computational models of human mental representations to predict people's behavioral and physiological responses to those entities. Though increasingly successful in their predictive capabilities, the implicit notion of "human" that they rely upon often glosses over individual-level differences in beliefs, attitudes, and associations, as well as group-level cultural constructs. In this paper, we model shared representations of food healthiness by aligning learned word representations with the consensus among a group of respondents. To do so, we extend Cultural Consensus Theory to include latent constructs structured as fine-tuned word representations. We then apply the model to a dataset of people's judgments of food healthiness. We show that our method creates a robust mapping between learned word representations and culturally constructed representations that guide consumer behavior.

Interlimb Transfer of Proprioceptive Recalibration and Effect of Body Posture

Through training using distorted vision, the perception of the trained limb position is shifted based on visual information. We investigated whether and how such proprioceptive recalibration transfers to untrained limbs. The results of experiments using human-like virtual limbs confirmed that transfer to some limbs occurred. The manner in which the transfer occurred varied according to the participants' body posture and the type of trained limb. When the participants sat, the recalibration transferred from one arm to another arm symmetrically around the body midline. Conversely, in the case of a sitting leg and standing arm, it was directly copied to another leg or arm.

Can higher social status of competitors cause decision makers to commit more errors?

The ability to make good decisions is critical in life. Although anecdotal and preliminary evidence suggests that social comparison could impair decision making, surprisingly little attention has been paid to such dynamics within cognitive science. The present study aimed to address this gap by exploring, via a sample of 1.5 million chess games and a fuzzy regression discontinuity design, whether higher status of competitors could cause individuals to commit more errors. Critically, chess data includes overt symbols of social status, viz. titles conferred at arbitrary thresholds of ratings that represent playing strength, and an objective measure of errors could be calculated by contrasting the moves that players chose in games against the optimal moves determined by powerful chess engines. I found no evidence that the mere presence of status titles impacted error rates.

A bounded rationality account of dependency length minimization in Hindi

The principle of DEPENDENCY LENGTH MINIMIZATION, which seeks to keep syntactically related words close in a sentence, is thought to universally shape the structure of human languages for effective communication. However, the extent to which dependency length minimization is applied in human language systems is not yet fully understood. Preverbally, the placement of long-before-short constituents and postverbally, short-before-long constituents are known to minimize overall dependency length of a sentence. In this study, we test the hypothesis that placing only the shortest preverbal constituent next to the main-verb explains word order preferences in Hindi (a SOV language) as opposed to the global minimization of dependency length. We characterize this approach as a least-effort strategy because it is a cost-effective way to shorten all dependencies between the verb and its preverbal dependencies. As such, this approach is consistent with the bounded-rationality perspective according to which decision making is governed by "fast but frugal" heuristics rather than by a search for optimal solutions. Consistent with this idea, our results indicate that actual corpus sentences in the Hindi-Urdu Treebank corpus are better explained by the least effort strategy than by global minimization of dependency lengths. Additionally, for the task of distinguishing corpus sentences from counterfactual variants, we find that the dependency length and constituent length of the constituent closest to the main verb are much better predictors of whether a sentence appeared in the corpus than total dependency length. Overall, our findings suggest that cognitive resource constraints play a crucial role in shaping natural languages.

Improving Ontology Translation from Disentangled Semantic and Language Representations

Ontology is the basis of knowledge representation, and it is necessary to translate ontologies that are normally expressed in English into other languages in order to achieve exchange across languages. Building a domain-specific translation system is essential due to the extremely focused words used and the inadequacy of contextual information. In this paper, we introduce disentangled representations under cross-lingual agreement to alleviate the aforementioned issues. We introduce semantic and language representations and integrate extra losses to induce disentangled representations that capture different information. To reduce the gap between the ontology label and the hypothesis generated by the translation model, we further integrate adversarial learning. In order to guide the generation of translation candidates, the semantic matching strategy is incorporated into the decoding phase. Experiments on the four English-to-German ontologies of different domains show that the proposed method achieves improvements over the baselines.

Object Focus and Preschoolers’ Relational Reasoning

Preschoolers have difficulties in relational reasoning tasks, which are usually attributed to their object focus. Object focus can be reduced by using basic shapes familiar to children and could differ across children. Therefore, in Experiment 1, we investigated 25 4-year-olds' performance in the relational match-to-sample task (RMTS), using basic shapes with training, and testing sameness and difference. In Experiment 2, 41 4- to 5-year-olds were tested in the RMTS, using basic shapes, manipulating familiarity, without training, and testing only sameness. We also investigated whether children's use of object property words (e.g., color, shape) was directly associated with their performance. Our results showed that children performed above the chance level when tested on sameness but not on difference (Experiment 1). Furthermore, basic shapes but not familiar shapes enhanced preschoolers' performance in the RMTS (Experiment 2). Children's use of object property words dampened their performance. These findings underline the importance of task- and child-related factors when investigating children's relational reasoning in particular and cognitive performance in general.

Changing Children’s Intergroup Biases Through Statistical Counterevidence

Biases about social groups emerge from a young age. This study examines whether statistically representative counterevidence – a randomly drawn sample from the social group – can change children’s attitudes and beliefs about minimally defined social groups. We found that 5- to 6-year-olds learned from the sample to change their attitudes and beliefs about minimal groups. However, they showed a negativity bias and an ingroup bias when they learned from the evidence. It was unclear whether 9- to 10-year-olds’ attitudes and beliefs can also be changed by this type of evidence. Future directions and implications of this study are discussed.

What inferences do people actually make upon encountering informationally redundant utterances? An individual differences study

Utterances mentioning a highly predictable event are known to elicit atypicality inferences (Kravtchenko and Demberg, 2015; 2022). In those studies, pragmatic inferences are measured based on typicality ratings. It is assumed that comprehenders notice the redundancy and "repair'' the utterance informativity by inferring that the mentioned event is atypical for the referent, resulting in a lower typicality rating. However, the actual inferences that people make have never been elicited. We extend the original experimental design by asking participants to explain their ratings and administering several individual differences tests. This allows us to test (1) whether low ratings indeed correspond to the assumed inferences (they mostly do, but occasionally participants seem to make the inference but then reject it and give high ratings), and (2) whether the tendency to make atypicality inferences is modulated by cognitive factors. We find that people with higher reasoning abilities are more likely to draw inferences.

Let's talk structure: the positive outcomes of structural thinking

Group disparities, such as the gender wage or racial achievement gap in the US, pervade societies. Unfortunately, children and adults often attribute these disparities to inherent (e.g. biological) features of groups, which leads to problematic outcomes (e.g., overgeneralizing, endorsing disparities). In contrast to inherent thinking, structural thinking about social disparities, which attributes disparities to a stable external structure, could lead to more positive social outcomes. Here, we induced biological, cultural, or structural thinking about an occupational disparity, and found that the latter caused adults (n = 90) to show more context-sensitive generalizations, judge the disparity as less acceptable, and provide more structural interventions. 7- to 9-year-olds (n = 70) showed similar but weaker results for more context-sensitive generalizations and judging the disparity as less acceptable. Cultural thinking showed an intermediate pattern between biological and structural thinking. Overall, structural thinking could be a fruitful way of mobilizing progress on social disparities.

A Unified Framework for Unseen Target Stance Detection based on Feature Enhancement via Graph Contrastive Learning

Stance detection for unseen targets is designed to automatically identify the user's stance or attitude towards various new targets that are constantly appearing with no labels. Inspired by work in cognitive science, we distinguish functions between systems for syntactic and semantic to enhance stance detection. First, we construct a dual-view graph and utilize unsupervised graph contrastive learning to capture target-invariant features influencing stance expression from a syntactic structure perspective. Second, we use an attention mechanism to learn the relationship between syntactic pattern features and a given target, and fuse the two parts to enhance the model's ability to predict unseen targets. Meanwhile, we employ the interactive GCN to maintain the global semantics of the dual-view graph fusion and ensure the stability and validity of the learned syntactic representations. Comprehensive experiments on stance detection of unseen targets verify the effectiveness and superiority of our proposed method.

Communicative reduction in referring expressions within a multi-player negotiation game

The ability to form novel conventions is a key signature of efficient linguistic communication. Reduction of referring expressions, one measure of convention formation, is found robustly in dyadic repeated reference games when the target images are initially difficult to name. In reference games, participants share the explicit goal of establishing joint reference. However, establishing reference is a key subgoal of many conversations where interlocuters have more complex goals. In the current work, we analyze a dataset where reference was embedded in strategic 3- player negotiation and coordination games. In these more complex games, we found that the patterns of reduction and convergence to conventions still held across two different incentive conditions, with some modest differences between the conditions.

Behavioral characteristics in general trust: an exploratory laboratory-based analysis using the ultimatum game

This exploratory study investigated how the combination of top-down and bottom-up processing influences decision-making for high and low trusters using the ultimatum game against a computer agent. We designed an experiment wherein (1) participants expected their partners to be humans or agents (top-down processes) and (2) agents used one of four different types of algorithmic behavior (bottom-up processes) to propose and respond. We found that high trusters made fairer decisions in the human condition than in the agent condition in the proposal phase, when opponents’ behaviors were not ambiguous but intentional. In the response phase, the higher the level of trust, the more likely they were to avoid unfairness for an opponent that proposed a distribution amount approved by the participant. Results suggest that, in interpersonal communication, high trusters flexibly use both types of cognitive processing to economically process information when developing representations of others and deciding on a response.

The Role of Causal Stability in Children's Active Exploration

Previous research documented adults’ preference for stable causal relationships that do not vary in strength across backgrounds (Vasilyeva, Blanchard, & Lombrozo, 2018). In this study, we investigate the role of causal stability in guiding children’s exploration behavior. We developed a computerized version of an active information-search paradigm to study how children dynamically explore different agents and backgrounds to learn more about their causal stability. Five- to seven-year-old children (n = 60) were presented with stable and unstable causes (i.e., causes with fixed or variable causal efficacy across backgrounds). We assessed children’s causal attributions of outcomes and their exploratory behavior as they tried out previously observed and novel causes across previously observed and novel backgrounds. We find that children in this age range acknowledge causal instability in their causal attributions, and they become increasingly adept at tracking causal efficacy across multiple factors simultaneously (causes and backgrounds), but this does not translate into a blanket preference for exploring stable or unstable causes. We suggest a possibility that causal (in)stability guides exploration in more subtle and indirect ways and discuss the implications of our findings for the development of active exploration.

Visual cueing affects the processing of grammatical structure: A self-paced reading study on non-canonical word order in Bulgarian

Visual attention can influence how language is processed. For instance, previous research showed that visual cueing of a referent affects the choice of a particular grammatical structure. The present study extended this research to language comprehension and investigated whether visually cueing either the subject or the object of an event affects the interpretation of a textual event description in Bulgarian with either a subject-initial or object-initial word order. The presence of a visual cue matching the sentence-initial argument decreased self-paced reading reaction times and altered the accuracy in response to a comprehension question. Differences were found depending on the gender of the referent, highlighting the interaction of the visual cue and other linguistic information such as case marking. This study demonstrates that visually induced "context" can affect the linguistic salience of a referent in discourse and illustrates the interaction of domain-general cognitive mechanisms in language processing.

Working memory updating modulates adaptation to speaker-specific use of uncertainty expressions

Listeners rapidly learn speaker-specific expectations and interpretations of words and phrases such as uncertainty expressions when they observe a speaker's use of these expressions. However, previous studies have exclusively examined this behavior in populations of listeners and it remains unclear to what extent there are systematic individual differences in listeners' adaptation behavior and, if such differences exist, whether they are linked to more general cognitive abilities. In this work, we first re-analyze the data by Schuster & Degen (2019) and show that listeners vary in the extent to which they adapt to different speakers. In a series of exploratory and confirmatory studies, we then show that the extent to which listeners update their expectations of different speakers is correlated with participant's score on the Keep Track Task (Yntema, 1963), which suggests that working memory control modulates listeners' semantic-pragmatic adaptation abilities.

Skirting the Sacred: Moral Violations Make Intentional Misunderstandings Worse

People engage in intentional misunderstandings to get around direct non-compliance. In other words, they use loopholes. Previous work showed that adults and children consider loophole behavior to be less costly than direct non-compliance (Bridgers, Schulz, & Ullman, 2021), and suggested this is a primary reason for their use: loopholes will land you in less trouble than defiance. However, we propose that this difference between loopholes and defiance will not hold for a specific, important context: moral violations. We replicate the finding that loopholes are less costly in a neutral context but find that engaging in loopholes in a moral context is as bad as non-compliance (Experiment 1, N=360). We then use a direct comparison between loopholes and non-compliance (N=150) to investigate whether in some contexts loopholes will be seen as even worse than non-compliance. We replicate the differential effect of the moral context from Experiment 1, but do not find a reversal. We discuss possible extensions and limitations, and consider why loopholes in moral violations may be uniquely unacceptable.

Causal Reasoning Across Agents and Objects

This work attempts to bridge the divide between accounts of causal reasoning with respect to agents and objects. We begin by examining the influence of animacy. In a collision-based context, we vary the animacy status of an object using 3D animations. By holding the fine-grained kinematics of the actual and counterfactual outcomes fixed across animate and inanimate conditions, we find that animacy itself has no effect on causal attribution judgments. Next, we test if causal judgments for animate and inanimate objects differ as a function of the counterfactuals they respectively afford in a disjunctive causal structure. Here, we find that the effect of perceived animacy on causal attribution is mediated by differences in counterfactual judgments. Finally, we introduce the known effect of prescriptive norm violations to this paradigm. Our results collectively highlight how normative expectations specify the counterfactual considerations that guide causal reasoning about both agents and objects.

Using Computational Models to Understand the Role and Nature of Valuation Bias in Mixed Gambles

It is a well-known observation that people tend to dislike risky situations that could potentially lead to a loss, a phenomenon that is called loss aversion. This is often explained using valuation bias, i.e., the subjective value of losses is larger than the subjective value of gains of equal magnitude. However, recent studies using the drift-diffusion model have shown that a pre-valuation bias towards rejection is also a primary determinant of loss-averse behavior. It has large contributions to model fits, predicts a key relationship between rejection rates and response times, and explains the most individual heterogeneity in the rejection rates of participants. We analyzed data from three previously published experiments using the drift-diffusion model and found that these findings generalize to them. However, we found that valuation bias plays the most important role in predicting how likely a person is to accept a given gamble. Our findings also showed that a person's loss aversion parameter, $\lambda$, which captures their propensity to avoid losses is closely related to valuation bias. These results combined highlight the importance of valuation bias in understanding people's choice patterns. Finally, using the leaky, competing accumulator model, we show strong mimicking between valuation bias and an attentional bias wherein people pay more attention to losses as compared to gains. This finding suggests that behaviors that seem to arise due to valuation bias may arise due to such an attentional bias.

Partial Word Learning from Referentially Ambiguous Naming Events: Evidence from a Human Simulation Paradigm

Children learn word meanings from their patterns of usage in their everyday input. This trivial statement is made more interesting by the fact that most patterns of word usage, even around language-learning children, are not particularly good at revealing word meaning. So, do children simply ignore much of their input and learn words primarily from the few instances of usage where word meaning is transparent? Or are there pieces of information in the sea of opaque word usage that would allow children to learn words slowly over time? In an adaptation of Gleitman and colleagues’ classic Human Simulation Paradigm (Gillette et al., 1999), the current study explores the kinds of input that contribute to learning. Our data suggest that the answer may depend on how “learning” is assessed.

Analogical Reasoning During Hypothesis Generation: The Effects of Object and Domain Similarities on Access and Transfer

In two experiments on analogical hypothesis generation, we factorially manipulated the presence of domain and object similarities between a base situation and a target phenomenon, and assessed their effects on the transfer of the source’s explanatory structure before and after an indication to use the base analog as a source for analogical explanations. The absence of any kind of surface similarity led to very low rates of spontaneous transfer. In both experiments, however, either kind of surface similarity sufficed to enhance the spontaneous transfer of the base explanation during the formulation of plausible hypotheses for the target. The transfer advantage of object and domain similarity cannot be attributed to the effect of these variables on post-access processes, since experimental conditions did not differ with regard to the ability to transfer the base explanation upon explicit request. The effect of domain similarity on spontaneous analogical explanation constitutes a relevant finding, especially given the lack of attention received by this dimension of similarity in behavioral studies and computer simulations of analogical retrieval.

Individuation, Categorization, and the Other-Race Effect in Face Recognition

Social-cognitive models propose that the other-race effect in face recognition is caused by different motivational tendencies when processing own- and other-race faces. More specifically, we tend to individuate own-race faces which facilitates face recognition, but racially categorize other-race faces which inhibits face recognition. This study tests whether a novel experimental manipulation aimed at promoting individuation or categorization encoding of faces moderates the other-race effect in an old/new recognition task. We found that categorization encoding eliminated the other-race effect when list length was short (Experiment 2) but not when list length was long (Experiment 1B). Inconsistent with social-cognitive predictions, individuation encoding failed to reduce the other-race effect, regardless of list length. We compare these findings with previous attempts to promote individuation and categorization encoding and suggest that the recognition benefits of individuation encoding might be more limited for faces that are most difficult to individuate i.e., other-race faces.

Are First Passage Time Distributions Necessary for Drift-Diffusion Modeling?

Drift diffusion models are used to model evidence accumulation in two-choice forced-tasks. The traditional approach to fitting Ratcliff’s standard drift diffusion model (where the drift and diffusion are constant) usually involves explicit modeling of the first passage time distributions of the upper and lower boundaries or likelihood approximations. We present the very first technique, to the best of our knowledge, that foregoes use of explicit modeling of the first passage time distributions with a random forest regressor. A random forest regression model that takes the first five moments of the response time distribution, and the upper boundary termination proportion, is used to predict the drift and diffusion parameters from response time data. A training set of response time samples of size 2500 from 121 distinct drift-diffusion pairs is used to train the random forest regressor. On a testing set of 10,000 distinct drift-diffusion combinations with response time sample sizes of 40, we find that our model surpasses techniques that make use of some form of analytical modeling of the first passage time distributions of the boundaries for prediction of the diffusion rate, but not the drift rate. We conclude that the application of machine learning to drift-diffusion modeling of empirical data is a topic worth further investigation.

Defendant character influences mock juror judgments of blame, guilt, and punishment

The present study explored how evaluations of a defendant’s character can influence mock jurors’ judgments using a beliefupdating paradigm. Participants (N=143) were shown a trial transcript in which we manipulated the defendant’s character by introducing an irrelevant moral behavior observed before the crime as well as prior conviction. We found that bad defendants were consistently judged to be more deserving of punishment than good defendants. While character information influenced judgments of guilt, blame, and intentionality immediately after it was presented, the effect diminished as participants received more information about the case, and ultimately did not shift their verdicts. In general, participants also mitigated moral judgments for good defendants rather than exacerbate judgments for bad defendants. Thematic analysis of judgment rationales also revealed that participants reasoned about actions, norms, and mental states when evaluating blame and punishment. We discuss the implications of this study in moral and legal decision-making.

Selective imitation on the basis of reward function similarity

Imitation is a key component of human social behavior, and is widely used by both children and adults as a way to navigate uncertain or unfamiliar situations. But in an environment populated by multiple heterogeneous agents pursuing different goals or objectives, indiscriminate imitation is unlikely to be an effective strategy—the imitator must instead determine who is most useful to copy. There are likely many factors that play into these judgements, depending on context and availability of information. Here we investigate the hypothesis that these decisions involve inferences about other agents’ reward functions. We suggest that people preferentially imitate the behavior of others they deem to have similar reward functions to their own. We further argue that these inferences can be made on the basis of very sparse or indirect data, by leveraging an inductive bias toward positing the existence of different groups or types of people with similar reward functions, allowing learners to select imitation targets without direct evidence of alignment.

Identifying Body Parts in the Spatial Context of Pairwise Relations: Human Psychophysics and Model Simulations

The ability to detect and analyze a human figure is key to our survival and social interactions. Efficient and robust identification of body parts can help to interpret images when bodies are partially occluded. While previous studies emphasized the configural processing of whole bodies using simplified stimuli, it still remains unclear how spatial contexts about body parts are integrated to resolve ambiguities (e.g., from occlusion) regarding the identities of or spatial relations among body parts. In a series of online experiments, we asked human observers to identify an ambiguous target body part in the presence of another “context” part. Our results showed that humans can use various amounts of spatial context to discount local ambiguities in natural images of pairs of parts, and are sensitive to low- and mid-level cues such as alignment and connectedness. Further simulations using deep convolutional neural networks (DCNNs) exhibited comparable similar sensitivity to spatial context variations, despite being trained solely on local part appearances without explicit prior knowledge of body structure. However, discrepancies between human and model performance were also observed, with humans showing greater sensitivity to spatial relations compared to the models. Our findings suggest that while both humans and models utilize low- and mid-level features for body part recognition, humans possess a stronger prior knowledge of body structure that in- fluences their perception. These results contribute to our understanding of how humans integrate spatial context to resolve ambiguities and provide insights into the computational mechanisms underlying body perception.

Theory of AI Mind: How adults and children reason about the ``mental states'' of conversational AI

Conversational AI devices are increasingly present in our lives and even used by children to ask questions, play, and learn. These entities not only blur the line between objects and agents—they are speakers (objects) that respond to speech and engage in conversations (agents)—but also operate differently from humans. Here we use a variant of a classic false-belief task to explore adults' and children's attributions of mental states to conversational AI versus human agents. While adults understood that two conversational AI devices, unlike two human agents, may share the same "beliefs" (Exp.1), 3- to 8-year-old children treated two conversational AI devices just like human agents (Exp.2); by 5 years of age, they expected the two devices to maintain separate beliefs rather than share the same belief, with hints of developmental change. Our results suggest that children initially rely on their understanding of agents to make sense of conversational AI.

Context Dependent Memory in the Wilds

Memory retrieval is influenced by both prior and current experiences. The various factors (e.g., frequency, recency, or similarity) may interfere during retrieval due to prior experiences, while the context-dependent memory effect may enhance based on present experiences. Most memory research has been limited to controlled laboratory settings, but this study aims to examine memory retrieval in a more natural setting by using a GPS application (e.g., Traccar Client) to track participants’ daily GPS locations every 60 seconds for 5 weeks. Participants were then asked to recall their locations at a specific time, choosing from all locations visited in the previous 4 weeks. Results demonstrated the existence of the context-dependent memory effect in real-world settings, with more frequent or recent visits leading to increased correct responses. This study is the first to use the current methodology to study the context-dependent memory effect and to measure an individual’s genuine memories in a more ecologically valid way.

Selectively Providing Reliance Calibration Cues With Reliance Prediction

For effective collaboration between humans and intelligent agents that employ machine learning for decision-making, humans must understand what agents can and cannot do to avoid over/under-reliance. A solution to this problem is adjusting human reliance through communication using reliance calibration cues (RCCs) to help humans assess agents' capabilities. Previous studies typically attempted to calibrate reliance by continuously presenting RCCs, and when an agent should provide RCCs remains an open question. To answer this, we propose Pred-RC, a method for selectively providing RCCs. Pred-RC uses a cognitive reliance model to predict whether a human will assign a task to an agent. By comparing the prediction results for both cases with and without an RCC, Pred-RC evaluates the influence of the RCC on human reliance. We tested Pred-RC in a human-AI collaboration task and found that it can successfully calibrate human reliance with a reduced number of RCCs.

Language Shifts the Representation of Sounds in Time: From Auditory Individuals to Auditory Ensembles

Objects can either be represented as independent individuals (“object-files”) or as members of a collection (an “ensemble”). Work over the past 40 years has explored these representational systems, largely in the visual domain. Far less is known about auditory objects. Here, we show that a property characteristic of visual object representation – that it can be modulated by linguistic framing – also applies to auditory objects. In particular, we show that using the expression “each sound” versus “every sound” can bias auditory object construal in the same way that using “each circle” versus “every circle” can bias visual object construal. These findings support the idea that object-files and ensembles are not limited to the visual domain, but are representational formats found more generally throughout cognition.

Recognizing Emotional Cues in Word Content versus Facial Expression: A Cross-cultural Comparison

Cultural background may shape how people attend to different emotional cues. Emotions can be perceived from both visual and auditory channels. This cross-cultural study investigated individuals’ attention to emotional cues in facial expressions and spoken words. The final sample consisted of 99 Singaporean Chinese and 81 German adults (Mage = 24.03 years, SDage = 6.29 years). In this online study, participants completed two tasks in which they were presented with emotional facial expressions and spoken words simultaneously. They were asked to judge the pleasantness of word meanings (Word task) or facial expressions (Face task) while ignoring the other aspect. Singaporean participants’ accuracies were significantly influenced by the word content while judging the pleasantness of facial expressions in the Face task. However, for German participants, there was no significant interference effect in either the Face or Word task.

Thinking about Thinking as Rational Computation

Theory of Mind enables us to attribute mental states to others. But we not only make inferences about mental states (like what someone believes or wants), but about mental processes (like if someone is distracted or whether they remember something). Here, we present a computational formalization of these kinds of inferences. We propose that inferences about mental processes are structured around a principle of rational mental effort: the expectation that other people allocate mental resources rationally so as to minimize thinking costs incurred while pursuing their goals. We develop this theory into a computational model in the context of the Rush Hour puzzle game. In two behavioral experiments testing different inferences about mental processing, we find that our model predicts participant judgments. This work advances our understanding of the richness of the human mind’s ability to think about other minds, and even about thinking itself.

Diachronic Language Change and Its Influence on Lexico-semantic Representations Across the Lifespan

Patterns of language use change over time and may reflect and/or impact lexico-semantic representations of individuals as they age. In the current study, we use distributional semantic word embeddings trained on corpora from different decades (HistWords) to examine language change. We first measured lexico-semantic organization in different age groups, using an open dataset of association norms, and tested how they may be related to language change. Then, using the diachronic word embeddings, we sampled English words that have changed in meaning and words that have maintained the same meaning/usage patterns between the 1950s and the 1990s. We tested how relatedness judgments for those words differ when paired with their “neighbors” from earlier vs. recent decades, for both younger and older adults. Our findings suggest that individuals continuously and rapidly update their lexico-semantic representations across the lifespan, such that earlier learned meanings have minimal impact on present-day representation.

Quantifying Bias in Library Classification Systems

Categorization is ubiquitous in human cognition and society, and impacts how we perceive and understand the world. In reflecting the needs and perspectives of their creators, no categorization system is entirely objective, and inbuilt biases can have harmful social consequences. Here, we propose methods for quantifying three kinds of category biases in hierarchical category systems. We present a study on two widely used library classification systems (the DDC and LCC) as large-scale examples of human categorization, and use our methods to quantify bias towards content associated with western (vs non-western) concepts in topic areas including history and religion. We find consistent evidence for western bias and show that the DDC tends to exhibit more western bias than the LCC. Our methods are general, and can be used to survey biases across topic areas, bias attributes, and hierarchical category systems.

Finding your Way Out: Planning Strategies in Human Maze-Solving Behavior

In many situations encountered in our daily lives where we have several options to choose from, we need to balance the amount of planning into the future with the number of alternatives we want to consider to achieve our long-term goals. A popular way to study behavior in these planning problems in controlled environments are maze-solving tasks since they can be precisely defined and controlled in terms of their topology. In our study, participants solved mazes that differed systematically in topological properties regulating the number of alternatives and depth of paths. Replicating previous results, we show the influence of these spatial features on performance and stopping times. Longer and more branched solution paths lead to more planning effort and longer solution times. Additionally, we measured subjects’ eye movements to investigate their planning horizon. Our results suggest that people decrease their planning depth with increasing number of alternatives.

Influences of Task Expectations and Failure Feedback on Learning from Subsequent Tasks

Previous research suggests that negative emotions invoked by failure feedback might lead people to tune out from the task, which is detrimental to their learning. However, failure feedback is pervasive in the real world and we need to identify ways we can learn from it optimally. In the current study, the participants’ (n = 218) task expectations were randomly set to be easy or hard. Then, the participants solved a novel type of equation problems that involved manipulation of researcher-invented symbols, followed by either success (“You solved the equations CORRECTLY!”) or failure feedback (“You solved the equations INCORRECTLY!”). Next, the participants were provided instruction about the rules of the equation tasks and solved posttest questions across two rounds. Across different learning outcomes, we identify the cases in which the influence of feedback is moderated by task difficulty expectations (on identical items), failure feedback results in similarly high performance with success feedback (on isomorphic items), and participants learn better when they receive failure than success feedback (at a new independent task). We conclude that the tune-out reactions to failure during feedback might be diminished, and even be reversed, after feedback. Keywords: feedback; learning from failure; task difficulty expectations; learning from errors; failure feedback

Does informativity modulate linearization preferences in reference production?

During referential communication, speaker choices regarding the syntactic encoding of their expressions can modulate the linear ordering of the properties necessary to identify the referent. We investigated whether such syntactic choices are influenced by the informativity of these properties in a given visual context, as quantified by Referential Entropy Reduction (RER). In two experiments, a maze-based sentence completion task was used to examine whether informativity of a particular property (animal or action) influenced the decision to produce pre- versus post-nominal modifications when describing animal-performing-action referents in a visual scene. While many participants used a fixed strategy, informativity did significantly influence linearization for the remaining participants, consistent with a maximal informativity strategy in which the high RER property is be encoded first. This suggests that speakers who vary their encodings are indeed sensitive to the informativity of properties in a visual scene, preferring syntactic linearization in which informative properties appear early.

Young Children and Adults Extend Novel Nouns to Objects not Places

Young children’s intuitions about the meanings of novel nouns have revealed foundational biases in language learning. Nevertheless, existing work on such word-learning biases has focused primarily on only one spatial domain to which nouns might refer—objects—not the large-scale and navigable places in which objects are situated. Previous research has nevertheless shown that adults and children treat objects and places differently not only in recognition and navigation tasks, but also in symbolic tasks, like drawing production. In a noun-extension task, we thus evaluate young children’s and adults’ word-learning biases across these two spatial domains—objects and places—and show that young children and adults treat objects and places differently in language: Young children and adults preferentially extend novel nouns to objects over places. This bias suggests a specific role for spatial domain in word learning and may reflect greater attention to objects over places in symbolic contexts like language.

Reasoning with online and offline knowledge

Knowledge affects how humans think and reason: people use background knowledge to interpret natural language, and reason over those interpretations. We show one way in which offline knowledge, which is stored in semantic memory, interacts with online knowledge, that is, knowledge acquired through the use of factive mental state verbs such as know and discover. The interaction tests a theory of human thinking that assumes people construct simulations of possibilities – mental models – when they reason. It predicts that offline knowledge can affect reasoning through a process known as modulation, which blocks the mental construction of possibilities; and that online knowledge can cause reasoners to make presuppositions about facts. It also describes the mechanisms by which the mind updates mental models and separates fact from belief. An experiment tested the theory and corroborated its predicted interaction effect. We discuss the results in light of recent proposals of reasoning with knowledge.

Characterizing tradeoffs between teaching via language and demonstrations in multi-agent systems

Humans teach others about the world through language and demonstration. When might one of these modalities be more effective than the other? In this work, we study the factors that modulate the effectiveness of language vs. demonstration using multi-agent systems to model human communication. Specifically, we train neural network agents to teach via language or demonstration in a grounded communication task, manipulating 1) the inherent difficulty of the task and 2) the competence of the teacher. We find that teaching by demonstration is more effective in the simplest settings, but language is more effective as task difficulty increases, due to its ability to generalize more effectively to unseen scenarios. Overall, these results provide converging evidence for a tradeoff between language and demonstration as teaching modalities in humans, and make the novel predictions that demonstration may be optimal for easy tasks, while language enables generalization in more challenging settings.

CO2 as “Carbon DiLoopy:" Boosting People’s Global Warming Acceptance and Concern by Explaining CO2’s Cognitive Effects

The public is united in understanding neither global warming’s (GW’s) causes nor its urgency. This experiment assesses a novel text (informally called “Carbon DiLoopy”) intended to spawn science-normative changes in people’s GW beliefs by explaining rising CO2’s negative cognitive effects––without mentioning GW or climate change. Thus, it represents an indirect way to increase GW concern. Two control/replication texts explained (a) the carbon cycle and (b) human-caused GW’s scientific consensus. All texts, containing roughly 400-words each, were assessed regarding their impacts in changing GW beliefs and attitudes. The carbon-cycle control text yielded expected null results. The scientific-consensus text caused gains in general concern about rising atmospheric CO2, and–– replicating past studies––in GW acceptance and concern. The novel diLoopy text induced gains in all three measured concern/acceptance dimensions: (1) CO2’s effects on cognition, (2) rising CO2 in general, and (3) GW. We also found no evidence of backfire or polarization effects.

Does the finder of alternatives intentionally seek information irrelevant to the trained procedure?

Why do humans search for better alternatives when a familiar trained procedure is sufficient to solve the problem? Such a question is important in explaining the flexibility of human thinking. This study investigated whether the finder of an alternative procedure intentionally seeks to access information irrelevant to the trained procedure while solving a problem using the trained procedure. The results show that finders intentionally sought more information irrelevant to the trained procedure, even when solving a problem using the procedure. In addition, differences in intentional search may be caused by resistance to the reinforcement of fixation on the trained procedure. This study provides evidence that the discovery of alternatives involves the tendency to intentionally search for information irrelevant to a familiar solution.

Representations of Abstract Relations in Early Childhood

In science, we use common graphical representations to indicate changes in events over time, independent of domain. Are children also sensitive to abstract patterns in the ways events change over time? In a series of four experiments, we show that young children (range: 48-84 months) distinguish different function families (Exp 1). Children can also distinguish specific function types within function families (e.g., between linear and sigmoid monotonic functions; Exp 2); nonetheless, they will group different function types within a family together, rather than with functions in a different family (Exp 3). Finally, we show that children’s sensitivity to functions is abstract, allowing them to match observable causes to verbal descriptions of their effects (Exp 4). These results suggest that although some aspects of function understanding, like learning how to interpret graphs, requires formal education, the ability to identify abstract functional relationships is intuitive and early emerging.

Coexistence Reasoning about Misfortune During COVID-19 is Associated with Positive Psychological Well-Being

Across cultures, people use natural and supernatural explanations to explain adverse life events, such as illness or death. Yet little is known about the psychological implications of this type of causal reasoning. Here, we ask, does explanatory coexistence help or hinder coping with significant misfortune? We examined this question through structured interviews with a diverse sample of 147 Los Angelinos who had suffered from severe illness or bereavement during the COVID-19 pandemic. As predicted, mean scores on the Posttraumatic Growth Inventory were significantly higher for participants who employed coexisting explanations for their misfortune than those who employed singular explanations (natural, supernatural) alone. These findings provide evidence of an association between coexistence reasoning about misfortune and positive post-event processing.

Omniscience errors in mental state reasoning

Young children make systematic mistakes when reasoning about what other agents know and believe -- mature mental state reasoning emerges around late childhood. We describe a novel class of errors that adult reasoners make when considering information about the mental states of others. Participants in two studies reasoned about common conditional reasoning inferences couched in terms of an agent’s knowledge or belief, e.g., Alia knows that if it’s rainy then the café is closed; It’ s rainy. What follows? They generated their responses using a novel sentence construction interface. Many participants spontaneously generated responses such as, Alia knows that the café is closed. This pattern reflects an “omniscience” error, i.e., one in which reasoners erroneously impute knowledge of a deductive consequence to an agent. We discuss the results in the context of recent proposals on epistemic inference.

A one-second wait improves judgment accuracy: A mouse tracking reveals cognitive processes during choice behaviors

It is generally difficult for people to make rational and accurate judgments under their limited cognitive resources. In this study, we propose an intervention to easily improve people’s judgmental accuracy with less workload by waiting for a short time at the beginning of a task. By using a simple binary choice task, we found that when a short (1s) waiting time was inserted, participants showed higher accuracy than when no waiting time was inserted, and they felt less mental workload than when a longer (2.5s) waiting time was inserted. To examine the underlying implicit cognitive processes, we applied mouse tracking approaches during choice behaviors. We found that the inserted time enhanced participants’ change of mind (i.e., they amended their initial wrong judgments). These results suggest that making people wait for only 1s will serve as a simple, effective, and resource-rational intervention to boost people’s accuracy of judgments. Because of its simplicity, we believe that this intervention has potential to be applied in various fields.

5. Abstracts

The Impact of the Balance between Trust in Advice and Confidence in Human Judgment on Advice Utilization

The extent to which people utilize advice from others differs depending on whether the source of the advice is an algorithm or a human. However, no unifying evidence can be used for advice design. Moreover, the use of advice given at intervals (e.g., 70–90%) has not been fully studied. This study proposed a three-step model of the cognitive process of the use of advice with intervals and conducted a simulation and four behavioral experiments. These experiments showed that differences in advice sources affected the cognitive process in which judges decide whether to update their initial judgment based on the advice; this cognitive process was influenced by the relative weight between their initial judgment and the advice interval. These results suggested that for judges to adjust their judgments, designing advice itself is insufficient and advice must be designed according to the relationship between the advice and judge’s judgments.

An Experimental and Computational Analysis of Agreement-based Moral Cognition

Contractualist moral theories hold that morality is primarily about acting according to what would be agreed by rational agents. The contractualist tradition has received little attention in empirical work compared to competing normative ethical theories that are primarily concerned with rules (deontology) and consequences (consequentialism). Recent theoretical developments and empirical results suggest that agreement-based considerations and forms of reasoning could play a central role in moral judgment and decision making. However, the computational foundations of moral contractualism have scarcely been explored. In this paper, we present a flexible experimental paradigm to study contractualist moral behavior in an incentivized setting. We then develop a simple computational model of agreement-based moral decision making that captures participants’ behavior in the task. Results suggest that agreement-based cognitive processes are required to explain the nuances of observed behavioral patterns. Contractualism could offer a fruitful framework to understand how humans coordinate their behavior in morally ambiguous contexts.

Contrastive Explanations for Recommendation Systems

We develop an automatic method that, given a contrastive query from the user, generates contrastive explanations based on items' features and users' preferences. That is, once receiving a recommendation, the users have the option to ask the system why it did not recommend a specific different item. Our method enables a recommendation system to reply with a meaningful and convincing personalized explanation. For example, the recommendation system may recommend a Samsung S22 phone. The user may ask the system why it did not recommend the Xiaomi 12. Based on the user's preferences, all other users' preferences, and the specific phones in question, our method might infer that a good camera is particularly important to the user, and thus, say that the Samsung S22 includes a better camera. We show that humans are more convinced that the recommended item is better than the contrastive item when using our contrastive explanations.

Crosslinguistic Consistency in the Interpretation of Logical Connectives: The case of English, Hungarian, Spanish, and Mandarin Chinese

Languages have constructions that convey the logical concepts of negation, conjunction, and disjunction. These constructions are often logically ambiguous. A disjunction can be exclusive (XOR) or inclusive (IOR), and a negative conjunction/disjunction can be an alternative denial (NAND) or joint denial (NOR). Previous studies have suggested that there is substantial crosslinguistic variation in the interpretation of logical constructions and that languages fall into two groups. The first group including English interprets a negative conjunction as alternative denial (NAND) and a negative disjunction as joint denial (NOR). The second group including Hungarian and Chinese has the opposite interpretation pattern. However, there have been few crosslinguistic studies on the adult interpretation of logical constructions. Using an card selection task, we tested speakers of English, Hungarian, Spanish, and Chinese on different logical constructions. We found that speakers of these languages showed consistency in their interpretations of these constructions in the question environment.

A Comparison of Human and Machine Performance in Object Recognition Using The ObjectNet Image Set

The best-performing artificial intelligence systems for object recognition are deep neural networks (DNNs). For several years now, engineers and neuroscientists have claimed that DNNs show human-level performance in object recognition. However, Barbu et al. (2019) reported accuracies of around 30% for state-of-the-art object recognition systems when testing on their better-controlled image set – ObjectNet. How do humans perform on ObjectNet? In our experiment, we tested 25 undergraduates' ability to classify the ten categories of objects in ObjectNet that the Deep Convolutional Neural Networks (DCNNs) found easiest, moderate and hardest. Although humans and DCNNs had similar overall accuracy levels, there were some everyday, basic-level categories for which machine performance was much lower than humans. The pattern of errors generated by the DCNNs was about as similar to human error patterns as the individual human error patterns were to each other. Implications of these results and plans for future work are discussed.

Expectation of temporal delays shapes Judgement of Causal Strength and Causal Structure

In order to accurately assess the causal relationship between variables, organisms utilise the length of temporal delays between events as a major source of information. Associative learning theories predict that judgements of causal strength uniformly decrease as temporal delays increase. Contrasting inferential learning theories, associative theories discount the influence of prior knowledge of existing causal mechanisms. In a total of two experiments, the present study demonstrates empirical evidence for inferential theories, that i) if prior knowledge suggests a long delay between cause and effect, reasoners regard variables long apart in time as equally causal as contiguous pairs ii) reasoners explain low causal strength by “re-attributing” the true cause to an alternative latent cause, altering the possible causal structure. Together, the study provides novel evidence for how people reason from temporal information towards causality.

LaDA: Latent Dialogue Action For Zero-shot Cross-lingual Neural Network Language Modeling

Cross-lingual adaptation has proven effective in spoken language understanding (SLU) systems with limited resources. Existing methods are frequently unsatisfactory for intent detection and slot filling, particularly for distant languages that differ significantly from the source language in scripts, morphology, and syntax. Latent Dialogue Action (LaDA) layer is proposed to optimize decoding strategy in order to address the aforementioned issues. The model consists of an additional layer of latent dialogue action. It enables our model to improve a system's capability of handling conversations with complex multilingual intent and slot values of distant languages. To the best of our knowledge, this is the first exhaustive investigation of the use of latent variables for optimizing cross-lingual SLU policy during the decode stage. LaDA obtains state-of-the-art results on public datasets for both zero-shot and few-shot adaptation.

Task Variation in Scalar Implicature Computation

Experimental research on the processing and acquisition of Scalar Implicatures (SIs) relies on behavioral tasks that purport to measure the rate at which SIs are computed. Two paradigms, the Truth Value Judgment Task (TVJT) and the Picture Selection Task (PST) have dominated the experimental pragmatics literature; however, it is still unclear how the selection of experimental paradigm affects the estimates of implicature rate. We report the results of two studies testing participants in both TVJT and PST using three different linguistic scales in English: “ad-hoc”, “or-and”, and “some-all”. In Experiment 1, the task variation was manipulated within subjects while in Experiment 2, it was manipulated between subjects. We found that the estimated rate of SI computation varied noticeably between these tasks. This suggests that the experimental paradigm itself has a significant impact on our estimates of the implicature rate and consequently psycholinguistic theories of implicature computation.

Investigation into Approximate Number System: Cognitive Underpinnings and Relationship with Arithmetic Fluency

The approximate number system, involved in the estimation of large numerosities, forms the basis for the development of mathematics ability. Although researchers have investigated the cognitive underpinnings of the approximate number system, the specific underlying cognitive abilities involved need better theorisation. There have also been conflicting findings regarding the relationship between the approximate number system and mathematics ability. Therefore, the current study had twofold objectives: 1) To investigate the cognitive underpinning of the approximate number system. 2) To test whether the approximate number system can predict mathematics ability. A total of 31 primary school children participated in the study. To measure approximate number system and mathematics ability, non-symbolic number comparison and arithmetic fluency tasks were used, respectively. Using repeated measure ANCOVA and multilinear regressions, results revealed that only inhibitory control played a significant role in the approximate number system. Furthermore, the approximate number system could not predict arithmetic fluency.

How descriptive norms shape reasoning about rules

Rules are central to the successful coordination of large-scale societies. However, official codified rules (like laws) and other important social information like descriptive (often implicit) norms can sometimes provide conflicting accounts of what someone should do. Here, we explored two situations in which official rules and descriptive norms conflict—with implications for enforcement and punishment. Experiment 1 looked in the US and Belgium and found that descriptive norms shape the extent to which violating a codified rule is seen as actually breaking a rule. In Experiment 2 we demonstrated that descriptive norms also influence who people think was the cause of a punishment—the rule enforcer, not the rule breaker, is seen as the cause when the rule is rarely enforced (i.e. descriptive norms of enforcement are low.) Overall, this work suggests that norms play a vital role in shaping how we understand official rules and those who break them.

ChatGPT: More Human-Like Than Computer-Like, but Not Necessarily in a Good Way

ChatGPT is a large language model developed by OpenAI as a conversational agent. ChatGPT was trained on data generated by humans and by receiving human feedback. This training process results in a bias toward humans' traits and preferences. In this paper, we stress multiple biases of ChatGPT, and show that its responses demonstrate many human traits. We Begin by showing a very high correlation between the frequency of digits generated by ChatGPT and humans' favorite numbers, with the most frequent digit generated by ChatGPT, matching humans' most favorable number, 7. We continue by showing that ChatGPT's responses in several social experiments are much closer to those of humans' than to those of fully rational agents. Finally, we show that several cognitive biases, known in humans, are also present in ChatGPT's responses.

Neural Delay & Desynchronization problems for the Simple Brain Time Theory

The brain time theory cannot account for an adaptive trade-off between, the speed of perceptual availability and the accuracy of temporal binding. It argues that the ordinality of neural processing of sensory features is isomorphic to the ordinality of our conscious representations of them. The theory thus proposes a simple solution to the temporal aspects of the binding problem i.e., how sensory features are integrated into coherent percepts when these features change over time and are processed at different times and speeds in the brain. However, this solution is unable to account for the trade-off set out here: either it errs on one side of the trade-off, or it gets the trade-off right at the cost of violating its two central theses. This is a problem as a theory of time perception must get this trade-off right to be a satisfactory solution to the temporal aspects of the binding problem.

Fermi problems as tools for understanding number generation and Benford’s law

Fermi problems are often taught in science and engineering as a technique for difficult numerical estimation in which a question is broken down into sub-questions that people can more confidently estimate. Thus, they may be tools providing insight into how people estimate numbers. However, there are no empirical studies into the effectiveness of Fermi problems. This study presented participants with the same questions as both Fermi problems (with sub-questions) and as non-Fermi problems (no sub-questions) and tested the hypothesis that participants’ estimates would be more accurate when presented with Fermi problems than non-Fermi problems. The same data tested a hypothesis that participants would better fit to Benford’s law when presented with Fermi problems than non-Fermi problems. Neither hypothesis was supported, although the strong fit of peoples estimates to Benford’s law was replicated. This first attempt to empirically examine Fermi problems pointed to ways to more rigorously test these hypotheses.

Predicting intentions: How do we predict other's action intentions?

Intention prediction often plays a crucial role in successful social interaction. Previous studies have attempted to understand this skill by focusing on the role of movement kinematics in isolation. However, this approach is limited as the same kinematics typically map to multiple action possibilities (affordances) and as a result, individual's also employ contextual information to predict others' intentions. In this study we present preliminary findings from a qualitative study aimed at investigating intention prediction in naturalistic contexts. Participants viewed an individual reaching for a cup with one of two object-directed intentions: to drink OR to clear the table. A third non-object-directed intention was also included where the observed individual placed their hand on the table next to the cup. For each intention the contextual information was varied by changing the environmental scene between (1) cups full of juice, (2) almost empty cups, and (3) half-empty cups. The findings reveal that participants perceived the cup's functional (most salient) affordance - drink - for the intention so far as the movement kinematics specified an object-directed intention (drink or clear) with a context that clearly afforded it (full and half-full cups). However, participants were also sensitive to the kinematic differences between the object-directed intentions when the context made the functional affordance seem improbable (almost empty cups).

What’s new in a name? Chinese people’s perception of the renaming of COVID-19

Following the abandonment of the three-year strict zero-COVID strategy in China, the National Health Commission of China announced the renaming of COVID-19 in Chinese from “新冠肺炎” (novel coronavirus pneumonia, NCP) to “新冠感染” (novel coronavirus infection, NCI) in December, 2022, which may help to reduce people’s fear of COVID-19. We investigated whether the name change affected Chinese people’s (N=1256) perception of COVID immediately after the announcement. The results showed that when directly asked about the difference between the two names, about 65% of the individuals perceived NCP as a more serious threat to health and more frightening than NCI. However, when the questions were posed indirectly, the effect of renaming interacted with individuals’ personal and indirect COVID experience. This study thus provides insight into the role of language in shaping people's perception, particularly its interaction with people’s bodily and knowledge/social experience.

Incorporating Mental State into Contrastive Learning for Fine-grained Implicit Hate Speech Classification

Many people have suffered harm as a result of hate speech on social media. The majority of research has focused on coarse-grained explicit hate speech detection while disregarding fine-grained implicit hate speech classification. It is crucial for more effectively combating hate speech. Although the language used in implicit hate speech may vary greatly, the mental states involved are usually the same. There are rarely similarities and differences between the mental states present in implicit hate speech examined. We create a module to infer mental states from implicit hate speech to close this gap. Mental states primarily refer to the speaker's intent and the reader's reaction. Then, we use them as the positive sample in contrastive learning. This strategy can pull the implicit hate speech which has similar mental states in similar representations and push away different ones. Comprehensive experiment results demonstrate superior classification performance and generalization of the proposed method.

Classification of Rule Learning Phases in Inductive Reasoning

Intelligent tutoring systems (ITS) afford complex learning environments in which inductive reasoning is engaged, but where sparse log data complicates the classification of cognitive states. Using a simple rule learning task, we explore the feasibility of verbal think aloud protocols to contribute to machine learning algorithms designed to classify states of rule search, rule discovery, and rule following. Domain-general versus domain-specific contributions to classification are considered through the use of isomorphic rule learning tasks with numeric and spatial stimuli. We trained and tested models both within and across task domains. Models including verbal data outperform models based only on log data with particular improvement in classifying rule discovery. These results provide a foundation for real-time classification of cognitive states during ITS use.

Structural Signatures of Individual Object and Event Units

The physical world provides humans with continuous streams of experience in both space and time. The human mind, however, can parse and organize this continuous input into discrete, individual units. In this work, we characterize the representational signatures of basic units of human experience across the spatial (object) and the temporal (event) domains. We propose that there are three shared, abstract signatures of individuation underlying the basic units of representation across the two domains. Specifically, individuals in both the spatial domain (objects) and temporal domain (bounded events) resist restructuring, have distinct parts, and do not tolerate breaks; non-canonical individuals in both the spatial domain (substances) and the temporal domain (unbounded events) lack these features. In a series of experiments, we confirm these principles and discuss their significance for cognitive and linguistic theories of objects and events.

Action decision congruence between human and deep reinforcement learning agents during a coordinated action task

Deep reinforcement learning (DRL) is capable of training agents that exceed human-levels of performance in multi-agent tasks. However, the behaviors exhibited by these agents are not guaranteed to be human-like or human-compatible. This poses a problem if the goal is to design agents capable of collaborating with humans or augmenting human actions in cooperative or team-based tasks. Indeed, recommender systems designed to augment human decision-making need to not only recommend actions that align with the task goal, but which also maintain coordinative behaviors between agents. The current study simultaneously explored skill learning performance of human learners when working alongside different artificial agents (AAs) during a collaborative problem-solving task, as well as evaluated the effectiveness of the same AAs as action decision recommender systems to aid learning. The action decisions of the AAs were either modelled by a heuristic model based on human performance or by a deep neural network trained by reinforcement learning using self-play. In addition to evaluating skill learning performance, the current study also tested the congruence between the decisions of the AAs with actual decisions made by humans. Results demonstrate that the performance of humans was significantly worse when working alongside the DRL AA compared to the heuristic AA. Additionally, the action decisions participants made also showed less allignment with the recommendations made by the DRL AA.

PAGAE: Improving Graph Autoencoder by Dual Enhanced Adversary

Autoencoder frameworks have received attention for graph embedding, particularly those utilizing generative adversarial networks (GAN). However, GAN-based frameworks do not fully utilize the original graph information and lack stable updates in the GAN component. To bridge this gap, we propose a dual-adversarial framework for graph embedding that expands mutual information (MI) in positive and negative samples for adversarial training using GAN. We further improve model performance by incorporating reinforcement learning ideas. Our framework includes two variants: a pessimistic adversarial graph autoencoder (PAGAE), and a pessimistic adversarial graph autoencoder with PO loss (PAGAEPO). Essentially, we present a pessimistic module to negative sample generator to boost original discriminator, thereby reinforcing the generator’s ability. Additionally, we designed a PO loss function on discriminator to stabilize the learning process and it will further improve the ability of model. Experimental results show that our models are competitive with state-of-the-art GAEs on benchmark datasets.

Hierarchical Grouping of Simple Visual Scenes

Human visual grouping processes consolidate independent visual objects into grouped visual features on the basis of shared characteristics; these visual features can themselves be grouped, resulting in a hierarchical representation of visual grouping information. In the present study, participants provided free-response groupings of a set of stimuli that contained consistent structural relationships between a limited set of visual features. These grouping patterns were evaluated for relationships between specific visual features and the participant’s grouping patterns. We observed that the relative size of the visual features differentiated groupings across levels of the grouping hierarchy, while the form of visual objects and features distinguished separate groups within a particular level of hierarchy. These consistent relationships between visual feature characteristics and placement within a grouping hierarchy can be leveraged to advance computational theories of human visual grouping behavior, which can in turn be applied to effective design for interfaces such as voter ballots.

Generating body images from distributed word representation

Communications are mediated by symbolic or quantitative representations. Symbolic interfaces use discrete representations such as language and icons, while quantitative interfaces use physical quantities such as speech and movement. The two media are processed complementarily in human-human / human-machine communications. This study proposes a method of transforming these two representations, especially for generating body gestures. Our method is based on distributed representations of words, in which the size image for words is computed from the axis whose poles correspond to “small” and “large” word images. In addition, the size image of the words is physically implemented as robot gestures. The proposed methods were evaluated by two online surveys. Summarizing the results, the authors claim the potential of developing artifacts exchanging qualitative and quantitative aspects of word representations.

Understanding the Time Course of Context-Based Incidental Word Learning

Reader’s ability to integrate words into the preceding linguistic context via inferences making is one fundamental component of reading comprehension. However, the underlying processes that contribute to effective word-to-text integration are not yet fully understood. In this study, we investigated the contribution of variables underlying lexical representations (e.g., vocabulary), and general reading skills (text comprehension) to the integration of words across sentences in a sample of adolescents. To do so, we implemented an event-related potentials experiment, in which we contrasted two sentential context conditions: a repetition condition and an inference condition. The cognitive cost of inferential processing was estimated by comparing the amplitude of the semantic N400 component in both conditions. Participants’ individual differences in vocabulary and reading comprehension were included to examine the role of different components in this process. The results showed that participants with lower vocabulary exhibited a larger and extended cognitive cost of inference-making in the N400 component relative to participants with a higher vocabulary. No such differences were observed for text comprehension groups. These results indicate that vocabulary reduced the cost of integration during inference processing. Our findings contribute to the understanding of the mechanisms underlying real-time text comprehension.

Modeling the impact of arousal on decision making with spiking neural networks

Complex decision making (DM) requires coordination of elementary information processes subserved by a distributed network of brain areas. Computational models help to understand these processes, but most of the existing models focus on simulating only one of the many parallel operations. One existing spiking neural (SNN) model (Duggins, Krzeminski, Eliasmith, Wichary, 2020) attempts to simulate DM holistically, however it does not take into account significant influence of emotional context of DM. To address this limitation, we propose to examine the impact of arousal-related neural gain modulation on DM using the mentioned SNN model. In this study we outline the methodology and perform successful in-silico validation of global gain modulation hypothesis with the SNN model of DM. To perform the simulation, we use a well-studied multi-attribute choice task and we validate simulation results against human behavioral data.

Asymmetric Effects of Shifting Trust in Pro- and Anti-Consensus Climate Scientists

The trustworthiness of experts underlies public perceptions in policy domains like climate change. While 97% of climate scientists assert that human-caused climate change is occurring, a far lower portion of Americans agree (60%), which may be due to the perception that there is a legitimate debate. Previous research has focused on strategies to increase trust in climate scientists, broadly. However, the relative credibility of the two camps of scientists is important in determining belief in climate change. Using an experiment that provides hypothetical information about the trustworthiness of pro-consensus scientists (those who believe in human-caused climate change) and anti-consensus scientists (those who do not), we show that trust in both groups has a causal effect on belief in climate change, but this is asymmetric. Interventions that decrease the relative trustworthiness of pro-consensus scientists are effective at decreasing belief in human-caused climate change while interventions to increase their trust are ineffective.

Semantic relatedness and retrieval from semantic memory

The structure of semantic memory has often been investigated by using studies that manipulate the semantic relatedness of stimulus items, and use facilitation and inhibition effects to make inferences about semantic memory's structure. In the current work, we present two experiments using the visual world paradigm where we systematically manipulate the semantic relatedness of the distractor item when a participant is asked to look at (Experiment 1) or click on (Experiment 2) a target image. Both experiments yielded consistent patterns of results such that there was more competition from the distractor image the more semantically related it was to the target. The graded effects observed provide additional evidence for a graded and feature-based representational structure in semantic memory.

Causal Structure and Argumentative Value

Recent years have seen a surge of interest in probabilistic measures of argument strength. Such measures have been used to elucidate longstanding questions of both theoretical and practical interest around fallacies of argumentation, scientific argument, legal argument, or argument and evidence aggregation. The measures typically used have drawn, in one form or other, on conditional probability as a central component for measuring argument quality, strength or confirmation. Work in this area has also consistently highlighted the value of Bayesian Belief Networks for computing argument strength in multi-argument (variable) contexts. However, to date, there has been no consideration of whether the key structural property underlying such networks – the notion of conditional independence- has a bearing on argument quality. In this paper, we consider the potential impact of causal structure on the utility of arguments.

The Impact of Personality for Solving Complex Problems

In our global and technical world, there is an increasing need for humans to solve complex, nontransparent and self-dynamic problems. There are vast differences between humans regarding their capability to solve such problems that go beyond classical intelligence and having an analytical mind. Factors such as personality, knowledge, and the motivation to engage in effortful cognitive problems can contribute to success. Only a few studies investigated the relationship between these ‘soft’ factors and complex problem-solving (CPS). In the following, we will investigate these factors in the CPS framework Tailorshop, a computer-based scenario to increase the company value by manipulating several variables. Results indicate that personality traits and the Need for Cognition are no successful predictors. Overall, the present study points out the tendency of relevant personality traits as CPS predictors.

Training-Induced Linguistic Relativity and Embodied Processing

Current theories suggest that linguistic relativity and language embodiment are two sides of the same coin and that language embodiment and “linguistic shortcut” are two alternative types of semantic processing. It can therefore be deduced that deep embodied simulation of linguistic labels may facilitate the corresponding linguistic relativity effect more than shallow linguistic-shortcut processing does. However, this theoretical deduction has not been tested empirically. Here, participants used two newly-trained labels of verb (in)transitivity in sentence contexts that were either simulated in a deep, embodied manner (visual imagery group) or processed via shallow linguistic-shortcut (linguistic associate group). Subsequently, the former group were more likely to categorize motion events based on motion transitivity than the latter group. We thus expanded the moderators of linguistic relativity to the types of semantic access. The findings are discussed in light of the relativist, the universalist, and the sociocultural view on language-cognition relationship.

The Influence of Lexical Frequency, Phonotactic Probability, and Neighborhood Density on Word Identification

I examine how lexical frequency, neighborhood density, and phonotactic probability predict word identification, as well as the ways that they relate to each other and to the segments within a word. In a two-alternative forced choice task for identification of monosyllabic English words in noise, accuracy was higher for words with higher lexical frequency, lower neighborhood density, and higher phonotactic probability. However, all three characteristics also differ based on the segments within a word. Adding vowel as a predictor of accuracy largely eliminated the observed effects of phonotactic probability and neighborhood density. This relationship with vowel quality might suggest two directions of effects that can influence misperception patterns and subsequently sound change: Phoneme frequency causing directional confusions, or directional confusions changing phoneme frequency.

Inverse Hypercorrection of Metacognitive Errors

We investigated whether metacognitive errors (e.g., over- and under-confidence) were subject to a hypercorrection effect – an asymmetric adjustment for high- vs. low-confidence errors – analogous to that which has been demonstrated with performance errors. In a cue-outcome prediction task, on each trial, participants indicated which word would follow a particular image, and rated how confident they were in their answer. Consistent with the conventional hypercorrection effect in performance accuracy, there was an asymmetry in the adjustment of confidence ratings that did not match performance. However, contrary to previous reports of performance hypercorrection, metacognitive adjustment was greater for under- than over-confident responses.

Motivational modulation of strategy choice and memory formation during emotion regulation

Robust evidence suggests that motivation increases both cognitive effort and memory encoding. Despite growing recognition that emotion regulation may be a motivated process, motivational effects on the cognitive mechanisms underlying emotion regulation and subsequent memory for encountered stimuli remain largely uncharacterized. We manipulated extrinsic and intrinsic motivation to down-regulate negative affect in an emotion regulation paradigm including emotional and neutral stimuli. Participants were trained in two regulation strategies (cognitive reappraisal and distraction) and reported trial-by-trial strategy use. Both extrinsic and intrinsic motivation were associated with decreased negative affect and a shift in regulation strategy use. Specifically, use of reappraisal (a cognitively effortful strategy) increased with motivation. 24- hour recognition memory for presented stimuli was modulated by both emotional content and motivation condition. These findings suggest that interacting motivational and cognitive processes during emotion regulation can adaptively shape subsequent memory for the encountered stimuli, with implications for both cognitive and clinical science.

Linguistic Pathways to Spatial Cognition: The relations between Chinese handwriting legibility and different spatial subskills

Numerous cross-cultural comparative studies have claimed East Asian children’s superiority in spatial cognition and the significance of intensive Chinese character writing experience in shaping this superiority. However, the specific mechanism/pathways behind this functioning remain unclear. To fill this gap, this study focused on the spatial-orientated legibility dimension and investigated the relationship between Chinese handwriting legibility and spatial cognition with different spatial subskills (i.e., visual-motor integration, spatial visualization, and mental rotation) in the particular Chinese cultural context. A total of 249 4th graders participated in the investigation and finished the relevant measurements. The results suggested that Chinese handwriting legibility had a strong relationship with visual-motor integration, and this relationship can be generalized to spatial visualization and mental rotation, thus confirming the close relationship between Chinese handwriting and spatial cognition and specifying the mechanism/pathways behind this relationship. Possible explanations for the results and implications for school practitioners are discussed.

Narrative flow quantification of autobiographies reveals signatures of memory retrieval

The development of large language models (LLMs) enables the investigation of cognitive phenomena at an unprecedented scale. We applied LLM-derived measures on large narrative datasets to characterize the structure and dynamics of memory retrieval. Specifically, we found that autobiographical narratives flow less linearly from sentence to sentence than biographical narratives. Furthermore, the treatment of topics within biographies tends to be more coherent and are also written at a higher level of complexity than autobiographies. In summary, the narrative flow differences suggest that when authors rely on their own memory, retrieval proceeds in a less organized manner likely reflecting spontaneous cueing of associated memories. Our results demonstrate the utility of applying LLMs to narrative text to study cognitive phenomena.

Bilingual students’ test-taking strategies in content subject assessments

This study examined the potential relationship among students’ test-taking strategies, language proficiency and performance through an eye-tracking experiment. 71 university students with different levels of English proficiency attempted carefully selected questions on their Biology knowledge. Their eye movement behavior was captured during the process and analyzed using both summary statistics of eye movement measures and gaze transition consistency as measured in entropy using Eye Movement analysis with Hidden Markov Models (EMHMM) to infer their test-taking strategies, which were then correlated with test performance measured in accuracy and reaction time. Results generally showed that eye movement behavior was associated with performance. In particular, gaze transition consistency predicted performance beyond summary statistics of eye movement measures. However, language proficiency was not associated with eye movement behavior or performance. These results may imply that participants’ moment-by-moment, flexible problem-solving strategies when taking the test would better predict their performance than their underlying language proficiency.

Teacher Cognition: A Model of How Teachers Build Distributed and Enactive Narratives, to Generate and Finetune Mechanism Concepts in Student Minds

Science teaching is a complex socio-cognitive practice, where teachers simultaneously collaborate with and influence student minds. This process is distributed across textbooks, explanations, blackboard activities, student questions, student performance, etc.; and enactive, as teachers act out scientific mechanisms using descriptions, gestures, teaching props, models etc. This complex process of Teacher Cognition (TC) is not well understood, as existing studies are disparate, and based on disjointed approaches. The lack of a TC theory limits the design of systematic education policies - currently based on intuitions about teacher cognition - leading to policy guidelines that teachers find difficult to implement. Recent developments in embodied/enactive cognition theory – particularly the enactive simulation model of language and the enactive simulation theory of other minds – provide useful ways to develop models of TC, especially related to science teaching. Here we extend and develop a finer-grained version of a recent enactive model of TC, using classroom data.

Exploring the relationship between popular culture and perceptions of social standing in Science, Technology, Engineering, Mathematics, and Medicine (STEMM) careers: a pilot study using path analysis

Popular culture consists of elements consumed by the general public, such as movies, television shows, and books, that reflect the attitudes, practices, beliefs, and cultural objects prevalent in society. Popular culture tends to influence numerous facets of one’s lifestyle, and both empirical and anecdotal evidence suggests that it plays a role in determining the career of nascent professionals. We conducted an anonymized survey to assess the effect of popular culture on pursuing a career in Science, Technology, Engineering, Mathematics, and Medicine (STEMM). Results from the path analysis identified the direct effect of different aspects of popular media on the perceptions of social standing in choosing a STEMM career.

Modeling Human Navigation in First-Person Herding Tasks

Modeling human navigation and route selection in complex environments has used path planning in the past and more recently, dynamical models. The current study modeled the navigational movement trajectories of human herder agents tasked with corralling an autonomous target agent into a specified containment zone. Analyses of trajectories of participants completing a first-person herding game revealed that their movement entailed sequential (i) approach and then (ii) corral phases that were invariant to the initial location of the herder and target agents. More importantly, the trajectories of human herders could be captured using a low-dimensional environmentally coupled dynamical model. The novelty of this work is in extending the previous research in modeling navigational behaviors of humans to include moving targets that need to be manipulated into a desired position. Implications of the findings for the development of artificial agents for human-machine herding, as well as more general human-machine teaming are discussed.

Word Prediction in Context: An Empirical Investigation of Core Vocabulary

Core vocabulary is a topic of huge interest in linguistics and has been studied from a wide variety of perspectives, such as language learning, dictionary studies, and cross-linguistically. In many of these conceptions, word frequency is widely considered the conventional measure of a word's coreness; however, this approach overlooks important aspects of mental representation like centrality in an associative semantic network. In this experiment, we compare different approaches to defining core words in a task that involves predicting missing words in sentences. Results showed that core words (regardless of definition) were easier to guess than non-core words, but that frequency-defined ones did not perform as well as expected given their higher predictability and the nature of the task. Analysis of incorrect responses also showed that people preferred to guess core words, simple synonyms, and words that are taxonomically related to the target. The findings suggest that how core vocabulary is defined depends in part on the nature of the task and that aspects of both mental representation and the linguistic environment play an important role.

Prospective versus reactive strategies in microworld performance

Cognitive flexibility is the ability to adapt to changing contingencies and demands over time. It is often assessed by complex problem solving tasks (e.g., microworld simulations), which require individuals to maintain a goal state over trials. Yet typically analyses summarises performance by mean accuracy per person, which assumes performance is a constant and ignores the trial-by-trial trajectory of individuals that reveal strategy . We tested n = 83 psychology students aged 16 to 45 in a microworld task under different conditions of stochasticity and delay. We modelled trial-by-trial performance according to a prospective model of the true task contingencies versus a reactive strategy adapting to feedback from the last trial,. Most individuals (~66%) appeared to adopt a reactive strategy, however when the task contingencies were immediate, people were more likely to act prospectively. Performance in the microworld task depends on the strategy which varies by person and with the task contingencies.

GCN-based Autism Spectrum Disorder Diagnosis via Convolutional Restructuring Attention

Brain function connectivity, derived from functional magnetic resonance imaging (fMRI), has enjoyed high popularity in studies of Autism Spectrum Disorder (ASD) diagnosis. Albeit rapid progress has been made, most studies still suffer from several knotty issues: (1) the hardship of effectively modeling the sophisticated brain neuronal connectivity; (2) the dimensionality explosion resulted from excessive voxels in each fMRI sample; (3) the poor interpretability giving rise to unpersuasive diagnosis. To ameliorate these issues, we propose a GCN-based model, namely ConResNet. Specifically, a convolutional restructuring attention is designed for efficiently ROI-interaction feature extraction. Besides, we embed a salient graph pooling method to sift salient nodes. Extensive experiments conducted on ABIDE dataset demonstrate ConResNet achieves state-of-the-art performance. Moreover, the most salient brain regions predicted by ConResNet are closely consistent with theoretical knowledge in the medical domain, providing potential biomarkers for ASD clinical diagnosis.

Intuitive theories of moral progress

Many consider the world to be morally better today than it was in the past and expect moral improvement to continue. How do people explain what drives this change? We identify two ways people might think about how moral progress occurs: that it is inevitable or driven by human action. In Study 1 (N=149), we find that those who more strongly believe that moral progress is driven by human action are more likely to believe that their own intervention is warranted to correct a moral setback. In Study 2 (N=145), we find that this translates to action: those who more strongly believe that moral progress is driven by human action report that they would donate more money to correct a moral setback. Together these studies suggest that beliefs about the mechanisms of moral progress have important implications for engaging in political action.

Exploring the impact of social anxiety on behavioural variability

Traditionally considered random noise, variability in behaviour in fact contains meaningful structure. Healthy functioning can be characterised by behavioural variability that is statistically self-similar (i.e., exhibits 1/f scaling), while disease or injury can result in deviations from this pattern. To date this work has focused predominantly on physical conditions. Little is known about the link between mental health and 1/f scaling. To address this gap, in the present study participants completed a simple walking task during which their gait variability was captured. Cognitive load, a factor known to impact 1/f scaling, was manipulated and symptoms of social anxiety assessed. As expected, being distracted by a cognitive task led to deviations in gait structure away from 1/f scaling. Notably, this effect was influenced by symptoms of social anxiety. This provides initial evidence that difference in mental health status impacts the structure of behaviour.

Cognitive limitation or sophistication? Probability matching, wavy recency, and underweighting of rare events are associated with pattern search

People’s exceptional ability to identify structure in an uncertain world is often taken as a hallmark feature of human cognition. Yet people search for patterns even in random sequences—a tendency argued to give rise to striking (and often suboptimal) behavioral phenomena in experience-based choice: probability matching, underweighting of rare events, and the wavy recency effect. We tested the role of pattern search across three types of choice paradigms: probability learning, decisions-from-experience, and a gamble with a fixed pattern. Additionally, we included a battery of cognitive ability tests. We found that probability matching, wavy recency, and underweighting of rare events in the absence of patterns were associated with participant’s ability to identify existing patterns. By contrast, we found no credible associations between these behaviors and participants’ thinking style, intelligence, or memory capacity. These results suggest that prominent deviations from maximization in experience-based choice are associated with people’s tendency to search for patterns.

Lexical diversity across monolingual and multilingual populations

Lexical diversity, the proportion of unique lemmas in a text, is known to vary across first language and second language production. It has been used by researchers as a proxy for language proficiency, particularly for classroom language learners. Lexical diversity has been found to vary by language background, potentially at least partly due to cross-linguistic influence. We investigated whether or how lexical diversity differs across groups with varied language backgrounds, namely monolinguals, bilinguals, adult language learners, and post-attrition speakers. We expected that these groups would show differing mean lexical diversity scores due to their different backgrounds with varying combinations of languages and levels of proficiency. Data from 114 participants were stratified and showed substantial between-group overlap in their lexical diversity scores. However, some significant by-group differences in lexical diversity were found. We discuss the implications of these results.

Leveraging Neural Networks for Feature Selection in Sentence Processing Models

Previous research under the cue-based retrieval framework has assumed that general and discrete retrieval cues, such as [+subject] and [+singular] are employed in selecting a retrieval target during dependency building. However, to explain the effects of semantic compatibility between the target and the head of a dependency, as demonstrated in Cunnings and Sturt (2018), an infinite amount of lexically specific features and retrieval cues are needed. Smith and Vasishth (2020) offered a principled way for feature selection using word embeddings. However, even with a giant corpus, some dependencies are missing. To solve the coverage problem, we leverage pre-trained neural language models (GPT-2) to select features. The metric used in this paper is highly correlated with those in Smith and Vasishth (2020) and can predict the results reported in Cunnings and Sturt (2018). We argue that it the method offers a broader-coverage yet convenient way for feature selection.

Behavioral estimates of conceptual structure are robust across tasks in humans but not large language models

Neural network models of language have long been used as a tool for developing hypotheses about conceptual representation in the mind and brain. For many years, such use involved extracting vector-space representations of words and using distances among these to predict or understand human behavior in various semantic tasks. In contemporary language AIs, however, it is possible to interrogate the latent structure of conceptual representations using methods nearly identical to those commonly used with human participants. The current work uses two common techniques borrowed from cognitive psychology to estimate and compare lexical-semantic structure in both humans and a well-known AI, the DaVinci variant of GPT-3. In humans, we show that conceptual structure is robust to differences in culture, language, and method of estimation. Structures estimated from AI behavior, while individually fairly consistent with those estimated from human behavior, depend much more upon the particular task used to generate behavior responses–responses generated by the very same model in the two tasks yield estimates of conceptual structure that cohere less with one another than do human structure estimates. The results suggest one important way that knowledge inhering in contemporary AIs can differ from human cognition.

Asymmetry in Political Information Processing: Congruency-based reasoning in belief revision and political decision making

In a political environment, people may use information selectively for reasoning and belief updation. We hypothesized that political reasoning is asymmetrical and is triggered only for belief-challenging information. The experiment had three stages. In the first stage, participants were presented with a fictional socio-economic and political background, then they voted on fictional candidates from two competing political parties, and finally rated the parties on how strongly they supported/opposed them. Subsequently, participants were presented with either congruent or incongruent information items and they rated their trust in them. Finally, participants again rated their preference for the political parties. For congruent information, the initial polarization of the participants did not significantly influence their trust rating for the information item, but it did affect their belief update. The effect was reversed for incongruent information. The findings indicate that individuals engage in behaviour that tries to reduce cognitive dissonance when they encounter incongruent information.

Attention in the mind’s eye: using the Navon attention task to track the way the grammatical structure of text passages modulate mental simulation of perspective

Behavioral and neuroanatomical studies show that reading comprehension is based on mental simulation, as modality-specific sensorimotor representations and processes related to the meaning of the text are activated during reading. Supporting this simulation view of reading comprehension, cognitive semantics analyses show that changes in lexico-grammatical features are accompanied by changes in mental simulation. We hypothesized that Academic Language (AL) texts with different linguistic structuring will alter students’ mental simulations, particularly their perspectives differently, which can be tracked through its effect on attention. We first changed the linguistic structuring of AL text – particularly nominalization – to change the encoded perspective. We then tracked the effect of this change on the global and local perspectives of student mental simulations, using the Navon task. Initial results show a trend where changes in the passage structure led to a local attention effect. We discuss the implications of this indicative result, and ongoing work to further examine this finding.

Effective utilization of anchor-biased estimates for the wisdom of crowds

We propose a method to use anchor-biased numerical estimates to enhance the wisdom of the crowd (i.e., aggregating individual estimates becomes more accurate). We predicted that better wisdom of crowds could be achieved by combining estimates affected by two sufficiently different anchors. We conducted a behavioral experiment on anchoring effects and computer simulations of the wisdom of crowds. The behavioral experiment revealed that anchor-biased estimates did not always produce larger errors but produced more accurate or less accurate estimates depending on the relationship between the anchor and true values. We also found that even in the absence of anchoring effects, the estimates were biased. The results of computer simulations revealed that robust and better wisdom of crowds could be achieved using the proposed method. Contrastingly, aggregating estimates that were not biased by anchors did not always lead to better wisdom of crowds.

Unifying exemplar and prototype models of categorization

Prototype and exemplar models have each found support in the cognitive science literature on human concepts and categorization. The two model classes have complementary strengths. Prototype models can be more computationally efficient and interpretable whereas exemplar models can represent vastly more complex relationships. Rather than posit one or the other, some researchers have proposed people shift between prototype and exemplar representations, for example over the course of learning. Other researchers have offered models that combine features of prototype and exemplar models. Here, we propose new approaches to reconciling prototype and exemplar models. We demonstrate how tune-able modifications to the k-nearest neighbor (kNN) classifier combined with clustering techniques can capture human performance as documented in cognitive science studies where people learn small, artificial concept structures, and can also scale to large machine learning contexts. Our findings show the value of fluid and flexible approaches in unifying prototype and exemplar representations.

Prime time to buy: an analysis of a televised Dutch Auction

We present an analysis of real-life televised Dutch Auctions, where over the course of 12 hours of auctions, bidders made over 5,000 purchases of 81 unique products. In the Dutch Auction format, multiples of an item (traditionally Dutch tulips) are for sale, where prices start high and decrease over time. In the televised version, viewers of the Price-Drop TV program could wait for the price to drop over time but needed to weigh this against the risk of being scooped by the collective bidding behaviour of other viewers. We conduct analyses of the auctions to: 1) demonstrate the features of sell-out auctions where all items are sold, 2) show how time pressure increases sales in the auction, and 3) how the type of price decrease, discrete vs. continuous, affects people's decision to bid on items. We conclude by proposing an iterative process model based on the framework of Prospect Theory to extend theoretically motivated, laboratory-based findings out to real-world purchasing behaviours.

What can MINERVA2 tell us about 'killing hope'? Investigating L2 Collocational Processing with a Memory Model

Collocations are semi-productive word combinations with one word used literally and one other figuratively, characterized by an arbitrary restriction on substitution (kill hope, #murder hope). They are notoriously difficult for L2 speakers to acquire, yet there is no processing model specific to collocations. The present study attempts to explain trends in L2 collocational processing as memory retrieval. Modifying MINERVA2, a frequency-based memory model, we simulate reaction times and compare them to data from 99 L1 and 230 L2 (L1 Portuguese) English speakers involving free combinations (eat cake, ‘comer bolos’), congruent (read minds, ‘ler mentes’) and incongruent collocations (kick habits, no equivalent translation in Portuguese), and nonsense baselines (#read cakes). Under the assumptions that the L2 lexicon develops conditioned on the L1 and that the L2 lexicon is sensitive to L1 frequencies, we report that MINERVA2 can predict processing trends in both L1 and L2 collocational processing.

Integrating in Interleaved Conflicting Documents: Content Preference and Epistemic Beliefs Matter

This study investigated the effect of interleaving and epistemic beliefs on the reading comprehension of multiple conflicting documents. Especially, it is assumed that interleaving may highlight the differences between documents through the discrimination and contrasting of the conflicts, thus improving the integration of information. A one-factor experiment was conducted (n = 179) to compare the effect of interleaving against spacing and blocking on reading comprehension. In addition, a mediation model was established based on the Documents Model Framework. Path analysis results indicated that interleaving influenced integration through germane cognitive load and viewpoint description. Whereas spacing facilitated source-content links through source memory but showed no influence on integration. Furthermore, epistemic belief moderated the effect of interleaving on germane cognitive load. Participants with high level of justification for knowing reported the lowest germane cognitive load in the interleaved condition. Potential mechanism of interleaving in multiple document reading was discussed.

Parental Mind-mindedness and Autonomy Granting are Associated with Singaporean Children’s Free Will Beliefs

Parents’ mind-mindedness may influence their provision of opportunities for children to make decisions on their own, which can influence children’s sense of agency and autonomy regarding choices. We examine this hypothesis by measuring children’s free will beliefs and testing its correlation with parental measures. In Study 1, mind-mindedness for 90 parents of 3- to 6-year-old Singaporean children were coded from their written descriptions of their children. Those who scored high on mind-mindedness had children who were more likely to endorse free will during an online interview. In Study 2, self-reported autonomy granting collected from 89 parents of 4- to 9-year-old Singaporean children were correlated with their children’s free will beliefs after controlling for children’s age, children’s gender, parent’s gender, and household income. These results suggest that children who are perceived and treated as autonomous by their parents may become more likely to exercise their autonomy to make difficult choices.

A Computational Model of Children's Learning and Use of Probabilities Across Different Ages

Recent empirical work has shown that human children are adept at learning and reasoning with probabilities. Here, we model a recent experiment investigating the development of school-age children's non-symbolic probability reasoning ability using the Neural Probability Learner and Sampler (NPLS) system. We demonstrate that NPLS can accurately simulate children’s probability judgments at different ages, tasks and difficulty levels to discriminate two probabilistic choices through accurate probability learning and sampling. An extension of NPLS using a skewed heuristic distribution can also model children’s tendency to wrongly select the outcome with more favorable items but less likely to draw the favorable ones when the probabilistic choices are similar. We discuss the roles of two model parameters that can be adjusted to simulate the probability matching versus probability maximization phenomena in children, and why frequency biases children’s probabilistic judgments.

Modeling Trust and Reliance with Wait Time in a Human-Robot Interaction

This study investigated how wait time influences trust in and reliance on a robot. Experiment 1 was conducted as an online experiment manipulating the wait time for the task partner's action from 1 to 20 seconds and the anthropomorphism of the partner. As a result, the anthropomorphism influenced trust in the partner and did not influence reliance on the partner. However, the wait time negatively influenced trust in and reliance on the partner. Moreover, a mediation effect of trust from the wait time on reliance on the partner was confirmed. Experiment 2 was conducted to confirm the effects of wait time on trust and reliance in a human-robot face-to-face situation. As a result, the same effects of wait time found in Experiment 1 were confirmed. This study revealed that wait time is a strong and controllable factor that influences trust in and reliance on a robot.

Human control redressed: Comparing AI and human predictability in a real-effort task

Predictability is a prerequisite for effective human control of artificial intelligence (AI). For example, the inability to predict the malfunctioning of AI impedes timely human intervention. In this paper, we employ a computerized navigation task, a lunar lander game, to investigate empirically how AI's predictability compares to humans' predictability. To this end, we ask participants to guess whether the landings of a spaceship in this game performed by AI and humans will succeed. We show that humans are worse at predicting AI performance than at predicting human performance in this environment. Significantly, participants underestimate the differences in the relative predictability of AI and, at times, overestimate their prediction skills. We link the difference in predictability to differences in the approaches, i.e. different landing patterns, employed by AI and humans to succeed in the task. These results highlight important differences in perception of AI and human with implications for human-computer interaction.

Causal perception in Guinea Baboons (Papio papio)

In humans, simple 2D visual displays of launching events (“Michottean launches”) can evoke the impression of causality. Direct launching events are regarded as causal, but similar events where a spatial and/or temporal gap is added between the movements of the two objects, as non-causal. In the present study, we investigated the evolutionary origins of this phenomenon and tested whether Guinea baboons (Papio papio) perceive causality in launching events. We used a discrimination and categorisation task of Michottean launches. Our results indicate that Guinea baboons discriminate between different events, but we did not find a learning advantage for a categorisation based on causality. This implies that they focused on the spatial and temporal gap to achieve accurate categorisation, but not on causality per se. Currently we cannot rule out that Guinea baboons have causal representations of Michottean events, but our findings point to a feature-based discrimination strategy in a sorting task.

Competing causal debates and the influence of source credibility on belief revision

This research explores the effects of the zero-sum fallacy and the interaction of source credibility in three experimental parts (Pilditch, Fenton & Lagnado, 2019). The zero-sum fallacy is a reasoning error wherein individuals presented with two equally plausible competing causal debates erroneously assume that neither can be true. Experiment 1 (N=16) was an unsuccessful replication of Pilditch and colleagues (2019) experiment 1 which previously significantly demonstrated the effects of the zero-sum fallacy. Experiment 2 (N=53), found significant results favouring the existence and robustness of the zero-sum fallacy using logically identical but contextually different experimental stimuli. Experiment 3 (N=101), found the zero-sum fallacy persisted when source credibility statements were incorporated, but that source credibility had a significant impact on participants’ reasoning. As part of explanatory analysis, data from all 3 experiments was subjected to Bayesian analysis.

Exploring the Effect of Recall Direction on False Memories in a DRM Paradigm

Presenting lists that share semantic or phonemic associations has been shown to elicit incorrect recall of a specific non-presented item; the false memory effect studied in the Deese-Roediger-McDermott (DRM) paradigm. Previous research indicates phonemic lists produce more false memories than semantic lists, and forward recall produces better recall accuracy than backward recall. The present study aimed to investigate the effect of recall direction and association on recall within the DRM paradigm. Participants were randomly allocated to forward (n=20) or backward (n=20) serial recall and were presented with six-item word lists containing semantic and phonemic associates of a non-presented lure. Results demonstrated an association effect for forward recall in lure occurrence, both recall direction and association for serial recall. Key findings support the notion that semantic association is a driver of recall accuracy and protects from memory errors.

Conceptual Integration and Semantic Relational Processing as Study Tasks to Promote Cued-Recall of Word Pairs

A basic question in the study of human cognition concerns the ability to encode and retrieve associations between pairs of meaningful elements such as words. We employed a standard version of a paired-associate learning task using novel, arbitrary word pairs (restricted to nouns) presented one pair at a time during study without direct warning of a memory test to follow. After a distraction task, participants were provided with a cued-recall task which provided either the first or second word from each of the studied pairings. This paradigm was used to investigate the impact of two study tasks inspired by research in the domain of higher-order cognition. Specifically, we proposed a benefit in cued recall due to either: 1) articulating a conceptual combination of the two provided concepts, i.e., generating a novel integrative concept that might be referenced by the noun-noun pairing; or 2) articulating a specific proposition about how the two provided concepts could interact by fulfilling the roles of a semantic relationship. To illustrate, a pair of words like "cloth" and "donkey" might be integrated via conceptual combination to refer to a stuffed animal or via relational linking to indicate that a cloth could be draped over a donkey. The control condition in the experimental design was a simple imagery-based support task to allow assessment of the impact of the novel support tasks above and beyond the role of imagery. The results show that both study tasks dramatically improve memory performance.

Learning Regularity in a Sequence of Decision-Making Tasks

When people make decisions, these are often sequential, and later decisions frequently depend on the outcome of the previous decision. For instance, a doctor will first decide on a patient's treatment and then its duration. Grammar learning shows that humans can learn temporal regularities between such sequential information. However, little is known about sequential category learning tasks. Here, we investigate learning of regularities between two categorization tasks and generalization to novel objects. In an experiment, we varied whether a contingency between the outcome of two categorization tasks existed and whether they were adjacent (the tasks followed each other) or non-adjacent (an intervening task in between). Participants learned to categorize the objects of the second task in adjacent condition better than other conditions. However, both regularity conditions were beneficial to categorize novel objects (generalization). The results show the importance of considering temporal regularities between decision tasks in theories of category learning.

Decoupling between categorization and attention optimization: An eye-tracking study

Selective attention has been a critical component in many models of categorization and category learning. However, previous developmental studies have found that children under age five exhibited inconsistent patterns of attention in categorization and recognition, which challenges the long-held assumption that categorization is often accompanied by attention optimization. We designed an eye-tracking study to directly investigate this dynamic relationship between attention optimization and categorization using the occlusion-based anticipatory eye movement paradigm. Adult participants were asked to learn artificial categories while their eye movements were recorded and their memory of features was tested. Surprisingly, while their categorization indicated selective attention on features determining category membership, their recognition and gaze data exhibited distributed attention among various features. These results suggest a potential decoupling between attention optimization and categorization.

Metacognition in architectural design

This poster presents a theoretical model description of metacognition in architectural design. The assumption is that metacognition happens when an architect expresses self-awareness and that’s likely to happen when he/she acquires a global view of the design process. During design, architects are immersed in a sequential decision-making process with limited links/references backwards and forwards to immediately adjacent design actions. At certain points during design, possibly evoked by a stimuli or a challenge, architects rise to a higher order awareness where they construct a global view of parts or the whole of the design process, and understand, in parts or in full, the structure and causalities underlying their design decisions within the context of the design process and perhaps within a wider context that is situated in the social world. This is where architects are likely to declare or express their level of confidence about the actions they have made thus far and set longer term strategies on how they are going to manage their design decisions.

Risk Literacy is Associated with Reduced Belief Bias in Medical Risk Evaluations: Evidence from Structural Modeling of Cognitive Abilities and Process Tracing

People’s reasoning is sometimes biased by their prior beliefs (i.e., belief bias), which can lead to errors in judgment and decision making (e.g., interpreting risks related to climate change, medical procedures, COVID-19). Although research has examined the role of some cognitive abilities in belief biases (e.g., working memory, general intelligence), to date we cannot find any other investigation that has directly examined the relations between decision making processes and statistical numeracy skills, which have been found to be among the most robust general predictors of risk literacy and general decision making skill (Cokely et al., 2012; 2018). To address this gap, we conducted a study designed to trace and model the relations between risk literacy, cognitive processes (as measured by a write-aloud protocol analysis), and judgment outcomes in an ecological (i.e., naturalistic) risky medical choice task.