Proceedings of the Annual Meeting of the Cognitive Science Society

Applied Category Theory (aCT) is both a language for describing and a method for analyzing the abstract structures that populate cognitive science. It can serve as the lingua franca for cross domain discussions, and as a mathematical tool for probing the consequences of a model or theory’s structure. As many cognitive scientists are unfamiliar with aCT this workshop will provide an introduction to its terminology and key features. The morning session will emphasize key concepts and tutorial exercises. The afternoon session will use recent research applications as case studies of its potential and as the basis for demonstrations of how it can be used productively.

As the theme of this year’s conference suggests, cognitive diversity among learners and educators is increasingly acknowledged. However, in our societies that increasingly require advanced education, training, and technical skills, the pressure to standardize learning objectives, delivery techniques and delivery tools, especially online, is high. In these situations, learners and educators of diverse cognitive phenotypes and abilities experience learning environments that are a poor match for their abilities, making effective delivery of educational content challenging. In addition to learning about our work developing cross-sensory interaction design principles, workshop participants will share lived experiences of the pandemic-induced experimentation in online learning over the past two years to co-design prototypes that address pain points identified by participants.

The lack of diverse participants in our research can lead to issues of generalizability, inability to address social and health disparities, and perpetuation of stereotypes, among many other issues. For example, it became quite clear during COVID-19 in the United States, the inequities that existed in health care access and quality and disparities in health outcomes for minoritized communities. Such inequities and disparities are also found in research practice in many fields, including cognitive science. For example, research with Western, educated, industrialized, rich, and democratic (WEIRD) populations has been highlighted as problematic (Ceci, Kahan, & Braman, 2010; Henrich, Heine, & Norenzayan, 2010), yet the inclusion of diverse participants in research remains lacking.

Drawing is a powerful cognitive technology for creating external representations of thought. While drawings have long provided inspiration to researchers in many areas of cognitive science, including psychology, machine learning, and neuroscience, these communities have not generally had opportunities to interact and share insights. The goal of this workshop is to bring together perspectives from multiple disciplines to explore the question of how humans use drawings to communicate knowledge, catalyzing new opportunities for multidisciplinary collaboration. We are introducing a novel ``flipped'' format wherein we will hold three virtual seminars in the weeks leading up to CogSci 2022, each highlighting insights from Machine Learning, Neuroscience, and Developmental Science, respectively. Holding these thematic seminars in advance will enable us to attract a broader audience for our event and focus on promoting informal interaction among workshop attendees at the on-site event.

How does the brain represent different modes of information? Can we design a system that can automatically understand what the user is thinking? We can make progress towards answering such questions by studying brain recordings from devices such as functional magnetic resonance imaging (fMRI). The brain encoding problem aims to automatically generate fMRI brain representations given a stimulus. The brain decoding problem is the inverse problem of reconstructing the stimuli given the fMRI brain representation. Both the brain encoding and decoding problems have been studied in detail in the past two decades and the foremost attraction of studying these solutions is that they serve as additional tools for basic research in cognitive science and cognitive neuroscience. Recently, inspired by the effectiveness of deep learning models for natural language processing and computer vision, such models have been applied for neuroscience as well. In this tutorial, we plan to discuss different kinds of stimulus representations, and popular encoding and decoding architectures in detail. The tutorial will provide a working knowledge of the state of the art methods for encoding and decoding, a thorough understanding of the literature, and a better understanding of the benefits and limitations of encoding/decoding with deep learning.

Is the mind equipped with universal biases? If so, how can we account for the well-recognized diversity of human cognition? And if universal biases do exist, what is their source—do they reflect innate principles of core knowledge, or can they emerge from domain-general pressures? Finally, how can we, cognitive scientists, “get” human nature better—minimizing the pitfalls of our intuitive cognitive biases and our narrow WEIRD perspective?

In this Symposium, we seek to highlight ongoing research in the cognitive sciences that addresses issues of race and cultural perception and bias, as well as linguistic diversity. These research questions are critical to ensuring that our theoretical understanding of different cognitive processes encompasses the diversity of the world we live in.

Visual Snow is a neurological condition that is experienced as an ‘augmented’ reality of auras, glowing lines, depression, anxiety and depersonalisation. Whilst Visual Snow produces a collection of different symptoms, it is clinically recognised. A commonly experienced visual symptom is described as the 'persistent effect of television “snow”', and was first described in the literature in 1995. Distorted Constellations is an immersive, sensory, labyrinthine environment and playful experience of an augmented reality interpretation of artist, Nwando Ebizie's unique perception of the world. This exhibit draws on Visual Snow experienced as experienced by the artist and informed by interdisciplinary research, including cognitive science. The exhibition, public events and recent inbuilt psychological study, embrace the subjective nature of perception and highlight a role for augmented reality art experiences as cognitive science experiments in public settings. This contributed symposia will stimulate debate and questions arising across the intersections of art, neurology, cognitive science and public participation to leverage understanding of reality and the self through interdisciplinary considerations of cognitive difference. The role of reflexive collaborative inquiry and active public participation in emergent research is considered as a way to offer socially responsible scientific tools to the cognitive science community.

Although sentence comprehension and production are increasingly often studied by combining computational modeling and human experiments, this approach remains mostly restricted to studies of monolingual or first-language (L1) processing. There are currently only very few sentence-level computational models of second-language (L2) or bilingual processing (Frank, 2021). This lack of computational specifications can hamper further progress in bilingualism research. Moreover, better understanding of bilingual processing will give more insights into more general mechanisms such as cognitive control processes involved while switching languages (Luk et al., 2012). Our symposium aims to bring together researchers from different labs and with different research traditions, working on the intersection of models and experiments in bilingual sentence processing. The symposium has four talks, by Edith Kaan (associate professor, specializing in psycholinguistics of bilingualism), Yung Han Khoe (PhD student, working on models of bilingual sentence production), Lin Chen (research associate with an expertise in reading processes), and a joint talk by Irene Winther (PhD student working on bilingual sentence processing) and Yevgen Matusevych (research associate in computational cognitive science of language). Finally, we will have a panel discussion to suggest how models could be challenged by experimental data, and provide new explanatory mechanisms. This discussion will be moderated by Xavier Hinaut (research scientist in computational neuroscience) and Stefan Frank (associate professor in computational psycholinguistics).

Humans are surprisingly adept at interpreting what is happening around them – they spontaneously and rapidly segment and organize their dynamic experience into coherent event construals. Such event construals may offer a starting point for assembling a linguistic description of the event during speaking (Levelt, 1989). However, the precise format of event representations and their mapping to language have remained elusive, partly because research on how people mentally segment and perceive events (see Radvansky & Zacks, 2014 for a review) has largely proceeded separately from analyses of how events are encoded in language (see Truswell, 2019 for a review).

AI systems that dynamically navigate the human world will sometimes need to predict and produce human-like moral judgments. This task requires integrating complex information about human moral cognition (what decision would humans make in this situation?), normative ethics (what is the right decision for an AI to make?), and artificial intelligence engineering (how can we implement this functionality in AI systems?). A range of solutions have begun to emerge within the cognitive science community to satisfy these three categories of demands. However, most solutions tend to satisfy some demands, while falling short on others. This symposium highlights four competing solutions for building AI with a human-like moral sense, with the goal of highlighting the strengths and weaknesses of each approach and how each might complement the others in development and deployment going forward.

Throughout the lifespan and across cultures, all human behavior happens in space. By early childhood, people are capable of navigating complex 3D environments, executing sophisticated motor plans, and coordinating action with others. They also use their representations of space to structure a variety of non-spatial concepts, including time, number, similarity, and emotion. How do people perform these cognitive feats? One source of insight comes from studying the diversity of spatial cognition: Although the physical properties of space are invariant, the way people typically conceptualize space varies radically across groups, between individuals, and over development. By studying this variation in spatial cognition, we can better understand the universal set of cognitive and neural mechanisms that underlie it, with implications for the cognitive sciences, education, and design.

One key dimension of individual differences that affects children’s development, interactional behavior, and cognitive processes is temperamental shyness–a tendency to be reluctant or anxious in the face of new social situations. Prior research has documented shyness holds the potential to negatively impact a child’s social functioning, psychological health, and language abilities. However, emerging research from different disciplinary angles sheds a more positive light on shyness by illustrating adaptive aspects such as benefits in social cognitive and communicative functioning (Viertel, 2019). Furthermore, there is accumulating evidence that shyness may not necessarily have a detrimental effect on language learning, especially when knowledge is assessed under familiar conditions (Kucker, Zimmerman, & Chmielewski, 2021; Tolksdorf, Viertel, & Rohlfing, 2021). However, although considered a ubiquitous phenomenon, the relation between shyness and other cognitive, perceptual, and social processes in childhood remains far from understood. Thus, by drawing together multiple levels of analyses and perspectives, the aim of this symposium is to emphasize the diverse manifestations of shyness in interactional settings and its impact on empathy, language, and social interactions.

Deductive domains are typical of many cognitive skills in that no single problem-solving strategy is always optimal for solving all problems. It was shown that students who know how and when to use each strategy (StrTime) outperformed those who know neither and stick to the default strategy (Default). In this work, students were trained on a logic tutor that supports a default forward-chaining and a backward-chaining (BC) strategy, then a probability tutor that only supports BC. We investigated three types of interventions on teaching the Default students how and when to use which strategy on the logic tutor: Example, Nudge and Presented. Meanwhile, StrTime students received no interventions. Overall, our results show that Nudge outperformed their Default peers and caught up with StrTime on both tutors.

Computational feasibility is a widespread concern that guides the framing and modeling of biological and artificial intelligence. The specification of cognitive system capacities is often shaped by unexamined intuitive assumptions about the search space and complexity of a subcomputation. However, a mistaken intuition might make such initial conceptualizations misleading for what empirical questions appear relevant later on. We undertake here computational-level modeling and complexity analyses of segmentation - a widely hypothesized subcomputation that plays a requisite role in explanations of capacities across domains - as a case study to show how crucial it is to formally assess these assumptions. We mathematically prove two sets of results regarding hardness and search space size that may run counter to intuition, and position their implications with respect to existing views on the subcapacity.

Research has documented children’s understanding that a choice made when constrained to a single option is a poor indicator of another person’s preference. However, when constraints are constant over time—as they often are in social contexts—they may lose their salience. We examined whether children (N = 133, 5- to 12-year-olds) were more likely to refrain from inferring that a constrained actor prefers their choice if they first observe unconstrained actors (Alternatives condition) compared to if they only observe constrained actors (Constant condition). Presence of alternatives was crossed with constraint type: either the second option was hard to access or there was no other option. In line with our predictions, results indicated that observing alternative situations with greater choice increased children’s subsequent attention to constraints. Effects were stronger for the hard to access constraint and for older children.

Since generations, singing and speech have been mainly transmitted orally. How does oral transmission shape the evolution of music? Here, we developed a method for conducting online transmission experiments, in which sung melodies are passed from one singer to the next. We show that cognitive and motor constraints play a profound role in the emergence of melodic structure. Specifically, initially random tones develop into more structured systems that increasingly reuse and combine fewer elements, making melodies easier to learn and transmit over time. We discuss how our findings are compatible with melodic universals found in most human cultures and culturally specific characteristics of participants' previous musical exposure. Overall, our method efficiently automates online singing experiments while enabling large-scale data collection using standard computers available to everyone. We see great potential in further extending this work to increase the efficiency, scalability, and diversity of research on cultural evolution and cognitive science.

Parents and teachers often encourage students (e.g., "You can do it!") when they encounter challenges, but these messages are not always effective. Whose encouragement motivates students the most, and why? Here we tested the hypothesis that others’ domain knowledge (e.g., knowledge about course materials) and ability knowledge (e.g., knowledge about students’ abilities in the course) each inform how students evaluate their encouragement. In a large-scale survey, we find that middle school students (n=288) and high school students (n=425) are most likely to seek out and be motivated by encouragement from someone with both domain and ability knowledge, rather than only one or the other. This effect emerged both when students reasoned about hypothetical classmates (Study 1a) and real people in their lives (Study 1b). Moreover, we find that confidence in others' performance estimates linearly increases when they have greater ability and domain knowledge (Study 1c). Collectively, this work suggests that students do not find all encouragement equally motivating. Rather, students find encouragement most motivating when the speaker has knowledge of their abilities and the domain.

Phonotactic and orthotactic constraints determine the possible spoken and written sequences of a language. Adult speakers quickly learn simple new phonotactic rules, but they only learn the more complex second-order rules (e.g., “/k/ is an onset only if the vowel is /æ/, but a coda if the vowel is /ɪ/”) after the first day of training, whereas children learn the same rules on the first day. In this study, we first show that adults learn simple new rules of sequencing in typing as quickly as in speaking. We then show that, despite a much higher error rate and opportunities for error-based learning, the timeline for learning the second-order rules in typing is similar to speaking. Finally, we demonstrate that what is learned in a second-order rule, as in the example above, is the coda —and not the onset— constraint, pointing to a chaining-type mechanism for learning new rules of sequencing. Keywords: orthotactic constraints; language production; typing; statistical learning

Joint pick and place tasks occur in many interpersonal scenarios, such as when two people pick up and pass dishes. Previous studies have demonstrated that low-dimensional models can accurately capture the dynamics of pick and place motor behaviors in a controlled 2D environment. The current study models the dynamics of pick-up and pass decisions within a less restrictive virtual reality mediated 3D joint pick and place task. Findings indicate that reach-normalized distance measures, between participants and objects/targets, could accurately predict pick-up and pass decisions. Findings also reveal that participants took longer to pick-up objects where division of labor boundaries were less obvious and tended to pass in locations maximizing the dyad’s efficiency. This study supports the notion that individuals are more likely to engage in interpersonal behavior when a task goal is perceived as difficult or unattainable (i.e., not afforded). Implications of findings for human-artificial agent interactions are discussed.

As the history of science has documented, there is an important role for thought experiments in scientific progress. Yet, there is very little empirical research about whether and how children learn from thought experiments. Here, we asked that question in the context of 6-year-olds’ developing theory of matter. At the outset of the study, over half of the children claimed that small pieces of matter weigh nothing at all. Children were randomly assigned to a Real (RE) and a Thought Experiment (TE) condition. The goal of each condition was to show – via demonstration in the RE and via mental simulation in the TE – that the weight of a single grain of rice can cause a card resting on a fulcrum to topple. We found that children simulated accurately in the TE, and they changed their judgments and justifications concerning the weight of small pieces equally from the TE and RE.

Recent research on thick terms like ‘rude’ and ‘friendly’ has revealed a polarity effect, according to which the evaluative content of positive thick terms like ‘friendly’ and ‘courageous’ can be more easily cancelled than the evaluative content of negative terms like ‘rude’ and ‘selfish’. In this paper, we study the polarity effect in greater detail. We first demonstrate that the polarity effect is insensitive to manipulations of embeddings (Study 1). Second, we show that the effect occurs not only for thick terms but also for thin terms such as ‘good’ or ‘bad’ (Study 2). We conclude that the polarity effect is indicative of a pervasive asymmetry that holds between positive and negative evaluative terms.

The extent to which people can infer new mathematical concepts in the absence of cultural support is not clear. We test such learning with a simple math concept: additive commutativity. Experimental work with children in industrialized cultures suggests that cultural support is necessary, since children take time to learn commutativity and ultimately show signs of knowing it after entering school. However, children are at a disadvantage in learning because they are not yet cognitively mature. Moreover, they have only had a short time to experience the world and possibly learn principles like commutativity on their own. Unschooled adults, on the other hand, may be in a better position to have inferred commutativity on their own. We test indigneous Amazonians with variable levels of math cultural supports, and find that those with low cultural supports do not show signs of knowing additive commutativity.

Semantic extension plays a key role in language change and grammaticalisation. Here we use a dyadic interaction paradigm to study semantic extension of novel labels in controlled circumstances. We ask whether participants will be able to (i) use highly accessible associations in the perceptual environment (colour-shape associations) to converge on a meaning for the novel labels, and (ii) extend these meanings to apply to both concrete targets (objects) and abstract targets (emotions). Further, given the argument that both metonymy and metaphor are important drivers of language change, we investigate whether participants will be able to draw on relations of contiguity (‘metonymic’ associations, e.g. colour-shape or object-colour) and relations of similarity (‘metaphorical’ associations, e.g. emotion-colour) to extend the meaning of labels.

From classrooms to dinner parties, many of our everyday conversations take place in larger groups where speakers address multiple listeners at once. Such multiparty settings raise a number of challenges for classical theories of communication, which largely focus on dyadic interactions. In this study, we investigated how speakers adapt their referring expressions over time as a function of the feedback they receive from multiple parties. We collected a large corpus of multiparty repeated reference games (98 games, 390 participants, 116K words) where speakers designed referring expressions for groups of 1 to 5 listeners. Larger groups tended to use more words total and to introduce more new words; nonetheless, most groups were able to converge to more efficient conventions regardless of the number of listeners.

We describe UMA (Unified Model of Arithmetic), a theory of children’s arithmetic implemented as a computational model. UMA extends a theory of fraction arithmetic (Braithwaite et al., 2017) to include arithmetic with whole numbers and decimals. We evaluated UMA in the domain of decimal arithmetic by training the model on problems from a math textbook series, then testing it on decimal arithmetic problems that were solved by 6th and 8th graders in a previous study. UMA’s test performance closely matched that of children, supporting three assumptions of the theory: (1) most errors reflect small deviations from standard procedures, (2) between-problem variations in error rates reflect the distribution of input that learners receive, and (3) individual differences in strategy use reflect underlying variation in learning parameters.

Syllogistic reasoning is one of the oldest domains of reasoning research and has made great advances in understanding and modeling human reasoning processes. However, the field was mostly focused on a traditional set of quantifiers originating in first-order logic, thereby neglecting the large variety of quantifiers humans use when engaging in reasoning in their everyday life. The present work makes three main contributions: (I) we conducted a study yielding a dataset covering all traditional syllogisms and tasks containing generalized quantifiers ``most'' and ``most not'', providing a starting point for existing theories and models to transition to generalized quantifiers. (II) based on the dataset, we analyze the impact that the additional quantifiers have on the reasoning behavior. (III) We investigated the reasoning behavior with respect to the difference between traditional and generalized quantifiers, gaining insights into some of the peculiarities of the domain of generalized syllogisms.

How do people build up trust with artificial agents? Here, we study a key component of interpersonal trust: people's ability to evaluate the competence of another agent across repeated interactions. Prior work has largely focused on appraisal of simple, static skills; in contrast, we probe competence evaluations in a rich setting with agents that learn over time. Participants played a video game involving physical reasoning paired with one of four artificial agents that suggested moves each round. We measure participants' decisions to accept or revise their partner's suggestions to understand how people evaluated their partner's ability. Overall, participants collaborated successfully with their agent partners; however, when revising their partner's suggestions, people made sophisticated inferences about the competence of their partner from prior behavior. Results provide a quantitative measure of how people integrate a partner's competence into their own decisions and may help facilitate better coordination between humans and artificial agents.

How do children learn to connect expressions (e.g "that red apple") to the real-world objects they refer to? The dominant view in developmental psychology is that children rely on descriptive information (red,apple). In contrast, linguistic theories of adult language attribute primacy to the grammar: words like "that" or "another" first establish the status of potential referents within the discourse context (old, new) before descriptions can factor in. These theories predict that reference can succeed even when the description does not match the referent. We explore this novel prediction in adults and children. Over three experiments, we found that (i) adults relied on the articles to identify the referent, even when the description did not fit, consistent with 'grammar-first' accounts; (ii) consistent with 'description-first' accounts, and unlike adults, 3-5yo children prioritized the descriptions provided by the nouns, despite being sensitive to grammatical information. This suggests that children connect expressions to referents differently from adults.

According to cross-situational learning, infants aggregate statistical information across naming events to resolve ambiguous word-referent mappings. While lab experiments show that learners are sensitive to these statistics, in studies using naturalistic stimuli, adults often fail to infer the correct referent. Here, we examined how young learners' input “in the wild” differs from laboratory experiments. We analyzed the temporal and spatial regularities of parent naming events in a naturalistic dataset of parent-child play and tested their effect on infants' visual attention. Parents were less likely to name the same toy twice than to name two different toys in sequence, except at short lags (0>t>5s). Most of the visual scenes accompanying naming events were composed of several toys of approximately equal (small) size. Child-attention to the target toy appeared to be modulated primarily by size. These results underscore the importance of quantifying naturalistic statistical regularities for understanding the mechanisms of word learning.

From birth, humans constantly make decisions about what to look at and for how long. Yet the mechanism behind such decision-making remains poorly understood. Here we present the rational action, noisy choice for habituation (RANCH) model. RANCH is a rational learning model that takes noisy perceptual samples from stimuli and makes sampling decisions based on Expected Information Gain (EIG). The model captures key patterns of looking time documented in developmental research: habituation and dishabituation. We evaluated the model with adult looking time collected from a paradigm analogous to the infant habituation paradigm. We compared RANCH with baseline models (no learning model, no perceptual noise model) and models with alternative linking hypotheses (Surprisal, KL divergence). We showed that 1) learning and perceptual noise are critical assumptions of the model, and 2) Surprisal and KL are good proxies for EIG under the current learning context.

Retrieval practice of information through testing has been shown to improve learning. So has studying examples. In this paper, we address inconsistencies in the literature concerning which of these two approaches is best. We test the hypothesis that learning depends on what is being learned; whereas practice emphasizes memorization, studying examples allows for selectivity of encoding, resulting in different information being learned. Accordingly, we predicted that practice will improve learning in situations that emphasize memorization (such as learning facts or simple associations), whereas studying examples will improve learning in situations where there are multiple pieces of information available and selectivity is necessary (such as when learning skills or procedures). We report evidence from a laboratory study using naturalistic materials showing results consistent with these predictions.

Using space to think about time appears to be a human universal, but the specifics of people’s space-time mappings vary across individuals and groups. What determines how people spatialize time in their minds? Here we hypothesized that motor experience contributes to individuals’ space-time mappings, independent of linguistic and cultural factors. Many everyday motor actions require using the hands in a particular sequence (e.g., positioning a nail with the nondominant hand before swinging the hammer with the dominant hand), establishing a correlation between space (left hand, right hand) and time (earlier action, later action) that reverses with handedness. These action sequences reinforce Westerners’ typical left-to-right mental timeline for right-handers, but contradict this timeline for left-handers. Accordingly, we find that right-handers associate leftward space with earlier times and rightward space with later times more strongly than left-handers, for whom cultural and bodily experiences present contrasting relationships between space and time.

Object-centric interactions provide rich learning moments for young children, including opportunities to discover word meanings. Children’s first-person object handling experiences, in particular, form a key source of input—one that varies across cultures and across development. Using daylong photo streams from child-worn cameras, we analyze >17k images to identify the frequency and targets of child object handling across the first four years in two small-scale subsistence farming communities on opposite sides of the globe (Rossel Papuan and Tseltal Mayan). Overall, we see general consistency in the distribution of object categories (e.g., consumables, mealtime tools, natural objects, etc.) handled by children across cultures and age, likely reflecting stable properties of children’s physical environments and day-to-day routines. However, the exact objects available to children vary both within and across communities and diversify with age. These various distributions of handling patterns are discussed in their relation to potential consequences for early learning.

Child-directed language (CDL) features words such as doggy, night-night, and tummy that are rarely used in adult-directed language (ADL). Characteristics of CDL variants, such as diminutivization and reduplication, explain why they may be learned and produced earlier by children. However, it is not yet clear how or when children switch to using ADL equivalents—dog, goodnight, stomach. Through analysis of speech transcripts from CHILDES and the Language Development Project corpus, we show that children significantly increase their production of ADL variants across age, with the average CDL-to-ADL transition point at 2.5 years. Many of the linguistic features that distinguish CDL vs. ADL registers (e.g., lexical and syntactic complexity) similarly differentiated the local speech contexts surrounding CDL vs. ADL variants in children’s input. Notably, these differences emerged even in speech that was primarily child-directed. Learners may therefore be able to capitalize on these linguistic cues to support their discovery of register along with context-appropriate CDL/ADL pair use.

Previous work has found competing evidence for how contextual diversity influences early word learning. In support of contextual diversity facilitating learning, the corpus-derived diversity metric from Hills, Maouene, Riordan, and Smith (2010) was found to correlate with earlier ages of acquisition in children. We extend this work to five languages, accounting for a nonlinear relationship between the raw contextual diversity metric and word log-frequencies, and we account for additional covariates such as word length, concreteness, and lexical class. In contrast with the original result, we find that contextually diverse words are acquired later by children across languages. Our findings support the hypothesis that contextual diversity introduces an excess of possible meanings for contextually diverse words, adding noise to the word learning process. This hindering effect overshadows any benefits of syntactic or semantic bootstrapping during early word learning, when children are still in the early stages of vocabulary and conceptual development.

Why does the mind wander? Recent theoretical models suggest mental content depends on a calculation that measures the expected rewards gained from the current task compared to other cognitive tasks and procedures. In Focused Attention Meditation (FA), participants practice attentional control by maintaining attention to an internal stimulus. Throughout the task, attentional lapses occur, in which there is an abrupt shift to mind wandering. We propose a model that formalizes attentional lapses as the interaction between a controller that boosts attentional resources to a target according to expected value calculations and a metacognitive monitoring procedure that stochastically observes internal contents. The model is applied to explain individual variation in button press data on an FA meditation task.

People are often reluctant to reconsider their choices, sticking with their goals even when it is clear that they would be better off abandoning them. Explanations for this abound, including loss aversion, sunk costs, social and reputational pressures, and resource rational consideration of the costs of replanning. Here we propose another hypothesis: In adopting a goal, you immediately reap the rewards of gaining information about what to do and how to act. Insofar as goals are rewarding in themselves, we predict that unless a goal is specifically devalued or the costs associated with it are very high, the default is not to engage in any reconsideration at all. We test this hypothesis by creating a stripped down scenario involving choices between two goals with transparently obvious cost differentials. The task is designed to minimize other factors that might contribute to goal persistence and indeed, we test both adults and very young children on virtually the same task to ensure that the cognitive load for adults is negligible. Both adults (Experiments 1-2) and 4-6-year-old children (Experiments 3-4) choose the less costly of two goals when shown the costs and goals together. However, when participants are shown the goals first, and only then shown that their chosen goal is more costly than the alternative, participants stick with higher cost goals, unless the goals are explicitly devalued.

This study investigated the neural substrate of processing gesture-speech semantic variation which arises when the same entity word is uttered along with an iconic gesture that depicts object knowledge or event knowledge of the entity concept, or with a beat gesture that enacts a rhythmic movement, or with a grooming gesture. The fMRI results attested that the observation of gestures with speech, as compared to speech alone, centers on the high-level visual processing and recognition of complex stimuli in the bilateral fusiform gyrus, and the association of various sensory information in spatio-motoric and semantic processing of the inputs in the left inferior/superior parietal gyrus, upon which multiple functional networks are engaged in response to cross-modal semantic variation. The visuo-sensorimotor network of gesture processing with speech does not resemble the processing of hand actions on real objects in the frontal and parietal lobes for the recognition of object-directed motor acts.

We introduce purely visual paradigms that convey the logical structure of illusory inferences from disjunction: (a AND b) OR c, a |- b. Although the logical information was conveyed entirely via non-linguistic means, we found that the visual paradigms induce reasoning fallacies, though less attractive than their linguistic counterparts. The visual paradigms highlight the role of alternative-based reasoning, or question-answer dynamics, as they control for narrowly interpretive processes that confound the study of their linguistic counterparts. To our knowledge, this is the first work to develop visual paradigms that represent reasoning fallacies committed by adults and involve multiple logical operators non-trivially embedded. Previous studies focused on pre-verbal children or non-human animals, and for this reason limited the scope of research to visually representing logically simple, valid inferences.

Human language offers a powerful window into our thoughts -- we tell stories, give explanations, and express our beliefs and goals through words. Abundant evidence also suggests that language plays a developmental role in structuring our learning. Here, we ask: how much of human-like thinking can be captured by learning statistical patterns in language alone? We first contribute a new challenge benchmark for comparing humans and distributional large language models (LLMs). Our benchmark contains two problem-solving domains (planning and explanation generation) and is designed to require generalization to new, out-of-distribution problems expressed in language. We find that humans are far more robust than LLMs on this benchmark. Next, we propose a hybrid Parse-and-Solve model, which augments distributional LLMs with a structured symbolic reasoning module. We find that this model shows more robust adaptation to out-of-distribution planning problems, demonstrating the promise of hybrid AI models for more human-like reasoning.

The last ten years have seen a steady increase in vignette-based research investigating the folk concept of pain. That research challenges the standard view of pain, according to which pains are unpleasant feelings. However, the results of these studies also suggest that the concept of pain is ambiguous and difficult to pin down. This paper approaches the topic from a new angle, using linguistic tests to decipher what people communicate when making statements such as ‘I have a pain in my arm’. The results suggest that first-person pain reports semantically entail information about both an unpleasant feeling and a disruptive bodily state. This speaks in favor of a pluralist view on the semantic meaning of pain.

The question of why human language relies so heavily on vague terms has received a great deal of attention from philosophers, linguists, and more recently cognitive scientists, yet much less is known about their effect on other aspects of language use. In this paper, we propose a model for the interaction between vagueness and implicatures, an important pragmatic phenomenon, incorporating recent work in the RSA framework and insights from the philosophical literature on vagueness. We show that the model offers a good fit of data from earlier studies, and discuss the scope of the model more broadly.

Knowledge of cause and effect allows people to navigate and understand the complex systems of the world. Despite the importance of causal knowledge to everyday reasoning, little is known about how people transfer causal knowledge learned in one situation to novel contexts. In two experiments, we examine when people choose to generalize two types of causal knowledge, causal mechanisms (Experiment 1) and causal strength (Experiment 2), across various domains. We find that people willingly transfer causal knowledge to novel contexts when the entities in those contexts share high categorical relatedness with the source of the causal knowledge. The extent to which people are willing to transfer causal knowledge decreases as category similarity decreases. We discuss future research that could delineate the boundaries of causal transfer.

The external provision of examples has proven the most successful approach to aid learning and application of declarative concepts (i.e., abstract concepts denoted by key terms and short definitions that can be applied to a wide variety of scenarios). The current experiment sought to further this line of research by exploring the effect of using thematically varied examples on learners' ability to classify novel exemplars and near misses of five system principles that cut across thematic domains. Results revealed that thematic variation increased learners’ ability to reject near-misses and, more crucially, to classify novel exemplars from domains not covered by the studied examples. The fact that this enhanced flexibility was unaccompanied by poorer performance in rejecting near misses or classifying new items from domains covered by the learned examples renders this strategy readily applicable in instructional settings. We end by discussing possible mechanisms that could potentially explain the observed advantage of thematically varied examples.

Directed forgetting (DF) studies demonstrate that humans can intentionally forget item information. In the presented study, participants learned three lists of words. After studying the first two lists (L0+L1), we cued half of the participants to forget these lists before learning a new list (L2), the other half remembered all three lists. Typically, such a forget instruction impedes recall of previously-studied to-be-forgotten words but enhances memory for subsequent to-be-remembered items. Instead of recalling the words, we asked participants to select the list a word was studied in, assessing how DF affected both item- and list-memory. In line with the context-change hypothesis, list-memory for L1 did not differ between the two groups suggesting that even if recall of to-be-forgotten words is typically impaired, list-memory is still intact. Furthermore, after the forget instruction, participants’ list-memory was enhanced particularly for early L2 words, providing evidence for a reset of encoding or rehearsal processes.

We investigate how humans leverage sparse observations of social interaction to infer the rich internal structure of human social collectives. We propose a computational model of this process which integrates a domain-general structure learning mechanism with domain-specific knowledge about social contexts (i.e.: “intuitive sociologies”). We test our model in two experiments where participants observe a sequence of animated interactions between agents, and then assign the agents to groups according to their role or type within the social collective. Crucially, the two experiments depict different types of social interactions which reflect different types of underlying social structures. The patterns of correspondence between model predictions and human data support our account, and demonstrate the importance of both general statistical learning and specific social knowledge when reasoning about social collectives. Keywords: Social Inference; Intergroup Cognition; Computational Modeling

The capacity to generate recursive sequences is argued to be a behavioral marker of rich, algorithmic cognition, like that found perhaps distinctively in humans. However, the precise mechanisms underlying recursive sequence generation remain mysterious. We investigate three potential building blocks of recursive pattern processing: hierarchical reasoning, ordinal reasoning, and associative chaining. We develop a Bayesian mixture model to quantify the extent to which these three cognitive mechanisms contribute to a center-embedded sequence generation task. We further test whether recursive rule discovery depends upon relational information (either perceptual or conceptual) present in the task stimuli. The presence of relational information facilitates hierarchical reasoning that drives the generation of recursive sequences across various depths of center-embedding. Without relational information, the use of ordinal reasoning predominates. Our results suggest that hierarchical reasoning is an important building block of recursive pattern processing and can be deployed across embedding depths and relational domains.

While widely used in psycholinguistics, the linking hypothesis for eye movements in the visual world paradigm is still poorly understood. Recent work on linking hypotheses for referential tasks in particular has found mixed support for the 'Referential Belief Link': that the proportion of looks to a referent in a time window reflects participants' degree of belief that the referent is the intended target in that time window. Here we test the hypothesis that participants' expectations for the utterances observed in an experiment modulate the extent to which the Referential Belief Link holds. This hypothesis is motivated by a simple idea: when utterances are unexpected, listeners engage in additional reasoning to make sense of the observed signal. In a re-analysis of a previous eye movement and incremental decision task dataset, in conjunction with two novel production experiments, we find that the more surprising an observed utterance is, the smaller the correlation between explicit and implicit beliefs is. We discuss the importance of participants' production expectations in research using the visual world paradigm.

How do humans generalise to make better decisions? Previous work has investigated this question using reward-guided decision-making tasks with low-dimensional and artificial stimuli. In this paper, we extend this work by presenting participants with a naturalistic decision-making task, in which options were images of real-world objects and the underlying reward function was based on one of their latent dimensions. Even though participants received no explicit instruction about object features, they quickly learned to do the task and generalised to unseen objects. To understand how they accomplished this, we tested a range of computational models and found that human behaviour is overall best explained by a linear model but that participants' strategies changed during the experiment. Lastly, we showed that combining pixel-based representations extracted from convolutional neural networks with the original latent dimensions further improved our models. Taken together, our study offers new insights into human decision making under naturalistic settings.

Prior work has shown a “natural” preference in the Verb Phrase for direct object Nouns to linearly precede the Verb. There is also evidence of a “natural” preference in the Noun Phrase to order Nouns before Adjectives. Given this, we asked how domain-general biases like regularization and language-specific biases like the preference for “natural” orders could jointly contribute to the emergence of these two common word orders cross-linguistically. Using a silent gesture paradigm (in which we presented iconic gestures without speech), we exposed different participants to competing Verb Phrase (NounVerb vs. VerbNoun) and Noun Phrase (NounAdj vs. AdjNoun) word orders at varying frequencies. In Noun Phrase contrast conditions, we found that regularization was greatest when the domain-general bias towards regularization and the linguistic bias to order Nouns before Adjectives were aligned. In Verb Phrase conditions, participants regularized to the same extent regardless of input: They opted for greater regularity, even at the expense of aligning with underlying word order biases. We discuss the implications of our work for understanding the effects of domain-general biases on language.

We explore whether people can recognise the epistemic goal of active learners. In a novel online experiment, 110 adults watched screen recordings of other adults (``players'') manipulating objects in a 2D simulated physical microworld. Players had the goal of either identifying the magnet-like force connecting two of the objects, or their relative masses. Observers were asked to identify the learning goal of the player. By drawing from a previously collected dataset of active physical learning interactions and an ideal observer analysis, we manipulated how informative the players' actions are about the target property, and observers' level of access to the players' micro-control actions. We found observers were better at identifying the goals of successful players and of players trying to identify force, while the micro-dynamic evidence improved accuracy on identifying the mass goal. We use mixed methods to explore what cues observers used to make these judgments.

Common critiques of natural language processing (NLP) methods cite their lack of multimodal sensory information, claiming an inability to learn situated, action-oriented relations through language alone. Barsalou’s (1983) theory of ad hoc categories, which are formed from to achieve goals in real-world scenarios, correspond theoretically to those types of relations with which language models ought to have great difficulty. Recent NLP models have developed dynamic approaches to word representations, where the same word can have different encodings depending on the context in which it appears. Testing these models using categorization tasks with human response data demonstrates that situated properties may be partially captured through semantic analysis. We discuss possible ways in which different notions of situatedness may be distinguished for future development and testing of NLP models.

What is the function of immature vocalizing in early learning environments? Previous work on infants in the US indicates that prelinguistic vocalizations elicit caregiver speech which is simplified in its linguistic structure. However, there is substantial cross-cultural variation in the extent to which children’s vocalizations elicit responses from caregivers. In the current study we ask whether children’s vocalizations elicit similar changes in their immediate caregivers’ speech structure across two cultural sites with differing perspectives on how to interact with infants and young children. Here we compare Tseltal Mayan and US caregivers’ verbal responses to their children’s vocalizations. Similar to findings from US dyads, we found that children from the Tseltal community regulate the statistical structure of caregivers’ speech simply by vocalizing. Following the interaction burst hypothesis, where clusters of child-adult contingent response alternations facilitate learning from limited input, we reveal a stable source of information facilitating language learning within ongoing interaction.

The Simon, Stroop, and flanker tasks are commonly used to investigate cognitive control. However, it remains unclear whether these three tasks in fact measure the same cognitive abilities and in the same proportion. We take a developmental approach to this question: if the tasks all roughly measure the same capacity, they should show similar patterns of age-related change. We present data from two massive online studies: Study 1 included 9,642 participants 10 to 80 years of age who completed the Simon and Stroop tasks, and Study 2 included 13,448 participants 10 to 79 years of age who completed the flanker task. The results revealed markedly different developmental trajectories among the tasks, with only the flanker task following an inverted U-shaped trajectory. These findings caution against using standard congruency tasks to draw general conclusions about the development of cognitive control and underscore the importance of developing more psychometrically rigorous measures.

People detect painful expressions more easily in members of their racial ingroup than outgroup. Here, we wanted to investigate this racial bias with a machine learning model trained to detect activations of different action units of painful facial expressions. We examined whether the model detected higher action unit activation for European than African faces when trained on datasets with mostly White faces. To control for confounding variables, pictures of faces were generated with the FaceGen Modeller. Results revealed that there exist differences in the visual detectability of some facial muscle activations due to skin color or other race-dependent facial features. Despite the bias towards European looking faces in the training data, some activations were more easily detectable in African faces. Thus, neither the perceptual detectability, nor the larger exposure to own-race faces seems to solely explain the racial bias in pain detection.

Diversity of social identities can improve the performance of groups through varied cognitive and communicative pathways. Recently, research efforts have focused on identifying when we should expect to see these potential benefits in real-world settings. While most research to date has studied this topic at individual and interpersonal levels, in this paper, we develop an agent-based model to explore how various aspects of homophily, the tendency of individuals to associate with similar others, affects performance at a larger scale. Study 1 examines how two types of homophily---identity-driven and opinion-driven---impact collective performance on a sequential decision-making task via modulating network formation and trust relations. Study 2 considers how the presence of identity-based conformity pressure can affect the findings from the first study. Overall, we find that the effect of homophily on performance is complex, depending on the operative dimensions of similarity, mediating pathways, and the specific outcome of interest. Finally, we discuss the implications of our results for policy interventions aiming to improve group performance.

Do people attribute functions to gendered social categories? (For instance, is there something men or mothers are for?) And if so, do such attributions of function have consequences for normative judgments about what members of these social categories ought to do? In the current study, participants (N = 366) rated their agreement with 15 statements about the “true functions” of different social categories, in triads of matched masculine, feminine, and superordinate categories (e.g., fathers, mothers, and parents). Participants endorsed functional claims more for some social categories (e.g., parents) than others (e.g., kids), and their background beliefs about gender predicted variation in functional reasoning. However, across categories, participants judged that fulfilling true functions was ‘natural’ for members of the category, and they judged that category members ought to fulfill their true functions.

Linguistic alignment---the contingent reuse of our interlocutors' language at all levels of linguistic structure---pervades human dialogue. Here, we design unique measures to capture the degree of linguistic alignment between interlocutors' linguistic representations at three levels of structure: lexical, syntactic, and semantic. We track these measures in a longitudinal dataset of early conversations between caregivers and children with and without perinatal brain injury. Specifically, we test the predictions of the well-known Interactive Alignment Model, taking advantage of the variability within our sample in terms of the strength of interlocutors' linguistic representations, whether owed to age or injury. Ultimately, we find inconsistent support for the (largely untested) predictions of the Interactive Alignment Model, pointing to a need for new quantitative accounts of the mechanisms underlying linguistic alignment. Our results regarding the trajectory of interactive alignment broadly replicate developmental trends documented by other researchers, though analyses linking concurrent vocabulary and child alignment, as well as caregiver alignment and later child vocabulary---defy predictions from previous work. Our goal with these analyses is to start a conversation regarding the mechanisms underlying linguistic alignment, and to inform theories of how interactive linguistic experience supports language development.

We see the external world as consisting not only of objects and their parts, but also of relations that hold between them. Visual analogy, which depends on similarities between relations, provides a clear example of how perception supports reasoning. Here we report an experiment in which we quantitatively measured the human ability to find analogical mappings between parts of different objects, where the objects to be compared were drawn either from the same category (e.g., images of two mammals, such as a dog and a horse), or from two dissimilar categories (e.g., a chair image mapped to a cat image). Humans showed systematic mapping patterns, but with greater variability in mapping responses when objects were drawn from dissimilar categories. We simulated the human response of analogical mapping using a computational model of mapping between 3D objects, visiPAM (visual Probabilistic Analogical Mapping). VisiPAM takes point-cloud representations of two 3D objects as inputs, and outputs the mapping between analogous parts of the two objects. VisiPAM consists of a visual module that constructs structural representations of individual objects, and a reasoning module that identifies a probabilistic mapping between parts of the two 3D objects. Model simulations not only capture the qualitative pattern of human mapping performance cross conditions, but also approach human-level reliability in solving visual analogy problems.

The assumption of an inverse S-shaped probability weighting function allows cumulative prospect theory to explain several well-established regularities in risky choice between monetary lotteries. Empirical evidence indicates that in choices between options with nonmonetary outcomes, the shape of the weighting function is strongly influenced by the negative emotions often associated with these outcomes. In its current form, however, cumulative prospect theory is silent with respect to how to formally integrate the influence of affective processes on the shape of the weighting function. Here, we propose an affective probability weighting function in which the two main features of the weighting function, probability sensitivity and elevation, gradually change with the affective value of the nonmonetary outcomes. We test our proposition in a model competition with three data sets. The results show that the affective probability weighting function improves the ability of (cumulative) prospect theory to predict choices between options with nonmonetary outcomes. We observed approximately linear probability weighting for the least affective nonmonetary outcomes and probability neglect for the worst or multiple outcomes. These findings demonstrate that integrating the effect of affective processes in formal decision models is crucial for advancing the understanding of choices between nonmonetary risky options---and thus ensuring the generalizability of the models beyond choices between monetary lotteries.

Distributed vector representations are a key bridging point between connectionist and symbolic representations of cognition. It is unclear how uncertainty should be modelled in systems using such representations. One may place vector-valued distributions over vector representations, although that may assign non-zero probabilities to vector symbols that cannot occur. In this paper we discuss how bundles of symbols in Vector Symbolic Architectures (VSAs) can be understood as defining an object that has a relationship to a probability distribution, and how statements in VSAs can be understood as being analogous to probabilistic statements. We sketch novel designs for networks that compute entropy and mutual information. In this paper we restrict ourselves to operators proposed for Holographic Reduced Representations, and representing real-valued data. However, we suggest that the methods presented in this paper should translate to any VSA where the dot product between fractionally bound symbols induces a valid kernel.

Many non-geographic factors influence spatial judgments, which implies that spatial representations are not metrically veridical. We investigated the influence of social and political factors in the geopolitical context of Cyprus–an island divided since 1974 into the Turkish Cypriot and Greek Cypriot communities in the north and south, respectively. Participants (249 Greek Cypriots, 322 Turkish Cypriots) indicated their familiarity with 19 towns, estimated town locations, and the straight-line distance between those towns. They also rated their attitudes toward the other community. Cypriots underestimated distances and contracted the placement of towns within the other community more so than within their own community. Moreover, those more willing to live together with Cypriots from the other community underestimated distances between towns, whereas those less willing to live together overestimated distances. The results support the notion that representations of global-scale environments have multi-faceted origins, including sociopolitical factors not usually associated with spatial representations.

It has been proposed that a drive for efficient communication shapes systems of semantic categories across languages. Recent work in this vein has increasingly emphasized communicative need: how often a particular object or idea will need to be referenced. Many studies assume for simplicity that the distribution of need across referents is the same for different cultures, and that this need distribution can be reliably inferred from corpora. In contrast, we elicited culture-specific estimates of communicative need from native speakers of English and Chinese. We compared those need distributions to each other and to a corpus-based need distribution, and we assessed the efficiency of the English and Chinese naming systems for the semantic domain of household containers under different need distributions. Our results suggest that languages reflect culture-specific need patterns, and that subjective estimates are sometimes superior to corpus data as a measure of need.

The law of product liability starts with the idea that a product should safely perform the function that it is for: a plaintiff can recover if she used the product for its intended purpose, but perhaps not if she misused the product. Previous work in psychology has suggested that people reason about artifacts in terms of their purpose. Yet, no work has tested the effect of misuse on judgment and decision-making, in particular in the context of product liability, or creator accountability. Two studies (N = 280, N = 282) show a robust effect of misuse on liability judgments, such that people are less likely to blame the creator in the case of misuse (vs. normal use). Additionally, both studies show a consistent pattern with regard to the role of individual differences in narrow teleology. When a product is misused, individual differences in teleology are strongly associated with liability judgments, but there is no such association when the product is used normally. This asymmetry suggests that judgments of misuse may be best explained in terms of what objects are for.

Children increasingly rely on the internet for information. In this study, children ages 7-10 (n=80) indicated whether a human source or Google could answer questions involving past, present, or future events, and which informant would be better able to do so. Children indicated that Google could accurately answer questions more frequently than the human could, and they were least likely to indicate that either informant could answer questions about the future. Children selected Google as the better informant across all question types, but they did so most frequently in the future condition. Children’s responses also varied such that as the age of the participants increased, they judged the person as less able to answer questions about current events and Google as better able to do so. Children believe that search engines can accurately answer questions more often than a person can, perhaps reflecting their exposure to digital learning environments.

Research on sound symbolism has shown that speakers of different languages associate specific consonants and vowels with round and pointy shapes, a phenomenon commonly dubbed the bouba-kiki effect. Most of this work rely on forced-choice tasks in which participants assign previously crafted pseudowords (like kiki or bouba) to visual stimuli. Here we investigate this phenomenon with a production written task. Participants had to create a new word they thought would be a good name for round and spiky images. In this less constrained task, spiky images received names with more high/front vowels and voiceless stops, while round shapes were named with more back/rounded vowels and lateral and nasal consonants. In great part these results replicate previous findings, showing that participants recourse to sound symbolism even when the task at hand gives them more freedom to create names to abstract shapes.

Inferences of other’s emotion states are influenced by multiple sources including target cues such as facial movements and the situational context. Our understanding of how information from these cues is integrated is limited, however. We examined whether people integrate information from faces and situations to infer emotions as predicted by an existing model of affective cognition. We applied a Bayesian cue-integration model to a dataset that includes a variety of complex social situations that reflect the heterogeneity of emotion contexts in social lives. Results indicate that when viewing both faces and situations, situation information alone predicted people’s inference about emotions better than Bayesian cue-integration model. However, there was some variability in this pattern across emotion categories as the Bayesian cue-integration model best predicted inferences for emotion categories of amusement and happiness. These findings better our understanding of the interplay between facial and situational cues in informing emotion inferences.

To achieve goals, people leverage cognitive control to adjust how they process information. Here we show that frequent adjustments in information processing strategies (e.g., response threshold) within a single task give rise to reconfiguration costs. In two experiments we induced different performance goals in a Stroop task via explicit instruction or incentives, and these goals either varied or were fixed across different blocks. Across both experiments, we find smaller adjustments in control intensity when people frequently adjust the amount of control they exert, relative to blocks in which they don’t. We show that these results can be accounted for with a model that maximizes reward rate while minimizing reconfiguration costs (proportional to the Euclidean distances between the previous and current control signals). These findings suggest that cognitive control adjustments are regularized to constrain larger adjustments in control, which has important implications for computational modeling and measurement of motivated cognitive control.

We extend our previous neural dynamic models of visual search and scene memory (Grieben et al. (2020); Grieben and Schöner (2021)) to move beyond classical “laboratory” stimuli. The new model can autonomously explore a natural scene and build a scene memory of recognized objects and their locations. It is also capable of guided visual search for object categories in the scene. This is achieved by learning object templates for object recognition, and feature guidance templates for visual search and associating them to categorical concepts. We address how preattentive shape can be extracted from the visual input and how scene guidance, specifically, scene gramar (Võ, 2021), emerge. For the first time, we embed feature extraction by a headless deep convolutional neural network (CNN) in a neural dynamic (DFT) architecture by learning a mapping from the distributed feature representation of the CNN to the localist representation of a dynamic neural field.

Speakers often use different names to refer to the same entity (e.g., "woman" vs. "tennis player"). We explore factors that affect naming variation for visually presented objects, analyzing a large dataset of object names with realistic images, and focusing on two factors: visual typicality (of objects and contexts) and name frequency. We develop a computational approach to estimate typicality, and not only study object names used by most annotators (top names), but also the second most used ones (alternative names). Our results show that variation increases for objects that are less typical for their top name, more typical for their alternative name, or whose top name has relatively low frequency. Context typicality does not show a general effect. Overall, we show that characteristics relating to name candidates beyond the top name are informative of naming variation, and the potential of using computational methods to inform models of human object naming.

Online communities rely on their members to understand and follow community norms, which they learn by observing others and the consequences of their behavior, seeing codes of conduct, and receiving feedback via moderation. Here, to determine the contribution of each source of learning to the preservation of a social norm, we extend cultural consensus theory, a mathematical framework for identifying the cultural consensus in a community. In particular, we extend the model to include learning from experience, centralized moderation, and decentralized moderation, three features commonly found in online communities. We then apply the extended model to data from an online community dedicated to preserving a norm related to the psychophysical scaling of intersubjective notions of beauty derived from facial aesthetics. We find that users' perceptual alignment with the norm before enculturation predicts involvement in the community and that experience in the community is an important indicator for group perceptual learning.

Semantic features have been playing a central role in investigating the nature of our conceptual representations. Yet the time and effort required to sample features from human raters has restricted their use to a limited set of manually curated concepts. Given recent success of transformer-based language models, we asked whether it was possible to use such models to automatically generate meaningful lists of properties for arbitrary object concepts and whether these models would produce features similar to those found in humans. We probed a GPT-3 model to generate semantic features for 1,854 objects and compared them to existing human feature norms. GPT-3 showed a similar distribution in the types of features and similar performance in predicting similarity, relatedness, and category membership. Together these results highlight the potential of large language models to capture important facets of human knowledge and yield a new approach for automatically generating interpretable feature sets.

Some linguistic features are more readily learned than others, and are thereby more likely to be maintained in diachronic language change, giving rise to typological universals. Less readily learned features may give rise to typological gaps. We consider an apparent typological gap—that a morphologically superlative determiner (e.g., gleebest in "gleebest of the cows") with a negative meaning is cross-linguistically unattested—and ask whether it reflects an underlying learning bias. We find 4-year-olds know that such determiners indicate quantity (replicating Wellwood, Gagliardi, & Lidz, 2016), but only when positive (‘most’), but not negative (‘least’). Importantly, the observed bias is not specific to the apparent typological gap: same-age children showed difficulty learning the negative meaning of a non-superlative determiner, though such meanings are attested. The data thus suggest that children are generally biased against negativity, consistent with much prior work on conceptual bias and language learning/processing.

We introduce a new empirical paradigm for studying naturalistic active learning, as well as new computational tools for jointly modeling algorithmic and rational theories of information search. Subjects in our task can ask questions and learn about hundreds of everyday items, but must retrieve queried items from memory. In order to maximize information gain, subjects need to retrieve sequences of dissimilar items. We find that subjects are not able to do this. Instead, associative memory mechanisms lead to the successive retrieval of similar items, an established memory effect known as semantic congruence. The extent of semantic congruence (and thus suboptimality) is unaffected by task instructions and incentives, though subjects are able to identify efficient query sequences when given a choice. Overall, our results indicate that subjects can distinguish between optimal and suboptimal search if explicitly asked to do so, but have difficulty implementing optimal search from memory. We conclude that associative memory processes place critical restrictions on people’s ability to ask good questions in naturalistic active learning tasks.

High-level cognition relies on the ability to determine what the relevant possibilities are in the face of incomplete information. For example, causal judgments require reasoning about the relevant counterfactual possibilities in the context in which the actual events occurred. Similarly, to judge that someone is morally responsible for a given action requires assessing what other actions were available to the agent in the context they acted within. Defining the set of relevant possibilities in a context---what we call the contextual 'modal space'---is computationally intractable both because such contexts are not well defined and because there are indefinitely many possibilities that one could consider. Interestingly though, high-level judgments that require reasoning over such possibilities are often made quickly and effortlessly, suggesting that they may recruit a heuristic or implicit representation of the contextually relevant modal space. In this paper we (1) introduce a sampling approach for empirically describing contextual modal spaces, (2) find that modal spaces are shaped by statistical and prescriptive normality, (3) demonstrate that the contextual modal spaces play a domain general role in high-level cognition in the sense that the same modal space is recruited across different domains of reasoning, and finally (4) we provide evidence that such judgments are made on the basis of heuristic representations of the contextual modal space rather than on-line sampling of relevant alternatives.

Decision makers can infer social statistics (e.g., the relative frequency of health risks or consumer preferences in the population) by drawing on samples from their personal social networks. In light of the growing use of the Internet, much of people’s social interactions occur online (e.g., via social media) rather than offline (e.g., via face-to-face contact). Here, we examine to what extent sampling of social network members from memory (social sampling) is affected by whether one usually has online vs. offline contact to a person. In our study, participants judged the popularity of holiday destinations and recalled people in their own online and offline social networks who had vacationed at each destination. Additionally, participants indicated the respective contact mode (offline, online, or mixed) and social category (self, family member, friend, or acquaintance) of each recalled person. We used a hierarchical Bayesian modeling approach to contrast two variants of a cognitive model that assumes sequential and limited search—the social-circle model. The variants assumed the search process underlying social sampling to be guided by either contact mode (online vs. offline) or social category. The model comparison further included an exhaustive sampling strategy and guessing. The majority of participants was best described by a limited rather than an exhaustive search strategy or guessing. Additionally, more than a third of participants were best described by the variant of the social-circle model assuming search to be guided by contact mode. Interestingly, participants who followed this search strategy also relied more strongly on their own experiences than participants who probed their memory by social category. Overall, these results provide the first evidence that contact mode affects social sampling from memory.

Typological data shows a tendency for languages to exhibit harmonic (i.e. consistent) ordering between heads and dependents. However, some categories seem to contradict this tendency. Here we investigate one such case, the order of the noun with respect to two dependents—adjectives, which tend to follow the noun and genitives which precede. We report two silent gesture experiments examining (i) whether there are cognitive biases favouring postnominal adjective and prenominal genitive order in a single trial judgement task, and (ii) if those preferences continue to influence order when participants learn a complete word order system. Our results shed light on how biases for individual categories of elements interact with biases that affect the wider linguistic system. While participants strongly prefer postnominal adjectives and prenominal genitives when these are judged in isolation, when they learn a system of ordering, these biases are obscured and (at least in some cases) harmony emerges.

Paradigms using the free recall of word lists have furthered our understanding of the organizational structure of memory by elucidating the role of contextual associations on memory search. We adapted the traditional word list-learning paradigm to investigate whether emphasizing contextual associations between items influences subsequent retrieval. Specifically, we introduced a review period between encoding and recall of word lists where items were repeated to highlight either the temporal or semantic associations at encoding. We found that temporal review led to stronger temporal clustering compared to a semantic or control review, and semantic review led to stronger semantic clustering compared to a temporal or control review. Moreover, participants recalled more list items when semantic associations were emphasized, with the degree of semantic clustering at recall predicting memory performance. These results demonstrate that emphasizing contextual associations during a repeated viewing after initial encoding can affect subsequent memory organization and recall.

How altruistic behavior evolves despite its evolutionary cost is still an intriguing question. Using Neural Network and Gradient descent algorithms, we proposed a mixed computational model of fitness competition among three artificial agents (predator, altruistic prey, recipient prey), in the zero-sum game environment. We found that altruism emerged without direct reciprocity when the predator invested in altruism aiming to use the prey’s altruistic behavior as “bait” to fish for more prey. For the decisive factor of this mechanism, we demonstrated that the long-term decision-making of a predator enhanced its investment in the prey's altruistic behavior, which leads to a significant increase in altruism and fitness in altruistic prey. We interpreted our findings from economic, evolutionary, and psychological perspectives, connecting zero-sum economies, K-selection, and third-party emotional decision-making to the emergence and maintenance of altruistic behavior.

It is commonly believed that expressions visually signal rich diagnostic information to human observers. We studied how observers interpret the dynamic expressions that people spontaneously produced during a real-life high-stakes televised game. We find that human observers are remarkably poor at recovering what events elicited others' facial and bodily expressions. Beyond simple inaccuracy, people's causal reasoning exhibits systematic model-based patterns of errors. We show that latent emotion representations can explain people's reasoning about the unseen causes of expressions. A hierarchical Bayesian model simulates which events people infer to be the cause of others' expressions by comparing the emotions inferred from the expressions against the emotions people were predicted to experience in various situations. This causal model provides a close, parameter-free fit to human causal judgments, suggesting that humans interpret expressions in the context of emotion predictions generated by a causally-structured mental model of other minds.

Rational inference over a noisy channel can potentially explain readers’ occasional misreading. We tested if the prior probability of an intended message modulates the rate of misreading a transposed-word sentence as grammatical. In Experiment 1 we manipulated the cloze probability of a word given its full context (Because my socks had holes, I bought a new pair/pack) but found no reliable effect on the rate of noticing word transpositions (pair new vs. pack new). In Experiment 2 we manipulated the 4-gram frequency of the sequence ending with the transposed words and again found no effect (I always know what mean they vs. love they). We conclude readers do not effectively exploit full-context information to derive nonliteral messages. Despite the results of Experiment 2, comparison of error rates across conditions in several experiments suggests a role for local ngram statistics, though perhaps only in a restricted range of ngram frequency.

The way two people choose to share food reveals how close their relationship is. Very close relationships alleviate the discomfort of exchanging saliva. We measure human inferences about relationships from observed food sharing actions with variable risks of saliva exchange; and then use a formal model of inverse planning to quantitatively capture these inferences. The model that best fits human judgments construes food sharing as a rational communicative social action, according to which actions are chosen both to maximize comfort given a relationship, and to communicate about the relationship itself.

Many computational models of reasoning rely on explicit relation representations to account for human cognitive capacities such as analogical reasoning. Relational luring, a phenomenon observed in recognition memory, has been interpreted as evidence that explicit relation representations also impact episodic memory; however, this assumption has not been rigorously assessed by computational modeling. We implemented an established model of recognition memory, the Generalized Context Model (GCM), as a framework for simulating human performance on an old/new recognition task that elicits relational luring. Within this basic theoretical framework, we compared representations based on explicit relations, lexical semantics (i.e., individual word meanings), and a combination of the two. We compared the same alternative representations as predictors of accuracy in solving explicit verbal analogies. In accord with previous work, we found that explicit relation representations are necessary for modeling analogical reasoning. In contrast, preliminary simulations incorporating model parameters optimized to fit human data reproduce relational luring using any of the alternative representations, including one based on non-relational lexical semantics. Further work on model comparisons is needed to examine the contributions of lexical semantics and relations on the luring effect in recognition memory.

A key property of human cognition is its ability to generate novel predictions about unfamiliar situations by completing a partially-specified relation or an analogy. Here, we present a computational model capable of producing generative inferences from relations and analogs. This model, BART-Gen, operates on explicit representations of relations learned by BART (Bayesian Analogy with Relational Transformations), to achieve two related forms of generative inference: reasoning from a single relation, and reasoning from an analog. In the first form, a reasoner completes a partially-specified instance of a stated relation (e.g., robin is a type of ____). In the second, a reasoner completes a target analog based on a stated source analog (e.g., sedan:car :: robin:____). We compare the performance of BART-Gen with that of BERT, a popular model for Natural Language Processing (NLP) that is trained on sentence completion tasks and that does not rely on explicit representations of relations. Across simulations and human experiments, we show that BART-Gen produces more human-like responses for generative inferences from relations and analogs than does the NLP model. These results demonstrate the essential role of explicit relation representations in human generative reasoning.

Can we know a word by the company it keeps? Aspects of meaning that concern physical interactions might be particularly difficult to learn from language alone. Glenberg & Robertson (2000) found that although human comprehenders were sensitive to the distinction between afforded and nonafforded actions, distributional semantic models were not. We tested whether technological advances have made distributional models more sensitive to affordances by replicating their experiment with modern Neural Language Models (NLMs). We found that only one NLM (GPT-3) was sensitive to the affordedness of actions. Moreover, GPT-3 accounted for only one third of the effect of affordedness on human sensibility judgments. These results imply that people use processes that go beyond distributional statistics to understand linguistic expressions, and that NLP systems may need to be augmented with such capabilities.

Consider a causal claim like “Tom caused the train delay.” Previous research has shown that the extent to which Tom is seen to act intentionally (i.e., through his own agency) affects the extent to which people agree with this claim. But is this effect of perceived agency a unique phenomenon to causal judgments? Two experiments suggest this may not be the case. Study 1 finds that perceived agency affects people’s under- standing of both causal and non-causal events. Study 2 then finds that while perceptions of agency were similarly involved in people’s understanding of causal and non-causal events, they affected only cases where these events were brought about by animate agents (e.g., people). These results thus suggest that perceptions of agency may have a much more general influence in how people understand events involving agents, and therefore in how they understand the sentences that describe them. We discuss implications for causal cognition, broader research in agency, and the intersections between both and linguistics.

Literature has suggested that self-faces are processed differently at various stages of information processing. Although mechanisms like familiarity, implicit positive attitude, emotional arousal, dual-coding, and dopamine reward pathway have been theorized to explain this effect, it may share a fundamental basis in the attentional mechanism resulting in perceptual prioritization for self-face. In this study, we have assessed the attentional bias resulting from the self-face (over other familiar and unfamiliar faces), by using face pairs as cues before a dot-probe task. We looked at reaction time and its underlying latent variables as a function of face pairs used as cues. We find that both self-face and familiar face result in a faster reaction time for subsequent stimuli at cued locations. Though self-face shows this advantage for both short and long cue-time, a familiar face shows the advantage only for longer cue-time. We also found that drift rate bias is found for the location where self-face is presented. Familiar faces show a prior bias (z) as the reason for underlying advantage. We conclude that although, self-face, as well as familiar faces, might bias the processing of subsequent stimuli the underlying latent factor might differ.

Global Workspace Theory (GWT) is a prominent account of cognitive access in humans. In the decades since its proposal, there have been a number of computational models developed to study the hypothetical dynamics of the global workspace, most of which are hand-designed to reflect the expectations of the theory. Here we examine a recently successful general deep learning architecture, the Perceiver, as a potential theoretical candidate for the global workspace. We find that despite being developed in an unrelated context, the Perceiver meets a number of theoretical requirements of the global workspace. More importantly, it demonstrates empirical behavior consistent with that expected by GWT in both attentional control and working memory tasks drawn from the cognitive science literature. Taken together, this evidence suggests that the Perceiver and related models may be a useful tool for studying the global workspace and its potential realization in both artificial and biological agents.

Children's early language skill has been linked to later educational outcomes, making it important to measure early language accurately. Parent-reported instruments such as the Communicative Development Inventories (CDIs) have been shown to provide reliable and valid measures of children's aggregate early language skill. However, CDIs contain hundreds of vocabulary items, some of which may not be heard (and thus learned) equally often by children of varying backgrounds. This study used a database of American English CDIs to identify words demonstrating strong bias for particular demographic groups of children, on dimensions of sex (male vs. female), race (white vs. non-white), and maternal education (high vs. low). For each dimension, we identified dozens of strongly biased items, and showed that eliminating even some of these items can reduce the magnitude of differences between groups. Additionally, we investigated how well the relative frequency of words spoken to young girls vs. boys predicted sex-based word learning bias, and discuss possible sources of demographic differences in early word learning.

Enabling efficient communication in artificial agents brings us closer to machines that can cooperate with each other and with human partners. Hand-engineered approaches have substantial limitations, leading to increased interest in methods for communication to emerge autonomously between artificial agents. Most of the research in the field explores unsituated communication in one-step referential tasks. The tasks are not temporally interactive and lack time pressures typically present in natural communication and language learning. In these settings, agents can successfully learn what to communicate but not when or whether to communicate. Here, we extend the literature by assessing emergence of communication between reinforcement learning agents in a temporally interactive, cooperative task of navigating a gridworld environment. We show that, through multi-step interactions, agents develop just-in-time messaging protocols that enable them to successfully solve the task. With memory—which provides flexibility around message timing—agent pairs converge to a look-ahead communication protocol, finding an optimal solution to the task more quickly than without memory. Lastly, we explore situated communication, enabling the acting agent to choose when and whether to communicate. With the opportunity cost of forgoing an action to communicate, the acting agent learns to solicit information sparingly, in line with the Gricean Maxim of quantity. Our results point towards the importance of studying language emergence through situated communication in multi-step interactions.

We explored use of Recurrence Quantification Analysis (RQA) of speech rhythm data from mental-health counseling sessions for prediction of quality of psychotherapy. Time-series of inter-syllable intervals (ISIs) were extracted from 239 counseling sessions conducted by 12 therapists who repeatedly interacted with 30 clients. We found a negative association between recurrence metrics and client-rated session quality and a negative link between percent of laminarity and therapist-rated session quality, after controlling for self-reported client depression and distress measures and duration of speech sound within a session. Placing value on reduced recurrence in patterns of ISIs, and especially reduced degree of a dyadic system remaining in the same speech-rhythm pattern may be indicative of a desire for variation in content and strategies of client-therapist interaction. These exploratory findings point to the possibility of RQA-based automated systems to capture the ‘footprint’ of the non-verbal dynamic that is indicative of successful mental-health counseling.

A key feature of attention is that it moves over time, guided both exogenously by changing external circumstances and endogenously by internal cognitive states. However, the endogenous mechanisms guiding the movement of attention in the absence of external cues remain poorly understood. This paper develops and validates a computational model of how internal attentional states, motivated by the Adaptive Gain Theory of the locus coeruleus-norepinephrine (LC-NE) system, can guide the movement of visual attention over time. By fitting our model to young children's gaze data as they perform a visual object tracking task, we investigate developmental changes in higher-order patterns of attending behavior between 3.5-6 years of age, and we hypothesize about how the LC-NE system might mediate these changes.

To defend or burnish our moral reputation, we often tell moral narratives. Moral narratives describe morally relevant actions and explanations of those actions, detailing how people acted and why they did so. When and why does communication about moral events include descriptions of peoples’ actions and explanations? We hypothesize that informational, reputational, and presentational goals of narrators shape whether their communication contains clear actions and explanations. We asked a group of “narrators” to communicate with other people following a moral decision. Another group of “audience” members judged them based on their chosen statement. We find that the informational and reputational goals of narrators can explain what information they decide to reveal. Narrators choose what to say based on inferring the audience’s likely perceptions but underestimate how much audiences in fact expect answers to “what” and “why”. Audiences, however, do not always perceive the lack of expected information as indicative of deceit.

The same causal system can be accurately described in many ways. What governs the evaluation of these choices? We propose a novel, formal account of causal evaluation according to which evaluations of causal claims reflect the joint demands of maximal informativeness and maximal compression. Across two experiments, we show that evaluations of more and less compressed causal claims are sensitive to the amount of information lost by choosing the more compressed causal claim over a less compressed one, regardless of whether the compression is realized by coarsening a single variable or by eliding a background condition. This offers a unified account of two dimensions along which causal claims are evaluated (proportionality and stability), and contributes to a more general picture of human cognition according to which the capacity to create compressed (causal) representations plays a central role.

“Each” and “every” can be used to express the same truth-conditions but differ in their contexts of use. We adopt a particular psycho-semantic proposal about the meanings of these universal quantifiers: “each” has a meaning that interfaces with the psychological system for representing object-files whereas “every” has a meaning that interfaces with the psychological system for representing ensembles. In five experiments (n=798 total) we demonstrate that this mentalistic account correctly predicts newly-observed constraints on how “each” and “every” are pragmatically used. More generally, these results demonstrate that canonical patterns of language use are affected in predictable ways by fine-grained differences in semantic representations and the cognitive systems to which those representations connect. By treating the output of semantics as mental representations that are more finely articulated than truth-conditions—and by taking seriously the relationship between linguistic meanings and non-linguistic cognitive systems—we can explain otherwise puzzling patterns of language use.

The literature suggests several hypotheses explaining adaptive vs. maladaptive character of task unrelated thoughts (TUT). However, it is still not clear what particular features can differentiate adaptive TUT from its maladaptive form. The main aim of the present study was to test the content and the context regulation hypothesis using daily sampling, that is to verify how TUT and task features are linked to momentary mood. 214 participants assessed their trait TUT through self-reported questionnaires and underwent a 7-day ecological momentary assessment of mood, TUT, and task characteristics measured 7 times by day. The results suggest that TUT particular features (i.e. lack of control, delay from the present moment, valence) are linked to both, lower mood valence and higher anxiety. Moreover control over the thoughts moderates the link between task characteristic (effort required by the task) and participants’ mood. Thus, from the clinical perspective, it seems more justified to take into account the particular TUT features instead of distinguishing specific TUT type (e.g. mind-wandering or rumination).

Language is a unique hallmark of humans, it is both learned and symbolic, which poses the problem of emergence: if neither form nor meaning is known, how can individuals communicate in the first place? The current study replicates work that investigates the emergence of signal forms and meanings and explores how Personal Need for Structure (PNS) of interacting partners can aid or hinder the emergence of communicative systems. We include an existing measure of personal need for structure to investigate its relationship with the emergence of such systems while participants play the embodied communication game (ECG). Similar to the original study, our work shows that a bootstrapping process and sufficient common ground are integral to the recognition of signalhood. Moreover, this process appears to be more successful for individuals who respond differently to a lack of structure as compared to their interaction partner. Contrary to what is usually assumed, our results indicate that not only shared expectations and biases seem to matter in communicative tasks, but that diversity in biases of communication partners can also be beneficial for the emergence of new communication systems.

Communication involves searching for optimal utterances within memory and then evaluating those utterances against a target goal. This task is substantially harder when informa- tion about multiple concepts has to be communicated, such as describing how music and tides are similar. Whether the search process for this challenging communicative task con- verges onto the optimal response relatively quickly, or involves more strategic decision-making to evaluate different candi- dates remains understudied. In this work, speakers gener- ated single word “clues” that would enable a listener to cor- rectly identify a pair of words among several distractor words. Speakers and listeners generated candidates before producing final responses. Each player was biased towards the first can- didate(s) they generated, even when this candidate was sub- optimal compared to other candidates, as was the case for less related concepts. Furthermore, straying away from the initial semantic “patch” of responses decreased accuracy in the game. Overall, these findings suggest that individuals tend to identify the relevant semantic cluster early on during semantic search, and are likely to employ the “take-the-first” strategy for select- ing utterances in ambiguous, ill-defined semantic contexts.

There are conflicting theories about how people reason through cause and effect. A key distinction between two prominent accounts pertains to whether, in judging an event’s causal relevance, people preferentially consider what actually happened (as predicted by process theories) or whether they also consider what could have happened under different conditions (as predicted by counterfactual theories). Toward adjudicating between these theories, the current work used eye tracking and Gaussian Process modeling to investigate how people form causal judgments retrospectively and in the absence of ongoing visual input. Participants played a virtual ball-shooting game: after choosing to move left or right, they encoded a video of the actual outcome and then were prompted to mentally simulate either (a) what actually happened, (b) what could have happened, or (c) what caused the outcome to happen while looking at a blank screen. During causal judgment, we found evidence that participants visually mentally simulated counterfactual possibilities: they moved their eyes in similar patterns as when they imagined a counterfactual alternative. Altogether, these results favor counterfactual theories of causal reasoning, demonstrate how visual mental simulation can support this reasoning, and provide a novel methodological approach for using eye movements to investigate causal reasoning and counterfactual thinking more broadly.

Models in cognitive science are often restricted for the sake of interpretability, and as a result may miss patterns in the data that are instead classified as noise. In contrast, deep neural networks can detect almost any pattern given sufficient data, but have only recently been applied to large-scale data sets and tasks for which there already exist process-level models to compare against. Here, we train deep neural networks to predict human play in 4-in-a-row, a combinatorial game of intermediate complexity, using a data set of 10,874,547 games. We compare these networks to a planning model based on a heuristic function and tree search, and make suggestions for model improvements based on this analysis. This work provides the foundation for estimating a noise ceiling on massive data sets as well as systematically investigating the processes underlying human sequential decision-making.

The introduction of large-scale data sets in psychology allows for more robust accounts of various cognitive mechanisms, one of which is human learning. However, these data sets provide participants with complete autonomy over their own participation in the task, and therefore require precisely studying the factors influencing dropout alongside learning. In this work, we present such a data set where 1,234,844 participants play 10,874,547 games of a challenging variant of tic-tac-toe. We establish that there is a correlation between task performance and total experience, and independently analyze participants’ dropout behavior and learning trajectories. We find evidence for stopping patterns as a function of playing strength and investigate the processes underlying playing strength increases with experience using a set of metrics derived from a planning model. Finally, we develop a joint model to account for both dropout and learning functions which replicates our empirical findings.

Redundant modification in referring expression production varies both within language (e.g., English speakers produce more redundant color than size modifiers) and cross-linguistically (e.g., English speakers produce more redundant color modifiers than Spanish speakers). It is an open question whether these asymmetries are the result of asymmetries in the general referential utility of color and size modifiers or of incremental language processing pressures. Cross-linguistic investigations of redundant modification are important to this debate: similar cross-linguistic rates of redundant modification would suggest a strong role for general referential utility. In contrast, lower prevalence of redundant modification in languages with post-nominal modification suggests a strong role for incrementality. Here, we test whether differences in redundant adjective use are systematic for a particularly interesting language: Central Taurus Sign Language. As a language in its infancy, CTSL has no established conventions, and therefore provides us with a unique opportunity to explore how redundancy emerges in the initial stages of language formation. We evaluate different computational models of referring expression that each make different assumptions regarding the source of asymmetries in the production of redundant modifiers.

Rules undoubtedly guide our moral lives. Simple moral rules prohibit lying, cheating, and stealing, for instance. But the moral mind is more flexible than a theory based only on rule-adherence can account for. In this paper, we look at one particular kind of flexibility: the ability to figure out when it is okay to break a moral rule. We elicit judgments of the moral acceptability of breaking a simple rule: it's wrong to cut in line. We created a video game environment in which agents attempt to gather water -- and sometimes must stand in line behind others to do so. Subjects watch clips of the game being played and make judgments of the moral acceptability of cutting in line across a wide range of spatio-temporally varied and dynamic scenarios. Our data suggests that subjects make judgments by using a generative understanding of the underlying function of the rule about waiting in line. We further show that our data cannot be accounted for by either 1) simple rule adherence or 2) utility maximization.

Listeners adapt to specific speakers' speech cue distributions and generalize the adaptation to the perception of a different speaker. It remains unclear whether listeners track and generalize the distributional statistics of raw, un-normalized cues or normalized cue distributions relative to the speaker's acoustic space. To address this question, we adopted a perceptual generalization paradigm to investigate whether manipulating context properties of a training speaker (Female A)'s speech would lead to different categorization results of critical phonemes in a test speaker (Female B)'s speech. Experiment 1 showed that learning Female A's speech containing the same set of sibilants but shifted vowel formants would lead to different categorization of Female B's sibilants: listeners exposed to raised vowel formants were more likely to identify an "s" and those exposed to lowered vowel formants were more likely to identify "sh" in Female B's speech, compared with participants exposed to unaltered vowel contexts. Experiment 2 showed that learning of Female A's speech containing the same set of stops but manipulated context duration would lead to different categorization results of Female B's stops: listeners temporally exposed to expanded context were more likely to identify a "t" and those exposed to compressed contexts were less likely to identify a "t" in Female B's speech, compared to participants exposed to unaltered temporal cues. These results suggest that listeners keep track of normalized cue distributions relative to the speaker's acoustic space and generalize those distributions to guide their speech perception behaviors.

Previous studies have shown that representationally complex referents are encoded slower into working memory (WM) but are retrieved faster (Hofmeister, 2011; Karimi & Ferreira, 2016). However, the cost of maintaining complex representations is still not well understood. Through two self-paced reading experiments, we investigated the cost of encoding, maintaining and retrieving complex representations in WM. While we replicated the facilitatory effect during retrieval, the slowdown during encoding was not consistent across our experiments. More critically, for the first time, our experiments demonstrated that maintaining complex representations in WM is less costly than maintaining their simple counterparts. Furthermore, we found that WM maintenance cost is reduced because complex target noun phrases are more distinct from other competing referents in WM than simple ones. Overall, our results showed that the semantic elaboration of complex representations can reduce maintenance cost and provided new perspectives into this understudied WM process.

Self-directed learners are often described as ‘intuitive scientists’, yet they also tend to struggle in assessments of their scientific reasoning. We investigate a novel explanation for this apparent gap between formal and informal scientific inquiry. Specifically, we consider whether learners’ documented failure to correctly apply the control of variables strategy might stem from a mismatch between their causal intuitions and task presentation. Children (7- and 9-year-olds) and adults were tested on a version of a traditional multivariate reasoning task (Tschirgi, 1980) that we modified to clarify ambiguous elements of the causal logic. A significant majority of participants in all age groups selected informative experiments on this modified task, avoiding confounded actions with positive tangible outcomes. This finding contrasts with the longstanding claim that learners do not correctly employ control of variables without extensive training and suggests that self-directed scientific inquiry may be intuitively suited to support causal learning goals.

Understanding human morality is of major interest across the cognitive and behavioral sciences. Empirical approaches often focus on two theories from moral philosophy — consequentialism and deontology —, explaining moral cognition by appealing to either calculation of consequences, adherence to rules, or both. By contrast, a third influential philosophical tradition — contractualism — has received little empirical investigation. According to contractualism, ethics is a matter of forming, adhering to, and enforcing (hypothetical) agreements. Drawing upon virtual bargaining — a recent psychological proposal that models social interactions in contractualist terms — we investigate moral contractualism in five preregistered online experiments (n = 3,636). We find that characteristically contractualist concerns (e.g., agreement, consent, mutual interests) heavily shape incentivized decisions in a new experimental game designed to split apart contractualism from consequentialism and deontology. Moreover, they influence moral judgments in three distinct settings. Contractualist reasoning may play a central role in human morality.

Source-goal events involve an object moving from the Source to the Goal. In this work, we focus on the representation of the object, which has received relatively less attention in the study of Source-goal events. Specifically, this study aims to investigate the mapping between language and mental representations of object locations in transfer-of-possession events (e.g. throwing, giving). We investigate two different grammatical factors that may influence the representation of object location in transfer-of-possession events: (a) grammatical aspect (e.g. threw vs. was throwing) and (b) verb semantics (guaranteed transfer, e.g. give vs. no guaranteed transfer, e.g. throw). We conducted a visual-world eye-tracking study using a novel webcam-based eye-tracking paradigm (Webgazer; Papoutsaki et al., 2016) to investigate how grammatical aspect and verb semantics in the linguistic input guide the real-time and final representations of object locations. We show that grammatical cues guide the real-time and final representations of object locations.

Capturing the structure of human conceptual knowledge is a challenging but fundamental task. The most prominent approach, Multidimensional Scaling (MDS), usually requires many similarity judgments, which leads to long experiments, and only provides a representation of a fixed set of stimuli. In contrast, we present a more flexible method that can generalize to novel stimuli. This method uses a child-friendly task that allows researchers to uncover the development of categories with fewer participant judgments. We evaluate this approach on simulated data and find that it can accurately reveal representations even when trained on data generated by groups that categorize differently. We then analyze data from the World Color Survey and find that we can recover language-specific color organization. Finally, we use the method in a novel developmental experiment and find age-dependent differences in how fruit categories are structured. These results suggest that our method is widely applicable in developmental tasks.

“Slavery ended in the United States because slavery is morally wrong.” This explanation does not seem to fit the typical criteria for explaining an event, since it appeals to ethics rather than causal factors as the reason for this social change. But do people perceive these ethical claims as explanatory, and if so, why? In Study 1, we find that people accept ethical explanations for social change and that this is predicted by their meta-ethical beliefs in moral progress and moral objectivism, suggesting that they treat morality somewhat akin to a causal force. In Study 2, we find that people recognize this relationship between ethical explanations and meta-ethical commitments, using the former to make inferences about individuals’ beliefs in moral progress and objectivism. Together these studies demonstrate that our moral commitments shape our judgments of explanations and that explanations shape our moral inferences about others.

Humans have the exceptional ability to efficiently structure past knowledge during learning to enable fast generalization. Xia and Collins (2021) evaluated this ability in a hierarchically structured, sequential decision-making task, where participants could build “options” (strategy “chunks”) at multiple levels of temporal and state abstraction. A quantitative model, the Option Model, captured the transfer effects observed in human participants, suggesting that humans create and com- pose hierarchical options and use them to explore novel con- texts. However, it is not well understood how learning in a new context is attributed to new and old options (i.e., the credit assignment problem). In a new context with new contingencies, where participants can recompose some aspects of previously learned options, do they reliably create new options or overwrite existing ones? Does the credit assignment de- pend on how similar the new option is to an old one? In our experiment, two groups of participants (n=124 and n=104) learned hierarchically structured options, experienced different amounts of negative transfer in a new option context, and were subsequently tested on the previously learned options. Behavioral analysis showed that old options were successfully reused without interference, and new options were appropriately created and credited. This credit assignment did not depend on how similar the new option was to the old option, showing great flexibility and precision in human hierarchical learning. These behavioral results were captured by the Option Model, providing further evidence for option learning and transfer in humans.

What information guides individuals to trust an algorithm? We examine this question across three experiments that consistently found explanations and relative performance information increased trust in an algorithm relative to a human expert. Strikingly however, in only 23% of responses (414/1800) did an individual’s preferred agent for a task (e.g., driving a car) change from human to algorithm. Thus, initial preferences were ‘sticky’ and largely resistant to large shifts in trust. We discuss theoretical and practical implications of this work and identify important contributions to our understanding of how summaries of information can improve people’s willingness to trust decision aid algorithms.

Question asking is a powerful means by which humans learn. However, asking a question requires searching through a massive space of possible questions to find a single question that is relevant and informative. How do humans efficiently accomplish this task? Drawing on prior research on other decision problems, we propose that the search for new questions is constrained by those encountered in the past, so that people frequently reuse questions (or parts of questions) rather than generating new questions "from scratch." We find empirical support for this prediction, and we find that this "question reuse" has consequences for the informational value of people's questions. Taken together, this research sheds new light on the mechanisms behind human question asking abilities and, more generally, how we narrow down a large space of possibilities to find a single solution.

Affective factors such as anxiety, confidence, and motivation can impair and enhance task performance. Here, we used drift diffusion modeling (DDM) to examine how these variables affect visualization, manipulation, and decision making on a mental rotation task (MRT). The effects of affective factors on visuospatial reasoning are largely unknown, perhaps in part because analyses are generally concerned with overall accuracy and reaction time (RT), without decomposing the stages of processing. With DDM, we decompose performance on a MRT into separate processing components, particularly the speed of information update (drift rate) and the amount of evidence accumulation (decision threshold). 106 adult participants performed a two-alternative forced-choice (2-AFC) MRT, and throughout, they rated their levels of anxiety, confidence, and motivation. We found that although anxiety, confidence, and motivation all impacted drift rate, only confidence affected the decision threshold. Moreover, we observed a unique role for confidence in mediating the links between gender and model parameters, as well as a unique moderating role of motivation in this mediation. Altogether, these findings shed light on the interrelations between affective factors in accounting for mental rotation performance in men and women, including the unique combination of confidence and motivation in explaining the gender difference in mental rotation performance.

In many real-world settings, it is crucially vital for agents to learn to communicate and cooperate. Different cooperation models have been proposed to represent cooperative relations among agents. However, the intensity of the cooperative relation has not received much attention. In particular, how it varied with spatial-temporal information has not been studied deeply. In this paper, we propose a temporal dynamic weighted graph convolution based multi-agent reinforcement learning framework (TWG-Q). We design a weighted graph convolutional network to capture cooperative information among agents. On top of that, a temporal weight learning mechanism is introduced to characterize intensities of cooperations. We design a novel temporal convolutional network in the temporal dimension to extract effective features for the multi-agent reinforcement learning. Extensive experiments show that our method significantly improves the performance of multi-agent reinforcement learning on the public benchmark of micromanagement tasks in StarCraft II.

Spatial perspective taking, in which people mentally adopt another person's view of the world, is a crucial component of everyday communication. We investigate spatial perspective taking in listeners interpreting ambiguous instructions from a partner, looking at how this behaviour varies with a human vs. computer partner (Exp. 1 and 2), and with individual differences in social and cognitive abilities (Exp. 3). Listeners' perspective taking tendencies vary with their individual differences in spatial orientation ability, with more othercentricism associated with better spatial orientation. In addition, partner identity influences perspective taking; however, in contrast to previous work, we find higher levels of egocentricism with a computer than a human partner.

Human language permits us to call to mind objects, events, and ideas that we cannot witness directly. This capacity requires that one links words not only to their referents, but to mental representations of those referents. Together with the recognition that words are used intentionally for communication, this link constitutes ‘verbal reference.’ Although the development of verbal reference is a pivotal achievement, questions concerning its origins remain. To address this gap, we investigate infants’ ability to establish a representation of an object that is hidden from view based on language input and to learn its name.

This study aimed to characterize the developmental trajectories of different cognitive component processes underlying planning decisions. Participants (ages 8-25 years) completed a planning task called Four-in-a-row. We used computational modeling to distinguish between three cognitive component processes of planning: planning depth, heuristic quality, and attentional oversights, each of which three contributed to better playing strength, but differed in their developmental trajectories. Specifically, from early to mid-adolescence, heuristic quality rapidly improved and contributed to better playing strength. From mid to late-adolescence, planning depth increased and supported better playing strength. Fewer attentional oversights were associated with better playing strength and this relation did not show age differences. Together, these results reveal sequential development of the cognitive component processes underlying planning, with early refinement of heuristic strategies, and gradual increases into young adulthood in the number of considered future actions, states, and outcomes. These findings provide a more complete account of the development of planning and its component processes.

Being able to learn from small amounts of data is a key characteristic of human intelligence, but exactly how small? In this paper, we introduce a novel experimental paradigm that allows us to examine classification in an extremely data-scarce setting, asking whether humans can learn more categories than they have exemplars (i.e., can humans do "less-than-one shot'' learning?). An experiment conducted using this paradigm reveals that people are capable of learning in such settings, and provides several insights into underlying mechanisms. First, people can accurately infer and represent high-dimensional feature spaces from very little data. Second, having inferred the relevant spaces, people use a form of prototype-based categorization (as opposed to exemplar-based) to make categorical inferences. Finally, systematic, machine-learnable patterns in responses indicate that people may have efficient inductive biases for dealing with this class of data-scarce problems.

Responsive predicates are clause-embedding predicates like English 'know' and 'guess' that can take both declarative and interrogative clausal complements. The meanings of responsive predicates when they take a declarative complement and when they take an interrogative complement are hypothesized to be constrained in systematic ways across languages, suggesting that these constraints represent semantic universals. We report an artificial language learning experiment showing that one of these proposed constraints is indeed reflected in the inferences participants make while learning a novel responsive predicate. Our results add support to a growing body of evidence linking semantic universals to learning.

A hallmark of human memory is the ability to integrate discrete experiences into cognitive maps. A fundamental form of this integration is transitive inference (TI), in which overlapping premises (A < B, B < C) are integrated into a unified representation of a relational hierarchy (A < B < C). Few existing theories provide a mechanistic account of this construction of relational knowledge and how it is shaped by different training conditions. This study builds on recent findings that TI is facilitated by chaining of overlapping premises, with a new behavioral experiment confirming an advantage over non-overlapping sequences matched for premise frequency and spacing. A subsequent simulation study shows that the chaining effect is captured by a particle filter which performs approximate Bayesian inference about the latent hierarchy. These results provide a better understanding of how chaining shapes the construction of relational knowledge in the face of uncertainty and forgetting.

Over the last 50 years, there have been efforts on behalf of the US government to simplify public legal documents for the benefit of society at large . However, there has been no systematic evaluation of how effective these efforts--collectively referred to as the ``plain language movement''--have been. Here we report the results of a large-scale longitudinal corpus analysis (n$\approx$225 million words), in which we compare every law passed by congress between 1951 to 2009 (as well as concurrent resolutions and proclamations), with a comparably sized sample of English texts from four different genres published during the same time period. We find that laws remain laden with features associated with processing difficulty--including center-embedding, passive voice, low-frequency jargon and capitalization--relative to each of the four baseline genres of English, and that the prevalence of these features has not meaningfully declined since the onset of the plain language movement (in some cases, their prevalence has increased). These findings suggest that top-down efforts to simplify legal language have thus far remained largely ineffectual, despite the apparent tractability of these changes, raising and informing difficult questions of law and public policy.

The traveling salesperson problem (TSP) is an NP-Hard problem that computers find difficult to solve. Humans are surprisingly good at solving the TSP, with solutions within 10% of optimal for problems with up to 100 points, constructed in time linear with the number of points. We propose that humans solve the TSP by initially clustering the points and then connecting them first within and then between clusters. In this study, 67 participants first clustered 40 stimuli and then solved them as TSPs. Strikingly, participants' TSP solutions perfectly followed their clusters for 52% of the stimuli. Further, participants' TSP solutions' were more congruent with their clusters for stimuli with statistically higher levels of clustered structure. This provides strong evidence for the clustering proposal. Random TSP solutions, however, showed no such congruence to cluster structure. These findings suggest that clustering might be a fundamental ability for reasoning about graph-theoretic algorithmic problems.

Linguistic descriptions of complex events have to map their temporal structure onto language. Formal accounts of embedded tense have argued that syntax mirrors event structure: Following directly from the syntactic properties of relative clauses, in complex sentences, events described by a relative clause are interpreted only relative to the utterance time and bear no temporal relation to the events of a matrix clause. From an event structural perspective, however, the temporal relationships between events do not have to mirror syntactic relations; rather, a central, salient event may anchor peripheral situations in time independent of its syntactic encoding. In two studies in English and German, we test which interpretations are accessible for past-under-past relative clauses, showing that tense interpretation in relative clauses is dependent on the matrix clause – at least when the matrix sentence describes a salient anchoring event, and the relative clause a backgrounded situation. Our results challenge the assumption that syntactic dependencies determine the temporal construal of events and provide new insight into how temporal semantic features are mapped onto linguistic structure.

What we do depends on what we know. But sometimes we try to decouple our behavior from our knowledge so that we appear not to know what we really do know. Such pretense behavior requires understanding how we would behave with different knowledge — and so provides a window into counterfactual self-simulation. However, little research has characterized and evaluated pretense relative to non-pretense behavior. Here, in a large-scale, pre-registered experiment, subjects played normal games of Battleships (trying to sink ships hidden in a grid), as well as ‘pretend’ games, where they were told all the ships’ locations but had to pretend they were playing normally. Relative to normal games, ‘pretend’ games demonstrated similar but exaggerated behavioral patterns. Furthermore, pretenders played rationally, but less so than non-pretenders. Despite these differences, ‘judge’ participants could not detect ‘pretend’ games. We discuss implications of these findings for accounts of theory of mind and metacognition.

New analyses of pseudo-homophone RTs (e.g., BRANE) from two published lexical decision studies clarify lexical involvement in pseudo-homophone processing and challenge widespread assumptions about word frequency effects. First, RTs increased along with increases in the proportion of base- word letters that appeared in the pseudo-homophone (e.g., WHELT-WELT slower than PHAWT – FOUGHT) suggesting that “No” decision-making is slowed by mutually reinforcing activation in phonological and orthographic representations of base word knowledge. Second, effects of base-word frequency were either extremely weak or nonexistent among pseudo-homophones that contained most or all the letters that make up their base word. In contrast, among pseudo-homophones that shared fewer letters with their base word (e.g., “PHAWT”), RTs for items derived from high-frequency base words were faster than RTs for items derived from low-frequency base words. These findings (i) challenge the ubiquitous assumption that lexical representations are frequency sensitive and (ii) suggest that lexical decision involves a spell-check.

People generally prefer functional explanations over mechanistic ones. Why? One possibility is that people value functional information more. But another possibility is that people don't have an overall preference for functional explanations; Instead, people might just expect this information to precede mechanistic information. Here, we ask whether people have preferences for the order of functional and mechanistic information in explanations. In a first set of studies, we show that adults do in fact prefer that functional information precedes mechanistic. In a second set of studies, we show that people have a more general preference for explanations to address the whole before parts. Finally, we show that the preference for function to precede mechanism may be related to the broader whole-before-parts preference.

Artificial language learning research has shown that, under some conditions, adult speakers tend to probability-match to inconsistent variation in their input, while in others, they regularize by reducing that variation. We demonstrate that this framework can characterize speaker behavior in a natural-language morphological inflection task: the lexicon can be used to estimate variation in speaker productions. In the task of German plural inflection, we find that speakers probability-match a lexical distribution conditioned on phonology, and largely disregard an alternative possible strategy of conditional regularization based on grammatical gender.

Statistical descriptions of reaction times are central components of quantitative attention models. It is often assumed that total reaction time is comprised of various components, e.g. sensory delays, decision making and motor execution contributions. We use machine learning to decompose observed total reaction times into sensorimotor and decision components, and evaluate which model assumptions maximize approximate Bayesian model evidence (free energy or evidence lower bound). We find that an inverse Gaussian decision time distribution combined with a very narrow Gaussian sensorimotor distribution can best explain human reaction time data. We also model outliers explicitly by a uniform background distribution. We find that the model assigns a small fraction of datapoints to this outlier distribution.

Research in cognitive science indicates that the effects of corrective feedback are powerful, but quite variable. Stemming from the Feedback Intervention Theory, we tested two features of feedback that may influence the learner’s attention to the self and ultimately their performance. In this zoom-based experiment, 6 to 8 year old children (N = 102) completed an online learning activity focused on solving mathematical equivalence problems. During the learning activity, children were assigned to different conditions which varied both feedback source (person vs. computer) and feedback type (correct-answer vs. correct-answer+verification). Feedback source was found to affect accuracy, where computer-based feedback resulted in higher accuracy compared to person-based feedback. Additionally, feedback type was found to affect strategy variability. Low knowledge children were more likely to use a variety of different strategies when they received correct-answer only feedback, while high knowledge children were less affected by the presence or absence of verification cues.

Numerous psychological findings have shown that mere exposure to ideas makes those ideas seem more true, a finding commonly referred to as the “illusory truth” effect (e.g. Hasheret al., 1977). In the presence of pervasive misinformation, this basic feature of cognition may undermine the functioning of a democratic society (Pennycook et al., 2018). However, genuine beliefs do not only produce judgments of truth, they also imply other beliefs and drive decision-making. Here, we sought to examine whether mere exposure to statements produces genuine beliefs by examining whether people draw inferences from statements after mere exposure. Surprisingly, and in contrast to familiarity-based accounts of the illusory truth effect (e.g. Dechene et al., 2010), we found that exposure to “premise” statements affected participants’ truth ratings for novel "implied" statements. This “illusory implication” effect suggests that exposure to false statements has further-reaching impacts than previously thought and calls for a new mechanistic account of these effects.

Traditional laboratory studies have found that people are more likely to retrieve surface matches than distant analogs, suggesting that superficial similarities exert a stronger influence than structural similarities on retrieval. However, it has been contended that the observed supremacy of surface similarity may have originated in experimental conditions that are unfairly adverse for the retrieval of distant analogs, as well as in a faulty separation between surface and structural similarity during the construction of surface matches. In two experiments, we presented a target item that maintained only superficial similarities with one extra-experimental source and only structural similarities with another one. By using natural items, we were able to avoid the shallow processing often attributed to experimental analogs, while carefully controlling that surface matches did not maintain structural similarities. Converging with traditional results, our data showed a more frequent retrieval of surface matches than of distant analogs, indicating a supremacy of superficial similarities during retrieval.

We study the wisdom of the crowd in the context of spatial knowledge, asking participants to identify US states and African countries on unlabeled tile maps. We use two question frames, asking participants to select where the target is present or eliminate where it is absent. Participants generally display overconfidence, often selecting small regions that do not include the target. We find strong wisdom of the crowd effects by aggregating participants' responses, especially by weighting the individual responses according to the size of their selection. The weighted crowd outperforms all but a few participants for the US states and all participants for the African countries. We also find wisdom of the crowd within effects, by aggregating the present and absent frames for the same participant. We discuss the implications of our findings for understanding how people express uncertain spatial knowledge and the potential use of crowd aggregation in real-world applications.

Studies have found that when innovation involves recombining cultural traits, partially-connected populations produce higher levels of cultural complexity than fully-connected populations by avoiding cultural homogenization. However, population connectedness is only one of many factors that could promote cultural diversity and thus cultural complexity. Here, we examine whether people's preference for copying members of their own social group could also fill this role. Our simulations reveal that even in fully-connected populations, ingroup-biased transmission results in greater cultural complexity than unbiased transmission. Moreover, in partially-connected populations, this bias interacts with population structure to produce even higher levels of cultural complexity than population structure alone. Finally, by incorporating population turnover into our model, we shed light on the trade-off between promoting cultural diversity versus limiting cultural loss.

There is growing evidence that spatial abilities can be improved through training, including participation in hobbies and everyday activities that involve spatial thinking. In order to better assess the contributions of everyday spatial activities to the development of spatial skills, we developed a new self-report questionnaire of spatial activities by adding updates and navigation activities to an existing questionnaire. A principal component analysis revealed five interpretable components which were compared to measures of perspective taking, mental rotation and two other self-report scales. Small but significant correlations were found between the ‘navigation’ component of the spatial activities questionnaire and a self-report measure of sense of direction, as well as self-reported childhood wayfinding experience. No sex difference was found on the ‘navigation’ component. This questionnaire is currently being used in a large study of spatial abilities.

What kinds of arguments do people make, and what effect do they have on others? Normative constraints on argument-making are as old as philosophy itself, but little is known about the diversity of arguments made in practice. We use NLP tools to extract patterns of argument-making from the Reddit site "Change My View'' (r/CMV). This reveals six distinct argument patterns: not just the familiar deductive and inductive forms, but also arguments about definitions, relevance, possibility and cause, and personal experience. Data from r/CMV also reveal differences in efficacy: personal experience and, to a lesser extent, arguments about causation and examples, are most likely to shift a person's view, while arguments about relevance and presumption are the least. Finally, our methods reveal a gradient of argument-making preferences among users: a two-axis model, of "personal—impersonal'' and "concrete—abstract'', can account for nearly 80% of the strategy variance between individuals.

The current work tests the hypothesis that the island status of clausal adjuncts, as determined by judgments on wh-questions, are predicted by the degree of “backgroundedness” of the adjuncts, as determined by a separate negation task. Results of two experiments support the hypothesis that acceptability of extraction from adjuncts in wh-questions is inversely correlated with the degree to which the adjunct is backgrounded in discourse. Taken together, results show that temporal clausal adjuncts (headed by before, after, while) are stronger islands than adjuncts that are causal (here, headed by to or by). This demonstrates that adjuncts differ in degree of island status, depending on their meaning, despite parallel syntactic structure.

Recent experimental and computational modelling work has found that languages are shaped by the referential context in which they operate. Wray and Grace (2007) argue that even compositionality, traditionally regarded as a universal and fundamental feature of human languages, may have only culturally evolved in response to changing social contexts. But how can the referential contexts of individual interactions come to shape the level of compositionality in the language of an entire community? To explore this question, we propose an iterated hierarchical Bayesian model that shows how partner-specific linguistic innovations can be generalized as community-wide features via a context-sensitive pathway. Our simulations show that the degree of compositionality that evolves in the language of a community depends on the communicative needs of its members, but also on the degree of user uncertainty over the nature of those needs, and on the level of heterogeneity in the community's needs.

Beyond seemingly lower-level features such as color and motion, visual perception also recovers properties that are more commonly associated with higher-level thought — as when an upwardly accelerating object is seen as self-propelled, and resisting the force of gravity. Past work has explored how speed changes drive the perception of physical forces, but might the reverse also be true? Does seeing a speed change as resisting the force of gravity make us more likely to notice it in the first place? In four experiments, observers were more sensitive to objects’ accelerations when they moved upward (i.e. when those accelerations were opposite to gravity), and they were more sensitive to objects’ decelerations when they moved downward (i.e. when those decelerations appeared as ‘braking’ against gravity). We conclude that the perception of physical forces is not merely an outcome of visual processing, but also determines the perception of other, seemingly lower-level, features of how objects move.

Children start to communicate and use language in social interactions from the very early stages in development. This allows them to experiment with their current linguistic knowledge and receive valuable feedback from their interlocutors. We conducted a large-scale corpus study to examine the quality of positive and negative Communicative Feedback signals that caregivers provide in terms of time-contingent responses and clarification requests. We found evidence for the effect of such feedback in supporting children’s production of intelligible speech. The broad impact of this paper is to highlight how general social feedback mechanisms that govern human communication can also support child language acquisition.

The current study investigates children’s understanding of the social dynamics of complex groups. We asked children to use relative differences in intragroup status to predict the behaviors of individuals. Specifically, who do children (ages 3 to 10, n = 120) and adults (n = 34) believe a subordinate “worker” would be loyal to (another worker or to their “boss”), and whom the worker would prefer to socialize with? Young children predicted that workers would be loyal to other workers, but as age increased so did children’s tendency to predict that workers would be loyal to bosses. Regardless of age, children and adults believed that workers would prefer to spend time with other workers. These results have important implications for how children understand and navigate nuanced power differentials within a group.

Participants learned to classify a set of rock images into geologically-defined science categories. We then investigated the nature of their category-based memory representations by collecting old-new recognition data in a subsequent transfer phase. An exemplar model provided better qualitative accounts of the old-new recognition data than did a prototype or clustering model. However, to account for the variability in recognition probabilities among the old training items themselves, a hybrid-similarity exemplar model was needed that took account of distinctive features present in the items. The study is among the first to use computational models for making detailed quantitative predictions of old-new recognition probabilities for individual items embedded in complex, high-dimensional similarity spaces.

Behavioural cascades through social reinforcement are ubiquitous in human and animal groups. Nonetheless, we only have a rudimentary understanding of which choices are more likely to initiate cascades. Here we investigate the role of response time (RT) asymmetries (i.e., one choice alternative being selected faster than the other) in shaping behavioural cascades by combining an empirical and modelling approach. RT asymmetries are found in a wide range of decision-making contexts, including police shooting, risky choice, and memory retrieval. How they shape collective dynamics, is, however, unknown. Applying evidence accumulation models to analyse behaviour in a sequential choice paradigm, we show that RT asymmetries crucially shape behavioural cascades. Using simulations, we show that especially start point biases (and to a less extent varying drift rates) can initiate cascades, as they lead to rapid choices for one choice alternative. Our results highlight the importance of RT asymmetries in shaping collective dynamics.

Adult speakers rarely produce a verb that does not agree with its subject in number, unless the sentence contains nouns with clashing pluralities. For example, a sentence such as “The key next to the cabinets…”, sometimes elicits a plural verb, and such attraction errors are more common with singular than plural heads (the attraction asymmetry). Both attraction and attraction asymmetry have been instrumental in understanding the computations underlying agreement production. Interestingly, developmental studies of agreement have often found very different patterns of agreement errors in children, leading to the conclusion of different mechanisms for agreement production in children and adults. Using a referential communication game, we demonstrate that English-speaking children as young as 5 years of age show robust agreement attraction. Children 6 years and older also demonstrate the attraction asymmetry. These findings support similar mechanisms underlying agreement production in children and adults.

Categorical perception involves our perceptual system creating sharp boundaries along an objectively continuous stimulus property, such as the discrete colors of the rainbow being perceived despite continuous change in wavelength. The same mechanism is thought to take place in facial emotion perception. But how are emotions at these boundaries perceived? We presented participants with morphed emotional faces made by blending different emotional expressions in equal proportions. Next, we asked participants to respond freely to these ambiguous face morphs and examined these responses via natural language processing methods. The results showed that participants used many more labels than those related to the categories which went into the morphs. These results can inform theories on categorical facial perception as well as the mental representation of facial expressions.

Central to rationality is the ability to think about our beliefs, and make sure they are based on good reasons. If we have several conflicting reasons to believe something, we wait for more information before making a decision, perhaps rechecking our original reasons before proceeding. In these studies, we presented great apes and young children with conflicting evidence about the location of a reward. We found that apes double-checked the evidence for their original choice before making a final decision, revealing an awareness of their own beliefs and reasoning not hitherto documented. Young children, in contrast, were more sensitive to peer disagreement than conflicting physical evidence, illustrating the distinctively social nature of human rationality.

The human capacity for causal judgment has long been thought to depend on an ability to consider counterfactual alternatives: the lightning strike caused the forest fire because had it not struck, the forest fire would not have ensued. To accommodate psychological effects on causal judgment, a range of recent accounts of causal judgment have proposed that people probabilistically sample counterfactual alternatives from which they compute a graded index of causal strength. While such models have had success in describing the influence of probability on causal judgments, among other effects, we show that these models make further untested predictions: probability should also influence people's metacognitive confidence in their causal judgments. In a large (N=3020) sample of participants in a causal judgment task, we found evidence that normality indeed influences people's confidence in their causal judgments and that these influences were predicted by a counterfactual sampling model. We take this result as supporting evidence for existing Bayesian accounts of causal judgment.

People have a remarkable ability to remain steadfast in their beliefs in the face of large-scale disagreement. This has important consequences (e.g., societal polarization), yet its psychological underpinnings are poorly understood. In this paper, we answer foundational questions regarding belief persistence, from its prevalence to variability. Across two Experiments (N = 356, N = 354), we find that participants are aware of societal disagreement about controversial issues, yet overwhelmingly (~85%) do not question their views if asked to reflect on this disagreement. Both studies provide evidence that explanations for persistence vary across domains, with epistemic and meta-epistemic explanations among the most prevalent.

Despite progress in understanding the sources of difficulty in solving insight problems, how new ideas are discovered, implemented, and learned is poorly understood. We report an experiment testing a theory of how individuals use failed attempts to discover new ideas. We compared performance on the nine-dot problem with a variant requiring solution using three lines rather than four. Results supported predictions that the three-line variant is easier than the four-line, and that transfer of solution knowledge from the three- to the four-line version is facilitative, but not vice-versa. Additionally, varying the spacing between dots facilitated discovery and transfer of solutions in both variants. Our theory specifies a priority order for seeking new ideas that offers a partial solution to the frame problem. Individuals first seek ideas from the problem statement and attempts they make. Only when these sources fail do they resort to searching memory or the external task environment.

We present a new cognitive architecture that combines two neurobiologically-plausible computational elements: (1) a variant of predictive processing known as neural generative coding and (2) hyperdimensional / vector-symbolic models of human memory. We draw inspiration from well-known cognitive architectures such as ACT-R, Soar, Leabra, and Spaun/Nengo. Our cognitive architecture, the COGnitive Neural GENerative system (CogNGen), is in broad agreement with these architectures, but provides a level of detail between ACT-R’s high-level, symbolic description of human cognition and Spaun’s low-level neurobiological description. CogNGen creates the groundwork for developing agents that learn from diverse tasks and model human performance at larger scales than what is possible with existent cognitive architectures. We test CogNGen on a set of maze-learning tasks, including mazes that test short-term memory and planning, and find that the synergy between its predictive processing and vector-symbolic components allow it to master the maze tasks.

Human behavior is modulated by financial incentives, but it is not well understood what types of behavior are immune to incentive and why. The cognitive processes underlying behavior appear to create restrictions on the effect an individual's motivation will have on their performance. We investigate a classic category learning task for which the effect of financial incentives is still unknown (Shepard, Hovland, & Jenkins, 1961). Across four renditions of a category learning experiment, we find no effect of incentive on performance. On a fifth experiment requiring category recognition but not learning, we find a large effect on response time and small effect on task performance. Humans appear to selectively apply more effort in valuable contexts, but the effort is disproportionate with the performance improvement. Taken together, the results suggest that performance in tasks which require novel inductive insights are relatively immune to financial incentive, while tasks that require rote perseverance of a fixed strategy are more malleable.

Human adults can figure out what happened by combining evidence from different sensory modalities, such as vision and sound. How does the ability to integrate multi-modal information develop in early childhood? Inspired by prior computational work and behavioral studies with adults, we examined 3- to 8-year-old children's ability to reason about the physical trajectory of a ball that was dropped into an occluded Plinko box. Children had to infer in which one of three holes the ball was dropped based on visual information (i.e., where the ball landed) and auditory information (i.e., the sounds of the ball colliding with parts of the box). We compare children's responses to the predictions of four computational models. The results suggest that although even the youngest children make systematic judgments rather than randomly guessing, children's ability to integrate visual and auditory evidence continues to develop into late childhood.

Language and gender are inextricably linked; we regularly make reference to the genders of individuals around us, and the language used to do so recursively feeds the biases we hold about gender in the social world. What has been left under-investigated is the role that individual, rather than societally-held, ideologies about gender play in the linguistic system. In two web-based studies, we investigate the processing and production of gender-neutral role nouns such as congressperson as a function of individual gender ideology and political alignment. Our results indicate an asymmetry between the processing and production of such nouns: while individuals’ gender ideologies do not modulate processing, they do interact with political party in production tasks such that Democratic participants with more progressive gender ideologies produce more gender-neutral role nouns. We argue that these forms have become linguistic resources for indexing social progressiveness, leading to their use by Democrats and avoidance by Republicans.

Dogs have a unique evolutionary relationship with humans and are relied upon in a variety of working roles, yet little is known about the kinds of visual information available to them, as well as how they direct their attention within their environment. The present study, inspired by comparable work in infants, aimed to categorize the visual statistics (specifically the identity of objects) available to dogs during a common event in their daily lives, a walk. Using a head-mounted eye-tracking apparatus that was custom designed for dogs, four dogs walked on a pre-determined route outdoors under naturalistic conditions generating a total of 49,431 frames for analysis. On average, there were few individual differences between dogs. Dogs looked proportionally more to people and plants than to other object categories in their environment, like the sky which they appeared to consider as background. The results of this project provide a foundational step towards understanding how dogs’ look at and interact with their physical world, opening up avenues for future research into how they complete tasks, and learn and make decisions, both independently and with a human social partner.

An examination of emotion recognition and response to music can isolate perception and experience of emotion from the potentially confounding effects of other social cues (e.g., faces). Participants aged 5-6-years-old listened to clips of calm, scary, and sad music and either identified the emotional content of the music or reported on the feelings elicited by the music clip. Children correctly identified the emotions and reported feeling the emotions conveyed in music above chance. Accurately recognizing and resonating with the emotion conveyed were correlated, although the relationship varied as a function of child characteristics. Specifically, children whose parents reported them as showing more prosocial behavior had significantly greater alignment between emotion recognition and resonation. Results provide new insights into emotion perception in the absence of direct social signals and provide evidence that children’s ability to perceive and resonate with the emotion conveyed through music differs depending on key socioemotional characteristics.

A number of findings suggest that people’s expectations about the future are unrealistically optimistic (e.g. Sharot et al., 2011). This bias is thought to result from the “motivational modulation” of evidence, driven by the desire to feel positively about one’s own future (Sharot, 2011). However, evaluating “bias” in belief revision requires careful comparison against a rational standard, and recent arguments and findings (Shah et al., 2016) give reason to doubt much of the evidence for optimism bias. Descriptive Bayesian models allow for a direct comparison of human belief updating against the Bayesian rational standard (Tauber et al., 2017). Here, these analyses indicate widespread “conservatism,” or weaker-than-rational belief revision. However, in contrast to the widely-reported “optimism bias,” participants more commonly displayed pessimism than optimism in their belief revision. Both effects were marked by significant heterogeneity, with a sizable fraction of participants engaging in largely rational updating.

Investigation of the relationship between cognitive function and academic performance has recently pivoted from differences in intelligence to executive function. To date, these studies have focused disproportionately on samples recruited from Western countries, despite evidence in support of cultural differences in these putative relationships. To address this gap, the present study investigated whether differences in inhibitory and/or attentional control could predict academic performance in a sample of Chinese adolescents (n=42). Participants reported on demographic details and completed both the Simon task and Attention Network Test. Data were analyzed using multiple linear regression controlling for gender, age, SES, English language proficiency, processing speed, and fluid intelligence. Results showed that one index of inhibitory control derived from flanker task performance explained a significant amount of unique variance in academic performance. Our findings provide evidence that executive function, specifically inhibitory control, plays a significant role in academic performance.

Because there are many situations in our daily life in which the option space is not discrete but continuous, recently developed decision models have been able to examine the cognitive processes underlying choice in laboratory tasks with a continuous outcome space. One of the most important of these continuous models is the circular diffusion model (CDM) by Smith, which has been shown to account for continuous space data from a wide range of paradigms, including color identification, orientation, brightness, pricing. However, in addition to the inherent complexity of this model, it has become more complex in order to predict reliable data patterns, making it a tool only for experts. Here we propose a more easy version of the CDM, the EZ version, to fit the model on continuous scale data. The EZ-CDM for continuous choice space tasks can estimate the parameter values for the cognitive processes underlying without considering the response time distribution but only using traditionally favored summary statistics (i.e. the mean and variance of response time, and angular variance of accuracy.) by simple formulas that can be computed easily and needs neither theoretical knowledge of model fitting nor much programming skills. Here, we formulate the EZ method and show that, despite being easy and fast to calculate, it’s performance in recovering true parameters is acceptable.

Prior research suggests that humans rationally integrate semantic expectations and the likelihood of noise corruptions in robust language comprehension. For such inferences to be maximally useful in normal communication, people should be sensitive to the fine-grained statistical structure of likely errors that vary across contexts, but this has not yet been shown. Here we hypothesize that a rational language user should represent fine-grained patterns of potential mistakes. To test this hypothesis, we employ a novel free-form text editing task, where participants are asked to edit sentences with subject-verb agreement errors, among stimuli with various type of other errors. We build a Bayesian cognitive model to infer the parameters of the model of errors, based on the observed frequency of the type of edits and judgments of plausibility of sentences without agreement errors in an independent norming study. Results suggest that the full Bayesian model explains the editing choice data better than alternative models that attribute the variance in human behaviors to either prior or context-insensitive error likelihood. Furthermore, the estimated likelihood parameters show a pattern in the distribution of errors qualitatively similar to that reported in empirical research of language production, suggesting that humans' intuitive theory of errors may be rational as to represent language-wise statistical structure of errors in the environment. These results provide quantitative evidence that humans closely track prior statistics over linguistic forms and deploy a context-sensitive model of errors in reasoning about the cause of problem from erroneous input.

When deciding whether and how to punish, people consider not only the potential direct consequences, but also, how their choice will affect observers’ judgements about the values and motives underlying the choice. We formalize the decision to punish as a rational communicative social action (RCSA). The model generates rational decisions to punish, incorporating anticipated observers’ judgements obtained from a recursive model of inference using an intuitive theory of mind. Using this model, we synthesize patterns of human punishment from recently published papers. RCSA thus offers a formal model of the cognitive process that humans use to balance preferences for how they are perceived, with other goals for punishing.

While research has documented that children can compensate for overt cues to teaching inefficacy through exploration of novel solutions, an important question is whether children use exploration to detect inefficacy. Further, to move beyond ineffective teaching, learners must prioritize their own ideas. Thus, girls could be disadvantaged due to a greater emphasis on people-pleasing in their socialization. We tested 7- to 10-year-olds using a novel, video-game paradigm. Children were shown ineffective instruction but could only discover its inefficacy by independently attempting the solution. Children generally attempted the taught solution successfully and rationally traded-off between instruction and exploration. However, gender differences emerged in exploration, solving, and learning even after controlling for video game experience and teacher gender. These results have important implications, as girls may have a greater need to move beyond ineffective teaching when exposed to sexist content or beliefs.

One of the most influential modern theories of morality, Moral Foundations Theory, proposes that morality is formed on innate and shared modular foundations. Psychologists have studied the conceptual development of these moral foundations in childhood, but there exists no comprehensive effort on characterizing the early emergence of moral foundations in naturalistic settings. We explore the emerging order of moral foundations through child and caretaker speech. Using computational methods, we contribute an annotated dataset of moral utterances and find that the individualizing foundations emerge earlier than the binding foundations. Furthermore, caretakers tend to talk more about fairness and degradation, while children talk more about cheating. These results are robust across child gender, family's social class, and race.

Languages often express grammatical information through inflectional morphology, in which grammatical features are grouped into strings of morphemes. In this work, we propose that cross-linguistic generalizations about morphological fusion, in which multiple features are expressed through one morpheme, can be explained in part by optimization of processing efficiency, as formalized using the memory--surprisal tradeoff of Hahn et al. (2021). We show in a toy setting that fusion of highly informative neighboring morphemes can lead to greater processing efficiency under our processing model. Next, based on paradigm and frequency data from four languages, we consider both total fusion and gradable fusion using empirical measures developed by Rathi et al. (2021), and find that the degree of fusion is predicted by closeness of optimal morpheme ordering as determined by optimization of processing efficiency. Finally, we show that optimization of processing efficiency can successfully predict typological patterns involving suppletion.

Category learning is a fundamental skill across modalities. Previous studies have investigated how children learn categories, primarily focusing on a single modality within a study. As a result, it is not well understood how the same children approach category learning tasks across modalities. In this study, we investigate 7–12-year-old children’s ability to learn rule-based or information-integration categories in the auditory and visual modalities. Our results indicate that children learn and generalize their knowledge better for visual than auditory categories, regardless of category type, and for rule-based than information-integration categories, regardless of modality. Even so, learning was strongly correlated across all tasks. Children overwhelmingly used unidimensional rule-based strategies to learn, regardless of whether it was optimal for the task. These results demonstrate that there are individual differences in children’s ability to learn perceptual categories across modalities and suggest that category learning in children is both category- and modality-general.

In an experimental study, we investigated how social interaction dynamics affect collective creativity. Pairs of participants collaborated in a computer game, creating “beautiful and interesting” shapes by moving tiles on a large touchscreen. We identified naturally emerging interaction styles by applying k-means clustering on participants’ tile moves. The game allowed us to quantify the unfolding creative process in a well-defined search space. Pairs characterized by a single dominating member tended to visit fewer areas of the solution space, stay there longer and created on average more (but less original) shapes. In contrast, pairs that took turns with every tile move tended to explore more, stayed in each area of the solution space for less time and created fewer (but more original) shapes. While previous literature found conflicting effects of ‘creating with another’, the current paper suggests naturally emerging interaction styles as a differentiating factor underlying how collective creativity unfolds.

Accounts of human and machine concept learning face a fundamental challenge. Some approaches, notably deep learning, frequently achieve human-level performance on specific tasks but lack consistently human-like—i.e. generalizable, composable, and explainable—solutions. Others, including classic symbolic accounts, produce human-like hypotheses but scale poorly. We present a model of learning which is both human-level and human-like. It represents concepts as program-like expressions formed by applying a series of higher-order inferences that iteratively revise preexisting concepts into novel target concepts. Learning seeks the best combination of revisions under a Bayesian score. This model predicts learning behavior in 392 humans over 100 computationally sophisticated concepts more accurately than alternative models (Enumerate, Metagol, RobustFill, Fleet) while using three orders of magnitude less computation. This work shows how humans plausibly construct sophisticated algorithmic representations, a necessity for compelling human-like artificial intelligence.

Neural networks struggle in continual learning settings from catastrophic forgetting: when trials are blocked, new learning can overwrite the learning from previous blocks. Humans learn effectively in these settings, in some cases even showing an advantage of blocking, suggesting the brain contains mechanisms to overcome this problem. Here, we build on previous work and show that neural networks equipped with a mechanism for cognitive control do not exhibit catastrophic forgetting when trials are blocked. We further show an advantage of blocking over interleaving when there is a bias for active maintenance in the control signal, implying a tradeoff between maintenance and the strength of control. Analyses of map-like representations learned by the networks provided additional insights into these mechanisms. Our work highlights the potential of cognitive control to aid continual learning in neural networks, and offers an explanation for the advantage of blocking that has been observed in humans.

We present a neural dynamic model that perceptually grounds nested noun phrases, i.e., noun phrases that contain further (possibly also nested) noun phrases as parts. The model receives input from the visual array and a representation of a noun phrase from language processing. It organizes a search for the denoted object in the visual scene. The model is a neural dynamic architecture of interacting neural populations which has clear interfaces with perceptual processes. It solves a set of theoretical challenges, including the problem of keeping a nested structure in short-term memory in a way that solves the problem of 2 and massive binding problem emphasized by Jackendoff (2002). The model organizes a search for the objects that are referenced in that structure. We motivate the model, demonstrate simulation results, and discuss how it differs from related models.

As efficient systems of communication, languages are usually expected to map meanings to forms in a one-to-one way, using for example the same affix form (e.g., -s in English) every time a particular meaning is intended (e.g., plural number), and placing affixes with the same meaning consistently in the same position (e.g., always suffixal). Forms and positional rules extending over contexts with a common meaning (e.g., plural in 1PL, 2PL, 3PL) are thus considered natural, and those extending over contexts with no consistent common meaning (e.g., 1PL and 3SG) are considered unnatural. Natural patterns are most common cross-linguistically, and most learnable in experiments; however, little is yet know about differences between unnatural classes. In this study we explore syncretism (i.e., use of the same form in different functions) and affix position in the domain of person and number agreement in verbs, both cross-linguistically and in artificial language learning experiments. Results from the two approaches and both phenomena converge in finding a gradient of (un)naturalness. Rather than a dichotomous natural/unnatural distinction, we found that both cross-linguistic frequency and learnability are proportional to the amount of shared feature values among the contexts requiring the same form or position. We argue that a cognitive bias towards similarity-based structure explains our experimental results and could be driving the patterns observed in natural languages.

Infant points are often accompanied by preverbal vocalisations but little is known about their acoustic properties. In this study we explore the role of point-accompanying preverbal infant vocalisations in expressing intention, declarative or imperative. We test whether the interpretation of these preverbal expressions is independent from the communicative and cultural context by assessing Swiss adult speakers’ ability to interpret preverbal point-accompanying vocalisations from Shipibo-Konibo (Peru) children. Results suggest that adults can easily distinguish declarative and imperative preverbal pointing acts by their accompanying vocalisations. We thus show that acoustic properties of these vocalisations contain a rich amount of information which is understood by members of a different culture. This hints towards a larger preverbal repertoire of communicative tools than previously assumed.

There is a pressing need to inform the public and drive personal and political action to mitigate climate change. Recent theorizing suggests that people’s intuitive theories may be key levers for affecting attitude and behavior change (Weisman & Markman, 2017). We asked 400 participants to estimate the probability of different future events related to climate change. Our findings indicate that people hold coherent theories of climate change, that these theories were predictive of policy positions, and that they varied across individuals and across partisan groups. In particular, political independents and Republicans’s causal models underestimated the impacts of climate change. We also examined an educational intervention that explains a key mechanism of climate change (Ranney & Clark, 2016). Unfortunately, while the intervention increased mechanistic knowledge, it did not affect participants’ beliefs about climate outcomes. Nevertheless, the coherence of participants’ intuitive theories gives hope that other educational interventions could have meaningful and systematic effects on policy attitudes and political behaviors.

Cardiopulmonary resuscitation (CPR) is the single greatest determinant in survival from cardiac arrest and is an essential part of every medical professional’s toolkit. Effectively responding to cardiac arrests requires CPR proficiency, which is best retained through frequent training. One such model is Resuscitation Quality Improvement (RQI) that requires users to exhibit proficient performance in compression and ventilation skills every 3 months. While frequent refresher trainings are superior to the traditional 24-month intervals (Cheng et al., 2020), they still need to consider inter-individual differences. Healthcare environments are rife for personalized, adaptive approaches to tracing users’ proficiency over time. To this end, we explore how users that perform similarly over time can be clustered together. Such performance profiles could ultimately enhance the benefits of frequent training with personalized efficiency for users with different needs. With the long-term goal of building an adaptive scheduling tool in mind, we present some initial explorations in this domain. Using k-means clustering, we show that a small number of clusters seems sufficient to create meaningful performance profiles to make out-of-sample predictions. Furthermore, our simulation study suggests that fuzzy membership to said clusters can be leveraged to enhance predictions. We discuss potential next steps in which these fuzzy performance profiles can be employed by more powerful predictive models to move the field towards personalized, adaptive training schedules that improve learning efficiency with the goal of increasing survival outcomes after cardiac arrest.

The opportunistic assimilation hypothesis posits that struggling and failing to solve a problem creates failure indexes, or long-term memory traces of the problem, that creates sensitivity to environment hints that trigger insight experiences. Past laboratory research has cast doubt on the usefulness of such hints during incubation breaks, but laboratory work is limited in its ability to recreate the diversity of stimuli in everyday life the opportunistic assimilation hypothesis requires. The current paper evaluates the insight experiences of over 150 participants who solved an insight math puzzle outside the lab for the presence of incidental hints that aided with problem solving. Across two studies, participants reported that chance hints in the wild had helped them to solve the puzzle and triggered insight moments. This suggests that opportunistic assimilation may play a role in everyday insight experiences and should not be discounted in future research.

Many tasks that are easy for humans are difficult for machines. Particularly, while humans excel at tasks that require generalising across problems, machine systems notably struggle. One such task is the Synthetic Visual Reasoning Test (SVRT). The SVRT consists of a range of problems where simple visual stimuli must be categorised into one of two categories based on an unknown rule that must be induced. Conventional machine learning approaches perform well only when trained to categorise based on a single rule and are unable to generalise without extensive additional training to tasks with any additional rules. Multiple theories of higher-level cognition posit that humans solve such tasks using structured relational representations. Specifically, people learn rules based on structured representations that generalise to novel instances quickly and easily. We believe it is possible to model this approach in a single system which learns all the required relational representations from scratch and performs tasks such as SVRT in a single run. Here, we present a system which expands the DORA/LISA architecture and augments the existing model with principally novel components, namely a) visual reasoning based on the established theories of recognition by components; b) the process of learning complex relational representations by synthesis (in addition to learning by analysis). The proposed augmented model matches human behaviour on SVRT problems. Moreover, the proposed system stands as a more realistic account of human cognition, wherein rather than using tools that have been shown successful in the machine learning field to inform psychological theorising, we use established psychological theories to inform developing a machine system.

Iconicity refers to a resemblance between word form and meaning. Previous work has shown that iconic words are learned earlier and processed faster. Here we examined whether iconicity would also affect a recognition memory task. We also manipulated the level at which items were encoded—with a focus on either their meaning or their form—in order to gain insight into the mechanism by which iconicity would affect memory. In comparison with non-iconic words, iconic words were associated with a higher false alarm rate, a lower d’ score, and a lower criterion C. We did not observe any interaction between iconicity and encoding condition. We also conducted an analysis of recognition memory megastudy data and found that iconicity was predictive of higher false alarms and a lower criterion C across 1,646 items. We interpret these results as suggesting that iconicity leads to a feeling of familiarity in recognition memory.

Modern Hopfield networks (HNs) exhibit properties of a Content Addressable Memory (CAM) that can store and retrieve a large number of memories. They also provide a basis for modelling associative memory in humans. However, the implementation of these networks is often not biologically plausible as they assume the strengths of synaptic connections are symmetric, and utilize functions that rely on many-body synapses. More biologically realistic versions of Modern HNs have been proposed, although these implementations often still utilize the softmax function. Computing the softmax for a single node requires the knowledge of all other neurons, and thus still poses a degree of biological implausibility. We present a Modern HN that uses a version of softmax that can be computed in a more bio-realistic way, and hence moves us closer to a model of memory that is biologically sound. We also show that our proposed network can learn the connection weights using a local learning rule, derived from gradient descent on the energy function. Finally, we verify that our proposed biological network behaves like a Modern HN and explore some of its other interesting properties.

According to the embodied cognition view, retrieving the meaning of action-related language requires the participation of sensorimotor processes. In consequence, an increasing number of neurostimulation (TMS and tDCS) studies have tried to test this idea. In the present study, we aim to evaluate the evidential value of this body of research (N = 43) by means of p-curve analyses. Our results suggest that the published studies so far do not yet allow to establish if they explore real effects beyond a reasonable doubt. We also found that these studies are quite underpowered (estimated power < 30%), which suggests that a large percentage of these findings are, in fact, false-positive results. In sum, our study suggests that the results derived from brain stimulation studies of embodied semantics are not as reliable as would be desirable. We give some recommendations that will be important for future research on this topic.

Resolving overloading in communication requires attention to context. Previous research has found that the mutual assumption of cooperation during communication can act as a powerful constraint, allowing successful resolution under ambiguity. In this study, we investigate two specific types of cooperative context used in a communicative task which arise from different sources: beliefs and actions. In belief-driven communication, signals are interpreted in context of what else a speaker could have said about the world. Here communicators assume that the speaker aims to change the listener's beliefs by providing the most straightforward signal. In action-driven communication, signals are considered in terms of what a speaker can reasonably ask others to do. Signaling can be sensitive to utility considerations of acting and interacting in the physical world. Through a communication game, we tested how listeners would interpret an ambiguous signal using belief-driven or action-driven strategies. We find that while no one strategy is dominant overall, individuals are highly consistent in which strategy they employ when forced to decide.

Where do mathematical insights come from? According to classic accounts, creativity is a multi-stage process that involves combining ideas in novel ways. Evidence for these accounts, however, is drawn from artificial lab-based settings or is zoomed out from the messy, moment-to-moment details of discovery. Here, we examine a video corpus of expert mathematicians generating proofs in an ecologically valid setting. We find that mathematicians begin by creating a variety of inscriptions. They then interact with these inscriptions through gaze, speech, gesture, and writing. When they experience an insight, however, their interactions become unpredictable, and they begin to connect inscriptions in novel ways (quantified by an information-theoretic measure, surprisal). Expert mathematical discovery, we conclude, exhibits the stages and combinatorial processing that have been proposed to characterize creativity. Even at the pinnacle of abstraction, at the highest levels of expertise, new ideas are born when the body discovers unexpected affinities among ideas.

Recent work has raised a question about whether adult language learners take advantage of indirect negative evidence (here, statistical preemption) while learning a new language. Statistical preemption predicts that exposure to conventional formulations results in better recognition that unconventional formulations are unacceptable. In a preregistered study, 61 undergraduates enrolled in Spanish classes were exposed to instances of conventional constructions in Spanish for 3 days to determine whether the exposure would bring their responses to unconventional formulations into closer alignment with those of native Spanish speakers. Judgment data confirms an effect of statistical preemption: students showed an increased recognition of the fact that unconventional (unwitnessed) formulations were unacceptable. These results are consistent with the idea that learning a new language is, in large part, learning which formulations to avoid: learning what not to say.

Human collaboration often involves a decision to pursue one out of multiple comparable goals, in which case it is challenging to remain committed to the same goal collectively. Philosophical theories as well as empirical evidence from developmental psychology suggest that humans, having shared intentionality as an underlying cognitive structure, may be able to form joint commitment in pursuing a collective goal without communication. By conducting experiments in a real-time cooperative hunting game that heavily relies on visual perception, we demonstrated that humans established and maintained robust cooperation with high-quality hunting, even with a large number of potential targets. Additionally, we showed that a Bayesian imagined “We” (IW) model within a joint commitment framework, could capture humans’ robustness in resisting alternative targets with relatively high quality of hunting. This poses a contrast with a Reward Sharing (RS) model that, despite performing proficiently in pursuing a single goal, mostly exhibited low-quality hunting and whose teaming fell apart as available targets increased. In a hybrid team simulation experiment, the IW model could better mimic the intentions of human hunters compared to the RS model. Together, the success of the persevered group commitment in humans suggests that shared intentionality is a pivotal element in human cooperation. Moreover, the similarity between the performance of humans and the IW model sheds light on the computational formulation of shared intentionality and further advances our understanding of the nature of cooperation.

People often segment spaces into hierarchically structured subspaces. Judgments about inter-point distance and direction are more accurate within than between segments. However, especially in large-scale complex spaces, segmentation may be necessary for flexible navigation. In this study, we looked at spatial segmentation in a real-life city. We asked citizens of Istanbul, a transcontinental city spread over Europe and Asia with natural waterways that divide it into multiple neighborhoods, to indicate how they segment their city and to perform spatial judgments between well-known landmarks. We examined segmentation effects for divisions they endorsed, and for those others use but they do not report using. Additionally, we examined the impact of gender, age, time spent in the city, and frequency of using connecting routes and bridges. We replicated basic segmentation effects for the primary division, used by all, between the European and Asian sides. For the European side, which has a geographic boundary (The Golden Horn), segmentation impaired the accuracy of spatial representation of participants. For the Asian side, where there is a potential division that is more notional, we found different effects. Individual’s age, sex, time spent in the city, and frequency of using connecting routes also influenced spatial judgments. These results suggest that (i) spatial segmentation effects exist in the real-world, (ii) segmentation in a city-scale environment is differently affected by physical and conceptual boundaries, and (iii) sex, age, and navigation experiences are associated with the cognitive representation of a city.

Children learn to extend novel category labels to objects with the same shape, a phenomenon known as the shape bias. Inspired by these findings, Geirhos et al. (2019) examined whether deep neural networks (DNNs) show a shape or texture bias by constructing images with conflicting shape and texture cues. They found that DNNs strongly preferred to classify familiar objects based on texture as opposed to shape. However, there are a number of differences between how the networks were tested in this study versus how children are typically tested. In this work, we re-examine the inductive biases of neural networks by adapting the stimuli and procedure from Geirhos et al. (2019) to more closely follow the developmental paradigm and test on a range of neural networks. We find that DNNs exhibit a preference for shape rather than texture when tested under conditions that more closely replicate the developmental procedure.

Humans are uniquely adept at extracting structure from the world around them. It is well known that people often form hierarchical task representations during learning, even when a task does not explicitly necessitate a hierarchical representation. Still, how individuals capitalize on this structure to facilitate behavior is an open question. In the present study, we address this question by carefully examining patterns of response time switch costs in a hierarchically structured visuomotor association learning task, to adjudicate between multiple models of behavior. We find evidence that participants do appear to navigate through a hierarchical representation of stimulus-response associations as they prepare responses, rather than maintaining a non-hierarchical, flat model or being primarily affected by changes in stimulus features. These results establish the existence of hierarchical mental representations even for static visuomotor mappings, and imply that such representations are internally navigated in an orderly manner during action selection.

Words in human languages cluster together in phonological neighborhoods more closely than would be expected by chance. But why? One explanation is that large neighborhoods are directly selected for, possibly because they scaffold word learning and production. But it's also possible that they emerge as a byproduct of other constraints or selection pressures operating over real lexica. We advance one such selection pressure as a candidate explanation. A pressure to avoid overloading unique wordforms with homophones may lead to clusters of words that are not identical but similar. Using simulated baselines, we test the viability of this alternative account. We find that a pressure against loading too many meanings on unique wordforms––paired with the phonotactics of a target language––produces lexica with neighborhoods that are at least as large on average as those in real lexica. This does not rule out the possibility of a pro-neighborhood pressure, but it does demonstrate the viability of a parsimonious alternative account based on a pressure against homonymy for which there is independent evidence.

When people evaluate explanations in uncertain situations, the latent scope bias occurs. It refers to the tendency to perceive explanations that do not include unobservable events as plausible. Previous studies have proposed the inferred evidence account, which states that the bias is caused by underestimating the occurrence probability of unobservable events. Additionally, this account assumes that humans use Bayesian probability reasoning in evaluating such explanations. However, previous studies on this bias have not examined the Bayesian probabilistic reasoning component. This study measured subjective probabilities of explanations and modeled the reasoning process. As a result, it was found that latent scope bias is caused by Bayesian probabilistic reasoning, compared to the inference using psychological utility. The results also suggest that there are considerable individual differences in the occurrence of latent scope bias. These results support the inferred evidence account. Future studies are required to investigate the factors causing such individual differences.

When socially evaluating a speaker, listeners partially rely on context-dependent expectations, giving greater social penalties for using a marked form in a less expected context. The nature of listeners’ expectations can be based on the context in which a form is produced in the current utterance (local use), as well as cumulative information about the context in which a form tends to be produced (typical use). This paper asks which of these kinds of expectations about the English sociolinguistic variable (ING), as in talking vs. talkin, is most relevant to listeners in making social judgments. Results indicate that (ING) words’ typical grammatical functions (as a noun vs. a verb) contributed to social judgments, while the local grammatical use of (ING) words did not, supporting usage-based theories and raising new questions about the cognitive mechanisms that underlie social evaluations based on speech.

Most studies of human category learning involve category structures that do not change, or that change in a way that is independent of people's categorization behavior. We consider the situation in which successful category learning causes categories to change. In an experiment, participants learned from feedback whether animals are healthy or diseased. Once their categorization accuracy was near-perfect, the category structure changed so that different animals became diseased. Based on exploratory data analysis and the application of two category learning models, we argue that, once they detect a category change, people retain what they have learned about healthy animals, but reset what they have learned about diseased animals. We discuss future modeling goals and emphasize the need for learning models to study situations in which people's behavior impacts the dynamics of the environment in which learning takes place.

Do children learn language from the words that they produce themselves? Because children know that they have imperfect knowledge of language, they could simply ignore their own productions. However, children could also learn from their productions -- using what they say and how their caregivers respond to update their language models. Using irregular plurals as a case study, we conducted a large-scale corpus analysis and two experimental studies to understand the role of children's productions and caregivers' responses in language learning. We demonstrate that children do learn from their own production, with errorful utterances leading to more errors. However, at least in some contexts, children can use implicit corrections from parents to offset the negative effects of their errors. Children thus appear to learn not only from their caregivers' productions, but also from their own productions and from the relationship between the two.

A central question in cognitive science is how semantic information is mentally represented. Two dominant theories of semantic representation are language-based distributional semantic models (which suggest that word meaning is based on which words co-occur in language) and semantic networks based on word associations (which suggest that words are represented as a network in which words with closer meanings are more closely linked). We investigate the issue of semantic representation through the lens of core vocabulary -- the set of words that are most central in the mental lexicon -- which these two theories make different predictions about. We report on the results of an experiment that tests which measure of core vocabulary most closely aligns with human behaviour in a word-guessing game where the aim was to identify a target word given a set of semantically related words as hints. Target and hint words, which varied across trials, were generated from different core vocabulary lists corresponding to these different theories. Results revealed that the type of hint words did not affect performance, but that better performance was attained for target words derived from word associations than from natural language distributional statistics. Follow-up analyses ruled out several alternate explanations. Our results suggest that the semantic information reflected in word associations may be more involved in the efficient identification of lexical meaning.

Limited evidence shows that visual input can facilitate learning novel sound-to-meaning mappings that are crucial to learning a second language. However, the mechanisms by which visual information influences auditory learning are still unclear. Here, we investigate to what extent visual input can lead to effective learning in another domain. We trained atonal speakers with Mandarin tones in 4 conditions: Auditory Only (AO) where only auditory tones were given as input; Animated Contour (AC) where moving visual pitch contours indicating the dynamic changes of tones were given in addition to auditory tones; Static Contour (SC) where static visual pitch contours were given in addition to auditory tones; Incongruent Contour (IC) where mismatched pitch contours were given in addition to auditory tones. The results show the advantage of AC and SC over AO in learning tonal categories and that IC inhibits learning, suggesting that extracting ‘compatible’ properties cross modalities benefits learning most.

The well-known CRT is a test designed to measure a person’s ‘cognitive reflection’ and used as a predictor of decision making ability. Within the literature, however there is a growing consensus that it shares the majority of its variance with numerical and other cognitive abilities and thus the question increasingly asked is whether it has predictive power beyond existing measures. That is, is there something unique captured by the CRT? This study examines the CRT in parallel with a wide range of individual differences measures reflecting aspects of intelligence (8 CHC broad ability factors), personality (the Big 5 and 30 facets), other decision styles (5 measures) and attention (12 measures covering six aspects of attention). Results indicate that the CRT is, primarily, a cognitive measure, strongly linked to fluid, crystallized and quantitative ability but may also be capturing some distinct aspects of attention relating to the ability to ignore distractors.

Intuitively, adding uninformed individuals to a group should undermine group efficiency, as they create coordination costs while lacking the expertise to meaningfully contribute. However, uninformed individuals may be able to overcome deadlocks in otherwise polarized groups by heightening conformity pressures. Modeling group members’ decision making using a sequential sampling model based on Decision Field Theory (DFT: Busemeyer & Townsend, 1993), we present an existence proof of how ignorance can, in contrast to intuition and prominent economic accounts, facilitate improved group decision making. The implications of these findings for cognitive science, organizational behavior, and social impact are discussed.

The free recall of triadic comparisons, a task used in clinical settings, presents a unique analysis challenge for many memory models, because learning occurs incidentally and items are presented multiple times in triads. To account for this design, we extend the SIMPLE (Brown et al., 2007) model of memory, which assumes to-be-remembered items are stored as separate logarithmically-compressed temporal traces. The ability to retrieve these traces depends on the acuity of memory probes and the semantic similarity between the items represented by the traces. We applied this model to a real-world clinical data set including healthy controls, people with mild cognitive impairment (MCI), and people with Alzheimer’s dementia. We found that people with MCI had lower acuity than healthy controls, but both groups placed roughly equal weight on temporal and semantic cues. People with dementia had both lower acuity and placed much more weight on temporal cues than semantic cues.

Labeling social groups can increase social essentialism (e.g., beliefs that group members are fundamentally the same), leading to greater discrimination and stigmatization. Labels can also increase stigma about mental illness (MI). Some mental health professionals claim that using "person-first" language can reduce stigma, but there is little empirical support for this, and no studies have investigated the relation between person-first language and social essentialism. Here, 513 adults read vignettes describing characters with MI, using person-first (e.g., "a person with autism"), identity-first (e.g., "an autistic person"), or generic noun language (e.g., "an autistic"). We assessed participants' stigmatizing and essentialist beliefs about characters and their MI. Reported stigma and essentialism were correlated. Person-first language reduced stigmatizing beliefs about individuals with some disorders, e.g., depression, but not others, e.g., autism. Relative to generic nouns, person-first language reduced essentialist beliefs about real mental illnesses, but not novel ones.

Demonstratives---simple referential devices like this and that---are linguistic universals, but their meaning varies cross-linguistically. In languages like English and Italian, demonstratives are thought to encode the referent's distance from the producer (e.g., that one means ``the one far away from me"), while in others, like Portuguese and Spanish, they encode relative distance from both producer and receiver (e.g., aquel means ``the one far away from both of us"). Here we propose that demonstratives are also sensitive to the receiver's focus of attention, hence requiring a deeper form of social cognition than previously thought. We provide initial empirical and computational evidence for this idea, suggesting that producers use demonstratives to redirect the receiver's attention towards the intended referent, rather than only to indicate its physical distance.

Do infants’ social evaluations privilege the outcomes of others’ actions, or the beliefs underlying those actions? In two experiments, 8-month-old infants viewed a protagonist who sought to grasp one of two toys, each inside a different box, as two other agents observed. Then, while the protagonist was away, the toys exchanged locations, either in the presence or absence of the two other agents. Thus, the agents had either true or false beliefs about the toys’ locations. When the protagonist returned, one agent opened the box that now contained the protagonist’s desired toy, whereas the other opened the box that previously contained that toy. When agents had true beliefs about the desired toy’s location, infants preferred the agent who opened the box containing that toy. When agents had false beliefs about that location, infants instead preferred the agent who opened the opposite box. Thus, infants' social evaluations privilege agents’ beliefs.

Previous work suggests that established memories can both facilitate and interfere with new learning in adults. We predicted that children may not show these same effects of prior knowledge, as there is emerging evidence that they are less likely to relate new experiences to existing memories. To test this hypothesis, we had children (4-11 years) and young adults (17-33 years) complete a spatial learning task inspired by rodent model paradigms. Subjects first formed strong memories for object-location maps and then learned new locations, some of which could be incorporated into a learned map. We found that same-day prior spatial knowledge had a different impact on learning in children and adults: Adults demonstrated marginal proactive interference while children showed slight proactive facilitation, if anything. Our results suggest there are developmental differences in the effects of prior knowledge on learning, perhaps due to immature associative memory formation and/or activation mechanisms in children.

Suppose we observe something happen in an interaction be- tween two objects A and B. Can we then predict what will hap- pen in an interaction between A and C, or between B and C? Recent research, inspired by work on the “causal asymmetry”, suggests that people use cues to causal agency to guide object- based generalization decisions, even in relatively abstract set- tings. When object A possesses cues to causal agency (e.g. it moves, remains stable throughout the interaction), people tend to predict that what happened will probably also occur in an interaction between A and C, but not between B and C. Here we replicate and extend this work, with the goal of identify- ing the cues that people use to determine that an object is a causal agent. In four experiments, we manipulate three prop- erties of the agent and recipient objects. We find that people anchor their inductive generalizations around the agent object when that object possesses all three cues to causal agency, but removing either cue abolishes the asymmetry.

The learning of rule-plus-exception categories relies on pattern integration and differentiation, but how the representations of rule-followers and exceptions develop through these two operations remains obscure. Here, we inspected the representational shifts in rule-plus-exception category learning by fitting a computational model to behavioral categorization data. We found that exceptions were differentiated from rule-followers within and between categories through learning. The distanced rule-follower and exception representations in each category formed distinct clusters that together constituted a hierarchically structured categorical representation. Moreover, exception learning increased the representational overlap between rule-followers of opposite categories, thereby blurring the category boundary. Our findings illuminate the representational dynamic underlying the acquisition of rule-plus-exception categories and highlight the roles of pattern integration and differentiation in category learning.

Compounding is a common type of word formation extensively studied in linguistics and cognitive psychology. A growing line of research suggests that the lexicon supports efficient communication by balancing informativeness and simplicity. We propose that the formation of novel compounds reflects a similar tradeoff between informativeness and word length. We formalize this hypothesis in information-theoretic terms and develop a computational procedure to evaluate our hypothesis on English noun compounds that emerged over the past century. We find that attested compounds achieve more efficient tradeoffs between informativeness and word length than do alternative word forms. Our work demonstrates how word formation and compositionality can be connected with information-theoretic approaches to the design of the lexicon.

Cue-based retrieval theories of sentence processing assume that subject-verb dependencies are resolved through a content-addressable search in memory. The model assumes that multiple nouns with similar syntactic or semantic features increase dependency completion difficulty. English eyetracking data (reading) are consistent with model predictions; interestingly, a similar experiment with German --a language marking case overtly-- suggests that only syntactic features affect dependency completion difficulty. Why would German show different behavior than English? Using a computational implementation of the cue-based retrieval model and model comparison using Bayes factors, we show that the reason is systematic variation at the individual-participant level: German participants overwhelmingly give higher weighting to syntactic cues over semantic cues, whereas English participants mostly give equal weighting to syntactic and semantic cues. The richer morphosyntax of German leads to syntactic cues being favoured; if such cues are largely absent (as in English) the parser relies on both cue types equally.

Three-dimensional objects pose a challenge for our visual system, since we can only view objects from a single limited perspective at a given moment. Previous work found that given a limited perspective, infants represent 3D objects as complete volumes. Our study replicated this finding in 4- to 7-year-olds and adults, using an explicit prediction measure rather than looking times. We also explored whether humans have a bias to represent visually limited 3D objects as symmetrical rather than asymmetrical across shape, size, texture, and color. Overall, there was an above-chance preference for full volumetric and symmetrical object completion that increased with age. Low-level perceptual similarity of choices did not predict participants’ choices. Moreover, we evaluated ResNet-50 neural networks on the same tasks: they represented objects as complete volumes, but did not show substantial preference for symmetrical 3D representations. This raises the possibility that incorporating human symmetry biases could improve computer vision.

Word meanings extend over time due to a functional need for maintaining communicative expressivity within a compact lexicon. Previous scholars have suggested that word meanings extend via a process of chaining, whereby novel items link to existing ones close in semantic space. Recent work has formalized this idea using computational models grounded typically in the exemplar and prototype theories of categorization that are either memory-intensive or simplistic in representation. We propose an alternative account of chaining that optimizes cognitive efficiency by trading off representational accuracy with memory complexity. We operationalize this efficient chaining as an infinite mixture model and show how it constructs the internal representations of word meaning adaptively through time while predicting the historical development of English verb meanings with precision and limited resources.

Despite the variability of music worldwide, some types of human songs share basic acoustic characteristics. For example, dance songs tend to be loud and rhythmic, whereas lullabies tend to be quiet and melodious. Prior studies with western English-speaking participants have shown that this enables listeners to infer aspects of a singer’s behavior, despite being unfamiliar with the singer’s culture and language. Here, we test whether these intuitions are shared across a diversity of languages and human societies, with 5524 people from 49 industrialised countries comprising 28 languages, and 116 people in 3 small-scale societies with limited access to global media. Each made inferences about the behavioral contexts of 118 songs from 86 societies. Both groups reliably identified the behavioral functions of dance songs, lullabies, and healing songs. Linguistic and geographical proximity between listeners and singers was minimally predictive of accuracy, demonstrating a degree of cultural invariance in music perception.

The ability to take action according to a partially defined plan allows humans to resolve a distant future, even when steps are missing between the present and that future. Adhering to this partial plan requires an intentional commitment that curbs distracting desires conflicting with the planned course of action, enabling humans to act coherently over long horizons. This research (N = 50, 23 boys, ages 5-6, Chinese) explored the cognitive development of commitment to partial plans in a sequential decision-making task, and its correlation to participant capacity for attentional control. Our results suggest that only 6-year-olds committed to partial plans, and moreover, that in both age groups, intentional commitment was positively correlated with the use of proactive control. These findings indicate that intentional commitment does not develop simultaneously with the understanding of intention at infancy, but rather matures gradually in parallel with the development of attentional control.

Correlation judgments are at the core of belief formation. In previous studies of correlation judgment from 2D scatterplots, observers underestimate correlations, and display stronger underestimation biases when the scatterplot is shown in a landscape view than in a portrait view. Yet, it is unclear how these biases arise. Here, we propose that observers are Bayesian learners who perform “mental regression” using the observed data points in graph. Accordingly, judgment errors can arise from biased visual information sampling. We test our model’s predictions with two eye-tracking experiments and find that the Bayesian learning model, applied to information obtained from visual fixation data, replicates classic behavioral findings. The model also predicts trial-level estimation biases at a high accuracy level. Our study shows how computational models trained on process-level data can shed light on the cognitive mechanisms underlying belief formation, and yield theory-driven practical implications for data visualization and statistical communication.

Skill learning usually unfolds exponentially — we improve rapidly early on, and then performance levels off. However, we do not know whether people’s representations of skill learning accurately reflect this fact. Here, we asked people to predict the learning trajectory for a novel visuomotor task, “Lollitoss.” First, we established that skill learning unfolds exponentially on Lollitoss (Exp. 1). Across two experiments probing people’s trial-by-trial predictions of learning in Lollitoss using direct performance (Exp. 2a) and likelihood estimates (Exp. 2b), we found that people accurately represent the learning curve as exponential. However, we also found systematic errors - people think individuals start out better, make less errors, and learn slower in the task than in reality. Taken together, we find that people are surprisingly accurate at representing the overall shape of learning, but misestimate certain features, like the rate of learning, which may potentially have downstream effects on self-directed learning.

Picture book reading is a common word-learning context from which parents repeatedly name objects to their child and it has been found to facilitate early word learning. To learn the correct word-object mappings in a book-reading context, infants need to be able to link what they see with what they hear. However, given multiple objects on every book page, it is not clear how infants direct their attention to objects named by parents. The aim of the current study is to examine how infants mechanistically discover the correct word-object mappings during book reading in real time. We used head-mounted eye-tracking during parent-infant picture book reading and measured the infant's moment-by-moment visual attention to the named referent. We also examined how gesture cues provided by both the child and the parent may influence infants' attention to the named target. We found that although parents provided many object labels during book reading, infants were not able to attend to the named objects easily. However, their abilities to follow and use gestures to direct the other social partner’s attention increase the chance of looking at the named target during parent naming.

We study the effect of Dutch proficiency on immigrants’ labour market performance, savings and financial wealth in the Netherlands. Different from past research, we had participants (N=659) take a language proficiency test apart from self-reported assessments, and measured participants’ IQ, patience, saving intention, risk aversion, self-control, temporal focus, etc. to better control for individual characteristics. Immigrants’ labour market performance and financial wealth were initially surveyed in 2016, and then again in 2020-2021. We find that Dutch proficiency affects immigrants’ earnings (employment probabilities; income; hourly wages) in 2016 and predicts participants’ earnings in 2021 even after controlling for the baseline in 2016, individual characteristics and demographic information. Furthermore, the results for the first time reveal that language proficiency can also predict immigrants’ current and future savings and financial wealth. Importantly, using an instrumental variables approach we show that language proficiency has a causal effect. Our findings have important theoretical and policy implications.

Here we examined whether one’s perceptual style in viewing own- and other-race faces is associated with performance and bias in social categorization by race, and whether mask use modulates the perceptual style and social categorization effects. We found that Asian participants who adopted more eyes-focused eye movement patterns when viewing Asian faces had a larger bias to judge 50% Asian-Caucasian face morphs as Asian. However, although mask use made participants’ viewing pattern more eyes-focused, it did not change this bias in judging morphed faces, or other-race advantage in social categorization speed. These results suggest that information from the eye region may be sufficient to induce these social categorization effects, and that transient perceptual input change due to mask use does not modulate these social categorization effects. Thus, effects and biases in social categorization may be impervious to mask use. These findings have important implications for social interaction during the pandemic.

How do humans produce and comprehend language in pragmatic ways? A variety of models of pragmatic inferences have been proposed, and these models are typically evaluated on their ability to account for human inferences in reference game experiments. However, these experiments are not tailored to target theoretical differences between models or clearly tease apart model predictions. We propose an optimal experiment design approach to systematically construct reference games that can optimally differentiate between models of human pragmatic reasoning. We demonstrate this approach and apply it to four models that have been debated in the literature: Grammar-based, Iterated Best Response (IBR), Rational Speech Act (RSA), and a recent variant of RSA grounded in rate-distortion theory (RD-RSA). Using these optimal reference game experiments, we find empirical evidence favoring iterated rationality models over the grammar-based model, as well as support for the relevance of rate-distortion theory to human pragmatic inferences. These results suggest that our optimal reference game design framework may help adjudicate between computational theories of pragmatic reasoning.

Children do not learn language from passively analyzing correlations between language and observations, but from interaction with caregivers or peers. The non-nativist approach claims that the main driver of language learning should be to achieve communicative goals. Imitation, on the other hand, is another natural desire that many argue influences language learning. However, there are still gaps in the research on what roles communicative goals and imitating linguistic input play in language acquisition, due to the difficulty of performing comprehensive experiments with human learners. In this paper, we propose a computational framework using simulated experiments that allows us to compare the roles of the two drivers. Specifically, we simulate a two-way communication game between a speaker, corresponding to a language learner, and a listener, corresponding to a caregiver or teacher. The speaker's communicative goals are modeled as rewards for successful completion of a referential game, and imitation is performed by mimicking feedback from the listener. The listener adaptively chooses to give feedback and makes choices based on the speaker's utterances. With empirical results on naturalistic visual and language data, we find that communicative goals play an important role in driving language learning, whereas imitation accelerates the learning process. We also find that (1) models trained with communicative goals tend to use minimal vocabulary and utterances and overextend them to concepts outside the original word meanings; (2) the strategy with which the listener provides feedback also influences the learning results and speed. Code and data for replicating the experiments are available\footnote{\url{https://bit.ly/interactgym}} to spur future research on models for computational studies of language learning.

Historically, metaphors have facilitated creative change across multiple disparate domains. Similarly, human adults use metaphors to guide their everyday thinking and reasoning. While previous research in cognitive development has demonstrated that preschoolers understand metaphors, less is known about how preschoolers might learn from metaphors. The current experiments investigate whether preschoolers can use novel metaphors to make additional inferences about artifacts. Experiment 1 demonstrates that both four-year-olds and adults who hear novel positive and negative metaphors (e.g., “Daxes are clouds. Daxes are not suns.”) can form additional inferences based on these metaphor (e.g., that daxes let out water rather than light up). Experiment 2 conceptually replicates this result using a modified paradigm with only positive metaphors (e.g., “Daxes are clouds”). Taken together, these findings suggest that children can not only understand, but also learn from, metaphors. Consequently, metaphors may be a powerful learning mechanism in both adulthood and early childhood.

Recent work has shown that the brain organizes abstract, non-spatial relationships between entities into map-like representations. However, an animal’s objectives often depend on only a subset of the features of the environment. Under these circumstances, cognitive control – the capacity to flexibly select the features most relevant in the current context – becomes paramount. Here, we explore the relationship between cognitive control and the geometry of map-like representations by combining fMRI with neural network modeling. We find that brain areas including hippocampus and entorhinal cortex spontaneously organize pairwise relationships into 2D map-like representations, and that this 2D structure was controlled by compressing task-irrelevant dimensions in areas of prefrontal and parietal cortex. Our neural network model reproduced these findings and additionally predicted warping in the geometry along a context-invariant axis. This prediction was confirmed with fMRI, which showed that the degree of warping was correlated with individual differences in cognitive control.

Temporal perspectives have been studied as a part of spatial thinking of time. They allow us to place ourselves and temporal events on a timeline, making it easier to spatialize time. This study investigates how we adopt temporal perspectives in temporal gestures. We asked participants to retell temporal scenarios written in the Moving-Ego (ME), Moving-Time (MT), and Time-Reference-Point (Time-RP) perspectives in spontaneous and elicited gesture conditions. Participants adopted temporal perspectives similarly regardless of the gesture condition, with few differences. Our results showed that participants’ temporal gestures resonated better with the Ego-Reference-Point versus Time-Reference-Point distinction. Participants produced more ME and Time-RP gestures for the corresponding scenarios and speech, however the MT perspective was not adopted more than the others in any condition. Our findings show that we incorporate temporal perspectives into our temporal gestures to a considerable extent, however, the classical ME and MT classification may not hold for temporal gestures.

Making inferences about claims we do not have direct experience with is a common feature of everyday life. In these situations, it makes sense to consult others: an apparent consensus may be a useful cue to the truth of a claim. This strategy is not without its challenges. The utility of a consensus should depend in part on the sources of evidence that underlie it. If each person based their conclusion on independent data then the fact that they agree is informative. If, instead, everyone relied on the same primary source, the consensus is less meaningful. However, the extent to which people are actually sensitive to this kind of source independence is still unclear. Here, we present the results of three experiments that examine this issue in a social media setting, by varying the sources of primary data cited via retweets. In each experiment, participants rated their agreement with 12 different claims before and after reading four tweets that were retweeted on the basis of either the same or different primary data. We found that people were sensitive to source independence only when it was clear that the tweeters had relied on the primary data to reach their conclusion. Implications for existing research are discussed.

Contemporary models of subjective probability distortions assume that distortions arise during probability encoding. However, such assumptions are inconsistent with the ability of humans to retrieve probabilities veridically in some elicitation formats. We present a sampling-based model of probability judgment for risky prospects that assumes that probability distortions occur because people read out probability judgments as biased averages from working memory contents. Simulations demonstrate that this model shows the classic inverse-S shaped distortion of probability judgments using only retrieval-stage assumptions. The model further predicts that observers with greater working memory capacity would show larger probability distortions on average, which should lead to a particular fourfold pattern of risk preference as a function of working memory capacity. Using cognitive ability measurements as a proxy for working memory capacity, we conducted an experiment with human participants and found results consistent with the model's predictions as well as previous empirical studies. Our results support a role for sampling during assessment of risky prospects, which in turn explains differences in probability distortions seen across different elicitation methods.

Traditionally, theory and practice of Cognitive Control are linked via literature reviews by human domain experts. This approach, however, is inadequate to track the ever-growing literature. It may also be biased, and yield redundancies and confusion. Here we present an alternative approach. We performed automated text analyses on a large body of scientific texts to create a joint representation of tasks and constructs. More specifically, 385,705 scientific abstracts were first mapped into an embedding space using a transformers-based language model. Document embeddings were then used to identify a task-construct graph embedding that grounds constructs on tasks and supports nuanced meaning of the constructs by taking advantage of constrained random walks in the graph. This joint task-construct graph embedding, can be queried to generate task batteries targeting specific constructs, may reveal knowledge gaps in the literature, and inspire new tasks and novel hypotheses.

We investigated how the properties of lexical items, which label object features, affect concept tokening. We addressed this issue by modeling data from three sources: (1) norms obtained from a dataset of 78,000 features to a set of pictures representing living and nonliving objects; (2) accuracy data from a picture-word priming congruency task with stimuli presented for 50-60 milliseconds; and (3) corpus data on the lexical properties of four different social usage count measures. We conducted two sets of analyses: one relying on sample count-based measures (i.e., measures based on the norming study: sample frequency, cue validity, feature distinctiveness), and a second relying on the social usage count-based measures (i.e., word frequency (WF), contextual diversity (CD), discourse contextual diversity (DCD), and user contextual diversity (UCD). Contrasting count and social usage-based measures allowed us to gain insight into the contribution of diverse semantic and socially oriented contextual measures of lexical items, and how they may affect concept tokening. Our results show that cue validity and feature distinctiveness were negative predictors of participants’ accuracy to congruency decisions—an effect which was more pronounced for distinctive features of living things. There was also a noticeable advantage for the UCD and DCD variables, over CD and WF. Overall, our results suggest that the conceptual system may be organized as a function of both, intrinsic properties of object features and usage based contextual measures of lexical items that label these features.

We examined the effects of multilingualism and bi-dialectalism on irony interpretation by comparing multilingual, bi-dialectal, and monolingual young adults. We used an act-out task with three Meaning (literal positive, literal negative, ironic) and four Cue conditions (context-only, intonation-only, intonation + face, context + intonation + face). Results revealed that irony interpretation was (1) difficult, as shown by slower and less accurate responses to ironic compared to literal items; (2) facilitated by the presence of more ironic cues. Moreover, evidence suggested that linguistic context had a greater and facilitative effect on the speed of irony processing compared to literal meanings. Finally, we found no evidence for group differences in accuracy or speed of irony processing or in the way that different (combinations of) cues affected irony. Overall, our findings support a view of multilingual pragmatics according to which pragmatic interpretation is no different in multilinguals compared to monolinguals.

Failing to come up with a word or name is a fairly common experience that is exacerbated in older adulthood and among populations with language impairments, and yet the mechanisms underlying lexical retrieval remain fairly understudied. In this work, we introduce and evaluate a series of nested computational models of lexical retrieval that combine semantic representations derived from a distributional semantic model with a process model to account for behavioral performance in a primed lexical retrieval task. The models were tested on a behavioral data set where participants attempted to retrieve answers to descriptions of low-frequency words and were provided a semantically and/or phonologically related prime word before the retrieval attempt. Model comparisons indicated that a model that emphasized semantic activations from the description and phonological activations from the prime word best accounted for the overall data. Additionally, incorrect responses and metacognitive judgments indicating that participants had other words in mind were associated with models that instead emphasized semantic activations from the prime word. Taken together, these results identify the locus of lexical retrieval failures and offer the opportunity to investigate broader questions about semantic memory retrieval.

Navigation is a process that humans use to get from A to B. Landmarks used during navigation and wayfinding can address different sensory modalities. We examined landmark information in four different variants: as a written word, as a spoken word, as a picture, or as an odor. Our 51 participants were separated into four groups. Each group received one specific variant of landmark information integrated into a learning and wayfinding video of a virtual maze with 12 intersections. At each intersection, one landmark information was presented. To assess how well the relevant landmarks could be distinguished from unknown distractor items of the same condition, the experiment concluded with a recognition phase, where 24 stimuli were presented (12 landmarks + 12 distractors). Relative frequencies of correct responses and mean response times were measured for wayfinding and recognition. Odors lead to similar correctness in wayfinding compared to the more common landmarks (pictures, written and spoken words), even though requiring longer response times. We stepped away from the traditional but limited view on landmarks towards a more holistic (i.e. including all senses) view of human orientation. Implications for future scientific research are being discussed.

Although expectancy effects induced by placebo treatment are reported to attenuate depressive symptoms in the long run, mechanisms underlying situational dynamics are not well understood. Improved reward learning has been discussed as a candidate mediator for effects of positive expectancies on more positive mood. Here, we fitted a series of Q-learning models to measure the effect of sham antidepressant treatment vs. open-label placebo in a probabilistic reinforcement learning task. Treatment effects were observed mainly in those Q-learning models justified by the task structure. Additionally, interindividual variability remained the largest origin of unexplained variance in predictive match across models. These findings provide further support for the role of expectancies in reward learning. They also highlight the need for unraveling individual differences in cognitive mechanisms that account for differences in reward learning, and obtaining reliable estimates for them.

Gestures facilitate speech production by helping speakers reduce cognitive load. Studies on gesture-speech interaction mostly examined the effect of representational gestures on spatial contexts. However, abstract deictics (e.g., pointing at objects that are not visually present) might also have a role in facilitating cognitive processes. The present study investigated the effect of gestures on disfluency rates by presenting a narrative task in three conditions: spontaneous, restricted, and encouraged gesture use. We found that disfluency rates across three conditions did not significantly differ. The use of abstract deictics in the spontaneous gesture use condition was a significant predictor of disfluency rates in the gesture restricted condition. Results indicate that gestures’ facilitative roles might be manifested differently depending on the context. Abstract deictics might also benefit speakers, especially in a narrative context. Studying abstract deictics can provide new insights on gesture and speech production interaction.

How people learn about events often varies with some events perceived in their entirety and others are inferred based on the available evidence. Here, we investigate how children and adults linguistically encode the sources of their event knowledge. We focus on Turkish – a language that obligatorily encodes source of information for past events using two evidentiality markers. Children (4- to 5-year-olds and 6- to 7- year-olds) and adults watched and described events that they directly saw or inferred based on visual cues with manipulated degrees of indirectness. Overall, participants modified the evidential marking in their descriptions depending on (a) whether they saw or inferred the event and (b) the indirectness of the visual cues giving rise to an inference. There were no differences across age groups. These findings suggest that Turkish-speaking adults’ and children’s use of evidential markers are sensitive to the indirectness of the inferential evidence for events.

The "myside bias'' in evaluating arguments is an empirically well-confirmed phenomenon that consists of overweighting arguments that endorse one's beliefs or attack alternative beliefs while underweighting arguments that attack one's beliefs or defend alternative beliefs. This paper makes two contributions: First, it proposes a probabilistic model that adequately captures three salient features of myside bias in argument evaluation. Second, it provides a Bayesian justification of this model, thus showing that myside bias has a rational Bayesian explanation under certain conditions.

Biological brains learn much more quickly than standard deep neural network reinforcement learning algorithms. One reason for this is that the deep neural networks need to learn a representation that is appropriate for the task at hand, whilst biological systems already possess an appropriate representation. Here, we bypass this problem by imposing on the neural network a representation based on what is observed in biology, such as grid cells. This study explores the impact of using a biologically-inspired grid-cell representation vs. a one-hot representation, on the speed at which a Temporal Difference-based Actor-Critic network learns to solve a simple 2D grid-world reinforcement learning task. The results suggest that the use of grid cells does promote faster learning. Furthermore, the grid cells implemented here have the potential for accurately representing unbounded continuous space. Thus, their promising performance on this discrete task acts as a first step in exploring their utility for reinforcement learning in continuous space.

Memory is limited in capacity, which means that we must choose what information to prioritize for storage. Part of knowing what to prioritize is predicting future needs. For example, if you view a 3D object, later on you may wish to recall exactly how it was oriented. Alternatively, you might need to remember its shape, independent of viewpoint. Given this kind of uncertainty, a good strategy would be to store multiple kinds of information about the objects we observe, and then decode in a task-dependent manner. We tested whether people apply these strategies in the specific domain of short-term memory for novel faces. To test whether people store various kinds of information about a face, and then decode in a task-dependent manner, we modeled their responses in a memory task using features (extracted from deep neural networks) that varied in how much 3D information they carried. We found strong evidence for a mixed-storage strategy, which did not vary in response to task demands. Our results suggest that in order to fully understand resource allocation and retrieval strategies in human memory, it may be critical to consider not just the distribution over tasks in people's natural environments, but also task uncertainty at the time of encoding.

This paper proposes new empirical classifiers for evaluative concepts, including thin concepts like 'good' or 'bad' and thick concepts such as 'honest' or 'disgusting', based on quantitative corpus linguistics. Prior work in experimental philosophy has shown that sentiment analysis can be used to track differences between concept classes. Building on this, Task 1 investigates whether the relationship between sentiment and evaluativeness is parabolic rather than linear. Task 2 extends this question to the differences between evaluative and non-evaluative concept classes. The results of both Tasks show that the linear and the parabolic logistic regression classifiers perform equally well. Interestingly, this study also finds that adjectives attributed to animate entities (e.g. "generous customer") generally have a higher probability to be evaluative concepts than those attributed to inanimate entities (e.g."dry soil").

Mental simulation is a powerful cognitive capacity that underlies people's ability to draw inferences about what happened in the past from the present. Recent work suggests that eye-tracking can be used as a window through which one can study the process of mental simulation in intuitive physics tasks. In our experiment, participants have to figure out in which of three holes a ball was dropped in a virtual Plinko box. We develop a computational model of human intuitive physical reasoning in Plinko that runs repeated simulations in a noisy physics simulator in order to infer in which hole the ball was dropped. We evaluate our model's behavior against multiple human data signals: trial judgments, response times, and eye-movement data. We find that a model that sequentially samples simulations while balancing uncertainty and reward best explains the patterns of participant behavior we observe in these three signals.

We explore the impact of speaker identity on the interpretation of number words in a T(ruth)-V(alue) J(udgment) task – a paradigm in which respondents assess whether a given description appropriately represents a given body of facts. We find that imprecise statements from speakers socially expected to be less precise – i.e. “Chill” ones – are rejected at a higher rate, and thus held to more stringent evaluation standards, than those from speakers socially expected to speak more precisely – i.e. “Nerdy” ones, and especially so when participants do not identify with the speaker’s properties. This shows that TVJ assessments are impacted by respondents’ social perception of the speaker; but that they are affected by social considerations in a different way from other experimental tasks similarly tapping into meaning interpretation, suggesting a nuanced interplay between social information and pragmatic reasoning

People waiting to receive information about a personally relevant future event often become increasingly pessimistic as the event draws near. These temporal declines in expectations have been demonstrated robustly across both naturalistic and laboratory settings. However, the low-level cognitive processes that give rise to temporal declines in expectations remain unclear. Here, we investigated the temporal boundary conditions of this effect. In a controlled cognitive task involving repeated probabilistic gambles, we assessed the dynamics of participants' reward expectations over a 12-second waiting period prior to revelation of the gamble outcome. Across two experiments (total N = 120), we found no evidence for temporal declines in expectations over this short waiting period, no matter whether expectations were measured via direct probability report (Experiment 1) or via an incentive-compatible `cash-out' decision (Experiment 2). These results demonstrate that temporal declines in expectations are not an invariant characteristic of human expectations regarding personally relevant future events.

Other people's mental states---what they want, what they know, and how they combine the two to act---are structured by the experiences that they've had. In line with this, we propose that inferences about other people's experiences are a central, but often neglected, aspect of human Theory of Mind. We explore this idea by presenting and testing a computational model that jointly infers others' desires, knowledge, and experience. We find that, by focusing inferences on others' experience, our model can make richer inferences about other's knowledge than would be otherwise possible. Our model quantitatively fits participant judgments on two experiments above an and beyond an alternative model. Overall, our work extends the richness of human Theory of Mind judgements that can be formalized as Bayesian inference over a generative model.

The order in which options are presented influences choice in ways that parallel primacy and recency effects in memory, but the depth of this connection remains underexplored. I present sequences of art to experimental participants who select their favorite pieces, and find evidence that cognitive load can selectively weaken choice primacy or recency depending on its timing, analogous to established findings in memory research. The data suggests that primacy is reduced by an externally-imposed distractor task in between each option or by natural fatigue, while recency is reduced by an extra delay containing a distractor after the last option is presented. Thus, order effects in choice may be predictably modulated by the targeted disruption of processing.

Young children tend to prioritize objects over layouts in their drawings, often juxtaposing “floating” objects in the picture plane instead of grounding those objects in drawn representations of the extended layout. In the present study, we explore whether implicitly directing children’s attention to elements of the extended layout through a drawing’s communicative goal—to indicate the location of a hidden target to someone else—might lead children to draw more layout information. By comparing children’s drawings to a different group of children’s verbal descriptions, moreover, we explore how communicative medium affects children’s inclusion of layout and object information. If attention modulates children’s symbolic communication about layouts and objects, then children should both draw and talk about layouts and objects when they are relevant to the communicative task. If there are challenges or advantages specific to either medium, then children might treat layouts and objects differently when drawing versus describing them. We find evidence for both of these possibilities: Attention affects what children include in symbolic communication, like drawings and language, but children are more concise in their inclusion of relevant layout or object information in language versus drawings.

An important step in children's socio-cognitive development is learning how to engage in coordinated conversations. This requires not only becoming competent speakers but also active listeners. This paper studies children's use of backchannel signaling (e.g., "yeah!" or a head nod) when in the listener's role during conversations with their caregivers via video call. While previous work had found backchannel to be still immature in middle childhood (i.e., 6 to 11 years of age), our use of both more natural/spontaneous conversational settings and more adequate controls allowed us to reveal that school-age children are strikingly close to adult-level mastery in many measures of backchanneling. The broader impact of this paper is to highlight the crucial role of social context in evaluating children's conversational abilities.

It is commonly assumed that there are qualitatively distinct cognitive strategies that underlie decision making. Because cognitive strategies differ in how information is processed, predecisional information search offers a window onto these strategies. Using a bottom-up approach, we examine whether predecisional information search actually reflects the use of distinct strategies. Specifically, we investigate the extent to which the heterogeneity in people's predecisional information search in a risky choice task reflects qualitatively distinct patterns that should emerge when people use distinct strategies. Our analysis takes into account the distribution of attention across attributes and transitions between attributes. Using cluster analysis, we find just two qualitatively different clusters with low separability: one characterized by balanced attention to all attributes and by transitions occurring mostly within the same option, and one characterized by a focus on outcome information and by frequent attribute-wise transitions. These two clusters were also associated with differences in people's choice behavior. The distribution of these clusters varied considerably across individuals, but less so across choice problems, suggesting that information search is not necessarily guided by features of the choice problem—this result challenges current theories on strategy selection. Our results challenge the common assumption that heterogeneity in predecisional information search is differentiated along clearly distinct information processing policies. Instead, the differentiation seems to fall into just two broad clusters—one resembling rational principles of expectation computation, the other reflecting heuristic principles that neglect probabilities—with considerable variability within each cluster.

Learning to read is a critical skill; yet only a small portion of children in the United States are reading at or above grade level. Attention is one crucial process that affects the acquisition of reading skills. The process involves selectively choosing task relevant information and requires monitoring competing demands. Many books for beginning readers include illustrations, but this design choice may require learners to split their attention between multiple sources of information. This study employed eye tracking to examine whether embedding text within illustrations in children’s e-books inadvertently induces attentional competition. The results showed that spatially separating illustrations from the text in beginning reader books reduces attentional competition and improves children’s reading comprehension. This study shows that changes to the design of books for beginning readers can help promote literacy development in children.

Different domains exhibit different degrees of lexical precision. Existing work has suggested that communicative need may modulate the precision of word meaning in individual domains. We extend this proposal across domains by asking why languages have more precise vocabulary in some domains than others. We hypothesize that lexical precision for a domain reflects how frequently speakers need to refer to it. We test this proposal using a cross-linguistic dataset of word-concept mappings for nine diverse domains from seven languages, and word frequencies from independent corpora. We find that the more frequent domains (except for kinship) tend to be more precise in every language, supporting a domain-level account of efficient communication on the precision of the lexicon.

The ability to categorize visual information is essential for human cognition. Often, this categorization is achieved via internalized rules. In rule-based categorization tasks, participants categorize stimuli according to given decision rules. In this study, we created a framework aimed at measuring the respective impact of single memory operations on task performance. We present a study investigating two central mental operations - the addition of a new and the update of an existing rule - by confronting participants with Alien images they needed to assign to planets. Both conditions showed interference effects for task performance with previously learned ones. We found improved categorization task performance when old and new rules were in accordance, but no significant effect for conflicting situations. Our experimental setting promises to be well-suited to investigate the impact of memory operations on participants' behavior in a controlled environment.

In today’s politically polarized environment, college students need strategies to discern trustworthy information. Educational interventions have had modest success in teaching students to fact-check online information using lateral reading, i.e., leaving the original content to investigate information sources and claims. College students (N = 157, M = 20.2 years (SD = 4.0), 61.8% F) completed a semester-long online curriculum teaching fact-checking via lateral reading. Students made gains in their lateral reading attitudes (i.e., preference for fact-checking using lateral reading strategies) and use of lateral reading. Preference predicted use at posttest, but not at pretest. At posttest, preference also partially mediated the effect of reading comprehension on use. The majority of students mentioned cognitive and/or contextual factors when explaining how the Internet contributes to political polarization, though their awareness of such factors did not increase post-intervention.

Many models in Cognitive Science require data to calibrate parameters. Some modelers calibrate their models’ parameters for each individual in a data set, and others work at the aggregate level. Generally, the accuracy of a model is judged by the degree to which human data are replicated, and the model parameters are interpreted accordingly. It is not too surprising that models that are developed for a particular task and fit to each individual’s data in such a task replicate the human data well. The question is, however, whether those models can make predictions in the absence of human data. In this paper, we present a theory-driven model of a well-known sequential decision task (the Balloon Analog Risk Task, BART) which is able to make predictions in the absence of human data. The cognitive model is grounded on the processes and mechanisms of Instance-Based Learning (IBL) Theory of experiential choice. We demonstrate the simulation predictions from an IBL model and those of a well-known model of the BART, which depends on the fits to human data. We further show that when making predictions without data, the IBL model provides predictions that are both theoretically founded and accurate, while the Two-Parameter model performs much worse than when fit to data. We conclude with a discussion of the benefits of making theory-based predictions in the absence of human data for our community.

While an apparent tendency for negative markers to appear before the verb has been observed in typology, language acquisition, and language emergence, it remains uncertain what factors may motivate such a preference. The present study uses an artificial language learning paradigm to test the existence of learning asymmetries consistent with Neg-First preferences in English speakers. The study further incorporates a dyadic interaction task to investigate proposals that the Neg-First tendency is driven by communicative factors. Results show that learners overall produced more preverbal negation than was found in the input language, consistent with a Neg-First bias. However, interaction only induced greater preverbal negation use when preverbal negation was the majority word order in the input language. This does not support the proposal that communication generally promotes a Neg-First bias, but is suggestive of greater regularization when a production bias is aligned with a bias to eliminate variability during communication.

Fast and efficient correction of speech errors is essential to effective communication. Yet, despite several accounts of error detection, no computational account exists to explain how humans repair their speech errors. This paper proposes the first such model. We demonstrate that a simple automatic mechanism can form the basis of most repairs. We then demonstrate that augmenting the model with a conflict-based monitoring-control loop allows it to capture more nuanced findings in human speech error repair data.

The present paper reports an experiment (N=254) testing two views of how reasoners learn and generalize potentially complex causal knowledge. Previous work has focused on reasoners’ ability to learn rules describing how pre-defined candidate causes combine, potentially interactively, to produce an outcome in a domain. This empirical-function learning view predicts that reasoners would generalize an acquired combination rule based on similarity to stimuli they experienced in the domain. An alternative causal-invariance view goes beyond empirical learning: it allows for the possibility that one’s current representation may not yield useable (i.e., invariant) causal knowledge –– knowledge that holds true when applied. Accordingly, because useable causal knowledge is the evident aspiration of causal induction, this view posits that deviation from causal invariance is a criterion for knowledge revision. The criterion shapes the empirical functions learned and retained. A discriminating test is whether reasoners would re-represent interacting causes as a whole cause that does not interact with other causes, even when in their relevant experience all (pre-defined) causes in the domain interact. Our results favor the causal-invariance view.

Polarity-sensitivity is a typologically general linguistic phenomenon. We focus on negative polarity items (NPIs, e.g. English 'any') -- expressions that are licensed only in negative contexts. The relevant notion of 'negative context' could be defined lexically, syntactically or semantically. There is psycholinguistic evidence in favour of semantics as a driving factor for some NPIs in a couple of languages (Chemla, Homer, & Rothschild, 2011; Denić, Homer, Rothschild, & Chemla, 2021). Testing the scale of this analysis as a potential cross-linguistic universal experimentally is extremely hard. We turn to recent multilingual pre-trained language models -- multilingual BERT (Devlin, Chang, Lee, & Toutanova, 2018) and XLM-RoBERTa (Conneau et al., 2019) -- and evaluate the models' recognition of polarity-sensitivity and its cross-lingual generality. Further, using the artificial language learning paradigm, we look for the connection in neural language models between semantic profiles of expressions and their ability to license NPIs. We find evidence for such connection for negation but not for other items we study.

Recent evidence indicates that children represent and learn multiple meanings of ambiguous words from early in development (e.g., mail letter, alphabetic letter). This raises the question of which naturalistic factors might allow young children to resolve lexical ambiguities. Previous research has shown that children’s processing of ambiguous words is facilitated by verb-related information. However, it is still unclear whether such facilitation comes from bottom-up (lexical associations) or top-down information sources (verb-event structures). In this study, we leveraged a large sense-annotated child-directed speech corpus to disentangle the effect of bottom-up lexical and top-down event structure cues. Preliminary results show that 4-year-olds might rely on verb-event structures when these are put in competition with lexical association. We discuss implications for theories of sentence parsing and word learning.

Third party punishment (TPP), or altruistic punishment, is specifically human prosocial behavior. TPP denotes the administration of a sanction to a transgressor by an individual that is not affected by the transgression. In some evolutionary accounts, TPP is considered crucial for the stability of cooperation and solidarity in larger groups formed by genetically unrelated individuals. Belief in free will (BFW), on the other hand, is the idea that humans have control over their behavior. BFW is a human universal notion that, in some studies, has been found to be supportive of prosocial behavior. In our study, we examined the effect of BFW on TPP under high and low affect scenarios through optical brain imaging (fNIRS). We hypothesized that in low affect cases, there would be a positive correlation between the strength of the BFW and the severity of the punishment inflicted. Obtained results and related statistical analyses indicate that participants with higher degree of BFW have more neural activation in their right dorsolateral prefrontal cortex (DLPFC) (hbo and hbt measures) in high affect scenarios, whereas the participants with lower degree of BFW have higher levels of neural activation in the medial PFC (hbo and hbt measures) in low affect scenarios. These empirical findings are in line with the research findings in the relevant academic literature and support the hypothesis that the degree of BFW influences punishment decisions.

Readers rely on sentence context to generate predictions about the upcoming words so that processing of their visual forms is less necessary. Consequently, processing of an orthographic neighbor of a strongly predicted word is facilitated by that context (as indicated by a reduced N400 ERP amplitude), regardless of the perceived item’s lexicality (i.e., whether it is a real word or a pseudoword). The current study investigated whether lexicality becomes important when the sentence context is less helpful in generating predictions. Our findings indicate that in weakly constraining sentences, the lexical status of a word impacts word recognition processes as indicated by a left anterior negativity, suggesting that readers rely on sublexical properties of words in the absence of strong expectations.

We investigated the influence of the COVID-19 pandemic on young people’s value temporal focus, religiosity, and time spatialization. Samples of young participants from eight cultures (Americans, Spaniards, Serbs, Bosniaks, Croats, Moroccans, Turks, and Chinese) collected before the pandemic (N = 497, mean age = 21.09) were matched with samples collected during the first confinement period (N = 497, mean age = 20.96). Our results in study 1 showed that during the pandemic, young adults were less religious, more future-focused, and placed the future in front to of them in a greater extent. In study 2, using the whole sample collected during the pandemic (N = 893, mean age = 21.94), we observed that the more affected the participants were by the pandemic, the greater their future focus, the lower their religiosity, and the greater their tendency to locate the future in front. These pattern of results held in most cultures.

Human decision-making is filled with numerous paradoxes and violations of rationality principles. A particularly notable example is violation of cumulative independence (VoCI). Recently, there has been a surge of interest in theorizing and developing a resource-rational foundation for many such phenomena. Here we ask whether VoCI could be given a resource-rational basis too. To what extent could VoCI be explained in terms of the optimal use of limited cognitive resources? In this work, we look at VoCI through the lens of modern psychological theories of bounded rationality, presenting the first resource rational account of VoCI.We discuss the implications of our work for risky decision-making, and more broadly, human rationality.

In this work we introduce a structured signaling game, an extension of the classical signaling game with a similarity structure between meanings in the context, along with a variant of the Rational Speech Act (RSA) framework which we call structured-RSA (sRSA) for pragmatic reasoning in structured domains. We explore the behavior of the sRSA in the domain of color and show that pragmatic agents using sRSA on top of semantic representations, derived from the World Color Survey, attain efficiency very close to the information theoretic limit after only 1 or 2 levels of recursion. We also explore the interaction between pragmatic reasoning and learning in multi-agent reinforcement learning framework. Our results illustrate that artificial agents using sRSA develop communication closer to the information theoretic frontier compared to agents using RSA and just reinforcement learning. We also find that the ambiguity of the semantic representation increases as the pragmatic agents are allowed to perform deeper reasoning about each other during learning.

Is a cow more closely related to grass or to a chicken? Responses vary by culture and age, among other factors. Those from western societies (or independent-leaning regions within interdependent non-western societies) are more likely to endorse the taxonomic match, the chicken, over the thematic match, grass (Chiu, 1972; Talhelm et al., 2014). This preference has been documented -- largely in western cultures -- to increase over development (e.g., Smiley & Brown, 1979). While neither development nor culture occur independently of the other, comparisons across these areas are problematic. We address one potential barrier to comparing cultural and developmental research using this classic paradigm -- stimulus format -- and show that the use of text (versus image) stimuli can bias participants toward taxonomic responding in some contexts. We present stimuli designed for cross-cultural use with children and adults and document country, regional, and demographic variation across the US and Italy.

Simple perceptual decision-making tasks such as the Stroop and flanker tasks are popular as a method of measuring individual variation in the processing of conflicting visual stimuli--for instance, the difference in accuracy on stimuli with and without conflict. A major challenge in applying these tasks, for instance, to compare two different populations of subjects, is the low reliability of the nonparametric measures of performance in the tasks. Here, we model dynamic adjustments in decision policies often seen in human behavior, thereby capturing trial-by-trial variation in decision policies, in addition to the classically used average statistics. We propose a recurrent network model to capture behavioral strategies in the task in a model-agnostic manner, and to overcome small-sample learning challenges by pooling across subjects. We show that by splitting the learning into a complex, shared meta-model and simple subject-specific parameters, we learn significantly better predictive models, and also identify latent dimensions indexing the decision policy that may serve as a better measure of individual differences in the task.

Previous research demonstrated an asymmetry between Sources and Goals in people’s linguistic and non-linguistic encoding of motion events: when describing events such as a fairy going from a tree to a flower, people mention the Goal (“to a flower”) more often than the Source (“from a tree”) and are better at detecting Goal changes in a Same-different memory test. Many take these findings as evidence for a homology between linguistic and conceptual representations: an unmentioned event component is also conceptually less robust. Here, we show that the nonlinguistic Source-Goal asymmetry disappears when memory is probed with a Forced-choice task instead of a Same-different task. We argue that, despite frequent absence from linguistic descriptions, Sources are robust in event memory, but not attended to during Same-different tests due to people’s task-relevance assumption. This result bears on the nature of the Source-Goal asymmetry and calls for a finer-grained account for language-cognition homology.

Decision makers face a nontrivial problem when evaluating how much time to invest in an uncertain future prospect. Un- conditional persistence is not always advantageous; rather, different levels of persistence are favored in environments with different temporal statistics. Previous studies using foraging- like decision-making tasks have found that people can rapidly recalibrate their persistence behavior—becoming either more or less willing to tolerate delay—after a short period of direct experience with the temporal statistics of a new environment. Furthermore, substantial individual variation is apparent both in baseline levels of persistence and in the flexibility of re- calibration across environments. However, it is unknown to what degree such variation reflects trait-like individual differences in contrast to session-specific measurement noise. Here we investigated the test-retest reliability of individual variation in behavioral persistence in a computerized decision-making task. We conducted an online experiment in which participants (n=141 after exclusions) performed the task on two occasions separated by a three-week interval. We evaluated the test- retest reliability of several behavior-derived indices, including: a descriptive estimate of overall willingness to wait, a contrast measure reflecting flexibility of recalibration across environments, and individual-level parameter estimates derived from a reinforcement learning model of adaptive persistence. The results showed strong evidence for stable, trait-like individual variation in multiple aspects of persistence-related decision- making behavior. Our findings establish a foundation for future investigations of associations between task-derived parameters of decision behavior and other cognitive and motivational traits.

As widely acknowledged in the bilingualism literature, language experience is multifaceted, complex, and dynamic; it cannot be simply reduced to single dimensions or categories. However, cognitive science research outside of bi/multilingualism does not always take into account this fact. Within a population of Hindi-Urdu speakers, we show that proxy categories based on `native speaker' identification or residential history do not neatly map onto patterns of language experience, despite the common assumption that these bring about sufficient homogeneity. Moreover, compared to variables derived from gradient measures of language experience, these proxies do not robustly predict linguistic behavior in the form of acceptability judgments in Hindi-Urdu. In demonstrating alternative approaches to operationalizing language experience, we argue for all language researchers to move past relying on underspecified and ideologically-linked concepts, in favor of more intentional, nuanced, and rigorous testing of experiential factors underlying language processing.

The ancient Greek hero Ulysses chose to bind himself to resist the temptation of Sirens, highlighting the fact that humans may voluntarily sacrifice their freedom of choice to achieve committed goals. In this work, we propose a computational model for such commitment under the framework of Bayesian Theory of Mind. The model is based on the idea that even when alone, humans act to better demonstrate their intentions to an imagined third-party observer (ITO) censoring their actions. Our model successfully captures the Ulysses-constraint of freedom, as the freedom confuses the ITO’s inference of their intention. We further show that, trajectories generated both by human actors and actors modeled with ITO censorship are easy to interpret both in the eyes of an actual human ob- server and an ITO. The results demonstrate that under conflict- ing desires, humans achieve commitment by spontaneously censoring their actions with an internalized theory of mind.

Burns & Krygier (2015) demonstrated that people could exhibit a strong bias towards the smaller first digits, in a way similar to that described by Benford’s law. This paper sought to expand the scope of this phenomenon and to test a possible explanation, the Recognition Hypothesis that a Benford bias is due to life-long environmental exposure to this statistical relationship. Participants completed three numerical tasks: A Generation Task requiring answering trivia questions; a Selection Task requiring selecting between two numerical responses; and an Estimation task requiring estimating the number of jelly beans in a jar. The results found no evidence of any first digit effect in the Recognition Task, some evidence of Benford bias in the Generation Task and strong evidence in the Estimation Task. Future research should focus on alternatives to the Recognition Hypothesis and investigate the parameters of Benford bias in generation tasks

Categorical perception (CP) facilitates the discrimination of stimuli belonging to different categories relative to those from the same category. Effects of CP on the discrimination of color and shape have been attributed to the top-down modulation of visual perception by the left-lateralized language processes. We used a divided visual field (DVF) search paradigm to investigate the prospective effects of CP for face identity and gender processing. Consistent with visual processing of face identity in the right hemisphere, we found CP facilitated perception only in the left visual field (LVF). In contrast, and consistent with language-induced CP, we observed a between-category advantage for processing face gender only in the right visual field (RVF). Taken together, our results suggest that language-induced CP plays a role in the category-based visual processing of faces by the left hemisphere, but face familiarity processing might be dependent on different, identity-specialized networks in the right hemisphere.

One of the key issues in visual word recognition is the role of orthographic overlap in priming. However, most research investigating this topic has focused on priming with orthographic neighbors. In this study, we investigate priming effects of word-final letter overlap and their interaction with word frequency, prime duration, and morphology. In Experiment 1 with briefly presented primes (SOA=34 ms, N = 123), we obtained similar facilitation from non-morphological overlap (compel-TRAVEL) and inflectional suffix overlap (turned-CALLED), regardless of word frequency. In Experiment 2 when primes were fully recognizable (SOA=150 ms, N=123), only non-morphological overlap showed inhibition among lower frequency prime words. These results are inconsistent with predictions of the Interactive Activation model (McCelland and Rumelhart, 1981), and suggest (i) different weights of inhibition and facilitation depending on prime duration and morphological structure of words as well as (ii) the involvement of a reset mechanism in long SOA conditions.

Word learning is characterized by a bias for mapping meanings at the “basic”-level such as apple, as opposed to a subordinate-level like red apple (Markman, 1990). The fact that learners nevertheless acquire subordinate nouns has been attributed to properties of the referential world that co-occur with the word (e.g., Xu and Tanenbaum, 2007b; Spencer et al., 2011). However, learners may also make inferences about the informativity of labels as intentional linguistic acts. We investigated whether learners exploit information about semantic contrast to generalize word meanings beyond the basic level. Experiment 1 found that the introduction of a labelled alternative at the subordinate level (green apple) eliminated the basic-level bias. Experiment 2 found that the presence of the alternative exemplar without a label merely suppressed the bias. We propose that the acquisition of subordinate-level meanings is facilitated by expectations of informativity which allow learners to enter the relevant alternatives into consideration.

While statistical learning is a well-established language learning mechanism, its usefulness in multiple language contexts is more unknown. A phenomenon known as entrenchment has been proposed, in which learning one language prevents the acquisition of a second language in the same speech stream. The observed L1 advantage or primacy effect has been previously mitigated with various cues to the presence of a second structure (L2). The present study manipulates the number of transitions between L1 and L2 to influence entrenchment. One condition was designed to replicate previous findings of entrenchment and the other was designed to overcome entrenchment. We find that adding more transitions between languages did not increase L2 learning, and second language learning is more dependent on the first learned language than on manipulations of the transitions between languages.

Prior work suggests children understand how speech conveys information and influences others’ minds. Although these studies have focused on communication under ideal conditions, auditory noise plagues the real world, often corrupting the transmission of information. The current study examines how children reason about the impact of auditory noise on communication. Children (N=72, Age:3;0-5;11) watched scenarios where a teacher tells a learner about two toys, but loud auditory noise masks one of the explanations. When asked which toy the learner wants to hear about again, children were more likely to select the noise-masked toy when the learner knew about neither toy (No Knowledge) than when he already knew about the masked toy (Partial Knowledge). However, their preference for the masked toy also increased with age in both conditions. Overall, these results demonstrate children's developing understanding of when and how communication affects listeners' knowledge and information-seeking behaviors.

When a language offers multiple options for expressing the same meaning, what principles govern a speaker's choice? Two well-known principles proposed for explaining wide-ranging speaker preference are Uniform Information Density and Availability-Based Production. Here we test the predictions of these theories in a previously uninvestigated case of speaker choice. Russian has two ways of expressing the comparative: an \textsc{explicit} option (\textit{Ona bystree chem ja}/She fast{\sc-comp} than me{\sc-nom}) and a \textsc{genitive} option (\textit{Ona bystree menya/She fast{\sc-comp} me{\sc-gen}}). We lay out several potential predictions of each theory for speaker choice in the Russian comparative construction, including effects of post-comparative word predictability, phrase length, syntactic complexity, and semantic association between the comparative adjective and subsequent noun. In a corpus study, we find that the explicit construction is used preferentially when the post-comparative noun phrase is longer, has a relative clause, and is less semantically associated with the comparative adjective. A follow-up production experiment using visual scene stimuli to elicit comparative sentences replicates the corpus finding that Russian native speakers prefer the explicit form when post-comparative phrases are longer. These findings offer no clear support for the predictions of Uniform Information Density, but are broadly supportive of Availability-Based Production, with the explicit option serving as an unreduced form that eases speakers' planning of complex or low-availability utterances. Code for this study is available at https://github.mit.edu/thclark/russian_uid

Technology has been used in the service of learning for a long time. Nowadays, the use of Artificial Intelligence (AI) is developing but its acceptability among future teachers still needs to be investigated. Moreover, differences between elementary and middle-school teachers could arise, due to the comparison between their role and those of technology involving AI. The current study aims at evaluating the acceptability of technology involving AI among future teachers, using a well-known model and more specifically regarding several tasks. Results show that elementary school teachers expect more performance from technology involving AI, but mainly for a use of content generation (e.g., course content, exercises). Middle-school teachers are more willing to accept technology involving AI for more high added value tasks such as help in writing learning or in diagnosing learning difficulties. Future studies should focus on identifying action levers to favor higher acceptability and actual use.

Adaptive scheduling systems aim to estimate the ability of an individual in order to prescribe a personalized training schedule. These adaptive systems are often founded on regularities of human memory such as a learning, forgetting, and the spacing effect. One such model which has been developed to both account for regularities of memory and be used in applied contexts is the Predictive Performance Equation (PPE). One limitation of the PPE is that it is only able to account for and incorporate information about a participant’s accuracy on a task and cannot take into account additional performance measures such as reaction time. To expand the PPE, we propose a simple extension to the model, allowing it to account for both accuracy and reaction time measures. Our paper reports the extension to the PPE as well as a formal model comparison to another model of learning and retention (Pavlik and Anderson, 2005). The results of our model comparison reveal that the extended PPE can both better account and predict an individual’s performance than Pavlik and Anderson (2005) model.

Opponent Colors Theory advances that four colors have special status and are yoked in opponent fashion (yellow-versus-blue, and red-versus-green). Classic hue cancelation studies provide evidence for this theory: people readily pick out colors that are neither red nor green, usually yellow. Here we conducted a version of a hue-cancelation experiment with the Tsimane’ people, a non-industrialized culture in the Amazon. Tsimane’ speakers readily identified reddish and greenish color chips, but they showed idiosyncratic choices when asked to identify a color that is neither reddish nor greenish, unlike English speakers who consistently select focal yellow. The Tsimane’ participants who also spoke Spanish and had a consistent label for English “yellow” (“amarillo”), performed similarly to the Tsimane’ monolinguals, suggesting that simply having a label for “yellow” is not sufficient to explain the consistency of English speakers. The results add to a growing body of evidence that does not support Opponent Colors Theory.

This study aimed to explore cross-sectional associations between executive function (EF), and community and household factors (household SES, caregiver education, home learning environment, caregiver/child interaction, caregiver wellbeing, and exposure to community violence) in a sample of children from very low-SES settings in Cape Town, South Africa. Results revealed that children exposed to higher levels of violence perform worse on inhibition tasks. No other associations were significant, highlighting the need to reassess how researchers can better understand these settings and the effects on EF development.

Generalizations are a fundamental linguistic tool for efficiently passing along information. To interpret the intended strength of a generalization, listeners rely on prior knowledge. Experienced and inexperienced listeners may interpret the same generalization differently, potentially leading to miscommunication. Speakers could mitigate such miscommunication by avoiding generalizations that inexperienced listeners are likely to misinterpret. However, experienced speakers may struggle to understand the perspective of an inexperienced listener. The present study examined whether experienced speakers adjust their use of generalizations based on the expertise of their intended audience. Results showed that any such adjustments are minimal and insufficient to avoid miscommunication as operationally defined. Future research may clarify the practical impact of such miscommunication by examining how generalizations are used in relation to speakers’ and listeners’ goals.

When judging the relative difficulty of impossible actions within the context of a magical world like that of Harry Potter, individuals honor real-world causal principles (e.g., assuming that heavier objects would be harder to levitate than lighter ones even though levitation itself is impossible; Shtulman & Morgan, 2017). We examined whether this effect persists when events are presented outside of this context. U.S. (Studies 1 and 2) and Chinese (Study 2) adults were asked to rate the relative difficulty of two impossible events that varied according to an irrelevant causal principle in one of three contexts: present science, future science, or magical. Though Chinese and U.S. adults honored irrelevant causal principles to a similar degree across the three contexts, Chinese adults’ confidence in their judgments varied by context. Additionally, individual differences in cognitive reflection (U.S.) and fantasy engagement (Chinese) related to judgments. Findings indicate that adults honor irrelevant causal constraints when reasoning about the impossible across multiple contexts, though subtle differences exist at both the cultural and individual level.

Syntactic harmony occurs when heads and dependents align within and across different types of phrases in a language. Harmony is a well-known (statistical) typological universal: in most languages, many if not all heads and dependents are consistently ordered (i.e., either head-dependent, or dependent-head). Despite decades of work, from every conceivable theoretical perspective, the origins of syntactic harmony remain opaque. However, recent work using artificial language learning has suggested that harmonic patterns are easier to learn than their non-harmonic counter-parts. Thus at least part of the explanation for this tendency may be linked to learning. Here, we explore whether the mechanism behind the learning bias for syntactic harmony is fundamentally domain-general by instantiating harmony in non-linguistic stimuli. Our findings support the claim that the origins of syntactic harmony lie in a domain-general bias for simplicity acting on linearized, language-specific categories.

Referring expression understanding and generation are critical for robots to communicate about the world around them. Recently there have been significant advances on the problem of referring expression understanding, also known as reference resolution, with researchers presenting approaches to both incremental reference resolution (i.e., processing referring expressions word by word in real-time as they are spoken) and open-world reference resolution (i.e., resolving references both to known and previously unknown entities). In this work, we combine insights from these approaches to present IPOWER: the first algorithm for performing reference resolution incrementally in open-world environments.

Each grammatical construction has its own function, and typically multiple constructions are combined to express a message. When the functions of two constructions conflict in a way that cannot be reconciled, their combination is judged ungrammatical. Here we consider one such type of case: “syntactic island violations.” Specifically, we consider combinations of wh-questions with 11 other constructions. Wh-questions request direct information about a particular constituent. Using a new Discourse task, we quantify how directly 11 constructions convey information in simple declarative sentences. Results demonstrate acceptability judgments on the wh-questions correlate with the degree to which the 11 constructions convey information directly. Thus, we argue that degrees of unacceptability of “island violations” result from the extent to which the discourse functions of the constructions involved conflict (N=240).

Data were collected from a brain-computer interface speller that utilized the P3b as a control signal. Stimuli consisted of letters and their "segments". Importantly, different letters were made up of different numbers of segments from a 10 segment library. Subjects were instructed to mentally note whenever segments from their letter (targets) were flashed. We found P3b amplitudes of target segments decreased as the number of segments in a letter (target letter complexity) increased. In contrast, the P3b attenuation was not affected by the total number of letters a segment belonged to (segment frequency). These results may reflect higher task difficulty caused by increased working memory load with increased target letter complexity. Alternatively, it's possible that despite the target rate being fixed at 30% within each block, subjects erroneously believed the target rate increased with target letter complexity. Further work to disentangle these possibilities may enrich our understanding of the P3b.

Humans are able to intentionally forget declarative memory content as demonstrated in directed-forgetting (DF) experiments. Yet, only few studies assessed whether DF affects associations in procedural memory. We tested how the intention to remember/forget a stimulus affected the formation and/or retrieval of stimulus-response (S-R) associations. To do so, we combined an item-specific priming paradigm with list-method DF. We did not find an impact of the intention to remember/forget on either the retrieval of existing or the formation of new S-R associations: Although participants formed S-R associations (evident in decreasing RTs over stimulis’ prime instances), their persisting activation did not impact on RTs in a subsequent item-recognition-test. Potentially, processes contributing to item recognition impeded S-R retrieval. This finding is informative for future studies aiming to assess how intention differentially affects procedural and declarative memory. We formulate experimental design recommendations for future studies assessing the impact of DF on item-specific S-R associations.

Inspired by notions of intrinsic motivation (Schmidhuber, 2010) and play as proposing and solving arbitrary problems (Chu and Schulz, 2020b), we report initial progress toward computational modeling of playful goal generation. We create an embodied, 3D environment resembling a child's room, and ask study participants to play in the environment and then create a scorable game. We propose to model games using a domain-specific language, which represents each game as a computer program. These programs act as reward-generating functions, mapping states visited by an agent as they play a game to the score they should receive. We then analyze our corpus of program representations to highlight four key aspects of human games that would contribute to constructing effective computational models of game generation: creativity, compositionality, common sense, and context sensitivity.

Human social interactions require understanding and predict- ing other people’s behavior. A growing body of work has found that these inferences are structured around an assumption that agents act rationally and efficiently in space. While powerful, this view treats action understanding in a vacuum, ignoring that much social inference happens in the context of familiar, hierarchically structured events (e.g.: buying groceries, ordering in a restaurant). We propose that social and world knowledge is critical for efficiently interpreting behavior and test this idea through a simple block-building paradigm, where participants infer an agent’s sub-task (study 1a), next action (study 1b), and higher-level goal (study 1c), from very sparse observations. We compare these inferences against a Bayesian model of goal inference that exploits task structure to interpret agents’ actions. This model fit participant judgments with high quantitative accuracy, highlighting how world knowledge may help support social inferences in a rich and powerful way. Keywords: Computational modeling; Social cognition

According to the Language Familiarity Effect (LFE), people are better at discriminating between speakers of their native language. Although this cognitive effect was largely studied in the literature, experiments have only been conducted on a limited number of language pairs and their results only show the presence of the effect without yielding a gradual measure that may vary across language pairs. In this work, we show that the computational model of LFE introduced by Thorburn, Feldman, and Schatz (2019) can address these two limitations. In a first experiment, we attest to this model's capacity to obtain a gradual measure of the LFE by replicating behavioural findings on native and accented speech. In a second experiment, we evaluate LFE on a large number of language pairs, including many which have never been tested on humans. We show that the effect is replicated across a wide array of languages, providing further evidence of its universality. Building on the gradual measure of LFE, we also show that languages belonging to the same family yield smaller scores, supporting the idea of an effect of language distance on LFE.

A foundational hypothesis in cognitive science is that some of human thinking happens in a language of thought (LoT), which is universal across humans (Fodor, 1975). According to this hypothesis, words in different natural languages are labels for primitive concepts or their combinations in LoT. What are LoT's primitives? This is a major challenge because LoT is not directly observable, and thus needs to be inferred or 'reverse-engineered'. We put forward a novel approach to reverse-engineering LoT, capitalizing on the existing knowledge about the optimization of the trade-off between complexity and informativeness in natural languages.

Attention is widely considered a core process of Executive Function (EF), but it is not clear if it is a separable or integral component of EF in preschool children. Preschool children (n=137) completed a battery of tasks which included EF (i.e., response inhibition, working memory) and attentional control (AC) processes (i.e., sustained attention, selective attention). Confirmatory Factor Analyses (CFA) indicated that a two-factor model with EF and AC as separate factors fit the data better than a unitary one-factor model. These findings are consistent with the view that EF and AC are developing at different rates during the preschool years, and thus are not yet fully integrated in the processing of information. The implications of how EF and AC should be conceptualized in early childhood are discussed.

Cognitive theories for reasoning are about understanding how humans come to conclusions from a set of premises. Starting from hypothetical thoughts, we are interested which are the implications behind basic everyday language and how do we reason with them. A widely studied topic is whether cognitive theories can account for typical reasoning tasks and be confirmed by own empirical experiments. This paper takes a different view and we do not propose a theory, but instead take findings from the literature and show how these, formalized as cognitive principles within a logical framework, can establish a quantitative notion of reasoning, which we call plausibility. For this purpose, we employ techniques from non-monotonic reasoning and computer science, namely, a solving paradigm called answer set programming (ASP). Finally, we can fruitfully use plausibility reasoning in ASP to test the effects of an existing experiment and explain different majority responses.

Causal learning is shaped by people’s prior beliefs, including their expectations. In this paper, we specifically examine expectations of determinism: do they vary with perceptual features of physical causal events, and how do they influence subsequent causal learning from data? We show that perceptual features lead adults to different expectations of determinism for different causes of launching (Exps. 1A & 1B). Those expectations lead to significant differences in responses to causal “failures”; that is, we show a difference in violation-of-expectation effect after a failed launch (Exp. 2). Actual data can reduce or eliminate the impact of these expectations, but they do not override the effect of perceptual features (Exp. 3). Overall, spatiotemporal contiguity cues and expectation of determinism have similar effects on causal learning outcomes, but neither is fully reducible to the other.

Some sentences are hard to read, and we don’t fully understand why. Memory-based and expectation-based constraints both attempt to explain sentence processing difficulties, and decades of sentence processing literature have found evidence in support of both theories. We further investigate theories of sentence processing by exploring subject- and object-extracted relative clause processing in Standard Arabic. We conducted a self-paced reading task and found that SRCs are easier to process than ORCs in Arabic, in line with expectation-based theories. A follow-up analysis of comprehension question answers revealed that when suggested with the possibility of a noisy interpretation, readers preferentially accept an SRC interpretation over an ORC interpretation. Our future research will explore these findings and test the threshold for acceptance of noisy interpretations.

Theories suggest that representational gestures depicting properties of referents in accompanying speech could facilitate language production and comprehension. In order to shed light on how gesture and speech are coordinated during production, we investigate whether representational gestures are time-locked to the onset of utterances (hence planned when full events are encoded) or Lexical Affiliates (LAs; words most closely aligned with the gesture meaning; hence planned when individual concepts are encoded) in a large corpus of naturalistic conversation (n = 1803 gestures from n = 24 speakers). Our data shows that representational gestures are more tightly tied to LA onsets than utterance onsets, which is consistent with theories of multimodal communication in which gestures aid conceptual packaging or retrieval of individual concepts rather than events. We also demonstrate that in naturalistic speech, representational gestures tend to precede their LAs by around 370ms, which means that they could plausibly allow for an addressee to predict upcoming words (ter Bekke, Drijvers & Holler, 2021; Ferré, 2010; Habets et al., 2011).

It was recently found that letter-shapes have a non-arbitrary relation with their canonical pronunciations, in multiple orthographies and quantified across the whole of each orthography: letters that look similar tend to have similar pronunciations. Similarly, there is phonosemantic systematicity at the word level: words that sound similar tend to have similar meanings. We investigated for the first time whether a similar systematicity exists in Chinese characters. We measured all the pairwise phonological distances and all the pairwise orthographic distances of the 58 Chinese pictographic characters that are taught to year 1 and 2 in Chinese primary schools. The correlation was tested between the two lists of distances and verified by a Mantel test. We found a significant negative correlation between characters and their segmental pronunciations: characters that look similar tend to have dissimilar segmental pronunciations. This contrasts with the positive correlations found in previous similar research with alphabetic writing. We conclude, first, that questions of systematicity in the Chinese writing system are tractable in the same terms and by the same methodology as that applied to alphabetic writing systems. Second, segment-based processing requires to be augmented by tones for there to be systematicity that is comparable to that found in alphabetic writing systems. Any non-arbitrary relation between letter shapes and sounds may help bootstrap the acquisition of literacy.

We investigated whether the representation of relational categories is different from that of featural categories. Earlier work has suggested an extreme-value hypothesis: when a category is defined in terms of a relation, exemplars with exaggerated values along this stimulus dimension are judged as better members of the category. Featural categories, on the other hand, are not exaggerated. To test this hypothesis, we trained participants to categorize two fictional diseases defined either by a deterministic relation or a deterministic feature. After the categorization task was mastered up to a predefined learning criterion, we provided a graphical user interface that enabled participants to construct good examples of the acquired categories by adjusting the stimulus attributes. We constructed a novel index of relational exaggeration based on residual deviations from a non-exaggerated response strategy. These results supported the extreme-value hypothesis. This replicates and extends an earlier quasi-experimental study (Du et al., 2021).

Social environments often impose tradeoffs between pursuing personal goals and maintaining a favorable reputation. We studied how individuals navigate these tradeoffs using Reinforcement Learning (RL), paying particular attention to the role of social value orientation (SVO). We had human participants play an interated Trust Game against various software opponents and analyzed the behaviors. We then incorporated RL into two cognitive models, trained these RL agents against the same software opponents, and performed similar analyses. Our results show that the RL agents reproduce many interesting features in the human data, such as the dynamics of convergence during learning and the tendency to defect once reciprocation becomes impossible. We also endowed some of our agents with SVO by incorporating terms for altruism and inequality aversion into their reward functions. These prosocial agents differed from proself agents in ways that resembled the differences between prosocial and proself participants. This suggests that RL is a useful framework for understanding how people use feedback to make social decisions.

Path integration, the ability to maintain an estimate of one's location by continuously integrating self-motion cues, is a vital component of the brain's navigation system. We present a spiking neural network model of path integration derived from a starting assumption that the brain represents continuous variables, such as spatial coordinates, using Spatial Semantic Pointers (SSPs). SSPs are a representation for encoding continuous variables as high-dimensional vectors, and can also be used to create structured, hierarchical representations for neural cognitive modelling. Path integration can be performed by a recurrently-connected neural network using SSP representations. Unlike past work, we show that our model can be used to continuously update variables of any dimensionality. We demonstrate that symbol-like object representations can be bound to continuous SSP representations. Specifically, we incorporate a simple model of working memory to remember environment maps with such symbol-like representations situated in 2D space.

English is the dominant language in computer science. In addition to English-based academic papers, English is frequently the only language provided in introduction sections and manuals of command and software libraries, which are essential aspects of computer programming. Hence, English-as-a-second-language (ESL) learners may have difficulty studying computer science because they must learn this field while also learning English. Despite this problem, few studies have assessed the difficulty level of computer science texts for ESL learners. Ideally, the difficulty levels of texts are assessed by having groups of ESL learners read them. However, owing to the excessive time and financial costs involved, such practices can be impractical. Hence, using two highly accurate automatic readability assessors based on natural language processing (NLP) techniques, we assessed the readability of various computer-science-related texts for ESL learners. The first assessor is based on state-of-the-art deep transfer learning, and the second is based on classical machine learning and applied linguistics. For training the assessors, we used a standard corpus employed in NLP, which was annotated by professional English teachers to evaluate the readability of the texts for ESL learners. To conduct the experiments, we built a collection of computer science texts ranging from academic papers to software manuals (READMEs) crawled from a source-code hosting website, namely GitHub. The experimental results showed that intermediate ESL learners were able to read most of the computer science related texts.

With artificial intelligence technologies becoming commonplace today, enhancing the efficiency of human-artificial agent (AA) interactions has become increasingly important. A growing body of research has revealed how dynamic motor primitives (DMPs) of human perceptual-motor behavior can be used to create ‘human-like’ AAs, primarily focusing on cooperative tasks. Using air hockey as a representative task, the current experiment is the first part of a large study aimed at determining the utility of DMP-based models for developing ‘human-like’ competitive AAs. Participants played against a preliminary DMP model and the differences in their behaviors were analyzed. Based on these observed differences, a revised model is proposed, with preliminary results revealing that the new model exhibits behaviors more consistent with those of humans. A major implication of this work is that it presents a framework for creating ‘human-like’ AAs that capture the essential human decision and movement dynamics without requiring large human gameplay datasets.

The propensity for people to avoid mentally demanding tasks in the absence of reward is well documented. As a result, humans are often described as cognitive misers. This characterisation, while consistent with the psychological literature, contradicts everyday instances of effort being sought: reading, board games, and brain-teasing puzzles. Such examples however are markedly different from the types of tasks typically used in the mental effort literature (e.g., working memory tasks, demand selection tasks). The current set of experiments assessed whether the type of task (i.e., N-Back, Number Sequence Problems [NSP], or Anagrams) affects people’s aversion to, or desire for, increased effort. On average, across 3 experiments, participants showed an aversion to effort regardless of whether the effort required was more attentional (N-Back) or cognitive (NSP and anagrams) in nature, and were willing to forgo financial reward in order to avoid more difficult tasks. A minority of participants, however, sought more effortful tasks for equal or lesser reward.

Sequential sampling models have provided accurate accounts of people’s choice, response time, and preference strength in value-based decision-making tasks. Conventionally, these models are developed as Markov-type processes (such as random walks or diffusion processes) following the Kolmogorov axioms. Quantum probability theory has been proposed as an alternative framework upon which to develop models of cognition, including quantum random walk models. When modeling people's behavior during decision-making tasks, previous work has demonstrated that both the Markov and quantum models have their respective strengths. Recently, the open system model, which is a hybrid version of the Markov and quantum models, has been shown to provide a more accurate account of preference strength compared to the Markov and quantum models in isolation. In this work, we extend the open system model to make predictions on pairwise choice and response time and compare it to the Markov and quantum random walk models.

Reasoning and reflective thought are critical to the study and practice of philosophy. However, findings from social cognition have challenged the extent to which many decisions are driven by explicit reasoning. We report an experiment that examines how reflective thinking impacts subjects’ judgements on various philosophical topics. Subjects were presented various scenarios on common philosophical topics (e.g., mind-body dualism); each scenario stated a given position. Some subjects were asked to indicate the extent to which they endorsed these positions (control), whereas others were asked to engage in a reflective thinking task before making this choice. Our results revealed that the reflective thinking group was more skeptical of the scenarios’ stated positions than the control group, but this effect depended on the topic of the scenarios. Thus, reflective thinking and reasoning do indeed seem to impact philosophical judgments, but this effect seems to depend on the topic under consideration.

The comprehension of counterfactual statements (‘If there had been zebras, there would have been lions’) has been subject to much research, but two key questions remain: Can comprehenders interpret counterfactuals without relying on causal inferences? And can comprehenders reach the actual state interpretation relying only on grammatical cues, or is this interpretation triggered by communicative goals? We answer these questions by relying on non-causal counterfactuals, and by manipulating the Question under Discussion between experiments: In Exp. 1, we replicate Orenes et al. (2019), using a web-based eye-tracking paradigm. In Exp. 2, we make the QuD explicit by asking about the actual state of affairs. The results reveal that making a contextually relevant alternative explicit via the QuD shifts counterfactual interpretation, but in general, the suppositional state interpretation is preferred in non-causal counterfactuals. These results imply that the driving forces behind counterfactual processing are pragmatic, not syntactic.

Learning to write is characterized by bottom-up mimicking of characters and top-down writing from memory. We introduce a CNN-RNN model that implements both pathways: It can (i) directly write a letter by generating a motion trajectory given an image, (ii) first classify the character in the image and then determine its motion trajectory `from memory', or (iii) use a combination of both pathways. The results show that, in one-shot and few-shot learning, the model profits from different combinations of the pathways: The generation of different character variants works best when the top-down is supported by the bottom-up pathway. Refilling occluded images of efficiently learned characters works best when using the top-down pathway alone. Overall, the architecture implies that a weighted merge of bottom-up and top-down information into a latent, generative code fosters the development of compositional encodings, which can be reused in efficient learning tasks.

We explore pupil diameter (PD) as estimator of Sense of Embodiment (SoE) using data of three user studies. We hypothesize that pupil diameter reflects SoE in a direct and indirect way. If individuals feel strongly embodied, presenting an emotional stimulus like a threat to the surrogate will produce a strong response, as if the stimulus would be presented to their own body. This would lead to a positive correlation between SoE and pupil dilation during the presentation of emotional stimuli. Besides this direct effect, there may also be an indirect effect. It is postulated that higher degrees of embodiment reduce workload when controlling a surrogate. This indirect effect of embodiment through lower workload on the PD would result in a negative correlation between SoE and PD since lower workload results in smaller PD. These direct and indirect effects were partially confirmed by the results of three experiments. We observed that PD and SoE are positive and direct correlated in case of emotional stimuli subjected to the surrogate (e.g. a threat), and that PD tended to be smaller for participants who experienced a condition designed to provide high SoE compared to one designed to provide low SoE.

Speakers' referential expressions often depart from communicative ideals in ways that help illuminate the nature of pragmatic language use. Patterns of overmodification, in which a speaker uses a modifier that is redundant given their communicative goal, have proven especially informative in this regard. It seems likely that these patterns are shaped by the environment a speaker is exposed to in complex ways. Unfortunately, systematically manipulating these factors during human language acquisition is impossible. In this paper, we propose to address this limitation by adopting neural networks (NN) as learning agents. By systematically varying the environments in which these agents are trained, while keeping the NN architecture constant, we show that overmodification is more likely with environmental features that are infrequent or salient. We show that these findings emerge naturally in the context of a probabilistic model of pragmatic communication.

The purpose of this paper is to present a novel conceptualisation of an intuitive, primitive form of trust termed proto-trust. This concept is proposed in order to account for the many different senses, types and domains in which trust has traditionally been defined and theorised. A brief review of the literature on affective and intuitive trust is presented, informing the definition and formalisation of proto-trust. Following this, a preliminary empirical investigation of proto-trust is described, where intuitive trust assessments are compared to analytical trust decisions, under various attribution prompts. Results showed effects of attribution prompts on changes to trust assessments from intuitive to deliberative decisions. In addition, qualitative data are presented for the various reasons participants gave for their trust decisions. One of these reasons (emotional reaction) was found to affect the degree of difference between intuitive and deliberative trust assessments.

Dementia is a common and debilitating condition that typically gives rise to increasing language impairment. There is a need to understand the nature of this impairment further so that therapies may be developed, particularly in the case of bilinguals. This paper extends BiLex, an existing computational model of bilingual lexical access, to simulate language decline in dementia. Six lesion types are evaluated for their ability to reproduce the pattern of decline in the semantic variant primary progressive aphasia (svPPA) subtype of dementia. Semantic memory lesions reproduce this pattern of decline best in monolinguals, and further suggest patterns that are likely to be found in longitudinal data from bilingual dementia patients in the future.

Racial and gender bias, from advertisement to political rhetoric, is ubiquitous in persuasion. However, the impact of bias on persuasive discourse is often muddied by intent and framing. Reasoners practicing anti-racism may be more likely to scrutinize racially-specific arguments, while arguments made by women may only be diminished when they are emotionally charged. We sought to study how humans evaluate interpretive arguments, what makes certain arguments persuasive, and the impact of bias and emotionality on persuasiveness. We found that shallow heuristics such as argument length and readability are poor indicators for persuasive impact, but reasoners are more likely to be persuaded by arguments made by White people, particularly White women. Further, no difference was observed based on a reasoner's ability to see the arguer's face, implying that judgments are made solely by name recognition. Our focus on written arguments has broad implications for information literacy and racial justice.

Morphological syncretism occurs in languages when one morphological category ‘merges’ with another. Cross-linguistic research on the prevalence and types of syncretic patterns has revealed that some types of syncretism are more common than others. For example, syncretism in nominal morphology is more likely to occur in non-singular categories (Baerman et al., 2005). In two artificial language learning experiments, participants were exposed to words from a miniature language with suffix markings for gender (feminine and masculine) and number (singular, dual, and plural). Participants in Experiment 1 showed no evidence of a bias for syncretism in non-singular forms. However, participants in Experiment 2 showed a general bias to infer that a suffix that marked a novel category should be identical to a known form. This bias was strongest for non-singular items, in line with the cross-linguistic typology of syncretism. Implications for learnability and typology are discussed.

As algorithmic tools increasingly aid experts in making consequential decisions, the need to understand the precise factors that mediate their influence has grown commensurately. In this paper, we present a crowdsourcing vignette study designed to assess the impacts of two plausible factors on AI-informed decision-making. First, we examine homophily---do people defer more to models that tend to agree with them?---by manipulating the agreement during training between participants and the algorithmic tool. Second, we considered incentives---how do people incorporate a (known) cost structure in the hybrid decision-making setting?---by varying rewards associated with true positives vs. true negatives. Surprisingly, we found limited influence of either homophily and no evidence of incentive effects, despite participants performing similarly to previous studies. Higher levels of agreement between the participant and the AI tool yielded more confident predictions, but only when outcome feedback was absent. These results highlight the complexity of characterizing human-algorithm interactions, and suggest that findings from social psychology may require re-examination when humans interact with algorithms.

What is the relationship between trust and perceived agency? The present study experimentally investigated the effect of people’s perception of a robot’s compliance (and resistance) to social norms on their evaluation of a robot’s perceived agency, performance trust, and moral trust. Participants reported a norm-conforming robot to have higher perceived agency and a greater sense of trust than a robot that violated social norms. We also found that perceived agency, regardless of how much a robot followed norms, was correlated with trust. We interpret this finding as evidence that as people see a robot as having agency, they trust it more.

Elicited imitation is a widely used method for testing a child's knowledge of a language for scientific or clinical purposes. A child hears an utterance and is asked to repeat what they have heard. While it is assumed that their fluency or speed in doing so is contingent on their linguistic competence, little is known about the cognitive mechanisms and/or representations involved. To explore this, we train an encoder-decoder model, consisting of recurrent neural networks, to encode and reproduce a corpus of child-directed speech and then test its performance on the experimental task of Bannard and Matthews (2008). In that study pre-school children were asked to repeat high- and low-frequency four-word sequences in which the first three words were identical (e.g., sit in your chair and sit in your truck) and the final words and bigrams were closely matched for frequency. We find that like those children our model makes more errors on the initial three words when they are part of a low-frequency than a high-frequency sequence, despite the fact that the words being repeated are identical. We explore why this might be and pinpoint some possible similarities between the model and child language processing.

How and why are analogies spontaneously generated? Despite the prominence of analogy in learning and reasoning, there is little research on whether and how analogy is spontaneously generated in everyday settings. Here we fill this gap by gathering parents' answers to children's real questions, and examining analogy use in parental explanations. Study 1 found that parents used analogy spontaneously in their explanations, despite no prompt nor mention of analogy in the instruction. Study 2 found that these analogical explanations were rated highly by parents, schoolteachers, and university students alike. In Study 3, six-year-olds also rated good analogical explanations highly, but unlike their parents, did not rate them higher than causal, non-analogical explanations. We discuss what makes an analogy a good explanation, and how theories from both explanation and analogy research explain one’s motivation for spontaneously generating analogies.

We explore whether younger children (4- and 5-year-olds) and older children (9- and 10-year-olds) expect a costly signaler (someone who engages in a costly action) to be a more committed group member than someone who engages in a comparatively less costly action. In Experiment 1 (N=173), older children and adults—but not younger children—expect a costly signaler wants to be in a group more than a control, and they give more positive evaluations of the costly signaler than the control. In Experiment 2 (N=84; ongoing), employing a different manipulation of cost both younger and older children infer that a costly signaler wants their goal more than the control, but they make different evaluations of the costly signaler depending on whether they exerted effort on behalf of a group versus an individual. Future research may be needed to rule out alternative explanations.

Reasoning about sampling distributions is notably challenging for humans. It has been argued that the complexity involved in sampling processes can be facilitated by interactive computer simulations that allow learners to experiment with variables. In the current study, we compared the effects of learning sampling distributions through a simulation-based learning (SBL) versus direct instruction (DI) method. While both conditions resulted in similar improvement in rule learning and graph identification, neither condition improved more distant transfer of concepts. Furthermore, the simulation-based learning method resulted in unintuitive and surprising kinds of misconceptions about how sample size affects estimation of parameters while the direct instruction group used correct intuitive judgments more often. We argue that similar perceptual properties of different sampling processes in the SBL condition overrode learners’ intuitions and led them to make conceptual confusions that they would not typically make. We conclude that conceptually important differences should be grounded in easily interpretable and distinguishable perceptual representations in simulation-based learning methods. Keywords: education; statistics; learning with simulations; sampling distributions

Decades of feedback research have suggested that feedback is more effective in correcting errors than confirming the right responses. A study conducted by Eskreis-Winkler and Fishbach (2019) challenged this notion by showing that people learn less from feedback that indicates their answer is incorrect (failure feedback) than feedback that indicates their answer is correct (success feedback) even after incentivizing learning, manipulating response correctness, and controlling for background knowledge and mental inferences required for learning across conditions. Across two randomized experiments, we extended this work to investigate whether changing the focus of feedback from the self (“You answered this question correct/incorrect!”) to the task (“The answer was correct/incorrect!”) would reduce the difference between success and failure feedback. We replicated the previous study’s main finding that people learn less from failure feedback than success feedback. However, the focus of feedback message (task vs self) did not have the hypothesized effect. We suggest future research further investigate the impact of feedback focus using in-person experimental settings with more powerful designs and we recommend a set of motivational factors to investigate to determine how learning from failure feedback can be optimized. Keywords: learning; education; feedback; motivation; ego threat; replication

Many of the real world phenomena that cognizers must grapple with are continuous, not only in the values they can take, but also in how these values change over time. The mind must somehow abstract from these inputs to extract useful discrete concepts such as objects, events and causal relationships. We investigate several factors that affect basic inferences about causal relationships between continuous variables based on observations in continuous time. In a novel experiment, we explore the ways in which causal judgments are sensitive to factors that relate to causal inductive biases (e.g. causal lags, the direction of variation) and causal perception (e.g. the range and rapidity of variation). We argue standard statistical time-series models have limited utility in accounting for human sensitivity to these factors. We suggest further work is needed to fully understand the cognitive processes that underlie causal induction from time-series information.

The application of artificial intelligence (AI) for text generation in creative domains raises questions regarding the credibility of AI-generated content. In two studies, we explored if readers can differentiate between AI-based and human-written texts (generated based on the first line of texts and poems of classic authors) and how the stylistic qualities of these texts are rated. Participants read 9 AI-based continuations and either 9 human-written continuations (Study 1, N=120) or 9 original continuations (Study 2, N=302). Participants' task was to decide whether a continuation was written with an AI-tool or not, to indicate their confidence in each decision, and to assess the stylistic text quality. Results showed that participants generally had low accuracy for differentiating between text types but were overconfident in their decisions. Regarding the assessment of stylistic quality, AI-continuations were perceived as less well-written, inspiring, fascinating, interesting, and aesthetic than both human-written and original continuations. Keywords: Cognition, Artificial Intelligence, Literature, NLP, GPT-2

In many real-life situations, we make decisions between a defined set of options, which can be either discrete (as when deciding between going on driving and stopping the car) or continuous (as when stirring the wheel, the possible range of angles goes from $-30$ to $30$ degrees). However, most computational models for decision-making focus on decisions between a discrete set of options. While there are a few sequential sampling models that can explain behavioral patterns (i.e., choices and response times) of decisions in a continuous option space (i.e., the CDM and the SCDM), these models have a few limitations. For example, these models assume no leakage in the evidence accumulation process and no spatial inhibition (i.e., inhibition among different areas of the option space depending on their distance to each other). In this paper, we propose a novel sequential sampling model based on an existing computational model (i.e., the leaky competing accumulator model) for decisions in a continuous option space. Our proposed model includes leakage and spatial inhibition and is thus more biologically plausible.

Previous demonstrations of false memories for predicted but not presented words used slow encoding and immediate retrieval conditions, potentially exacerbating false memory effects. We present two experiments that investigated whether false memories also occur under self-paced encoding and delayed retrieval conditions, and whether false memories are reduced when the initial prediction was disconfirmed by an implausible word, thought to elicit false memory suppression. Results showed that previous demonstrations of false memories were not contingent on the task conditions: False memories also occur when language processing is self-paced, and they affect longer-term memory structures. Crucially, false memories emerged regardless of whether the prediction-disconfirming word was plausible or not. Results are evaluated against a recent psycho-linguistic account that makes diverging predictions regarding the processing consequences of mild and severe violations of plausibility.

Recent years have witnessed significant advances in our understanding of bounds on rationality in both cognitive psychology and economics. These two fields have been making separate progress, but time is ripe for unifying these efforts. In this article, we introduce recently developed economic tools, themselves rooted in the psychometric tradition, to quantify individual differences in the capacity for cognitive control. These tools suggest that a reliable assessment of the capacity for cognitive control may be accomplished by examining task performance as a function of reward. We demonstrate through simulation studies that an incentive-informed measure of task performance does a better job of recovering individual differences in one’s capacity for cognitive control, compared to the commonly used congruency effect. Furthermore, we show that the economic approach can be used to predict control-dependent behavior across different task settings. We conclude by discussing future directions for the fruitful integration of behavioral economics and cognitive psychology with the aim of improved measurement of individual differences in the capacity for cognitive control.

Parallel accounts of interference resulting from the generation of related words can be found in the retrieval-induced forgetting (RIF) and the cumulative semantic interference literatures. Recent work on the language production side suggests that the same adaptive learning process may underlie both. However, the literatures remain separate. They use different procedures and dependent measures, and theoretical accounts focus on underlying conceptual representations (memory research) vs. conceptual-lexical links (language research). We propose that the accounts should be reconciled. As an initial step toward this goal we combined a retrieval/generation procedure with a continuous picture-naming test phase to assess their combined effects on interference. We observed both costs and benefits in error data. There were more naming errors (including many time-outs) for non-generated items from activated categories and fewer for previously generated items. Perhaps due to a too-severe cutoff, naming times did not show a RIF influence, only a marginal facilitation effect for generated items. However, naming time showed typical cumulative interference within the picture-naming phase independent of previous retrieval experience. Future work will investigate the locus of interference in conceptual memory representations versus in links to word representations with the goal of producing a unified account of semantic interference.

This study examines (1) whether infant-directed speech (IDS) facilitates children’s word learning compared to adult-directed speech (ADS); and (2) the link between the prosody of IDS in word-learning contexts and children’s word learning from ADS and IDS. Twenty-four Dutch mother-child dyads participated when children were 18 and 24 months old. We collect mothers’ ADS and IDS at both ages and test children’s word learning from ADS and IDS at 24 months using an Intermodal Preferential Looking Paradigm (IPLP). We find that Dutch 24-month-old children could reliably learn novel words from both ADS and IDS, and IDS had a facilitative effect. Also, children’s word learning from IDS (but not ADS) is predicted by IDS pitch range when mothers introduce unfamiliar words to children at 18 months. Our findings contribute to an understanding of the role of IDS prosody in language development, highlighting both individual differences and contextual differences in IDS prosody.

The impressive recent performance of large language models such as GPT-3 has led many to wonder to what extent they can serve as models of general intelligence or are similar to human cognition. We address this issue by applying GPT-3 to a classic problem in human inductive reasoning known as property induction. Our results suggest that while GPT-3 can qualitatively mimic human performance for some inductive phenomena (especially those that depend primarily on similarity relationships), it reasons in a qualitatively distinct way on phenomena that require more theoretical understanding. We propose that this emerges due to the reasoning abilities of GPT-3 rather than its underlying representations, and suggest that increasing its scale is unlikely to change this pattern.

When people think about the features of common objects, like scissors, they often spontaneously recall a central feature: scissors cut things. They tend not to recall other features of scissors, e.g., that they have handles. The present paper posits a novel explanation for the behavior: the features people recall first and most often reflect semantic generalizations of kinds. A recent taxonomy of such generalizations suggests that people represent privileged links between kinds and their features known as principled connections (Prasada et al., 2013). Several tests diagnose principled connections: for instance, principled connections reflect norms, so one way to diagnose the presence of a principled connection is to test the acceptability of sentences of the form all normal Xs have feature Y, as in all normal cars have four wheels. We tested whether participants accept generalizations about the normality of features produced in a semantic feature production task (Experiments 1 and 2) as well as self-referential generalizations (Experiment 3). The experiments provided participants with generalizations about features listed first and most often as well as features that people list less frequently. They found that people readily accepted generalizations that diagnose the presence of principled connections. The results corroborate the view that principled connections help people recall the features of conceptual categories.

Children’s production of mental state verbs can reveal evidence of their theory of mind and general cognitive development. Children produce a certain class of mental state verbs, namely desire verbs such as want, wish, and hope, early in development. Among these desire verbs, they produce want the most frequently. We report on a corpus study of 450+ instances of want as gathered from children’s dialogues with caretakers in the CHILDES database. We developed a novel coding scheme to measure children’s use and understanding of want utterances: i.e., we sought to track the contents of their desires and the agents children predicated desires about. We report on the frequencies of these features across the ages of 2- 4, and highlight noteworthy trends in the way children learn to use want. Children appear to talk about their own desires most often; they primarily use questions to talk about second person desires; and they desire more complex objects as they mature. We describe how these patterns of linguistic competency may serve as an index of a developing theory of mind.

Team science research heavily relies on communication data—that is, data derived from audio, video, or text-chat communication streams between team members. Between transcription and content analysis, significant overhead is required to work with these data. Recent developments in natural language processing (NLP) may help ameliorate time constraints in this domain. Using transcript data, the present study, presented as a proof-of-concept, assesses how the BERT NLP model performs in a team communication categorization task, in comparison to ground truth measures. This work builds upon past work that relied on human-coded transcripts to identify phase transitions in team collaboration. Results suggest BERT’s capabilities at phase change detection are promising for experienced teams, though further iteration is needed on the methods in the current study. Applications of this work extend to real-time collaboration with an artificial agent, as this requires the real-time semantic processing of human communication data.

We demonstrate how medical-image classification decisions can be denoised by aggregating decisions on similar images. In our algorithm, the final decision on a target image is cancerous if a percentage t of the k most similar images are cancerous, else it is not cancerous. Similarity between images is calculated as the distance between representations from an artificial neural network. We vary k and t for novice and expert participants using data from Trueblood et al. (2018) and Trueblood et al. (2021). We show that increasing k improves performance for novices, with their performance approaching that of experts. We also show that the algorithm is biased towards identifying cancerous cells, which is reflected in the representational space. The percentage t allows greater control over sensitivity and specificity and can be used to debias decisions. This algorithm is less effective for experts, partially explained by them giving similar responses on similar images.

Category learning is our ability to generalize across experiences and apply existing knowledge to new situations. Many real-world categories adhere to a “rule-plus-exceptions” structure, wherein most items are rule-followers, but a subset of “exceptions” violate category rules. Rule-plus-exception learning seems tightly coupled with hippocampal function. Though past work has demonstrated that prediction error drives hippocampus to form distinct representations of exceptions, limited work has investigated how this process impacts existing rule-follower representations. Here we use a neural network model of hippocampus to quantify how rule- follower representations are altered by the introduction of exceptions. By recording model representations of rule- followers before and after exceptions are introduced, we computed the shift in rule-follower representation elicited by exceptions. A rule-follower’s similarity to exceptions along category-relevant, but not irrelevant, dimensions predicted its degree of representational shift. This work furthers our understanding of how hippocampus supports the integration of surprising information in dynamic environments.

In some contexts, human learning greatly exceeds what the sparsity of the available data seems to allow, while in others, it can fall short, despite vast amounts of data. This apparent contradiction has led to separate explanations of humans being equipped either with background knowledge that enhances their learning or with suboptimal mechanisms that hinder it. Here, we reconcile these findings by recognising learners can be uncertain about two structural properties of environments: 1) is there only one generative model or are there multiple ones switching across time; 2) how stochastic are the generative models. We show that optimal learning under these conditions of uncertainty results in learning trade-offs: e.g., a prior for determinism fosters fast initial learning but renders learners susceptible to low asymptotic performance, when faced with high model-stochasticity. Our results reveal the existence of optimal-paths-to-not-learning and reconcile within a coherent framework, phenomena previously considered disparate.

We examine how the variation present in a Latino variety of English spoken by Miami-based Cuban Americans, which is not a foreign accent, affects processing for two distinct listener populations, General American English listeners and LA-based Mexican American English listeners. Past research has appealed to notions of standardness and familiarity when explaining processing costs associated with foreign and regional accents. Studying two listener populations that have different relationships with standard and Latino varieties of English has the potential to disentangle these factors (i.e. familiarity, standardness). Through three semantic priming experiments, which measure online processing, it’s shown that the variation present in Cuban American speech does not affect priming facilitation for General American English listeners or LA-based Mexican American listeners, suggesting that our human processing system is generally flexible at accommodating variation and that it’s worth studying the effects of variation at levels beyond the extremes.

Detecting analogy is an important high-level cognitive skill that is involved in many aspects of human reasoning. While Structure Mapping Theory (Gentner, 1983) is a well-recognized high-level theory of analogy, it lacks a neural process implementation that links to perception and attention. Avoiding algorithmic computation on ungrounded symbols, we present a dynamic neural architecture built from interacting neural populations that establishes analogy between objects in two visually presented scenes. Consistent with SMT, it accounts for how humans find such analogies.

The presence of semantic information in multivariate patterns of neural activity has been explored as a method of measuring knowledge and learning. Using fMRI, we investigated whether novice learners of American Sign Language (ASL) showed overlapping representations of semantic categories for words presented in a well-known (English) or newly learned (ASL) language. We find evidence of neural patterns that were partially shared between sign and speech in novice participants. This result provides evidence for the influence of even brief learning on neural representations in cross-modality language processing.

In order to probe the production of kinematic cues to signal boundaries in action sequences, adults performed sequences of three actions on an object, with or without an action boundary following the second action. Movement of the hand was recorded via motion tracking, and it was found that the boundary was marked by a lengthening of the pre-boundary action and by a pause. These cues are also found in prosody to signal phrase boundaries in speech, suggesting a close coupling of the mechanisms underlying boundary production in both domains.

Humans differ greatly in their ability to use language. Contemporary psycholinguistic theories assume that individual differences in language skills arise from variability in linguistic experience and in general cognitive skills. While much previous research has tested the involvement of select verbal and non-verbal variables in select domains of linguistic processing, comprehensive characterizations of the relationships among the skills underlying language use are rare. We contribute to such a research program by re-analyzing a publicly available set of data from 112 young adults tested on 33 behavioral tests. The tests assessed nine key constructs reflecting linguistic processing skills, linguistic experience and general cognitive skills. Correlation and hierarchical clustering analyses of the test scores showed that most of the tests assumed to measure the same construct correlated moderately to strongly and largely clustered together. Furthermore, the results suggest important roles of processing speed in comprehension, and of linguistic experience in production.

The ability to perceptually “reweight” acoustic dimensions in response to changes in distributional statistics is known as dimension-based statistical learning (DBSL). However, it is currently unknown whether DBSL imposes a cognitive load. Older adults, who typically have age-related declines in cognitive ability, may be sensitive to this load. We examined young and older adults’ categorization of beer and pier sounds when the statistical relationship between VOT and F0 was consistent with that of American English, followed by a condition in which those statistics were reversed. Listeners made categorization decisions on each stimulus (Experiment 1), or after passive exposure to a string of stimuli (Experiment 2). In both experiments, younger and older participants demonstrated DBSL following exposure to the reversed statistics. Older adults tracked distributional statistics even when learning required accumulation of statistics over 8 sec, suggesting that rapid adaptation to regularities in speech input is robust across differing perceptual loads.

Groups of individuals need to coordinate in many real world domains. However, coordination failure is common and not well understood. There are few coordination measurements, analyses focus on averaged data, and models lack coordination strategies and clear correspondence to cognitive mechanisms. Here, we present a thorough analysis of human data from a difficult coordination scenario and a cognitive model implemented within the ACT-R cognitive architecture to fit and explain the data. Data were explored to better understand coordination strategies and group dynamics. The cognitive model included pre-game preferences, coordination strategies like signaling, and other player choice predictions. This work highlights the need for deeper data explorations and presents challenges for modeling related to coordination dynamics, strategies, and how players form beliefs about others.

We investigated neural networks’ ability to generalize during visual object recognition. In three experiments, we show that while basic multilayer neural networks easily learn to classify the objects on which they are trained, they show serious difficulties transferring that knowledge to novel items. However, our experiments also show that when the previously trained networks are then trained on the novel items, they learn to respond correctly to the novel items much faster than untrained networks. This shows that these networks are learning abstract representations that go beyond the simple items on which they were trained. We argue that this demonstrates that regarding abstract rule learning, the problem with neural networks is not their inability to learn abstractions, but their ability to apply that knowledge when classifying new objects.

A quantum walk model is developed for emotion transmission in serial reproduction of narratives. The readers' emotions are represented by density operators, and the influences of the narratives on the readers' emotions are modeled by applying the controlled unitary operators to the density operators. The performance of the quantum model is evaluated on a large corpus of narratives, compared to that of the Bayesian Markov chain model. The quantum model not only outperforms the Bayesian model for all five emotion transmissions presented in the corpus but can also account for order effects in serial reproductions. These results suggest a promising first step towards extending quantum-like models to explain group-level cognition.

Children produce increasingly more recognizable drawings of object concepts throughout childhood. What drives this improvement? Here we explore the role of children's ability to include relevant parts of those objects in their drawings. We crowdsourced part tags for every pen stroke in 2,160 drawings of 16 common object categories that had been produced by children between 4 and 8 years old. These part decompositions revealed both substantial variation in the number and kind of parts that children emphasized, as well as a non-monotonic relationship between the number of parts that children drew and how recognizable their drawing was. We plan to publicly release these data to catalyze further investigation of how children's drawings change across development.

Adults show numerical interference during discrete proportional reasoning. Although children’s similar errors are attributed to incorrect counting strategies, it is unlikely that adults use a counting strategy. We investigate two behavioral phenomena of proportional reasoning, numerical interference errors and holistic ratio-dependent responding, and use a Bayesian model-based approach to test whether these behavioral patterns can be explained by adults’ differential use of numerator comparison versus proportion comparison strategies. We find evidence of numerator interference and holistic ratio dependent responding for both discrete (i.e., individual dots) and continuous (i.e., undivided pie charts) proportions, but numerical interference is stronger for discrete stimuli. Importantly, adults’ continuous proportion comparisons were best captured by a proportion strategy, whereas discrete proportion comparisons showed a mixed pattern, with a slight preference for a numerator strategy. These findings provide insight into the mechanisms underlying proportional reasoning and provide a novel model-based approach for investigating strategy use.

Adults can rapidly learn new first-order phonotactic constraints like /f/ only occurs at the beginning of syllables, by producing strings of nonsense syllables such as "hes feng neg kem". The learning is measured by observing their speech errors, e.g., whether /f/’s then always slip to syllable onset position. Context-dependent (second-order) constraints such as /f/ occurs at the beginning of syllables if the vowel is /æ/, but occurs at the end of syllables if the vowel is /ı/ can be learned as well, but errors only follow these constraints after a period of sleep. It has been suggested that the knowledge of newly-learned second-order constraints is isolated from English knowledge in a separate "mini-grammar" and that the creation of the mini�grammar requires a period of sleep. The present study investigates the mini-grammar notion in the learning of first-order constraints, which are learned quickly in a single session. We interleaved trials in which participants produced strings of nonsense syllables with trials in which they repeated English sentences. The English sentences and nonsense sequences either showed the same consonant-position constraints or the opposite constraints. Speech error data showed that the English sentences interfered with the learning of the first-order constraints within the nonsense sequences, suggesting that the constraints in the nonsense context were not separated from ordinary English in a mini-grammar. We hypothesize that the formation of mini-grammars may require consolidation and that no mini�grammar is created for first-order constraint learning.

Because of its importance in academic achievement, especially in mathematics, training cognitive flexibility at school is a major issue. The present research investigates the effectiveness of a school-based intervention to improve proportion arithmetic problem solving. The study was conducted with 5th graders of 10 classes from 5 high-priority education schools in the Paris region. Students of the control and experimental groups took part in 8 learning sessions about proportion problem solving. The experimental group’s training focused on comparing and flexibly categorizing the problems in the hopes to help students achieve a deeper understanding of proportion problems. Results show that training flexible categorization allowed the experimental group to progress more than the control group, in both categorization and solving tasks. The educational implications of our results are discussed.

The contingency symmetry inference, the inference to generalize a learned contingency to a reverse direction, is known to be extremely difficult for non-human animal species (Lionello-DeNolf, 2009). In contrast, humans are known to have the “affirming the consequent fallacy”, which reverses the antecedent and the consequence (if P then Q: Q therefore P). The contingency symmetry bias has been long discussed in relation to the ontogenesis of language learning, as word learning requires understanding of bidirectional relationship between symbols and objects. But how this bias emerges has not been known. This research tested whether 8-month-old human infants have this bias on a matching-to-sample task. The results demonstrated the possession of this bias in human infants before they start active word learning. This bias is likely a uniquely human cognitive bias, which may explain why only humans have language.

Understanding abstract relations, and reasoning about various instantiations of the same relation, is an important marker in human cognition. Here we focus on development of understanding for the concept of antonymy. We examined whether four- and five-year-olds (N= 67) are able to complete an analogy task involving antonyms, whether language cues facilitate children’s ability to reason about the antonym relation, and how their performance compares with that of two vector-based computational models. We found that explicit relation labels in the form of a relation phrase (“opposites”) improved performance on the task for five-year-olds but not four-year-olds. Five-year-old (but not four-year-old) children were more accurate for adjective and verb antonyms than for noun antonyms. Two computational models showed substantial variability in performance across different lexical classes, and in some cases fell short of children’s accuracy levels. These results suggest that young children acquire a solid understanding of the abstract relation of opposites, and can generalize it to various instantiations across different lexical classes. These developmental results challenge relation models based on vector semantics, and highlight the importance of examining performance across different parts of speech.

Little research to date has examined how listeners integrate cues to non-native speaker identity in real time sentence processing. Here, we examine listeners’ interpretation of the semantic and socio-pragmatic content of utterances produced by either a foreign accented speaker or a native speaker. Overall, our findings suggest that processing speed was slower in the presence of foreign accents. However, the extra perceptual demands of processing unfamiliar accents did not translate into listeners’ accuracy rates, and in certain sentence contexts, non-native speakers were also more likely to elicit higher semantic or pragmatic interpretation accuracy. Our findings show that non-native speaker identity plays an important role in listeners’ sentence interpretations.

Recent work suggests that learning perceptual classifications can be enhanced by combining single item classifications with adaptive comparisons triggered by each learner’s confusions. Here, we asked whether learning might work equally well using all comparison trials. In a face identification paradigm, we tested single item classifications, paired comparisons, and dual instance classifications that resembled comparisons but required two identification responses. In initial results, the comparisons condition showed evidence of greater efficiency (learning gain divided by trials or time invested). We suspected that this effect may have been driven by easier attainment of mastery criteria in the comparisons condition, and a negatively accelerated learning curve. To test this idea, we fit learning curves and found data consistent with the same underlying learning rate in all conditions. These results suggest that paired comparison trials may be as effective in driving learning of multiple perceptual classifications as more demanding single item classifications.

Logico-semantic theories have long noted parallels between the linguistic representation of temporal entities (events) and spatial entities (objects): bounded (or telic) predicates such as fix a car resemble count nouns such as sandcastle because they are “atoms” with well-defined boundaries. By contrast, unbounded (or atelic) phrases such as drive a car resemble mass nouns such as sand in that they are unspecified for atomic features. Here, we show for the first time that there are similarities in the perceptual-cognitive representation of events and objects in non-linguistic tasks. Specifically, after viewers form a bounded or an unbounded event category, they can extend the category to objects or substances respectively (Experiment 1). Furthermore, viewers can intuitively make event-to-object mappings that respect atomicity (Experiment 2). These striking similarities between the mental representation of events and objects have implications for current theories of event cognition, as well as the relationship between language and thought.

Events are considered as temporal segments with a beginning and an endpoint. Philosophical and linguistic literature on events distinguishes between bounded events that include distinct temporal stages leading to culmination (e.g., fix a car) and unbounded events that include largely undifferentiated stages and lack an inherent endpoint (e.g., drive a car). The present study shows that event viewers spontaneously compute this distinction through an interruption detection task. People watched videos of bounded or unbounded events with a visual interruption lasting .03s at the midpoint or close to the endpoint of the event stimulus. People indicated whether they saw an interruption after each video (Experiments 1) or responded as soon as possible during each video (Experiment 2). In both cases, the endpoint-midpoint difference depended on whether participants were watching a bounded or an unbounded event. As people perceive dynamic events, they spontaneously track boundedness, or the internal temporal structure of events.

Although pointing is sparse, overloaded, and indirect, it allows humans to effectively decode shared information, (ex)change their minds, and plan accordingly. Pointing is an invitation to jointly attend to an object, which triggers the mutual inference between agents of each other’s mind. Relevance is a fundamental assumption underlying all human communication, including pointing. We define relevance as how much a signaler’s belief can make a positive difference to its receiver’s well being. We build a Theory of Mind (ToM) model to test our definition of relevance and use pointing as a case study. In two experiments, we test our relevance model in a classic artificial intelligence (AI) task, the Wumpus world, with the key difference that there is a guide that points to help a hunter. Agents with our relevance model gain significantly higher rewards than agents who ignore signals from the guide. Agents with our model also achieve better performance than agents who receive an additional observation of the environment. The results show that the power of pointing comes from the ToM inference of relevance, rather than providing more precise individual perception.

This paper presents a connectionist network simulation of Livesey and McLaren’s (2009) results. In that paper they showed that participants with post-discrimination gradients that were initially peak shifted became monotonic as they were exposed to the full range of test stimuli. While the authors suggest that this is the result of rule-based processes ‘taking over’ responding, we show how a connectionist network with an attentional parameter and realistic activation functions for the input can simulate both the peak shifted and monotonic gradients. Although we do not infer that the monotonic gradient obtained in peak shift paradigms is entirely the result of associative, rather than propositional processes, we suggest that perhaps it is a change in the allocation of attention, in conjunction with the underlying representational structures used for the stimuli that facilitates rule induction in this case.

When Arabic speakers write in their dialect, they have the choice of using either the standard Arabic script or the non-standard Roman script. Arabizi writing is a new emerging writing system that Arabic speakers use to type their dialects utilizing Roman characters. Although Arabizi is not standardized, people have developed an efficient way to communicate through it. One phenomenon that emerged with this new system is vowel dropping. In this paper, we approach this phenomenon from the perspective of communicative efficiency. We study the informativity of short and long spellings of words and investigate whether the predictability of the word in certain contexts impacts whether the vowel is dropped in that word.

Children’s early word learning is to a large extent driven by the prevalence of words in their language environment, with words that are spoken more often to children being learned earlier. However, children receive language from a variety of sources, including books, television, and movies meant for children, as well as speech and media that is meant for adults, but over- heard by children. Despite considerable similarity of word frequency distributions from these different input sources, there is also significant and predictable variability between them. For example, function words are far more frequent in books than in everyday speech, while early-learned nouns (e.g., ‘ball’ and ‘mommy’) are more frequent in child-directed speech than in other sources. Children receive a mixture of these different frequency distributions. The goal of this paper is to better understand the shared and unique variance in these input sources – in both English and French – and to evaluate how predictive these distributions are of children’s early word learning.

Making rational judgments is not always easy. Given that aggregation of the distributed labor force through Internet has become common, a simple and cost-effective solution is needed to improve worker performance. We tested the hypothesis that enforcing a certain decision time boosts job performance by not allowing workers to provide answers within a certain short time after presenting the task. We used the binary judgment tasks, and job performance with various enforced decision times were compared. Two behavioral experiments with physicians (N = 628) demonstrated that job performance was improved by enforcing a one-second decision time; this did not affect the cognitive load of physicians. Furthermore, it was suggested that adding a one-second decision time induced the optimal trade-off between the worker’s performance and cognitive load. Our results show that focusing on resource rationality could lead to simple and cost-effective solutions to real-world problems by boosting workers’ job performance.

We investigate the way people judge how helpful a review is in informing their decision as to whether to make a purchase. In particular, we are interested in how the summary statistics an individual sees influences judgments of a review’s helpfulness. We find perceived helpfulness of a given review decreases as the star rating of that review gets further from the mean rating. Additionally, participants were more likely to search for reviews close to the mean. Both of these findings are consistent with confirmation bias. We explore, but do not find support for, alternative possible explanations.

How do humans explain and cognize visual information? Why do AI explanations in radiology, despite their remarkable accuracy, fail to gain human trust? In a study of 13 radiology practitioners, we found that AI explanations of x-rays differ from human explanations in 3 ways. The first concerns visual reasoning and evidence: how humans get other humans to see an interpretation’s validity. Machine learned classifications lack this evidentiary grounding, and consequently XAI explanations like heat maps fail to meet many users' needs. The second concerns the varying needs of interlocutors. Predictably, explanations suitable for experts and novices differ; presuppositions on explainee knowledge and goals inform explanation content. Pragmatics matter. The third difference concerns how linguistic terms and phrases are used to hedge uncertainty. There is no reason XAI might not satisfy these human requirements. To do so, however, will require deeper theories of human explanation.

Proficiency in math is critically important given its implications for education and daily life (e.g., finances, health). However, math is a challenging subject, and proficiency requires a complex interplay of content knowledge and general cognitive processes, including Executive Function (EF). In this exploratory study, we used heat maps to examine whether participants' self-reported attention to strategy-specific components of fraction arithmetic equations (i.e., operations, numerators, denominators) was related to their EF and task performance. Our results indicated that participants with stronger EF (indexed by a numerical stroop task) obtained higher fraction arithmetic scores and were also more likely to attend to strategy-specific components in the fraction problems. Additionally, a positive correlation was found between participants’ selection of strategy-specific components and their fraction arithmetic accuracy. Keywords: Fraction arithmetic; Strategy Reports; Executive Function; Inhibitory Control; Attention

In this paper, we have studied the impact of clickbait headlines on the distribution of visual attention on hyperlinked news articles. Visual attention is a driving factor in ad-based revenue models that support online journalism. Importantly, it is also an indicator of cognitive processes involved in reading and comprehension. We hypothesize that articles with clickbait headlines receive lesser visual attention when controlled for articles’ content. This is based on the premise that a significant proportion of clicks on clickbait headlines are driven by readers’ specific epistemic curiosity rather than knowledge acquisition. An eye-tracker setup was used to infer visual attention from the gaze-fixation analysis conducted on data from 60 participants. Our results suggest that clickbait headlines significantly reduce the visual attention on news articles. Though, article content comprehension measured by a recall test was comparable for clickbait and non-clickbait headlines. Our findings add to the discussions on the cognitive attention and the implications of using clickbait headlines for news publishers, newsreaders, and advertising agencies alike.

Metonymy is regarded as a universally shared cognitive phenomenon; as such, humans are taken to effortlessly produce and comprehend metonymic senses. However, experimental studies on metonymy have been focused on Western societies, and the linguistic data backing up claims of universality has not been large enough to provide conclusive evidence. We introduce a large-scale analysis of metonymy based on a lexical corpus of 20~thousand metonymy instances from 189 languages and 69 genera. No prior study, to our knowledge, is based on linguistic coverage as broad as ours. Drawing on corpus and statistical analysis, evidence of universality is found at three levels: systematic metonymy in general, particular metonymy patterns, and specific metonymy concepts. These findings imply that a shared conceptual structure for these patterns and concepts holds across societies.

Dopaminergic reward prediction error neurons in the midbrain are the most prominent type of neurons encoding rewards. To explain the coding properties of these neurons, we apply the efficient coding framework to derive how neurons should encode rewards to maximize efficiency. The optimal populations qualitatively explain two recently made observations about real reward prediction error neurons: First, reward prediction error neurons represent rewards relative to a range of quantiles of the expected reward distribution, not relative to a single value. Second, the tuning of these neurons is asymmetric around their base firing rate and the asymmetry of each neuron is related to its threshold quantile. Furthermore, we achieve a good quantitative agreement with the neuronal recordings that were recently used to establish distributional reinforcement learning as a mechanistic explanation for these observations. Our analyses suggest the new interpretation that reward prediction error neurons might efficiently encode reward. Furthermore, it establishes an interesting theoretical link to the sensory processing literature, where efficient coding principles were developed.

Loaded language is an umbrella term for words, phrases, and overall rhetorical strategies that have strong emotional implications and intent to sway others. Belief in conspiracy theories is tied to a range of strong emotions (van Prooijen and Douglas, 2018). Accordingly, language with strong emotional and persuasive content may be expressed by people experiencing the strong emotions associated with conspiracy theorizing. In this research, we examine multiple types of loaded language in two online parenting forums: one historically against vaccination, and another historically accepting of vaccination. It is well-established that conspiracy theories are the most influential contributor to anti-vaccination views (Hornsey et al., 2018) and anti-vaccination beliefs are strongly correlated with belief in unrelated conspiracy theories (Goldberg & Richey, 2020). Results indicate that users of an anti-vaccination forum use a greater frequency of loaded language to express themselves than users of a vaccination-neutral forum.

People prefer complex explanations for complex phenomena, but make better choices when given only the information required. Thus there is a tension between the information people want, and the information they are able to use effectively. However, little is known about how the specific types of information included in causal models influences how people perceive them. We examine how omitting information influences how people reason about causal models, varying whether commonly known or unexpected information is removed (Experiment 1) or which parts of a causal path are omitted (Experiment 2). We find that omitting causal information participants expect to see lowers ratings of trust and other factors, while omitting less commonly known information improves ratings. However, causal paths can be simplified without harming perceptions of diagrams.

Visual working memory and visual mental imagery both involve the use of internal visual representations, and they likely have overlapping neural substrates. However, research on people with “aphantasia,” or a lack of visual imagery, has not found any evidence that aphantasics are impaired on visual working memory tasks, possibly because they can use non-visual strategies. We designed a task intended to prevent compensatory strategies, and also to explore what happens when aphantasics are required to shift the focus of attention between items in working memory. We found that aphantasics were not significantly different from controls, either when maintaining or shifting the focus of attention. Explanations include non-visual memory strategies, but also the possibility that aphantasics can store information in visual working memory without conscious awareness. Future research should combine behavioral methods with neuroimaging to investigate how aphantasics encode working memory representations.

One of the key features of natural languages is that they exhibit long-distance filler-gap dependencies (FGDs): In the sentence `What do you think the pilot sent __?' the wh-filler what is interpreted as the object of the verb sent across multiple words. The ability to establish FGDs is thought to require hierarchical syntactic structure. However, recent research suggests that recurrent neural networks (RNNs) without specific hierarchical bias can learn complex generalizations about wh-questions in English from raw text data (Wilcox et al. 2018; 2019). Across two experiments, we probe the generality of this result by testing whether a long short-term memory (LSTM) RNN model can learn basic generalizations about FGDs in Norwegian. Testing Norwegian allows us to assess whether previous results were due to distributional statistics of the English input or whether models can extract similar generalizations in languages with different syntactic distributions. We also test the model's performance on two different FGDs: wh-questions and relative clauses, allowing us to determine if the model learns abstract generalizations about FGDs that extend beyond a single construction type. Results from Experiment 1 suggest that the model expects fillers to be paired with gaps and that this expectation generalizes across different syntactic positions. Results from Experiment 2 suggest that the model's expectations are largely unaffected by the increased linear distance between the filler and the gap. Our findings provide support for the conclusion that LSTM RNN's ability to learn basic generalizations about FGDs is robust across dependency type and language.

We investigated the influence of visual information on interpersonal coordination of head- and body- movement during dyadic conversations. Visual information was manipulated by locating a partition at a halfway point between participants. Interpersonal coordination dynamics between head- and body- movement was also compared. To quantify the amount of such movement, human pose estimation software was used. The time series of each body part were submitted to the cross-recurrence quantification analysis to assess the degree of coordination. We hypothesized that unavailability of visual information increase interpersonal bodily coordination and experimental manipulation affects interpersonal coordination during conversation but does differently between head- and body- movement levels. As predicted, results revealed that occlusion of visual information increased head-movement coordination between participants while no significant difference was found in body-movement coordination between conditions. Further investigations on the mechanism of such different influences of perceptual information on coordination dynamics at multiple levels should be pursued.

We investigated whether schema congruency differentially affects low level sensory processing (eye gaze) compared to higher-level cognition (memory). Participants performed a two-phase eye tracking task; first a baseline phase with only congruent cartoon events, and subsequently an experimental phase in which the same events were adapted to remain congruent or become incongruent to a theme. Results revealed that participants became quicker in recognizing the congruent cartoon events compared to incongruent in the experimental phase, indicating improved memory for congruent cartoon events. No mean difference in gaze towards congruent versus incongruent events was observed. Surprisingly, a slight bias towards gazing to the left side of the screen in the baseline phase diminished during the experimental phase, indicating that the schema congruency manipulation might affect gaze behavior. Taken together, our results suggest that our schema congruency manipulation affects gaze behavior and memory, but further eye tracking analysis could reveal the dynamic nature of this effect.

In the debate between single-route and dual-route models of verb inflection, the dissociation between regular and irregular verbs in the non-fluent variety of aphasia has been a key sticking point for the proponents of the single-route model. This paper adopts a state-of-the-art neural model which has previously been used to learn inflectional morphology, and shows that it can also be used to model data from non-fluent aphasia. This challenges the assumption that a dual-route model is necessary to capture apparent dissociations in aphasia data and encourages a reanalysis of the deficits involved in non-fluent aphasia.

Empirical studies have demonstrated that when comprehenders are faced with informationally redundant utterances, they may make pragmatic inferences to accommodate the informationally redundant utterance (Kravtchenko & Demberg, 2015. Previous work has also shown that the strength of these inferences depends on prominence of the redundant utterance – if it is stressed prosodically, marked with an exclamation mark, or introduced with a discourse marker such as “Oh yeah”, atypicality inferences are stronger (Kravtchenko & Demberg, 2015; 2022; Ryzhova & Demberg, 2020). The goal of the present paper is to demonstrate how both the atypicality inference and the effect of prominence can be modelled using the rational speech act (RSA) framework. We show that atypicality inferences can be captured by introducing joint reasoning about the habituality of events, following Degen, Tessler, and Goodman (2015); Goodman and Frank (2016). However, we find that joint reasoning models principally cannot account for the effect of differences in utterance prominence. This is because prominence markers do not contribute to the truth-conditional meaning. We then proceed to demonstrate that leveraging a noisy channel model, which has previously been used to model low-level acoustic perception (Bergen & Goodman, 2015), can successfully account for the empirically observed patterns of utterance prominence.

The study examined the role of reading test strategies in reading comprehension performance of children by analyzing their eye-movements during a reading comprehension test. The Eye Movement analysis with Hidden Markov Model (EMHMM) with co-clustering discovered two representative eye movement pattern groups, with one more flexibly attending to either the passage beginning or the question in the beginning of the test displayed and attending to more contextual information in answering inferential questions. Participants adopted the more strategic pattern outperformed the other group in cognitive-linguistic skills and reading comprehension. Also, by quantifying participants’ eye movement behaviors along the contrast between the two pattern groups, their eye movement behavior explained unique variance on reading comprehension performance beyond general cognitive abilities and reading-related cognitive-linguistic skills. Thus, reading test strategy plays an important role in accounting for reading comprehension performance. These findings have important educational implications on teaching reading test strategies to help children improve comprehension performance.

A question that has long interested cognitive scientists is how to best represent the different emotions we experience and attribute to others. For example, constructionist and appraisal theories propose that differences between emotions can be captured in part by their variation along a set of appraisal dimensions. More recently, researchers have used language models to capture the differences across different emotion terms. Both approaches allow us to represent emotions as occupying different locations in high-dimensional representational spaces. To ask how well these different approaches capture the similarity between emotion concepts, we collected pairwise similarity and appraisal feature ratings for 58 different emotion concepts and then employed representational similarity analysis to investigate the overlap between people’s pairwise similarity judgments and emotion similarity in a 14-dimensional appraisal space and three word embedding spaces from two word2vec models (300 dimensions) and the newer GPT-3 model (12288 dimensions). The results indicate that while there is a high correlation between appraisal feature-based similarity and pairwise similarity judgments, word embedding-based similarity exhibits lower correlations, though GPT-3 showed much better performance than the word2vec models. Finally, characterizing the errors made by word embedding models showed that they can be largely attributed to an over-reliance on the valence of emotion concepts.

Agreement attraction (i.e. facilitatory interference manifested by sped-up reading times) observed in establishing subject-verb number agreement by comprehenders when reading ungrammatical sentences with number-matching attractor nouns, has been long-established and cross-linguistically validated. For languages with rich inflectional morphology, case syncretism has been suggested to play a role in the phenomenon. In the present self-paced reading study on Czech, we show that unlike in other languages, facilitatory interference is not observed and that not even case syncretism is sufficient for its appearance. We put forward several possible explanations for this anomaly exhibited by Czech compared to other languages. We propose that the lack of semantic agreement in the language could be one of these. Finally, we discuss the implications of these results for the models of long-distance dependency resolution in comprehension.

Conversations sometimes include speech errors that are repaired. But what do speakers and listeners remember, the error, the repair, or both? In three experiments, we investigated this question by having speakers give instructions for clicking on pictures (Exp 1) or by having listeners follow those instructions by clicking on the referenced pictures (Exps 2 and 3), followed by a surprise recognition test for the spoken words. Results of Exps 1 and 2 showed that both speakers and listeners have better memory for errors than repairs. Exp 3 managed to reverse this pattern by preventing listeners from clicking on the objects that were the referents of speech errors. Collectively, these results suggest superior memory for errors, not when they are simply perceived, but when they are tied to an action.

The ability to infer general characteristics of populations from specific instances is critical for reasoning. While there is evidence of this capacity in infancy, prior work has not examined children’s ability to use these second-order inferences to make predictions about future outcomes. In the current study, 3-year-olds observed balls drawn at random from two containers. In one sample each ball was a different color. The other sample consisted of balls of only one (Experiment 1) or two (Experiment 2) colors. Children were asked which of the containers was more likely to contain a novel colored ball. A significant majority of children chose the more variable sample’s container. This suggests that 3-year-olds are not only able to make inferences about hidden populations from the variability of observed samples, but also use those inferences to reason beyond their direct experience.

Infants’ earliest words are learned by observation of the referent world, but substantial research suggests such learning is highly error-prone. However, recent work suggests that even learners’ incorrect guesses may fall within the correct meaning’s semantic neighborhood—enabling learners to converge on the correct meaning across exposures. Here, we evaluate the semantic similarity of adults’ hypothesized word meanings in a cross-situational word-learning task. We find evidence for a weak semantic neighborhood effect: incorrect guesses are judged as similar to correct meanings (Study 1). However, this effect is not associated with successful word-learning. While learners tend to provide similar, internally consistent guesses across exposures, their accurate guesses are not similar to their previous guesses (Study 2). Moreover, incorrect guesses similar to the target do not increase accuracy on the subsequent exposure (Study 3). These results suggest early word-learning is driven by cues available in-the-moment, not by gradual exploration of semantic space.

This study investigates how L2 learners achieve the ‘good-enough’ comprehension in Korean. We focus on a suffixal passive construction, given the scarcity of this construction in the L2 textbook input. Results from acceptability judgement and self-paced reading tasks suggest two aspects of L2 comprehension. First, L1 and L2 comprehension do not qualitatively differ regarding ‘good-enough’ processing: the L2 processor utilises both heuristic and algorithmic parsing to reduce the burden of work at hand. Second, the divergence of L1 and L2 processing behaviours during comprehension may originate from various factors around L2 learners (e.g., L2 input, L1–L2 interface, task types), which are assumed to anchor the noisier representations of L2 knowledge.

Accessibility plays a major role in speech production. Here we investigate and measure four factors that influence speakers to produce one word over another more optimal word form. Three experiments asked participants to label images of insects and instruments. Participants were incentivized to produce an accurate specific label (e.g., bee), over a more general label (e.g., insect), so that specific labels were more optimal. Each of three experiments manipulated a different factor that could influence accessibility – word frequency, priming, and interference – and all experiments additionally varied whether labels had to be produced under time pressure or not. Results showed that each variable significantly influenced the accessibility of labels: participants produced more specific labels when those labels were higher frequency, when they were primed, when a visually-similar label had not been primed, and when participants were unconstrained by time pressure. These findings demonstrate that multiple factors influence the accessibility of familiar words during production, regularly leading participants to rely on “good-enough” rather than optimal options to convey their message.

The Covid-19 pandemic altered workplaces. For those with ‘office jobs,’ this meant working ‘virtually,’ or remotely, from home. This transition forced organizations and workplaces to exercise flexibility, adapt workflows and rely on Information and Communication Technologies (ICTs) to work remotely. However, Blind and Partially Sighted Individuals (BPSI) face challenges accessing work digitally and remote communications through ICTs. In response, we report on the results of our longitudinal participatory design study investigating the impact of working and training over a distance for BPSI. What emerged is a conceptual model to assist in understanding how ICT interfaces convey spatial-topological cues for the construction of shared intentionality in virtual work environments. The implications of our model could be significant, as it aids understanding of what is lost and gained when transitioning to virtual work environments. This could inform the development of ICTs with cross-sensory interaction and national accessibility policies for the workplace.

Measuring the emotion in words is valuable in that it analyzes emotions through language. However, it is difficult to find such measurements in low-resource languages. In this paper, we proposed a method to expand the affective lexicon by utilizing the context of words. The proposed model predicted the Valence and Arousal values of words using their dictionary definitions. In Study 1, we reviewed previous studies about the Korean affective lexicon and integrated data from these studies. The model was trained to minimize the MSE error between the Valence and Arousal values of the words and their predictions. We then checked the distribution of Valence and Arousal values of Korean vocabulary by applying our model to the Korean dictionary. In Study 2, a new affective lexicon was built to empirically validate our model. We found a negatively biased error pattern on model predictions and discussed why it happened.

When a group member commits wrongdoing, people sometimes assign responsibility and blame not only to the wrongdoer but also to other members of the same group. We examined such assignment of collective responsibility in the context of exploitation of one family by another. Participants were recruited from the United States and South Korea, which are known to vary in cultural norms and endorsement of collectivistic values. Participants in both countries rated the degree to which an agent (grandson) should be held responsible for his grandfather’s exploitation of a victimized family, while varying the closeness of familial connection. Participants’ responsibility judgments showed sensitivity to whether the grandson received financial benefit from the wrongdoer and to the perceived closeness between the grandson and the wrongdoer. Korean participants imposed greater responsibility on the agent than did American participants. Implications for understanding the influence of social norms on moral judgments are discussed.

Biodiversity is a complex concept entailing scientific and political aspects. The usage of analogies, especially metaphors, that have positive influences on the understanding of complex concepts, on attitudes and behaviors, seems an interesting strategy to achieve this goal. Based on biodiversity analogies elaborated by 259 participants, a first study aims to identify two important protective approaches: preservationism that encourages humankind to limit their intervention on nature and conservationism that allows humankind to exploit nature with parsimony. We analyzed their analogies and results highlight three major groups: a scientific, a conservationist and a preservationist dimension. A second study investigates the effects of metaphorical framing on environmental attitudes and behaviors. 277 University students read a short text framing biodiversity with a preservationist or conservationist metaphor or without metaphor framing. A decision-making task and an environmental concern scale were completed. Results showed an effect of the conservationist metaphor on the decision-making task.

Elicitation methods, such as asking people to produce the deciles of a distribution, are standard practices in policy or applied statistics. However, these approaches often only capture a rough outline of what people know. We investigated whether tasks in which participants generate random sequences of items can be used to elicit people’s implicit beliefs about the distribution of these items. Because it remains unclear if, and at what level of detail, people represent distributions, we applied both decile elicitation and random generation tasks to uncover the kinds of environmental statistics investigated by Griffiths and Tenenbaum (2006). We found that random generation is competitive with decile elicitation in predicting participants’ expectations. Both random generation and decile elicitation revealed that people know the rough shapes of environmental distributions. Random generation, however, goes beyond decile elicitation in establishing the novel finding that people are aware of fine details of environmental distributions

How are experiences of events used to update knowledge of predictive relations in semantic memory? We examined the roles of anterior-lateral entorhinal cortex (alEC), important for encoding recently experienced temporal relations, and middle temporal gyrus (MTG), involved in familiar event concepts. Participants underwent fMRI during exposure to novel event sequences and a memory probe phase (Session 1) and the same process a week later (Session 2). Across distinct sequences, predictive relations among similar events could either be Consistent, or the roles of the events could swap (Inconsistent). We examined the effect of Consistency on the strength of relational memory content. Areas that integrate across diverse experiences should be aided in the Consistent condition. We found that alEC performed this integrative role in Session 1, and at Session 2, similar effects were also observed in MTG. We suggest that these areas both contribute to building relational knowledge from experience.

Written Chinese sentences consist of a series of characters without word boundary information. Here we examined whether word boundary information facilitated Chinese sentence reading comprehension in children as beginning readers. Primary grade 2-3 children read age-appropriate sentences with either spacing or shading contrast to mark word boundaries and answered related comprehension questions. Compared with regular sentences without word boundary information, spacing significantly impaired comprehension accuracy and reduced eye movement consistency during reading as measured in entropy, and the decrease in accuracy was associated with decrease in consistency of eye gaze transitions during reading. This result suggested that the performance impairment may be related to disturbances to their immature visual routine for reading that may be inconsistent with the provided word boundary information. In contrast, using shading contrast did not change children’s reading performance or eye movement consistency. These findings have important implications for ways to facilitate reading development in children.

Analogy problems involving multiple ordered relations of the same type create mapping ambiguity, requiring some mechanism for relational integration to achieve mapping accuracy. We address the question of whether the integration of ordered relations depends on their logical form alone, or on semantic representations that differ across relation types. We developed a triplet mapping task that provides a basic paradigm to investigate analogical reasoning with simple relational structures. Experimental results showed that mapping performance differed across orderings based on category, linear order, and causal relations, providing evidence that each transitive relation has its own semantic representation. Hence, human analogical mapping of ordered relations does not depend solely on their formal property of transitivity. Instead, human ability to solve mapping problems by integrating relations relies on the semantics of relation representations. We also compared human performance to the performance of several vector-based computational models of analogy. These models performed above chance but fell short of human performance for some relations, highlighting the need for further model development.

A set of fundamental principles governs our reasoning about objects since infancy. Studies have 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. However, little is known about whether these principles can be revised given counterevidence. In the present experiments, we demonstrate that although adults have strong prior beliefs about these principles, they can revise these beliefs in a specific, virtual world, when they observe multiple pieces of counterevidence.

Despite substantial progress in artificial intelligence (AI), little is known about people’s moral intuitions towards AI systems. Given that politico-moral intuitions often influence judgements in non-rational ways, we investigated participants’ willingness to act on verdicts provided by an expert AI system, trust in AI, and perceived fairness of AI as a function of the AI system’s (dis)agreement with their pre-existing politico-moral beliefs across various morally contentious issues. Results show belief alignment triggered a willingness to act on AI verdicts but did not increase trust or fairness perception of the AI. This result was unaffected by general AI attitudes. Our findings suggest a disassociation between acceptance of AI recommendations and judgements of trust/fairness of the AI, and that such acceptance is partly driven by alignment with pre-existing intuitions.

Previous studies have claimed that language structures tend to minimize the linear distance between syntactic heads and their dependents, a principle known as Dependency Length Minimization (DLM). These studies, however, have largely focused on written modality. In this study we examine the role of dependency length in acceptability ratings of English and Hindi, two typologically distinct languages, using audio stimuli. With double PP constructions as a test case, our results demonstrate no effect of DLM, suggesting the preference for shorter dependencies is different in acceptability and written texts. These findings are further supported with corpus analysis of a total of 10 treebanks for the two languages, which shows additional language-specific differences in the extent of DLM. We discuss the implications of our work and call for more careful consideration of linguistic and modality-specific diversity when it comes to processing-based claims about language typology.

Second-language (L2) learning is characterized by both positive and negative transfer from the first language (L1). However, psycholinguistic studies focus on a few syntactic phenomena and L1-L2 pairs at a time, resulting in an incomplete picture. We apply machine learning to seven learner corpora in English and Spanish with 39 language pairs, showing that statistical models combined with simple $n$-grams of part-of-speech tags and syntactic dependency relations achieve good performance in recovering the L1, indicating structural transfer from L1 to L2. Further machine learning using a rich hand-curated linguistic feature set allowed us to identify aspects of L2 linguistic structure particularly influenced by L1 (verbal morphology, average dependency tree parse depth, and headedness of clausal structures) as well as those with minimal influence (distributions of dependency relations, basic word orders, or non-projective dependencies).

A recent proposal of syntactic satiation claims that it is driven by adaptation: comprehenders track and update their beliefs about the probability of observing certain sentences, leading to subsequent increases in the acceptability of those sentences. This leaves open what the representational targets of satiation are, that is: what is the tracked information that belief update is based on? In two acceptability judgment experiments, we show that exposure to one type of island violation can lead to the satiation of another island type, suggesting that island type-general representations are tracked by comprehenders in addition to island type-specific representations. The same experimental paradigm can be used for further exploration of the representational targets of satiation.

Human reinforcement learning (RL) is characterized by different challenges. Exploration has been studied extensively in multi-armed bandits, while planning has been investigated in multi-step decision tasks. More recent work has added structured rewards to study generalization. However, past studies have often focused on a single one of these aspects, making it hard to compare results. We propose a generative model for constructing correlated trees to provide a unified and scalable method for studying exploration, planning, and generalization in a single task. In an online experiment, we find that people use structure (when provided) to generalize and perform uncertainty-directed exploration, with structure helping more in larger environments. In environments without structure, exploration becomes more random and more planning is needed. All behavioral effects are captured in a single model with recoverable parameters. In conclusion, our results connect past research on human RL in one framework using correlated trees.

Politeness is a social linguistic phenomenon. Modeling polite language production and understanding is difficult, as it may contradict conversational maxims and is shaped by extralinguistic social influences, such as the speaker-hearer relationship. This paper extends Yoon et al.'s (2016) Rational Speech Act-based model of politeness by mapping speaker-hearer relationship influences to the utility weights of the model and instantiates it in German. Three online experiments, for empirical analysis and collection of behavioural data for model training and evaluation, are presented. These confirm the influence of the speaker-hearer relations on indirect politeness. Furthermore, two versions of the model are trained and evaluated to find out which part of the model is better suited for the integration of social influences. Overall, both model versions yielded similar results and were able to predict the meaning of polite speech acts.

Humans must often make decisions in temporally autoregressive environments (e.g., weather, stock market). Here, current states of the environment regress on their previous states (either across consecutive timesteps or from several timesteps back in a patterned fashion). The current work investigates people’s abilities to utilize previous states of autoregressive sequences as cues to its current state. In Experiment 1 we determine whether utilization of autoregressions reduces as the temporal distance of the predictive timestep increases; and in Experiment 2 we explore whether participants’ utilization of previous timesteps in predictions compete such that they reduce utilization of one timestep when increasing utilization of another timestep. We also fit data from both experiments with a trial-by-trial decision model. Overall, we find that participants significantly reduced utilization of a cue with its increased temporal distance. However, we obtained less conclusive results on competition among timestep cues. These results can explain people’s predictions in sequential decision tasks (e.g., their tendencies to perceive clumpiness in random environments).

Sequential learning in artificial neural networks is known to trigger catastrophic interference (CI), where previously learned skills are forgotten after learning new skills. This is in direct contrast to humans’ ability to learn increasingly complex skills across the lifespan without major instances of CI. The present work builds on techniques for mitigating CI that have been proposed in prior work. Anderson and Schooler (1991) first documented that the memory environment has a lawful structure. Following from their observation, we constructed a training environment where previously mastered tasks (Boolean functions) decrease in frequency over time according to a power law. It was predicted that training in this environment would (1) mitigate CI, (2) replicate human performance in learning curves following a power law of practice, and (3) promote positive transfer of training to new skills, all without the need to posit additional mechanisms. The present results support all three predictions.

Rule-based learning is an important aspect of language acquisition. Yang (2005,2016) proposed the Tolerance Principle (TP) to predict when a rule will be formed by the language learner. We present the derivation of the TP as originally proposed and test it on both hypothetical data and corpus data from 8 children. Results for the hypothetical data contradict the TP’s predictions, as do the data from 7 of the 8 children. We conclude that the original form of the TP does not explain rule-learning.

We explore contextual adaptation of referring expressions with respect to referential ambiguity and communicative intention. We focus not only on whether people adapt, but also on how by contrasting lexical specification (e.g., "batter") and syntactic modification (e.g., "man in white pants") when discriminating between objects in natural scenes (e.g., a batter wearing white pants and a referee). There are three main results. First, we replicate that speakers adapt their expressions to avoid ambiguity. Second, communicative intention has an effect: participants tended to use more specific names in a discrimination task than in a descriptive task, even without referential ambiguity in the context. Third, when given the choice, participants tended to prefer more specific words over adding modification - that is, using lexical rather than syntactic means to resolve ambiguity. This suggests that it may be less demanding to increase informativity of referring expressions with lexical specification than syntactic modification.

We follow up on recent work demonstrating clear advantages of lexical-to-sublexical feedback in the TRACE model of spoken word recognition. The prior work compared accuracy and recognition times in TRACE with feedback on or off as progressively more noise was added to inputs. Recognition times were faster with feedback at every level of noise, and there was an accuracy advantage for feedback with noise added to inputs. However, a recent article claims that those results must be an artifact of converting activations to response probabilities, because feedback could only reinforce the “status quo.” That is, the claim is that given noisy inputs, feedback must reinforce all inputs equally, whether driven by signal or noise. We demonstrate that the feedback advantage replicates with raw activations. We also demonstrate that lexical feedback selectively reinforces lexically-coherent input patterns – that is, signal over noise – and explain how that behavior emerges naturally in interactive activation.

Many memory models suggest self-terminating backward scanning along a memory representation. In these models, time to retrieve a particular item from memory could depend on how far in the past the item was presented or on the number of items presented since that item. To investigate which of these two types of memory representation is more likely, we designed a relative Judgment of Recency (JOR) task with variable presentation rates. The variable presentation rate deconfounded the age of memory and the number of intervening items. Our results favor the hypothesis that memory representation is temporally organized. This result is important for advancing memory models and for building stronger ties between cognitive and neural models of memory.

A critical part of language comprehension is inferring omitted but plausible information from linguistic descriptions of events. For instance, the verb phrase ‘preserve vegetable’ implies the instrument vinegar whereas ‘preserve fruit’ implies dehydrator. We studied the ability of distributional semantic models to perform this kind of semantic inference after being trained on an artificial corpus with strictly controlled constraints on which verb phrases occur with which instruments. Importantly, the ability to infer omitted but plausible instruments in our task requires compositional generalization. We found that contemporary neural network models fall short generalizing learned selectional constraints, and that a graph-based distributional semantic model trained on constituency-parsed data and equipped with a spreading-activation procedure for calculating semantic relatedness, achieves perfect performance. Our findings shed light on the mechanisms that give rise to compositional generalization, and using graphs to model semantic memory.

Over the last few decades, Causal Model Theory (CMT) has become a dominant framework for human causal-based reasoning, including categorization and inference. CMT prescribes how people should reason about probabilistic events in terms of causal models. In typical causal-based categorization experiments, subjects are provided with verbal descriptions of causally linked features, generally including probabilistic information. Another line of research focuses on perceived or experienced causal events, rather than on verbal descriptions. In this work we asked whether effects which are consistent with CMT, and that have been obtained with verbal descriptions, generalize to visually perceived events. In two experiments, we presented subjects with videos of a 3D A→B causal event rather than verbal descriptions. In Exp. 1, we found that subjects who saw the causal event did not show the coherence effect in categorization (i.e., subjects tend to rate the null ¬A¬B event as a category member). However, subjects who did see the null event during training did show the effect. In Exp. 2, we ruled out the possibility that Exp. 1’s results were simply an effect of how frequently events were experienced during training. We conclude that a one-shot perceived causal event is not sufficient for people to show causal-based reasoning as CMT predicts.

Machine learning models that automatically assess reasoning quality are trained on human-annotated written products. These “gold-standard” corpora are typically created by prompting annotators to choose, using a forced choice design, which of two products presented side by side is the most convincing, contains the strongest evidence or would be adopted by more people. Despite the increase in popularity of using a forced choice design for assessing quality of reasoning (QoR), no study to date has established the validity and reliability of such a method. In two studies, we simultaneously presented two products of reasoning to participants and asked them to identify which product was ‘better justified’ through a forced choice design. We investigated the criterion validity and inter-rater reliability of the forced choice protocol by assessing the relationship between QoR, measured using the forced choice protocol, and accuracy in objectively answerable problems using naive raters sampled from MTurk (Study 1) and experts (Study 2), respectively. In both studies products that were closer to the correct answer and products generated by larger teams were consistently preferred. Experts were substantially better at picking the reasoning products that corresponded to accurate answers. Perhaps the most surprising finding was just how rapidly raters made judgements regarding reasoning: On average, both novices and experts made reliable decisions in under 15 seconds. We conclude that forced choice is a valid and reliable method of assessing QoR.

Similarity judgments provide a well-established method for accessing mental representations, with applications in psychology, neuroscience and machine learning. However, collecting similarity judgments can be prohibitively expensive for naturalistic datasets as the number of comparisons grows quadratically in the number of stimuli. One way to tackle this problem is to construct approximation procedures that rely on more accessible proxies for predicting similarity. Here we leverage recent advances in language models and online recruitment, proposing an efficient domain-general procedure for predicting human similarity judgments based on text descriptions. Intuitively, similar stimuli are likely to evoke similar descriptions, allowing us to use description similarity to predict pairwise similarity judgments. Crucially, the number of descriptions required grows only linearly with the number of stimuli, drastically reducing the amount of data required. We test this procedure on six datasets of naturalistic images and show that our models outperform previous approaches based on visual information.

Efforts have always been deployed to surpass limitations in human cognitive abilities to enhance aspects such as task accuracy, work effectiveness and error management. Cognitive enhancement is a field aiming at improving human cognition in order to overcome those limitations. It bears important interest from the human factors community given its potential for reducing errors in complex operational environments. Yet, cognitive enhancement strategies are rarely used outside the lab and practical applications are scarce. The current paper presents a brief summary of the literature on human cognitive enhancement and discusses key operational applications of the main methods reported in this field. Using a human factors perspective, the paper also outlines how such techniques could be integrated into decision-support tools to support operators facing cognitive challenges in complex operational domains, including those experiencing functional limitations preventing them to contribute to the workforce.

Causal explanations are important guides to understanding the world. While research suggests people prefer simple explanations, a seeming notable exception exists in the widespread endorsement of conspiracy theories. Researchers have described conspiracy theories as causally complex explanations of world events. We examined whether the lay public agrees with this assessment and sees conspiracy theories as complex explanations, as well as how perceptions of complexity relate to believability of these explanations. We tested publicly available (Experiment 1) and experimenter-generated (Experiment 2) conspiracy theories, alongside fact-based explanations for the same events. We asked participants to rate the complexity of each explanation, along with how believable they find the explanation. Participants across studies rated the conspiracy theory explanations as more complex. Interestingly, complexity was positively correlated with believability of the conspiracy theory, but not fact-based, explanations. We discuss what these findings suggest for the causal explanation field and our understanding of conspiracy theories.

Previous research indicates that using the internet in knowledge related tasks increases overestimation. We attempted to replicate this finding and extended previous research by explicitly manipulating the standards that participants used for the explanatory knowledge task in order to reduce the metacognitive bias. We conducted a 2x2 within-subject experiment with N = 166 participants. Replicating previous findings, the results show significantly more overestimation in Internet than in No-Internet conditions. However, with an alignment to external standards participants elicited more accurate metacognitive judgments. We conclude that explicit standards may be an important factor in knowledge-related activities involving the internet because of their effect on metacognitive judgments. On a theoretical level, this has implications for determining the basis of overestimation in knowledge tasks with the internet. On a practical level, providing external standards could be a feasible aid for buffering against this bias, for example in the educational context.

Humans use both linear and hierarchical representations in language processing, and the exact role of each has been debated. One domain where hierarchical processing is important is noun phrases. English noun phrases have a fixed order of prenominal modifiers: demonstratives - numerals - adjectives (these two green vases). However, when English speakers learn an artificial language with postnominal modifiers, instead of reproducing this linear order they preserve the distance between each modifier and the noun (vases green two these). This has been explained by a hierarchical homomorphism bias. Here, we investigate whether RNNs exhibit this bias. We pre-train one linear and two hierarchical models on English and expose them to a small artificial language. We then test them on noun phrases from a study with humans and find that only the hierarchical models can exhibit the bias, supporting the idea that homomorphic word order preferences arise from hierarchical, and not linear relations.

While population-level models often provide a good fit to the data, they may mask meaningful individual differences. Exploring individual differences can also be beneficial for gaining a better understanding of the processes that underlie pragmatic phenomena. In this study, we investigate whether the substantial differences in performance on a pragmatic reference game can be traced back to cognitive or socio-pragmatic traits. We observe a significant effect of the ability to inhibit an intuitive response and of abstract reasoning ability. In contrast, we do not find evidence that socio-pragmatic abilities or working memory capacity influence pragmatic responding.

One of the advantages of using metaphorical expressions over literal ones might be that speakers can convey not only the intended property, but also its degree. For example, when hearing “John is a shark”, the listener might infer that the speaker aims to communicate that John is as mean as a typical shark. We present experimental findings supporting this hypothesis, along with a novel metaphor interpretation model, which is implemented within the Rational Speech Act framework. We compare our model's predictions to those of an existing RSA model of metaphor understanding, within which the listener infers just the presence or absence of a feature as opposed to its degree, and find that our model produces a significantly better fit.

Learning to establish joint reference is an important milestone of communicative and linguistic development. Pointing is one of the first entry points into this process, since gestures often precede verbal communication. During early development, as well as later language use, pointing and linguistic utterances interact in many ways, complementing each other. However, little is known about the development of this relationship during development. In this paper, we focus on the development of the co-occurrence of finger pointing and accompanying utterances in two different cultures: Russia and Chintang (Sino-Tibetan, Eastern Nepal). We show that despite the differences in environment, the development of finger pointing and accompanying language use show substantial similarities. Early on, a larger proportion of points is not accompanied by language. As the children's linguistic abilities develop, children first use language to specify what is being pointed at, and later elaborate on some aspect of the referent.

The following studies investigated the perceptual processes that are the basis of the face inversion effect (FIE). We evaluated the effects of disrupting holistic information conveyed by the face contour/outline. In Experiment 1 (n=144) we blurred the contour of the faces and using an old/new recognition task we found that a robust inversion effect similar to that for normal faces remains for these new no-contour faces. However, a significant reduction in overall performance was found for no-contour vs normal faces. In Experiment 2 (n=74) instead of blurring we inserted a novel face contour to replace the normal one and found the same pattern of results as in Experiment 1. Our results suggest that the holistic information provided by the face contour does not on its own influence the FIE, however it plays a role in face recognition more generally.

It is intuitive to believe that humans take considerations of mental effort into account when making decisions. However, it has proved difficult to differentiate theories of mental effort in the absence of direct measurements of this psychological construct. Existing measurements of mental effort using response times and revealed preferences have low reliability. In this paper, we present a new experimental task - selecting between visuomotor lotteries using eye-tracking for sampling lotteries - that enables direct measurement of mental effort. Unlike response time-based measures, effort measurements in this task are not confounded by actual effort allocation. Unlike revealed preference-based measures, effort measurements in this task are acquired on a natural scale unitized by automatic visual selection processes. We also report results from a simple experiment conducted using this task, which reproduce existing findings of costly effort-aversion, and also demonstrate adaptive adjustment of mental effort.

User-generated ratings — often elicited and presented as “star ratings” — have become a ubiquitous feature of the online consumer experience. While most research agrees that these user-generated ratings influence individual consumer decisions and overall consumer demand, there is less consensus as to whether user- generated ratings help consumers make better, welfare-enhancing decisions. In this manuscript, we expound on an intrinsic problem with the use of user-generated ratings in product choice decisions. Specifically, product ratings are typically given in an isolated (non-comparative) context, but are typically used in a comparative context, where relative differences in ratings may not reflect relative differences in quality. We provide a simple empirical demonstration of how this structural misalignment can lead consumers to choose suboptimal products and, ultimately, yield reduced consumer welfare.

In the field of cognitive archaeology, the origin of art has been recurrently explained as a result of the transition to a fully symbolic mind in our species, H. sapiens. Recent data is challenging that view as increasing evidence shows that the cognitive differences between ‘premodern’ and modern human populations are smaller than previously thought. Yet, possible cases of Neanderthal and other hominin art are few and far between, rendering artistic practices mainly a H. sapiens phenomenon. To explain this, it is necessary to redefine art and understand it not only as the product of cognitive operations, but as a behavior embedded in modern human social interactions.

The human ability to generalize beyond interpolation, often called extrapolation or symbol-binding, is challenging to recreate with computational models. Biologically plausible models incorporating indirection mechanisms have demonstrated strong performance in this regard. Deep learning approaches such as Long Short-Term Memory (LSTM) and Transformers have shown varying degrees of success, but recent work has suggested that Transformers are capable of extrapolation as well. We evaluate the capabilities of the above approaches on a series of increasingly complex sentence-processing tasks to infer the capacity of each individual architecture to extrapolate sentential roles across novel word fillers. We confirm that the Transformer does possess superior abstraction capabilities compared to LSTM. However, what it does not possess is extrapolation capabilities, as evidenced by clear performance disparities on novel filler tasks as compared to working memory-based indirection models.

Recent work in judgment and decision making has focused on which actions people consider when solving open-ended problems and found that the actions that come to mind tend to have particular features, such as having a high historical value. Here, we pursue the idea that the process of generating actions for decision-making tasks may actually reflect more general mechanisms for generating kinds of things. We provide evidence that what comes to mind may simply be a reflection of participants sampling from the most relevant part of the representational space they use to encode the type of thing they are generating. In this paper, we (1) introduce an approach for empirically describing a category in terms of the features that people use to represent category members, and for locating category members within that feature space, (2) show that certain locations in a category's feature space predict an item's likelihood of coming to mind, (3) introduce an approach for understanding the relevance of various features to people's representations of category members, and (4) show that features which are most involved in people's representations of category members are also predictors of what comes to mind within a category. We close by proposing that features that are most relevant to our representations of category members and predict coming to mind are those for which it has been historically useful to have information about during past experiences with the category in question.

To what extent can experience from language contribute to our conceptual knowledge? Computational explorations of this question have shed light on the ability of powerful neural language models (LMs)---informed solely through text input---to encode and elicit information about concepts and properties. To extend this line of research, we present a framework that uses neural-network language models (LMs) to perform property induction---a task in which humans generalize novel property knowledge (has sesamoid bones) from one or more concepts (robins) to others (sparrow, canary). Patterns of property induction observed in humans have shed considerable light on the nature and organization of human conceptual knowledge. Inspired by this insight, we use our framework to explore the property inductions of LMs, and find that they show an inductive preference to generalize novel properties on the basis of category membership, suggesting the presence of a taxonomic bias in their representations.

Although the relationship between the sound of each word and its meaning is generally arbitrary, onomatopoeias are said to have the unarbitrary link, which called sound-symbolism, between them. In this study, we investigated whether sound symbolic words are widely common in a class of onomatopoeias in some natural language. We conducted an experiment, which asked Japanese and non-Japanese speakers to match each given Japanese-onomatopoeia-like sound with a shape to which the sound referred. The result of analysis showed a similar structure for both speaker groups, in which round shapes were associated to a particular set of sounds and pointed shapes associated to the other set of sounds. Moreover, the round/ pointed shapes are correlated to pseudo onomatopoeias with sonorants/ fricative phonetic features. This finding supports the sound symbolic hypothesis asserting that the major component of Japanese onomatopoeias forms a bouba-kiki like sound-shape correspondence even for non-Japanese speakers.

Knowledge diversity is widely acknowledged to be beneficial for collaborative groups engaged in problem solving. An experiment was conducted to determine whether knowledge diversity and assigned task roles for members in an online virtual collaborative group affect group task performance and individual learning and transfer, and to explore the role of explanations as a mediating variable in these effects. Two control conditions were included that involved individual work, with and without self-explanations. Results showed that the frequency of explanations in dyadic discourse were correlated with individual learning, and that groups with knowledge diversity tend to use more explanations. These findings suggest that discussing explanations is a key feature of successful group work that contributes to determining how much individual learning occurs and how well it transfers from collaborative activities to similar, novel tasks.

This study sought to replicate the effect of observing pitch gesture and clarify the effect of observing representational gesture on L2 lexical tone learning and to explore the influences of individual differences in lexical and non-lexical tone perception on these effects. The results revealed that observing representational gestures facilitates lexical tone discrimination, albeit to a lesser extent than observing pitch gestures, suggesting that task difficulty may influence its effect. Moreover, they revealed that individual differences in non-speech tone perception predict discrimination of lexical tones learned by observing pitch gesture and no gesture, but not representational gesture. Together, these findings suggest that task difficulty as well as individual differences in sensitivity to non-speech sounds influence the effects of observing gesture on novel L2 speech sound learning.

Are left handers more creative than right handers? In both popular belief and scientific literature, left-handedness is linked with higher creativity. Here, we evaluate whether left handers are better at divergent thinking, and whether left handers are overrepresented in creative professions, in a qualitative review supported by meta-analysis. We argue that plausible mechanisms for a creativity-handedness link can be found within influential theories of the neural basis of creativity. However, our meta-analysis does not find evidence that left or mixed handers are better at divergent thinking; in fact, right handers score slightly higher on the Alternate Uses Test. Additionally, we conclude that while left and mixed handers may be overrepresented in Art and Music, they are underrepresented in creative professions in general. We find that although both right and left handers tend to believe that left handers are more creative, this belief is not supported by the available empirical evidence.

To explain how perception processes are performed, understanding how continuous sensory streams are temporally segmented into discrete units is central. This is particularly the case in speech perception where temporal segmentation is key for identifying linguistic units contained between consecutive events in time. We propose an original probabilistic construct, that we call "Bayesian gates", to segment temporally continuous streams of sensory stimuli into sequences of decoders. We first define Bayesian gates mathematically and describe their properties. We then illustrate their behavior in the context of a model of word recognition in speech perception. We show that, based on an event detection module, they sequentially parse the acoustic stimulus, so that each syllable decoder only processes a segment of the sensory signal.

Integrating visual representations in an interactive learning activity effectively scaffolds performance and learning. However, it is unclear whether and how sustaining or interleaving visual scaffolding helps learners solve problems efficiently and learn from problem solving. We conducted a classroom study with 63 middle-school students in which we tested whether sustaining or interleaving a particular form of visual scaffolding, called anticipatory diagrammatic self-explanation in an Intelligent Tutoring System, helps students’ learning and performance in the domain of early algebra. Sustaining visual scaffolding during problem solving helped students solve problems efficiently with no negative effects on learning. However, in-depth log data analyses suggest that interleaving visual scaffolding allowed students to practice important skills that may help them in later phases of algebra learning. This paper extends scientific understanding that sustaining visual scaffold does not over-scaffold student learning in the early phase of skill acquisition in algebra.

The present study investigates young children’s reasoning about who owns the information users share with apps. 87 children ages 5-years to 10-years were asked to judge who owned two types of information after it had been willingly shared by users: general and personal information. Based on an informational autonomy account, we predicted that young children would judge that the user owns their personal information but not their general information. We found that by 8-years-old children were indeed more likely to judge that users own the personal information they share with apps than they were to judge that users own the general information they share with them. However, younger children judged that the general information was owned by the user at similar rates to the personal information. Further exploration of our data suggests these changes are likely driven by beliefs about the ownership of general information.

Mental imagery is theorized to play a key role in mood and mood disorders due to the emotional impact of visualizations and biases in the processing of negative versus positive imagery. Although differences in emotional imagery have been linked to mental health outcomes, it is unclear if individuals experiencing emotional distress differ in their baseline ability to generate mental images (i.e., ‘imagery ability’). Recent research has highlighted linkages between imagery ability and facets of trait mindfulness, such as the tendency to observe and describe inner thoughts. Thus, we suspected that individual differences in trait mindfulness may help explain inconsistent findings regarding the relationship between imagery ability and emotional distress. A path analysis revealed that trait mindfulness significantly and fully mediated the relationship between imagery vividness and depression, indicating that mindfulness is a critical aspect of imagery phenomenology, as well as emphasizing the importance of mindfulness to mental health.

Many decision problems can be divided into three parts: Generating a set of options to consider, evaluating them, and choosing the best. Prior models often assume that the ``consideration set'' is established in a single step prior to evaluation. Alternatively, people may dynamically and continually assess whether to expand the consideration set based on the quality of the actions considered so far. We use modeling to derive a signature property of dynamic consideration set construction and then demonstrate it in two experiments on human participants.

Computational cognitive theories of Autism Spectrum Disorder have received renewed attention in recent years. Consistent with the predictive processing framework, ASD has been re-conceptualized as a disorder of aberrant prediction and learning-rate estimation involving multiple levels of a putative cognitive computational hierarchy. Specifically, behavioral symptoms of individuals with ASD might manifest due to an aberrant overestimation of the volatility of environmental contingencies (i.e. tendency of change in cue-outcome probabilities) which in turn might induce a dysfunctional setting of learning rates. In this work, we attempted to conceptually replicate computational modeling analyses of an impactful study of the recent ASD modeling literature in an independent sample of subjects. We were not able to replicate some prior reported effects likely due to differences in model architecture and cognitive task setup. We found statistical trends in similar directions.

The Rescorla-Wagner model has seen widespread success in modelling not only its original target of animal learning, but also several areas of human learning. However, despite its success, a number of studies with humans have found effects that are not predicted by the model, thus inspiring proposals for modifications to the model. One such proposal, by Van Hamme and Wasserman (1994, VHW), is that humans not only learn from present cues to all (present and absent) outcomes, as in the original model, but also learn from the absence of cues. They set out to test this hypothesis with a causal rating experiment. However, behaviour in learning studies may depend on the task. We propose that error-driven learning should be considered to be a form of implicit learning and that the results of VHW’s contingency judgement task might stem from explicit strategies involving logic and reasoning. The present study investigates this question by a) running simulations with both the original and modified versions of the model; b) replicating the VHW experiment (Experiment 1); and c) extending the experiment with new stimuli and by including unseen stimuli following the learning phases (Experiment 2). Simulations show that the VHW modified model predicts that cues learnt at the beginning will be unlearnt when absent over the following blocks, so that they become negative predictors over time. In contrast, the original RW predicts that the absent cues remain steady (positive) predictors over the blocks. Results showed no significant difference in cue assignment between training and test, in line with the original RW model. Moreover, predictive cues in the training phase showed significantly higher ratings than a new cue introduced in the test phase, at least in some cases, also partially supporting the original RW. We propose that in the development of human learning theory, attention should be paid to whether the behaviour (or other learning data) to be modelled results from implicit learning or involves higher level cognitive processes. We suggest that the RW may best capture implicit error-driven learning.

The Shapley value is one of the most important normative division schemes in cooperative game theory, satisfying basic axioms. However, some allocation according to the Shapley value may seem unfair to humans. In this paper, we develop an automatic method that generates intuitive explanations for a Shapley-based payoff allocation, which utilizes the basic axioms. Given any coalitional game, our method decomposes it to sub-games, for which it is easy to generate verbal explanations, and shows that the given game is composed of the sub-games. Since the payoff allocation for each sub-game is perceived as fair, the Shapley-based payoff allocation for the given game should seem fair as well. We run an experiment with 630 human participants and show that when applying our method, humans perceive the Shapley-based payoff allocation as more fair than the Shapley-based payoff allocation without any explanation or with explanations generated by other methods.

The doctrine of double effect (DDE) explains that it may be permissible to cause harm as a foreseen side-effect of an action that brings about a good result but impermissible to cause harm as a means of bringing about the same good result. The DDE is commonly illustrated with the Trolley Problem, which along with similarly structured examples, have become widely popular as a tool for studying moral psychology and have been taken to demonstrate a universal feature of moral judgment. Across two studies, we investigate how consenting to being harmed interacts with the Doctrine of Double Effect. Specifically, we ask whether (1) harming someone as a means becomes morally acceptable when that person consents to being used as a means, and (2) whether the distinction between harming as a means vs. side-effect persists even when the person being harmed consents. We find that consent significantly interacts with the DDE.

How much information can people gain from being lied to? We propose that people can infer the truth from false messages if two preconditions are met: (1) bigger lies are more costly, and (2) speakers have known, directional deception goals. We tested this with a marble-flipping task in which a judge tried to accurately estimate the number of sampled marbles, while a sender attempted to make the judge over- or underestimate. The sender could produce larger lies about the number of marbles drawn by physically clicking marbles along a lower or higher cost function. We found that judges took into consideration both the senders' goals and costs to correct for bias introduced by senders' lies. Our paradigm allows us to show that a large amount of the variation can be explained by people correcting others' lies based on the lies that they themselves would produce.

Several popular sequence-based and pretrained language models have been found to be successful for text-driven prediction of brain activations. However, these models still lack long-term cognitive plausibility as well as insights on the underlying neural substrate mechanisms. This paper studies the influence of context representations of different language models such as sequence-based models: Long short-term memory networks (LSTMs) and ELMo, and popular pretrained Transformer language model (Longformer). In particular, we study how the internal hidden representations align with the brain activity observed via fMRI when the subjects listen to several narrative stories. We use brain imaging recordings of subjects listening to narrative stories to interpret word and sequence embeddings. We further investigate how the representations of language models layers reveal better semantic context during listening. Experiments across all language model representations provide the following cognitive insights: (i) the representations of LSTM cell states are better aligned with brain recordings than LSTM (hidden state), the cell state activity can represent more long-term information, (ii) the representations of ELMo and Longformer display a good predictive performance across brain regions for listening stimuli; (iii) Posterior Medial Cortex (PMC), Temporo-Parieto-Occipital junction (TPOJ), and Dorsal Frontal Lobe (DFL) have higher correlation versus Early Auditory (EAC) and Auditory Association Cortex (AAC).

The representation of time depends heavily on spatial skills. Saj et al. (2014) demonstrated that left-hemispatial neglect patients, who lost the ability to detect objects in their left visual field, have a selective deficit in remembering items corresponding to the past, i.e., the left side of their mental timeline. The current study used the same memory task but tested neurotypical individuals (N = 76) to examine whether individual differences in spatial ability as well as learning order (chronological vs. random) predict how well participants remember items and associations between the item and time (past or future). Our results indicate that higher spatial ability and chronological learning both lead to better memory. This study is among the first to demonstrate how individual differences may impact time representation and memory that relies on a mental timeline.

Navigation is a fundamental cognitive function of virtually all moving animals. Several navigation strategies require an estimate of the current travelling direction that is updated continuously. In the central complex of the insect brain, multimodal cues are fused into a compass-like head direction representation. Based on the proposed connectivity of columnar neurons in the central complex of the desert locust we designed a computational model to examine how these neurons could maintain a stable representation of heading direction and how shifts occur by optic flow signals when the animal turns. Our proposed model architecture shows that the activity of head direction-encoding CL1a neurons remains stable if the activity of a second class of columnar neurons, CL2, is exactly the same. Shifts occur via modulation of the network connectivity. Our model can be used to deduce testable hypotheses where data are lacking, inspiring new avenues of experimental investigations.

In an eye-tracking during reading experiment, we examined how rapidly two types of photo-sentence relations impact sentence comprehension. The relation between a photo of a cultural figure and a year in an ensuing sentence (either the cultural figure in the photo was either alive in that year or not) was contrasted with relations between the same photo and achievements or facts about the cultural figure (e.g., a film they had vs. had not starred in). Longer reading times were observed at the regions containing mismatching year or fact, with more robust effects when data were filtered to include only trials in which participants had accurate prior knowledge about the cultural figure, their accomplishments, and their lifetime. These findings indicate that specific long-term knowledge is activated by a picture of a known figure and is rapidly available during language processing.

When considering the location of objects and places, we often take perspectives in reference to ourselves or someone/something else. Using ourselves as a reference is considered using an egocentric reference frame, while using something external as a reference is considered using an allocentric reference frame. Of interest is the similarity of how these reference frames inform our understanding of both spatial and social cognitive processes. Similar to how we understand objects in relation to ourselves or an external reference, mentalizing and theory of mind processes have also been described using reference frames. Whether there is a common mechanism for using reference frames for processing both spatial and social information is unclear. The present study explored this idea with an online study where participants performed both a spatial and social (i.e., mentalizing) perspective taking task, along with questionnaire gauging personality, visualization ability, and anxiety. Participants who were better at taking someone else’s spatial perspective tended to be better at mentalizing. This relationship was not present when taking one’s own spatial perspective or when mentalizing was not necessary. We provide preliminary evidence that reference frames contribute to both spatial and social cognitive processes.

The Principle of Sufficient Reason (PSR) has been an influential thesis since the earliest stages of western philosophy. According to a simple version of the PSR, for every fact, there must be an explanation of that fact. In the present research, we investigate whether people presuppose a PSR-like principle in judgment. Across four studies (N = 1,007 in total, U.S., Prolific), we find that participants consistently presuppose PSR in judgments about candidate explananda. Such judgments predictably track the metaphysical aspects relevant to the PSR (Study 1) and diverge from related epistemic judgments (Study 2) and value judgments (Study 3). Moreover, we find participants’ PSR-affirming judgments apply to a large set of facts that were sampled from random Wikipedia entries (Studies 4). These findings suggest that certain metaphysical judgments play an important role in our explanatory activities, one that is distinct from the role of the epistemic and value judgments that have been the focus of much recent work in cognitive psychology and philosophy of science.

This visual world eye-tracking pilot study investigates the comprehension of register variants (Stelzen_colloquial vs. Beine_standard transl:.‘’legs’) in a German target sentence when this target sentence (mis)matches the formality of a preceding context sentence, given the object argument either matches or mismatches verb meaning constraints (e.g. Ich rasiere bald meine Beine/Stelzen/#Autos/#Karren, transt.: ‘I shave my legs_standard/legs_coll/#cars_standard/#cars_coll’). The aim of this study is to examine whether register congruence rapidly interacts with verb-argument semantic relations. LME results (n=9) show a main effect of verb-argument congruence but no main effect of formality-register congruence at the region between the verb onset and object-argument onset, indicating that verb-argument relations are computed and used rapidly in online language comprehension. These pilot results suggest that situation formality may indeed modulate verb-argument congruency processing, possibly indicating that standard language processing mechanisms interact closely with register representations.

Following Griffiths and Tenenbaum (2006), we explore whether people use relevant social information to improve their already nearly optimal predictions about quantities in everyday events. We tested this question in two experiments involving quantities in three domains: cake baking times, movie runtimes, and podcast lengths. In Experiment 1, we found that participants were sensitive to the difference between relevant and irrelevant social information. In Experiment 2, we found that people consistently used relevant social information to adjust their predictions in the expected directions. We introduce an optimal social prediction model but find that it does not consistently perform better at accounting for our participants' social predictions than an optimal non-social prediction model. We conclude by discussing whether people use social information for prediction in an optimal way.

This paper explores the value of skepticism towards the Approximate Number System (ANS). I sketch some of the main arguments levied against ANS-based interpretations of numerical cognition data and argue that there are empirical and conceptual reasons to reject wholesale replacement of the ANS with an Analog Magnitude System (AMS). To simplify the discussion, I focus for the most part on a recent critical review representative of this new wave of revisionist skepticism (Leibovich, T., Katzin, N., Harel, M., & Henik, A., 2017). I start with a brief review of some of the reasons offered to deny that experiments studying our numerical abilities reveal the presence of a system dedicated to representing quantities of discrete objects, before turning briefly to empirical responses to these worries. I then offer a few reflections on why even if the empirical rebuttal were to fail, there are conceptual reasons to doubt that we are only equipped with an AMS. While some of these reasons involve methodological implications of AMS-based theories, other conceptual reasons to doubt AMS skepticism revolve around how ANS-skepticism seems to go against the history of the relation between the continuous and the discrete, and how one cannot be derived from the other. I then end with a potential reply to my worries involving an appeal to the Object-File System (OFS) as a source of discrete content in our numerical abilities and find it wanting.

The French phonological rule of liaison, whereby certain underlying word-final consonants surface only when the following word starts with a vowel, sometimes creates homophony. For instance, un œuf ‘an egg’ and un neuf ‘a nine’ are both pronounced [ɛ̃.nœf]. While homophony is cross-linguistically frequent, there is evidence that it is constrained in various ways. Here, we quantify liaison-induced homophony by comparing its occurrence in real French to that in a benchmark consisting of versions of French with modified liaison consonants. We find that liaison induces more homophony in the benchmark than in real French. This is the first evidence that a phonological rule that applies across words is subject to an anti-homophony bias.

Ways in which ovarian hormones affect cognition have been long overlooked in psychology and neuroscience research despite strong evidence of their effects on the brain. In order to address this gap, we study performance on a rule-plus-exception category learning task, a complex task that requires careful coordination of core cognitive mechanisms, across the menstrual cycle. Results show that the menstrual cycle distinctly affects learning of exceptions in a manner that matches the typical estradiol cycle. Furthermore, participants in their high estradiol phase outperform participants in their low estradiol phase, and show steeper learning slopes than men in exception-learning. These results provide novel evidence of the role of estradiol in category learning, underscore the importance of recruiting diverse samples in cognitive neuroscience research, and highlight the ways in which cognition varies throughout the fundamental biological cycles of the human experience.

A capacity for transitive inference (i.e. if aRb and bRc then aRc) was thought to be uniquely human. However, evidence of transitive inference in other species suggests that this capacity is ubiquitous throughout the animal kingdom. This apparent ubiquity raises two basic questions for cognitive science. (1) Why is transitive inference so prevalent? (2) What is special about transitive inference in (adult) humans? Formal (category theory) methods are used to address these questions. To the first question, different (implicit and explicit) forms of transitive inference follow from a common (universal) operation over the premises, aRb and bRc, i.e. a category theory version of transitive closure, hence the ubiquity of this capacity. To the second question, this construction involves rapid (one-shot) premise integration in older humans, but not other cohorts. This formal comparison points to rapid encoding and integration of relational data as underlying the evolution and development of higher cognitive capacities.

In decision-making situations that arise repeatedly, there are tradeoffs between: (i) acquiring new information to facilitate future, related decisions (exploration) and (ii) using existing information to secure expected outcomes (exploitation). Exploration choices have been well characterized in nonsocial contexts, but choices to explore (or not) in social environments are less well understood. Social environments are of particular interest because a key factor that increases exploration in nonsocial contexts is environmental uncertainty, and the social world is appreciated to be highly uncertain. Here, participants searched for rewards in a series of grids that were either described as comprising real people distributing previously-earned points (social context) or as the result of a computer algorithm or natural phenomenon (nonsocial context). Participants explored more, and earned fewer rewards, in the social versus nonsocial context, suggesting that social uncertainty prompted exploration at the cost of task-relevant goals.

Formal mathematical reasoning is unique in its precision: any valid conclusion can be justified by a sequence of base axioms. But human-written proofs or solutions rarely operate at that level. Instead, obvious steps are skipped to provide a simple, lucid argument. This is especially important in an educational setting, where too many details in an example solution, or too few, can confuse a student. What are the key steps for humans in a given formal solution? We investigate several computational hypotheses in the context of equation solving. Specifically, we take a reinforcement learning agent that solves equations using low-level axioms, and propose a series of methods for abstracting its solutions by selecting key steps. We consider methods based on the semantic distance between subsequent steps, based on the steps with the highest uncertainty for the agent, and based on transitions between latent "high-level skills" learned from a large number of agent-produced solutions. In a human evaluation we find that skill-base simplifications were judged most useful. These results suggest new directions for understanding human mathematical reasoning.

Determining others’ motor competence is critical for action prediction and social decision making. One aspect of competence judgements involves assessing how costly a given action is for a particular agent (e.g., whether climbing 4 floors of stairs is a piece of cake or a tough physical exercise). Such information is not given away by the agents’ physical appearance but can be inferred based on their behavior. Across two looking-time experiments, we show that 10-month-olds can infer and compare agent-specific costs of different actions. After being familiarized with agent A jumping over low obstacles and walking around high obstacles, and agent B jumping over both low and high obstacles, infants worked out that for B jumping bears little cost, while for A jumping high is more costly than detouring the obstacles by walking. Furthermore, they used this motor competence judgements to predict both agents’ actions in a new environment. These findings suggest that basic building blocks competence evaluations are available in infancy and may be rooted in infants’ action interpretation skills.

Humans routinely monitor social interactions to learn about the relational make-up of their groups and select social partners. It is unclear however whether social interactions primarily invite inferences about the dispositions of the participants involved or about underlying social relations. In the present study we tested which of these two inferences children and adults draw when observing interactions based on dominance. Children expected dominants to prevail over previous subordinates but did not generalize this expectation to interactions with novel agents, whereas adults did. These results suggest that children interpreted dominance as specific to a particular social relation, whereas adults interpreted it as a stable, target-invariant trait. This asymmetry supports the proposal that children may first interpret social interactions through a relational stance, and only later in development apprehend them through the lenses of trait attribution.

Quantifying the meaning of a word is a complex challenge. Humans can encode semantic information along a large and diverse range of semantic dimensions for any given word. Whilst a number of studies have applied a range of techniques to quantify word meaning along specific dimensions, little work has focussed on the socio-semantic dimensions of meaning. Here, we present data that quantifies the socio-semantic representations of 2,700 Czech words along the dimensions of gender, location, political, valence and age. We also demonstrate the utility of the data set by calculating an estimate of socio-semantic similarity between all words, which can be used to identify words that are either proximally close or distant in socio-semantic space.

How do people respond to a question when they are not certain of the answer? Probabilistic theories of cognition assume that the mind represents probability distributions over possible answers, but in practice people rarely recite these probability distributions out loud: instead they make simple guesses. Consider how you would express your belief about how many people live in the European Union. You would probably not say ``a Gaussian with mean 300 million and standard deviation 50 million" -- you would make a simple guess, such as "between 200 and 400 million". Here we present a simple rational analysis of these guesses. We assume that communicating the full probability distribution in one's head would take too much time, so people offer simple guesses in order to communicate a compressed version of this distribution. Drawing on information theory, we show that it is possible to measure how well a guess encodes a given probability distribution, and suggest that people tend to make guesses that provide the best such encoding. Two experiments provide preliminary evidence for the model. Our theory explains from first principles why guesses seem to strike a balance between accuracy and informativeness.

Apperly and Butterfill (2009) argue that adult theory of mind (ToM) requires two parallel systems. One system, efficient but inflexible, enables rapid judgements by operating without explicit modeling of beliefs, while a separate, effortful system, enables richer predictions over more complex belief encodings. Here, we agree with their qualitative distinction but propose a different model: a single process, but with effortful re-representation leading to two phases of ToM reasoning. Efficient reasoning, in our view, occurs over representations that include actions, but not necessarily explicit belief states. Effortful reasoning, then, involves re-representation of these initial encodings in order to handle errors, resolve real-world conflicts, and fully account for others’ belief states. We present an implemented computational model, based in memory retrieval and structural alignment, that illustrates our approach.

We describe two experiments designed to test whether the ease with which people can label features of the environment influences human reinforcement learning. The first experiment presents evidence that people are more efficient at learning to discern relevant features of a task when candidate features are easier to name. The second experiment shows that learning what action to take in a given state is easier when states have more readily nameable verbal labels, an effect that was especially pronounced in environments with more states. The interaction between CLIP, a state-of-the-art AI model trained to map images to natural language concepts, and established human RL algorithms, captures the key effects without the need to specify condition-specific parameters. These results suggest a possible role for language information in how humans represent the environment when learning from trial and error.

Query-Based Memory (QBM) models are heavily used in machine learning, though their relevance to human cognition is unclear. In this paper, we explore QBM models through both formal exploration and a simulation study to address this question. We found that QBM models are theoretically motivated, as they approximate rational induction with neurally-plausible mechanisms. Additionally, a simple implementation of the model could readily reproduce four benchmark findings in infant statistical learning. These results provide an encouraging starting point for further research using these formal tools to understand cognition across development.

Although language is critical to supporting morality within society, it is not clear how moral language itself evolved. We investigate the evolution of moral semantics, hypothesizing that words evolved to take on moral meanings from concrete experiences through metaphorization. We test this hypothesis by analyzing moral semantic change in words from the Moral Foundations Dictionary and the Historical Thesaurus of English over the past hundreds of years. In contrast with the observation that words become concrete over time, we demonstrate that moral words in the English lexicon undergo concrete-to-abstract shifts, reflecting systematic metaphorical mappings to the moral domain. Our results provide large-scale evidence for the role of metaphor in the historical development of the English moral lexicon.

Language use can be characterised as transparent, stating facts about the world, or non-transparent, requiring additional meaning to be inferred. The challenge faced by addressees is recognizing when language use is transparent or not. The current study investigates two factors that may influence how readily participants interpret utterances as instances of transparent or non-transparent language use; speaker knowledgeability and utterance form. When utterances involved negation participants were more likely to recognize this as non-transparent language use and infer that the situation is usually different. Whereas speaker knowledge did not influence how utterances were understood.

The MINERVA 2 (Hintzman, 1984) model of human memory has been used to simulate a variety of cognitive phenomena. These simulations, however, describe cogni-tive phenomena at Marr’s (1982) representation/algorithm level, with little effort to link the core assumptions of the model to an underlying neural implementation (however, see Kelly et al., 2017). This article describes a possible neural implementation of MINERVA 2—one that is sim-ple and arguably biologically plausible. This implementa-tion suggests a novel method for generating response la-tencies and provides a concrete example to support Marr’s claim that the representations and algorithms that mediate human performance in a variety of different cognitive tasks (e.g., decision making; Dougherty, Gettys, & Ogden, 1999) can be investigated and simulated without reference to their underlying neural implementation.

According to the diversity-beats-ability theorem, groups of diverse problem solvers can outperform groups of high-ability problem solvers (Hong and Page 2004). This striking claim about the power of cognitive diversity is highly influential within and outside academia, from democratic theory to management of teams in professional organizations. Our replication and analysis of the models used by Hong and Page suggests, however, that both the binary string model and its one-dimensional variant are inadequate for exploring the trade-off between cognitive diversity and ability. Diversity may sometimes beat ability, but the models fail to provide reliable evidence of if and when it does so. We suggest ways in which these important model templates can be improved.

Learning frequently forces us to rely on the good judgment and epistemic vigilance of sources with no more firsthand knowledge of a topic than ourselves, but who may have more second or third- hand knowledge. Yet, being forced to rely on their judgment doesn’t prevent us from evaluating their judgment: one might trust information because it was passed on to you by someone whose epistemic vigilance you trust, but reject it from someone whom you believe lacks good judgment. We present two experiments suggest that by integrating affective cues like anger and surprise along with perceptual access and consensus, children infer what others believe and what the correct answer to a question is. We discuss implications for consensus-based social learning strategies.

We suggest that some of the mechanisms underlying network effects on cultural evolution are intuitively accessible to laypeople, and may be part of the suite of social learning strategies underlying the human capacity for cumulative culture. Interest in the psychological mechanisms underlying this capacity typically focuses on learners’ ability to identify reliable sources and capacity for high-fidelity imitation. Yet, at the population level, research suggests that network structures themselves may influence cumulative learning by changing individuals’ explore-exploit patterns. In our experiments, adults infer that more proximal or distal clusters in a fragmented network will have more similar or dissimilar technological “styles”, and prefer to seek advice from more distant experts when asked to innovate, but more proximate experts when asked to remember. Commonsense intuitions about how social networks shape our access to information and diversity- fidelity tradeoffs for memory and innovation may make us more effective social learners.

Holistic and analytic thinking styles are well-documented in cultural psychology. However, recent studies suggest that language potentially mediates the influence of culture on thinking styles. The overarching goal of this study is to examine how verbal labels impact people’s thinking styles. Study 1 sought to examine whether thinking styles in a classic triad task could depend on verbal or pictorial formats. Although we observed a significant correlation between performance in verbal and picture triad tasks, more participants were classified as holistic thinkers with a verbal compared to a picture triad task. In Study 2, we examined whether participants could shift their thinking styles in the verbal triad task after being primed to focus on categorical associations. We found that females were influenced by this prime and displayed more analytic thinking. Our results suggest that language can influence thinking styles and that thinking styles are context-dependent.

Humans, even from infancy, are capable of unsupervised (“sta- tistical”) learning of linguistic information. However, it re- mains unclear which of the myriad algorithms for unsuper- vised learning captures human abilities. This matters because unsupervised learning algorithms vary greatly in how much can be learned how quickly. Thus, which algorithm(s) humans use may place a strong bound on how much of language can ac- tually be learned in an unsupervised fashion. As a step towards more precisely characterizing human unsupervised learning capabilities, we quantitatively synthesize the literature on adult unsupervised (“statistical”) word segmentation. Unfortunately, most confidence intervals were very large, and few moderators were found to be significant. These findings are consistent with prior work suggesting low power and precision in the litera- ture. Constraining theory will require more, higher-powered studies.

Learning to identify a person’s voice is a key component of speech perception. In this study, we use a categorization framework to provide insights about the mechanisms supporting talker identification. Native Mandarin Chinese listeners learned to categorize sentences in three tasks with different language contexts – native Mandarin talkers speaking Mandarin, native English talkers speaking English, and native Mandarin talkers speaking English. We compared learning when listeners received fully informative or minimal feedback. Using decision bound models, we examined the strategies participants used in each of the three tasks. Regardless of language context, full feedback was initially better for learning than minimal feedback but was no different after the second block. Across tasks, participants often used strategies based on mean fundamental frequency to separate the talkers. These results demonstrate that talker identification is a categorization problem, which enables leveraging existing category learning frameworks to understand the mechanisms of this important ability.

In an agent-based simulation, we investigate the implications of social interaction and cognitive diversity on creative processes of divergent thinking. Agents performed a verbal association task individually and jointly in pairs. We created pairs of varying cognitive diversity by manipulating properties of the vector spaces defining their semantic memory. We find that cognitive diversity positively stimulates the flexibility of agents’ collective cognitive search, giving rise to higher fluency (more solutions) and originality (more ‘rare’ solutions). While cognitively similar agents tend to exploit local semantic neighborhoods, diversity promotes more explorative search, with longer distances traveled in semantic space. This helps diverse pairs reach more distant areas of semantic space and escape cognitive fixation. However, our model also suggests that too high levels of diversity can have detrimental effects, as overly exploratory behaviors make pairs leave solution saturated areas prematurely and increase the risk of reaching semantic “dead ends”.

The debate of whether bilingualism provides a cognitive and or linguistic advantage is a lasting one. Underlying this debate is the idea that an additional language shapes cognition and linguistic processing. The current research analyzes a behavioral dataset containing individuals’ performance in different general cognitive and linguistic tests using a machine learning approach to classify individuals as bilinguals or multilinguals based on their performance. Using an extreme gradient boosting model, we were able to achieve a balanced accuracy of 77%. High scores on a prescriptive grammar test, a verbal fluency test, and a picture naming test were predictive for multilingualism. The implications of the reported results for the field and future research are discussed.

Do young children have a teleological conception of the essence of natural kinds? We tested this by examining how the preservation or alteration of an animal’s purpose affected children’s persistence judgments (N = 40, ages 4 - 12, Mean Age = 7.04, 61% female). We found that even when surface-level features of an animal (e.g., a bee) were preserved, if the entity’s purpose changed (e.g., the bee now spins webs), children were more likely to categorize the entity as a member of a different natural kind (e.g., a spider) and these effects were similar in magnitude to altering the surface-features of a natural kind. Our results suggest that we might view teleological properties as partially constitutive of the essence of natural kinds.

Variation in second language acquisition is evident from earliest stages. This study examined effects of learning tasks (retrieval practice, comprehension, verbal repetition) on comprehension of Turkish as a new language. Undergraduates (N = 156) engaged with Turkish spoken dialogues in a computer-assisted language learning session via Zoom, with learning tasks manipulated between-subjects. Participants completed pre/posttests assessing comprehension of Turkish number and case marking, a vocabulary test, and open-response questions gauging explicit awareness. The retrieval-practice group showed highest performance overall, after controlling for significant effects of nonverbal ability and pretest. For comprehension of number/case marking, the comprehension group performed comparably to the retrieval-practice group. For vocabulary comprehension, the verbal-repetition group performed comparably to the retrieval-practice group. Differential performance associated with learning tasks indicates benefits of testing and production and aligns with transfer-appropriate processing. As predicted by the noticing hypothesis, explicit awareness of number and case marking correlated with comprehension accuracy.

Neural language models are commonly used to study language processing in human speakers, and several studies trained such models on two languages to simulate bilingual speakers. Surprisingly, no work systematically evaluates different neural architectures on bilingual speakers’ data, despite the abundance of such studies in the monolingual domain. In this work, we take the first step in this direction. We train three neural architectures (SRN, LSTM, and Transformer) on Dutch and English data and evaluate them on two data sets from experimental studies. Our goal is to investigate which architectures can reproduce the cognate facilitation effect and grammaticality illusion observed in bilingual speakers. While all three architectures can correctly predict the cognate effect, only the SRN succeeds at the grammaticality illusion. We additionally show how the observed patterns change as a function of the models’ hidden layer size, a hyperparameter that we argue may be more important in bilingual models.

When people who are generally loyal or fair have a momentary lapse in their moral behavior, how does that impact whether we continue to perceive them as loyal or fair people––and how is this shaped by our relative valuation of loyalty and fairness? Reasoning from an error-management perspective suggests the Moral Stringency Hypothesis: Upon witnessing a moral lapse, we should be especially prone to discount category membership for our most deeply valued moral categories, given the potential costs of affiliating with people who do not reliably adhere to our core moral values. However, another line of research on conceptions of the "true self" suggests the opposite. Specifically, the True Self Hypothesis predicts that we should reliably project our most strongly held moral values onto others, even after a lapse. Across two studies (N = 720), we found consistent evidence favoring the Moral Stringency Hypothesis over the True Self Hypothesis.

Military deployments often expose personnel to highly threatening and stressful circumstances that put them at greater risk for developing Posttraumatic Stress Disorder (PTSD). PTSD may alter internal processes that affect one’s ability to maintain situational awareness (SA). Military personnel conducting patrols must maintain SA to search for threats, with potentially life-threatening consequences if SA drops. Here an exploratory analyses was conducted to determine whether there were differences in performance and eye gaze behavior between those with and without PTSD during a free-viewing visual search task conducted in a virtual desktop environment. Cognitive workload was increased through an additional auditory Math Task. While performance did not differ significantly between the two Groups, key differences in gaze behavior were found. Results showed that those with PTSD viewed significantly more trail markers, had increased duration of individual fixations overall, and decreased fixation and saccade rates during the Math Task. These results appear consistent with previous findings suggesting those with PTSD may have difficulty disengaging from stimuli.

Systematic generalization is the ability to combine known parts into novel meaning; an important aspect of efficient human learning, but a weakness of neural network learning. In this work, we investigate how two well-known modeling principles --- modularity and data augmentation --- affect systematic generalization of neural networks in grounded language learning. We analyze how large the vocabulary needs to be to achieve systematic generalization and how similar the augmented data needs to be to the problem at hand. Our findings show that even in the controlled setting of a synthetic benchmark, achieving systematic generalization remains very difficult. After training on an augmented dataset with almost forty times more adverbs than the original problem, a non-modular baseline is not able to systematically generalize to a novel combination of a known verb and adverb. When separating the task into cognitive processes like perception and navigation, a modular neural network is able to utilize the augmented data and generalize more systematically, achieving 70% and 40% exact match increase over state-of-the-art on two gSCAN tests that have not previously been improved. We hope that this work gives insight into the drivers of systematic generalization, and what we still need to improve for neural networks to learn more like humans do.

Temporal Binding (TB) is standardly regarded as an implicit measure of the sense of agency (Haggard, 2017). Though the TB effect is robust, an underlying mechanism has not been agreed upon (Hoerl et al., 2020). Here we propose a memory process as an explanation for the observed error in two publicly available datasets. We first replotted the data and found that on average, across both experiments, participants overestimate the length of the shortest timing interval and underestimate the longest interval, a classic regression to the mean pattern. Summary statistics extracted from the data from each experiment were then used as parameters in a simple Bayesian model of memory. Model simulations reproduced the behavioral data for almost all timing intervals and experimental trial-types across two experiments. Adjusting one of the parameters in the model (prior mean for actions) resulted in an improved qualitative fit. We suggest that other more likely sources of error, apart from experienced agency, may account for this result.

Subway systems in large cities witness high volumes of commuter traffic, with crowded coaches and limited seats. In such scenarios, commuters often carefully position themselves in strategic locations with the aim of maximizing their chances of getting a place to sit. While user behavior in subways around the world have been the focus of multiple studies in the past, these everyday acts of ‘optimal decision making’ is of particular interest to the cognitive scientist. This paper inquires into commuter positioning choices in simulated subway coaches, within the framework of Bayesian probabilistic modelling. Data on preferred standing positions were collected across 20 subjects for 30 co-passenger configurations, through an interactive computer game. A generative model based on a Bayesian network involving three key spatial parameters was constructed, and used for inferring preferred positions conditioned on the specific configurations. The model was able to accurately simulate the quick and intuitive decisions made by the players under constraints of time, and also effectively capture noise in responses across subjects

Images are a powerful medium known to induce empathy and emotional response in people. In political protests it has the power for a people-initiated policy change and signifies the deep symbolism of a political system. In this study, we aim to quantify the range of emotional connection a person experiences for photographs of a farmers' protest.The protest was the headlines in all media at the time this experiment was conducted and had polarized public opinion. Each photograph is identified to have a set of physical and semantic features. The three selected features were presence of police, gender and close-up (vs.long-shot) in the frame. The intensity on a range of emotions (fear, disgust, anger, sadness, optimism, pessimism, surprise, shock, happiness, and respect) experienced by the viewer for each feature was collected. By statistical and dimensionality analyses, we isolate and identify influencing factors in an image. We found that the presence of police in aggressive actions and close-up shots of had the highest variation in the emotional responses of participants. Interestingly, the gender of the protesters did not show statistically significant effects. The findings from the exploratory investigation highlights the powerful role photographic features have on emotional responses of people, an understudied but critical factor in a world immersed in social media.

Research in event cognition has focused on how people perceive and remember events under experimental conditions. This research study aims to explore the temporal duration of self-reported events from daily life (Sreekumar, et al., 2018; Zhuang, et al., 2012). The small amount of prior work that exists suggests that daily event durations have a Gaussian distribution and that people have prior beliefs that reflect this reality (Griffiths & Tenenbaum, 2006). Forty-eight participants provided activity duration data as they went about their everyday lives for 14 days. Descriptive analyses and activity duration modeling (mixture models of gaussian, gamma, normal and exponential distributions) were used to characterize event durations within activity types. Results show that most of the events present an exponential pattern of durations, while others show a bimodal pattern. Although some preplanned events have a characteristic time, many daily events have a substantial exponential component.

In order to successfully guide generalization of knowledge, category representation needs to be both: flexible enough to account for new evidence and stable enough to resist harmful change. Here we present a set of experiments designed to test how items that violate our expectations (i.e., category exceptions) affect category representation. Specifically, we wanted to know whether learning a category exception can change category boundaries. Does learning about penguins changes the way we think about birds? Do features of penguins contribute to making decisions as to whether a novel item is a bird? Across two experiments we found evidence that exceptions can change category boundaries and thus significantly affect future generalization. We discuss implications these findings have for the extent models of category learning and memory.

Math anxiety (MA) is negatively related to math performance. One proposed intervention with potential to disrupt the MA-math performance link is expressive writing. The current study aimed to conceptually replicate Park and colleagues (2014). In that study, the authors concluded that expressive writing effectively boosted math anxious students’ performance. In our current sample of 168 college students, participants randomly assigned to the expressive writing condition were no more accurate at posttest than were other participants assigned to a math self-concept intervention, active control, or passive control. Additionally, participants in the math self-concept and active control conditions reported lower state MA immediately following the intervention; participants in the expressive writing and passive control conditions reported no differences between pretest and posttest state MA. The current study provides boundary conditions for the effectiveness of expressive writing interventions in ameliorating MA during difficult math tasks and illuminates potential mechanisms underlying MA.

In polarized political climates, debate is ubiquitous but minds rarely change. This raises a question: what causes people to update their views? Recent work has shown that people are persuaded more by experienced-based explanations rather than factual ones. Yet, facts surely play (or ought to play) an important role in political discourse. Is it possible to leverage the persuasive power of personal experiences without sacrificing factual information? In Experiments 1 and 2, we replicate and build on previous findings showing that people who offer experienced-based (vs. fact-based) explanations are perceived as more rational and worthy of respect. In Experiment 3, we show that more complex explanations combining factual information with personal examples reveal more nuanced results. Collectively, this work sheds new light on how experienced-based and fact-based evidence can be used to persuade.

Early language learning relies on statistical regularities that exist across timescales in infants’ lives. Two types of these statistical regularities are the routine activities that make up their day, such as mealtime and play, and the real-time repeated behaviors that make up the moment-by-moment dynamics of those routines. These two types of regularities are different in nature and are embedded at two different temporal scales, which led to divergent research in the literature – those who collect long-form recordings and observations of at-home behavior and those who use eye trackers and micro-level analyses to quantify real-time behavior in laboratories. The goal of present paper is to jointly examine and connect the statistical regularities at these two timescales. Towards this goal, we brought wearable eye trackers to English- and Spanish-speaking families’ homes to record parent and toddler visual attention during daily routines. We transcribed parent speech during object play and mealtime and coded toddler visual attention during naming moments. We found that parents and toddlers jointly interacted with the unique vocabularies of the two activities. Although naming and attention were more coordinated during object play, mealtime still afforded opportunities for high-quality naming moments. Our results lay the building blocks for connecting these two lines of research and demonstrate the feasibility of at-home data collection with eye trackers.

Understanding how people generalize and extrapolate from limited amounts of data remains an outstanding challenge. We study this question in the domain of scalar function learning, and propose a simple model based on the Principle of Maxi- mum Entropy (Jaynes, 1957). Through computational model- ing, we demonstrate that the theory makes two specific predic- tions about peoples’ extrapolation judgments, that we validate through experiments. Moreover, we show that existing Gaus- sian Process models of function learning cannot account for these effects.

When assessing the quality of a machine, people might consider the machine’s outputs—how well it serves its function. Alternatively, people might also consider the efficiency of the machine. We investigated this possibility in two experiments (N = 392). In each experiment, participants saw pairs of machines, one with simple inside parts and one with more complex inside parts. Machines either had the same output or unknown outputs, and people judged which of the two machines was better. When the machines had the same output, participants in both experiments judged that machines with simpler inside parts were better than ones with more complex insides. However, when machines’ functions were unknown, people predominantly judged that machines with complex insides were better. Together, our work shows that people consider both parts and functions of machines when inferring quality.

Effective practice is key to learning. Yet, it is unclear whether young children have the ability to make effective and adaptive training choices. In this project, we investigated 4- to 7-year-old children’s (n=146) ability to tailor their training strategies to optimize performance outcomes. Children were presented with one easy and one difficult guessing game and were asked to choose which game they wanted to practice. Crucially, before they chose, they were told that they would eventually be tested either on the game of their choice (Choice condition) or on the game the computer would randomly pick (Random condition). Contrary to our hypotheses, we found that condition per se did not predict children’s training choices. However, we found that older children were more likely to make effective and adaptive training choices than younger children. Overall, our results indicate that children’s training choices improve from ages 4 to 7 and inform the development of interventions to support strategic learning.

Models of word meaning, like the Topics model (Griffiths et al., 2007) and word2vec (Mikolov et al., 2013), condense word-by-context co-occurrence statistics to induce representations that organize words along semantically relevant dimensions (e.g., synonymy, antonymy, hyponymy etc.). However, their reliance on latent representations leaves them vulnerable to interference and makes them slow learners. We show how it is possible to construct the meaning of words online during retrieval to avoid these limitations. We implement our spreading activation account of word meaning in an associative net, a one-layer highly recurrent network of associations, called a Dynamic-Eigen-Net, that we developed to address the limitations of earlier variants of associative nets when scaling up to deal with unstructured input domains such as natural language text. After fixing the corpus across models, we show that spreading activation using a Dynamic-Eigen-Net outperforms the Topics model and word2vec in several cases when predicting human free associations and word similarity ratings. We argue in favour of the Dynamic-Eigen-Net as a fast learner that is not subject to catastrophic interference, and present it as an example of delegating the induction of latent relationships to process assumptions instead of assumptions about representation.

We tackle the problem of an agent interacting with humans in a general-sum environment, i.e., a non-zero sum, non-fully cooperative setting, where the agent's goal is to increase its own utility. We show that when data is limited, building an accurate human model is very challenging, and that a reinforcement learning agent, which is based on this data, does not perform well in practice. Therefore, we propose that the agent should try maximizing a linear combination of the human's utility and its own utility rather than simply trying to maximize only its own utility.

Orthographic depth, the consistency of grapheme-phoneme correspondence, influences brain activation in multilinguals’ first (L1) and second language (L2). The intrinsic functional connectivity of cross-language transfer was investigated between two groups of multilinguals, those whose L2 orthography is deeper than their L1 (S-to-D group) and those whose L2 orthography is shallower than their L1 (D-to-S group). We focused on two seed regions: the Visual Word Form Area (VWFA) and the left posterior supramarginal gyrus (pSMG). stronger connectivity was found between the left pSMG and the right precuneus in multilinguals who spoke at least three languages (trilinguals) compared to those who only spoke two languages (bilinguals). Follow-up analyses revealed that this difference was driven by stronger intrinsic connectivity in D-to-S trilinguals compared to the S-to-D trilinguals. Multilinguals’ intrinsic functional connectivity is shaped by the orthographic distance between L1 and L2, as well as differences between bilingualism and trilingualism.

In virtually every activity we engage in — from analyzing economic trends, to predicting which of two football teams is more likely to win a game — our minds are tasked with separating signal from noise. Such computations benefit from the fact that our minds are highly attuned to the statistical structure of the world. But how quickly do we detect statistical structure — and to what extent is our sensitivity to structure rooted in perceptual processes? To address this, we asked observers to judge whether briefly presented visual stimuli were generated randomly or non-randomly. In as little as a tenth of a second, people exhibited the same stable biases of statistical perception that they exhibit in classic cognitive tasks (i.e., without time constraints). These results suggest that certain biases of subjective probability may arise not from how we think about randomness, but from how we perceive statistical information in the first place.

In creativity, the importance of interaction with the environment through bodily movement and perceptual information acquired therein has been discussed anecdotally. However, past creativity studies have mainly focused on the connection of creativity with memory and knowledge and the relationship between creativity and cognitive manipulations. The above process of bodily movement and environment was not sufficiently discussed. In this study, we developed a model of the above process and partially checked its validity through an experiment. Our model and the results of our experiment suggested the following processes. The interaction with the environment through the bodily movement changes the content and quality of the ideas generated. That interaction also changes the content of the cognitive manipulations in the idea generation. The above change in the cognitive manipulations partially described the change in the content and quality of the ideas. In these processes, the acquisition of perceptual information that differs greatly from the prediction has an important function. The dynamical relationship between the bodily movement, perception, and cognition in creative activities will require further investigation.

This study investigates how neural networks reveal developmental trajectories of child language, focusing on the Agent-First strategy in comprehension of an active transitive construction in Korean. We develop three models (LSTM; BERT; GPT-2) and measure their classification performance on the test stimuli used in Shin (2021) involving scrambling and omission of constructional components at varying degrees. Results show that, despite some compatibility of these models’ performance with the children’s response patterns, their performance does not fully approximate the children’s utilisation of this strategy, demonstrating by-model and by-condition asymmetries. This study’s findings suggest that neural networks can utilise information about formal co-occurrences to access the intended message to a certain degree, but the outcome of this process may be substantially different from how a child (as a developing processor) engages in comprehension. This implies some limits of neural networks on revealing the developmental trajectories of child language.

Intuitive explanations for natural phenomena are typically our default explanations, even after we have learned more accurate, scientific explanations (Shtulman & Valcarcel, 2012). The current study examined whether priming students with scientific images improves their ability to verify counterintuitive scientific statements, like “bacteria need nutrients” and “bubbles have weight.” Participants (100 college undergraduates) verified scientific statements interspersed with images relevant to the predicates of those statements; the images depicted either schematic diagrams (scientific primes) or everyday scenes (intuitive primes). Scientific primes increased the accuracy of participants’ responses, relative to intuitive primes, but not the speed of those responses, indicating that scientific primes facilitate a preference for scientific ideas over intuitive ones but do not eliminate the initial conflict between them.

Several studies have reported that numerical magnitudes biases temporal judgments, i.e., large numerical magnitude, were perceived to last longer than small numerical magnitude. However, these predictions have been predominantly verified only when the large and small numerical magnitudes were presented in an intermixed fashion where numerical magnitudes varied randomly from trial to trial. We conducted two experiments (Blocked-magnitude and Mixed-Magnitude) using a temporal bisection paradigm to investigate whether numerical context affects temporal processing in a sub-second timescale. The numbers were presented with varying durations. Participants were asked to judge whether the presented durations were shorter or longer. The results suggest that the temporal judgments were affected when small and large numbers were randomly presented in an intermixed manner. However, such effects disappeared when the number magnitudes were presented separately. These results indicate the modulation of attention in number-time interaction, and such crosstalk may not require a generalized magnitude system.

We present a neural-network computational model of a recent experiment revealing that chimpanzees show some ability to reason probabilistically. Specifically, we show that the neural probability learner and sampler (NPLS) system can account for both success by chimpanzees and better performance by human controls. NPLS effectively combines learning probability distributions with sampling from those learned distributions to guide action choices. Because NPLS also simulates learning and use of probability distributions by human infants, this brings us closer to a unifying model of probabilistic reasoning, across various age groups and species.

A widely researched question in bilingualism asks whether bilinguals’ vocabulary growth is equal to or lower than that of monolinguals. Some studies have found smaller vocabularies in bilingual toddlers than monolingual toddlers when comparing in one language, but others have found no significant group differences. We compared 12 to 32-month-old bilingual toddlers growing up in the UK with English and one additional language (AL) to age-matched UK English monolinguals. We evaluated both vocabulary size in English and conceptual vocabulary. Bilinguals’ English vocabulary sizes in both comprehension and production were significantly smaller than monolinguals’ after controlling for age and socioeconomic status. This was seen across bilinguals of different levels of language dominance. The bilingual lag in vocabulary size was smaller when calculated using conceptual vocabulary but still significant for both comprehension and production. We discuss the implications for measurements of bilingual toddlers’ vocabulary size.

Knowing more than one language provides a speaker with an increased pool of linguistic experiences and concepts. This expanded language knowledge is thought to benefit bilingual speakers on standardized tests of creative ability. However, relatively little research has explored bilingual performance on tests of linguistic creativity. In this study we compare the production of creative, attenuated descriptions produced by English L1 and English L2 speakers. Using computational measures of text similarity, we find that English L2 answers were significantly less similar than L1 answers, suggesting a greater number of concepts and topics were used by the L2 participants. Additionally, unsupervised cluster analysis found no strong differences in the number of cluster topics between the L1 and L2 data. As such, the L2 answers contained more breadth, whereas the L1 answers contained more depth. The results may reflect fundamental differences in the storage and use of L1/L2 language knowledge.

To enable natural and fluid human-robot interactions, robots need to not only be able to communicate with humans through natural language, but also do so in a way that complies with the norms of human interaction, such as politeness norms. Doing so is particularly challenging, however, in part due to the sensitivity of such norms to a host of different contextual and intentional factors. In this work, we explore computational models of context-sensitive human politeness norms, using explainable machine learning models to demonstrate the value of both speaker intention and task context in predicting adherence with indirect speech norms. We argue that this type of model, if integrated into a robot cognitive architecture, could be highly successful at enabling robots to predict when they themselves should similarly adhere to these norms.

Computational models of verbal analogy and relational similarity judgments can employ different types of vector representations of word meanings (embeddings) generated by machine-learning algorithms. An important question is whether human-like relational processing depends on explicit representations of relations (i.e., representations separable from those of the concepts being related), or whether implicit relation representations suffice. Earlier machine-learning models produced static embeddings for individual words, identical across all contexts. However, more recent Large Language Models (LLMs), which use transformer architectures applied to much larger training corpora, are able to produce contextualized embeddings that have the potential to capture implicit knowledge of semantic relations. Here we compare multiple models based on different types of embeddings to human data concerning judgments of relational similarity and solutions of verbal analogy problems. For two datasets, a model that learns explicit representations of relations, Bayesian Analogy with Relational Transformations (BART), captured human performance more successfully than either a model using static embeddings (Word2vec) or models using contextualized embeddings created by LLMs (BERT, RoBERTa, and GPT-2). These findings support the proposal that human thinking depends on representations that separate relations from the concepts they relate.

The introduction of deep learning and CNNs to image recognition problems has led to state-of-the-art classification accuracy. However, CNNs exacerbate the issue of algorithm explainability due to deep learning’s black box nature. Numerous explainable AI (XAI) algorithms have been developed that provide developers insight into the operations of deep learning. We aim to make XAI explanations more user-centric by introducing modifications to existing XAI algorithms based on cognitive theory. The goal of this research is to yield intuitive XAI explanations that more closely resemble explanations given by experts in the domain of bird watching. Using an existing base XAI algorithm, we conducted two user studies with expert bird watchers and found that our novel averaged and contrasting XAI algorithms are significantly preferred over the base XAI algorithm for bird identification.

This study explored how sociocultural pressures and internalized beauty ideals play a role in how women and men perceive the attractiveness of different body types of the same and opposite gender. Results showed that when judging the attractiveness of bodies of the same gender, internalized beauty ideals have different effects on women and men. Women’s judgments of the attractiveness of female bodies are predicted by the pressure exerted by a thin beauty ideal, while men’s judgments of the attractiveness of male bodies are predicted instead by a muscular beauty ideal. Attractiveness judgments for bodies of the opposite gender are influenced by the pressure to be thin and the perceived influence of significant others. Sociocultural pressures also have a stronger effect on women than men. These findings offer an initial window into the distinct factors that shape body image construction for the digital generation of women and men.

One of the ways to make reinforcement learning (RL) more ef- ficient is by utilizing human advice. Because human advice is expensive, the central question in advice-based reinforcement learning is, how to decide in which states the agent should ask for advice. To approach this challenge, various advice strate- gies have been proposed. Although all of these strategies dis- tribute advice more efficiently than naive strategies (such as choosing random states), they rely solely on the agent’s inter- nal representation of the task (the action-value function, the policy, etc.) and therefore, are rather inefficient when this rep- resentation is not accurate, in particular, in the early stages of the learning process. To address this weakness, we propose an approach to advice-based RL, in which the human’s role is not limited to giving advice in chosen states, but also includes hint- ing apriori (before the learning procedure) which sub-domains of the state space require more advice. Specifically, we sug- gest different ways to improve any given advice strategy by utilizing the concept of critical states: states in which it is very important to choose the correct action. Finally, we present ex- periments in 2 environments that validate the efficiency of our approach.

There has been a small but now growing interest in studying decision making in real-world contexts where part of the problem faced by decision makers is to generate candidate options they will actually decide between. While some of this work has employed large decision spaces where options are discrete and valuation is computationally tractable (e.g., chess), very little work has focused on genuinely open-ended decision contexts that more closely mirror mundane real-world decisions. This paper leverages large language models to investigate how people generate options when facing genuinely open-ended problems. Across three experiments, we apply semantic similarity and sentiment analyses to the options that participants sequentially generate for real-world decision problems. We find that the first options generated tend to be sampled from a relatively local region of semantic space and are typically of high value. As additional options are generated, they become increasingly dissimilar and are of lower value. These patterns held both at the level of individual option generation trajectories within a given participant and at the level of individual differences across participants.

Even simple, ambiguous signals can have a rich interpretation when viewed in the context of an interaction in a shared environment. We create a model called Paternalistic Communication by combining an existing model of overloaded language -- Rational Speech Acts (RSA) -- with a full agent model of Theory of Mind (ToM). This integration allows signals to be processed in conjunction with common ground in a principled manner dependent on task-dependent action utilities. This modeling perspective treats communication as a way to coordinate diverging perspectives in a cooperative setting. Under Paternalistic Communication, a speaker decides what to say by predicting their partner’s reaction based on the information in common ground and then evaluates those reactions using their own mind which may contain additional information. We demonstrate the flexibility and performance of Paternalistic Communication in a case study with ambiguous signaling through a set of simulations.

Who we choose to learn from is influenced by the relative confidence of potential informants. More confident advisers are preferred based on an assumption that confidence is a good indicator of accuracy. However, oftentimes, accuracy and confidence are not calibrated, either due to strategic manipulations of confidence or unintentional failures of metacognition. When accuracy information is readily available, people are additionally vigilant to the calibration of informants, penalizing incorrect, yet confident advisers (Tenney et al., 2007). The current experiment tested whether participants can leverage inferences about two advisers' calibration profiles to make optimal trial-by-trial decisions. We predicted that choice of advisers reflects relative differences in the advisers' probability of being correct given their stated confidence (recalibrated confidence), as opposed to stated confidence differences. The prediction was not supported by data, but calibration had a modulating effect on choices, as more confident advisers were more influential only when they were also calibrated.

A common result in novel word generalization is that comparison settings (i.e., several stimuli introduced simultaneously) favor conceptualization and generalization. We investigated which type of items four-, five- and six-year-old children would choose as referents in a free-choice novel noun generalization task. We manipulated the generalization items availability at test (i.e., generalization stimuli introduced sequentially or simultaneously). We also manipulated the semantic distance between items. In a signal detection theory framework, results showed that a simultaneous presentation of generalization items improves children’s sensitivity and helps them use a neutral strategy to generalize. Conceptual distance at learning also affects generalization performance. We discuss the cognitive constraints that both types of presentation bring into the task, and how distance might impede or favor conceptual alignments.

A central component of the predictive coding theoretical framework concerns the comparison between predictions and sensory decoding. In the probabilistic setting, this takes the form of assessing the similarity or distance between probability distributions. However, such similarity or distance measures are not associated with explicit probabilistic models, making their assumptions implicit. In this paper, we explore an original variation on probabilistic coherence variables; we define a probabilistic component, that we call a "Bayesian comparator", that mathematically yields a particular similarity measure. A geometrical analogy suggests two variants of this measure. We apply these similarity measures to simulate the comparison of known, predicted patterns to patterns from sensory decoding, first in a simple, illustrative model, and second, in a previous model of visual word recognition. Experimental results suggest that the variant that is scaled by the norms of both predicted and perceived probability distributions yields better robustness and more desirable dynamics.

Speech errors are often perceived as categorical substitutions of one sound for another, but phonetic analyses have consistently revealed that errorful productions retain a phonetic trace of the target category. These trace effects have been taken as evidence for the simultaneous activation of multiple categories, both exerting influence on speech production. We develop a dynamic neural field model of voice onset time (VOT) planning, showing how multiple activated categories can be resolved in the field to show trace effects. We evaluate model predictions against measurements of VOT for voiced and voiceless stops in speech error experiments and naturalistic corpora.

The Aha-Accuracy effect refers to the finding that experiencing an Aha! moment is associated with reaching correct solutions on insight problems. Because this effect has generally been demonstrated with verbal problems, this study tested for this effect on spatial problems (matchstick arithmetic). In addition, this study also explored the effect of hints on the Aha! experience and the Aha-Accuracy effect. Overall, there was no Aha-Accuracy effect in the no-hint control condition. There was an Aha-Accuracy effect in the hint condition, but it was limited to problems with solutions that were not directly cued by the hint. When the hint was directly relevant for solution, then many participants were able to reach a correct solution without an Aha! experience. These findings provide evidence that providing hints may not simply increase the likelihood of reaching a solution, but it may also alter the Aha! experience.

Collectible items, such as stamps, coins, paintings, and trading cards, are often valued for their rarity. A side effect of rarer items being more highly valued is that they are also more often traded, discussed, and displayed. A new collector's experience of the category defined by a set collectible items is thus heavily biased towards the rare items. Theories of category learning predict that these conditions make for a uniquely challenging environment in which to learn a category because rarity-based sampling can invert the distribution of associated attribute frequencies. Here, we show that under these conditions, the demand for rarity is self-defeating: when newcomers do not correct for the sampling bias present in their experience, they will have a distorted sense of the category and misunderstand which items are in fact rare, causing rarity to become devalued over time. We find evidence for this dynamic in the context of The Bored Ape Yacht Club (BAYC), a collection of 10,000 non-fungible tokens (NFTs), each with a set of attributes that vary in rarity. We demonstrate that, in line with our theory, over time the influx of newcomers learning about BAYC has been associated with a decrease in the demand for tokens with rare attributes.

To explain the importance of interaction for a truthful performance in acting, the present study captures the characteristics of interaction and attempts to probe the underlying intrapersonal changes through interaction during an acting course which emphasizes paying attention to a partner. Novice participants tend to change their way of communication as the course progresses, the pattern of which further differs from that of professional actors. While actors devote themselves more to the connection with their partner and demonstrate more balanced communication, novices rely on general inference to speculate about others’ affective states. This study offers a new perspective to elucidate the construction of interaction in acting, and emphasizes the significance of involvement in interaction when applying acting approaches to general training with the aim of improving social understanding.

Can people learn causal relationships about the world from someone’s emotions? We present a computational model integrating observational causal learning with emotional information, which uses emotional displays to disambiguate the beliefs, desires, and knowledge of other agents, in turn allowing causal inferences about the world. We compared our model predictions to human causal judgements on two observational learning tasks involving multiple possible causes or multiple possible outcomes. Across three studies (N = 129,127,125), emotional displays (compared to actions alone) led people to interpret agents’ beliefs differently, which in some contexts resulted in different causal inferences. Our model closely reflected these patterns of belief and causal inference and revealed new insights on how people learn causal relationships from others’ emotions.

Fundamental concepts in biology are often challenging to understand. More strikingly, studies also report incorrect or incomplete understanding of such concepts for undergraduate natural science students even after instruction. Recent research suggests that embedding conceptual information in a narrative could support students’ learning process and facilitate conceptual change. Therefore, we designed learning materials covering complex concepts in biology either in the form of a narrative presenting the to-be-learned concepts in a historical context or as an expository text as control. We then assessed conceptual understanding and potential learning mechanisms. Results indicate that students learned from narrative texts and expository texts to a similar extent. However, if the prior knowledge was higher, the effect on learning was bigger in the narrative group than in the expository group. Moreover, the narrative led to better enjoyment and a higher germane cognitive load than the expository text material.

Given considerable advancements in automated driving systems, the day when autonomous vehicles will be regularly present in our everyday life is impending. It is, therefore, very significant to put emphasis on the effect that giving up autonomy might have on an individual. We take into consideration an experimental data set regarding participants' reported discomfort levels to tackle the following questions: How can we represent a discomfort measurement in a meaningful way? Using this representation, can future discomfort reactions be predicted? We identify key features, identify baseline models, and develop a new approach based on the k-nearest neighbor model to considerably improve the prediction of individual user's discomfort measurements. A discussion of limits and potentials concludes the paper.

Contrary to the classic idea of arbitrariness in mappings between words and meanings, many languages have words that mimic the sounds of their referents (onomatopoeia) and other subtler sound symbolic associations. However, our knowledge concerning the characteristics of sound-meaning links is still limited. Previous research mostly focused on languages with a large (e.g., Japanese) or limited (e.g., English) inventory of sound symbolic words. We conducted a word-production study with native speakers of Turkish, a language with a moderate amount of sound symbolic words, and examined links between sound properties (e.g., voiced vs. voiceless) and semantic dimensions (e.g., size, speed) in describing motions. Some of the sound-meaning links identified were the links found in Japanese and English samples in previous studies (Saji et al., 2019), whereas many seem to be specific to Turkish. This study provides initial evidence for language-specific sound symbolism in Turkish and links that are consistent across languages.

In many areas like economics, finance, and health, people make judgmental forecasts looking at previous time series data. In such efforts, either tabular presentations or graphs are utilized, where graphs can be in different formats like bars, lines or points. Different presentations may cause certain biases stemming from bottom-up processing. To delineate such perceptually driven biases in judgmental forecasting, we investigated the effect of graph format (line, bar, point) and trend type (upwards, downwards, flat) on judgmental point forecasts when no domain information was provided. Bringing together perspectives from graph processing, visualization and forecasting literatures, our major goals were to determine which graph formats lead to more accurate forecasts and whether bar graphs lead to mean reversion bias or within-the-bar bias in forecasts. Additionally, we wanted to determine whether asymmetric damping observed in sales forecasts of downward vs. upward trended series were confounded by graph characteristics. We found that forecasts in line and point graphs were less biased than those in bar graphs; forecasts based on bar graphs depicting trended data exhibited mean reversion bias. We also observed a general positivity bias in forecasts for all trend types in line and point graphs. This implied trend following forecasts in upward trends and mean reverting forecasts in downward trends revealing an asymmetricity in the absence of context as well.

Social learning is essential to survival. It is likely to evolve when it is more efficient than asocial, trial-and-error learning. The consensus in cultural evolutionary theory holds that some amount of environmental variability and uncertainty about the best decisions are necessary for social learning to evolve. However, current models for the evolution of social learning tend to conflate forms of uncertainty, and rarely consider different ones in tandem. Moreover, many models are limited by considering only two possible behaviors and environmental states. Here we use evolutionary agent-based modeling to identify the complex ways in which different forms of uncertainty affect social learning. We model a time-varying environment with dozens of possible behaviors performed by agents engaging in individual and social learning. We show that ambiguous payoffs, larger possible decision sets, and shorter agent lifespans sometimes increase social learning prevalence, as expected. However we also find which concrete uncertainty conditions cause evolution to select against social learning.

The human voice effectively communicates a range of emotions with nuanced variations in acoustics. Existing emotional speech corpora are limited in that they are either (a) highly curated to induce specific emotions with predefined categories that may not capture the full extent of emotional experiences, or (b) entangled in their semantic and prosodic cues, limiting the ability to study these cues separately. To overcome this challenge, we propose a new approach called 'Genetic Algorithm with People' (GAP), which integrates human decision and production into a genetic algorithm. In our design, we allow creators and raters to jointly optimize the emotional prosody over generations. We demonstrate that GAP can efficiently sample from the emotional speech space and capture a broad range of emotions, and show comparable results to state-of-the-art emotional speech corpora. GAP is language-independent and supports large crowd-sourcing, thus can support future large-scale cross-cultural research.

Transfer of cognitive abilities has often been described in regard to Working Memory, while little has been said about Cognitive Control. Recent studies have proposed that congruency cues can be used to investigate learning-guided cognitive control adaptations in a trial-by-trial fashion during conflict tasks. In the present study, we employed congruency cues within an inducer/diagnostic paradigm to (1) induce a control learning between cue and string congruency in a Flanker task variant and (2) test whether this learning could transfer to a different Flanker variant. Results provided evidence that participants can learn to strategically employ congruency cues to adapt their cognitive control and that these learned control strategies/routines can be transferred to a very similar task variant (near transfer). Further experiments will be performed to explore the extent of this transfer.

People talk about time using the language of space. The future is "ahead." Endless events are "long." Cross-linguistically, these conventions exhibit both universality and striking diversity. These mappings in language, therefore, might originate from a combination of shared cognitive biases and sociocultural processes. To investigate the mechanisms involved in the emergence of space-time mappings - and linguistic metaphor more broadly - we conducted an experiment in which participants had to communicate about abstract temporal concepts using entirely spatial signals. The spatial signals developed by one pair of participants were then transmitted to the next pair, creating chains of multiple generations. Together, these processes of interaction and transmission sometimes generated fully systematic, compositional systems - although sometimes also generated systems that lacked structure entirely. The deciding factor may have been how people responded to errors - with incremental adjustments or radical reconfiguration. Systematic metaphors, therefore, may emerge from a heterogeneous mix of mechanisms.

Math talk has implications for the development of numerical concepts. Research suggests that when caregivers talk about natural numbers (1, 2, 3…), it may enhance children’s later math knowledge. Natural numbers have physical quantities that children can observe, yet abstract numerical concepts do not have such observable quantities. In this analysis, we examined how zero occurs in math talk. Using the CHILDES American English corpora (MacWhinney, 2000), we examined the frequency and nature of math talk about zero in naturalistic interactions between 2- to 6-year-olds and other speakers. Input from other speakers increased in frequency and complexity across development. Input with zero in symbolic sentential contexts (e.g., “one and zero make ten”) and cardinal sentential contexts (e.g., “zero means nothing”) increased with development. Children’s production of zero did not change in frequency or context. These results have implications for the concepts about zero children may bring to formal education.

One pedagogical technique that promotes conceptual understanding in mathematics learners is self-explanation integrated with worked examples (e.g., Rittle-Johnson et al., 2017). In this work, we implemented self-explanations with worked examples (correct and erroneous) in a software-based Intelligent Tutoring System (ITS) for learning algebra. We developed an approach to eliciting self-explanations in which the ITS guided students to select explanations that were conceptually rich in nature. Students who used the ITS with self-explanations scored higher on a posttest that included items tapping both conceptual and procedural knowledge than did students who used a version of the ITS that included only traditional problem-solving practice. This study replicates previous findings that self-explanation and worked examples in an ITS can foster algebra learning (Booth et al., 2013). Further, this study extends prior work to show that guiding students towards conceptual explanations is beneficial.

Cognitive processes that underpin performance on a given task may vary both within and across individuals. Yet, it is unclear how individual-specific versus shared cognitive processes each support behaviour. Here, we used a functional magnetic resonance imaging (fMRI) pattern classifier approach to ask how individual-specific and shared neural cognitive states differently relate to an individual’s ability to detect consecutive repeats in semantic (story) meaning versus perceptual (artist style) dimensions of illustrations that depicted well-known stories. Both states were related to participants’ task performance overall but differently for story versus artist style behaviours: individual-specific states were related to story performance, whereas shared states were related to artist style performance. These findings suggest that behaviours relying upon prior knowledge—likely varying across individuals—may be supported by idiosyncratic versus shared states. In contrast, unfamiliar judgments associated with a smaller number of eligible strategies may be supported by a state shared across individuals.

The study reported here examined the role of expertise in the composite face effect which constitutes better recognition of the top half of a face when in composite with a congruent vs. an incongruent (in terms of response required) bottom half. Experiment 1a (n=96) used prototype-defined artificial stimuli (checkerboards) to investigate the composite effect. The advantage of using these stimuli is that expertise can be fully controlled. Experiment 1b (n=96) aimed to replicate the composite effect in face stimuli which served as a control and provided a direct comparison of the composite effect between face and checkerboard stimuli. A full experimental design including congruent/incongruent aligned and misaligned composites was used in both experiments to measure the composite effect. Experiment 1a revealed that the composite effect could not be obtained in checkerboard composites. Experiment 1b confirmed the robust composite face effect. We interpret our results as suggesting that expertise/perceptual learning does not contribute to the composite effect for faces.

We examined how rhythmic activities affect children’s perspective-taking in a referential communication task with 69 Chinese 5- to 6-year-old children. The child first played an instrument with a virtual partner in one of three coordination conditions: synchrony, asynchrony, and antiphase synchrony. Eye movements were then monitored with the partner giving instructions to identify a shape referent which included a pre-nominal scalar adjective (e.g., big cubic block). Participants with awareness of their partner’s perspective could, in principle, identify the intended referent before the shape was named when the target contrast (a small cubic block) was in shared ground whereas a competitor contrast was occluded for the partner. Children in the asynchrony and antiphase synchrony conditions, but not the synchrony condition, showed anticipatory looks to the target, suggesting that playing instruments asynchronously or in alternation facilitates perspective-taking, likely by training self-other discrimination and inhibitory control.