Proceedings of the Annual Meeting of the Cognitive Science Society

Please, see the workshop's website here: https://sites.google.com/view/cogconcepts The goal of this workshop is to initiate an interdisciplinary conversation about reconceptualizing cognitive science disciplines. This workshop will bring together researchers proposing new conceptualizations in their disciplines, cognitive scientists investigating the mechanisms of concept learning and the role of concepts in human cognition, researchers building infrastructures to study and improve cognitive concepts, and philosophers analyzing scientific conceptualizations. The workshop will include activities which will prompt the audience to think about the conceptual foundations of their respective areas, and about ways to improve these foundations. These activities are designed to maximize audience participation and include panel discussions, as well as mind-matching sessions. One of the outcomes of the workshop is identifying the diversity of approaches for improving cognitive science concepts that could be relevant to both discipline-wide and more specific efforts.

Compositionality, or the ability to build complex representations from discrete elements, is an essential ingredient of human intelligence. Compositionality enables people to think productively, learn fast from limited experience, and generalize knowledge to new contexts without re-learning from scratch. It is also essential in information processing systems to efficiently represent structured data and has seen application in compression and symbolic Artificial Intelligence (AI). Historically, the notion of compositionality played a central role in linguistic theory and philosophy of mind. More recently, it is attracting a surge of interest throughout the domains of cognitive science. Compositional processes are leveraged for elucidating the nature of mental representations in cognition (Dehaene et al., 2022), understanding the functional organisation of the brain (Agrawal et al., 2019), or building Artificial Intelligence systems that are robust to changes in the environment (Hupkes et al., 2020).

In-context learning refers to the ability of a neural network to learn from information presented in its context. While traditional learning in neural networks requires adjusting network weights for every new task, in-context learning operates purely by updating internal activations without needing any updates to network weights. The emergence of this ability in large language models has led to a paradigm shift in machine learning and has forced researchers to reconceptualize how they think about learning in neural networks. Looking beyond language models, we can find in-context learning in many computational models relevant to cognitive science, including those that emerge from meta-learning.

The field of psychometrics has undergone substantial evolution over the past several decades, both in terms of advances in methodology and improved software and hardware for deploying new methods. Despite these strides, many of these developments have not been integrated into the broader field of psychology, as highlighted by Embretson (2005) and Borsboom (2006). Understanding and incorporating these psychometric advances is crucial to enable cognitive scientists to address growing concerns about validity and reliability, as well as to develop robust theoretical frameworks for understanding cognition.

In recent years, the field of computer graphics has achieved its longstanding dream of photorealism: modern graphics algorithms produce images that are indistinguishable from reality. Much like art at the advent of photography, then, computer graphics is now turning its gaze to the beholder: researchers are increasingly looking to cognitive science to engineer new modes of visual expression. Recent work has sought to apply insights from cognitive science to a variety of traditional graphics topics: from taking a perceptual approach to perspective, to studying the theory of mind behind animation, to applying theories of abstraction learning to build tools for geometry processing. At the same time, a wave of recent work in cognitive science has addressed fundamental questions about visual expression: for example, how humans understand and create sketches, shapes, and symbols. The field has also benefited greatly from tools and methods from computer graphics: differentiable rendering, physics simulation, and game engines have become indispensable in modeling human perception and intuitive physics. Recognizing this growing interdisciplinary exchange of ideas, we are proposing a workshop to begin building formal bridges between the cognitive science and computer graphics communities.

Cognitive science research has far-reaching implications, but many graduate students are trained solely for tenure-track faculty positions. Academic training develops a wide range of skills in behavioral research, literature reviewing, data analysis, scientific publishing, grant writing, teaching, and student mentorship. These skills have direct application in many other careers, but training within academia typically neglects to address how these skills translate to other work environments and career paths. As growth in the number of doctoral trainees continues to outpace permanent academic positions (Kolata, 2016; Larson et al., 2013; Lederman, 2016), more doctoral recipients have been seeking employment beyond faculty positions and academia (National Science Board, 2018). Those who are interested in exploring alternative career paths may not know where to turn for guidance. Our goal in this professional development workshop is to offer such guidance and an opportunity to network with scholars in similar situations.

We have a simple thesis: the relationship between academic and industry-based cognitive science is broken, but can be fixed. Over the last few decades, there has been a huge increase in the representation of cognitive science in industry. Beyond just machine learning, businesses are increasingly interested in human behavior and cognitive processes. Large proportions of our Ph.D. students, post-docs, and even faculty choose to go through a largely one-way door to corporate jobs in data science, behavioral experimentation, machine learning, user experience, and elsewhere. Currently, people who choose industry careers often lose their social and intellectual networks and their ability to return to tenure-track positions. Valuable insights from industry about memory, decision-making, learning, emotion, distributed cognition, and much more never return to the academic community. We believe that deep, theory driven, theory building work is being done in industry settings–and that the rift between communities makes all our work less effective

To communicate is to move. There is no way around that. If we pick up comprehensive handbooks or introductory texts in movement science (Hong and Bartlett (2008)) we see that there is very rich knowledge and tractable mathematical models about different aspects of movements. Yet, we find no chapter on communicative movements. While the field of speech motor control is a developed area on its own (Parrell and Lammert (2019)), there is no movement science of communication proper, which would include whole-body-, hand-gestural-, signed-, and inter-bodily actions.

In this Symposium we link different perspectives and traditions of research on language and concepts in the cognitive sciences to better understand the process of abstraction from the social-interactive standpoint. In contrast to the usual benefits of abstraction – such as the ability to categorize and generalize – we underscore the possibility of abstract concepts and words to remain underspecified, “open to the context, the suggestions of others” (Borghi, 2023) or to processes of real-time negotiations (Christiansen & Chater, 2022). This is enabled by their largely non-perceptual rela-tional character (Gentner, 2005) and by the fact that they arise in cognitive systems continually embedded and active in their environments (Cisek 1999; Mannella & Tummolini 2023).

Events are a fundamental part of human experience. Research on event cognition is rapidly developing and is revealing central aspects of how humans perceive, conceptualize, communicate about, and remember events. This symposium offers an interdisciplinary look at recent advances in the study of event cognition. The symposium brings together cognitive scientists from across continents, who are experts on the subject. The symposium contributors come from a variety of backgrounds and disciplines in developmental psychology, cognitive psychology, neuro-computational psychology, and linguistics. They combine a variety of innovative and integrative approaches and methodologies and study diverse populations across the lifespan and across languages. The overall goal of this symposium is to foster an interdisciplinary conversation on different aspects of event cognition.

Goals are a central pillar of everyday mental activity. From finding your way home to solving a puzzle and ordering food delivery, much of human action and cognition is goal-directed. Perhaps unsurprisingly, theories of goals are a central focus in the psychology of motivation (Elliott & Dweck, 1988), in social and personality psychology (Fishbach & Ferguson, 2007), as well as research aimed at understanding factors contributing to task achievement in educational and industrial settings (Ames & Ames, 1984; Locke & Latham, 2002). In this symposium, we highlight recent work emphasizing a goal-centric outlook on learning, exploration, and communication.

Reducing inequality, mitigating climate change, and responding to public health crises are large-scale goals that require the cooperation and coordination of many individuals. These goals cannot be achieved by one individual alone, and contributing is not always beneficial to each individual. And yet, individuals must contribute in order to make a difference. How do we hold individuals and groups responsible for collective action?

Predicting the actions and reactions of others is crucial to suc- cessful social interaction. When deciding whether to bluff in a game of poker, we consider the chances that the other players will fold or continue to play and unmask our bluff. When deciding whether to tell our boss that their plans are likely to have adverse effects, we consider a range of reac- tions, from being grateful for our honesty to being dismissed out of spite. Such predictions are highly uncertain and com- plex, not least because the other’s (re)actions usually result from them making equally complex and uncertain inferences about us. Nevertheless, we are often remarkably successful – although sometimes utterly wrong – in our social inferences. How do we explain these successes and failures?

Deep Learning is a neural network approach where a network of multiple layers is trained to process complex data. Applications for deep learning include recognizing complex patterns in pictures, text, and sounds to, in some cases, produce insights into how human process information. Deep learning has garnered widescale interest, fostered by advances in, for instance, natural language processing and the release of innovative tools like Chat GPT. But does deep learning have implications for theory in the cognitive sciences; that is, is deep learning the answer for understanding human cognitive dynamics?

Tracing the evolutionary milestones of our species has been the focus of much exciting research. Yet, we are still unable to ‘locate’ the divergence point of our cognitive evolution, which has made us so unique in the animal kingdom (Tomasello & Rakoczy, 2003). We have identified a gap in our understanding, and that is the absence of a systematic exploration into the symbiotic relationship between Homo sapiens and fungi. This highly interdisciplinary symposium aims to address this oversight, emphasizing the important – yet underappreciated – role of fungi in cognitive contexts and challenge traditional views of cognition.

Large language models (LLMs) like OpenAI’s GPT-4 (OpenAI et al., 2023), or Google’s PaLM (Chowdhery et al., 2022) generate text responses to user-generated text prompts. In contrast to work that evaluates the extent to which model-generated text coheres with linguistic rules (i.e., formal competence) (Chomsky et al., 2023; Piantadosi, 2023), the present symposium discusses the work of cognitive scientists aimed at assessing the extent and manner in which LLMs show effective understanding, reasoning and decision making, capacities associated with human higher cognition (i.e., functional competence) (Binz & Schulz, 2023; Mahowald et al., 2023; Webb et al., 2023). Given both their expertise and their interest in clarifying the nature of human thinking, cognitive scientists are in a unique position both to carefully evaluate LLMs’ capacity for thought (Bhatia, 2023; Han et al., 2024; Mitchell, 2023) and to benefit from them as methodological and theoretical tools. This symposium will thus be of interest not only to cognitive scientists concerned with machine intelligence, but also to those looking to incorporate advances in artificial intelligence with their study of human intelligence.

Humans engage in a wide variety of search behaviors during their lifetime. These search behaviors may be external such as foraging for food in the wild, or searching for mates, or internal, such as searching for concepts in memory. Decades of work on search processes among humans has suggested that these two types of search behaviors share common characteristics and physiological mechanisms. Despite this progress, understanding the dynamics of internal search is an ongoing challenge in the field.

The brain evolved as a sensory-motor machine that drives behavior while being linked to the world through sensors. Human cognition abstracts from these sensory-motor roots but retains intimate ties. The brain's structure reflects this history. How do neural processes at different distances from the sensory and motor surfaces integrate to achieve meaningful and grounded cognition? This is a challenge given the time-continuous and graded nature of sensory-motor processing, which enables continuous online updating. It is also a major challenge to understanding development and autonomous learning, in which the coupling across functional boundaries evolves under the influence of online activation patterns.

In this session, representatives from IES, NSF, UNESCO, and ERC will discuss intellectual, sociological, and practical issues that arise in doing research in Cognitive Science, Education, and at their intersection. Some of these issues are due generally to the multidisciplinary nature of the work, but others are specific to education. The mobilization of research knowledge and human capital for translation to practice and policy remains a significant challenge that each of these agencies seeks to address. The speakers will highlight relevant initiatives and point to similarities and differences in the grant review processes between programs, providing tips for successful grant writing along the way. They will discuss where their programs are placed in relation to one another in the funding landscape and along the continuum from the most basic to the most applied research – and the extent that such a distinction is meaningful. They will also contrast their funding emphases and the implications those emphases have for the kinds of projects that can be engaged.

Time words like ‘yesterday’ and ‘tomorrow’ are abstract, and are interpreted relative to the context in which they are produced: the word ‘tomorrow’ refers to a different point in time now than in 24 hours. We tested 112 3- to 5-year-old Hindi-speaking children on their knowledge of ‘yesterday’ and ‘tomorrow’, which are represented by the same word in Hindi-Urdu: ‘kal’. We found that Hindi learners performed better than English learners when tested on actual past and future events, but that performance for hypothetical events was poor for both groups. Compatible with a “syntactic bootstrapping” account, we conclude that syntactic tense information – which is necessary for differentiating ‘yesterday’ from ‘tomorrow’ in Hindi – may play a stronger role in learning these words than mapping of specific words to particular past and future events (“event mapping”).

We investigated the impact of teachers’ multimodal cues on L2 word learning in naturalistic teaching. 169 university students randomly watched 12 of 54 clips of English vocabulary instructions and took subsequent word recognition and learning tests. The learning outcomes were analysed as a function of teachers’ prosodic, linguistic and gestural input during the instruction of each vocabulary while controlling for students’ characteristics and varying teachers’ influences. Results showed that a shorter mean length of utterances, fewer L2 English words, and more questions for students and “phrase” teaching predicted better learning outcomes. Furthermore, students learning improved with teachers’ slower speaking rate but fewer pauses and more iconic gestures. These results were robust even after controlling for other significant factors such as students’ English proficiency, working memory, degree of liking of teachers and different teachers. Overall, multimodal cues enhance L2 vocabulary learning, with implications for educators, linguists, and cognitive scientists.

Reduced cognitive function in major depression (MDD) is often interpreted as a reduced ability to exert cognitive control. Here we used the Effort Foraging Task to test the hypothesis that reduced cognitive function may be due, in part, to decreased willingness to exert control in MDD because of increased cognitive effort "costs". Contrary to our predictions, neither cognitive nor physical effort costs differed with MDD diagnosis (N MDD=52, N Comparisons=27). However, we found distinct patterns of symptom relationships for cognitive and physical effort costs. In MDD, greater anxiety symptoms were selectively associated with lower cognitive, but not physical effort cost (i.e. greater willingness to exert cognitive effort), whereas greater anhedonia and behavioral apathy symptoms were selectively associated with increased physical (but not cognitive) effort costs. These findings support the measurement of both cognitive and physical effort as decision-making function markers that may inform heterogeneity of MDD.

Analogical studies demonstrate that participants often fail to retrieve a well-learned base analog during the subsequent processing of a semantically-distant target analog. We evaluated whether presenting the target analog before the base analog increases analogical retrieval during hypothesis-generation. Experiment 1 revealed a higher rate of analogical retrieval when the target analog preceded the base analog, as compared to the traditional “base-target” sequence. Using a factorial design, Experiment 2 assessed whether spontaneously acknowledging the relevance of a subsequently encountered explanation for resuming a failed explanatory attempt requires the presence of structural similarities between the base and target situations. Results demonstrated that the primary contributor to spontaneous reactivation of a failed explanatory attempt is the presentation of an analogous phenomenon, while the presence of a useful explanation alone did not yield a significant impact. These findings contribute valuable insights to the dynamics of analogical retrieval and offer relevant implications for educational strategies.

Emotions, including stress and anxiety, strongly influence cognition and learning experiences. This study investigates the impacts of stress on cognitive load during learning, considering baseline anxiety levels and fluctuating stress. With a focus on technology-based learning, a web-based HTML introduction module was used. Using a social stress test, 15 participants underwent a stressful situation during learning, while the control group of 15 were in a neutral condition. Results indicate significantly elevated stress levels in the experimental group throughout the experiment, with a corresponding decrease in learning performance. For high perceived difficulty, the stressed condition demonstrated a significant increase in response time compared to the control condition. In contrast, when experiencing low perceived difficulty, a significant difference in response time across conditions was not found. Findings emphasise the importance of managing stress in educational contexts to optimise learning outcomes in the evolving landscape of technology-based learning.

Here we used deep neural network + hidden Markov model (DNN+HMM) to provide a computational account for the relationship among holistic processing (HP), face scanning pattern and face recognition performance. The model accounted for the positive associations between HP and eyes-focused face scanning pattern/face recognition performance observed in the literature regardless of the version of the composite task used to measure HP. Interestingly, we observed a quadratic relationship between HP and face scanning pattern, where models being highly eyes-focused or highly nose-focused had lower HP. By inspecting fixation locations and associated attention window size in the model and XAI methods, we found that the eyes- and nose-focused models both developed local and holistic internal representations during training, and their difference was in the temporal dynamics of how these representations were used. Our findings demonstrated how computational modeling could unravel the mechanisms underlying cognition not readily observable in human data.

Electroencephalogram (EEG) signals are physiological indicators of brain activity, offering the advantage of high temporal resolution for capturing subtle emotional changes and providing rich information for emotion recognition. However, extracting effective features from EEG data with a low signal-to-noise ratio poses a significant challenge that hinders progress in this research field. To address this issue, we propose a multi-view time-frequency contrastive learning framework called MV-TFCL to enhance the information representation capability of EEG signals from multiple perspectives. Firstly, we introduce a recursive neural network based on multi-scale time-frequency consistency, which integrates global semantic information across different scales through gated units. To our knowledge, this is the first proposal of the theory of multi-scale time-frequency consistency applied in emotion recognition research. Subsequently, we design a tree-structured time-frequency encoder to capture local semantic information within the time-frequency domain. Finally, we incorporate semantic consistency constraints from both global and local perspectives to learn more generalizable and robust features. Extensive experimental results on two publicly available datasets demonstrate the effectiveness and superiority of our proposed method.

The framework of event perception suggests that people segment continuous perceptual input into discrete events by forming mental representations of ongoing activity. Prior work extending the segmentation framework to emotion perception shows that a richer emotion vocabulary is associated with segmentation of emotion events in greater agreement with the cultural ingroup. However, little is known about how labeling behaviors themselves shape the segmentation of emotion events. Here, we look at the effect of labeling on emotion segmentation. Participants were randomly assigned to simply segment videos into discrete emotion events or to segment only when an emotion label is available and to label the segmented event. We found that compared to the group that segmented without providing labels, the group that segmented with explicit labeling behaviors were less sensitive at discriminating emotion events from non-emotion events and more conservative to identify an emotion event. The results are discussed with respect to competing theoretical accounts of the impact of labeling on emotion perception and suggest that the conceptual broadening account (where labels invoke idiographic emotion representations) may best account for the findings.

Inspired by inductive reasoning models, we test whether generalized shared reality (i.e., the sense of being on the same page) arises through probabilistic inference about latent commonalities. Using a naturalistic text-based chat paradigm, we manipulated whether conversation partners discussed a belief they shared, a belief on which their opinions differed, or a random prompt. Participants discussing shared opinions reported experiencing greater shared reality compared to those discussing differences or random topics. Moreover, participants who made broader inferences about additional beliefs they might share with their partners also reported greater shared reality. While discussing shared opinions can induce an overall greater sense of shared reality, participants discussing differences leveraged their conversation to establish shared realities about other topics. We demonstrate that shared reality can emerge in multiple ways during initial interactions, establishing a foundation for future mechanistic investigations within an inductive inference framework.

Since their inception, drift diffusion models have been applied across a wide range of disciplines within psychology to uncover the mental processes that underlie perception, attention, and cognitive control. Our studies contribute to ongoing efforts to extend these models to abstract, social reasoning processes like moral or legal judgment. We presented participants with a set of social rules, while manipulating whether various behaviors violated the rule’s letter and/or its purpose–––two independent standards by which to decide what constitutes a transgression. In this framework, cases that violate or comply with both a rule’s text and its purpose can be seen as congruent or ‘easy’ cases, and cases that elicit opposing verdicts as incongruent or ‘hard’ cases–––in a manner analogous to widely-studied conflict tasks in cognitive psychology. We recorded 34,573 decisions made by 364 participants under soft time pressure, and investigated whether hierarchical drift diffusion modeling could explain various behavioral patterns in our data. This approach yielded three key insights: (1) judgments of conviction were faster than judgments of acquittal owing to an overall bias (z parameter) toward conviction; (2) incongruent cases produced longer reaction times than congruent cases (an interference effect), due to differences in the rate of evidence accumulation (v parameter) across case-types; and (3) increases in the ratio of congruent-to-incongruent cases amplified the interference effect on reaction times, by fostering greater response caution—revealed by a larger threshold (or a parameter). Thus, our studies document dissociable effects of the drift diffusion components on rule-based decision-making, and illustrate how the cognitive processes that subserve abstract and social decision-making tasks, such as the enforcement of communal and legal rules, may be illuminated through the drift diffusion framework.

Neuroimaging, especially through Functional Magnetic Resonance Imaging (fMRI), plays a pivotal role in understanding brain activity by leveraging blood-oxygen level dependent (BOLD) signals to estimate neural activities across the brain. The interpretation of these signals through functional connectivity (FC) matrices facilitates the application of Graph Neural Networks (GNN) for analyzing brain network structures, offering insights into both normal and abnormal brain functions. Despite the potential of centralized learning methods in this domain, challenges related to data privacy and the feasibility of sharing sensitive medical datasets across institutions limit their application. This study introduces the Federated Graph Learning Framework for Brain Connectome (FGLBC), addressing these concerns. This novel approach enables the collaborative training of GNN models across multiple entities, such as hospitals, without compromising data privacy. The FGLBC framework implements a privacy-preserving local GNN training (PPGT) algorithm that incorporates Differential Privacy (DP) to safeguard sensitive information during model training. Furthermore, we introduce a unique similarity-weighted aggregation (SWA) algorithm that enhances the aggregation process, thereby boosting the global model's utility and performance. Our comprehensive evaluation across benchmark datasets demonstrates that the FGLBC not only preserves user privacy but also achieves or surpasses the performance of existing methods.

Outward differences between cultures are very salient, with Western and East Asian cultures as a prominent comparison pair. A large literature describes cross-cultural variation in cognition, but relatively less research has explored the developmental origins of this variation. This study helps to fill the empirical gap by replicating four prominent findings documenting cross-cultural differences in children’s reasoning, visual attention, and social cognition in a cross-sectional sample of 240 3-12-year-olds from the US and China. We observe cross-cultural differences in three of the four tasks and describe the distinct developmental trajectory that each task follows throughout early and middle childhood.

Cognitive processes underlie economic relations. In this paper, we develop a conceptual, mathematical, and computational framework for modeling market exchange as a series of dynamically interacting cognitive processes. Specifically, we show how advertisers can build trust and gain confidence in their pricing power to the point that they erode trust and undermine the efficacy of their advertising. Customers conversely orient towards advertisers seeking information or turn away from them as unreliable communicators. These behaviors and the patterns they generate occur inside a state space of unallocated perceived value. They constitute a small subset of the full range of possible strategic and adaptive responses that define cognitive microeconomics.

Humans extract useful abstractions of the world from noisy sensory data. Serial reproduction allows us to study how people construe the world through a paradigm similar to the game of telephone, where one person observes a stimulus and reproduces it for the next to form a chain of reproductions. Past serial reproduction experiments typically employ a single sensory modality, but humans often communicate abstractions of the world to each other through language. To investigate the effect language on the formation of abstractions, we implement a novel multimodal serial reproduction framework by asking people who receive a visual stimulus to reproduce it in a linguistic format, and vice versa. We ran unimodal and multimodal chains with both humans and GPT-4 and find that adding language as a modality has a larger effect on human reproductions than GPT-4's. This suggests human visual and linguistic representations are more dissociable than those of GPT-4.

Aphantasia, namely the inability to voluntarily form visual mental imagery, does not, counterintuitively, impair the affected from successfully performing mental imagery tasks. One way of explaining this finding is to posit that aphantasics, despite their claim to the contrary, can form visual imagery, a position here referred to as aphantasia skepticism. This article outlines and rejects two types of aphantasia skepticism and argues that the position results from what is coined the visualizer’s fallacy, namely the false belief that visual mental imagery is necessary to carry out mental imagery tasks. Furthermore, it is argued that the visualizer’s fallacy and the resulting aphantasia skepticism are not only potentially harmful to aphantasics but may also lead to an impoverished view of the dynamics of cognition in general.

Spontaneous recovery of fear after extinction is a well-established behavioral phenomenon. Different theories in psychology account for spontaneous recovery by proposing that it may result from temporal weighting, reduced processing of stimuli over time, enhanced salience of adverse events or return of the acquisition context. We propose a novel mechanism of spontaneous recovery: selective maintenance of adverse events, and ground this mechanism in a computational model of latent cause inference. To investigate the proposed mechanism, we collected behavioral data with an aversive conditioning and extinction task (N=280) and fit the data with computational models formalizing our and others’ theories. Quantitative and qualitative model comparisons indicated that selective maintenance of adverse events accounts for spontaneous recovery better than alternative theories. As spontaneous recovery of fear after extinction can serve as a model of relapse after exposure therapy, we use this mechanistic understanding of spontaneous recovery to propose and simulate the effect of add-on interventions to prevent relapse after exposure therapy.

Pointing is an intuitive and commonplace communication modality. In human-robot collaborative tasks, human pointing has been modeled using a variety of approaches, such as the forearm vector or the vector from eye to hand. However, models of the human pointing vector have not been uniformly or comprehensively evaluated. We performed a user study to compare five different representations of the pointing vector and their accuracies in identifying the human's intended target in an object selection task. We also compare the vectors' performances to that of domestic dogs to assess a non-human baseline known to be successful at following human points. Additionally, we developed an observation model to transform the vector into a probability map for object search. We implemented our system on our robot, enabling it to locate and fetch the user's desired objects efficiently and accurately.

Counterfactual reasoning is at the centre of human daily life and plays a key role in shaping our moral and social judgments. Its effect on moral judgment in adulthood, such as justifying immoral behavior (e.g., “If you had not left your phone on the table, it would not have been stolen.”), has been studied for years. However, we still know very little about when counterfactual reasoning starts to affect humans' moral judgments. To test this, we examined the effect of better and worse counterfactual alternatives on 5-6-year-olds’ (N = 91) moral and happiness judgments. We found that children judged social exclusion (e.g., a new kid has to play alone while other children play together) as less morally acceptable after imagining how it could have been better (e.g., the new kid and other children at the school could have played all together), but, contrary to past work with adults, they did not justify it after imagining how it could have been even worse (e.g., the other children could have broken the new kid’s toy). However, children’s happiness judgments showed the opposite effect: they reported feeling happier about reality after imagining a worse counterfactual alternative compared to children who only thought about what actually happened. Keywords: counterfactuals; moral judgment; children; happiness judgment

We examine the relation between event segmentation in language and cognition in the domain of motion events, focusing on Turkish, a verb-framed language that segments motion paths in separate linguistic units (verb clauses). We compare motion events that have a path change to those that did not have a path change. In the linguistic task, participants were more likely to use multiple verb phrases when describing events that had a path change compared to those that did not have a path change. In the non-linguistic Dwell Time task, participants viewed self-paced slideshows of still images sampled from the motion event videos in the linguistic task. Dwell times for slides corresponding to path changes were not significantly longer than those for temporally similar slides in the events without a path change. These findings suggest that event units in language may not have strong and stable influences on event segmentation in cognition.

Pragmatic theories assume that during communicative exchanges humans strive to be optimally informative and spontaneously adjust their communicative signals to satisfy their addressee’s epistemic needs. To investigate this ability in infants, we designed a task in which 18-month-olds had to point at the target object they wanted to receive. In Experiment 1, we found that when the target was placed behind a distractor object, infants appropriately modified their pointing to avoid mistakenly indicating the distractor to their partner. When the objects were covered, and their communicative partner had no information (Experiment 2) or incorrect information (Experiment 3) about the target’s location – as opposed to being knowledgeable about it – infants pointed at the target more often and employed modified pointing more frequently when it was necessary. This demonstrates that 18-month-olds can take into account their communicative partner’s epistemic states and provide her with relevant information through optimally informative deictic gestures.

Comparison of visibility inferences across congenitally blind and sighted people provides insight into the contribution of first-person sensory experience to intuitive theories. We hypothesized that both groups understand others’ visual experiences via an intuitive theory incorporating variables known to influence visual psychophysics (distance, looking duration, and feature size). Adults born blind (n=20) and sighted (n=40) listened to short scenarios that described an observer looking at another person from different distances and for varying durations. Participants rated how likely the observer would perceive appearance features of the person that varied in size (e.g., eye color vs. hat). A probabilistic formalization of intuitive visibility fit the ratings with high accuracy across scenarios and features. Model parameters were qualitatively identical across groups but blind adults weighted distance and size less. A quantitative and generative intuitive theory of vision develops without first-person sensory access, possibly through linguistic communication, and is fine-tuned by visual experience.

This study delves into how various musical factors influence the experience of auditory illusions, building on Diana Deutsch's scale illusion experiments and subsequent studies. Exploring the interaction between scale mode and timbre, this study assesses their influence on auditory misperceptions, while also considering the impact of an individual's musical training and ability to discern absolute pitch. Participants were divided into non-musicians, musicians with absolute pitch, and musicians with relative pitch, and were exposed to stimuli modified across three scale modes (tonal, dissonant, atonal) and two timbres (same, different). The findings suggest that scale illusions occur less frequently with different timbres and vary with scale mode. Crucially, the absolute pitch ability appears to have a more significant impact on the perception of illusions than the duration of musical training. This research contributes to understanding the complex interplay between various factors in auditory perception and the mechanisms behind the experience of auditory illusions.

Previous research has shown that our subjective sense of certainty doesn't always accurately reflect the strength of the evidence that has been presented to us. We investigate several key factors that drive children's certainty using a Boolean concept learning task. We created an idealized learning model to predict children's accuracy and certainty during the experiment, given past evidence that they have seen in the task, and we compared its predictions with our behavioral results. Our results suggest that while predictors from the idealized learning model capture children's accuracy, behavioral predictors generated by the behavioral data can better predict children's certainty. We also show that younger children's certainty can be explained by the idealized learning model, while older children's certainty is primarily predicted by how well they observed themselves doing in the experiment.

This paper introduces a novel perspective to significantly mitigate catastrophic forgetting in continuous learning (CL), which emphasizes models' capacity to preserve existing knowledge and assimilate new information. Current replay-based methods treat every task and data sample equally and thus can not fully exploit the potential of the replay buffer. In response, we propose COgnitive REplay (CORE), which draws inspiration from human cognitive review processes. CORE includes two key strategies: Adaptive Quantity Allocation and Quality-Focused Data Selection. The former adaptively modulates the replay buffer allocation for each task based on its forgetting rate, while the latter guarantees the inclusion of representative data that best encapsulates the characteristics of each task within the buffer. Our approach achieves an average accuracy of 37.95\% on split-CIFAR10, surpassing the best baseline method by 6.52\%. Additionally, it significantly enhances the accuracy of the poorest-performing task by 6.30\% compared to the top baseline. Code is available at https://github.com/sterzhang/CORE.

Functional relations are prevalent in everyday life and science. Do children have intuitive knowledge of functional relations, and can they learn these relations by active information gathering (i.e., choosing a few input values and observing the corresponding outputs)? We found that 6- to 9-year-olds can learn different families of functions (linear, Gaussian, and exponential) through both informative data provided by an experimenter and data they gather from the environment for themselves. Overall, children learn linear functions more accurately than non-linear functions. When choosing data points to learn about, some children select highly similar points that only shed light on a narrow region of a function, while others choose more variable inputs and gain a more holistic view of a function. Children who use this latter, globally informative strategy have higher learning accuracy, particularly for non-linear functions. Results suggest that children are in the process of developing effective strategies for active function learning.

As a foundation for social interaction, interpersonal coordination is facilitated by positive social qualities (e.g., cooperation), but undermined in negative contexts (e.g., conflict). Exactly how social factors shape coordination is less clear. Previous literature notes that the way people attend to others impacts how interactions unfold. It is possible therefore, that patterns of social attention also govern coordination. We examined this proposition by using virtual reality to investigate how attentional focus (self vs. other) and partner gaze (direct vs. averted) influence the spontaneous emergence of coordination. The results indicated that: (i) coordination was enhanced in the other (cf. self) focus condition; (ii) coordination was diminished in the averted (cf. direct) gaze condition. These findings suggest that changes in social attention impact interpersonal coordination. More broadly, this work provides further evidence that the emergence of interpersonal coordination fluctuates as a function of social context.

Adults are known to have superior working memory to children, but whether this improvement is driven primarily by differences in storage capacity or attentional control is debated. In particular, the understanding of how capacity and control influence the development of working memory is hampered by the fact that most theorizing about the effect of variation in either on behavior has been verbal. To address this, we extended a computational model of working memory to clearly separate the contributions of capacity and control, fitting the model to a recent developmental study. We find that the combined influence of capacity and control on working memory may be more complicated than previously appreciated. In particular, the general pattern of qualitative differences between children and adults could be produced by increasing either capacity or control alone. These results point to a need for additional experimental paradigms to clearly parse the differential impact of working memory components.

This review explores the complex relationship between Fear of Falling (FoF) and Fear of Heights (FoH), and their impact on human postural control. FoF encompasses a spectrum of psychological and physiological responses that dynamically influence postural control, while FoH involves perceptual distortions and heightened physiological arousal in response to elevated environments. Through a comprehensive literature review, we examine the research methods and findings of studies on FoF and FoH. We further propose that Optimal Feedback Control (OFC) theory is a suitable framework to understand the computational aspects of how these fears modify postural control. We aim to provide a nuanced understanding of FoF and FoH, not only as psychological phenomena but as complex, dynamic interactions of cognitive, physiological, and motor processes influencing an individual’s interaction with their environment.

As AI systems like language models are increasingly integrated into decision-making processes affecting people's lives, it's critical to ensure that these systems have sound moral reasoning. To test whether they do, we need to develop systematic evaluations. We provide a framework that uses a language model to translate causal graphs that capture key aspects of moral dilemmas into prompt templates. With this framework, we procedurally generated a large and diverse set of moral dilemmas---the OffTheRails benchmark---consisting of 50 scenarios and 400 unique test items. We collected moral permissibility and intention judgments from human participants for a subset of our items and compared these judgments to those from two language models (GPT-4 and Claude-2) across eight conditions. We find that moral dilemmas in which the harm is a necessary means (as compared to a side effect) resulted in lower permissibility and higher intention ratings for both participants and language models. The same pattern was observed for evitable versus inevitable harmful outcomes. However, there was no clear effect of whether the harm resulted from an agent's action versus from having omitted to act. We discuss limitations of our prompt generation pipeline and opportunities for improving scenarios to increase the strength of experimental effects.

Children rapidly learn to use language to effect a variety of communicative acts, such as proposing actions, asking questions, and making promises. While prior work has characterized this development in cross-sectional corpora, these analyses have been unable to comprehensively track individual differences in children's acquisition of communicative acts. We analyzed a longitudinal corpus of parent-child interactions from ages 14 to 58 months. We find that children's repertoires of communicative acts diversify over this period, with stable individual differences in the diversity of children's communicative act repertoires. Further, the diversities of parents' and children's communicative act repertoires are correlated. Children with more diverse communicative act repertoires also have larger vocabularies and use more diverse syntactic frames, suggesting links between discourse development and lexical and syntactic knowledge. Taken together, this work provides new insight into individual trajectories of communicative development and connections between communicative act use and other levels of language structure.

Spatial cognition is fundamental to human behavior, but people differ in how they remember spatial relations, variably using body-based (egocentric) and environment-based (allocentric) spatial reference frames. Despite decades of study, the causes of this variation and flexibility in spatial memory remain unclear. Here we show that people spontaneously use different reference frames on different spatial axes at the same time. When remembering the placement of a target object in a 2-dimensional array, Indigenous Tsimane' adults preferentially used allocentric space to determine lateral placement and egocentric space to determine sagittal placement in the same action. This effect of axis was also significant among US university students, whose overall preference for egocentric space was stronger on the sagittal than lateral axis. These findings support a novel account of spatial cognitive diversity and suggest that people across cultures habitually integrate egocentric and allocentric spatial reference frames into the same action.

Temporal language grounding (TLG) aims to localize query-related events in videos, which explores how to cognize relationships of video content with language descriptions. According to selective visual attention mechanism in cognitive science, people’s cognition and understanding of what happens often rely on dynamic foreground information in the video. Nonetheless, background usually predominates the scenes so that query-related visual features and irrelevant ones are confused. Thus, we propose a Foreground Enhanced Network (FEN) to diminish the background effect from two aspects. FEN at first in spatial dimension explicitly models the evolving foreground in video features by removing relatively unchanged background content. Besides, we propose a progressive contrastive sample generation module to gradually learn the differences between the predicted proposal and its elongated proposals that include the former as a portion, thereby distinguishing similar neighborhood frames. Experiments on two common-used datasets show the efficacy of our model.

Human learning does not stop at solving a single problem. Instead, we seek new challenges, define new goals, and come up with new ideas. What drives people to disrupt the existing conceptual landscape and create new things? Here, we examine the decision to create new things under different levels of expected returns. We formalize innovation as stochastically recombining existing ideas, where successful and more complex combinations generate higher returns. This formalization allows us to cast innovation-seeking as a Markov decision process, and derive optimal policies under different settings. Data collected through an online behavioral experiment confirm our prediction that people should invest more time and effort in seeking innovations when they know the chances of success are high and the potential new ideas would be rewarding. However, people also deviate from being optimal, both innovating more and less than they should in different settings.

Computational psycholinguistics has traditionally employed a complexity metric called Node Count, which counts the number of syntactic nodes representing syntactic structures and predicts processing costs in human sentence processing. However, Node Count does not dissociate distinct syntactic operations deriving those syntactic structures, so that how much processing cost each syntactic operation induces remains to be investigated. In this paper, we introduce a novel complexity metric dubbed Composition Count, which counts the number of syntactic operations deriving syntactic structures, allowing us to understand the computational system of human sentence processing from the derivational, not representational, perspective. Specifically, employing Combinatory Categorial Grammar (CCG) which is equipped with multiple syntactic operations and thus suitable for the purpose here, we investigate (i) how much distinct syntactic operations of CCG contribute to predicting human reading times, and (ii) whether the same holds across languages. The results demonstrate that distinct syntactic operations of CCG have independent and cross-linguistic contributions to predicting human reading times, while Node Count turns out not to be robust cross-linguistically. In conclusion, these results strongly suggest the importance of Composition Count to dissociate distinct syntactic operations, not whole syntactic representations, and understand the computational system of human sentence processing.

Whereas cognitive models of learning often assume direct experience with both the features of an event and with a true label or outcome, much of everyday learning arises from hearing the opinions of others, without direct access to either the experience or the ground truth outcome. We consider how people can learn which opinions to trust in such scenarios by extending the hedge algorithm: a classic solution for learning from diverse information sources. We first introduce a semi-supervised variant we call the delusional hedge capable of learning from both supervised and unsupervised experiences. In two experiments, we examine the alignment between human judgments and predictions from the standard hedge, the delusional hedge, and a heuristic baseline model. Results indicate that humans effectively incorporate both labeled and unlabeled information in a manner consistent with the delusional hedge algorithm---suggesting that human learners not only gauge the accuracy of information sources but also their consistency with other reliable sources. The findings advance our understanding of human learning from diverse opinions, with implications for the development of algorithms that better capture how people learn to weigh conflicting information sources.

Understanding spoken words requires listeners to integrate large amounts of linguistic information over time. There has been considerable debate about how semantic context preceding or following a target word affects its recognition, with preceding semantic context often viewed as a constraint on possible future words, and following semantic context as a mechanism for disambiguating previous ambiguous input. Surprisingly, no studies have directly compared whether the timing of semantic context influences spoken word recognition. The current study manipulates the acoustic-perceptual features of a target word, a semantic cue elsewhere in the sentence biasing toward one interpretation, and the location of the semantic context. We find that the two cues are additively integrated in participants' word identification responses, and that semantic context affects categorization the same regardless of where it appears relative to the target word. This suggests that listeners can optimally integrate acoustic-perceptual and semantic information across time.

A wealth of evidence indicates that children use their developing linguistic knowledge to incrementally interpret speech and predict upcoming reference to objects. For verbs, determiners, case-markers, and adjectives, hearing linguistic information that sufficiently constrains referent choice leads to anticipatory eye-movements. There is, however, limited evidence about whether children also use spatial prepositions predictively. This is surprising and theoretically important: spatial prepositions provide abstract semantic information that must interface with spatial properties of, and relations between, objects in the world. Making this connection may develop late because of the complex mapping required. In a visual-world eye-tracking task, we find that adults and 4-year-olds hearing 'inside' (but not 'near') look predictively to objects that afford the property of containment. We conclude that children make predictions about the geometric properties of objects from spatial terms that specify these properties, suggesting real-time use of language to guide analysis of objects in the visual world.

Prior studies of Theory of Mind have primarily asked observers to predict others’ actions given their beliefs and desires, or to infer agents’ beliefs and desires given observed actions. However, if Theory of Mind is genuinely a causal theory, people should also be able to plan interventions on others’ mental states to change their behavior. The intuitive causal model of Theory of Mind predicts an asymmetry: one has to instill both the relevant belief and desire to cause an agent to act; however, to prevent a likely action, it suffices to remove either the relevant belief or desire. Here, we use these asymmetric causal interventions to probe the structure of Theory of Mind. In Experiments 1 and 2, both adults (N=80) and older children (N=42, 8-10 years) distinguished generative and preventative cases: selecting interventions on both mental states (both belief and desire) to induce an agent to act and just one of the mental states (either belief or desire) to prevent an action. However, younger children (N =42, 5-7 years) did not. To probe this age difference, in Experiment 3, we asked younger children(N=42, 5-7 years) just to predict the outcome of others’ mental state interventions. Children predicted that interventions were more likely to prevent actions than to cause them, but failed to predict that intervening on both the relevant beliefs and desires is more likely to generate a novel action than intervening on either alone. These findings suggest that by eight to ten years old, people represent the causal structure of Theory of Mind and can selectively intervene on beliefs and desires to induce and prevent others’ actions.

To fully understand human memory, it is necessary to understand its lifespan development. However, memory assessments often rely on significantly different methodologies for different age groups, and their results are typically not directly comparable. In this paper, we present a quantitative assessment of memory function spanning an age range of five to 85 years that is based on a model-based memory assessment. This approach yields a uniform metric that is directly interpretable and can be compared across different tasks and materials that are appropriate for different age groups. The results show a robust U-shape function, with long-term memory function at age 5 being comparable to that of cognitively impaired elderly individuals. These results and the method utilized could provide a new foundation for future studies on memory development across life stages.

Daily experiences are conceptualized as events involving multiple participants and their relations (i.e., thematic roles). When describing events, speakers often do not include all event participants involved. Here, we explore how underlying conceptual requirements and language-specific encoding options influence the content of event descriptions in speech and gesture in two typologically different languages (English, Turkish). Focusing on conceptually peripheral instruments whose status is highly debated, we manipulated the conceptual status of event participants by including events that ‘require’ or ‘allow’ otherwise syntactically optional instruments. Results showed that the require-allow distinction did not manifest uniformly in Turkish and English in speech, gesture, or when both modalities were considered. However, mention of highly optional event participants (e.g., allowed instruments) was affected by language-specific syntactic encoding options. We conclude that, under more naturalistic elicitation conditions, planning descriptions of instrument events is more heavily affected by language-specific encoding than conceptual prominence of the roles.

Exploration in sequential decision problems is a computationally challenging problem. Yet, animals exhibit effective exploration strategies, discovering shortcuts and efficient routes toward rewarding sites. Characterizing this efficiency in animal exploration is an important goal in many areas of research, from ecology to psychology and neuroscience to machine learning. In this study, we aim to understand the exploration behavior of animals freely navigating a complex maze with many decision points. We propose an algorithm based on a few simple principles of animal movement from foraging studies in ecology and formalized using reinforcement learning. Our approach not only captures the search efficiency and turning biases of real animals but also uncovers longer spatial and temporal dependencies in the decisions of animals during their exploration of the maze. Through this work, we aspire to unveil a novel approach in cognitive science of drawing interdisciplinary inspiration to advancing the field's understanding of complex decision-making.

Whereas principles of communicative efficiency and legal doctrine dictate that laws be comprehensible to the common world, empirical evidence suggests legal documents are largely incomprehensible to lawyers and laypeople alike. Here, a corpus analysis (n=59 million words) first replicated and extended prior work revealing laws to contain strikingly higher rates of complex syntactic structures relative to six baseline genres of English. Next, two pre-registered text generation experiments (n=280) tested two leading hypotheses regarding how these complex structures enter into legal documents in the first place. In line with the \textit{magic spell hypothesis}, we found people tasked with writing official laws wrote in a more convoluted manner than when tasked with writing unofficial legal texts of equivalent conceptual complexity. Contrary to the \textit{copy-and-edit hypothesis}, we did not find evidence that people editing a legal document wrote in a more convoluted manner than when writing the same document from scratch. From a cognitive perspective, these results suggest law to be a rare exception to the general tendency in human language towards communicative efficiency. In particular, these findings indicate law's complexity to be derived from its performativity, whereby low-frequency structures may be inserted to signal law’s authoritative, world-state-altering nature, at the cost of increased processing demands on readers. From a law and policy perspective, these results suggest that the tension between the ubiquity and impenetrability of the law is not an inherent one, and that laws can be simplified without a loss or distortion of communicative content.

Children learn powerful internal models of the world around them from a few years of egocentric visual experience. Can such internal models be learned from a child's visual experience with highly generic learning algorithms or do they require strong inductive biases? Recent advances in collecting large-scale, longitudinal, developmentally realistic video datasets and generic self-supervised learning (SSL) algorithms are allowing us to begin to tackle this nature vs. nurture question. However, existing work typically focuses on image-based SSL algorithms and visual capabilities that can be learned from static images (e.g. object recognition), thus ignoring temporal aspects of the world. To close this gap, here we train self-supervised video models on longitudinal, egocentric headcam recordings collected from a child over a two year period in their early development (6-31 months). The resulting models are highly effective at facilitating the learning of action concepts from a small number of labeled examples; they have favorable data size scaling properties; and they display emergent video interpolation capabilities. Video models also learn more robust object representations than image-based models trained with the exact same data. These results suggest that important temporal aspects of a child's internal model of the world may be learnable from their visual experience using highly generic learning algorithms and without strong inductive biases.

Philosophers have long been arguing the precise semantics of different deontic terms within normative statements. However, little research has been done on the human reasoning side of understanding such terms. In this paper, we propose a normative scheme with bitstring semantics that is expressive enough to cover the basic normative concepts in most mainstream schemes proposed in deontic logic research. Even though further confirmation is needed, our explorative experiments on human deontic reasoning have shown results that are consistent with our proposed scheme.

In this paper, we explored the attribution of causal responsibility in a causal chain of events, where an agent A instructs an intermediate agent B to execute some harmful action which leads to a bad outcome. In Study 1, participants judged B to be more causally responsible, more blameworthy, and more deserving of punishment than A. In Study 2, we explored the effect of proximity on judgments of the two agents by adding a third, subsequent contributing cause, such that B’s action no longer directly caused the final outcome. Participants judged both agents A and B to be less causally responsible and deserving of punishment (but not less blameworthy) when they were less proximal to the outcome, and there were no differences in judgments between the two agents. In Study 3, we varied whether each of the two agents (A and B) intended for the final outcome to occur. We find an interaction between role and intent, where participants only mitigated judgments for A when A did not intend for the outcome to occur – regardless of B’s intent. We discuss possible explanations for our findings and its implications for moral and legal decision-making.

Spatial demonstratives are deictic expressions used to point to a referent with language. In the standard view, they encode a spatial proximal\distal contrast between “near” (this) and “far” (that) from the speaker. Several studies have shown that such contrast maps on a perceptual contrast between peripersonal and extrapersonal space. Still, other factors beyond spatial distance influence demonstrative choice. Here we investigate whether the proximal/distal contrast maps also onto a more general contrast between being in physical control/not in control of a target referent. Participants were presented with two circles (red and blue) on a screen. They had to move them with the mouse to find the target circle (the one with two gaps). One circle followed the mouse trajectory (controllable), while the other moved randomly in the center of the screen (not controllable). Unknown to the participants, the gaps only appeared if the stimuli crossed a distance threshold. Importantly, participants had to use stimulus controllability to solve the task. They were instructed to answer by indicating the target to the experimenter using this/that and red/blue (in Italian questo/quello and rosso/blu). Results show that participants used the proximal demonstrative more frequently to refer to the target stimulus when in control. These findings suggest that, similarly to spatial distance, physical control influences demonstrative choice.

Speakers are often over-informative, referring to the color and shape of a referent even when all objects in a scene are unique. Interestingly, this helps listeners locate the target. If speakers are indeed sensitive to listeners’ online processing demands, they should be more over-informative when addressing someone whose processing is especially slow. Here we show that English-speaking adults produce more redundant color adjectives when speaking to children than adults (Exp 1); that although Spanish-speakers produce fewer redundant color adjectives than English-speakers overall, they too do so more often for children (Exp 2); that these results are independent of experience with young children (Exp 3), and that children themselves (ages 4-10) are more over-informative when speaking to younger children than adults (Exps 4 and 5). Collectively, these results suggest that sensitivity to listeners’ online processing demands is robust, emerges early in development, and may be especially tailored to young learners.

An important issue in the semantic memory literature concerns the relative importance of experience-based sensorimotor versus language corpus-based distributional information in conceptual representations. Here we examine how each sort of information is associated with the EEG response to words in a property verification task in which participants indicated whether or not a property term (such as ”red”) is typically obtained for a concept term (such as ”APPLE”). To define and measure each type of information, we operationalized distributional and sensorimotor information using cosine distance measurements derived from GloVe Embeddings and Lancaster Sensorimotor Norms respectively. We then modeled single-trial EEG responses to property words in a property verification task using regression models. Our findings indicate that semantic processing in this task simultaneously incorporates distributional and sensorimotor information, and their contribution is shaped by task-relevant linguistic context. We aim for our study to contribute to a critical examination of such information operationalizations and also encourage a systematic evaluation of their performance across tasks, particularly for EEG measurements.

Adults routinely tailor their communication to others' auditory access, such as substituting gestures for speech in noisy environments. Yet, assessing the effectiveness of different communicative acts given others’ perceptual access—especially when it differs from one's own—requires mental-state reasoning, which undergoes significant developmental change. Can young children tailor their communication to others’ auditory access? In Study 1, parental report (n=98) indicated that most children, by age 4, adjust their communicative behaviors in noisy settings. Study 2 elicited these behaviors experimentally with 4- to 5-year-olds (n=68). Children taught how a novel toy works to a learner who wore headphones playing either loud music or nothing. Children were more likely to use physical demonstrations, and less likely to use verbal explanations, when the learner's auditory access was obstructed. These findings illustrate how mental-state reasoning might support children's ability to communicate successfully across perceptually-compromised contexts and individuals.

In spoken languages, new information is often expressed with a longer duration than given information. We investigated whether signers use duration to mark information status. Fifty deaf Chinese Sign Language (CSL) signers retold a cartoon clip, and we examined how they tracked references. The results showed that CSL signers mostly used nominals, classifiers and constructed actions, but rarely used any pointing or zero anaphora. When focusing on nominals, newly introduced references had a longer duration than the maintained and re-introduced ones, while the durations of maintained and re-introduced nominals did not differ. Additionally, there was a gradient decrease in sign duration over the first three mentions followed by an increase for the fourth and fifth mentions. Furthermore, between two nominal mentions, the more non-nominal referring there were, the shorter the duration of the current nominal mention. Thus, CSL signers vary the duration of nominals to indicate the degree of accessibility.

An important aspect of language comprehension is learning and generalizing complex lexical relations. For instance, having learned that the phrase preserve cucumbers predicts vinegar and that preserve berries predicts dehydrator, one should be able to infer that the novel phrase preserve peppers is more compatible with vinegar, because pepper is more similar to cucumber. We studied the ability to perform such (compositional) generalization in distributional models trained on an artificial corpus with strict semantic regularities. We found that word-encoding models failed to learn the multi-way lexical dependencies. Recurrent neural networks learned those dependencies but struggled to generalize to novel combinations. Only mini GPT-2, a minified version of the Transformer GPT-2, succeeded in both learning and generalization. Because successful generalization in our tasks requires capturing the relationship between a phrase and a word, we argue that mini GPT-2 acquired hierarchical representations that approximate phrase structure. Our results show that, compared to older models, Transformers are architecturally advantaged to perform compositional generalization.

Humans have increasing opportunities to offload internal cognitive demand, such as by setting reminders to aid future memory performance. Here, we examine how children begin to balance mind and world: weighing up when to offload cognition and when to rely on their unaided capacities. Australian children aged 6 to 9 years (N = 120) were tasked with remembering the locations of 1, 3, 5, and 7 targets hidden under 25 cups. In the critical test phase, children were provided with a limited number of ‘tokens’ to distribute across trials, which they could use to mark target locations and assist future performance. Following the final search period, children were invited to evaluate and adjust their initial allocation. Results showed that 8- to 9-year-olds prospectively allocated proportionately more tokens to difficult trials, whereas 6- to 7-year-olds did so only in retrospect. Throughout childhood, humans become increasingly adept at balancing internal and external cognition.

Events of social exchange, such as givings and tradings, are uniquely prevalent in human societies and cognitively privileged even at early stages of development. Such events may be represented as having 3 or even 4 participants. To do so in visual working memory would be at the limit of the system, which throughout development can track only 3 to 4 items. Using a case study of trading, we ask (i) whether adults can track all four participants in a trading scene, and (ii) whether they do so by chunking the scene into two giving events, each with 3 participants, to avoid placing the visual working memory system at its limit. We find that adults represent this scene under a 4-participant concept, and do not view the trade as two sequential giving events. We discuss further implications for event perception and verb learning in development.

When reasoning about a claim, it makes sense to be more persuaded if lots of other people agree. But, there are many factors that make weighing the evidence behind a consensus complicated. For example, a consensus might be more or less informative depending on the type of claim, or whether each consensus member formed their opinions independently. These factors might also influence people differently depending on their own assumptions or preferences. In this study we used a mock social media paradigm to assess how persuaded people were by two factors: the presence of consensus (no consensus vs. consensus), and source independence (a consensus based on independent information sources vs. a consensus formed off shared, dependent sources). We varied these factors at both the group and individual level. At the group level, we assessed a third factor: whether people were influenced by the type of claim being reasoned about (we assessed 60 different claims divided into 4 categories). Almost everyone was more persuaded by consensus trials compared to no consensus trials. However, the strength of this effect was credibly stronger if the claim was likely to have a ground truth. We found that around one third of participants were sensitive to source independence. Of these, three quarters were more persuaded by a consensus based on independent sources, but the quarter who were more persuaded by dependent sources were persuaded just as strongly.

During sentence processing, comprehenders have to maintain a mapping between lexical items and their position in the sentence (syntactic position). We propose a model of morpheme-position binding in working memory, based on models such as 'serial-order-in-a-box' and its SOB-complex-span version. Like those working memory models, our sentence processing version derives a range of attested memory interference effects from the process of item-position binding. We present simulation results capturing similarity-based interference and item-distortion. These two major classes of interference effects have not received a unified account before, and are not fully captured by cue-based retrieval models.

Dual language learners (DLLs) constitute a large portion of the population, but relatively little is known about the best ways in which to assess their vocabulary knowledge. Past research has used both conceptual vocabulary knowledge, assessing whether a child knows a word in either language, as well as total vocabulary knowledge, assessing what words a child knows in each language separately. The present work uses neural networks to predict specific word learning for individual Cantonese-English DLLs. As its input, The model utilizes word2vec embeddings that either represent children’s’ conceptual word knowledge or total word knowledge. We find that using total word knowledge results in higher predictive accuracy, suggesting that knowing what specific words DLLs know in each of their languages provides the most accurate picture of DLLs’ vocabulary knowledge. The present work has many implications for both identification of at-risk individuals and the creation of learning materials for DLL populations.

This study investigates how people assign blame to autonomous vehicles (AVs) when involved in an accident. Our experiment (N = 2647) revealed that people placed more blame on AVs than on human drivers when accident details were unspecified. To examine whether people assess major classes of blame-relevant information differently for AVs and humans, we developed a causal model and introduced a novel concept of prevention effort, which emerged as a crucial factor for blame judgement alongside intentionality. Finally, we addressed the “many hands” problem by exploring how people assign blame to entities associated with AVs and human drivers, such as the car company or an accident victim. Our findings showed that people assigned high blame to these entities in scenarios involving AVs, but not with human drivers. This necessitates adapting a model of blame for AVs to include other agents and thus allow for blame allocation “outside” of autonomous vehicles.

Humans seem to arbitrate between automatic and controlled processing by optimizing a trade-off between cognitive effort and performance. Previous research has described ways of how these costs and benefits can be quantified and how the trade-off between them can be performed. However, it remains unclear how the costs should be weighed relative to the benefits and how the cost of the arbitration mechanism itself factors in. Here, we derive measures for these separate factors from a single objective: the variational free energy. We demonstrate that by minimizing this objective, the trade-off between automatic and controlled processing as well as meta-control is optimized implicitly. As a proof of concept, we show that the congruency and proportion congruency effects in the Stroop task directly result from this optimization, given an environment with specific statistical regularities.

Adults often hold different goals for children's achievement: Sometimes adults want children to learn as much as possible, while at other times adults discount children's learning in favor of high performance. How do children reason about the achievement goals adults have for them? Across 3 preregistered studies (n = 120), we asked whether 5- and 6-year-old children understand the causal relationship between adults' achievement goals, their task choices, and children's competence. In Experiment 1, we found adults are more likely to give harder tasks to children when they hold learning versus performance goals and when the child is more competent. In Experiment 2, we found that children make similar inferences about adults' task selections given the adult's achievement goal and the receiving child's competence. Finally, in Experiment 3, children inferred that adults would pick harder tasks for them when they possessed a learning goal versus a performance goal, which matched their own task choice given the same achievement goals. Thus, young children can infer the relationship between adults' child-directed achievement goals and actions and may use this information to learn about what adults prioritize for children across contexts.

Surprisal values from large language models (LLMs) have been used to model the amplitude of the N400. This ERP component is sensitive not only to contextual word expectancy but also to semantic association, such that unexpected but associated words do not always induce an N400 increase. While LLMs are also sensitive to association, it remains unclear how they behave in these cases. Moreover, another ERP component, the P600, has shown graded sensitivity to plausibility-driven expectancy, while remaining insensitive to association; however, its relationship to LLM surprisal is not well researched yet. In an rERP analysis, we evaluate surprisal values of two unidirectional transformers on their ability to model N400 and P600 effects observed in three German ERP studies isolating the effects of association, plausibility, and expectancy. We find that surprisal predicts an N400 increase for associated but implausible words, even when no such increase was observed in humans. Furthermore, LLM surprisal accounts for P600 effects elicited by violations of selectional restrictions, but captures neither P600 effects from more subtle script knowledge violations nor graded P600 modulations. The results of our investigation call into question the extent to which LLM surprisal offers an accurate characterisation of the functional generators of either the N400 or P600.

Aspectual coercion occurs when there is a semantic mismatch between constituents in terms of their lexical aspect. Despite the long psycholinguistic history of this phenomenon, we currently lack direct measures of how people interpret coerced sentences. We introduce a novel method combining aspectual comprehension with event cognition, allowing us to detect changes in how individuals construe events after reading sentences with varying aspectual information. This study involved two experiments where participants read sentences—either telic or atelic, with or without coercion—followed by a video clip related to the sentence. They assessed if the actor completed the task and identified any brief interruptions during the event, located at the midpoint or late points. The focus was on whether coerced sentences altered participants' event construals, impacting their responses. Results uncovered distinct cognitive responses to aspectual coercion and highlighted differences between coercion types. This method advances our understanding of how lexical aspect influences event representation, offering insights into the nuanced effects of aspectual coercion on cognitive processing and event perception.

Adults often learn new semantic information in face-to-face communication with other adults (e.g., teachers, colleagues). More knowledgeable individuals provide an ensemble of multimodal behaviours that can shape the information that their interlocutors learn. Using the naturalistic ECOLANG corpus of dyadic conversations, we ask whether multimodal behaviours (pitch, speaking rate, representational gestures, points, object manipulations, and gaze) support adults’ semantic learning of unknown objects above and beyond verbal properties of utterances (number of utterances, lexical diversity, mean length of utterances, concreteness) and learners’ individual differences (vocabulary, working memory). We found that individual differences, pointing and object manipulations affected learning, with verbal and multimodal factors also interacting to predict adult semantic learning. Our results highlight the relevance of accounts of multimodal learning in adulthood and the importance of considering naturalistic interaction in its complexity to understand the factors that influence adult learning.

Reservoir computing (RC) offers distinct advantages in extracting spatiotemporal information with low training costs by separating recurrent neural networks into a fixed network with recurrent connections. The quality of the fixed network, known as the reservoir, plays a pivotal role in the performance of the RC system. Our work aims to provide a unified synaptic development framework for RC, constructing a more biologically plausible reservoir to model and understand the neural networks development within the human brain. In this paper, we propose an Autonomous Synaptic Development Reservoir Computing model (ASD-RC) based on an adaptive network of phase oscillators. The reservoir autonomously adjusts the distribution of connection weights in response to external stimuli, forming a task-specific structure. Through experiments and theoretical analyses, we demonstrate that ASD-RC can emulate various synaptic development rules of biological neural networks in \textit{vivo}, including the Hebbian rule and STDP. Furthermore, experiments reveal that combining different development rules can enhance performance on prediction tasks compared to using a single development rule, showcasing the emergence and effects of synergistic development that improve information processing capacity.

We study referential communication about concepts at different levels of abstraction in an interactive concept-level reference game. To better understand processes of abstraction, we investigate superordinate referring expressions (animal). Previous work identified two main factors that influence speakers’ choice of referring expressions for concepts: the immediate context and the basic-level effect, i.e. a preference for basic-level terms such as dog. Here we introduce a new concept-level reference game that allows us to study differences in the basic-level effect between comprehension and production and to elicit superordinate referring expressions experimentally. We find that superordinate referring expressions become relevant for groups of objects. Further, we reproduce the basic-level effect in production but not in comprehension. In conclusion, even though basic-level terms are most readily accessible, speakers tailor their expressions to the context, allowing the listener to identify the target concept.

Computational neuromodeling methods for evaluating representational dynamics involve intricate analysis choices at every stage of the analysis pipeline. Analysis choices for data processing pipelines are generally chosen based upon end to end accuracy metrics and corresponding performance metrics. Psychology research has recently begun to acknowledge the importance of controlling for potential bias introduced by degrees of freedom in data analysis, with specification curve analysis introduced as a principled method for correcting for such biases. In this paper, we conduct a specification curve analysis (SCA) for representational similarity analysis pipelines reported in the literature for fMRI and EEG datasets, respectively. We show that EEG-based RSA analyses are relatively robust to alternative specifications but that fMRI based analyses are not. Using a novel decision-tree analysis to supplement SCA, we present a potentially more robust pipeline for such analyses.

Telicity is an important semantic feature pointing to event construal: telic verb phrases denote bounded events with an inherent endpoint while atelic verb phrases denote unbounded events without such an endpoint. Languages encode telicity in different ways. Unlike English, Mandarin lacks an overt count-mass distinction and allows bare noun objects to form verb phrases. Would this cross-linguistic difference influence event perception? Experiment 1 elicited descriptions of bounded vs. unbounded events from English and Mandarin native speakers. A clear cross-linguistic difference was found: English speakers mostly used telic predicates for bounded events and atelic predicates for unbounded events while Mandarin speakers gave atelic predicates with bare noun objects for both event types. Experiment 2 explored how English and Mandarin speakers tracked the temporal structure of bounded vs. unbounded events. The two language groups performed similarly. The way people describe events may not affect the way they track event temporal profiles.

In this paper, we test the effect of manipulating discrimination difficulty on subsequent generalization of learning and in particular, on the peak shift effect. Participants learned a discrimination where one stimulus led to an outcome (S+) and another stimulus led to no outcome (S-). Difficulty was manipulated by varying the degree of similarity between the S+ and S- across groups (easy/medium/hard). In contrast to similar studies in animals, we found that increasing the difficulty of the discrimination resulted in less peak shift. Using a hierarchical mixture model, we characterize the effects of discrimination difficulty on relational- and similarity based responding, and show for the first time, a similar mixture of responding on stimulus identification gradients. We conclude that peak shift on generalization and identification measures can be explained by mixtures of participants responding in different ways.

Decoding naturalistic stimuli from neural recordings is a significant challenge in systems neuroscience, primarily due to the high-dimensional and nonlinear nature of stimulus-response interactions, and is further exacerbated by the limited availability and noisiness of neural data. While contemporary approaches that incorporate generative models, such as Generative Adversarial Networks (GANs), attempt to address these issues by mapping neural responses to latent representations, they do not fully overcome these obstacles. In this work, we present a novel paradigm of differential neural decoding (dicoding) that focuses on the relative changes in response patterns, which not only expands the neural training data quadratically but also inherently denoises it. To determine the corresponding stimulus changes, this method leverages the Euclidean and feature-disentangled properties of the underlying latents through vector arithmetic. As such, we not only effectively exploit the latent space but also achieve semantically meaningful latent offsets in the context of the stimuli, resulting in improved sample efficiency. We trained a decoder to predict changes in latent vectors based on the corresponding changes in neural responses. The absolute latent vector itself was derived by vector addition of the predicted latent change (indicative of stimulus shift) to a reference latent, which was fed to the generator for the reconstruction of the perceived stimulus. Our results show that this geometrically principled approach facilitates more effective reconstruction of naturalistic stimuli from noisy and limited neural data.

Food neophobia – the reluctance to eat novel food – has been associated with poorer performance in category-based tasks within the food domain among preschoolers. This research aims to unravel this negative relationship and determine if this association is specific to food items or reflects general cognitive rigidity in considering alternative ways to represent entities. In study 1, 123 children between 3 and 6 years were tested on an inductive reasoning task, comparing food and animals. In study 2, 112 children aged 4 to 6 engaged in a cross-categorization task comparing food, animals and artifacts. Results indicated that neophobic children exhibited poorer induction and cross-categorization performance in all domains compared to their neophilic counterparts. These findings highlight the importance of child characteristics in shaping the general development of category-based abilities and suggest that food neophobia, rather than a fear of novelty, reflects instead difficulties in changing perspectives once items have been classified.

What factors contribute to the growth of children’s early vocabulary? One method for exploring this question is investigating predictors (e.g., frequency) that differentiate words learnt earlier from those learnt later. A more comprehensive account requires the examination of multiple language families and multiple levels of linguistic representation (e.g., phonological, morphosyntactic, semantic). Here, we studied 10 predictors of word ages of acquisition across 27 languages and dialects. We found that words that were more frequent, concrete, and associated with babies were learnt earlier, whereas words that had greater length in phonemes and mean length of utterance were learnt later. There was no reliable effect of other morphosyntactic predictors, or of phonological neighbourhood. We found evidence of a tradeoff between a language’s morphological complexity and the effect of syntactic complexity for predicates, supporting the competition model. Predictor coefficients revealed broad consistency across all languages, along with variability that reflected language family classifications.

Retrieval practice—the process of actively calling information to mind rather than passively studying materials—has been proven to be a highly effective learning strategy. However, only recently, researchers have started to examine differences between learners in terms of the optimal conditions of retrieval practice in applied educational settings. In this study (N = 118), we focus on learners with dyslexia: a group that is usually not included in the retrieval practice literature. We compare their performance to the performance of typical learners in an adaptive, retrieval practice-based word learning task using both typing-based and speech-based response conditions. We find that typical learners outperform learners with dyslexia when they are asked to respond by typing, but that this difference is much smaller when learners can respond by speech. These results can contribute to the development of educational technology that allows for effective and inclusive learning in neurodivergent individuals.

The Abstraction Reasoning Corpus (ARC) is a visual analogical reasoning test designed for humans and machines (Chollet, 2019). We compared human and large language model (LLM) performance on a new child-friendly set of ARC items. Results show that both children and adults outperform most LLMs on these tasks. Error analysis revealed a similar "fallback" solution strategy in LLMs and young children, where part of the analogy is simply copied. In addition, we found two other error types, one based on seemingly grasping key concepts (e.g., Inside-Outside) and the other based on simple combinations of analogy input matrices. On the whole, "concept" errors were more common in humans, and "matrix" errors were more common in LLMs. This study sheds new light on LLM reasoning ability and the extent to which we can use error analyses and comparisons with human development to understand how LLMs solve visual analogies.

What impacts what we remember about objects we have just encountered? Influential theories of learning suggest that more active engagement leads to stronger memories than passive observation. However, it is not clear which aspects of interaction lead to stronger memories, nor what kinds of memories are supported by active engagement. Here we conduct several experiments to investigate the impact of assembling an object on subsequent recognition and recall performance. We found that reconstructing a block tower by copying it part-by-part could impair subsequent memory for that tower, compared to passively viewing that tower. By contrast, when participants initially encoded each tower by building it from working memory, their subsequent recall was enhanced relative to when they held the tower in working memory without building it. Together our results suggest a complex relationship between the nature of our interactions with objects and our subsequent memories of them.

English allows for both compounds (e.g., London-made) and phrasal paraphrases (e.g., made in London). While these constructions have roughly the same truth-conditional meaning, we hypothesize that the compound allows less freedom to express the nature of the semantic relationship between the participle and the pre-participle nominal. We thus predict that the pre-participle slot is more constrained than the equivalent position in the phrasal construction. We test this prediction in a large corpus by measuring the entropy of corresponding nominal slots, conditional on the participle used. That is, we compare the entropy of α in compound construction slots like “α-[V]ed” to the entropy of α in phrasal constructions like “[V]ed by α” for a given verb V. As predicted, there is significantly lower entropy in the compound construction than in the phrasal construction. We consider how these predictions follow from more general grammatical properties and processing factors.

We offer philosophical motivations for a method we call Virtual World Cognitive Science (VW CogSci), in which re- searchers use virtual embodied agents that are embedded in virtual worlds to explore questions in the field of Cognitive Science. We focus on questions about mental and linguistic representation and the ways that such computational modeling can add rigor to philosophical thought experiments, as well as the terminology used in the scientific study of such representations. We find that this method forces researchers to take a god’s-eye view when describing dynamical relationships be- tween entities in minds and entities in an environment in a way that eliminates the need for problematic talk of belief and concept *types*, such as *the belief that cats are silly*, and *the concept CAT*, while preserving belief and concept *tokens* in individual cognizers’ minds. We conclude with some further key advantages of VW CogSci for the scientific study of mental and linguistic representation and for Cognitive Science more broadly.

We offer a computational framework for modeling explanation as cooperative rational communication. Under our framework, when an explainer is faced with a ``why?'' question, they reason about the question-asker's current mental model, and intervene on that mental model in order to maximize the listener's future utility. We instantiate our framework in a planning domain, and show that our framework can model human explanations about plans across a wide variety of scenarios.

Disparate impact rules are formally neutral but indirectly discriminate against protected groups (i.e., by targeting a characteristic that is more prevalent in a given group). Because these rules are not obviously malicious, they have been widely enacted to circumvent policies against explicit discrimination. In a series of four experiments, we show that adults and children are sensitive to the moral implications of disparate impact rules. However, we also find that they are more accepting of these rules when strong justification is provided, compared to rules with no justification. Crucially, demographic differences also impact people's judgments of disparate impact rules and their creators. We find that conservatives and those from groups not directly affected by the rule tend to be more accepting of it. By studying people's reasoning about disparate impact rules, this work aims to identify the mechanisms by which these rules may evade detection. Finally, we discuss how these insights may inform the development of interventions that highlight the problematic effects of indirectly discriminatory policies.

Advancements in deep neural networks have led to significant progress in computer vision and natural language processing. These networks, trained on real-world stimuli, develop high-level feature representations of stimuli. It is hypothesized that these representations, stemming from different inputs, should converge into similar conceptual systems, as they reflect various perspectives of the same underlying reality. This paper examines the degree to which different conceptual systems can be aligned in an unsupervised manner, using feature-based representations from deep neural networks. Our investigation centers on the alignment between the image and word representations produced by diverse neural networks, emphasizing those trained via self-supervised learning methods. Subsequently, to probe comparable alignment patterns in human learning, we extend this examination to models trained on developmental headcam data from children. Our findings reveal a more pronounced alignment in models trained through self-supervised learning compared to supervised learning, effectively uncovering higher-level structural connections among categories. However, this alignment was notably absent in models trained with limited developmental headcam data, suggesting more data, more inductive biases, or more supervision are needed to establish alignment from realistic input.

Studies of meaning in human and primate communication face, in principle, similar methodological problems. In both cases, meaning is not observable directly, but must be inferred from more indirect sources, such as directly observable behavior. Recent work in probabilistic cognitive modeling of language use has therefore developed methods of inferring latent se- mantic meaning through the lens of a probabilistic model of language use. In this paper, we explore how to adapt such an approach for insightful investigations of primate communication. Towards this end, we develop a suitable probabilistic model of processes that generate communicative behavior by making use of functionally specified latent meaning representations. As a proof of concept, we apply this model to a rich, annotated data set of orangutan communicative dyadic interaction and conclude that explicit probabilistic modeling can provide additional insights for the study of animal communication pertaining to the context-dependent nature of signals and the gradual evolution of human communication systems.

This study investigates the phenomenon of verbal overshadowing in olfaction. It focuses on how odor recognition is impacted after individuals sniffed and then described odors. Three key findings emerged. First, participants who refrained from describing a previously encountered target odor (control group) showed significantly superior performance in recognizing the target odor compared to those who had described it (verbal group). Second, the verbal overshadowing effect tended to diminish or completely disappear when participants were required to respond rapidly. Third, providing participants with instructions highlighting potential conflicts between olfactory and verbal representations did not alleviate the influence of the verbal overshadowing effect. To conclude, describing an odor elaborately can adversely affect odor memory, even when one is aware of this, but this is mitigated under speeded conditions.

Music-making relies on precise temporal control and mutual coordination among performers, particularly to maintain tempo. We evaluate the impact of human-machine interaction and rhythmic subdivisions on tempo change in musical trios. A synchronization-continuation task was performed by trios of human participants interacting with confederates or with algorithmic (i.e. machine) models. Sounded tone onsets were produced by a linear error-correction model, delay-coupled model, and Kuramoto model that replaced a human participant. Inter-onset intervals were examined from participants who performed rhythms in both in-phase and anti-phase conditions while a third group member was either a human or algorithmic model. Trios drifted toward faster tempi more when they contained a human than an algorithmic model. Tempo drift also increased for the aligned rhythms (in-phase) compared to rhythms with rhythmic subdivisions (anti-phase). Finally, the tested algorithmic models replicated the confederate’s tempo drift without the use of any period correction mechanisms. This research advances our understanding of unintentional tempo drift, offering insights into ensemble dynamics and models of temporal coordination in groups. Implications for musical coordination and avenues for future research are discussed.

Moral inference is an emerging topic of critical importance in artificial intelligence. The contemporary approach often relies on language modelling to infer moral relevance or moral properties of a concept such as "smoking". This approach demands complex parameterisation and costly computation, and it tends to disconnect with psychological accounts of moralization. We present a simple cognitive model for moral inference grounded in theories of moralization. Our model builds on word association network known to capture human semantics and draws on rich psychological data. We demonstrate that our moral association graph model performs competitively to state-of-the-art language models, where we evaluate them against a comprehensive set of data for automated inference of moral norms and moral judgment of concepts, and in-context moral inference. Moreover, we show that our model discovers intuitive concepts underlying moral judgment and is applicable to informing short-term temporal changes in moral perception.

This pupillometric study investigates the relevance of domain-final intonation for attention-orienting in German, employing a changing-state oddball paradigm with rising, falling and neutral intonation on deviant stimuli. Pupil dilation responses (PDR) to deviants were shown to be affected by their intonation contours, strengthening the case for the role of intonational edge tones in attention-orienting. Moreover, the magnitude and duration of the PDR response was higher for rises than falls, indicating the fundamental role of intonational rises for the activation of the attention-orienting mechanism in speech perception.

We investigate whether more accurate representation of syntactic information in Transformer-based language models is associated with better alignment to brain activity. We use fMRI recordings from a large dataset (MOUS) of a Dutch sentence reading task, and perform Representational Similarity Analysis to measure alignment with 2 mono- and 3 multilingual language models. We focus on activity in a region known for syntactic processing (the Left posterior Medial Temporal Gyrus). We correlate model-brain similarity scores with the accuracy of dependency structures extracted from model internal states using a labelled structural probe. We report three key findings: 1) Accuracy of syntactic dependency representations correlates with brain similarity, 2) The link between brain similarity and dependency accuracy persists regardless of sentence complexity, although 3) Sentence complexity decreases dependency accuracy while increasing brain similarity. These results highlight how interpretable, linguistic features such as syntactic dependencies can mediate the similarity between language models and brains

While Vector Symbolic Architectures (VSAs) are promising for modelling spatial cognition, their application is currently limited to artificially generated images and simple spatial queries. We propose VSA4VQA – a novel 4D implementation of VSAs that implements a mental representation of natural images for the challenging task of Visual Question Answering (VQA). VSA4VQA is the first model to scale a VSA to complex spatial queries. Our method is based on the Semantic Pointer Architecture (SPA) to encode objects in a hyperdimensional vector space. To encode natural images, we extend the SPA to include dimensions for object’s width and height in addition to their spatial location. To perform spatial queries we further introduce learned spatial query masks and integrate a pre-trained vision-language model for answering attribute-related questions. We evaluate our method on the GQA benchmark dataset and show that it can effectively encode natural images, achieving competitive performance to state-of-the-art deep learning methods for zero-shot VQA.

Video recording is a widely used tool for studying animal behavior, especially in fields such as primatology. Primatologists rely on video data to analyze and research topics such as social grooming to uncover subtle mechanisms behind complex social behavior and structures. Insights into these social behaviors may provide us with a better understanding of our closest living relatives, but also new theories and insights into our own behavior. However, analyzing this type of data using manual annotation is currently a time-consuming task. Here we present an end-to-end pipeline to track chimpanzee (Pan troglodytes) poses using DeepLabCut (DLC) which then serves as input to a support vector machine. This classifier was trained to detect role transitions within grooming interactions. We replicate a recent study showing that DLC has usability value for chimpanzee data collected in natural environments. Our combined method of tracking and classification is remarkably successful in detecting the presence of grooming, indicating the directionality and a change in turn with an accuracy above 86% on unseen videos. We can identify particular pose features used in the classification of grooming, which will contribute to the exploration of turn-taking dynamics on a scale that would otherwise be difficult to attain with traditional methods. Finally, our pipeline can in principle be applied to recognize a variety of other socially interactive behaviors that are largely recognizable by (joint) postural states.

People thinking creatively will shift their bodies, wander around, move. Why? Here we investigate one explanation: Movement is a canny strategy for changing the information that is available visually, in ways that facilitate insight. We first analyzed video footage of mathematicians engrossed in creative thought. We found that sudden "aha" insights were reliably preceded by movements away far from the blackboard, as if mathematicians were stepping back to "see the big picture." To confirm the causal impact of changing proximity on creativity, we conducted an experiment that manipulated proximity to a whiteboard while participants worked on insight puzzles represented by diagrams. Participants had greater creative success when they could survey the entire whiteboard from a distance. Whether in real-world expert reasoning or a controlled experiment, movements away and toward visual representations facilitated insight. Wandering is sometimes a kind of epistemic action, facilitating the discovery of novel connections.

It has long been assumed that infants' ability to discriminate between languages stems from their sensitivity to speech rhythm, i.e., organized temporal structure of vowels and consonants in a language. However, the relationship between speech rhythm and language discrimination has not been directly demonstrated. Here, we use computational modeling and train models of speech perception with and without access to information about rhythm. We test these models on language discrimination, and find that access to rhythm does not affect the success of the model in replicating infant language discrimination results. Our findings challenge the relationship between rhythm and language discrimination, and have implications for theories of language acquisition.

There has been exciting recent progress in computational modeling of moral cognition. Work in this area tends to describe the cognitive mechanisms of human moral judgment using symbolic models, which are interpretable and written in terms of representations that carry meaning. However, these models fall short of capturing the full human capacity to make moral judgments in that they fail to process natural language inputs but instead rely on formal problem specifications. The inability to interface with natural language also limits the usefulness of symbolic models. Meanwhile, there have been steady advances in conversational AI systems built using large language models (LLMs) that interface with natural language. However, these systems fall short as models of human reasoning, particularly in the domain of morality. In this paper we explore the possibility of building neuro-symbolic models of human moral cognition that use the strengths of LLMs to interface with natural language (specifically, to extract morally relevant features from it) and the strengths of symbolic approaches to reason over representations. Our goal is to construct a model of human moral cognition that interfaces with natural language, predicts human moral judgment with high accuracy, and does so in a way that is transparent and interpretable.

Social media is often used as a platform where individuals engage in debate regarding topics that are important to them. Not all arguments are equally convincing, and whilst a given argument may be persuasive to some people, it is often seen as inadequate by others. We are interested in both the individual and argument level differences that make ‘everyday’ arguments such as those on social media persuasive. In a replication of our Everyday Belief Bias Task (Deans-Browne & Singmann, 2024), we investigate this question using a paradigm that consists of two parts. In the first part, we measure participant’s individual beliefs about eight claims each referring to a political topic (e.g., Abortion should be legal). In the second part, participants rated an argument for each of these claims that was deemed as either good, inconsistent (containing internal inconsistencies), or authority-based (being centered around appeals to authority). We replicated the belief consistency effect – participants preferred arguments that were also in line with their beliefs. We also found that authority-based arguments were rated as worse than inconsistent arguments, and that both types of arguments were rated as worse than good arguments. The implications are first that people do not evaluate arguments independently of the background beliefs held about them. Secondly, people are willing to ignore inconsistencies in arguments more than they are willing to accept the endorsement of authority figures as adequate evidence for arguments.

Our visual system tends to prioritise novel information, and this allocation of attention, as examined with the Visual Paired Comparison Task (VPC), is taken as an indirect index of memory processes. At present, research on the emergence of a novelty preference (NP) remains unclear about its temporal dynamics and agnostic about the role that the organisation of conceptual knowledge may play in it. These two gaps are addressed in this eye-tracking study, which adapts the VPC task to enable a finer temporal tracking of the NP while manipulating categorical and functional relationships between pairs of real-world visual objects to examine the impact conceptual associations bear on it. We found that NP significantly increases with increasing delay between the familiarisation and the test phase, especially for pairs of objects that were both categorically and functionally related (e.g., dart/dartboard). Our findings provide fresh evidence about the interplay between overt attention, conceptual knowledge and memory processes on novelty preference while offering valuable insights into the temporal dynamics of NP and its conceptual implications for mechanisms governing visual short-term memory.

Toddlers are sensitive to the reliability of speakers in their environment \cite{koenig2010}. While previous work suggests that children prefer labels from reliable speakers, it remains unclear how these social representations guide lower-level information-seeking processes that affect speaker preferences. The current study introduces a gaze-contingent eye-tracking paradigm to investigate how children engage in sampling during word learning. Toddlers (22-24m) view videos of two speakers labeling familiar objects; one speaker provides reliable labels and the other speaker provides unreliable labels. Toddlers then sample novel labels from the speakers using a gaze-contingent interface: only the speaker they are fixating on provides a novel label. Preliminary data (N = 18) suggests that participants prefer to sample first from the reliable speaker over the unreliable speaker. However, there is little difference in overall sampling preferences. Our findings suggest that toddlers can assess speaker reliability, but remain open to exploring information from unreliable speakers.

Early word learning involves mapping individual words to their meanings and building organized semantic representations among words. Previous corpus-based studies (e.g., using text from websites, newspapers, child-directed speech corpora) demonstrated that linguistic information such as word co-occurrence alone is sufficient to build semantically organized word knowledge. The present study explored two new research directions to advance understanding of how infants acquire semantically organized word knowledge. First, infants in the real world hear words surrounded by contextual information. Going beyond inferring semantic knowledge merely from language input, we examined the role of extra-linguistic contextual information in learning semantic knowledge. Second, previous research relies on large amounts of linguistic data to demonstrate in-principle learning, which is unrealistic compared with the input children receive. Here, we showed that incorporating extra-linguistic information provides an efficient mechanism through which semantic knowledge can be acquired with a small amount of data infants perceive in everyday learning contexts, such as toy play.

Toddlers can only say one or two words at a time. What do they choose to talk about? We report preliminary results (N=167; mean; 19.5 months) from a pre-registered online experiment on productive language. Toddlers saw six movies. A curtain opened on an introductory scene, the parent closed their eyes, and a new event happened. The curtain closed and the child was asked what happened. On two trials the unseen event was new to the parent (Novel event); on two trials, one of two animals ate the only food in the scene (Agent ambiguous); on two trials, the only animal ate one of the two foods (Patient ambiguous). We predicted that toddlers would selectively generate informative utterances (i.e., referring to the novel event, the agent, and the patient, respectively). Toddlers' productive language was indeed sensitive to what listeners’ know; however, unlike adults, they selectively referred to information in common ground.

People who have no experience with programming can create informal programs to rearrange the order of cars in trains. To find out whether they rely on kinematic mental simulations, the current studies examined participants’ eye movements in two experiments in which participants performed various moves and rearrangements on a railway consisting of a main track running from left to right and a siding entered from and exited to the left track. In Experiment 1, they had to imagine different sorts of single moves of cars on the railway. The sequences of their fixations resembled iconic gestures: they tended to look at the starting location of the imagined move, and then at its final location. In Experiment 2, the task was to create descriptions of how to solve four sorts of rearrangements that differ in their Kolmogorov complexity. It predicted the time to find the correct solution and the relative number and duration of fixations recorded during the description of each move for rearrangements of different complexity. Participants were more likely to fixate on the symbols on the cars than anything else, and they fixated longer when the rearrangement was more difficult. They also tended to fixate regions of the tracks where a car’s movement began or ended, as if they were imagining a car moving along the tracks. The results suggest that humans rely on a kinematic mental simulation when creating informal algorithms.

Learning names for novel objects has been shown to be impacted by the context in which they appear. Manipulations of context, therefore, provide a key pathway to explore these learning dynamics. Here we use a neural process model that instantiates the details of ‘context’ to generate novel, counterintuitive predictions about how similarity in object properties influence learning. Specifically, we use a dynamic field model, WOLVES, to simulate and predict learning in a cross-situational word learning task in two conditions: one where the two objects presented on each learning trial are metrically similar in a property (‘NEAR’) and another condition where the two objects are always dissimilar (‘FAR’). WOLVES predicts—counterintuitively—that participants should learn better in the ‘NEAR’ condition (where objects are potentially confusable) than in ‘FAR’ condition (where objects are distinctive). We then tested this prediction empirically, finding support for the novel prediction. This study shows the utility of process models which instantiate the details of ‘context’ during learning and provides support for WOLVES. We know of no other theory of cross-situational word learning that captures these novel findings.

Infants’ bodies, brains, and environments are ever-changing. Although this continuous transformation is a fundamental feature of development, how infants actively adapt and learn amidst such volatility is still unknown. To address this, we devised a novel learning task in which the location of a reward was systematically altered, transitioning from stable to volatile periods. Through computational modelling, we inferred from the infants’ gaze and pupil data the learning processes that enabled them to navigate these changing environments. We found that infants’ tonic pupil size reflected trial-by-trial changes in the level of environmental volatility. Moreover, phasic changes in pupil size when observing the reward indicated that infants relied on the information about volatility to optimize their learning. This resulted in the successful performance of the task, as indicated by the pattern of anticipatory looks to the correct reward locations. Together, these results identify the active role that infants play in adapting to change.

Seeing a person preparing to perform an action and later remembering having seen subsequent phases of the action, but not previous phases. This is what a theory on the role of the motor system in the creation and recovery of memories predicts can happen. We investigate memory for action after viewing an image representing an actor acting on a series of everyday objects. The participants in one experiment viewed a series of still photos of unfolding actions on objects (e.g., blowing the nose), and 15 minutes later they were asked to complete a recognition task. At recognition, participants viewed photos representing temporally distant moments, backward or forward in time compared to the original, along with the same photos seen at encoding. Results showed that participants tended to accept forward photos more than backward photos. In a pilot study, we explored the role of the temporal distance between encoding and recognition. Results showed that when 3 days elapsed between the encoding and recognition phases, participants did not tend to accept forward photos more than backward photos.

In this paper we investigate whether subject doubling in French is affected by the Uniform Information Density (UID) principle, which states that speakers prefer language encoding that minimizes fluctuations in information density. We show that, other factors being controlled, speakers are more likely to double the NP subject when it has a high surprisal, thus providing further empirical evidence to the UID principle which predicts a surprisal-redundancy trade-off as a property of natural languages. We argue for the importance of employing GPT-2 to investigate complex linguistic phenomena such as subject doubling, as it enables the estimation of subject surprisal by considering a rather large conversational context, a task made possible by powerful language models that incorporate linguistic knowledge through pre-training on extensive datasets.

Choice deadlines are commonly imposed in decision-making research to incentivize speedy responses and sustained attention to the task settings. However, computational models of choice and response times routinely overlook this deadline, instead simply omitting trials past the deadline from further analysis. This choice is made under the implicit assumption that parameter estimation is not significantly affected by ignoring these omissions. Using new tools from likelihood-free inference, here we elucidate the degree to which omitting omissions, even in seemingly benign settings, can lead researchers astray. We explore the phenomenon using a Sequential Sampling Model (SSM) with collapsing boundaries as a test-bed.

Generic statements (e.g., “girls wear makeup”) tie properties to groups and are a common way of transmitting stereotypes. One natural but untested way that people might try to undermine these statements is by making a similar statement about salient but not mentioned contrastive groups (e.g., “boys can wear makeup too”). Do can generics license the same judgments as do generics? Four studies examined how adults judge two novel groups when one group does a property (e.g., Zarpies make pizzas) while the other group can do the property (e.g., Gorps can make pizzas too). Compared to do generics, adults consistently judged groups described with can generics to be less likely to have, less interested in, less competent at, and for it to be less permissible for them to do the property. Overall, these results suggest that can generics are unlikely to be an effective means at equating beliefs about two groups.

This study explores human capabilities in distinguishing and recognizing entities that change over time from those that do not. We specifically investigate the linguistic distinction between "individual-level predicates" (ILPs) and "stage-level predicates" (SLPs). Our empirical approach focuses on how humans visually distinguish these two types. We performed a corpus analysis, in which a set of image captions were randomly extracted and annotated by experts with either SLP or ILP labels. The findings indicated a predominance of SLPs over ILPs in the image captions. We then performed automatic annotation on a large dataset of image captions and conducted a machine-learning experiment on image classification based on ILSs and SLPs. Our results demonstrated that SLPs were identified with high accuracy, while ILPs were identified with about chance level, substantially lower than human capabilities. Given the analyses, we discuss what features of the image contribute to distinguishing between ILPs and SLPs.

Content-curating algorithms provide a crucial service for social media users by surfacing relevant content, but they can also bring about harms when their objectives are misaligned with user values and welfare. Yet, potential behavioral consequences of this alignment problem remain understudied in controlled experiments. In a preregistered, two-wave, collaborative filtering experiment, we demonstrate that small changes to the metrics used for sampling and ranking posts affect the beliefs people form. Our results show observable differences in two types of outcomes within statisticized groups: belief accuracy and consensus. We find partial support for hypotheses that the recently proposed approaches of "bridging-based ranking" and "intelligence-based ranking" promote consensus and belief accuracy, respectively. We also find that while personalized, engagement-based ranking promotes posts that participants perceive favorably, it simultaneously leads those participants to form more polarized and less accurate beliefs than any of the other algorithms considered.

The cognitive processes underlying mental folding have been investigated for decades, while the neural correlates associated with this spatial transformation are barely understood. This study combines cognitive modeling with EEG recordings from 41 subjects to investigate the general mechanisms of mental spatial transformation. By linking model-based simulation and electrocortical activity, we identified brain areas involved during mental folding. Our novel approach showed active central parietal and left parietal, as well as occipital areas during spatial storage, while the right parietal cortex was associated with spatial transformation. The left occipital and parietal regions were active especially during visual baseline trials, while the right parietal region exhibited stronger activity for more difficult folding trials, replicating previous results. The varying activation patterns imply different cognitive loads for storage and for transformation depending on task difficulty.

Typical models of learning assume incremental estimation of continuously-varying decision variables like expected rewards. However, this class of models fails to capture more idiosyncratic, discrete heuristics and strategies that people and animals appear to exhibit. Despite recent advances in strategy discovery using tools like recurrent networks that generalize the classic models, the resulting strategies are often onerous to interpret, making connections to cognition difficult to establish. We use Bayesian program induction to discover strategies implemented by programs, letting the simplicity of strategies trade off against their effectiveness. Focusing on bandit tasks, we find strategies that are difficult or unexpected with classical incremental learning, like asymmetric learning from rewarded and unrewarded trials, adaptive horizon-dependent random exploration, and discrete state switching.

Current models of cognitive control frame its allocation as a process of expected utility maximization. The benefits of a candidate action are weighed against the costs of that control allocation (e.g. opportunity costs). Recent theorizing has found that it is normative to account for the value of learning when determining control allocation. Here, we sought to test whether learning expectations could explain people's initial control allocation in a standard dot-motion perceptual task. We found that subjects' initial skill level and learning rate in a first block were able to predict their initial willingness to accumulate evidence in a second block, interpreted as a greater control allocation for the task. Our findings support the hypothesis that agents consider the learnability of a task when deciding how much cognitive control to allocate to that task.

Innovation in children is typically studied by examining their capacity to create novel tools. However, innovation also involves recognising the future utility of a solution. Across two experiments, we examined children’s capacity to recognise and construct a tool for future uses. Experiment One presented 3- to 5-year-olds (N=55) with a future-directed problem-solving task. When given a tool construction opportunity in anticipation of returning to the task, only 5-year-olds made the correctly shaped tool above chance levels. Experiment Two assessed 3- to 7-year-olds’ (N=92) capacity to build a tool with future, as well as present, utility in mind. Age was positively associated with constructing a tool of greater utility than necessary to solve the present task. Children’s propensity to construct longer tools was associated with their capacity to prepare for two alternative possibilities on a secondary task, suggesting performance on our innovation task reflects emerging future-oriented cognition.

Decisions about extinction risks are ubiquitous in everyday life and for our continued existence as a species. We introduce a new risky-choice task that can be used to study this topic: The Extinction Gambling Task. Here, we investigate two versions of this task: a Keep variant, where participants cannot accumulate any more earnings after the extinction event, and a Lose variant, where extinction also wipes out all previous earnings. We derive optimal solutions for both variants and compare them to behavioural data. Our findings suggest that people understand the difference between the two variants and their behaviour is qualitatively in line with the optimal solution. Further, we find evidence for risk-aversion in the Keep condition but not in the Lose condition. We hope that this task can facilitate further research on this vital topic.

Compositional generalization that requires production and comprehension of novel _structures_ through observed constituent parts has been shown to be challenging for even very powerful neural network models of language. However, one of the test cases that poses the greatest difficulty---generalization of modifiers to unobserved syntactic positions---has not been empirically attested in human learners under the same exposure conditions assumed by these tests. In this work, we test adult human learners on whether they generalize or withhold the production of modification in novel syntactic positions using artificial language learning. We find that adult native speakers of English are biased towards producing modifiers in unobserved positions (therefore producing novel structures), even when they only observe modification in a single syntactic position, and even when the knowledge of their native language actively biases them against the plausibility of the target structures.

Humans preferentially recall items that are presented in close temporal proximity together -- a phenomenon known as the "temporal contiguity effect". In this study, we investigate whether this phenomenon emerges naturally when training a recurrent neural network with episodic memory on free recall tasks. The model learns to recall items in the order they were presented, consistent with the human contiguity effect. The strength of this effect predicts the performance of individual networks, mirroring experimental findings in humans where stronger contiguity effects predict higher recall performance. The contiguity effect in the model is supported by a neural representation of item index, resembling the `method of loci'. This differs from prominent computational models of human memory, which use a slow decay of past information to guide sequential retrieval. Our findings provide insights into the mechanisms underlying episodic memory and pave the way for future studies of its interactions with other cognitive processes.

In term series problems where multiple mental models can be constructed, partial-order models can be created as mental representations, which make it easier to perceive the symmetry of the terms. To test these hypotheses, we categorized multi-model (indeterminate) term series problems according to the patterns of partial-order models that could be constructed, and analyzed the reasoning performance for each pattern. These results suggest that reasoners tend to use the symmetry of terms to reduce the cognitive load of reasoning. Analysis of the patterns of incorrect answers also suggests that attempts to exploit the symmetry of the term may be biased, leading to errors in reasoning.

Cognitive control is thought to regulate the conflict between stability---maintaining the current task in the face of distraction---and flexibility---switching to a new task of greater priority. However, evidence conflicts regarding when and to what extent stability and flexibility trade-off. A normative theory of flexibility and stability may help clarify when and why we should expect such trade-offs to occur. Towards such a theory, we model task-switching as a problem of decision-making under uncertainty, in which the decision-maker must simultaneously infer both the identity of a stimulus and the task governing the correct response to that stimulus. We find that optimal behavior is either extremely stable or extremely flexible, but not both, indicating a normative basis for a trade-off between the two. However, we also show that a sub-optimal but more realistic decision-maker exhibits behavior between these two extremes, and more closely resembles experimental data.

Prospect Theory has been highly influential; however its experimental paradigm lacks higher orders of uncertainty. To introduce this, participants are asked to imagine themselves facing a choice between two bags containing 100,000 blue or red balls in unknown proportions. A red ball wins £500. Participants are shown samples from each bag; e.g., 5 balls from Bag 1 (3 red) and 100 balls from Bag 2 (55 red). The bags can be represented by distributions with Bag 1 having a higher mean probability estimate (60% vs 55%), but more variance (second order uncertainty) in that estimate. By varying observed frequencies and gain vs loss formats, we seek to determine if classic findings remain when higher order uncertainties are present. Results consistent with the four-fold pattern are seen for gains (uncertainty seeking at low probability values, uncertainty aversion at high probability values) but for losses, uncertainty aversion is seen at all values.

We test whether the support one holds about an event is influenced by other hypotheses. We addressed this by examining context effects in subjective probabilities (SPs) when forecasting NCAA men's basketball team rankings. A challenge in investigating context effects with naturalistic stimuli is the need to model the different representations of the options. To do so, we adapted the Spatial Arrangement method to capture individual representations and developed an algorithm to select stimuli. We asked participants steeped in basketball knowledge to create spatial maps for 50 teams. They were then presented with customized triplets of teams and asked to estimate their SP that one team would outrank the others. The study uncovered context effects in SPs, and moderators of the effects. Our findings suggest that similar cognitive processes may govern the construction of belief and preference and highlight the importance of modeling mental representations to understand forecasting scenarios.

The recent upswing in both use and acceptance of they/them as a singular pronoun has led to it becoming potentially ambiguous between singular and plural interpretations in cases like “Alex went running with Liz. They fell down” in which Alex is known to use they/them pronouns. The current work uniquely investigates how children interpret they in these ambiguous cases. Specifically, 5-year-olds, 8-year-olds, and an adult control group underwent a partial replication of Arnold et al. (2021), wherein they answered comprehension questions regarding a series of two-sentence stories. Results show that children can successfully map the pronoun they onto a singular individual when there are no plural competitors and that they interpret ambiguous they similarly to adults, although 5-year-olds interpret this pronoun as singular more often than 8-year-olds. These findings indicate that older children potentially undergo a form of overregularization of they due to grammatical rules enforced at school.

Autoregressive Large Language Models (LLMs) trained for next-word prediction have demonstrated remarkable proficiency at producing coherent text. But are they equally adept at forming coherent probability judgments? We use probabilistic identities and repeated judgments to assess the coherence of probability judgments made by LLMs. Our results show that the judgments produced by these models are often incoherent, displaying human-like systematic deviations from the rules of probability theory. Moreover, when prompted to judge the same event, the mean-variance relationship of probability judgments produced by LLMs shows an inverted-U-shaped like that seen in humans. We propose that these deviations from rationality can be explained by linking autoregressive LLMs to implicit Bayesian inference and drawing parallels with the Bayesian Sampler model of human probability judgments.

We conduct a large-scale study of online community variation in language. We show that factors of efficient communication, which have been shown to drive crosslinguistic variation in lexical semantic systems, also play a role in within-language variation across 1926 English-language Reddit communities. We study variation in stancetaking behaviour, a domain where efficient communication may be influenced by social motivations for language use. We find that communities indeed have efficient stancetaking systems, particularly with respect to their own communicative needs. However, contrasting with crosslinguistic work, we find that communities are often not optimized for their needs. Moreover, we find that community-level social factors correlate with how optimized they are. These results highlight the importance of accounting for social pressures for language use when studying how efficient communication drives variation.

Helping is a universal human behavior, and is a core aspect of a functioning society. However, the decision to provide help, and what type of help to provide, is a complex cognitive calculation that weights many costs and benefits simultaneously. In this paper, we explore how various costs influence the moment-to-moment decision to help in a simple video game. Participants were paired with another human participant and were asked to make repeated decisions that could benefit either themselves or their partner. Several preregistered manipulations altered the cost each person paid for actions in the environment, the intrinsic resource capacity of individuals to perform the task, the visibility of the other player's score, and the affordances within the environment for helping. The results give novel insight into the cost-benefit analyses that people apply when providing help, and highlight the role of reciprocity in influencing helping decisions.

Math anxiety is a pervasive issue in higher education that is often associated with poor performance outcomes. A hypothesized reason for this association is that individuals with math anxiety experience negative and intrusive thoughts related to the situation, their performance, and its consequences. These distractions are thought to be specific to math-related contexts. However, recent empirical evidence from the test anxiety literature calls the anxiety-distraction association into question. Here, we demonstrate that (a) math anxiety is associated with higher average reports of negative distraction, (b) that math anxiety-induced distraction is specific to the math problem-solving domain, and (c) that test anxiety also accounts for higher ratings of math-specific negative distraction. Investigating potential mechanisms underlying the math anxiety–poor math performance relationship is necessary for implementing effective interventions that foster math success, both in educational settings and in everyday life.

Entities in the spatial domain (objects) and the temporal domain (events) are characterized by parallel distinctions that are supported by a shared notion of individuation that runs across domains. This work investigates whether conceptual considerations of individuation are language-independent. We test speakers of English, which uses count-mass syntax and telicity to mark linguistic individuals in the nominal and verbal domain respectively, and Mandarin, which lacks these linguistic features. Our results throw light onto the nature of entity categories in the human mind: both English-speaking and Mandarin-speaking viewers process individuated and non-individuated entities differently, with only the former having a well-defined (temporal/spatial) structure with integrally-ordered, distinct parts. Crucially, these features of non-linguistic individuation are conceptualized in similar ways cross-linguistically and are potentially universal.

Our choices often require us to prioritize some features of our rich sensory experience over others. Past work suggests that humans solve this problem by focusing on relevant information while discarding that which is irrelevant. Yet learning which features to prioritize requires extensive experience. Moreover, features that are irrelevant now may become relevant in the future. One way to address these issues is by sampling individual richly encoded experiences from episodic memory. Here we hypothesize that episodic memory is used to guide decisions based on multiple features of past events. We test this hypothesis using an experiment in which participants made choices about the value of features that were present in multiple past experiences. We find evidence suggesting that participants used episodic memories to flexibly access features of past events during decision making. Overall, these results suggest that episodic memory promotes adaptive decisions when knowledge of multiple features is necessary.

Independent pieces of corroborating evidence should provide stronger support to a hypothesis than dependent pieces of evidence. Overlooking the inferiority of dependent relative to independent items of evidence can lead to a chain reaction of double-counting evidence, over-estimating the probability that the fact under consideration is true, and making wrongful decisions. Within fictitious scenarios, we investigate people’s sensitivity to the independency advantage. We assess their ability to integrate multiple items of evidence that come from (in)dependent sources who differ in reliability. We find that participants properly perceive dependencies when explicitly asked but fail to distinguish the probative value of dependent versus independent evidence in their belief updating. Still, individuals who perceive a strong dependence between sources treat the evidence as being more redundant. We find no dependency-related effects on participants’ individual Bayesian network model predictions. Potential reasons why participants perceive (in)dependencies and yet (mostly) fail to discount for them are discussed.

This study investigates whether homophone competitors are activated during typewriting and to which extent such activation is modulated by syntactic category. In two experiments, we compared the typewriting of homophone pairs in high vs. low conflict sentences (i.e., both homophones vs. only one homophone in the sentence, respectively) in a sentence dictation task (Experiment 1) and in a question-answering task (Experiment 2). The homophone pairs either belonged to the same or different syntactic categories. In Experiment 1, we found a homophone interference effect in accuracy, independent of conflict and syntactic category. In Experiment 2, this effect was replicated, but in addition, participants were slower to type homophones in a high vs. a low conflict context. Our results show a robust, lexically-situated homophone interference effect, regardless of conflict and syntactic category, but when deeper processing of the sentence is involved, conflict starts to play a role.

Recent research indicates that paired comparisons can accelerate perceptual learning of challenging dermatological lesion categories. Here we investigated whether the role of object categories as targets or distractors differentially influences learning outcomes. The frequency with which a given category occupied the target position was manipulated across three learning conditions: Always-Never, where half of 10 categories were always shown as target and the other half never shown as target; Often-Rarely, where half of categories appeared 75% as targets and 25% as distractors, with reversed presentation frequency for the other half; and Equal Split learning, in which all categories appeared as targets or distractors equally often. After learning, transfer results indicated that all conditions yielded equivalent overall learning, but categories prioritized more often as targets exhibited greater learning gains. These findings implicate differential processing of images in comparisons, even when no information regarding target vs. distractor was given prior to feedback.

When adults estimate meaningful numbers their distribution of first-digits is strongly biased towards Benford’s Law. Insight into why this bias emerges could be gained by examining when it emerges in children. Three hypotheses were formulated: the Representation Hypothesis predicted this distribution can be found in all grades; the Integration Hypothesis predicted a leap in Benford bias from Grade 3 to 4 due to increased mathematical knowledge; and the Distribution Hypothesis proposed a gradual increase across grades due to implicit learning. 151 children in Grades 2 to 4 were asked to estimate numbers based on images and questions. Results showed a strong Benford bias in all three grades but a significant leap from Grade 2 to 3. This was evidence for both the Representation and Integration Hypotheses. Therefore, Benford bias may develop in children due to how they represent numbers, or develop complex mathematical processes, or perhaps some combination of these.

Science can be viewed as a collective, epistemic endeavor. However, a variety of factors- such as the publish-or-perish culture, institutional incentives, and publishers who favor novel and positive findings- may challenge the ability of science to accurately aggregate information about the world. Evidence of the shortcomings in the current structure of science can be seen in the replication crisis that faces psychology and other disciplines. We analyze scientific publishing through the lens of cultural evolution, framing the scientific process as a multi-generational interplay between scientists and publishers in a multi-armed bandit setting. We examine the dynamics of this model through simulations, exploring the effect that different publication policies have on the accuracy of the published scientific record. Our findings highlight the need for replications and caution against behaviors that prioritize factors uncorrelated with result accuracy.

The study focuses on the mechanisms through which dance brings people together. We recorded 7 improvised dance duets and asked 5 skilled improvisers to rate the perceived togetherness in the recorded dances. Subsequently, we employed pose tracking techniques and developed a quantitative measure of the stability of interpersonal movement coordination between dancers, demonstrating that it strongly correlates with experts' togetherness ratings. Based on follow-up interviews, we revealed that experts' understanding of togetherness converges to a stable construct, involving a state of responsive, mindful attention. This construct can be grounded in the objective properties of movement coordination. These properties can be framed within the context of dynamical systems, suggesting potential systemic organization principles, such as moment-to-moment adaptation, that promote togetherness. Our mixed-methods research has implications for various fields, including psychology, cognitive science, and art studies.

We conduct tests of a hybrid-similarity exemplar model on its ability to account for the context-dependent memorability of items embedded in high-dimensional category spaces. According to the model, recognition judgments are based on the summed similarity of test items to studied exemplars. The model allows for the idea that “self-similarity” among objects differs due to matching on highly salient distinctive features. Participants viewed a study list of rock images belonging to geologically defined categories where the number of studied items from each category was manipulated, and their old-new recognition performance was then tested. With a minimum of parameter estimation, the model provided good accounts of changing levels of memorability due to contextual effects of category size, within- and between-category similarity, and the presence of distinctive features. We discuss future directions for improving upon the current predictions from the model.

Do children reason that people in close relationships accurately represent each other’s minds? In two experiments (total N = 123), we found that 7- to 9-year-old children from the US (i) reason that people who are close will accurately represent each other’s goals and desires and (ii) infer that people are socially close when they accurately predict each other’s emotional states. These findings suggest that children reason flexibly about mental state attributions within close relationships.

Construal Level Theory (CLT) suggests that we represent objects close to us in a concrete and modal fashion, and that representations become more abstract and amodal with increasing distance from ourselves. Evidence for such an association of abstraction level and distance comes from the Implicit Association Test (IAT), where participants are faster when pressing one key for “near” and “concrete” and another key for “far” and “abstract” targets (congruent), than when “near” is paired with “abstract” and “far” with “concrete” (incongruent). However, previous experiments might have confounded distance and abstraction by employing inherently near and far targets (e.g., CHAIR vs. SUN) that might also differ in their abstractness. Here, we thus experimentally induced different distances in a learning phase before a subsequent IAT task. Even with this controlled distance manipulation, a pronounced congruency effect emerged, providing further support for an association of distance and abstraction level as suggested by CLT.

Dyadic social interaction is a complex coordination task involving a large number of interconnected variables. Previous research has shown that metastability -- persistence for an extended, but impermanent, period of time in a non-stable state of a system -- can be a useful lens for understanding what makes an interaction successful. However, this framework has thus far only been applied to para-conversational signals like heart rate and prosody -- not to the semantic content of a conversation. Here, we present pink noise analysis of semantic trajectories as a metric for conversational success and apply this technique to a large open conversation dataset. Our results demonstrate that pink noise in a conversation predicts a host of variables representing participants' perception of conversation quality. These results have implications for optimizing a whole host of difficult dyadic conversations -- like those between political partisans -- and human-computer interactions, with applications for improving large language models' adaptability.

We are able to learn from others through a combination of trust and vigilance: we trust and believe people who are reliable and have our interests at heart; we ignore those who are incompetent or self-interested. While past work has studied how others' competence influences social learning, relatively little attention has been paid to how others' motivations influence such processes. To address this gap, we develop a Bayesian model of vigilance that considers the speaker's instrumental self-interest, and test predictions of this model through an experiment. In accordance with our model, participants become more vigilant when informants stand to benefit from influencing their actions. When perceived self-interest is maximal, testimony can be discounted wholesale, rendering middle ground increasingly difficult, if not impossible, to find. Our results have implications for research on polarization, misinformation, and disagreement.

People often select only a few events when explaining what happened. What drives people’s explanation selection? Prior research argued that people’s explanation choices are affected by event normality and causal structure. Here, we propose a new model of these existing findings and test its predictions in a novel experiment. The model predicts that speakers value accuracy and relevance. They choose explanations that are true, and that communicate useful information to the listener. We test the model’s predictions empirically by manipulating what goals a listener has and what actions they can take. Across twelve experimental conditions, we find that our model accurately predicts that people like to choose explanations that communicate optimal interventions

When a human and an AI agent collaborate to complete a task and something goes wrong, who is responsible? Prior work has developed theories to describe how people assign responsibility to individuals in teams. However, there has been little work studying the cognitive processes that underlie responsibility judgments in human-AI collaborations, especially for tasks comprising a sequence of interdependent actions. In this work, we take a step towards filling this gap. Using semi-autonomous driving as a paradigm, we develop an environment that simulates stylized cases of human-AI collaboration using a generative model of agent behavior. We propose a model of responsibility that considers how unexpected an agent’s action was, and what would have happened had they acted differently. We test the model’s predictions empirically and find that in addition to action expectations and counterfactual considerations, participants’ responsibility judgments are also affected by how much each agent actually contributed to the outcome.

How do humans decide what to look at and when to stop looking? The Rational Action, Noisy Choice for Habituation (RANCH) model formulates looking behaviors as a rational information acquisition process. RANCH instantiates a hypothesis about the perceptual encoding process using a neural network-derived embedding space, which allows it to operate on raw images. In this paper, we show that the model not only captures key looking time patterns such as habituation and dishabituation, but also makes fine-grained, out-of-sample predictions about magnitudes of dishabituation to previously unseen stimuli. We validated those predictions experimentally with a self-paced looking time task in adults (N = 468). We also show that model fits are robust across parameters, but that assumptions about the perceptual encoding process, the learning process and the decision process are all critical for predicting human performance.

The control-of-variables strategy is often considered to be the superior strategy when children learn from experiments. However, by simulating Bayesian likelihoods of outcomes from a water displacement task, we show that certain confounded comparisons may support belief revision better than controlled comparisons. We tested this assumption by experimentally varying the types of comparisons that participants observed in a learning task involving balls of different sizes and materials (N = 90, age range 6- to 9-yrs). In the Size, Material, and Mixed conditions we presented controlled comparisons. In the Confounded Incongruent Condition, we presented confounded comparisons in which the larger ball was made of the heavier material. In line with our hypotheses, children in the Confounded Incongruent Condition revised their beliefs more than children in the other conditions, as indicated by higher transfer test scores. These findings suggest that confounded comparisons may in fact sometimes provide more optimal information for learning.

Metaphors are often used to intuitively communicate about movement. Here, expert pianists played two melodies while keeping eight different metaphors in mind, contrasting arousal level, valence direction, and metaphor type (action-related and emotion-related metaphors). Measures of keystroke timing and velocity were analyzed to assess the relative contribution of metaphor content and melodic note sequence to motor performance, alongside ratings of semantic similarity between metaphors. Using Bayesian multilevel models, results indicate that the arousal level of the metaphor has the most influence on keystroke force, average tempo, and tempo variability. Additionally, interactions with valence are seen for the timing measures, and for both valence and type in force. No effects of the melody sequence were found. Similarity ratings of metaphor pairs indicate that mental similarities largely mirror performance similarities. These findings show the potential effects of mental imagery on motor performance and have implications for teaching complex movements in practical settings.

There is considerable debate about the origin, mechanism, and extent of humans’ capacity for recursive mindreading: the ability to represent beliefs about beliefs about beliefs (and so on). Here we quantify the extent to which language exposure could support this ability, using a Large Language Model (LLM) as an operationalization of distributional language knowledge. We replicate and extend O’Grady, et al. (2015)’s finding that humans can mindread up to 7 levels of embedding using both their original method and a stricter measure. In Experiment 2, we find that GPT-3, an LLM, performs comparably to humans up to 4 levels of embedding, but falters on higher levels, despite being near ceiling on 7th-order non-mental control questions. The results suggest that distributional information (and the transformer architecture in particular) can be used to track complex recursive concepts (including mental states), but that human mentalizing likely draws on resources beyond distributional likelihood.

This paper uses phylogenetic modeling to investigate the evolutionary mechanisms responsible for the maintenance of sound symbolism in the world's languages. Applying our model to sound-meaning correspondences reported in the literature, we find that many previously established associations are weaker than expected when analyzed using our framework. This is possibly because certain sound-meaning associations are artifacts of slow-changing vocabulary items rather than specific preferences for certain sounds in words with certain meanings. For sound-meaning associations for which we find evidence, the maintenance of sound symbolism appears to be due to a tendency to preserve words in certain meanings if certain sounds are present.

Attribution theory aims to explain people’s judgments about the cause of some behavior or outcome, often involving other people. The theory has proven to be broadly applicable and points towards important aspects of human cognition. This relevance is perhaps unsurprising given that attribution theory is a type of causal inference. However, there has been relatively little work on attribution theory in relation to causal learning. More specifically, previous literature has mostly examined attributions and their behavioral and motivational outcomes following a single observation, rather than capturing the dynamics of causal attribution (i.e., how those judgments shift as people observe more vignettes and thereby learn about the situation). We thus ran an exploratory study using a vignette design to investigate whether attributions and their outcomes change across multiple instances of observation and behavior adaptation.

The amount of resources someone has can influence their emotional responses to events. Two preregistered experiments investigated whether adults and children consider others' resource quantities when inferring their emotions. Sixty adults (Experiment 1) and 135 8-10-year-olds (Experiment 2) saw stories about people wanting an item but differing in the number of items they have enough money to buy (ranging from 1 to 5). Participants rated how these people felt both when buying the item and when losing it. Both adults and children judged that the fewer resources someone has, the sadder they felt when the item was lost, and the bigger emotional change they experienced (relative to when buying the item). Adults also judged that the impact of resource scarcity on emotion was most significant when the person had depleted all their resources, as opposed to still retaining some to influence the negative outcome, and this pattern is emerging in children. These findings suggest that even when the same negative event occurs, adults and children as young as 8 consider others' available resources when inferring their emotional responses to the event.

Often the only source of information for learning a word is its surrounding language context. For example, even without seeing a rambutan, one can learn that it is a fruit just from hearing “I like sweet, juicy rambutans”. What processes foster learning words from context? We investigated candidate processes that can unfold when the context precedes a new word and can foster learning via prediction, versus when the context occurs after and can only be used retroactively. We particularly sought to illuminate a role for working memory in linking a new word to the meaning implied by its context. Experiment 1 probed word learning during reading with eye tracking, and Experiment 2 probed word learning from speech. We found convergent evidence that regardless of whether the context precedes or follows a new word, word learning depends on maintaining the context in working memory while linking it to a new word.

Speech perception requires inferring category membership from varied acoustic cues, with listeners adeptly adjusting cue utilization upon encountering novel speech inputs. This adaptivity has been examined through the dimension-based statistical learning (DBSL) paradigm, which reveals that listeners can quickly de-emphasize secondary cues when cue correlations deviate from long-term expectations, resulting in cue-reweighting. Although multiple accounts of cue-reweighting have been proposed, direct comparisons of these accounts against human perceptual data are scarce. This study evaluates three computational models–cue normalization, Bayesian ideal adaptor, and error-driven learning–against classic DBSL findings to elucidate how cue reweighting supports adaptation to new speech patterns. These models differ in how they map cues onto categories for categorization and in how recent exposure to atypical input patterns influences this mapping. Our results show that both the error-driven learning and ideal adaptor models effectively capture the key patterns of cue-reweighting phenomena, whereas prelinguistic cue normalization does not. This comparison not only highlights the models' relative efficacy but also advances our understanding of the dynamic processes underlying speech perception adaptation.

An agent-based model for semantic search and retrieval in memory is proposed. The model seeks to generate verbal fluency lists with properties similar to those generated by humans in the semantic fluency task. This model is compared to a random walk in a semantic network in its ability to adjust to the results of 141 undergraduate students in the semantic fluency task in eight different outcomes. We found that the agent-based model fits participants' results better than the random walk model. The results were consistent with optimal foraging theories, and the distributions of the total number of words, similarities, and frequency values were similar to those generated by participants. The potential uses of this model as a virtual environment to experiment with the search and retrieval process in semantic memory are discussed.

Simulating sampling algorithms with people has proven a useful method for efficiently probing and understanding their mental representations. We propose that the same methods can be used to study the representations of Large Language Models (LLMs). While one can always directly prompt either humans or LLMs to disclose their mental representations introspectively, we show that increased efficiency can be achieved by using LLMs as elements of a sampling algorithm. We explore the extent to which we recover human-like representations when LLMs are interrogated with Direct Sampling and Markov chain Monte Carlo (MCMC). We found a significant increase in efficiency and performance using adaptive sampling algorithms based on MCMC. We also highlight the potential of our method to yield a more general method of conducting Bayesian inference with LLMs.

Large scale neural models show impressive performance across a wide array of linguistic tasks. Despite this they remain, largely, black-boxes - learning vector-representations of their input that prove difficult to interpret. This limits our ability to understand what they learn, and when the learn it, or characterise why they often fail to generalise systematically. To address this we introduce a novel approach to interpretability that looks at the mapping a model learns from sentences to representations as a kind of language in its own right. In doing so we introduce a set of information-theoretic measures that quantify how structured a model's representations are with respect to its input, and when during training that structure arises. Our measures are fast to compute, grounded in linguistic theory, and can predict which models will generalise best based on their representations. We use these measures to describe two distinct phases of training a transformer: an initial phase of in-distribution learning which reduces task loss, then a second stage where representations becoming robust to noise. Generalisation performance begins to increase during this second phase, drawing a link between generalisation and robustness to noise. Finally we look at how model size affects the structure of the representational space, showing that larger models ultimately compress their representations more than their smaller counterparts.

Question asking is a key tool for learning, especially in childhood. However, formulating good questions is challenging. In any given situation, many questions are possible but only few are informative. In the present work, we investigate two ways 5- to 10-year-olds and adults simplify the challenge of formulating questions: by reusing previous questions, and by remixing components of previous questions to form new questions. Our experimental results suggest that children and adults reuse and remix questions and adaptively modulate reuse depending on how informative a question will be in a particular situation. This work shows that task-relevant experience asking questions provides fodder for future questions, simplifying the challenge of inquiry and enabling effective learning.

We explore different aspects of cognitive diversity and its effect on the success of group deliberation. To evaluate this, we use 500 dialogues from small, online groups discussing the Wason Card Selection task - the DeliData corpus. Leveraging the corpus, we perform quantitative analysis evaluating three different measures of cognitive diversity. First, we analyse the effect of group size as a proxy measure for diversity. Second, we evaluate the effect of the size of the initial idea pool. Finally, we look into the content of the discussion by analysing discussed solutions, discussion patterns, and how conversational probing can improve those characteristics. Despite the reputation of groups for compounding bias, we show that small groups can, through dialogue, overcome intuitive biases and improve individual decision-making. Across a large sample and different operationalisations, we consistently find that greater cognitive diversity is associated with more successful group deliberation. Code and data used for the analysis are available in the repository: https://github.com/gkaradzhov/cognitive-diversity-groups-cogsci24

Generalization to new tasks requires learning of task representations that accurately reflect the similarity structure of the task space. Here, we argue that episodic memory (EM) plays an essential role in this process by stabilizing task representations, thereby supporting the accumulation of structured knowledge. We demonstrate this using a neural network model that infers task representations that minimize the current task's objective function; crucially, the model can retrieve previously encoded task representations from EM and use these to initialize the task inference process. With EM, the model succeeds in learning the underlying task structure; without EM, task representations drift and the network fails to learn the structure. We further show that EM errors can support structure learning by promoting the activation of similar task representations in tasks with similar sensory inputs. Overall, this model provides a novel account of how EM supports the acquisition of structured task representations.

Social rewards shape human behavior. During development, a caregiver guides a learner’s behavior towards culturally aligned goals and values. How do these behaviors persist and generalize when the caregiver is no longer present, and the learner must continue autonomously? Here, we propose a model of value internalization where social feedback trains an internal social reward (ISR) model that generates internal rewards when social rewards are unavailable. Through empirical simulations, we show that an ISR model prevents agents from unlearning socialized behaviors and enables generalization in out-of-distribution tasks. Incomplete internalization, akin to "reward hacking" on the ISR, is observed when the model is undertrained. Finally, we show that our model internalizes prosocial behavior in a multi-agent environment. Our work provides a framework for understanding how humans acquire and generalize values and offers insights for aligning AI with human values.

When humans communicate, they typically adapt to their conversational partner in how they speak, and in how they interpret what the conversational partner says. In the area of pragmatic language comprehension, there is so far little work that has studied the individual differences between listeners with respect to adapting to a given speaker. We investigated which individual cognitive factors correlate with listener's ability to associate speakers with humorous utterances. We found that working memory updating (as measured by the Keeping Track Task) was a significant predictor of adaptation to the speaker. These findings are in line with a recent related study (Schuster et al., 2023) which investigated speaker-specific adaptation to the use of uncertainty expressions. We furthermore observe a correlation between speaker adaptation and the Faux Pas Test. This task is used for measuring theory of mind abilities and is believed to specifically tap into intention recognition, an ability which is also very relevant to joke comprehension.

This study introduces a novel approach for quantifying individual differences in print exposure through the integration of distributional semantics with the Author Production Test (APT). By employing the Universal Sentence Encoder to generate vector representations of authors from their works, we constructed 'participant vectors' reflecting the aggregated author vectors individuals produced in the APT and 'genre vectors' capturing the representative characteristics of each literary genre. By analyzing the cosine similarities between participant and genre vectors, we objectively estimated individuals' genre preferences. The results demonstrated a significant correlation between these objective measures and self-reported genre preferences, particularly for older frequent readers, highlighting the method's effectiveness. Our findings offer a promising avenue for the objective measurement of print exposure, with potential implications for developing personalized models of lexical behavior.

We provide a generalized, normative model of visual detection that accounts for key asymmetries between decisions about presence and about absence. In our model, decisions about presence are made based on the visibility of presented stimuli, but decisions about absence are made based on counterfactual visibility: beliefs about the degree to which a stimulus would have been visible if present. Behavioral patterns in visual detection experiments under different levels of partial occlusion validate key model predictions. Specifically, we find that unlike decisions about presence, the confidence and speed of decisions about absence are largely independent of perceptual evidence, but are sensitive to the counterfactual visibility of absent stimuli. Finally, we reveal robust individual differences in counterfactual perception, with some participants systematically incorporating counterfactual visibility into perceptual decisions in a different fashion from others. We discuss implications for the varied and inferential nature of visual perception more broadly.

We propose that kinds relate to particular things either constitutively or contingently. Taxonomic categories of animals and artifacts constitutively relate their members: DOG and CAR group things by aspects of the forms of their matter; the forms that make them things instead of stuff. Categories of things in roles or with diseases contingently relate to their members: LAWYER and DIABETIC group things by forms other than the forms that make them things. We confirm this distinction in five experiments with American adults.

Building on counterfactual theories of causal-selection, according to which humans intuitively evaluate the causal responsibility of events, we developed an experimental paradigm to examine the effect of causal-selection explanations on abductive causal inference. In our experiment, participants attempted to infer the rule responsible for winning outcomes of random draws from urns with varying sampling probabilities. Participants who were provided with causal-selection judgments as explanations for the outcomes made significantly closer inferences to the rule than those relying on observations alone, or on other explanations of causal relevance. We mirror these empirical results with a computational model of inference from explanation leveraging the theories of causal selection.

Aesthetic appreciation is inherently multidimensional: many different stimulus dimensions (e.g., colors, shapes, sizes) contribute to our aesthetic experience. However, most studies in empirical aesthetics used either non-parametrically controlled multidimensional or parametrically controlled unidimensional stimuli, preventing insight into the relative contribution of each stimulus dimension or any potential interactions between them to perceptual and aesthetic evaluations. To adress this gap we combined two recent developments: the Order & Complexity Toolbox for Aesthetics (Van Geert, Bossens, & Wagemans, 2023) for generating multidimensional parametrically controlled stimuli, and Gibbs Sampling with People (Harrison et al., 2020) for efficiently characterizing subjective evaluations in multidimensional stimulus space. We show the advantages of this new approach by estimating multidimensional probability distributions for both aesthetic (pleasure and interest) and perceptual evaluations (order and complexity) in two visual multidimensional parametric stimulus spaces, and we compare our results with findings from earlier studies that used either non-parametric or unidimensional stimuli.

In this paper, we report three reading time (RT) experiments (one using self-paced reading and two using A-Maze) that tested the cognitive mechanisms underlying the processing of classifier-noun dependencies in Mandarin Chinese (MC). We leveraged prenominal relative clauses and the contrast between general and specific classifiers in MC, which offered a good testing ground for existing theories of sentence processing. Results from the A-Maze experiments showed both locality and expectation effects. More importantly, we observed an interaction between locality and expectation in the way of Information Locality (Futrell, 2019; Futrell, Gibson, & Levy, 2020): Expectation-driven facilitation was highly constrained by locality effects. To capture the results, we implemented a resource-rational Lossy-Context Surprisal model (Hahn et al., 2022) for MC, which successfully replicated the key patterns in the A-Maze experiments.

What drives lexical alignment in the context of language emergence? We test the theory that limited context promotes alignment, because individuals cannot make use of iconic mappings between shared meanings and forms. Using a novel referential communication paradigm where participants use pre-recorded gesture videos to communicate, we test different context conditions. We find, unexpectedly, no alignment differences between dyads with shared context and dyads with limited context, even though the former have fewer communicative errors. Importantly, we do observe differences when it comes to the iconic strategies used: less shared context promotes the use of (shared) visual iconicity.

Expanding on previous research highlighting the learning benefits of errors, this study explores the enduring effects of error-induced learning. Using an adaptive fact-learning system, 23 participants engaged in recognition, recall, and error tasks, with repeated testing for memory assessment. Initial findings echoed previous results: items learned through errors initially took longer to retrieve. However, a significant shift occurred over time; error items demonstrated faster retrieval speeds compared to study items, and, most notably, they exhibited greater resilience against forgetting. This study reaffirms the positive role of errors in learning and uncovers their contribution to enhanced long-term memory retention. These insights challenge traditional learning paradigms, advocating for an educational approach that recognizes and leverages the value of errors in learning processes.

The creation of sophisticated AI systems that are able to process and produce images and text creates new challenges in assessing the capabilities of those systems. We adapt a behavioral paradigm from developmental psychology to characterize the counting ability of a model that generates images from text. We show that three model scales of the Parti model (350m, 3B, and 20B parameters respectively) each have some counting ability, with a significant jump in performance between the 350m and 3B model scales. We also demonstrate that it is possible to interfere with these models' counting ability simply by incorporating unusual descriptive adjectives for the objects being counted into the text prompt. We analyze our results in the context of the knower-level theory of child number learning. Our results show that we can gain experimental intuition for how to probe model behavior by drawing from a rich literature of behavioral experiments on humans, and, perhaps most importantly, by adapting human developmental benchmarking paradigms to AI models, we can characterize and understand their behavior with respect to our own.

This paper explores the use of linguistic strategies, specifically discourse markers like ’but’, to express contrasts between expectations and reality when faced with unexpected events. The study concentrates on Denial of Expectation (DofE), the most powerful form of contrast, which arises when the expected value based on background assumptions is not met. The main focus of this paper is to model DofE as a weighted homogeneous relationship between object properties. The aim is to predict DofE for numerical properties in specific contexts. I aim to address a gap in previous models by considering the role of context. This is achieved by analyzing contrastive sentences from German car and motorcycle reviews. The research presents the concept of expectation intervals for scalar properties. These intervals align with expectations and exceeding them triggers a potential contrast. The study incorporates causality, expected behavior, and a shift function in selecting contrastive pairs, transforming the conditions into an algorithm. Keywords: contrast; computational and cognitive modeling; discourse analysis

We conducted a case study on how unreliable and/or unrepresentative stimuli in psycholinguistics research may impact the generalizability of experimental findings. Using the domain of lexical ambiguity as a foil, we analyzed 2033 unique words (6481 tokens) from 214 studies. Specifically, we examined how often studies agreed on the ambiguity types assigned to a word (i.e., homonymy, polysemy, and monosemy), and how well the words represented the populations underlying each ambiguity type. We observed far from perfect agreement in terms of how words are assigned to ambiguity types. We also observed that coverage of the populations is relatively poor and biased, leading to the use of a narrower set of words and associated properties. This raises concerns about the degree to which prior theoretical claims have strong empirical support, and offers targeted directions to improve research practices that are relevant to a broad set of domains.

High-frequency words are often assumed to be the most useful words for communication, as they provide the greatest coverage of texts. However, the relationship between text coverage and comprehension may not be straightforward -- some words may provide more information than others. In this study, we explore alternative methods of defining core vocabulary in addition to word frequency (e.g., words that are central hubs in semantic association networks). We report on the results of an empirical test of communicative utility using a text-based guessing game. We show that core words that reflect corpus-based distributional statistics (like frequency or co-occurrence centrality) were less useful for communication than others. This was evident both in terms of the size of the vocabulary that must be known and the proportion of the text that must be covered for successful communication.

Prevailing theories propose that confidence in two-alternative forced-choice decisions is based on the probability that the selected option is correct. However, recent findings from three-alternative tasks suggest that adults’ confidence might irrationally reflect the difference between the probabilities of the best and next-best options only, with other options disregarded. Using a novel probability task (in which participants guess the colour of a ball to be randomly selected from varying distributions) and a uniquely sensitive confidence measure, we investigated metacognition in multi-option decision making in children (N = 97, aged 6-9-years) and adults (N = 51). Contrary to previous findings, children’s and adults’ confidence was primarily explained by the probability of the best option. However, preliminary findings suggest that among older children and adults, additional irrelevant factors also accounted for unique variance in confidence. In some contexts, human confidence might be initially calibrated rationally but increasingly reflect irrational factors over development.

When children use online apps, they often share personal information, such as their name, address, and birthday. In the present study, we investigated the mental models children use to reason about what apps are allowed to do with personal data after it has been willingly shared with an app. 57 children ages 8- to 11-years-old were read a story in which they were asked to judge whether an online game (app) was allowed or not allowed to perform four different actions: looking, saving, selling, and showing. We compared these judgments to a comparison condition where we asked children what users themselves should be allowed to do with their data. We found that children viewed the app as less permitted to act on the data than users as well as some further differences by action-type. Our findings suggest that children use something akin to a “lending” model to conceptualize data transfers, in which apps have less rights than users despite the data being willingly transferred to the app. Our findings also suggest that children differentiate among the uses of information as children think certain actions by the app are less permissible than others (e.g., looking is more permissible than selling).

To succeed in social situations, we must learn how social cues predict subsequent events. How do we quickly form associations between a variety of social cues, such as individuals signaling their current emotion state, and social outcomes? To address this question, we developed a task in which participants viewed images of individuals conveying different emotions and searched among these images to gain rewards. Rewards were associated with either individuals’ identities or emotion cues. Across four experiments (N=720), individuals learned about rewards more efficiently from individual identity cues versus a wide variety of emotion cues. Participants also generalized cue-outcome associations more easily for individuals versus emotions. Learning was worse if participants experienced a change in the association rule, especially when switching from learning individual-based associations to emotion-based associations. Overall, we show that social cue type influences how associations between cues and rewards are learned, with implications for understanding learning in social contexts.

Social interaction plays a key role in children's development of language structure and use. In particular, children must successfully navigate the complex task of coordinating their communicative intents with people around them in early conversations. This study leveraged advanced NLP techniques to analyze a large corpus of child-caregiver conversations in the wild, combining methods for communicative intent inference and for turn contingency evaluation. Key findings include the prevalence of classic adjacency pairs like question-response; caregivers initiated the overwhelming majority of these sequences. We also document new developmental shifts in intent expression and an interesting dissociation between frequency vs. well-coordinated use across the early years of development. This framework offers a new approach to studying language development in its naturalistic, social context.

Recent work shows that participants make asymmetric causal inferences from apparently symmetric correlational statements (e.g., “A is associated with B”). Can we make sense of this behavior in terms of rational language use? Experiment 1 investigates these interpretive preferences—what we call “PACE effects”—in light of theoretical and experimental pragmatics and psycholinguistics. We uncover several linguistic factors that influence them, suggesting that a pragmatic explanation is possible. Yet, since PACE effects do not show that correlational language leads to causal implicatures strong enough to influence action choice in practical decision contexts, Experiment 2 offers new evidence from an experiment that explicitly compares the effects of causal vs. correlational claims on decision-making. Our results suggest that causal inferences from correlation language are an intricate, but possibly

Pronoun interpretation seems to be driven by structural factors, but also by factors related to meaning. In a forced-choice pronoun interpretation experiment, we compare the impact of the next-mention bias associated with transfer-of-possession-verbs on the interpretation of three Dutch pronominal forms that differ in the strength of their structural biases: reduced personal pronoun ze ‘she_reduced ’, full personal pronoun zij ‘she_full’, and demonstrative pronoun die ‘that’. In addition to replicating the common Goal-bias associated with transfer-of-possession verbs, results show significant differences in the proportion of pronoun resolved to the preceding subject between all three pronominal forms. However, the effect of the next-mention manipulation did not differ between pronominal forms. These findings are in line with a model of pronoun interpretation that combines structural and meaning-related factors, and present particularly strong evidence against models that posit that pronoun interpretation is the mirror image of pronoun production.

Word senses rise and fall due to a variety of causes. Previous research has explored how words grow novel senses, but the opposite problem of word sense decline is much less studied. Inspired by recent work on word decline, we investigate the cognitive factors that might explain the historical decline of word senses. We formalize a set of eight psycholinguistic predictors and assess their roles in discriminating declining senses from stable ones over the past two centuries in English. We find that semantic density, change in usage frequency in the semantic neighbourhood, and contextual diversity all predict word sense decline. Our study elucidates the cognitive underpinnings of word sense decline as the lexicon evolves.

Humans rely on our social networks to make more accurate inferences about the world. Yet it remains unclear how those inferences are shaped by the medium through which information is exchanged and beliefs are shared. In this paper, we report two experiments where participants (N=645) were asked to make inferences about an unknown probability distribution based on limited private observations. They exchanged messages with neighbors in a small social network and were asked to update their beliefs over repeated rounds. We compared three conditions: a unidirectional message medium, a constrained slider medium, and an interactive chat. All groups were able to converge toward more accurate inferences, but their convergence rates varied across conditions in ways not well-captured by common models. We argue that computational models of collective behavior must move beyond the assumption of direct belief transmission to capture the complexities of sharing information through natural language.

This study examines the development of children's gaze during face-to-face conversations, following up on previous work suggesting a protracted development in attending to the interlocutor's face. Using recent mobile eye-tracking technology, we observed children interacting with their parents at home in natural settings. In contrast to previous work, we found that children, even in early middle childhood, exhibit adult-like gaze patterns toward the interlocutor. However, differences emerge in gaze allocation between speaking and listening roles, indicating that while children may focus on faces similarly to adults, their use of gaze for social signaling, such as turn-taking cues, may still be maturing. The work underscores the critical role of social context in understanding the development of non-verbal behavior in face-to-face conversation.

Dependency length minimization is a universally observed quantitative property of natural languages. However, the extent of dependency length minimization, and the cognitive mechanisms through which the language processor achieves this minimization remain unclear. This research offers mechanistic insights by postulating that moving a short preverbal constituent next to the main verb explains preverbal constituent ordering decisions better than global minimization of dependency length in SOV languages. This approach constitutes a least-effort strategy because it’s just one operation but simultaneously reduces the length of all preverbal dependencies linked to the main verb. We corroborate this strategy using large-scale corpus evidence across all seven SOV languages that are prominently represented in the Universal Dependency Treebank. These findings align with the concept of bounded rationality, where decision-making is influenced by `quick-yet-economical' heuristics rather than exhaustive searches for optimal solutions. Overall, this work sheds light on the role of bounded rationality in linguistic decision-making and language evolution.

Eating onion ice cream is improbable, and levitating ice cream is impossible. But scooping ice cream using sadness is not just impossible: it is inconceivable. While prior work has examined the distinction between improbable and impossible events, there has been little empirical research on inconceivability. Here, we report a behavioral and computational study of inconceivability in three parts. First, we find that humans reliably categorize events as inconceivable, separate from probable, improbable, and impossible. Second, we find that we can decode the modal category of a sentence using language-model-derived estimates of subjective event probabilities. Third, we reproduce a recent finding that improbable events yield slowest response times in a possibility judgment task, and show that inconceivable events are faster to judge than impossible and improbable events. Overall, our results suggest that people distinguish the impossible from the inconceivable, and such distinctions may be based on graded rather than discrete judgments.

Languages spoken in larger populations seem to be relatively simple. One possible explanation is that this is a consequence of the simplifying influence of non-native speakers: adult learners tend to reduce complexity during learning, and large languages tend to have a higher proportion of non-native speakers. This Linguistic Niche Hypothesis, that languages adapt to their social niche, receives some statistical support from typological studies which show negative correlations between population size or number of non-native speakers and morphological complexity. Here I report an experimental test of this hypothesis, using two artificial language learning experiments to explore the impact of simplifications made by non-native-like early learners on morphological complexity. These experiments show that the presence of non-native-like early learners in a population can lead to the simplification of that language's morphology as a result of inter-generational language transmission, providing experimental support for the Linguistic Niche Hypothesis.

The present research evaluates a coherence-based network approach to moral judgement. Under this view, judgement is an outcome of achieving coherence between a network of causally interacting beliefs. Consistent with this, despite similar initial views, participants re-evaluated their beliefs and attitudes in support of their judgement, driving polarisation between individuals reporting competing judgements. Different properties of the dynamic network structure determined metacognitive properties of judgement such as confidence and perceived task difficulty. Whilst the judgement formation process involves revising beliefs and values to achieve a coherent arrangement, the nature of the judgement reached depends on the aggregate weight of these beliefs once the revision process is completed.

Transformer large language models (LLMs) perform exceptionally well across a variety of linguistic tasks. These models represent relationships between words in a sentence via “attention heads”, which assign weights between different words. Some attention heads automatically learn to “specialize” in identifying particular syntactic dependencies. Are syntactic computations in such heads encapsulated from non-syntactic information? Or are they penetrable to external information, like the human mind where information sources such as semantics influence parsing from the earliest moments? Here, we tested whether syntax-specialized attention heads in two LLMs (BERT, GPT-2) are modulated by the semantic plausibility of their preferred dependency. In 6 out of 7 cases, we found that implausible sentences reduce attention between words constituting a head’s preferred dependency. Therefore, even in heads that are best candidates for syntactic encapsulation, syntax is penetrable to semantics. These data are broadly consistent with the integration of syntax and semantics in human minds.

Speech perception involves storing and integrating sequentially presented items. Recent work in cognitive neuroscience has identified temporal and contextual characteristics in humans’ neural encoding of speech that may facilitate this temporal processing. In this study, we simulated similar analyses with representations extracted from a computational model that was trained on unlabelled speech with the learning objective of predicting upcoming acoustics. Our simulations revealed temporal dynamics similar to those in brain signals, implying that these properties can arise without linguistic knowledge. Another property shared between brains and the model is that the encoding patterns of phonemes support some degree of cross-context generalization. However, we found evidence that the effectiveness of these generalizations depends on the specific contexts, which suggests that this analysis alone is insufficient to support the presence of context-invariant encoding.

Both prediction and working memory constraints have been established as key factors in characterizing incremental sentence processing difficulty. Here we investigate the less explored question: Whether and how predictive expectation and working memory interact with each other using data from naturalistic reading time corpora. We provide broad-coverage evaluations of two hypotheses that make divergent predictions regarding the interaction of expectation and memory constraints: the Information Locality and Prediction Maintenance hypotheses. We first confirmed the predictions of both expectation- and working memory-based theories. Regarding their interactions, we find support the Information Locality hypothesis: Strong mutual predictability can enhance locality effects. We argue that future theory building in sentence processing should therefore take into consideration both prediction and memory constraints, as well as their potential interaction.

This paper tests the similarity in neural responses across repeated words and morphosyntactic features both within and between two languages. Prior work using priming has revealed robust cross-linguistic lexical effects and effects for shared grammatical form, such as argument structure; these methods have been less successful when applied to morphosyntactic features. Combining machine-learning based neural decoding with EEG data collected from Korean-English bilinguals we, first, replicate prior work showing successful classification of lexical items from EEG signals. We then extend this to demonstrate successful classification of morphosyntactic features of number and tense. Finally, we find that EEG decoding in one language does not successfully generalize to another, even when temporal differences are considered. Taken together, these results point to stable EEG representations for lexical items and morphosyntactic features, but suggest that these representations are different between the two languages investigated here.

The computational principles that underlie human concept learning have been debated in the literature for decades. Here, we formalize and test a new perspective that is grounded in rate-distortion theory (RDT), the mathematical theory of optimal (lossy) data compression, which has recently been gaining increasing popularity in cognitive science. More specifically, we characterize optimal conceptual systems as solutions to a special type of RDT problem, show how these optimal systems can generalize to unseen examples, and test their predictions for human behavior in three foundational concept-learning experiments. We find converging evidence that optimal compression may account for human concept learning. Our work also lends new insight into the relation between learnability and compressibility; integrates prototype, exemplar, and Bayesian approaches to human concepts within the RDT framework; and offers a potential theoretical link between concept learning and other cognitive functions that have been successfully characterized by efficient compression.

When children make decisions about whom to trust or learn from, they consider not only the informant’s reliability but also the social bond. Previous research often assigned a social label to informants without investigating how the interactive dynamics between informants and children influence learning and trust. This study investigates 3- to 6-year-old children’s preference towards informants who deliver instructions with or without coordination. In two experiments, children evaluated coordinative and non-coordinative informants on game-playing capability, willingness to engage with or learn from the informants, and selective trust in unrelated tasks. Children consistently preferred coordinative informants, perceiving them as more capable and trustworthy, over informants who demonstrated the information without coordinative turn-taking. This preference persisted across age groups, challenging previous notions about children's preference for information completeness. The findings highlight the prosocial effects of coordination, extending its influence beyond peer relationships to significantly impact selective trust when learning from knowledgeable individuals.

Children learn better through shared social experiences. Particularly, storytelling is a successful learning strategy that facilitates learning. These shared experiences are reflected in neural synchrony, which underlies predict understanding of the learned information. For adults, the scaffolding strategy, a shared social experience that involves active engagement rather than passive listening, has been shown to promote learning and has been linked with higher neural synchrony compared to passive learning. However, in the context of storytelling, it is unclear whether children will perform higher levels of neural synchrony as well as improved performances when they scaffold the learned information (tell a story about it) compared to when they passively listen. Here, we compare learning outcomes and neural basis of two learning strategies in young school-aged children in the context of storytelling.

Social norms are a hallmark of human social intelligence, yet the reasoning processes involved in norm formation have been difficult to capture with traditional modeling frameworks. We developed a computational model of norm formation as joint planning via theory-of-mind. The model is designed to capture the distinctively human ability to flexibly develop more complex norms in more complex situations, via simulation of joint decision-making with other agents over an extended time horizon. We evaluated the predictions of the model against participant interactions in a 2-player iterated decision-making task. Across 3 conditions our model captured the way participants balanced joint reward, fairness, and complexity when forming norms.

Are abstract sentences associated with specific constructs in dialogue, i.e., higher uncertainty, more curiosity and willingness to continue a conversation, and more causal questions? In three preregistered experiments we address these questions asking participants to evaluate the plausibility of linguistic exchanges referred to concrete and abstract concepts. Results support theories proposing that abstract concepts involve more inner monitoring and social dynamics compared to concrete concepts, and suggest that reaching alignment in dialogue is more effortful with abstract than with concrete concepts.

One feature of mental time travel is the ability to recursively embed temporal perspectives across different times: humans can remember how we anticipated the future and anticipate how we will remember the past. This recursive structure might be formalised in terms of a “grammar” that is reflective of but more general than linguistic notions of absolute and relative tense. Here I provide a foundation for this grammatical framework, emphasising a bounded (rather than unbounded) role of recursion in supporting mental time travel to a limited temporal depth and to actual and possible scenarios. Anticipated counterfactual thinking, for instance, entails three levels of mental time travel to a possible scenario (“in the future I will reflect on how my past self could have taken a different future action”) and is implicated in complex human decision-making. This perspective calls for further research into the nature and origins of recursive mental time travel.

We propose a neural dynamic architecture that models negation processing. The architecture receives a visual scene and a relational phrase like ``The blue object is not to the right of the yellow object'' or ``The blue object is to the right of the green object'' as input, and autonomously determines whether the phrase correctly describes the visual scene. The model is built out of empirically founded components for perceptually grounded cognition and constrained by neural principles. We demonstrate that the model can explain two commonly found reaction time effects: the negation effect in which reaction times are higher for negated than for affirmative phrases, and the polarity-by-truth-value interaction effect in which reaction times for false negated phrases are faster than those for true negated phrases whereas the opposite is true for affirmative phrases. The model is consistent with some aspects of the two-step simulation theory.

Recent studies link surprisal —a measure of conditional probability of words in context—to the N400 component size in event-related potentials (ERP), supporting a role for predictive coding in language comprehension. An alternative account argues that N400 variations are better explained by a retrieval mechanism sensitive to the semantic similarity between a word and its preceding context. Because jokes often rely on the presence of unexpected words that relate to the prior context multiple ways, they afford observation of the relative importance of contextual predictability and contextual similarity. We employed state-of-the-art machine learning to assess the surprisal and contextual semantic similarity of critical words in jokes and control stimuli. Using regression models to predict ERP, we found contextual similarity best explains N400 and P600 responses, supporting the semantic similarity account. Additionally, jokes elicit enhanced N400 and P600 responses that go beyond that attributable to their surprisal and contextual semantic similarity.

Contemporary formal models aim to capture group polarization as the result of deliberation between rational agents. Paradigmatic models do, however, rely on rather limited agents, casting doubt on the conclusion that group polarization can be rationally reconstructed. In this paper, we use a recently developed Bayesian agent-based model of deliberation to investigate this conclusion. This model avoids problems we identify in a group of influential Bayesian polarization models. Our case study shows that a simple mechanism produces realistic patterns of group polarization: limited exchange of evidence across a sparse social network. We reflect on what our results mean for our formal understanding of rational group polarization.

Recent research has provided experimental support for a new ``Inferentialist'' theory of conditionals, challenging the Equation P(If A, C) = P(C | A) and theories that support it. The key evidence comes from probability judgments involving conditionals whose antecedent and consequent are relevant vs. irrelevant to each other. Expanding on recent experimental work, we argue that Inferentialism has difficulty explaining the data. However, theories that support The Equation theory are well-placed to account for the results once we recognize an independent phenomenon of pragmatic intrusion on probability judgment - in this case, participants' tendency to assign lower probability to conditionals that are pragmatically incoherent.

Recent years have seen an explosion of theoretical interest, as well as increasingly fraught real-world debate, around issues to do with discourse participation. For example, marginalised groups may find themselves excluded or may exclude themselves from discourse contexts that are hostile. This not only has ethical implications, but likely impacts epistemic outcomes. The nature and scale of such outcomes remain difficult to estimate in practice. In this paper, we use agent-based modelling to explore the implications of a tendency toward `agreeableness' whereby agents might shape their communication so as to reduce direct conflict. Our simulations show that even mild tendencies to avoid disagreement can have significant consequences for information exchange and the resultant beliefs within a population.

Learning traps are false beliefs that entrench themselves by discouraging the exploration required to correct them. In previous lab experiments, these learning traps have proven remarkably difficult to prevent. Here, we investigate whether learning traps remain stable in contexts in which both individual and social learning are possible. In two of our three experiments, we found that learners trapped by a false belief were significantly more likely to escape a learning trap when they were able to observe another decision-maker's choices (without observing their outcomes). However, social learning was not a panacea. Social learning was constrained by the challenge of inferring others' beliefs, and trapped learners struggled to learn from partners with sub-optimal decision rules, even when their partner's choices were informative. Collectively, these results suggest that while social learning can help overcome the limits of individual learning, learning from others comes with its own challenges and limitations.

Human learning is sensitive to rule-like structure and the curriculum of examples used for training. In tasks governed by succinct rules, learning is more robust when related examples are blocked across trials, but in the absence of such rules, interleaving is more effective. To date, no neural model has simultaneously captured these seemingly contradictory effects. Here we show that this same tradeoff spontaneously emerges with “in-context learning” (ICL) both in neural networks trained with metalearning and in large language models (LLMs). ICL is the ability to learn new tasks “in context” — without weight changes — via an inner-loop algorithm implemented in activation dynamics. Experiments with pretrained LLMs and metalearning transformers show that ICL exhibits the blocking advantage demonstrated in humans on a task involving rule-like structure, and conversely, that concurrent in-weight learning reproduces the interleaving advantage observed in humans on tasks lacking such structure.

According to a common view in epistemology, a set of propositions is justified if it is coherent. Similarly, a new proposition should be accepted if it is coherent with the accepted body of beliefs. But what is coherence? And what, in turn, justifies the above claims? To answer these questions, various Bayesian measures of epistemic coherence have been proposed. Most of these measures are based on the prior probability distribution over the corresponding propositional variables. We criticize this ``static'' conceptualization of coherence and propose instead that the coherence of an information set is related to how well the information set responds when each of the propositions it contains is confirmed by an independent and partially reliable information source. The elaboration of this idea will show that the proposed ``dynamic'' perspective has several advantages and solves some open problems of coherentist epistemology. It also has implications for our understanding of reasoning and argumentation in science and beyond.

While people may be reluctant to explicitly state social stereotypes, their underlying beliefs may nonetheless leak out in subtler conversational cues, such as surprisal reactions that convey information about expectations. Across 3 experiments with adults and children (ages 4-9), we compare permissive responses ("Sure, you can have that one") that vary the presence of surprisal cues (interjections "oh!" and disfluencies "um"). In Experiment 1 (n = 120), children by 6-to-7 use surprisal reactions to infer that a boy more likely made a counter-stereotypical choice. In Experiment 2, we demonstrate that these cues are sufficient for children (n = 120) and adults (n = 80) to learn a novel expectation about a group of aliens. In Experiment 3, adults (n = 150) use the distribution of surprisal information to infer whether a novel behavior is gender-stereotyped. Across these experiments, we see emerging evidence that conversational feedback may provide a crucial and unappreciated avenue for the transmission of social beliefs.

It is uncontroversial that humans can represent possibilities, but it is debated what this claim amounts to. Under broad views of modal cognition, many representational and reasoning systems represent possibilities at multiple levels of cognitive architecture. Under narrow views of modal cognition, there exists a special kind of higher-level modal thought, that can be measured with purpose built non-verbal modal cognition tasks. Here we ask whether object tracking mechanisms that are assumed to lack the higher-level narrow modal capacity, show behavioral signatures that are assumed to require it. We find signature of modal representation in one task, but not another. The finding suggests that there is no clear difference between tasks that tap broad and narrow modal cognition, and invites a reassessment of the evidence for the latter.

Models based on the Transformer neural network architecture have seen success on a wide variety of tasks that appear to require complex ``cognitive branching''-- or the ability to maintain pursuit of one goal while accomplishing others. In cognitive neuroscience, success on such tasks is thought to rely on sophisticated frontostriatal mechanisms for selective gating, which enable role-addressable updating-- and later readout-- of information to and from distinct ``addresses'' of memory, in the form of clusters of neurons. However, Transformer models have no such mechanisms intentionally built-in. It is thus an open question how Transformers solve such tasks, and whether the mechanisms that emerge to help them to do so bear any resemblance to the gating mechanisms in the human brain. In this work, we analyze the mechanisms that emerge within a vanilla attention-only Transformer trained on a simple sequence modeling task inspired by a task explicitly designed to study working memory gating in computational cognitive neuroscience. We find that, as a result of training, the self-attention mechanism within the Transformer specializes in a way that mirrors the input and output gating mechanisms which were explicitly incorporated into earlier, more biologically-inspired architectures. These results suggest opportunities for future research on computational similarities between modern AI architectures and models of the human brain.

This study investigates cue-based memory retrieval during sentence processing. Cue-based retrieval theories argue that the parser uses lexical and structural information as retrieval cues to retrieve items from memory. Evidence for cue-based memory retrieval comes from research showing that non-target representations matching retrieval cues interfere with target retrieval. However, the degree of susceptibility to this similarity-based interference has been debated, having led to the development of different computational models. This study focuses on two cue-based models and tests their predictions in two experiments. The results suggested similarity-based interference, but its patterns were not fully compatible with these models. To reconcile these findings within the framework of cue-based memory retrieval, this paper presents a model that assigns substantial weight to the structure-based cue and incorporates the notions of initial retrieval and revision. Results from simulations indicate that the model incorporating these assumptions provides a better fit to the observed data.

This paper discusses an experiment conducted with two groups of participants, composed of musicians and non-musicians, in order to investigate the impact that the speed of a sound sequence and the interval size which selected sounds are played on the grouping of sounds into perceptual streams. Significant differences were observed between musicians and non-musicians with respect to the threshold sequence speed at which the sequence was split into two streams. In modern psychoacoustic studies, the qualifying criteria for listeners usually include otologically normal hearing (verified by audiometric test) and age. The differences in the results for the two groups suggest that the musical background of the participating listeners may be a vital factor. The criterion of musical education should be taken into account during experiments so that the results obtained are reliable, uniform and free from interpretive errors.

This study investigates the representation of garden-path sentences and its interaction with memory retrieval. Garden-path sentences are initially misanalysed, and the initial misrepresentations tend to affect language comprehension, even after revision. Memory retrieval targets items in memory based on their representations. Our main research question investigates whether memory retrieval targets initial misrepresentations or revised representations in garden-path sentences. Using the cue-based memory retrieval model, we generated predictions for potential processing patterns stemming from this research question. The experiments used lexicality maze, self-paced reading, and offline comprehension questions. The results showed largely similar processing patterns between garden-path and non-garden-path sentences, suggesting that initial misrepresentations do not affect memory retrieval.

To date, no account of lie-truth judgement formation has been capable of explaining how core cognitive mechanisms such as memory encoding and retrieval are employed to reach a judgement of either truth or lie. One account, the Adaptive Lie Detector theory (ALIED: Street, Bischof, Vadillo, & Kingstone, 2016) is sufficiently well defined that its assumptions may be implemented in a computational model. In this paper we describe our attempt to ground ALIED in the representations and mechanisms of the ACT-R cognitive architecture and then test the model by comparing it to human data from an experiment conducted by Street et al. (2016). The model provides a close fit to the human data and a plausible mechanistic account of how specific and general information are integrated in the formation of truth-lie judgements.

Prediction error, both at the level of sentence meaning and at the level of the next presented word, has been shown to successfully account for N400 amplitudes. Here we investigate whether people differ in the representational level at which they implicitly predict upcoming language. We computed a measure of prediction error at the level of sentence meaning (termed semantic update) and a measure of prediction error at the level of the next word (surprisal). Both measures significantly accounted for N400 amplitudes even when the other measure was controlled for. Most important for current purposes, both effects were significantly negatively correlated. Moreover, random-effects model comparison showed that individuals differ in whether their N400 amplitudes are driven by semantic update only, by surprisal only, or by both, and that the most common model in the population was either semantic update or the combined model but clearly not the pure surprisal model.

We assessed the goodness of fit of three models of vocabulary growth, with varying sensitivity to the structure of the environment and the learner's internal state, to estimated vocabulary growth trajectories in autistic and non-autistic children. We first computed word-level acquisition norms that indicate the vocabulary size at which individual words tend to be learned by each group. We then evaluated how well network growth models based on natural language co-occurrence structure and word associations account for variance in the autistic and non-autistic acquisition norms. In addition to replicating key observations from prior work and observing that the growth models explained similar amounts of variance in each group, we found that autistic vocabulary growth also exhibits growth consistent with "the lure of the associates" model. Thus, both groups leverage semantic structure in the learning environment for vocabulary development, but autistic vocabulary growth is also strongly influenced by existing vocabulary knowledge.

Across three experiments, we show that 15-month-old infants understand that arbitrary objects can be used as symbols. Experiment 1 shows that infants map geometric shapes (e.g., a triangle) onto familiar discourse referents (e.g., a duck) based on labeling (e.g., “Look, a duck!”). Experiment 2 shows that infants do not generalize these mappings to a new speaker. This rules out the alternative hypothesis that infants interpret the labeling events literally. Experiment 3 shows that infants are sensitive to the conceptual identity of the discourse referent. After being told that one shape represents an agent (e.g., a duck) and another shape represents a patient (e.g., a cup), infants attend differentially when the agent symbol moves towards the patient symbol than the opposite. This rules out the alternative hypothesis that infants interpret the labeling events as referential pacts. The findings jointly indicate that symbolic relations are easily activated and available early in human development.

Predicting Click-Through Rate (CTR) is crucial in product and content recommendation, as it involves estimating the likelihood of a user engaging with a specific advertisement or content link. This task encompasses understanding the complex cognitive processes behind human interactions with recommended content. Learning varied feature embeddings that reflect different cognitive responses in various circumstances is significantly important. However, traditional methods typically learn fixed feature representations, leading to suboptimal performance. Some recent approaches attempt to address this issue by learning bit-wise weights or augmented embeddings for feature representations, but suffer from uninformative or redundant features in the context. To tackle this problem, inspired by the Global Workspace Theory in conscious processing, which posits that only a specific subset of the product features are pertinent while the rest can be noisy and even detrimental to human-click behaviors, we propose a CTR model that enables Dynamic Embedding Learning with Truncated Conscious Attention for CTR prediction, termed DELTA. DELTA contains two key components: (I) conscious truncation module (CTM), which utilizes curriculum learning to apply adaptive truncation on attention weights to select the most critical feature in the context; (II) explicit embedding optimization (EEO), which applies an auxiliary task during training that directly and independently propagates the gradient from the loss layer to the embedding layer, thereby optimizing the embedding explicitly via linear feature crossing. Extensive experiments on five challenging CTR datasets demonstrate that DELTA achieves new state-of-the-art performance among current CTR methods.

One hallmark of human language is its combinatoriality---reusing a relatively small inventory of building blocks to create a far larger inventory of increasingly complex structures. In this paper, we explore the idea that combinatoriality in language reflects a human inductive bias toward representational efficiency in symbol systems. We develop a computational framework for discovering structure in a writing system. Built on top of state-of-the-art library learning and program synthesis techniques, our computational framework discovers known linguistic structures in the Chinese writing system and reveals how the system evolves towards simplification under pressures for representational efficiency. We demonstrate how a library learning approach, utilizing learned abstractions and compression, may help reveal the fundamental computational principles that underlie the creation of combinatorial structures in human cognition, and offer broader insights into the evolution of efficient communication systems.

How humans process and utilize experienced outcomes and actions to adapt to a constantly evolving and noisy world is an important area of research. We investigate the role of the pupil-linked arousal system in adaptive value-based decision-making in an uncertain and changing environment using a two-armed bandit task with occasional changes in reward contingencies. We find that pupil size fluctuation encodes reward- and uncertainty-related values across trials; moreover, pupil size reflects future-choice-dependent contributions of these variables to learning and decision-making: larger pupil encoding of reward prediction error (RPE) promotes reward-driven switches in choice, while larger pupil encodings of estimation uncertainty (EU) promotes uncertainty-driven switches in choice. Furthermore, individual differences in pupil’s encoding of RPE and EU correlate with individual variabilities in choice bias and task performance. Given the relationship of pupil size to noradrenergic and cholinergic modulations, these results provide insights into the computational and neural process underlying adaptive decision-making.

The ability to exert cognitive control is central to human brain function, facilitating goal-directed task performance. However, humans exhibit limitations in the duration over which they can exert cognitive control---a phenomenon referred to as cognitive fatigue. This study explores a computational rationale for cognitive fatigue in continual learning scenarios: cognitive fatigue serves to limit the extended performance of one task to avoid the forgetting of previously learned tasks. Our study employs a meta-learning framework, wherein cognitive control is optimally allocated to balance immediate task performance with forgetting of other tasks. We demonstrate that this model replicates common patterns of cognitive fatigue, such as performance degradation over time and sensitivity to reward. Furthermore, we discuss novel predictions, including variations in cognitive fatigue based on task representation overlap. This approach offers a novel perspective on the computational role of cognitive fatigue in neural systems.

Despite decades of study, we still know less than we would like about the association between joint attention (JA) and language acquisition. This is partly because of disagreements on how to operationalise JA. In this study, we examine the impact of applying two different, influential JA operationalisation schemes to the same dataset of child-caregiver interactions, to determine which yields a better fit to children’s later vocabulary size. Two coding schemes— one defining JA in terms of gaze overlap and one in terms of social aspects of shared attention—were applied to video-recordings of dyadic naturalistic toy-play interactions (N=45). We found that JA was predictive of later production vocabulary when operationalised as shared focus (study 1), but also that its operationalisation as shared social awareness increased its predictive power (study 2). Our results emphasise the critical role of methodological choices in understanding how and why JA is associated with vocabulary size.

Understanding referring expression generation has long been of interest to psycholinguistics, pragmatics, and sociolinguistics. Experimental data in the former two has shown that referring expression generation is modulated by both pragmatic and cognitive considerations, and the latter suggests that referring expressions have social meaning beyond their literal referential utility. This project integrates these three accounts by extending Burnett (2017)'s socially-enriched implementation of the Rational Speech Act (RSA) framework to account for variation in referring expressions used to denote transgender women in two politically opposed media corpora. Our findings highlight the utility of the RSA framework in explaining socially-modulated variation while also accounting for pragmatic and cognitive considerations. Finally, this paper contributes to growing literatures that address the relationship between (alt-)right ideologies about gender and language by highlighting the use of bioessentialist language such as 'biological male' in the propagation of anti-trans rhetoric in the United States.

Despite the centrality of caregiving relationships in the lives of infants, little is known about whether and how infants represent these relationships characterized by strong attachment and asymmetry in obligation and skills. The current studies (N=95) investigate whether 8-to-10-month-old infants attend to two cues—affiliative touch and physical size—to predict who will respond to distress. In Study 1 (n=49), infants expected larger characters to respond to the emotional needs of smaller characters, only when they saw affiliative touch (proportion looking time at large character: BF10=6.72). In Study 2 (n=46), they did not expect smaller characters to respond to larger characters (proportion looking time: BF10=0.17), suggesting they expect asymmetrical roles in caregiving relationships. Collectively, these findings suggest that humans have an early-emerging ability to recognize key relationships in their social world.

When do children understand that number words (such as ‘five’) refer to exact quantities and that the same number word can be used to label two sets whose items correspond 1-to-1 (e.g., if each bunny has a hat, and there are five hats, then there are five bunnies)? Two studies with English-speaking 2- to 5-year-olds revealed that children who could accurately count large sets (CP knowers) were able to infer that sets exhibiting 1-to-1 correspondence share the same number word, but not children who could not accurately count large sets (subset knowers). However, not all CP knowers made this inference, suggesting that learning to construct and label large sets is a critical but insufficient step in discovering that numbers represent exact quantities. CP knowers also failed to identify 1-to-1 corresponding sets when faced with sets that had an off-by-one difference, suggesting that children who could accurately count large sets used approximate magnitude to establish set equality, rather than 1-to-1 correspondence. These results suggest that children’s initial intuitions about numerical and set equality are based on approximation, not 1-to-1 correspondence, and that this occurs well after they have learned to count and construct large sets.

Code Knowledge Tracing (CKT) aims to model students' programming proficiency from their coding activities. Existing approaches mainly rely on answer records and lack problem descriptions and knowledge concepts, which fail to capture the inherent information. To solve this problem, we propose ECKT, an Enhanced Code Knowledge Tracing framework using Large Language Models (LLMs), which simulate human cognitive process through chain-of-thought prompting and adapts quickly to new tasks with limited data using few-shot learning. Specifically, ECKT generates detailed problem descriptions and knowledge concepts from student code, enhancing the model's understanding of programming concepts and proficiency. Additionally, ECKT incorporates task difficulty information by correlating problems with difficulty levels based on student performance scores. This integration allows for a more accurate assessment of student proficiency across varying levels of difficulty. Also, ECKT can explicitly capture the essential information of code and learn a better representation of them. Experimental results demonstrate that ECKT effectively improves the performance of knowledge tracing in programming education. This advancement not only supports personalized learning but also contributes to a deeper understanding of coding activities.

Alongside the linguistic input, young children leverage multimodal cues (e.g., prosody, gestures) to learn novel words in face-to-face interactions. It is unclear whether multimodal cues play a similar role in adults. Here, we used ECOLANG, a corpus of semi-naturalistic dyadic conversations where English-speaking adults incidentally learned about unknown objects and their names by interacting with a partner who knew those objects. We examined whether multimodal cues (prosodic, indexical, and iconic) predicted learners’ ability to learn the objects’ names, above and beyond individual differences and linguistic predictors. We found that the number of repetitions of the label predicted word learning. Additionally, learners with lower working memory abilities benefited from speakers producing representational gestures while labelling the unknown objects. We discuss implications for theories of word learning and approaches of situated cognition.

Emblems (also called conventional gestures) are a powerful, yet often overlooked part of humans’ communicative tool-kit. These gestures rapidly express encapsulated messages, such as waving a hand to greet someone and shoulder shrugging to reveal a lack of knowledge. We hypothesized that emblems are shaped by a universal pressure to reveal their communicative purpose, and they should therefore be unconfounded with movement typically produced to accomplish non-communicative goals. We present evidence for this hypothesis using a novel dataset of over 250 emblems from around the world: Over 95% of these gestures have forms that support observers’ inferences, suggesting that emblems are shaped to ease observers’ inferential burden. Finally, in a gesture-creation experiment, we show that these inference-guiding features emerge spontaneously without the need for observer feedback or cultural transmission. Taken together, these complementary approaches provide insight into how goal inferential processes may explain the shape of communicative actions across cultures.

Differentiating letter shapes accurately is an increasingly crucial competence. Are letters as distinctive as they could be? We used a unique dataset of crowdsourced letter descriptions across 43 writing systems to produce a comprehensive typology of letter shapes for these diverse scripts. We extracted from 19,591 letter classifications, contributed by 1,683 participants, enough features to provide a unique description of all letters in each system. We show that scripts, compared to phoneme inventories, are feature-extensive: they use additional features to do what could be done with a lower number of features, used more efficiently. Compared to 516 phoneme inventories from the P-base dataset, our 43 scripts have lower feature economy (fewer symbols for a given number of features) and lower feature informativeness (a less balanced distribution of feature values). Letter shapes, we argue, having more degrees of freedom than speech sounds, use features in a more wasteful way.

Category fluency is a widely studied cognitive task. Two major competing accounts have been proposed as the underlying retrieval mechanism: an optimal foraging process deliberately searching through memory (Hills et al., 2012) and a random walk sampling from a semantic network (Abbott et al., 2015). Evidence for both accounts has centered around predicting human patch switches, where both existing models of category fluency produce paradoxically identical results. We begin by peeling back the assumptions made by existing models, namely that each named exemplar only depends on the previous exemplar, by (i) adding an additional bias to model the category transition probability directly and (ii) relying on a large language model to predict based on the full prior exemplar sequence. Then, we present evidence towards resolving the disagreement between different models of foraging by reformulating them as sequence generators. For evaluation, we compare generated category fluency runs to a bank of humanwritten sequences by utilizing a metric based on n-gram overlap. We find that category switch predictors do not necessarily produce human-like sequences; rather, the additional biases used by the Hills et al. (2012) model are required to improve generation quality, which is further improved by our category modification. Even generating exclusively with an LLM requires an additional global cue to trigger the patch switching behavior during production. Further tests on only the search process on top of the semantic network highlight the importance of deterministic search in replicating human behavior.

In our cross-cultural corpus study of 332 comics, we asked whether animacy preference plays a role in comics. Are animates or inanimates more or less grammatically marked compared to each other, similarly to differential marking modulated by animacy in grammars of many languages? Following Opfer (2002), we considered the animacy preference as the expectation that only animates move in a goal-directed way. We focused on two visual morphological markings that indicate motion in comics and differ in their goal-directedness: the goal-directed motion lines (trailing a moving entity) and the non-goal-directed circumfixing lines (surrounding an entity). We found that inanimates are more marked by motion lines than animates in our data, while there is no difference between the two groups with circumfixing lines. This indicates that inanimates need to be marked by motion lines in order to signal their goal-directed movement, which is otherwise unexpected. We call this the principle of “Mark the unexpected!”.

Humans learn and generate languages, music, games, and seemingly limitless varieties of other structures across domains. Unlike many AI systems, we often do so from little data. How do we learn such large varieties of richly structured representations so efficiently? One possibility is that people ``learn by programming,'' synthesizing data-generating algorithms to explain what we observe. We examine the nature and origins of this learning mechanism in adults, children, and nonhuman primates (macaque monkeys), using a highly unconstrained sequence prediction task. Although adults and children quickly learn many richly structured sequences, monkeys learn only the simplest sequences (e.g. lines). We test multiple learning models, finding that adults are best explained by a ``Language of Thought''-like program-learning model and monkeys by a simpler extrapolation strategy. Children exhibit varied learning strategies but are best fit in aggregate by an intermediately expressive model. Paper available at https://sites.google.com/view/patternlearning.

Theory of Mind (ToM) is central to human social cognition, yet the roots of this capacity remain poorly understood. Both infants and nonhuman primates perform inconsistently on false belief tasks, limiting our understanding of the representations that characterize their ToM. Here, we seek to better understand this often-contradictory literature by dissecting these failures. Specifically, we focus on primates’ characteristic null performance on false belief tasks. Across three studies, we find that—despite succeeding on a closely-matched control—rhesus monkeys fail to predict how agents with false beliefs will behave even when the agents perform highly unexpected, unlikely actions. We interpret this pattern of performance as evidence that monkeys have no representation of another agent’s past awareness once the scene changes outside of that agent’s view. This work moves beyond the success/failure dichotomy typically used to assess ToM, and instead gives a more precise characterization of primates’ signature limits in ToM.

We can evaluate features of problems and their potential solutions well before we can effectively solve them. When considering a game we have never played, for instance, we might infer whether it is likely to be challenging, fair, or fun simply from hearing the game rules, prior to deciding whether to invest time in learning the game or trying to play it well. Many studies of game play have focused on optimality and expertise, characterizing how people and computational models play based on moderate to extensive search and after playing a game dozens (if not thousands or millions) of times. Here, we study how people reason about a range of simple but novel connect-n style board games. We ask people to judge how fair and how fun the games are from very little experience: just thinking about the game for a minute or so, before they have ever actually played with anyone else, and we propose a resource-limited model that captures their judgments using only a small number of partial game simulations and almost no lookahead search. For more information about this project, see https://sites.google.com/view/intuitive-game-theory

The decision to engage in lifelong learning often entails a motivational conflict, requiring individuals to balance potential benefits against the costs of engagement. Approach- avoidance motivation occurs when an action involves simultaneous positive and negative outcomes, necessitating a choice. This concept has primarily been studied in emotionally charged decisions linked to fear or anxiety, relevant for clinical settings. Our aim is to shift the focus to the cost of engagement in learning and educational settings. In a society marked by high demands and numerous tools for knowledge updating, lifelong learning may be beneficial for continuous individual development and societal contribution. We introduce a new framework that intricately connects motivation and learning processes with cognition, highlighting the pivotal roles of executive functions and decision-making processes. This article delves into the confluence of lifelong learning, cognitive conflict, and approach- avoidance motivation within the context of education and learning processes.

Studies of reinforcement learning in humans and animals have demonstrated a preference for options that yielded relatively better outcomes in the past, even when those options are associated with lower absolute reward. The present study tested whether large language models would exhibit a similar bias. We had gpt-4-1106-preview (GPT-4 Turbo) and Llama-2-70B make repeated choices between pairs of options with the goal of maximizing payoffs. A complete record of previous outcomes was included in each prompt. Both models exhibited relative value decision biases similar to those observed in humans and animals. Making relative comparisons among outcomes more explicit magnified the bias, whereas prompting the models to estimate expected outcomes caused the bias to disappear. These results have implications for the potential mechanisms that contribute to context-dependent choice in human agents.

People’s mental states constantly change as they navigate and interact with their environment. Accordingly, social reasoning requires us not only to represent mental states but also to understand the ways in which mental states tend to change. Despite their importance, relatively little is known about children’s understanding of the dynamics of mental states. To explore this question, we studied a common type of mental state change: knowledge gain. Specifically, we studied whether five- and six-year-olds distinguish between agents who gain knowledge and those who lose knowledge. In one condition, children saw an agent answer a two-alternative choice question incorrectly, followed by an identical-looking agent who answered the same question correctly (i.e., gaining knowledge). In another condition, children saw the reverse pattern (i.e., losing knowledge). Children were more likely to infer they had seen two different agents in the knowledge loss condition relative to the knowledge gain condition. These results suggest that children have intuitions about how epistemic states change and open new questions about children’s naive theories of mental state dynamics.

We readily get intuitions about a problem's complexity, how much thinking it will require to solve, and how long it should take, both for ourselves and for others. These intuitions allow us to make inferences about other people's mental processing---like whether they are thinking hard, remembering, or merely mind-wandering. But where do these intuitions come from? Prior work suggests that people try solving problems themselves so as to draw inferences about another person's thinking. If we use our own thinking to build up expectations about other people, does this introduce biases into our judgments? We present a behavioral experiment testing for effects of first-person thinking speed on judgments about another person's thinking in the puzzle game Rush Hour. Although participants overwhelmingly reported solving the puzzles themselves, we found no evidence for participants' thinking speeds influencing their judgments about another person's thinking, suggesting that people can correct for first-person biases.

Theory of Mind enables us to represent and reason about other people's mental states like beliefs and knowledge. By considering what other people know, this allows us to strategically construct believable lies. Previous work has shown that people construct lies to be consistent with others' beliefs even when those beliefs differ from their own. However, in most real world cases, we don't know everything that the other person knows. We propose that to produce believable lies, the sender considers what private information the receiver may have. Here, we develop our theory into a computational model and test it in a novel paradigm that allows us to distinguish between knowledge shared between the lie sender and receiver and knowledge private to the receiver. Our main model successfully captures how people lie in this paradigm over alternative models. Overall, our work furthers our understanding of human social cognition in adversarial situations.

We examine the heterogeneity of text-based behavioral interventions in a series of 5 preregistered studies across one in-person and 10 online panels, with over 11000 respondents in total. We observe large heterogeneity across settings and paradigms. Model the heterogeneity we introduce a framework that measures typically omitted moderators: Fluid Intelligence, Attentiveness, Crystallized Intelligence, and Experience. Variation in these factors are associated with different effect sizes and explain variations across samples. Moderators are associated with effect sizes through two paths, with the intensity of the manipulation and with the effect of the manipulation directly. Our results motivate observing these moderators and provide a theoretical and empirical framework for understanding and predicting varying effect sizes in the social sciences.

The complexity of everyday choices make them difficult to formally study. We address this challenge by constructing a dataset of over 100K real-life decision problems based on a combination of social media and large-scale survey data. Using large language models (LLMs) for automated coding, we are able to extract hundreds of choice attributes at play in these problems and map them onto a common representational space. This representation allows us to quantify both the content (e.g. broader themes) and the structure (e.g. specific tradeoffs) inherent in everyday choices. We also present subsets of these decision problems to human participants, and find consistency in choice patterns, allowing us to predict naturalistic choices with established decision models. Overall, our research provides new insights into the attributes and tradeoffs that underpin important life choices. In doing so, our work shows how LLM-based structure extraction can be used to study real-world cognition and behavior.

Children's acquisition of emotion words is a topic of interest across the domains of emotion research, early language acquisition, and social development. Prior research has shown a slow, gradual process of emotion word acquisition during ages 2–5 years and beyond. However, this research has used tasks that are demanding for young children, such as asking them to label or sort facial expressions. Here, in a preregistered study, we used a child-friendly looking-time paradigm---the "looking-while-listening'' task---to assess children's understanding of four emotion words, "happy,'' "sad,'' "angry,'' and "scared.'' We presented 64 two-year-olds (Mean age = 2.51, range: 2.00-2.97) with facial expressions and measured their preferential looking to the target face upon hearing an emotion word. Both younger and older two-year-olds showed above-chance performance when the target and distractor faces differed in valence (e.g., happy vs. sad). When the target and distractor faces were of the same valence (e.g., angry vs. sad), younger two-year-olds' results did not reach significance, but older two-year-olds' results were significantly above chance. These results suggest that within-valence mappings of emotion words to facial expressions emerges at least during the second half of age two. Full paper here: [https://osf.io/preprints/psyarxiv/nsq5t].

'Causal perception' describes the phenomenon wherein certain interactions between objects are automatically and irresistibly experienced as involving cause and effect. Previous work using retinotopically-specific visual adaptation paradigms has provided evidence that there is at least one specific causal event, 'launching', which is identified sufficiently early in visual processing that the visual system still operates using the surface of the retina as its frame of reference. Here, we demonstrate that there are in fact multiple 'causal perceptions', such that the visual system also detects a category of event described as 'entraining'. Using a novel ambiguous 'launch/push' display, we find that adapting to launching events leads to more ‘pushing’ reports, while adapting to entraining events leads to more 'launching' reports, and that these adaptation effects only occur for test events presented to the same location on the retina as the adaptation stream (i.e., are retinotopically specific). We discuss the implications of this finding for future work on causal perception and cognition.

Recent advancements in Artificial Intelligence (AI) have arguably enhanced human performance instead of supplanting it. Here we analyse 2.8 million decisions by elite chess players, a field emblematic of AI's application due to its complexity and objective measurability. We identify two AI milestones that correspond with substantial enhancements in top chess players' performance quality over the past four decades: the introduction of personal computers (PCs) and internet access in the late 1990s, and the advent of deep neural networks for chess in the late 2010s. The impact of these technologies, however, varied by age group; adult elite players derived considerable benefits from neural network-based chess computers, whereas younger top players were more influenced by the widespread availability of knowledge and PCs. Our findings underscore AI's potential to amplify human proficiency in complex tasks, highlighting the importance of tailored technological integration among elite performers.

Intentional visual imagery is a component of numerous aspects of cognition. Related to visual imagery, mental simulation plays a role in embodied theories of language comprehension that propose activation of modality-specific regions of the brain takes place as part of people understanding language. The extent to which the processes underlying conscious, voluntary visual imagery versus less conscious, more automatic mental simulation overlap is unclear. We investigated this issue by having aphantasics (people who are unable to experience conscious voluntary visual imagery) and control participants perform a property verification task in which they were asked whether a property is a physical part of an object (e.g., lion-tail). We manipulated the false trials in that the two words either were associated (semantically related) but did not form an object-part combination (monkey-banana), or were not associated (apple-cloud). Solomon and Barsalou (2004) demonstrated that word association influenced responses when the words in the false trials were not associated, whereas when they were related, perceptual measures most strongly influenced the results, indicating mental simulation. Here control participants and aphantasics demonstrated similar evidence of the use of both mental simulation and word association when verifying whether the words formed an object-part combination. These results provide evidence that visual imagery and mental simulation are at least somewhat separable cognitive processes.

Languages tend to describe who is doing what to whom by placing subjects before objects. This bias for agents is reflected in event cognition: agents capture more attention than patients in human adults and infants. We investigated whether this agent preference is unique to humans. We presented Guinea baboons (Papio papio, N = 13) with a change detection paradigm with chasing animations. The baboons had to respond to a colour change which was applied to either the chaser/agent or the chasee/patient. They were faster to detect a change to the chaser than to the chasee, which cannot be explained by low-level features in our stimuli. Our study suggests that baboons show an agent preference similar to human infants and adults. This may be an evolutionarily old mechanism that is shared between humans and other primates, which could have become externalised in language as a tendency to place the subject first.

A key aspect in child language development involves inducing the rules that determine the relations of the arguments to their verbal predicate, i.e., semantic roles. Here, we investigate whether child-directed speech facilitates learning ‘who does what to whom’ in English and Russian, two languages that strongly differ in their amount of case-marking and word order variation. We ask whether a contextual, distributional learner can more easily learn to assign semantic roles to arguments based on child-directed speech versus adult-directed speech. To this end, we represent the arguments of a verb with contextualised word embeddings extracted from neural language models. We compare the classification accuracy of semantic roles based on these representations between utterances extracted from corpora of child-directed speech and adult-directed speech. We further study to what extent semantic roles can be predicted based on arguments represented by different levels of information, such as non-contextualised representations, the position in the sentence, and case marking. We find that child-directed speech facilitates the learning of semantic roles, an important cornerstone for learning the morphosyntactic features of a language. However, the effect of child-directed speech is more pronounced in Russian than in English, indicating that child-directed speech may be optimised more strongly in a language where arguments are expressed in more varied forms and positions, as is the case in Russian.

Spontaneous internally directed attention, such as mind wandering, typically hinders performance in cognitive tasks. The impact of intentional internally directed attention (IDA) – for instance, deliberately thinking about past or future events – on task performance, however, remains unclear. In our study, we employed a dual-task paradigm that involved self-referential stimuli in a color-recall visual working memory task. This approach revealed that intentional IDA more significantly influences performance compared to intentional externally directed attention (EDA). We observed larger late positive potentials (LPP) over medial frontal sensors, suggesting sustained stimulus processing over frontal sensors under IDA. Additionally, we noted a pattern of neural activity associated with internal attention: event-related desynchronization (ERD) in the alpha band (8-12 Hz) during the encoding phase and event-related synchronization (ERS) in the delay phase. In contrast, the EDA condition was marked by theta (4-8 Hz) band ERS during the delay period. These findings highlight distinct behavioral impacts and neural patterns associated with internally versus externally directed attention in dual-task settings.

The study of mind perception, particularly how one perceives the mental states of `others,' has attracted considerable interest in cognitive science. The present study contributes to the investigation of mind perception in a human-robot interaction context, by testing a humanoid robot and a human and their communicative and noncommunicative actions. We examine mind perception across its two primary dimensions: Agency and Experience and in their High and Low ends. The novelty of our study lies in its real-time and implicit nature---both identified as crucial elements in current debates within the field. Our results indicate that testing physically present and active agents, as well as exposing participants to various types of live actions, influences mental capacity attributions across different capacities. Additionally, the integration of behavioral measurements alongside verbal data holds promise for a detailed interpretation of the mind perception process.

Emerging folklore in the cognitive sciences suggests that interpretability techniques to reverse-engineer artificial neural networks (ANNs) could speed up discovery and theory-building. For many researchers in psychology, linguistics, neuroscience, and artificial intelligence (AI), the full observability and perturbability of ANNs trained on complex tasks affords a shortcut to domain insights, cognitive theories, neurocognitive models, application improvement, and user safety. Folklore intuitions, however, are typically disconnected from other relevant knowledge. Here we examine these intuitions formally by drawing relevant connections to computational complexity theory. We model interpretability queries computationally and analyze their resource demands for biological/artificial high-level cognition. We prove mathematically that, contrary to folklore, basic circuit-finding queries in classic ANNs are already infeasibly demanding to answer even approximately. We discuss how interdisciplinary integration can mitigate this disconnect and situate the broader implications for the cognitive sciences, the philosophy of AI-fueled discovery, and AI ethics.

Most current research on commonsense question answering (CQA) has focused on proposing different techniques in natural language processing and text information retrieval. However, for human cognition, retrieving and organizing desired answers from text knowledge related to commonsense questions is far less intuitive and comprehensive than it is when using multi-modal knowledge, such as related images and videos. Motivated by this, we propose a framework for trying the acquisition of diverse modal information, and embedding and integrating it into CQA tasks, further improving the performance and user experience. Specifically, this paper proposes the integration of multi-modal knowledge, including images, image description statements, image scene graphs, and knowledge sub-graphs, into a CQA system. It introduces a parallel embedding technique for this multi-modal knowledge and employs an alignment-interaction-fusion mechanism to facilitate the seamless integration of this multi-modal knowledge. Through extensive experiments, the effectiveness and superiority of our proposed method are demonstrated.

The United Nations Security Council (UNSC) is entrusted with the responsibility of safeguarding global peace and security. Prominent global security concerns will be deliberated upon, and viewpoints will be presented within the UNSC. Analyzing the cognitive patterns from UNSC debates helps scholars gain insights into the intricacies of international relations and diplomatic discourse. In this study, our focus lies in the cognitive analysis of debates held within the UNSC. We employ metaphors and their associated concept mappings as a methodological tool to dissect the cognitive nuances present in the debates, spanning from January 1995 to December 2020. To undertake this extensive analysis from a large volume of documents, we leverage MetaPro, a state-of-the-art computational metaphor processing system to obtain the concept mappings of metaphors. We analyze cognitive variations by temporal and geographical variables. We also demonstrate the correlation between metaphor-reflected cognition and diplomatic behavior, and their recursive influence, based on large sample research. Our major finding highlights the mutual impacts of metaphorical cognition and voting behavior at the UN.

Conversation requires a substantial amount of coordination between dialogue participants, from managing turn taking to negotiating mutual understanding. Part of this coordination effort surfaces as the reuse of linguistic behaviour across speakers, a process often referred to as alignment. While the presence of linguistic alignment is well documented in the literature, several questions remain open, including the extent to which patterns of reuse across speakers have an impact on the emergence of labelling conventions for novel referents. In this study, we put forward a methodology for automatically detecting shared lemmatised constructions---expressions with a common lexical core used by both speakers within a dialogue---and apply it to a referential communication corpus where participants aim to identify novel objects for which no established labels exist. Our analyses uncover the usage patterns of shared constructions in interaction and reveal that features such as their frequency and the amount of different constructions used for a referent are associated with the degree of object labelling convergence the participants exhibit after social interaction. More generally, the present study shows that automatically detected shared constructions offer a useful level of analysis to investigate the dynamics of reference negotiation in dialogue.

Sleep staging holds significant importance in clinical medicine, aiding in the diagnosis of various disorders related to sleep and cognition. However, manually annotating a large amount of sleep data is time-consuming and labor-intensive, making it difficult to achieve. Efficiently utilizing these unannotated data poses a challenging task. We propose a novel self-supervised learning method with Temporal-split Contrastive and Electrode Autoencoder (TsC-EA) for sleep staging. We demonstrate that our method achieves state-of-the-art performance in self-supervised learning on SleepEDF and MASS-SS3. Moreover, experimental results indicate that our method can surpass the performance of supervised learning methods using only 10% of labeled data. Additionally, we explore the application of self-supervised learning in patients with disorder of consciousness. It can assist in diagnosing the severity of DoC through analysis of sleep staging. Staging the sleep patterns of patients with disorders of consciousness can help in diagnosing the severity of their condition.

Advancing intelligent systems requires efficient computational architectures built on emerging electronic computing devices, as well as effective biomimetic function simulation to improve overall intelligence. Here we design a memristor-based circuit inspired by self-awareness concepts. It effectively achieves bionic adaptive decision-making by mimicking habituation learning mechanisms. Memristors serve as foundational units in the circuit, facilitating the simulation of functions akin to biological neurons and synapses. They help implement key features such as information filtering, integration, and synaptic plasticity through concise circuit structures and efficient computing methods. Experimental results indicate that our circuit is capable of rapid and efficient information processing through in-memory analog computing, and it can make more reasonable and intelligent adaptive decisions by incorporating self-awareness concepts and biomimetic mechanisms. Extending this work to large-scale decision-making systems holds potential for intelligent platforms aiming to achieve advanced cognitive capabilities.

Empirical investigations demonstrate similar cognitive processing patterns for objects and sexualized women. However, sexual objectification (SO) extends beyond sexualized women. To explore SO, we apply eye-tracking technique in conjunction with local/global and body-inversion paradigms. Ninety-four college students participated in the study. The visual gaze on non-sexualized South-Asian wo(men) images and the response time in Navon task post-priming with upright and inverted images is analyzed. Results indicate that participants of both genders gaze objectify females. Interestingly, male images are also gaze objectified. A comparison of attention allocation to face versus sexual body parts in upright versus inverted female images shows a reduced face-to-body ratio for the latter orientation, indicating a gender-specific attention shift. Combining the two SO theories, the study objectively substantiates the claim that women undergo objectification in even in non-sexual attire.

We investigate the phenomenon of colexification, where a sin- gle wordform is associated with multiple meanings. Previ- ous research on colexification has primarily focused on em- pirical studies of different properties of the meanings that de- termine colexification, such as semantic similarity or meaning frequency. Meanwhile, little attention was paid to the word- forms’ properties, despite being the original approach advo- cated by Zipf. Our preregistered study examines whether word length influences word choice for colexification using a novel dyadic communication game (N = 64) and a computational model grounded in the Rational Speech Act (RSA) framework. Contrary to initial predictions, participants did not exhibit a strong preference for efficient colexification (namely colexi- fying multiple concepts using short words, when long alter- natives are available). The results align more closely with a simpler coordination model, where dyads align on a function- ing lexical convention with relatively little influence from the efficiency of that convention. Our study highlights the pos- sibility that colexification choices are strongly determined by the pressure for coordination, with weaker influences from se- mantic similarity or meaning frequency. This is most likely explained by weak pressure for efficiency in our experimental design.

Through comparing autistic and non-autistic adults in learning and recognizing masked faces, we found that although autistic participants generally had poorer face recognition performance than matched controls, the two groups were similarly impaired by mask use. Nevertheless, when viewing masked faces during learning, they showed reduced tendency to look at the eyes and reduced change in eye movement consistency as compared with controls; this was not observed during recognition. Across participants, selective attention ability and flexibility to change face scanning behavior according to mask conditions were two important factors accounting for individual differences in performance. Interestingly, autistic spectrum quotient accounted for additional variance when recognizing masked faces learned also with a mask, suggesting additional influence from one’s autistic traits that could have impacted face learning experience during development. Our findings have important implications for identifying vulnerable populations whose face recognition ability may be particularly affected by mask use.

A continual learning (CL) model is designed to solve the catastrophic forgetting problem, which damages the performance of neural networks by overwriting previous knowledge with new knowledge. The fundamental cause of this problem is that previous data is not available when training new data in the CL setting. The memory-based CL methods leverage a memory buffer to address this problem by storing a limited subset of previous data for replay, and most methods of this type adopt random storage and replay strategies. In the human brain, the hippocampus replays consolidated knowledge from the neocortex in a random manner, e.g., random dreaming. Inspired by this memory mechanism, we propose a memory-based method, which replays more consolidated memory data while maintaining the randomness. Our work highlights that random replaying is important for the CL model, which confirms the effectiveness of random dreaming in the human brain.

Homophony (i.e, multiple meanings expressed by the same form) is ubiquitous across the world’s languages. Despite its pervasiveness, not all instances of homophony are equally likely, which suggests that homophony is unlikely to be accidental. There is a growing body of literature which aims to thoroughly examine cross-linguistic regularities in patterns of homophony and explain these from constraints in language learning and use, both at the lexical and morphosyntactic levels. Here, we examine a specific case of homophony in pronominal paradigms, that is, the lack of a number distinction (singular vs plural) for a given person value (first, second and third), a phenomenon coined as horizontal homophony. Cysouw (2003) suggested that a lack of number distinction is more likely to be found in third person (i.e., 3SG=3PL) than in second (i.e., 2SG=2PL), and it is least frequently found in first person (i.e., 1SG=1PL). We refer to this generalisation as the Horizontal Homophony Hierarchy: 3 > 2 > 1 (where > represents frequency inequality). This generalisation was nevertheless only made via qualitative description and by raw counts, and merely described without motivated explanation. In this study we take a step back and present additional evidence supporting the Horizontal Homophony Hierarchy. First, we ascertain the robustness of this typological tendency through a statistical analysis using the largest cross-linguistic database of pronominal paradigms to date (926 languages from 229 different families). Next, we explore whether the Horizontal Homophony Hierarchy has a corresponding learning correlate, which would indicate that this asymmetry is at least partly rooted in a cognitive bias. Specifically, we examine asymmetries in how easily adult humans learn different types of horizontal homophony in an artificial language learning experiment. The results from our typological analysis corroborate a hierarchy of horizontal homophony 3 > 2 > 1 in the world’s languages. However, our experimental results provide evidence against a learning bias underlying the hierarchy, thus suggesting that motivated explanations of the typology (if any) are more likely to be found in alternative pressures such as communicative need and efficiency.

One of the core properties of human language is compositionality: the meaning of a sentence can be understood by the meaning of individual words and the rules for combining them (Szabó, 2020). We investigate the development of conceptual compositionality (the combination of concepts). In our study, 6- to 9-year-old children (N = 40) were shown a card with two objects (e.g., a car and a star). Participants were introduced to two characters (a robot and a wizard) that used their powers to change the objects in different ways (e.g., turning one object pink). In the test trials, participants were asked to predict what a card would look like after both characters used their powers on the same card. All participants successfully learned the characters’ powers, but only participants 7.5 years and older succeeded in the compositionality test trials. Our findings suggest that by age 7.5 children can successfully compose functions.

Language is fundamental in human cognition and communication, helping us to encode the world around us. Adjectives represent a linguistic form used extensively, particularly in the social domain. Adjectives vary in both their valence and their breadth (e.g., “punctual” is narrower than “dependable”). Variations in adjectival breadth have not been studied extensively but may have significant consequences across various domains. The present study explores how subtle distinctions in adjective use may relate to descriptions of experiences that people share. To assess linguistic breadth in online communication, we examine whether online reviews with different star ratings are associated with differences in adjectival breadth. Through an analysis of over 200,000 reviews from Amazon digital music (Study 1) and Yelp restaurants (Study 2), we find evidence that linguistic desirability and breadth of adjectives in reviews positively correlate with their ratings. Specifically, higher-rated reviews tend to use broader and more desirable adjectives. However, this relationship varies between product categories, with high-rated music reviews showing increased linguistic breadth and desirability, while top-rated restaurant reviews demonstrate a decrease in breadth. This paper contributes to understanding linguistic breadth in social media contexts, highlighting how subtle language variations in evaluations can reflect different cognitive and communicative processes.

Mandarin speakers have different space-time mappings than English speakers, but how Mandarin-speaking children spatialize time is unknown. We explored the development of 3D time-space representations in Chinese children aged 3 to 5. 145 Mandarin-speaking children, divided into three conditions (Exp1: horizontal, vertical, and Exp2: sagittal axes), undertook an MTL task for ten picture stories. We analysed their choices in 3-step temporal events, intending to test their sequential and directional preference of time (e.g., order vs. disorder; left-to- right vs. right-to-left). The results showed that Chinese children acquired sequential temporal representations on the horizontal and vertical axes at age 4, similar to English-speaking children. However, their directional preferences appeared earlier than English children (Exp1). Furthermore, the sagittal axis had different patterns: sequentiality emerged only at age 5, but directional preference still has not emerged in the whole 3- 5age group.These findings emphasize that language and culture impact children's conceptualization of time.

Although space helps children to grasp time, comprehending temporal metaphors remains challenging. Particularly, Mandarin has different degree of ambiguity in sagittal time-space metaphors, where ‘qian’ (front/past) expresses both future-in-front and past-in-front mappings but ‘hou’ (back/future) predominately expresses future-at-back mappings. Temporal metaphors with a longer duration unit (e.g., year vs. hour) also increase this challenge. We investigated: 1) when children understand sagittal time-space metaphors; 2) whether different degree of ambiguity leads children to having an asymmetric understanding of the past and future; 3) how the unit of temporal duration affects time understanding. 138 Mandarin-speaking children (3-5 years) undertook an 8-item sagittal time-space metaphors test. The results showed that age 5 is a milestone to understand sagittal time-space metaphors, and a longer unit of time duration and more ambiguous space-time metaphors hinder children’s time comprehension. This study reveals the development of time cognition in non-western children and demonstrates how language impacts cognition.

Humans are remarkably adept at inferring the causes of events in their environment; doing so often requires incorporating information from multiple sensory modalities. For instance, if a car slows down in front of us, inferences about why they did so are rapidly revised if we also hear sirens in the distance. Here, we investigate the ability to reconstruct others' actions and events from the past by integrating multimodal information. Participants were asked to infer which of two agents performed an action in a household setting given either visual evidence, auditory evidence, or both. We develop a computational model that makes inferences by generating multimodal simulations, and also evaluate our task on a large language model (GPT-4) and a large multimodal model (GPT-4V). We find that humans are relatively accurate overall and perform best when given multimodal evidence. GPT-4 and GPT-4V performance comes close overall, but is very weakly correlated with participants across individual trials. Meanwhile, the simulation model captures the pattern of human responses well. Multimodal event reconstruction represents a challenge for current AI systems, and frameworks that draw on the cognitive processes underlying people's ability to reconstruct events offer a promising avenue forward.

Standard Large Language Models (LLMs) evaluation contains many different queries from similar minimal contexts (e.g. multiple choice questions). Conclusions from such evaluations are little informative about models' behavior in different new contexts (e.g. in deployment). We argue that context-dependence should be studied as a property of LLMs. We study the stability of value expression over different contexts (conversation topics): Rank-order stability on the population (interpersonal) level, and Ipsative stability on the individual (intrapersonal). We observe consistent trends - Mixtral, Mistral, Qwen, and GPT-3.5 model families being more stable than LLaMa-2 and Phi - over those two types of stability, two different simulated populations, and even on a downstream behavioral task. Overall, LLMs exhibit low Rank-Order stability, highlighting the need for future research on role-playing LLMs, as well as on context-dependence in general. This paper provides a foundational step in that direction, and is the first study of value stability in LLMs.

Many challenging problems in linguistic analysis concern structures that have a hybrid character---they show evidence of belonging to two, independently motivated types. Proposals often assign them to one or the other class, requiring complication of the theory to handle their exceptionality. We suggest that there is no satisfactory answer to such conundrums under standard, type-based representational theories, for those theories are founded on discrete topologies. As an alternative, we propose “Metric Grammars”--grammatical systems founded on connected topologies. A metric grammar, a recurrent map that has a neural network at its core, changes its grammatical system slightly with each instance of language experience. Focusing on a grammaticalization episode from the history of English---the development of “sort of" and “kind of" from Noun-Preposition structures into adverbs---we provide evidence that metric grammars exhibit statistical anticipation of categorical change, a phenomenon that is difficult to account for with discrete-topology models.

There has been considerable interest exploring how the utility of an outcome impacts the probability with which it is mentally simulated. Earlier studies using varying methodologies have yielded divergent conclusions with different directions of the influence. To directly examine such mental process, we employed a random generation paradigm in which all the outcomes were either equally (i.e., followed a uniform distribution) or unequally (i.e., a binomial distribution) probable. While our results revealed individual differences in how the utility influenced responses, the overall findings suggested that it is the outcomes' probabilities, not their utilities, that guide this process. Notably, an initial utility-independent bias emerged, with individuals displaying a tendency to start with smaller values when all outcomes are equally likely. Our findings offer insights into the benefits of studying the mental sampling processes and provide empirical support for particular sampling models in this domain.

Contrasting the climate change emergency represents one of the major challenges of modern times. Knowing how people represent ecology-related phenomena is crucial to inform interventions aimed at promoting more effective pro-environmental behaviors. Despite this, literature on the topic is still scarce. To fill this gap, we asked 340 participants to rate 200 concepts—among which Ecological (N = 50, e.g., deforestation)—on numerous semantic dimensions (N = 39), drawing insights from the literature on conceptual organization. A Principal Component Analysis on our dataset revealed the presence of three major components explaining overall the variability of our set of concepts. Interestingly, Ecological concepts had a major role in all of them. Indeed, when compared to other conceptual categories—both related (i.e., Natural—e.g., water—and Geographical/Geopolitical—e.g., ocean, city) and not related (i.e., Technological—e.g., Internet) to the green domain—they figured among the most abstract (Component 1), impacting our political, social, and personal spheres (Component 2), scientific, emotionally charged, and evoking sensorimotor experiences (Component 3) concepts. Overall, our study has a threefold relevance. On a theoretical side, it can contribute to enriching theories on concepts by investigating a new semantic domain that jeopardizes the concrete-abstract dichotomy; on a scientific side, it might broaden categorization research by providing semantic norms for new conceptual domains (the TECo Database); on a societal side, it can enhance politics on these timely themes.

Previous studies have demonstrated that bilinguals have discrete representations for speech sounds that are phonemic in both of their languages. In a lexical identification task for Singapore Mandarin words 椅 (/i2/ ‘chair’) and 鱼 (/y2/ ‘fish’), we find steepness of the identification functions differs among bilinguals with different linguistic experience, with steeper slopes for early English-Mandarin bilinguals (for whom the /y/ vowel is phonemic) and shallower slopes for early English-Malay bilinguals (for whom /y/ is not phonemic, but is largely discriminable in the forced choice task). With nuanced language background information, this finding suggests that exposure to both /i/ and /y/ in early development shapes phonemic perception. Model comparisons demonstrate that continuous measures of early exposure are more powerful than simple categorical groupings of bilingual ‘type’. Continuous measures of bilingual exposure are therefore highlighted as useful tools in the investigation of phoneme perception.

Our ability to re-experience the events from our personal past tends to decline with age, which can have profound effects on well-being. HippoCamera is a smartphone-based application developed to mitigate age-related decline by guiding users to record and review cues for real-world events using established mnemonic strategies, with previous work demonstrating improved episodic recollection and enhanced hippocampal activity following use. Here, we followed-up with older adult participants who had used HippoCamera several years prior to investigate whether any benefits persisted following use. Using a mixed-methods approach, we found stronger subjective re-experiencing of events that were recorded with HippoCamera compared to those that were not. Further, participants reported extended benefits to their overall sense of meaning and well-being. These results provide preliminary evidence characterizing the long-lasting effects of a smartphone-based tool that improves memory for everyday events in aging.

This paper seeks to understand designers’ abstraction in ill- structured problem-solving. We utilize a protocol study with expert designers to empirically analyze the abstraction process in the latent need problem setting. A logic-based abstraction schema is found to model the process the designers employed. The study reveals how designers utilize this schema, detailing, developing, and evaluating solutions for ill-structured problems. It highlights the recursive nature of abstraction and raises questions about the termination of the process in ill- structured domains. We conclude by proposing a computational model to further evaluate abstraction in complex problem-solving scenarios.

Word Sense Disambiguation (WSD) has been a fundamental task for human language understanding. In specific contexts, a word may have different meanings. For rarely seen word senses, the disambiguation becomes challenging with limited examples. Meta-learning, as a widely adopted machine learning method for few-shot learning, addresses this by extracting metacognitive knowledge from training data, aiding models in "learning to learn". Hence, the advancement of meta-learning hinges on leveraging high-quality metacognitive knowledge. In light of this, we propose a Bi-Branch Meta-Learning method for WSD to enrich and accumulate metacognitive insights. Our method employs two branches during training and testing. During training, we use a bi-branch loss with original and augmented data from large language models to compensate for data scarcity. In testing, information from base classes generates bi-branch scores to refine predictions. Experiments show our method achieves a 74.3 F1 score in few-shot scenarios, demonstrating its potential for few-shot WSD.

Emotion recognition is crucial for enhancing human-computer interaction. Due to considerable individual differences in emotion manifestation, traditional models do not adapt well to new individuals. Moreover, existing algorithms typically focus on identifying a single emotion, overlooking intrinsic connections among multiple emotions. Therefore, we propose a multi-task adversarial domain adaption (MADA) model for EEG-based emotion recognition. First, domain matching is employed to identify the most similar individual from the dataset as the source domain, alleviating individual differences and reducing training time. Subsequently, multi-task learning is utilized to simultaneously classify multiple emotions, capturing their intrinsic connections. Finally, adversarial domain adaption is applied to learn the individual differences between the source and target domains. Cross-subject experiments on the DEAP dataset indicate that our model achieves accuracies of 78.08%, 68.36%, and 69.64% on the valence, arousal, and dominance, respectively, surpassing state-of-the-art methods. This indicates the effectiveness of our model in recognizing multi-dimensional emotions.

Humans are surrounded by dynamic, continuous streams of stimuli, yet the human mind segments these stimuli and organizes them into discrete event units. Theories of language production assume that segmenting and construing an event provides a starting point for speaking about the event (Levelt, 1989; Konopka & Brown-Schmidt, 2018). However, the precise units of event representation and their mapping to language remain elusive. In this work, we examine event unit formation in linguistic and conceptual event representations. Given cross-linguistic differences in motion event encoding (satellite vs. verb-framed languages), we investigate the extent to which such differences in forming linguistic motion event units affect how speakers of different languages form cognitive event units in non-linguistic tasks. We test English (satellite-framed) and Turkish (verb-framed) speakers on verbal and non-verbal motion event tasks. Our results show that speakers do not rely on the same event unit representations when verbalizing motion vs. identifying motion event units in non-verbal tasks. Therefore, we suggest that conceptual and linguistic event representations are related but distinct levels of event structure.

Although well-intentioned diversity, equity, and inclusion (DEI) initiatives aim to increase minority representation in elite groups, they can sometimes backfire by causing candidates to question whether they were selected for merit. Prior work in social psychology suggests that this effect is driven mainly by stereotype threat. Here, we propose a novel cognitive framework: DEI initiatives backfire due to causal inference. Specifically, when candidates hear that they were selected based on a DEI initiative and/or enter a group where they are a minority, they may hypothesize that their selection was based more on their identity and less on their merit. Across two pre-registered experiments manipulating selection messages (DEI vs. merit) and statistical gender representation (represented or under-represented in the selected group), we find evidence in favor of our hypothesis. DEI messages and under-representation independently caused successful candidates to attribute their selection more to their identity and less to their merit but did not directly impact perceptions of competence. A third pre-registered experiment revealed that women selectively rated themselves as less competent in DEI contexts when selection tasks were more difficult. Taken together, this work shows that people make different causal hypotheses about their selection into elite groups based on DEI messages and group composition in conjunction with selection task difficulty and their social identity. Importantly, this work paves the way for designing DEI-based initiatives that license more helpful causal inferences about success to ensure that minority candidates thrive in their positions.

The present study focuses on a type of seemingly arbitrary alternation in modern Russian. Specifically, we investigate the phenomenon of Complementizer Omission, i.e. the alternation between the presence and absence of complementizer with regard to the factors that potentially exert an influence on the alternation in Russian. The choice of alternating pairs is statistically modeled with mixed-effects logistic regression. We find that the complementizer is more likely to be absent in Russian when the matrix subject is a first-or-second-person pronoun, the matrix predicate has a high frequency and the onset of the complement clause is non-ambiguous and non-informational. The findings align well with the Grammaticalization theory, according to which the distribution of complementizer is partially driven by certain types of combinations of matrix subjects and verbs that have become grammaticalized as epistemic markers. Moreover, we argue that the results provide weak support for ambiguity avoidance at the general syntactic level and that the Uniform Information Density account more fully explains the alternation than the Availability account. As in Jaeger and Norcliffe (2009), we propose that more cross-linguistic research should be done on syntactic alternations as "even similar constructions may be processed differently in different languages".

The Event Segmentation Theory suggests that people naturally divide everyday experiences into distinct units, with event boundaries serving as anchors in long-term memory and aiding recall. These boundaries are ubiquitous in children’s daily experiences and may significantly influence learning. This study investigated how event boundaries affect novel category learning in young children. Specifically, 23 English-speaking three-year-olds learned novel object categories under two conditions. In the event boundary condition, objects were moved across two different background contexts, whereas in the control condition, they remained within the same backgrounds. We hypothesized that presenting objects across an event boundary would enhance generalization. Unexpectedly, both conditions yielded similar performance. An order effect emerged, with initially introduced categories showing better performance, suggesting the impact of task structure and children’s differing interpretations of event boundaries, particularly among females. This finding opens avenues for further investigation into the role of event boundaries in early category learning.

Saliency-based Explainable AI (XAI) methods have been commonly used for explaining computer vision models, but whether they could indeed enhance user understanding at different levels remains unclear. We showed that for object detection AI, presenting users with AI ’s output for a given input was sufficient for improving feature-level and some instance-level user understanding, particularly for false alarms, and providing saliency-based explanations did not have additional benefit. This was in contrast to previous research on image classification models where such explanations enhanced understanding. Analyses with human attention maps suggested that humans already attended to features important for AI’s output in object detection and thus could infer AI’s decision-making processes without saliency-based explanations. However, it did not enhance users’ ability to distinguish AI’s misses and hits, or system-level understanding. Therefore, the effectiveness of saliency-based explanations is task-dependent, and alternative XAI methods are required for object detection models to better enhance understanding.

While the ability of recursion is considered universally available, there are considerable cross- and with-linguistic differences regarding the rules for recursive embedding, which must be learned from language-specific experience. One proposal argues that the recursivity of a structure is learnable as a productive generalization from distributional information in non-embedded input, and adults can indeed use such distributional cues to acquire recursive structures in an artificial language. However, it is not yet known whether children can use distributional information in this way. In this work, we examine children’s distributional learning of recursive structures. We exposed children to non-embedded sentences in an artificial grammar, where we manipulated the productivity of the structure across conditions. At test, we found that children exposed to productive input were more likely to accept recursively embedded sentences unattested during the exposure phase. The results suggest that children can make use of distributional information to acquire recursive structures.

Social norms are often characterized as a system of rules that guide behavior. However, social norms also allow for flexibility; not entirely restricting individuals to one possible behavior. Here, we put forward an agent-based cognitive model that captures social norms as processes that are socially constructed through interactions between individuals. In this modelling work, we focus on the role of norm acquisition and conformity bias in both action production and inference-making. This computational cognitive model allows us to think about social norms along three dimensions: individual vs. collective, behavior vs. belief, and subjective vs. objective. Our simulation results show that increased conformity bias can induce misjudgments about the true desires of others and misalignment between different agents' perceptions of the social norm. However, if agents do not assume that others also conform in their behavior, this increased conformity bias does not necessarily lead to excessive misperceptions of the social norm.

Autonomous agents often need to decide between choosing actions that are familiar and have previously yielded positive results (exploitation) and seeking new information that could help uncover more effective actions (exploration). We present an “observe or bet” task that separates “pure exploration” from “pure exploitation”: 75 five-to-seven-year-old children, 60 adults and computational agents have to decide either to observe an outcome without reward, or to bet on an action without immediate feedback at varying probability levels. Their performances were measured against solutions from the partially observable Markov decision process and meta-RL models. Children and adults tended to choose observation more than both algorithm classes would suggest. Children also modulated their betting policy based on the probability structure and amount of evidence, exhibiting “hedging behavior” a strategy not evident in standard bandit tasks. The results provide a benchmark for reasoning about reward and information in humans and neural network models.

Research suggests that the meanings of iconic signs are not easily guessable by sign-naive people; however, some signs' meanings are more easily guessed than others'. What causes some signs to be more easily guessable (more transparent) than others is not well-understood. In our previous research, we showed that signs whose form is based on more cross-linguistically common underlying motivations were chosen as "better suited" to a meaning—that is, they are more transparent—than signs based on less common underlying motivations (Tkachman, Sadlier-Brown, Lo, & Hudson Kam, 2023). In the current study, we ask whether, in addition, iconicity affects a sign's transparency. We asked sign-naive English speakers to rate all the signs from our previous study for how iconic they are. We then reanalyzed the data from our previous study in light of the obtained iconicity ratings. Results show that when people are asked to choose between an attested sign for a given animal label and an unattested one (i.e., a sign for a different animal), iconicity ratings did not affect participants' preferences: attested signs are preferred regardless of how iconic they are. However, when participants are asked to choose between two attested signs with the same meaning (e.g., two signs for 'cat' from different sign languages), iconicity does appear to affect participants' choices: participants were more likely to pick the more cross-linguistically common sign if the difference in iconicity ratings between the two signs was bigger. These results shed additional light on the ongoing debate on the connection between iconicity and transparency: iconicity by itself does not make a sign transparent, but it can enhance transparency under certain conditions.

How do people perform general-purpose physical reasoning across a variety of scenarios in everyday life? Across two studies with seven different physical scenarios, we asked participants to predict whether or where two objects will make contact. People achieved high accuracy and were highly consistent with each other in their predictions. We hypothesize that this robust generalization is a consequence of mental simulations of noisy physics. We designed an "intuitive physics engine'' model to capture this generalizable simulation. We find that this model generalized in human-like ways to unseen stimuli and to a different query of predictions. We evaluated several state-of-the-art deep learning and scene feature models on the same task and found that they could not explain human predictions as well. This study provides evidence that human's robust generalization in physics predictions are supported by a probabilistic simulation model, and suggests the need for structure in learned dynamics models.

Large language models (LLMs) were trained to predict words without having explicit semantic word representations as humans do. Here we compared LLMs and humans in re-solving semantic ambiguities at the word/token level by ex-amining the case of segmenting overlapping ambiguous strings in Chinese sentence reading, where three characters “ABC” could be segmented in either “AB/C” or “A/BC” depending on the context. We showed that although LLMs performed worse than humans, they demonstrated a similar interaction effect between segmentation structure and word frequency order, suggesting that this effect observed in humans could be accounted for by statistical learning of word/token occurrence regularities without assuming an explicit semantic word representation. Nevertheless, across stimuli LLMs’ responses were not correlated with any hu-man performance or eye movement measures, suggesting differences in the underlying processing mechanisms. Thus, it is essential to understand these differences through XAI methods to facilitate LLM adoption.

Intentional binding (IB) is the experience of temporal interval compression between voluntary actions and subsequent events when the latter are perceived to be caused on purpose by the agent’s actions. It can be measured experimentally by comparing the judgments of temporal intervals between either a voluntary act or an external event, and a later sensory consequence. Evidence suggests this might be modulated by the emotional valence of the consequence. However, controversies have arisen over the consistency of the results and the methodology they were obtained with. Here, we aimed to measure this affective modulation using a two-interval forced-choice (2AFC) discrimination task and word stimuli. Three factors were employed: agency (agency and passive), emotional valence (neutral, positive, and negative words), and interval duration ratio determined based on individual values of just noticeable differences (JND). Participants had to judge which of two intervals presented in each trial was shorter. Generalized linear mixed model analysis indicated that there was an effect of IB, but no affective modulation. Dissociation of component mechanisms of SoA are discussed to better understand results and suggest further directions.

Because information theory equates information with event occurrence probabilities, when applying its methods, language researchers typically take the information provided by words to be their relative frequencies in a corpus. This implicitly assumes words occur uniformly across contexts, however empirically, word distributions are bursty: the likelihood of most words appearing in most contexts is small, whereas the likelihood of a word recurring in context is much higher. In an elicitation study we examined whether speakers are sensitive to the dynamic word occurrence probabilities this implies. Consistent with proposals that prenominal adjectives increase noun predictability, participants produced numerous seemingly redundant adjectives prior to unambiguous nouns at first mention. However, despite receiving no feedback, they produced significantly fewer adjectives before subsequent mentions of the same nouns, indicating they had re-evaluated their probabilities. These results support the idea that prenominal adjectives facilitate efficient communication, and that speakers' representations of lexical probabilities are dynamic.

Here we present a new Explainable AI (XAI) method to probe the functional partition in AI models by comparing features attended to at different layers with human attention driven by diverse task demands. We applied this method to explain an object detector Yolo-v5s in multi-category and single-category object detection tasks. We found that the model’s neck showed higher similarity to human attention during object detection, indicating a reliance on diagnostic features in the neck, whereas its backbone showed higher similarity to attention during passive viewing, indicating salient local features encoded. With this understanding of its functional partition, using Yolo-v5s as a model for human cognition, our comparative analysis against human attention when providing explanations for object detection revealed that humans attended to a combination of diagnostic and salient features during explaining multi-category general object detection but attended to mainly diagnostic features when explaining single-category human/vehicle detection in driving scenarios.

Video game playing is an extremely structured domain where algorithmic decision-making can be tested without adverse real-world consequences. While prevailing methods rely on image inputs to avoid the problem of hand-crafting state space representations, this approach systematically diverges from the way humans actually learn to play games. In this paper, we design object-based input representations that generalize well across a number of video games. Using these representations, we evaluate an agent's ability to learn games similar to an infant - with limited world experience, employing simple inductive biases derived from intuitive representations of physics from the real world. Using such biases, we construct an object category representation to be used by a Q-learning algorithm and assess how well it learns to play multiple games based on observed object affordances. Our results suggest that a human-like object interaction setup capably learns to play several video games, and demonstrates superior generalizability, particularly for unfamiliar objects. Further exploring such methods will allow machines to learn in a human-centric way, thus incorporating more human-like learning benefits.

In human social interactions, decisions are naturally influenced by both individual needs and the needs of others. However, it remains unclear whether cognitive robots exhibit similar needs-guided decision-making characteristics. In this study, we design a collaborative tracking task to evaluate this phenomenon. Specifically, we develop a needs-guided reinforcement learning framework that enables robots to autonomously learn and shape behavior by considering both their intrinsic needs and those of others. Our experiments highlight that the robots' inherent needs play a more crucial role in decision-making than the needs of others. In essence, our model establishes an interpretable foundation for applications in cognitive robotics.

Learning with dynamic facial expressions often results in higher face recognition performance than with static images. However, few studies have used both positive and negative facial expressions to investigate the effects of dynamic facial expression information on recognition memory. The present study examined whether the effect of dynamic facial expressions depends on the type of facial expression used during the learning and recognition phases. Participants viewed individuals with smiling or angry expressions in either static or dynamic images in a learning session. Participants then performed a recognition task using static images with neutral, angry, or smiling expressions. The results showed that when tested with the neutral static faces, the advantage of the dynamic expression was observed regardless of the facial expression during learning (Experiment 1). However, when tested with the angry static faces, the dynamic expression advantage was not observed, but the recognition performance was better for the faces learned with the angry static faces (i.e., identical to the faces in the recognition task) (Experiment 2). In the recognition task with the static smiling faces, the advantage of dynamic expression was again observed in addition to the emotion congruency effect (i.e., better performance for the faces learned with the smiling expression) (Experiment 3). These results suggest that the effect of dynamic facial expression information on recognition depends on the type of facial expression during learning and recognition.

How do people start conversations with someone they have never met before? In this project, we investigate the hypothesis that good starting topics facilitate transitions to many different topics. To test this, we leverage a dataset of unstructured, 10-minute conversations between pairs of strangers. Using natural language processing (NLP) and network approaches, we show that strangers begin their conversations with topics that are centrally located in a network of topic transitions. These “launch pad” topics are useful starting points because they are well-connected to other topics, potentially increasing the likelihood of finding common ground. These findings underscore the fact that it is not the semantic meaning of a topic that makes it an effective starting point, but rather its transition properties. This insight paves the way for future research to identify conversational launch pads in different populations, where common starting topics may differ widely but nonetheless hold similar network positions. When people start conversations, they begin the process of trying to understand and connect with another person’s mind. Here, we examine how this important process unfolds.

The complexity of visual stimuli plays an important role in many cognitive phenomena, including attention, engagement, memorability, time perception and aesthetic evaluation. Despite its importance, complexity is poorly understood and ironically, previous models of image complexity have been quite \textit{complex}. There have been many attempts to find handcrafted features that explain complexity, but these features are usually dataset specific, and hence fail to generalise. On the other hand, more recent work has employed deep neural networks to predict complexity, but these models remain difficult to interpret, and do not guide a theoretical understanding of the problem. Here we propose to model complexity using segment-based representations of images. We use state-of-the-art segmentation models, SAM and FC-CLIP, to quantify the number of segments at multiple granularities, and the number of classes in an image respectively. We find that complexity is well-explained by a simple linear model with these two features across six diverse image-sets of naturalistic scene and art images. This suggests that the complexity of images can be surprisingly simple.

Self-regulated learning influences students’ learning behaviors and is a significant academic performance factor. Resource management strategy based on self-regulated learning theory is an important indicator for students to demonstrate Self-regulated learning. However, current self-regulated learning and resource management strategy assessments still rely on subjective evaluations and self-assessments, which are time-consuming and laborious. Therefore, we propose a novel method combined with mobile sensing by collecting detailed learning strategy subscales and objective mobile sensing data from 211 college students to explore a new approach to assessing self-regulated learning and resource management strategy. We are the first to propose a mobile sensing approach for assessing self-regulated learning and learning strategies. The method studies the associations between the learning strategy subscales and these daily behavior patterns and presents features for behavior patterns from mobile sensing data. Our study helps to reveal new forms of assessing self-regulated learning and opens the way for personalized interventions.

Iconicity (the resemblance between form and meaning) exists in various modes of communication. This study investigated whether adults interpret iconicity in speech and gesture via a modality-independent ability. We tested 348 adult participants and assessed their ability to use iconic prosody and iconic gesture cues when interpreting novel verb meanings. We manipulated the rate of the spoken novel verbs (iconic prosody) and the rate of observed hand movements (iconic gestures) to be either fast or slow in two verb-action matching tasks. Adults could use these iconic speed cues to interpret novel verbs as referring to a fast or slow version of the same action. Adults showed similar performances in the two verb-action matching tasks: those who performed well in the iconic prosody task also performed well in the iconic gesture task. This positive correlation persisted even after controlling verbal working memory. Thus, we conclude that adults possess a modality-independent ability for interpreting iconicity.

Infants learn actively in their environments, shaping their own learning curricula. They learn about their environments’ affordances, that is, how local circumstances determine how their behavior can affect the environment. Here we model this type of behavior by means of a deep learning architecture. The architecture mediates between global cognitive map exploration and local affordance learning. Inference processes actively move the simulated agent towards regions where they expect affordance-related knowledge gain. We contrast three measures of uncertainty to guide this exploration: predicted uncertainty of a model, standard deviation between the means of several models (SD), and the Jensen-Shannon Divergence (JSD) between several models. We show that the first measure gets fooled by aleatoric uncertainty inherent in the envi- ronment, while the two other measures focus learning on epistemic uncertainty. JSD exhibits the most balanced exploration strategy. From a computational perspective, our model suggests three key ingredients for coordinating the active generation of learning curricula: (1) Navigation behavior needs to be coordinated with local motor behavior for enabling active affordance learning. (2) Affordances need to be encoded locally for acquiring generalized knowledge. (3) Effective active affordance learning mechanisms should use density comparison techniques for estimating expected knowledge gain. Future work may seek collaborations with developmental psychology to model active play in children in more realistic scenarios.

Studies suggest that infants track others’ beliefs based on visual information (Scott & Baillargeon, 2017 but see Dörrenberg, Rakoczy, Liszkowski, 2018). However, research targeting whether infants understand that others’ beliefs can be induced via communication is scarce, although most of the human belief-repertoire is acquired via communication. We presented eighteen-month-olds (Experiment1:N=34; Experiment2-replication:N=35) with a false belief (FB) scenario where the initial belief was induced via communication, aiming to measure their informative pointing (for an agent mistaken about a toy’s location compared to a true belief scenario). Instead of more pointing to the toy’s current location, in the FB condition we found an unexpected ‘altercentric’ effect: infants pointed more to the empty location where the agent falsely believed the object to be). Next, we asked whether infants show different altercentric effects for visually induced beliefs (Experiment3: N=35). Results replicated the altercentric effect, suggesting a potentially stronger encoding of visually induced beliefs.

The present study investigated the effects of presentational timing, operationalized as different levels of temporal overlap, on cross-modal priming of written words. We used a paradigm where the playback of spoken word primes was shifted relative to the presentation of written targets (asynchronous, partially overlapping, and synchronous presentation). Our participants (n = 48) carried out a speeded lexical decision task on the written targets. Presenting the spoken primes, albeit the words’ onset, before the written targets reduced lexical decision times to both words and pseudowords. Asynchronous presentation of the spoken primes resulted in the largest difference between word and pseudoword response times. We discuss our results in relation to the mental structure of human word knowledge and in the context of word form acquisition.

Previous work has shown that isolated non-canonical sentences with Object-before-Subject (OSV) order are initially harder to process than their canonical counterparts with Subject-before-Object (SOV) order. Although this difficulty diminishes with appropriate discourse context, the underlying cognitive factors responsible for alleviating processing challenges in OSV sentences remain a question. In this work, we test the hypothesis that dependency length minimization is a significant predictor of non-canonical (OSV) syntactic choices, especially when controlling for information status such as givenness and surprisal measures. We extract sentences from the Hindi-Urdu Treebank corpus (HUTB) that contain clearly-defined subjects and objects, systematically permute the preverbal constituents of those sentences, and deploy a classifier to distinguish between original corpus sentences and artificially generated alternatives. The classifier leverages various discourse-based and cognitive features, including dependency length, surprisal, and information status, to inform its predictions. Our results suggest that, although there exists a preference for minimizing dependency length in non-canonical corpus sentences amidst the generated variants, this factor does not significantly contribute in identifying corpus sentences above and beyond surprisal and givenness measures. Notably, discourse predictability emerges as the primary determinant of constituent-order preferences. These findings are further supported by human evaluations involving 44 native Hindi speakers. Overall, this work sheds light on the role of expectation adaptation in word-ordering decisions. We conclude by situating our results within the theories of discourse production and information locality.

Questions combine our mastery of language with our remarkable facility for reasoning about uncertainty. How do people navigate vast hypothesis spaces to pose informative questions given limited cognitive resources? We study these tradeoffs in a classic grounded question-asking task based on the board game Battleship. Our language-informed program sampling (LIPS) model uses large language models (LLMs) to generate natural language questions, translate them into symbolic programs, and evaluate their expected information gain. We find that with a surprisingly modest resource budget, this simple Monte Carlo optimization strategy yields informative questions that mirror human performance across varied Battleship board scenarios. In contrast, LLM-only baselines struggle to ground questions in the board state; notably, GPT-4V provides no improvement over non-visual baselines. Our results illustrate how Bayesian models of question-asking can leverage the statistics of language to capture human priors, while highlighting some shortcomings of pure LLMs as grounded reasoners.

The decision to explore a novel option or exploit a known one — referred to as the explore-exploit trade-off — has received much attention from diverse fields of research, ranging from computer science to developmental psychology. However, much of the work on this topic has focused exclusively on an individual agent acting alone, a scenario that does not fully capture the rich social dynamics of human decision-making. In particular, the presence and participation of others can theoretically influence the decision to explore or exploit. One factor which may affect how individuals navigate the explore-exploit tradeoff is the presence of caregivers, who can help buffer the downside costs of more exploratory decision making. Across two pre-registered studies, we investigated whether children and adults predicted more or less exploratory behavior in the presence of a caregiver. In Study 1, we presented U.S. American children (N=87, ages 4 to 8) with vignettes of other children faced with the choice of exploring a novel option or exploiting a known one across a range of domains. In the vignettes, the characters either faced these decisions alone or in the presence of a parent. In Study 2, we presented the same vignettes to U.S. American adults (N=79). Across both studies, and as predicted, we found that both children and adults believed others would be more exploratory in the presence of caregivers. These results add important nuance to our understanding of how individuals navigate the explore-exploit tradeoff, and highlight the role of the social context in shaping these decisions. We aim to build on these results on future work centralizing the role and function of care in decision-making and exploration.

Traditional event cognition research typically characterises events as continuous, each bounded by a single beginning and a single ending. Daily events, however, often seem to involve discontinuities. For instance, if one is in a meeting that is temporarily interrupted by a phone call, one retains two events—the meeting and the phone call—rather than three, which include the meeting before the phone call, the phone call itself, and the meeting subsequent to the call. This study explores what binds events together across these discontinuities in everyday life. We examined five potential binding factors: place, people, topic, activity, and goal. Fifty-one participants provided data on recent non-contiguous daily life events, revealing that 97% of these events were tied by the 'Activity' aspect, followed by the 'Place' aspect (82%) and 'Goal' aspect (56%). 'People' (48%) and 'Topic' aspects (24%) were less significant in unifying non-contiguous events. The proportion of each event aspect in non-contiguous events suggests a need to expand theories of event cognition to focus on what brings events together rather than solely on what separates them—a perspective often overlooked in cognitive event theories.

Sensation-seeking (SS) is characterised by a proclivity for intense experiences and disregard for potential aversive consequences. While SS is implicated as a vulnerability factor in various mental disorders, the underlying mechanisms remain elusive. Recent approaches propose an alternative perspective, suggesting that SS may be linked to highly explorative, and therefore risky, behaviours driven by a preference for informative environments. To probe this hypothesis, we reanalysed a dataset where participants chose to self-administer or avoid mild electric stimulation (MES) in an economic decision-making task. Contrary to previous interpretations associating higher sensation-seeking with the positive economic value of experiencing MES, Bayesian models of learning reveal an alternative account: sensation-seekers are more attuned to information about stimuli-shock contingencies. Specifically, high sensation-seeking individuals are less avoidant of information about the possibility of a shock, supporting the idea that sensation-seeking is linked to a preference for informative environments.

High-dimensional deep neural network representations of images and concepts can be aligned to predict human annotations of diverse stimuli. However, such alignment requires the costly collection of behavioral responses, such that, in practice, the deep-feature spaces are only ever sparsely sampled. Here, we propose an active learning approach to adaptively sample experimental stimuli to efficiently learn a Bayesian matrix factorization model with deep side information. We observe a significant efficiency gain over a passive baseline. Furthermore, with a sequential batched sampling strategy, the algorithm is applicable not only to small datasets collected from traditional laboratory experiments but also to settings where large-scale crowdsourced data collection is needed to accurately align the high-dimensional deep feature representations derived from pre-trained networks. This provides cost-effective solutions for collecting behavioral data and generating high-quality predictions in large-scale behavioral and cognitive studies.

Designing user-adaptive search systems necessitates modeling the user's knowledge state during information seeking. Gaze data offers insights into cognitive processes during task-based reading. Despite its potential, cognitive perspectives have been insufficiently explored in the representation of the user's knowledge state when designing search systems. We reanalyzed an eye-tracking dataset and constructed mixed-effects user models to identify which measurements of gaze activities (i.e., gaze metrics captured by eye trackers) are reflective of the user. Our study’s findings indicate that there are statistically significant correlations between gaze metrics that measure the variability of saccadic eye movement and search performance. The accuracy of answers has been significantly influenced by the interaction between the control of saccade trajectories, measured by the standard deviation of absolute saccadic directions and the difficulty of the search task. We discuss the implications of these findings for the design of search systems adaptable to the user’s state of knowledge.

Extensive instruction tuning of large language models(LLMs) has proven to be a powerful technique, extending the outstanding performance of general LLMs to new tasks. Consequently, the integration of state-of-the-art(SOTA) general open-source models with specific domains leverages instruction tuning to unlock the emerging capabilities of large language models(LLMs) in those domains. Current practices in instruction tuning often rely on expanding the size of the dataset without a clear strategy to ensure data quality. This can inadvertently introduce noise and degrade model performance. Furthermore, there is currently no unified approach for the quantity, quality, and diversity of instruction tuning data, as well as the methods for instruction tuning. As a result, this severely hampers the practicality and universality of instruction tuning.\ Addressing these issues, we propose a \textbf{UNI}fied \textbf{F}ramework for \textbf{I}nstruction \textbf{T}uning(\textbf{UNIFIT}), namely Concept Tree generation for instruction tuning data, instruction following difficulty selecting data for high-quality instruction tuning, and the incorporation of random noise embeddings(NE) to enhance model performance during tuning. Through experiments involving multiple models, domains, and orders of magnitude, our proposed instruction tuning framework not only enhances the diversity of instruction tuning data but also achieves a remarkable 60\% reduction in training time consumption, with a mere 6\% of all instruction tuning data, surpassing the performance of using all instruction tuning data by 11\%. This universally applicable instruction tuning framework signifies a substantial advancement in the generality of large language model instruction tuning, marking a revolutionary leap forward and promising efficiency gains while being resource-conscious.

In this study we investigated the role of interword spacing and its interaction with Arabic root frequencies by studying readers’ eye movement patterns when they read Arabic sentences. Our eye-tracking experiment results did not show any significant evidence for the interword spacing effect on Arabic word processing, which concurred with the earlier work by Leung et al. (2021). On the other hand, we replicated an earlier experiment conducted by Hermena et al. (2020) on the effect of Arabic root frequencies on word processing. Contrary to their finding, our results showed that words which differed in root frequencies significantly modulated eye movement measures. This provided another support to the status of Arabic non-concatenative roots as a morphological unit.

While ample work has examined how to increase empathy within situational contexts, little research has focused on how the language used to communicate with others may elicit empathy. Here, we investigate how (Study 1) the degree of a narrator's culpability and (Study 2) narrative framing of personal narratives (focusing on experienced sensations, emotions, or neither) affects feelings of empathy reported by listeners. Across our two studies, 901 participants read narratives describing common life events, rated their empathy towards the narrator, and were given an option to write a response to the narrator. Our findings indicate that people report less empathy towards narrators that caused their misfortune, although their written responses were more focused on the narrator. By contrast, however, highlighting sensory or emotional details in a narrative did not significantly impact the degree of empathy reported by listeners, yet still affected the language of responses produced by listeners.

Investors rely on judgmental heuristics and comparative analysis for future stock price prediction based on specific components of information in hand. Information components are used as anchors for price estimation. Through an eye-tracking experiment, we aim to understand the perceived significance of various formats of information, particularly focusing on graphical and numerical components, and to explore the influence of complex time-varying patterns in stock price line plots. Results show that graphical components capture higher visual attention. Participants are not always loss-averse and prominently exhibit disposition effects for investment decisions in profitable scenarios. The 52-week high is allotted the highest fixation duration, signifying its perception as a strong reference point. Investment choices were found to be varying based on levels of prior knowledge and experience. The visual gaze analysis provides behavioural insights into complex decision-making processes.

We compare storytelling in GPT-3.5, a recent large language model, with human storytelling. We hypothesized that GPT differs from humans in the kind of memories it possesses, and thus could perform differently on tasks influenced by memory, such as storytelling. We used an existing dataset of human stories, either recalled or imagined (Sap et al., 2022), and generated GPT stories with prompts designed to align with human instructions. We found that GPT's stories followed a common narrative flow of the story prompt (analogous to semantic memory in humans) more than details occurring in the specific context of the event (analogous to episodic memory in humans). Furthermore, despite lacking episodic details, GPT-generated stories exhibited language with greater word affect (valence, arousal, and dominance). When provided with examples of human stories (through few-shot prompting), GPT was unable to match its stories' narrative flow or affective aspects with human stories.

In recent years, with the widespread popularity of location-based social network platforms, the data generated by users on social networks has grown exponentially. There has been a growing focus on the problem of POI (Point-of-Interest) recommendations. Unlike traditional sequence recommendation that primarily considers the temporal dimension, POI recommendation needs to account for the influence of geographical information to a large extent. However, previous works in the graph construction process often only consider the places users have visited, neglecting those they haven't been to. To address this, we propose a Dual Weighted Graph Convolutional Network for POI recommendation called DualPOI. Specifically, we first leverage graph neural networks and attention mechanisms to capture users' local trajectory preferences for visited POIs. A delicately designed spatiotemporal encoder is conducted to model users' local spatiotemporal preferences. Subsequently, using a dual graph convolutional approach, we transfer the user's local preference information to a global scope, thereby modeling novel preferences for unvisited locations. Extensive experiments on four real-world datasets validate the effectiveness of our proposed method in enhancing the accuracy of POI recommendations. Comprehensive ablation studies and parameter analysis further confirm the efficacy of the proposed modules.

The multimodal deep neural networks, represented by CLIP, have generated rich downstream applications owing to their excellent performance, thus making understanding the decision-making process of CLIP an essential research topic. Due to the complex structure and the massive pre-training data, it is often regarded as a black-box model that is too difficult to understand and interpret. Concept-based models map the black-box visual representations extracted by deep neural networks onto a set of human-understandable concepts and use the concepts to make predictions, enhancing the transparency of the decision-making process. However, these methods involve the datasets labeled with fine-grained attributes by expert knowledge, which incur high costs and introduce excessive human prior knowledge and bias. In this paper, we observe the long-tail distribution of concepts, based on which we propose a two-stage Concept Selection Model (CSM) to mine core concepts without introducing any human priors. The concept greedy rough selection algorithm is applied to extract head concepts, and then the concept mask fine selection method performs the extraction of core concepts. Experiments show that our approach achieves comparable performance to end-to-end black-box models, and human evaluation demonstrates that the concepts discovered by our method are interpretable and comprehensible for humans.

Recent research has identified substantial individual differences in how people solve value-based tasks. Here, we examine such differences in the motivational Go/NoGo task, which orthogonalizes action and valence, using open-source data from 817 participants. Using computational modeling and behavioral analysis, we identified four distinct clusters of people. Three clusters corresponded to previous models of the task, including people with different learning rates for cues that signal rewarding and punishing states and with different sensitives for rewards and punishments. The fourth cluster of people acted like naïve reinforcement learners, with their responses shaped by outcomes in a manner that was independent of the state information provided by the cues. In addition to providing evidence that state-independent learning is a common disposition, we show that not considering such learning can dramatically affect the results of computational modeling. We discuss the implications for the modeling of data from heterogeneous populations.

Perceived dominance and trustworthiness have both been found to be positive predictors for a candidate’s electoral and employment success. On the other hand, compared to male faces, female faces exhibit a much stronger anti-correlation between perceived trustworthiness and dominance. Together, these two phenomena place women at a distinctive disadvantage to men in electoral and work settings. In this study, we conduct computational analyses on a gender- and race-balanced, publicly available face dataset to examine the provenance of the anti-correlation between perceived dominance and trustworthiness in female faces. By identifying and quantifying the facial features that contribute to each social trait, we find that the female anti-correlation stems predominantly from components unique to female faces (83\%), with the lip region being the main contributor (23\%). Visualization of face featural modifications show that the corners of the mouth curve up and down in opposite directions for perceived trustworthiness and dominance, respectively, in female faces, but in orthogonal directions the same in male faces. By correlating gender specific models with perceived demographic information, we find that female dominance ($F_D$) and trustworthiness ($F_T$) are correlated in opposite directions along most perceived gender, age and race-related demographic dimensions. Male dominance ($M_D$) and trustworthiness ($M_T$) , on the other hand, are correlated in the same direction along race-related dimensions, but otherwise share no significant demographic dimensions (age and gender). In particular, perceived sexual dimorphism strongly drives $F_D$, $F_T$, and $M_D$, but is absent for $M_T$, indicating sexual dimorphism is a strong contributor to the female anti-correlation.

People rapidly form first impressions based on facial appearances, which have significant real-life consequences. While various computational models have been developed to analyze how facial characteristics influence these impressions, they often have limitations, such as focusing on limited trait impressions, restricted facial characteristics, reliance on black-box machine learning methods, and dependency on manual annotations. In this study, we address these shortcomings by utilizing recent advancements in computer vision to extract human-interpretable, quantitative measures of facial characteristics (e.g., facial morphological features and skin color) and emotional attributes from face images. Using machine learning techniques, we modeled 34 first impressions and validated our model's generalizability and predictive accuracy with out-of-sample faces. Our model demonstrates the relative importance of facial characteristics and emotional attributes in shaping these 34 first impressions. Our results provide a comprehensive understanding of how various facial characteristics and emotional attributes collectively influence social biases.

Human decision-making is filled with a variety of paradoxes demonstrating deviations from rationality principles. Do state-of-the-art artificial intelligence (AI) models also manifest these paradoxes when making decisions? As a case study, in this work we investigate whether GPT-4, a recently released state-of-the-art language model, would show two well-known paradoxes in human decision-making: the Allais paradox and the Ellsberg paradox. We demonstrate that GPT-4 succeeds in the two variants of the Allais paradox (the common-consequence effect and the common-ratio effect) but fails in the case of the Ellsberg paradox. We also show that providing GPT-4 with high-level normative principles allows it to succeed in the Ellsberg paradox, thus elevating GPT-4’s decision-making rationality. We discuss the implications of our work for AI rationality enhancement and AI-assisted decision-making.

Causal reasoning enables us to explain the past, predict the future, and intervene in the present. Does the brain allocate specialized cortical regions to causal reasoning? And if so, are they involved in reasoning about both physical and social causal relationships, or are they domain-specific? In a pre-registered experiment (Exp 1) we scanned adults using fMRI while they matched physical and social causes to effects (e.g., ‘The car swerved to avoid a crash’ -> ‘Coffee spilled all over the car seat’; ‘He was late for work’ -> ‘Tom was scolded by his boss’) or physical and social descriptions of the same entity matched for difficulty and linguistic variables to the causal conditions (e.g., ‘The brightest object in the sky’-> ‘The closest star to earth’; ‘She works at a hotel’ -> ‘She brings in guests’ luggage’). A region in the left lateral prefrontal cortex (LPFC) responded significantly more strongly to causal than descriptive conditions in most subjects individually. Responses in this region in held-out data were high for both social and physical causal conditions, yet no greater than baseline for the two descriptive (non-causal) conditions. In a follow-up exploratory experiment (Exp 2), we tested a different task (answering causal versus non-causal questions about physical and social narratives, matched for linguistic variables). Again, we found that both the physical and social causal stimuli selectively engaged the LPFC region. Finally, in both experiments, we found that brain regions previously implicated in intuitive physical reasoning responded more to the physical causal than the physical non-causal stimuli. Collectively, these results suggest that a) a region in the LPFC is selectively engaged in causal reasoning independent of content domain and b) the hypothesized physics network (hPN) is selectively involved in physical causal reasoning across modalities (visual vs. linguistic).

The literature on conceptual combination has thus far been limited to research at the aggregate level investigating adjective-noun and noun-noun combinations. One well-established phenomenon within this literature is that of concept dominance (Hampton, 1988), which is the finding that the relative contribution of constituent concepts (for example sport or game) to their conjunction (sport that is also a game) is often very unequal. This exploratory study investigated individual differences in how people understand adjective-adjective-noun combinations, such as long blue coat. Participants rated images of coats varying along the perceptual dimensions of length and color for typicality in two different conjunctions, namely long blue coat and long purple coat. We used multidimensional scaling (MDS) to construct an aggregate coat space from similarity data collected with the Spatial Arrangement Method (SpAM). Using external unfolding, we modeled participants’ typicality judgements by representing their individual typicality data as vectors within the aggregate MDS space, such that orthogonal projections from the coats onto the vectors represent their perceived typicality in the conjunctions. We did not find strong evidence for concept dominance at the aggregate level; however, we did find evidence for concept dominance at the individual level, with marked individual differences in the extent of dominance and which dimension was dominant. The validation of external unfolding for research into conceptual combination comes with new research possibilities, several of which are proposed.

Following the increasing use of graphs to communicate statistical information in social media and news platforms, the occurrence of poorly designed misleading graphs has also risen. Thus, previous research has identified common misleading visual features of such graphs. Our study extends this research by empirically comparing the deceptive impact of 14 distinct misleading graph types on viewers’ understanding of the depicted data. We investigated the deceptive nature of these misleading graph types to identify those with the biggest potential to mislead viewers. Our findings indicate that misleading graphs significantly decreased viewers’ accuracy in interpreting data. While certain misleading graphs (e.g., graphs with inverted y-axis or manipulated time intervals) significantly impeded viewers’ accurate graph comprehension, other graphs (e.g., graphs using pictorial bars or graphs with compressed y-axis) had little misleading impact. By identifying misleading graphs that strongly affect viewers’ understanding about depicted data, we suggest that these misleading graphs should be the focus of educational interventions.

In teleoperation, the spatiotemporal calibration of the system can significantly impact both performance and user experience, which may not necessarily be causally linked. This study asks if Sense of Embodiment (SoE) varies with spatiotemporal calibration of a teleoperated system, which in turn affects task performance. Most SoE studies are passive and they do not represent a great paradigm to study the impact of calibration on SoE in active teleoperation. Therefore, we designed an active RHI in mixed reality where we manipulated both the spatial calibration (shifts) and visuo-proprioceptive synchronicity (temporal delay). We investigated if this manipulation affected performance, proprioceptive mapping, SoE and the perception of the setup as a mediator. The results suggest a potential direct influence of SoE on task performance, particularly through enhanced calibration due to synchronicity, indicating potential benefits for sustained usage. Additionally, SoE is explored comprehensively, employing multiple tests assessing implicit and explicit dimensions of calibration.

This study aimed to test the benefit of note-taking on pauses and learning in online educational videos. Participants (N = 72) were randomly assigned to one of two note-taking conditions (allowed or not) with the possibility to take pauses during a 10-minute online instructional video on the autonomic nervous system. The results did not reveal a significant correlation between note-taking and the number of pauses. Moreover, we observed no significant effect of note-taking on learning performance. However, prior knowledge and age affected significantly the relationship between pauses, note-taking and learning performance. We discuss the importance of prior knowledge and age for future research.

We investigated the human capacity to acquire multiple visuomotor mappings for de novo skills. Using a grid navigation paradigm, we tested whether contextual cues implemented as different "grid worlds", allow participants to learn two distinct key-mappings more efficiently. Our results indicate that when contextual information is provided, task performance is significantly better. The same held true for meta-reinforcement learning agents that differed in whether or not they receive contextual information when performing the task. We evaluated their accuracy in predicting human performance in the task and analyzed their internal representations. The results indicate that contextual cues allow the formation of separate representations in space and time when using different visuomotor mappings, whereas the absence of them favors sharing one representation. While both strategies can allow learning of multiple visuomotor mappings, we showed contextual cues provide a computational advantage in terms of how many mappings can be learned.

Prognostic assessment of patients with disorders of consciousness (DoC) remains one of the most challenging problems in contemporary medicine. The long treatment cycle and high costs of treatment are heavy burdens to our society. In this paper, we use deep network to investigate potential indicators of consciousness within brain signals of DoC patients. In the experiments, we study P300 and resting-state Electroencephalogram (rs-EEG) signals of 22 DoC patients to investigate neural correlation between brain signals and the improvement of consciousness. Synergistic integration of P300 and rs-EEG signals demonstrated superior predictive proficiency for cross-subject and cross-paradigm prognosis in DoC, achieving an accuracy of 81.1%. Our investigation is the first known to the literature to combine P300 and rs-EEG signals for analyzing DoC. This novel approach leverages advanced neural network models to elucidate the complex neural patterns associated with DoC, setting a precedent for future research in the field.

In social interactions, inferring the interaction partner’s hidden mental state is crucial for predicting their actions and optimiz- ing our responses. Effective models for this inference must account for how these mental states evolve due to the interac- tion history and environmental changes. For example, recog- nizing someone’s emotional state can help forecast their be- havior. Our study investigates how making these latent states visible influences decision-making in social interactions. Us- ing the repeated trust game paradigm, we show how to use hid- den Markov models (HMM) to formally represent latent state dependent strategies of the players. HMMs fitted to human dyadic play in the trust game are then used to specify adap- tive AI agents that simulate changes in mental dispositions of human players, such as the level of trust in the opponent, dur- ing a repeated interaction. Making these artificial HMM based agents take the role of the investor and interact with real hu- man trustees, we then explore how displaying “emotion” cues to the opponent’s latent state affects people’s actions. We find that the presence of cues was associated with more cooperative behavior from the human trustees, and that patterns of behav- ior that promote the maintenance of cooperation emerged in the presence of latent state cues and were transferred to set- tings where the cues were subsequently hidden.

In classroom interactions that take place over video conferencing platforms, teachers and students continue to gesture, but their bodies are neither physically copresent nor fully visible to each other. Do instructor gestures help learning in this context, as has been found for in-person learning and for video-based learning in lab experiments? We showed professors lecturing spontaneously with unscripted co-speech gestures. In some conditions, we cropped the video so only the top half of the professor's gesture space is available, or removed the video altogether. Results from our between-subjects experiment show that participants paid significantly more visual attention to the partial gesture condition than to stimuli where the gesturing was fully visible, and they scored significantly higher on an immediate comprehension test if they had seen lectures in the partially visible condition. This work raises further questions of how gestures help learning.

The proliferation of artificial intelligence (AI) agents has introduced a new dynamic into the human social environment. This study investigates prosocial behavior in a hybrid human-AI setting, particularly within a gaming environment. Many existing studies on prosocial behavior are conducted in economic game settings in which the agents' intentions, and whether or not prosocial actions offer benefits, are explicit. This project explores prosocial interactions in spatial environments where the need for help by another agent might not be immediately obvious, and where cognitive processes such as attention, and decision-making processes about the cost of helping are thus likely to play a role. In a baseline study (N = 177), we investigated the likelihood of human agents reciprocating prosocial behavior initiated by an AI player. Results indicated that the low saliency of the AI player's actions was a primary reason for non-reciprocation. A follow-up study (N = 164) tested whether increasing the salience of the AI's actions would enhance human prosocial responses. We found support for our hypothesis from analysis of the time-series data and participants' self-reported post-game questionnaires. This research contributes to the growing field of human-AI cooperation, outlining a vision for a future where technology actively contributes to our collective well-being, and opening up new possibilities for positive transformation in a world increasingly populated by intelligent machines.

People sometimes display moral flexibility by deciding that a commonly accepted moral norm ought not to apply in particular circumstances. But how? We explore this question in the context of queuing. We show that people’s judgements about the moral permissibility of queue-cutting can be explained through cognitive processes related to moral contractualism: universalization, virtual bargaining, and functional thinking. Participants were presented vignettes depicting prospective queue-cutters, and asked whether it was morally permissible to queue-cut in those circumstances. We model these judgements with reference to the existence of a game-theoretic equilibrium supporting queue cutting in a repeated game, and to considerations of whether queue cutting would subvert or enact the function of a queue: if you pay the waiting cost, you should get the reward. These results support the notion that moral flexibility is in part related to contractualist moral principles.

Existing models of the basal ganglia assume the existence of separate channels of neuron populations for representing each available action. This type of localist mapping limits models to small, discrete action spaces, since additional actions require additional channels, costing neural resources and imposing new connective tracts. In contrast, evidence suggests that the basal ganglia plays a role in the selection of both discrete action units, and continuously-valued action kinematics. In this work, we model the basal ganglia with distributed action representations, using high-dimensional vectors. This method lends itself to representing both discrete and continuous action spaces. Vectors that represent actions are weighted by a scalar value (their salience to the current task), and bundled together to form a single input vector. This paper provides an overview of the encoding method and network structure, as well as a demonstration of the model solving an action selection task using spiking neurons.

Contrary to common intuition, groups of people recalling information together remember less than the same number of individuals recalling alone (i.e., the collaborative inhibition effect). To understand this effect in a free recall task, we build a computational model of collaborative recall in groups, extended from the Context Maintenance and Retrieval (CMR) model which captures how individuals recall information alone (Polyn, Norman, & Kahana, 2009). We propose that in collaborative recall, one not only uses their previous recall as an internal retrieval cue, but also listens to someone else’s recall and uses it as an external retrieval cue. Attending to this cue updates the listener’s context to be more similar to the context of someone else’s recall. Over an existing dataset (Gates, Suchow, & Griffiths, 2022), we show that our model successfully captures the collaborative inhibition effects, as well as additional recall patterns such as recency and semantic clustering effects.

When a novel word refers to something in the world, how do learners decide whether that word have a more specific meaning (e.g., dog) or more general meaning (e.g., animal)? Here we focus on the role of semantic contrast between referential alternatives. We do this in the context of learning novel words cross-situationally, asking when learners adopt more specific meanings (resulting in homophonic words: e.g., ‘fami’ means both dog and butterfly) or adopt a single superordinate meaning (e.g., ‘fami’ means animal). We hypothesize that learners will be more likely to establish homophonous meanings when contrasting referents are from a neighboring category of the target, and more likely to establish a superordinate meaning when contrasting referents are from more distant categories. We also expect homophone learning to be more difficult because of its additional demands on learning and memory. Our predictions were borne out in a series of experiments and modeling.

of human language. We focus on the debate between gesture-first and vocalization-first theories. While some evidence supports the idea that gestures played a primary role in early communication, others argue that vocalizations are equally expressive. We think that methodological differences and biases in the choice of concepts may contribute to the challenge of comparing these modalities directly. For example, to what extent does selecting a certain concept from a semantic category matter to reproduce an effect? This and similar questions are explored in a data-driven way. First, we provide ratings on imagined expressibility of 207 concepts from an online experiment showing that people tend to rate gesture modality as better in expressing meaning compared to vocal modality. Second, we use the Bayesian posterior predictive distribution of these ratings to simulate new experiments where we vary the number of participants, number of concepts, and semantic categories to investigate how robust is the difference between gesture and vocal modality. Our results show that gesture modality is reliably different (i.e., affords higher expressibility) than vocal modality. However, the difference between the two is limited in terms of effect size (medium sizes by common standards) so one may question whether this difference is meaningful for bigger claims about early language evolution. This study further provides valuable information for further research on how to select stimuli and how to set up one’s design in a balanced way.

Part-whole relations and their representation play a vital role in perceptual organization and conceptual reasoning. It is critical for humans to parse visual scenes into objects and parts, and organize them into hierarchies. Few studies have examined how well neural networks learn part-whole hierarchies from visual inputs. In this paper, we introduce a new diagnostic dataset, CChar, to facilitate their understanding. It contains frame-based images of writing 6,840 Chinese characters and annotations on hierarchical structures. The results show that RNN and Transformer models could recognize a part of high-level components above strokes and illustrate a certain ability in learning part-whole hierarchies. However, these models do not have robust compositional reasoning. To identify the role of conceptual guidance in predicting hierarchical structures, we prepare visual features extracted by self-supervised and fine-tuned models, test them on generating hierarchical sequences, and observe that conceptual guidance is important to learn part-whole hierarchies. In addition, we also explore the relationship between the depth of hierarchies and model performance. It is found that RNNs perform worse as the hierarchies deepen, but the performance of Transformers becomes better with increasing depth.

Bipolar disorder (BD) is a mental health condition characterized by large fluctuations in goal-directed energy and mood. BD is defined by the presence of at least one lifetime episode of mania, a prolonged period of excessive goal-directed behavior, hyperactivity and elevated mood. Previous computational models of BD have primarily focused on explaining mood fluctuations in mania, placing less emphasis on goal-directed symptoms. In this work, we use reinforcement learning (RL), a principled model of goal-directed behavior and learning, to show how augmenting RL agents with \textit{dynamic self-efficacy beliefs} can give rise to goal-directed and mood symptoms characteristic of the mania phase of BD. Our simulations demonstrate that a model-free RL agent that dynamically updates its self-efficacy beliefs learns optimistic overgeneralized value representations. We suggest that these representations may underlie several behaviors associated with mania, such as increased motivational drive and faster initiation of approach behavior (i.e. impatience). We further show that agents with more sensitive self-efficacy beliefs display increased willingness to exert effort in order to achieve higher goals even in the face of costs, a characteristic that is observed in individuals at risk for BD. Finally, unrealistically high self-efficacy beliefs that emerged with learning were accompanied by behaviors such as distractibility and compulsive action selection that have clinical parallels to symptoms of mania.

This research tested and confirmed a novel hypothesis that similarity sets the ground for diversities to emerge, which then give rise to creativity. Adopting an experimental design, we recruited 66 typically-developing Chinese children (M = 6.04 years, SD = .28). First, in a Comparison task, these children were randomly assigned to name differences between two objects that were either highly similar (high-similarity condition) or dissimilar (low-similarity condition). Next, all children completed a divergent thinking task and received scores on fluency, originality, and usefulness. Results of t-tests showed that children of high-similarity condition reported both more surface (pertaining to perceptual features) and deep (pertaining to structural features) alignable differences and have on average a higher originality score, compared to children of low-similarity condition. Mediation analysis results further showed that the number of deep alignable differences mediated the effect of condition on children’s originality scores. This confirmed our expectation that the high similarity between objects facilitated children to generate more deep alignable differences, which subsequently facilitated children to generate more original ideas.

How does globalization impact the interaction between perception and language? Building on Berlin and Kay’s foundational study of color naming, we recruited 2,280 online participants speaking 22 different languages. We show that color naming maps differ structurally across languages, even among internet users living in (mostly) industrial societies. We use Large Language Models (LLMs) to simulate the limits of globalization by reproducing the naming task with a highly multilingual artificial agent with access to global digital information. We show that while the LLM has access to all languages, it has language-specific color representations and the number of color terms is correlated across humans and LLMs. However, LLMs use more color terms than humans, indicating differences in the representation. These results suggest that globalization has not removed cultural distinctions in color concepts, as language continues to be a key factor in the diversity of perception and meaning.

In this work, we propose a neural network model for free recall that draws direct parallels between neural machine translation (NMT) and cognitive models of memory search, specifically the Context Maintenance and Retrieval (CMR) model. We hypothesize that NMT advancements such as attention mechanisms (Luong et al., 2015) closely resemble how humans reactivate prior contexts (“mental time travel”; Tulving, 1985). To demonstrate these parallels, we train a seq2seq model with attention as a cognitive model of memory search and evaluate behavior against human free recall data. We find that the model can capture typical free recall patterns previously observed (Kahana et al., 2022); and after optimization, the model demonstrates the same optimal behavior as previously derived by the CMR model (Zhang, Griffiths, & Norman, 2023). Performing an ablation study, we demonstrate that behavioral differences between models with and without attention align with impaired behavior observed in hippocampal amnesia patients.

Despite considerable differences in the structures of the world's languages and child-rearing practices, children show remarkable cross-linguistic similarities in their early lexical development, including a preference for nouns. Here, we analyze children's early lexical production in naturalistic longitudinal corpora in a large-scale cross-linguistic comparison of 10 typologically highly diverse languages. We assess morphological complexity as a possible explanatory variable for children's higher noun-to-verb ratios and evaluate whether children's gradual increase in morphological productivity is correlated with their gradual decrease in noun-to-verb ratios towards the level found in their ambient language. We show that in languages with complex verb morphology, children exhibit a higher deviation in their noun-to-verb ratio compared to adults. This deviation gradually diminishes as they become more productive in the use of their target language. This effect holds across languages, despite their differences in morphological complexity.

Categories can be represented at different levels of abstraction, from prototypes focused on the most typical members to remembering all observed exemplars of the category. These representations have been explored in the context of supervised learning, where stimuli are presented with known category labels. We examine the benefits of prototype-based representations in a less-studied domain: semi-supervised learning, where agents must form unsupervised representations of stimuli before receiving category labels. We study this problem in a Bayesian unsupervised learning model called a variational auto-encoder, and we draw on recent advances in machine learning to implement a prior that encourages the model to use abstract prototypes to represent data. We apply this approach to image datasets and show that forming prototypes can improve semi-supervised category learning. Additionally, we study the latent embeddings of the models and show that these prototypes allow the models to form clustered representations without supervision, contributing to their success in downstream categorization performance.

Our remarkable ability to infer complex cause-effect relationships is thought to distinguish humans from all other species. Despite that causal inferencing pervades human cognition, it remains unclear whether this fundamental cognitive ability is supported by a unified, domain-general mechanism or multiple domain-specific mechanisms. Both the language and logical reasoning systems have been described as possible unified substrates of causal inferencing. The current study uses neuroimaging to offer insight into this debate. We specifically focus on the culturally universal and highly motivationally relevant case of inferring illness causes. Participants read causal and noncausal vignettes about illness and mechanical failure while undergoing fMRI. We find that inferring the causes of illness selectively activates the brain’s ‘animacy network,’ particularly the precuneus. By contrast, a domain-general (i.e., ‘content-invariant’) preference for causal inferencing did not emerge, including in the language and logical reasoning networks. Together, this evidence suggests that domain-specific mechanisms enable causal inferencing.

Collective improvisation is remarkable. When people improvise—whether dancing, making music, or conversing— they coordinate their behavior while exploring abstract spaces of movements, sounds, and ideas. How do improvisers navigate these abstract spaces? One possibility is that improvisation builds on foraging strategies used to search the physical world. Here, we investigate the dynamics of an especially complex and abstract form of collective improvisation: free jazz. We quantify how professional jazz ensembles navigate a space of sounds and show that it resembles a foraging strategy known as Area Restricted Search. In particular, ensembles change their playing dynamics in response to encounters with novel ‘soundworlds.’ Before encountering a new soundworld, ensembles engage in widespread exploration; immediately after, they shift to focused exploitation of the new sound. While collective improvisation pushes at our cognitive limits and is a paradigm of human creativity, it may build on evolutionarilyancient strategies for searching space.

Online mental health support plays a crucial role in addressing the mental health issues faced by modern individuals. However, delivering high-quality online mental health support presents a significant challenge. In response to this challenge, we introduce MentalBlend, a framework that leverages psychological counseling theories, including Cognitive-Behavioral Therapy, Dialectical Behavior Therapy, Person-Centered Therapy, and Reality Therapy, to guide large language models (LLMs) in offering professional online mental health support to individuals seeking assistance. Experimental evidence validates that the collaboration between LLMs and the MentalBlend framework results in the generation of responses that align with professional standards for online mental health support. Overall, our research aims to contribute to advancing the capabilities of LLMs in understanding the emotional backgrounds of help-seekers and delivering professional mental health support effectively.

There is wide agreement that the mind has different mechanisms it can use to make moral judgments. But how does it decide which one to use when? Recent theoretical work has suggested that people select mechanisms of moral judgment in a way that is resource-rational --- that is, by rationally trading off effort against utility. For instance, people may follow general rules in low-stakes situations, but engage more computationally intensive mechanisms such as consequentialist or contractualist reasoning when the stakes are high. Here, we evaluate whether humans and large language models (LLMs) exhibit resource-rational moral reasoning in two moral dilemmas by manipulating the stakes of each scenario. As predicted, we found that the higher the stakes, the more people employed a more effortful mechanism over following a general rule. However, there was mixed evidence for similar resource-rational moral reasoning in the LLMs. Our results provide evidence that people's moral judgments reflect resource-rational cognitive constraints, and they highlight the opportunities for developing AI systems better aligned with human moral values.

The desire to move to music appears to be a human universal. This behavioral response seems to be supported by a tight coupling of auditory and motor networks, even in the absence of overt movement. The prevailing theories explain this phenomenon either in terms of passive brain network entrainment to musical periodicity or motor system involvement in predictive coding. Both explanations recognize the role of rhythmic complexity in modulating motor activity. However, the precise nature of the relationship between rhythmic complexity and motor activity remains unclear. In this work, we conducted an fMRI literature review to examine this relationship. Out of 110 screened articles, 24 met inclusion criteria, reporting findings ranging from non-existent to linear or inverted-U-shaped. Underlying these findings, we encountered significant heterogeneity in the measurement and conceptualization of rhythmic complexity. We provide a summary of the relationships found, the approaches to measuring rhythmic complexity and the different types of tasks and stimuli used. We conclude that, in order to move forward, more agreement is needed regarding measures and notions of complexity.

Social media has affected how we relate to our body image. Digital makeovers have both reinforced existing beauty ideals and created new ones. This project investigated whether young adults’ detection of image filters was biased by internalized beauty ideals and gender. Participants completed a visual detection task (forced choice paradigm) where contrast filter correction was assessed for images of male and female bodies that were thin, average, and curvaceous/muscular. Results showed that people can detect filters and that accuracy is higher when filters are applied to bodies that represent the historical beauty ideals: thin female bodies and muscular male bodies. These findings suggest that the perception of low-level image features is biased to fit internalized beliefs about beauty

Emergent communication (EC) is the field that seeks to understand the mechanisms behind the emergence and evolution of natural language. In EC, the de facto standard has been using sequential architectures that have not explicitly incorporated the "tree-structured hierarchy" inherent in human language. This study utilizes a stack-based model called RL-SPINN, which learns tree structures through reinforcement learning without ground-truth parsing data, and acquires sentence representations according to these structures. We use this model as the basis for the understanding agents and investigate the extent to which the inductive bias of an architecture that explicitly utilizes tree structures affects the emergent language. The experimental results show that the emergent language generated by our model exhibits higher communication accuracy than those generated by other baselines in some settings. This work is the first to focus on the tree-structured hierarchy of language and suggests new directions for future research in EC.

This research explores the capabilities of Large Language Models (LLMs) to engage in abstract and concrete thought processes, challenging the common belief that LLMs are incapable of human-like, abstract thinking. Drawing upon the Construal Level Theory (Trope & Liberman, 2010), we demonstrate how prompts tailored for each construal level (abstract versus concrete) influence LLMs’ performance in tasks requiring different cognitive approaches. Our key findings include: 1) LLMs exhibit a statistically significant difference in construal level depending on the prompt conditions, and 2) LLMs display superior performance in tasks aligned with the prompted construal level; sentiment analysis in concrete conditions and natural language inference in abstract conditions. This research contributes to the scientific understanding of LLMs, offering practical insights for their effective use in tasks necessitating diverse cognitive capabilities.

When we communicate with others, we often repeat aspects of each other’s communicative behavior such as sentence structures and words. Such behavioral alignment has been mostly studied for speech or text. Yet, language use is mostly multimodal, flexibly using speech and gestures to convey messages. Here, we explore the use of alignment in speech (words) and co-speech gestures (iconic gestures) in a referential communication task aimed at finding labels for novel objects in interaction. In particular, we investigate how people flexibly use lexical and gestural alignment to create shared labels for novel objects and whether alignment in speech and gesture are related over time. The present study shows that interlocutors establish shared labels multimodally, and alignment in words and iconic gestures are used throughout the interaction. We also show that the amount of lexical alignment positively associates with the amount of gestural alignment over time, suggesting a close relationship between alignment in the vocal and manual modalities.

It is now well-established that the visual features of objects influence the sounds we make to refer to them. This is called sound symbolism. We present the results of a two-part study that explores the extent to which the visual features of writing systems correspond to the smallest spoken units of language. In Study 1, participants (n = 322) classified the shape of a set of glyphs, representative of the world’s script families. The purpose was to create an open-source database of normed glyphs for future research in cognitive linguistics. In Study 2, participants (n = 73) were prompted to select either a round or angular glyph after hearing one of two kinds of phonemes (vowel or consonant) from the International Phonetic Alphabet. Results from a logistic regression suggest that the type of sound had a significant effect on the choice of glyph, and that vowel sounds increased the likelihood of choosing round glyphs by 30%. The significant correlation between what subjects heard and their choice of glyph suggests that the effect may extend to such sound symbolic relations in real-world writing systems. Our ongoing research seeks to substantiate these findings with increased glyph contrast and more diverse populations.

Neural language models, particularly large-scale ones, have been consistently proven to be most effective in predicting brain neural activity across a range of studies. However, previous research overlooked the comparison of these models with psychologically plausible ones. Moreover, evaluations were reliant on limited, single-modality, and English cognitive datasets. To address these questions, we conducted an analysis comparing encoding performance of various neural language models and psychologically plausible models. Our study utilized extensive multi-modal cognitive datasets, examining bilingual word and discourse levels. Surprisingly, our findings revealed that psychologically plausible models outperformed neural language models across diverse contexts, encompassing different modalities such as fMRI and eye-tracking, and spanning languages from English to Chinese. Among psychologically plausible models, the one incorporating embodied information emerged as particularly exceptional. This model demonstrated superior performance at both word and discourse levels, exhibiting robust prediction of brain activation across numerous regions in both English and Chinese.

The European Upper Palaeolithic is rich in figurative cave art. In particular, prey animals are often depicted in simple sche- matic outlines. The role and function of these depictions is sub- ject of controversy with competing accounts represented in the literature. Here we apply eye-tracking to investigate partici- pants’ distribution of visual attention as a function of three hy- pothesized pragmatic functions of the cave art: aesthetic appre- ciation, narratives about animal behavior, and social learning of animal species. Results indicate vast variability in visual ex- ploration patterns across the viewing conditions, with more uniformly distributed attention in the aesthetics condition, more focus on legs and torso in the behavior condition, and more attention to the head regions in the species recognition condition. Findings are discussed in regards to the under- and over-specification of information in the animal paintings as a cue to their possible past function.

How we move and interact with our surroundings can reveal a lot about us as an individual. This study delves into the interplay of music, movement, and individual identity within the framework of embodied cognition. Drawing inspiration from Carlson et al. (2020)’s work, which showcased remarkably high accuracy in identifying individuals based on free-form dance movements in a marker-based setting, our investigation extends their work into two novel contexts: markerless-choreographic and marker-based-dyadic dance settings. In the choreographic setting, professional dancers perform identical routines in a markerless setting. In the dyadic setting, individuals danced with a partner. We found that the dancer identification accuracy was at least two times better than the chance level in the choreographic setting and notably high accuracy in the dyadic setting. These results showcase the robustness of Carlson et al. (2020)’s method in generalizing to new settings and the presence of motoric fingerprints in choreographic as well as dyadic settings.

Recently the principles of efficient communication have provided useful characterizations of semantic typology: the diversity of attested languages can be described by competing pressures for simplicity and informativeness. While this approach has achieved success in several semantic domains, the formalizations used to define complexity across domains vary. In this note, we list the conditions under which the two main approaches of defining complexity: channel rate and description length, unify and, thus, conclusions about near-optimal communicative efficiency generalize across formalizations. We illustrate this equivalence using simulations of communicative efficiency for Boolean concepts. We round out this note discussing the (un)importance of description languages and the limits on generalizing this equivalence for other behavioral targets for explanation.

Among the many fascinating findings to come out of the numerical cognition literature, subitizing – the ability to quickly, effortlessly, and accurately identify the number of items in small collections – holds a special place. Despite hundreds of studies probing this ability, the identity of the cognitive systems that explain its unique features remains unknown. One prominent account is that of Trick and Pylyshyn (1994), which is based on pre-attentive parallel individuation of visual indexes. Despite this account’s promise, a few researchers have questioned its validity, due to experiments showing that attentional load influences enumeration performance, which they interpret as invalidating a pre-attentional model of subitizing. The present discussion paper offers a novel re-interpretation of some studies on the nature of the relation between subitizing and attention to help clear up in which sense subitizing depends on attentive vs. pre-attentive processes, thereby providing a novel defense of Trick and Pylyshyn’s influential model.

Some German adjective-compound-noun constructions (‘severe storm.warning’) exhibit a bracketing paradox where an adjective semantically modifies the first noun N1 instead of the grammatically required last noun N2 thus violating compositionality. We present two experiments that examined the interpretation of nominal compounds and bracketing paradoxes. Experiment 1 showed that the semantic match of N1 and the adjective has a significant impact on the acceptability of Adj-N1N2 constructions. Experiment 2 probed the participants’ adjective attachment choices as well as the relationship between and attachment and acceptability: While N2 attachments were most common, many constructions received mixed and some consistently bracketing paradox interpretations. High ratings for Adj-N2 were predictive of N2 attachment, but high Adj-N1 ratings led to bracketing paradox interpretations. These results are partially against grammatical expectations and suggest competition between the nouns for modification, likely due to semantic and/or pragmatic factors.

Learning to understand and use agreement is an integral part of children's linguistic development. In Romance languages, this includes gender and number agreement between the controller and attributive or predicative adjectives or participles. We examine the development of this category in a case where children's task is complicated by syncretisms, multiple paradigms, and unequal input distributions. Romansh Tuatschin (Romance, Indo-European, Switzerland) presents children with two distinct paradigms for attributive (masculine and feminine only) and predicative (masculine, feminine, and neuter/unmarked) contexts of adjective and participle use. The masculine form in predicative use is the same as the neuter form in attributive usage. Thus the masculine form in these two paradigms differs. This could be challenging for the language learner. The distribution of these forms is heavily skewed towards the \emph{neuter} in predicative contexts but balanced in attributives. Examining production errors in children between 2;0 and 4;3, we evaluate the effects of frequency and syncretism and find that error-rate is affected by skewed distributions and less affected by syncretisms. This demonstrates the strong effect of input distributions on first language acquisition.

Language models (LMs) have demonstrated remarkable profi- ciency in generating linguistically coherent text, sparking dis- cussions about their relevance to understanding human lan- guage learnability. However, a significant gap exists between the training data for these models and the linguistic input a child receives. LMs are typically trained on data that is or- ders of magnitude larger and fundamentally different from child-directed speech (Warstadt & Bowman, 2022; Warstadt et al., 2023; Frank, 2023a). Addressing this discrepancy, our research focuses on training LMs on subsets of a sin- gle child’s linguistic input. Previously, Wang, Vong, Kim, and Lake (2023) found that LMs trained in this setting can form syntactic and semantic word clusters and develop sen- sitivity to certain linguistic phenomena, but they only consid- ered LSTMs and simpler neural networks trained from just one single-child dataset. Here, to examine the robustness of learn- ability from single-child input, we systematically train six dif- ferent model architectures on five datasets (3 single-child and 2 baselines). We find that the models trained on single-child datasets showed consistent results that matched with previous work, underscoring the robustness of forming meaningful syn- tactic and semantic representations from a subset of a child’s linguistic input. Keywords: learnability; single-child; distributional learning; robustness; language models

Many aspects of human learning have been proposed as a process of constructing mental programs: from acquiring symbolic number representations to intuitive theories about the world. In parallel, there is a long-tradition of using information processing to model human cognition through Rate Distortion Theory (RDT). Yet, it is still poorly understood how to apply RDT when mental representations take the form of programs. In this work, we adapt RDT by proposing a three way trade-off among rate (description length), distortion (error), and computational costs (search budget). We use simulations on a melody task to study the implications of this trade-off, and show that constructing a shared program library across tasks provides global benefits. However, this comes at the cost of sensitivity to curricula, which is also characteristic of human learners. Finally, we use methods from partial information decomposition to generate training curricula that induce more effective libraries and better generalization.

Collocations, semi-productive lexical combinations with one figurative and one literal word, are said to be a “pain in the neck” for researchers and L2 learners. The present study aims: (i) to conceptually replicate the processing costs incurred by L1 speakers when processing collocations using a larger and more diverse set of items, (ii) to use literalness judgements to test whether L1 speakers are aware of the semi-transparent meaning of a collocation, and (iii) to test whether the presence of processing costs associated with collocations can be predicted from literalness judgements. If so, we propose that literalness judgements could be used as a diagnostic for reli- ably identifying collocations. We replicate the L1 processing costs with a larger stimulus set and demonstrate that speakers are aware of the semi-transparent meaning of the collocation. We further show that L1 speaker judgements about the literal- ness of a word combination can be used to predict its status as a collocation.

Why do beliefs that gender differences are innate (i.e., gender essentialism) sometimes lead to normative judgments about how individual people ought to be? In the current study, we propose that a missing premise linking gender essentialism and normativity rests on the common folk-biological assumption that biological features serve a biological function. When participants (N = 289) learned that a novel feature of the gender category “mothers” was common and innate, they overwhelmingly assumed that it must have served some function across human history. When they learned that it served a historical function, they assumed that it must still be beneficial in today’s environment. When participants learned that the feature was beneficial, they judged that contemporary mothers ought to have it, and they were more willing to intervene to ensure that they would by constraining the choices of individual mothers. Thus, we suggest that essentialist assumptions can shape normative social judgments via the explanations people tend to generate about why certain features of natural kind categories become common to begin with. This finding articulates one manifestation of the naturalistic fallacy, with implications for policy debates about bodily autonomy and choice.

The mechanisms of learning stimulus-stimulus relationships are a longstanding research subject in psychology and neuroscience. Although traditional computational models provide valuable insights into learning processes, they often focus on the average behavior of a population. Individual learning trajectories, however, exhibit a diverse range of behaviors not captured by these models. In this paper, we compare sampling-based process-level models (i.e., particle filters) to representative associative and causal models (i.e., augmented Rescorla-Wagner and PowerPC) in their ability to capture individual learning behavior. We use likelihood-free inference incorporating machine-learned summary statistics for model estimation. We conduct a simulation study to demonstrate high model identifiability and test the models on an existing dataset and a newly conducted experiment which replicates and extends previous studies. We find that most participants are best explained by a particle filtering account, but more targeted experimental designs are required to estimate the best-fitting sub-type of these particle filter models.

We demonstrate an application for learning multiplication problems with an adaptive algorithm that is based on a computational cognitive model of the learner's memory. The application helps learners automatise and memorise multiplications through repeated practice over three levels of difficulty. In a naturalistic setting involving more than 500 primary school students (ages 6-10) who together recorded over 300,000 responses, we observed that performance improved as learners using the application progressed through the levels. A model-based analysis of performance revealed that learners' estimated speed of forgetting decreased from the second to the third level. This is consistent with a shift towards stronger declarative knowledge and/or more efficient computation procedures. The model also identified consistent differences in the difficulty of individual multiplication facts that persisted across levels. This study demonstrates the feasibility of using an adaptive fact learning application to help young learners master multiplication, an essential mathematical skill.

The present study examines how natural language aptitude subskills predict individual differences in learning Python and Java. Past work has demonstrated that overall performance on the Modern Language Aptitude Test (MLAT), a standardized measure of language aptitude, is a strong predictor of both the speed and accuracy with which individuals learn Python. However, language aptitude is a broad multidimensional construct made up of individual subskills. In the present study, we examine how two of these subskills - sensitivity to form and meaning mapping - relate to programming outcomes in both Python and Java. Results indicate that both sensitivity to form (MLAT IV) and meaning mapping (MLAT V) are related to programming acquisition in both languages - this relationship remains even after controlling for fluid intelligence. We also examined how programming skills tied to semantics and syntax related between Python and Java in a subset of learners who learned both languages. These results demonstrated that proficiency in Python predicted individual differences in both syntactic and semantic knowledge in Java. Taken together, these results further elucidate the role of natural language aptitude in programming learning and suggest that semantic and syntactic content may transfer across programming languages.

We investigated the relationship between visual experience and pragmatic development by testing the socio-communicative skills of a unique population: the Prakash children of India, who received treatment for congenital cataracts after years of visual deprivation. Using two different referential communication tasks, our study investigated Prakash’ children ability to produce sufficiently informative referential expressions (e.g., ‘the green pear’ or ‘the small plate’) and pay attention to their interlocutor’s face during the task (Experiment 1), as well as their ability to recognize a speaker’s referential intent through non-verbal cues such as head turning and pointing (Experiment 2). Our results show that Prakash children have strong pragmatic skills, but do not look at their interlocutor’s face as often as neurotypical children do. However, longitudinal analyses revealed an increase in face fixations, suggesting that over time, Prakash children come to utilize their improved visual skills for efficient referential communication.

Spatial cognition and spatial language are a core site for diversity, both within and across language communities. For instance, when describing motion events, speakers through speech and gesture may anchor information either (egocentrically) to their body or (allocentrically) to geographical landmarks in the environment. Here we investigate whether the use of such egocentric versus allocentric frames of reference in co-speech gesture indeed depends on both bodily and environmental axes. In a real-world experiment, members from the traditionally allocentric Balinese community were shown small-scale motion events and asked to retell them. To evaluate the potential influence of both types of axes on gestural frame of reference use, in a 2x2 between-participant design they were assigned to conditions that contrasted the body-anchored axis the motion events unfolded on with the underlying geographical environment-anchored axis. It was observed that the type of body-anchored axis significantly predicted frame of reference representation in participants’ gestures, consistent with previous research. The type of environment-anchored axes, however, did not affect characteristics of participants’ gestures. These findings advance our understanding of the intricate interplay between language, space, culture, and environment.

Recognizing written words involves identifying individual letters, as well as keeping track of specific positions of the letters. Interestingly, some languages show flexibility in letter- position encoding which is inferred by the observation that pseudowords formed by transposing internal letters of a word (e.g., jugde-JUDGE) can facilitate recognition of the given word. While research in English and other Indo-European languages have shown that readers can cope with such violations in the canonical order of letters in a word, research from other languages such as Arabic, Hebrew, and Korean show contrasting results. Such scenario creates a need of more research from different writing systems of the world, so that a universal model of word-recognition can be built. Therefore, in the current study, we investigated flexibility in letter-position encoding in Hindi (Devanagari script). Interestingly, we found evidence for flexibility in letter position encoding in Hindi similar to English and other Indo-European languages.

While meaning variation in common words across language and culture is well established, only a few studies have explicitly quantified how general such differences are and whether differences reflect slight variations in meaning or could be considered to map onto entirely distinct concepts for different groups. The present study aims to investigate the extent to which common words can be interpreted differently between groups of English-proficient native Chinese speakers and native English speakers. This was done through a free judgment of associative strength (JAS) task using 42 cue English nouns. Our findings revealed language-specific meanings across all 42 cue words, with strong evidence for language-specific meaning in nearly 95\% of nouns. To determine whether these words map onto entirely distinct language-specific concepts, we measured conceptual diversity using Latent Profile Analysis (LPA). The results of the LPA showed that nearly 69% of the cue words could be mapped onto more than one concept across all participants. Importantly, language differences were related to conceptual diversity in nearly 64% of words featuring multiple concepts. In sum, we found robust evidence of word meanings and conceptual variations among individuals across distinct linguistic and cultural backgrounds, even for common English words.

The complexity and unpredictability of a situation might contribute to how much an individual feels in control of their actions. Goal-directed behaviour tailored to different situations is enabled through a hierarchy of situated action control combining cognitive and sensorimotor control processes. We use eye-tracking to investigate the grounding of cognitive processes in the sensorimotor system. Our assumption is that different degrees of perceived control trigger cognitive states that are reflected in eye-movement behaviour. Utilizing a dynamic experimental environment, we investigate whether complexity and uncertainty of the situation are top-down processed into fixational eye movements. The distance to a reference point is affected by environmental complexity in all fixations; however environmental uncertainty is only incorporated in fixations that guide motor control. We discuss that these fixations are only executed under high sense of control when there are enough cognitive resources left to top-down process the environmental uncertainty into gaze allocation.

Cobweb, a human-like category learning system, differs from most cognitive science models in incrementally constructing hierarchically organized tree-like structures guided by the category utility measure. Prior studies have shown that Cobweb can capture psychological effects such as basic-level, typicality, and fan effects. However, a broader evaluation of Cobweb as a model of human categorization remains lacking. The current study addresses this gap. It establishes Cobweb's alignment with classical human category learning effects. It also explores Cobweb's flexibility to exhibit both exemplar- and prototype-like learning within a single framework. These findings set the stage for further research on Cobweb as a robust model of human category learning.

Command and control is a key activity in a war that determines whether the war is won or lost. A person's cognition can influence the decisions he makes in command and control. In this paper, we propose a method to assess the impact of cognitive manipulation techniques in command and control. We divide command and control activities into eight segments and use a hierarchical latent Dirichlet allocation model in natural language processing to discover the most important cognitive manipulation techniques at present. We completed the assessment process using the Quality Function Deployment Model. The results of the assessment show that judgement, decision-making, and planning in the command and control perspective are susceptible to cognitive manipulation techniques; the tactical level is more susceptible to cognitive manipulation techniques than the strategic campaign. Life sciences, digital technology, and other fields play a key role in command and control research under cognitive manipulation.

Viewers are sensitive to the distinction between visual events with an internal structure leading to a well-defined endpoint (bounded events) and events lacking this structure and a well-defined endpoint (unbounded events). Here, we asked whether boundedness could be represented in the auditory modality in a way similar to the visual modality. To investigate this question, we trained participants with visual and auditory events on bounded or unbounded event categories in a category identification task. Later, we tested whether they could abstract the internal temporal structure of events and extend the (un)boundedness category to new examples in the same modality. These findings suggest that the principles and constraints that apply to the basic units of human experience in the visual modality have their counterparts in the auditory modality.

Humans are efficient social learners who leverage social information to rapidly adapt to new environments, but the computations by which we combine social information with prior knowledge are poorly understood. We study social learning within the context of multi-armed bandits using a novel “asteroid mining” video game where participants learn through active play and passive observation of expert and novice players. We simulate human exploration and social learning using naive versions of Thompson and Upper Confidence Bound (UCB) solvers and hybrid models that use Thompson and UCB solvers for direct learning together with a multi-layer perceptron to estimate what should be learned from other players. Two variants of the hybrid models provide good, parameter-free fits to human performance across a range of learning conditions. Our work shows a route for integrating social learning into reinforcement learning models and suggests that human social learning conforms to the predictions of such models.

Compositionality is considered a key hallmark of human language. However, most research focuses on item-level compositionality, e.g., to what extent the meanings of phrases are composed of the meanings of their sub-parts, rather than on language-level compositionality, which is the degree to which possible combinations are utilized in practice during language use. Here, we propose a novel way to quantify the degree of language-level compositionality and apply it in the case of English adjective-noun combinations. Using corpus analyses, large language models, and human acceptability ratings, we find that (1) English only sparsely utilizes the compositional potential of adjective–noun combinations; and (2) LLMs struggle to predict human acceptability judgments of rare combinations. Taken together, our findings shed new light on the role of compositionality in language and highlight a challenging area for further improving LLMs.

Early language development, and vocabulary size specifically, is a predictor of well-being later in life, such as emotional development and academic achievement. Many successful vocabulary interventions for young children involve sharing a book with a caregiver, because storybooks are a good source of vocabulary that one might not otherwise encounter in everyday life. With the advent of Large Language Models (LLM), automatically generating stories has become a feasible way to tailor materials to the needs and interests of individual learners. Here we evaluate 1) whether parents of preschoolers find automatically generated stories containing specific vocabulary target words acceptable, and 2) whether preschoolers can learn these target words from being read the automatically generated stories. We find that parents overall consider automatically generated stories engaging, age- appropriate, and educational. In addition, children successfully learn the target words in the storybooks (compared to control words drawn from books not read). We conclude with a discussion on future work to improve the effectiveness of automatically generated stories to support robust vocabulary learning.

What does a social learner learn? Research has explored imitation-based social learning strategies as well as inverse reinforcement algorithms that estimate others' true reward function. In the current study, we propose that social learning may be more elaborate and develop a model of social learning using Bayesian inference that seeks to understand both the task an observed demonstrator is performing and the demonstrator itself. Using simulations, we show that the model is able to learn about the demonstrator when provided with full and partial information. We strengthen this point by asking the model to make inferences about missing choice and reward information. Last, we show that the model is able to represent one set of beliefs about the environment while attributing a distinct set of beliefs to the demonstrator. Thus, we move away from simple models of social learning, investigating inference-making as a core mechanism of social learning.

In this paper, we developed three ACT-R cognitive models to simulate the learning process of abacus gestures. Abacus gestures are mid-air gestures, each representing a number between 0 and 99. Our models learn to predict the response time of making an abacus gesture. We found the accuracy of a model's predictions depends on the structure of its declarative memory. A model with 100 chunks cannot simulate human response, whereas models using fewer chunks can, as segmenting chunks increase both the frequency and recency of information retrieval. Furthermore, our findings suggest that the mind is more likely to represent abacus gestures by dividing attention between two hands rather than memorizing and outputting all gestures directly. These insights have important implications for future research in cognitive science and human-computer interaction, particularly in developing vision and motor modules for mental states in existing cognitive architectures and designing intuitive and efficient mid-air gesture interfaces.

Large Language Models (LLMs), which match or exceed human performance on many linguistic tasks, are nonetheless commonly criticized for not “understanding” language. These critiques are hard to evaluate because they conflate “understanding” with reasoning and common sense—abilities that, in human minds, are dissociated from language processing per se. Here, we instead focus on a form of understanding that is tightly linked to language: mapping sentence structure onto an event description of “who did what to whom” (thematic roles). Whereas LLMs can be directly trained to solve to this task, we asked whether they naturally learn to extract such information during their regular, unsupervised training on word prediction. In two experiments, we evaluated sentence representations in two commonly used LLMs—BERT and GPT-2. Experiment 1 tested hidden representations distributed across all hidden units, and found an unexpected pattern: sentence pairs that had opposite (reversed) agent and patient, but shared syntax, were represented as more similar than pairs that shared the same agent and same patient, but differed in syntax. In contrast, human similarity judgments were driven by thematic role assignment. Experiment 2 asked whether thematic role information was localized to a subset of units and/or to attention heads. We found little evidence that this information was available in hidden units (with one exception). However, we found attention heads that reflected thematic roles independent of syntax. Therefore, some components within LLMs capture thematic roles, but such information exerts a much weaker influence on their sentence representations compared to its influence on human judgments.

Linguistic corpora have been a vital resource for understanding not only how we use language, but also how we process words and sentences. In order to better understand language processing, researchers have recently been creating corpora that integrate both traditional text annotations as well as behavioural measurements collected from human participants. In this paper we introduce the HeCz Corpus, which to our knowledge is the largest such example of a behavioural corpus, containing 1,919 newspaper headlines taken from a Czech language news website. The sample consisted of 1,872 participants, each reading approximately 120 headlines. Each headline was read using a self-paced reading, meaning that every word in the corpus can be analyzed for reading time. After reading each headline, each participant answered a question relating to a specific information contained within the headline, providing a measurement of comprehension. To facilitate better understanding of participant level variation in how the headlines are processed, we collected data on the participant’s mood state immediately prior to their participation, along with other basic demographic information. We also collected data from a subset of participants who read the stimuli in the initial testing round, but also completed the same experiment in a second round after a one-month gap, which can provide new insights into how texts are processed and understood when being re-read. In order to highlight the practical uses of the corpus, our analyses focus on how reading times are modulated by i) headline length in words, ii) trial order, and iii) testing round, in addition to examining the role of targeted information location in comprehension accuracy. HeCz thus provides a unique and novel resource that can be used by psycholinguists and cognitive scientists more generally, in order to gain new insights into how real-world language is processed and understood.

Adult language learners have difficulty segmenting words from continuous speech when the phonology is unfamiliar. Since speaker variability is known to improve acquisition of novel language structures, it could be processed in ways that bootstrap phonological patterns and enhance learners’ ability to segment words. To test this, the present experiment examined adult participants’ learning of a stream of statistically determined tri-syllabic words that were spoken by one or multiple speakers. Syllables were constructed with either English phonology or non-English phonology. Two tasks (target detection and two-alternative forced choice) assessed the extent of listeners’ sensitivity to language patterns and word segmentation. Results suggest speaker variability negatively impacted learners’ ability to track the underlying statistics. 2AFC word segmentation performance was poor—independent of speaker number; it is hypothesized that attentional demands of the target detection task conflicted with statistical word segmentation mechanisms.

Literary and aesthetic studies have suggested that readers can stay in indeterminate states where they hold multiple interpretations, and that this indeterminate interpretation state causes an aesthetic feeling, involvement, and understanding of art. To explore the indeterminate interpretation state, we employed a quantum cognition framework and conducted a reading experiment using a short story. The results suggest that readers' interpretations can be regarded as a superposition state corresponding to the indeterminate and polysemous interpretation of the story. We also estimated a time series of quantum indeterminacy and discussed the text features related to quantum indeterminacy.

People generally seek to minimize effort, including cognitive effort, but poetic language can be pleasurable while requiring effort to understand. The ‘optimal innovation hypothesis’ holds that this paradoxical relationship arises when a non-default interpretation is required and the default interpretation is easily available for comparison. A recent study of ease and pleasure during reading novel variations of familiar verb-based metaphors was partially consistent with this prediction. The present study replicated that pattern of partial support and examined how it is correlated with individual differences in verbal ability, personality (emotionality and openness to experience), and lifestyle/experience (engagement with creative hobbies). Correlations with individual differences tended to be very small and not statistically significant, with two exceptions. First, participants with better verbal ability tended to rate metaphors easier to understand, particularly for familiar metaphors, and a similar pattern was observed for the ‘openness to experience’ personality trait. Second, there was a positive association between engagement with creative hobbies and pleasure ratings specifically for the critical ‘optimal’ extension metaphors. These results provide a robust basis for future research on the aesthetic experience of metaphors and literary language in general.

People solve a myriad of coordination problems without explicit communication every day. A recent theoretical account, virtual bargaining, proposes that, to coordinate, we often simulate a negotiation process, and act according to what we would be most likely to agree to do if we were to bargain. But very often several equivalent tacit agreements — or virtual bargains — are available, which poses the challenge of figuring out which one to follow. Here we take inspiration from virtual bargaining to develop a cognitive modeling framework for dynamic coordination problems. We assume that players recognize their common goal, identify one or more possible tacit agreements based on situational features, observe the history of their partner’s choices to infer the most likely tacit agreement, and play their role in the joint plan. We test this approach in two experiments (n = 125 and n = 133) based on a dynamic coordination game designed to elicit agreement-based behavior. We fit our model at the individual level and compare its performance against alternative models. Across four different conditions, our model performs best among the set of models considered. Behavioral results are also consistent with players sustaining coordination and cooperation in the task by converging on tacitly agreed strategies or “virtual bargains”.

We present a neural process model of visual analogical mapping that receives image inputs and responds by spatially selecting a matching object to a cued object. The relational structure of the base scene is stored in a way that specifies the arguments of each relation, allowing mappings based on structural correspondence to be represented as proposed by the structure-mapping theory. All the processes in the model emerge out of coupled integro-differential equations modeling neural population activation dynamics. The mapping can be influenced by both featural and relational similarities. The developmental shift in mappings in the presence of a featural distractor can be accounted for by manipulating how well the model can maintain attention to relevant feature/relation dimensions, consistent with a hypothesis suggesting inhibitory as a key factor explaining the shift.

Category learning, a fundamental cognitive ability, is significantly influenced by variability. In this research, we propose a model describing how people adjust information search in self-regulated category learning to the level of category variability. Participants in the self-regulated category learning task sampled from two categories until they felt confident in categorizing novel objects. Our model assumes an influence of the variability of the focal and counter category on sampling by considering a within-category and between-category processes. In both processes, variability is quantified using an information-theoretic measure. Within this model, we test if a between-category process can be better conceptualized as either a contrasting or an assimilation process. The comparison of both processes support a between-category assimilation process, where the sample size adjusts to the counter category's variability. This novel focus sheds light on between-category dynamics, providing valuable insights into the mechanisms of category learning.

Reading acquisition primarily relies on orthographic learning. Behavioral studies show that familiarity with a novel word’s pronunciation facilitates learning, particularly in semantically meaningful contexts. Two main components of orthographic learning are commonly described: perceptual processing of the visual stimulus, to infer corresponding phonological rep- resentations, and “pronunciation correction”, to correct errors from perceptual processing. Currently, pronunciation correc- tion has not been featured in reading acquisition computa- tional models. This study uses BRAID-Acq, a reading ac- quisition model, to implement and compare two pronunciation correction mechanisms (an “online” and a “post-processing” variant). We simulated learning of words with and without prior phonological knowledge and explored the impact of con- text strength and size on learning. Results indicate that both mechanisms improve decoding. However, the post-processing mechanism induced implausible lexicalization for words with- out prior phonological knowledge, while the online mecha- nism did not. Overall, our simulation results suggest that pro- nunciation correction could be construed as an online process.

Autistic and neurotypical children do not handle audiovisual speech in the same manner. Current evidence suggests that this difference occurs at the level of cue combination. Here, we test whether differences in autistic and neurotypical audiovisual speech perception can be explained by a neural theory of sensory perception in autism, which proposes that heightened levels of neural excitation can account for sensory differences in autism. Through a linking hypothesis that integrates a standard probabilistic cognitive model of cue integration with representations of neural activity, we derive a model that can simulate audio-visual speech perception at a neural population level. Simulations of an audiovisual lexical identification task demonstrate that heightened levels of neural excitation at the level of cue combination cannot account for the observed differences in autistic and neurotypical children's audiovisual speech perception.

Despite computational algorithms outperforming humans in certain tasks, algorithmic advice is less used than human advice (algorithm aversion). Thus, algorithmic advice should be designed to avoid algorithm aversion. However, few studies have discussed the use of advice with an interval (e.g., 60.0 ± 2.0 %), a common format in algorithmic advice. This study confirmed in two behavioral experiments (N = 200) that differences in advice sources lead to differences in advice use, mainly by influencing the step at which the judge decides whether to ignore the advice. Therefore, we proposed to individualize the presentation of advice so that the advice would be such that decreases the rate advice being ignored. Our individualization of the advice presentation focused on the distance between the advice and the initial judgment, a significant factor in advice utilization. Another behavioral experiment (N = 100) confirmed that our proposed advice design overcomes differences among advisors.

Bilinguals show linguistic transfer effects at several processing levels. Focusing on phonology, we investigate the transfer of optional assimilation rules during speech production. Specifically, we examine to what extent bilinguals apply their native assimilation rule and/or fail to apply an L2 assimilation rule in their L2 speech. Both early and advanced late English-French bilinguals read a short French text. Using a speech recognizer with specific pronunciation variants, we found that the late bilinguals showed evidence for transfer of place assimilation, as well as a reduction in the amount of voicing assimilation compared to that of native French controls. The early bilinguals did not differ from the French controls in terms of place assimilation, but their voicing assimilation rate was intermediate between those of the French controls and the late bilinguals.

The sense of control (SoC) is the subjective feeling of being in control over an action, influenced by controllability, difficulty and feedback. However, it remains unclear how SoC is formed in multitasking scenarios. We conducted a study to analyze SoC and its impact on task-switching behavior in multitasking scenarios. Participants were required to perform two tasks in parallel while in control of one task at a time, requiring voluntary switching. We found that task-specific SoCs are influenced by the controllability and difficulty of each task. An overall SoC can be explained mainly by these task-specific SoCs. But, the overall SoC did not correlate with the frequency of task switches or the relative time spent on one task. Our analysis indicates that the SoC of a more control-demanding task has greater impact on the overall SoC and even affects the task-specific SoC of the other task, as well as task-switching behavior.

Working memory is widely assumed to underlie multi-step planning, where representations of possible future actions and rewards are iteratively updated before determining a choice. But most working memory research focuses on a context where stimuli are presented simultaneously and the value of encoding each stimulus is independent of others. It is unclear how working memory functions in planning scenarios where the rewards of future actions unfold over time, are retained in working memory, and must be integrated for plan selection. To bridge this gap, we adapted a version of the "mouselab task" in which participants sequentially observe the reward at each node in a decision tree before selecting a plan that maximizes cumulative rewards. We specified a theoretical model to characterize the optimal encoding and maintenance strategy for this task given the working memory constraints, which trades off the cost of storing information with the potential benefit of informing later choices. The model encoded rewards in choice-relevant plans more often, in particular, rewards on the best and (to a lesser extent) worst plans. We then tested this hypotheses on human participants, who showed the same pattern in the accuracy of their explicit recall. Our study thus establishes an empirical and theoretical foundation for models of how people encode and maintain information during planning.

Pragmatic implicature derivation presupposes that the cooperative principle is observed and critically depends on interlocutors expecting each other to behave cooperatively. It is much less clear, however, whether people extend this assumption to communication with artificial agents. People might therefore not draw the same pragmatic inferences when interacting with an artificial agent as they would with other conversationally competent humans, even if the agent is in principle believed to be similarly competent. In our study, we ask participants to interpret messages in a pragmatic reference game which they are told were generated by ChatGPT. Additionally, participants report whether they believe ChatGPT to be capable of the reasoning needed to select the optimal message. We observe a noteworthy discrepancy: in the reference game, participants interpret ChatGPT's messages less pragmatically than those of another adult human, but in the post-test questionnaire, they overwhelmingly rate ChatGPT's pragmatic ability very highly.

We investigated the impact of linguistic cues and autistic traits on lie detection. Adult participants (N = 125) judged suspects’ statements in a detective game. Untruthful statements were marked by semantic leakage. Literature indicates that liars use fewer first-person pronouns and mental-state terms than truth-tellers. We manipulated the untruthful statements for the presence/absence of these cues to test their effect on lie detection. The adults were 89% accurate in detecting lies. Mental-state terms did not affect accuracy, while presence of first-person pronouns hindered it. Having autistic traits did not influence lie detection. However, adults with higher autistic traits struggled to detect lies when these contained both a first-person pronoun and a mental-state term. Post-hoc analysis revealed lower lie detection accuracy for longer sentences. Our findings underscore the significance of semantic leakage in lie detection, with nuanced effects of linguistic cues on accuracy, particularly for adults with higher autistic traits.

In syllogistic reasoning research, humans are predominantly evaluated on their capabilities to judge whether a conclusion necessarily follows from a set of premises. To tackle this limitation, we build on work by Evans, Handley, Harper, and Johnson-Laird (1999), and present two studies where we asked participants for possible and likely conclusions. Combined with previous data (containing necessary), we present a comprehensive dataset with responses for all syllogisms, offering individual patterns for all three argument types - a first of its kind. We discovered that likely serves as a middle ground between possible and necessary, paving the way to further investigate biases and preferences. Generally, individuals were able to handle the different notions, yet tended to interpret quantifiers in a pragmatic way, overlooking logical implicatures. Finally, we tested mReasoner, an implementation of the Mental Model Theory, and concluded that it was not able to capture the patterns observed in our data.

The seminal dual coding theory by Paivio (1971) posited that non- verbal and verbal stimuli differ in their representational format, whereby the former activates a dual code while the latter only one. These differences in code have implications for tasks such as visual search. The current eye-tracking visual search study aims to re- evaluate this theoretical framework while examining the role played by semantic processing that has never been looked at before. We followed the original design by Paivio and Begg (1974), with participants searching for a target, cued either by a word or a picture, in an array of either words or pictures. The target could be either semantically related or unrelated to the other distractors. Corroborating original results, response times for correct trials were faster in pictorial arrays and substantially slower when a cued picture had to be found in a word array. Semantically unrelated targets were looked at faster for longer, leading to shorter search responses than semantically related targets. Critically, these effects driven by semantic relatedness were amplified when codes had to be converted (e.g., picture-to-word). Our findings refine our understanding of the role semantic processing plays in the representational format of words and pictures and the implications it carries for visual search.

Humans possess a remarkable capacity to recognize and manipulate abstract structure, which is especially apparent in the domain of geometry. Recent research in cognitive science suggests neural networks do not share this capacity, concluding that human geometric abilities come from discrete symbolic structure in human mental representations. However, progress in artificial intelligence (AI) suggests that neural networks begin to demonstrate more human-like reasoning after scaling up standard architectures in both model size and amount of training data. In this study, we revisit empirical results in cognitive science on geometric visual processing and identify three key biases in geometric visual processing: a sensitivity towards complexity, regularity, and the perception of parts and relations. We test tasks from the literature that probe these biases in humans and find that large pre-trained neural network models used in AI demonstrate more human-like abstract geometric processing.

Reinforcement learning is a powerful model of animal learning in brief, controlled experimental conditions, but does not readily explain the development of behavior over an animal's whole lifetime. In this paper, we describe a framework to address this shortcoming by introducing the single-life reinforcement learning setting to cognitive science. We construct an agent with two learning systems: an extrinsic learner that learns within a single lifetime, and an intrinsic learner that learns across lifetimes, equipping the agent with intrinsic motivation. We show that this model outperforms heuristic benchmarks and recapitulates a transition from exploratory to habit-driven behavior, while allowing the agent to learn an interpretable value function. We formulate a precise definition of intrinsic motivation and discuss the philosophical implications of using reinforcement learning as a model of behavior in the real world.

Efficient algorithms can enhance problem-solving in many cognitive domains but can be difficult to discover and use. For example, classical studies of seriation suggest that children struggle to apply algorithmic strategies in a simple sorting problem. We investigate the spontaneous discovery of algorithmic solutions across development. We gave children a variant of the sorting problem with hidden object ranks. Children sort animated bunnies into the right order, from the shortest to the tallest, when the bunnies are standing behind a wall so their heights are not visible. Children performed far above chance on this difficult sorting task, potentially because higher demands in memory and reasoning incentivized strategic behaviors. Children independently discovered at least two efficient algorithmic solutions to the sorting problem, Selection sort and Shaker sort. This result suggests that children are far more competent at sorting tasks than previous research would suggest. Additionally, older children were more efficient sorters than younger children. This suggests that competent performance on sorting tasks improves throughout development.

The discrete entities and explicit relations of symbolic systems make them transparent and easy to communicate. This contrasts with distributed systems, which tend to be opaque. This can lead us to pursue symbolic characterizations of human cognition. Symbolic interpretations can, however, oversimplify distributed systems. This is demonstrated in the developmental number cognition literature, where recent findings suggest a gradience of counting ability in children's learning. We take inspiration from these findings to explore the meaning of symbols in Recurrent Neural Networks (RNNs). We align recurrent neural representations with number symbols by causally intervening on the neural representations. We find that symbol-like representations of numbers do emerge in RNNs. We use this to inform the discussion on how neural systems represent quantity. We also show that the symbol-like representations evolve with learning, and continue to vary after the RNNs solve the task, demonstrating the graded nature of symbols in distributed systems.

Understanding how humans learn by themselves is crucial to develop interventions to prevent dropout and improve learner engagement. Classical learning curves were proposed to fit and describe experimental data involving enforced learning. However in real-world learning contexts such as MOOCs and hobbies, learners may quit - and often do. Even in formal settings such as college success typically requires intensive self-study outside lectures. Previous research in educational psychology supports a positive reciprocal relationship between motivation and achievement. Integrating insights from learning curves, forgetting curves and motivation-achievement cycles, we propose a formal Reciprocal-Practice-Success (RPS) model of learning ‘in the wild’. First, we describe the different components of the basic RPS model. Using simulations, we then show how long term learning outcomes critically depend on the shape of the learning curve. Concave curves lead to more consistent learning outcomes whereas S-shaped curves lead to either expertise or dropout. We also provide a dynamical systems version for the RPS model which shows similar qualitative behaviour. Through a bifurcation analysis of two controllable learning parameters - minimum practice rate and success sensitivity, we show which learner-specific interventions may be effective to preventing dropout. We also discuss theorized mechanisms which affect the inflection point of S-shaped learning curves such as task-complexity and relative feedback from failures vs. successes. These provide more task-specific interventions to lower quitting rates. Finally, we discuss possible extensions to the basic RPS model which will allow capturing spacing effects and insights from other motivation theories.

This study explores the way discourse structure information is used during encoding of linguistic representations, using the distinction between main and subordinate information as a case study. We use the two contrasting constructions: (a) “The singers\textsubscript{MAIN} who admired the violinists\textsubscript{MAIN} invited their mentors to the party”; (b) “The singers\textsubscript{MAIN}, who admired the violinists\textsubscript{SUBORDINATE}, invited their mentors to the party.” While both contain discourse-main information, (b) includes discourse-subordinate information in the clause ``who admired \textit{the violinists}.” Importantly, \textit{the singers} and \textit{the violinists} are both plausible antecedents for \textit{their}, but the overlap in discourse-information between the two NPs differs: (a) overlap ({MAIN, MAIN}); (b) no overlap ({MAIN, SUBORDINATE}). Through two web-based eye-tracking experiments using a visual world paradigm, we find that this overlap leads to competition between the two NPs, evidenced by eye-gaze differences, (a) < (b). We also find that this effect manifests early, even before retrieval, i.e., before pronoun resolution.

The spatial context of everyday speech to children is remarkably consistent. Words repeatedly occur in the same locations, and these words are learned earlier than those which are more scattered in use. Yet little is known about how spatial contextualization mediates this relationship. Does more constrained spatial context itself lead to better word learning? Or does it simply correlate with other informative cues in the input? Here, we assess how word learning is influenced by different levels of spatial contextualization in naturalistic scenes. We use different teaching methods (other-directed versus self-guided) as a proxy for distinguishing how the need for inductive inference mediates reliance on spatial context. We found that greater spatial contextualization led to better word learning, but only when inductive inference was needed. These findings suggest that learners can leverage spatial context to support word learning in the absence of rich linguistic input.

Our interaction with others largely hinges on how we semantically organize the social world. The organization of such conceptual information is not static—as we age, our experiences and ever-changing anatomy alter how we represent and arrange semantic information. How does semantic distance between concepts affect this organization, particularly for those with pathological deficits in semantic knowledge? Using triplet judgment responses collected from healthy participants, we compute an ordinal similarity embedding for a set of social words and images that vary in the dimensions of age and gender. We compare semantic distances between items in the space to patterns of error in a word-picture matching task performed by patients with semantic dementia (SD). Error patterns reveal that SD patients retain gender information more robustly than age information, and that age-related errors are a function of linear distance in age from a concept word. The distances between probed and exemplar items in the resulting conceptual map reflect error patterns in SD patient responses such that items semantically closer to a probed concept—in gender category or in linear age—are more likely to be erroneously chosen by patients in a word-picture matching task. To our knowledge, this is the first triplet embedding work to embed representations of words and images in a unified space, and to use this space to explain patterns of behavior in patients with impaired social semantic cognition.

The dominant mental timelines of native Chinese speakers (Exp1) and Mandarin learners of near-native proficiency (Exp2) was examined with the spontaneous gesture task. The results demonstrated that (1) both groups produced horizontal, vertical, sagittal, fused horizontal and vertical, and fused horizontal and sagittal gestures for all kinds of Chinese temporal words, indicating a strong preference for horizontal over vertical gestures. (2) Negligible correlations between immediate spatio-temporal metaphors and the mental timelines were observed, with an almost non-existent difference in gesture distribution across metaphorical types between the two groups. The findings indicate that (1) the horizontal mental timeline is the dominant timeline for two groups; (2) visuospatial experience exerts a greater influence on temporal cognition; (3) mental timelines formed by the long-term effects of language may operate beyond the immediate metaphors, similar to the horizontal gestures. A unified model proposing embodied experience as the mechanism for activating mental timelines is presented.

This study explores how L1 and L2 Chinese speakers use world knowledge and classifier information to predict fine-grained referent features. In a visual-world-paradigm eye-tracking experiment, participants were presented with two visual objects that were denoted by the same noun in Chinese but matched different shape classifiers. Meanwhile, they heard sentences containing world knowledge triggering context and classifiers. The effect of world knowledge has been differentiated from word-level associations. Native speakers generated anticipations about the shape/state features of the referents at an early processing stage and quickly integrated linguistic information with world knowledge upon hearing the classifiers. In contrast, L2 speakers show delayed, reduced anticipation based on world knowledge and minimal use of classifier cues. The findings reveal different cue-weighting strategies in L1 and L2 processing. Specifically, L2 speakers whose first languages lack obligatory classifiers do not employ classifier cues in a timely manner, even though the semantic meanings of shape classifiers are accessible to them. No evidence supports over-reliance on world knowledge in L2 processing. This study contributes to the understanding of L2 real-time processing, particularly in L2 speakers’ utility of linguistic and non-linguistic information in anticipating fine-grained referent features.

Team coordination is essential for effective performance during critical, stressful events. To better understand processes and states involved at multiple levels of team coordination, we assessed the correspondence between low- and high-level coordination in teams participating in simulation-based medical team training. We computed a measure of low-level team coordination with Multidimensional Recurrence Quantification Analysis, applied to arm movement, heart rate, and skin conductance data. High-level team coordination was captured by annotating video recordings for explicit and implicit, information and action coordination. Three linear mixed-effects model were run, each predicting a type of low-level coordination, based on high-level coordination annotations, accounting for multiple observations per team. Our findings showed that, compared to periods without annotated coordination, explicit- and implicit- information coordination corresponded to significantly different low-level team coordination across each of the studied modalities. Further research is required to assess additional factors related to the temporal variability observed in low-level coordination.

Human moral judgments are both precise, with clear intuitions about right and wrong, and at the same time obscure, as they seem to result from principles whose logic often escapes us. The development of Artificial Intelligence (AI) applications requires an understanding of this subtle logic if we are to embed moral considerations in artificial systems. Reinforcement Learning (RL) algorithms have emerged as a valuable interactive tool for investigating moral behavior. However, being value-based algorithms, they face difficulty when it comes to explaining deontological, non-consequentialist moral judgments. Here, in a multi-agent learning scenario based on the Producer-Scrounger Game, we show that RL agents can converge towards apparently non-consequentialist outcomes, provided the algorithm accounts for the temporal value of actions. The implications of our findings extend to integrating morality into AI agents by elucidating the interplay between learning strategies, characteristics for accounting temporal values, and methods of considering the opponent’s payoff.

Theory of mind is an essential ability for complex social interaction and collaboration. Researchers in cognitive science and psychology have previously sought to integrate theory of mind capabilities into artificial intelligence (AI) agents to improve collaborative abilities (Cuzzolin, Morelli, Cirstea, & Sarahakian, 2020). We introduce the Recurrent Conditional Variational Autoencoder (RCVAE), a novel model which leverages the ability of generative models to learn rich abstracted representations of contextual behaviors to predict behavioral intent from human behavioral trajectories. Advancing on current concept learning models, this model allows for the discovery of latent intent in human behavior trajectories, while maintaining the scalability and performance of generative AI models. We show that in the Overcooked-AI environment, the RCVAE outperforms baseline Long Short-Term Memory (LSTM) models in predicting intent, achieving higher prediction accuracy and greater predictive stability. The implications of these results are significant; the RCVAE's proficiency in learning the relationship between basic actions and resulting contextual behaviors represents a significant advancement in concept learning for behavioral intent prediction.

Sense of Agency (SoA) is a core concept related to our experience as intentional agents in our environment. Explicit and implicit measures have been used to study SoA. Recent findings suggest that the most common implicit measure, namely Temporal Binding (TB), may reflect memory processes rather than SoA. Here, we implemented two TB measures and an explicit measure in a novel goal-directed extended action task to better understand SoA measures. Participants either watched or produced dot movements to a target of choice and then estimated the duration between two tones that played either upon movement completion (TB1, akin to traditional TB studies) or based on the start and end of movements (TB2). Participants reported stronger explicit SoA during active than passive movements. Results from neither TB version aligned with prediction based on TB-accounts as a reflection of SoA. We discuss memory-based and scaling accounts as alternative interpretations for our data.

How, and how strongly, do default comprehension inferences shape verbal reasoning? When do they lead to fallacies? We address these questions for reasoning with polysemous verbs (verbs with distinct, but related senses) and ask when their use leads to fallacies of equivocation. The ‘linguistic salience bias hypothesis’ specifies conditions where subordinate uses of unbalanced polysemes trigger defeasible default inferences that are supported only by the dominant sense but influence further cognition, regardless. But does this happen even where the verb is preceded by disambiguating context that invites subordinate interpretations from the start? We present three experimental-philosophy studies that address this question: We use the psycholinguistic cancellation paradigm and fixation time measurements to examine inferences from polysemous appearance verbs. We find that default inferences can beat even preceding contextual information. Beyond their psycho-linguistic interest, findings have important philosophical consequences.

What makes Lassie a smart dog? People have strong intui-tions about dogs’ intelligence, yet the content and organiza-tion of these intuitions remain unknown. Two studies ex-amined the structure of laypeople’s concepts of dog intelli-gence, creating a conceptual map of what people represent as a “smart” or “dumb” dog. Study 1 elicited open-ended ideas about dog intelligence. We turned consistent themes into items in a 50-item survey. Study 2 asked participants to picture either a smart or dumb dog and rate that dog on the items derived from Study 1. Participants strongly agreed in their ratings of smart and dumb dogs, and we discovered a coherent dimensional structure underlying people’s intui-tions. They represent smart dogs as socially skilled with a good temperament, and dumb dogs as bad at physical rea-soning and avoiding threats. These representations align well with findings from canine research and with dog train-ers’ practical knowledge.

Most Chinese words are compounds formed through the combination of meaningful characters. Yet, due to compositional complexity, it is poorly understood how this combinatorial process affects the access to the whole-word meaning. In the present study, we turned to the recent development in compositional distributional semantics (Marelli et al., 2017), and employed a deep neural network to learn the less-than-systematic relationship between the constituent characters and the compound words. Based on the compositional representations derived from the computational model, we quantified compositionality as the degree of overlap between the compositional and the lexicalized representations as well as the degree of distinctness of the compositional representation. We observed that these two compositional attributes can affect compound recognition over and above the effects of constituent character features and compound features. Moreover, we found that this effect was increasingly stronger when holistic access to the compound meaning became more challenging. These findings therefore, from a computational perspective, provided new evidence for the combinatorial process involved in Chinese word recognition, which also shed light on the universal process of compound comprehension.

Humans can coordinate their behavior with others through interactions; however, not all pairs can coordinate. From the perspective of predictive processing, different social interaction patterns can be explained by the diversity of individuals’ belief strength. To investigate the relationship between coordination and belief strength, we conducted an interaction experiment using a Simon electronic light-sequence game in which participants memorize the order of color sequences. The results of our experiment, involving 23 pairs of participants, revealed diversity in the degree of coordination within pairs and the strength of belief between individuals. Our analysis supports the hypothesis that belief strength explains the success or failure of coordination: Coordination fails when both individuals in a pair have weak beliefs, whereas it succeeds when one person becomes the leader and the other becomes the follower because of the different strengths of their beliefs. Our findings suggest that predictive processing theory can be applied to situations involving social interactions.

This study investigates whether linguistic influences can affect the manifestation of lateral space-valence mappings in people’s minds. Although most oral languages and cultures of the world have expressions and conventions that associate the good with the right space, this association seems to be body- specific: while right-handers associate positive concepts with the right side and negative concepts with the left side, left- handers have the oppositive association, and the size of the effect of the body specificity does not vary with linguistic and cultural conventions. Thus, it is widely believed that this conceptual metaphor only depends on the body. However, sign languages do not seem to have any conventional association between lateral space and valence, and a recent study has shown that signers do not associate valence with lateral space, opening the possibility of a causal influence of language. The present study set to replicate this surprising and controversial finding by comparing a sign language group, consisting of Spanish and Chinese Sign Language users, and an oral Spanish control group on the widely applied “Bob” task in this field. Supporting prior findings, Spanish language participants associated the “good” with their dominant side of space, closely matching the anticipated proportion, but signers did not. This pattern of results can be explained by a strong linguistic influence on the formation of lateral associations of emotional valence, but we discuss some alternative possibilities.

The speech-to-song illusion is a robust psychological phenomenon whereby a spoken sentence sounds increasingly more musical as it is repeated. Despite decades of research, a complete formal account of this transformation is still lacking, and some of its nuanced characteristics, namely, that certain phrases appear to transform while others do not, is not well understood. Here we provide a formal account of this phenomenon, by recasting it as a statistical inference whereby a rational agent attempts to decide whether a sequence of utterances is more likely to have been produced in a song or speech. Using this approach and analyzing song and speech corpora, we further introduce a novel prose-to-lyrics illusion that is purely text-based. In this illusion, simply duplicating written sentences makes them appear more like song lyrics. We provide robust evidence for this new illusion in both human participants and large language models.

Speech production in aphasia is often described as “effortful”, though the consequences of consistent, high degrees of cognitive effort have not been explored. Using recent work on mental effort as a theoretical framework, the present study examined how effort-related fatigue produces decrements in performance in picture naming among participants with post-stroke aphasia. We analyzed three data sets from prior studies where participants completed a large picture naming test. Decreasing naming accuracy across trials was statistically significant in two of the three samples. There were also significant effects of practice (better performance on a second test administration), word frequency (better performance for more frequent words), and word length (better performance for shorter words). These results are the first concrete demonstration of fatigue affecting performance on a language task in post-stroke aphasia. They open a new avenue for research on mental effort/fatigue with potential implications for aphasia assessment, treatment, and management.

Prior literature shows that some mechanisms, e.g., commitment, could give rise to cooperation. However, participants' diverse propensities to cooperate may limit such mechanisms' effectiveness. Thus, we bring individual differences in their propensities to cooperate into the reasoning of long-term social dynamics of cooperation through an agent-based modeling approach. Our results suggest that commitment may still guarantee cooperation when individuals have different propensities to cooperate but has weaker effects, and the setups of commitment are also important. Our study highlights the importance of integrating individual preferences in analyzing collective dynamics of a population consisting of individuals of heterogeneous characteristics, thus offering implications to facilitate cooperation in rich real-world scenarios.

A speaker’s use of language is one of the most important indicators in detecting deception. To date, however, little research has focused on grammatical cues used in deceitful statements. One such cue is evidentiality which is the grammatical encoding for the source of information; i.e., whether the speaker has direct or indirect access to what they assert. This study investigates whether and how evidentiality coding in Turkish, an evidential language, interacts with producing deceitful and truthful narratives. Deceptive retellings were notably longer and syntactically more complex compared to truthful counterparts. Our hypothesis of increased past forms in deception was confirmed, alongside a heightened use of direct evidential inflection (–DI) in deceptive conditions. This exploration sheds light on the nuanced relationship between grammatical evidentiality and deceptive language use.

To successfully minimize losses or maximize gains, individuals must acquire a profound understanding of the rules and regularities in their environment. The current project centers on the impact of the environment on exploration and exploitation behavior. Therein, we compare costly exploration in environments, in which it is only possible to win (even though the size of the gains differs), only possible to lose, and mixed environments, in which one can win and lose. Participants engaged in a Multi-Armed Bandit task in three such conditions. Notably, participants exhibited reduced exploration in the gain domain compared to the loss domain, with the mixed domain falling in between. Interestingly, participants performed best in the mixed domain. Computational modeling of participants' choice behavior revealed that individuals tended to underestimate outcomes of unchosen options in the gain domain and overestimated them in the loss domain. We discuss two explanations for this pattern of findings: Either, effects are driven by the absolute difference between gains and losses or by the relative difference that individuals experience in relatively better or worse environments compared to their expectations (e.g., compared to previous blocks).

Is it tautological to call an action “wrongful discrimination?” Some philosophers and political theorists answer this question in the affirmative and claim that the term “discrimination” is intrinsically evaluative. Others agree that “discrimination” usually conveys the action’s moral wrongness but claim that the term can be used in a purely descriptive way. In this paper, we present two corpus studies and two experiments designed to test whether the folk concept of discrimination is evaluative. We demonstrate that the term has undergone a historical development and is nowadays no longer used purely descriptively. Further, we show that this evaluation cannot be cancelled without yielding a contradiction. We conclude that the descriptive use of “discriminatory” is a thing of the past.

Transcription with Incremental Presentation of the Stimulus (TIPS) is a novel approach relying on micro-behaviours proposed by Colarusso and colleagues (2023) to study users’ cognition with data visualizations. The study in this paper has two primary objectives: (a) investigate whether TIPS can measure an individual’s competence with data visualizations; and (b) explore the potential enhancement of TIPS measures by normalizing them with the individual’s performance on tests of visuo-spatial abilities and memory capacity. We test 30 participants with different expertise and cognitive skills. Results reveal that TIPS provides some promise for individual competence assessment, but only when normalized with the individual’s performance on a test of rigid transformation of mental images. Other tests measuring visuospatial abilities or memory capacity did not produce effective normalizations.

Classical approaches to studying insight problem-solving typically use specialized problems (e.g., nine-dot problem, compound-remote associates task) as stimuli together with verbal reports from subjects during problem-solving to reveal their thought processes, possibly adding other task-related metrics such as completion rate and physiological measures like eye fixation and neural activity. This approach has led to the claims that insight and creative thought require impasse and mental restructuring. What is missing from this literature is a cognitive process model of insight, and one reason for the lack of such a model is the lack of a unified, scalable, and tunable experimental framework with which to study human creative problem-solving with higher fidelity. In this paper, we introduce ESCAPE, an experimental paradigm using puzzle video games as stimuli which allow for the collection of process data that can serve as a basis for computational models. We have specifically developed a set of puzzle games based on this paradigm and conducted experiments that demonstrate the utility of the approach by revealing a set of computational principles that need to be accounted for by a theory of creative problems and the computational models based on it.

Large Language models (LLM) exhibit human-like proficiency in various tasks such as translation, question answering, essay writing, and programming. Emerging research explores the use of LLMs in collective problem-solving endeavors, such as tasks where groups try to uncover clues through discussions. Although prior work has investigated individual problem-solving tasks, leveraging LLM-powered agents for group consensus and decision-making remains largely unexplored. This research addresses this gap by (1) proposing an algorithm to enable free-form conversation in groups of LLM agents, (2) creating metrics to evaluate the human-likeness of the generated dialogue and problem-solving performance, and (3) evaluating LLM agent groups against human groups using an open source dataset. Our results reveal that LLM groups outperform human groups in problem-solving tasks. LLM groups also show a greater improvement in scores after participating in free discussions. In particular, analyses indicate that LLM agent groups exhibit more disagreements, complex statements, and a propensity for positive statements compared to human groups. The results shed light on the potential of LLMs to facilitate collective reasoning and provide insight into the dynamics of group interactions involving synthetic LLM agents.

Recent research has found that autistic individuals have poorer performance and lower eye movement consistency in face recognition, which may be related to less face processing experience due to lack of social interests. Here we showed that this phenomenon was not observed in visual search tasks, as autistic individuals and matched neurotypicals had similar hit rate and precision as well as eye movement behavior when searching for either social (human) or non-social (vehicle) stimuli. However, autistic individuals had longer search time and made more and longer fixations, suggesting difficulties in identifying potential targets. This difficulty was not limited to social stimuli, supporting a domain-general view of deficits in autism spectrum disorder (ASD). Our findings have important implications for understanding the core mechanisms underlying social-cognitive impairment in ASD.

People have strong intuitions about which colors do and do not match particular pieces of music — a phenomenon often conceptualized through semantic mediation. We further explored the specific strategies people employ to navigate these color-music associations which offers crucial insights to identifying the cognitive mechanisms that enable such cross-domain associations. We show that while some people rely more on intuition, other people actively seek justification of association by consulting common linguistic descriptors, emotional contents, and environmental cues. We found that more spontaneous strategies lessen the role of semantic mediation, while more evaluative strategies, especially those involving the use of language, amplify it. Notably, the use of evaluative strategies introduced an asymmetrical effect for matching and mismatching colors. Additionally, individuals employing similar strategies associated a given music excerpt with more similar colors, suggesting that strategy alignment enhances the consistency of color-music associations. Interestingly, this pattern of convergence was not observed among individuals who predominantly relied on guessing.

Although even infants appear to consider multiple possibilities, preschoolers often fail tasks that require reasoning about mutually exclusive alternatives. We review two explanations for this failure: (1) children have a minimal representation of possibility and fail to distinguish necessary from merely possible outcomes; and (2) children are sensitive to this distinction, but competing motivations (e.g., the tendency to explore) can lead to apparent failures. To test these hypotheses, we assessed 3- and 4-year-olds on a novel search task. Here, children searched for an object that was dropped from either a transparent (one necessary location) or opaque (two possible locations) set of inverted Y-shaped tubes. In Exp. 1, we found that children spent less time searching the first location when there were two possible candidates. Exp. 2 replicates these results in a digital task that does not require manual search.

In logical theories of meaning, threshold and prototype models are two distinctive formal approaches. In cognitive science literature, however, where the two models are operationalized, there is support for the use of a threshold model in categorization (Schmidt, Goodman, Barner, & Tenenbaum, 2009; Ramotowska, Haaf, Van Maanen, & Szymanik, 2022) as well as sup- port for the prototype model (Douven, 2016; Douven, Wenmackers, Jraissati, & Decock, 2017), and in many cases the two models are used interchangeably (Kruschke, 2008). We test for the case of relative gradable adjectives whether a) there is a difference between predicted degrees of membership from the two models when relying on explicit reports of threshold and prototype values, and b) which of the models better pre- dicts behavioral data from categorization tasks. Results suggest that prototype and threshold models are highly predictive of behaviour in a categorization task and that the two models yield similar results with a slight advantage of the threshold model.

Models of bottom-up visual attention such as the "saliency map" predict overt gaze under laboratory conditions while subjects view static images or videos while seated. Here, we show that the saliency map model predicts gaze at similar rates even when applied to video from a head-camera as part of a wearable eye-tracking system (Tobii Pro Glasses 2) while subjects drive an automobile or are passively driven while sitting in the front passenger-side seat. The ability of saliency to predict gaze varies depending on the driving task (saliency better predicts passenger gaze) and external conditions (saliency better predicts gaze at night). We further demonstrate that predictive performance is improved when the head-camera video is transformed to retinal coordinates before feeding it to the saliency model.

Alongside the advancement of large language models (LLMs), attention towards their limitations and potential risks has also increased. One common issue is hallucination, which occurs when LLMs generate inaccurate or irrelevant answers, especially for complex sentences. To address this issue, we propose a novel question preprocessing method inspired by how young children comprehend complex sentences. Our method consists of two modules: (1) hierarchical clause annotation (HCA)-based sentence decomposition, which breaks down complex sentences into one-verb-centered clauses, and (2) abstract meaning representation (AMR)-based clause rewriting, which reformulates the clauses based on AMR into the child-comprehensible subject-verb-object (SVO) structure. We evaluate our method on the question-answering dataset, TruthfulQA, and show that it can improve the truthfulness and informativeness of widely-used LLMs, LLaMA-7B, and LLaMA-2-7B-chat, preventing from generating hallucinated answers. Moreover, our method is highly efficient, as it does not require any pre-training, fine-tuning, or invoking larger-scale models.

We introduce the Curious About Uncertain Scene (CAUS) dataset, designed to enable Large Language Models, specifically GPT-4, to emulate human cognitive processes for resolving uncertainties. Leveraging this dataset, we investigate the potential of LLMs to engage in questioning effectively. Our approach involves providing scene descriptions embedded with uncertainties to stimulate the generation of reasoning and queries. The queries are then classified according to multi-dimensional criteria. All procedures are facilitated by a collaborative system involving both LLMs and human researchers. Our results demonstrate that GPT-4 can effectively generate pertinent questions and grasp their nuances, particularly when given appropriate context and instructions. The study suggests that incorporating human-like questioning into AI models improves their ability to manage uncertainties, paving the way for future advancements in Artificial Intelligence (AI).

Crossmodal correspondences are associations between perceptual features from different senses that aid in crossmodal binding. The semantic coding of these correspondences is expected to capture and mediate the emergence of perceptual crossmodal correspondences. However, the cross-cultural nature of such semantic coding has not been thoroughly studied. This research involved five languages across three different linguistic families (English, Dutch, Turkish, Chinese and Italian). Using distributional semantics, modality exclusivity norms and emotional lexicons, networks were constructed to represent semantic crossmodal correspondences and assess their relationship with Valence, Arousal and Dominance. Results indicate that emotions, particularly Valence and Dominance, play pivotal roles in shaping the structure of semantic crossmodal correspondences networks across languages. Moreover, the findings reveal that some types of semantic crossmodal correspondences might be shared among different languages in various language families, suggesting shared cognitive processes. This supports the significance of emotions as fundamental components in semantic crossmodal correspondences. Additionally, the study provides evidence supporting shared crossmodal correspondences among languages and cultures.

In this paper, we argue that action is involved in the creation and representation of perceptual objects. We introduce leading philosophical theories regarding the structure of perceptual objects in modality-independent and multisensory settings. These accounts omit action as a causal factor that can facilitate feature binding and serve as a structural component of perceptual objects. We argue that action does play this causal role due to the connections between the brain’s motor system and perceptual processing as evidenced by neurophysiological and behavioral studies. These data include research on view-independent representations, peripersonal space, and event file coding. We conclude that to omit the influence of the motor system on the structure of perceptual objects is to have an incomplete account of object perception. Motor action is often required to drive the integration of sensory features into corresponding perceptual objects.

Everyday tasks demand attentional resources to perceive, process, and respond to important information. Attempting to complete multiple tasks simultaneously, that is, multitasking, necessarily requires more resources than completing either task alone. Allocating common resources among two or more difficult tasks will lead to competition and result in performance deficits to one or more of the to-be-completed tasks. Multiple resource theory suggests separate pools for perceiving (aural, visual, tactile), processing (verbal, spatial), and responding (vocal, manual), but a common overarching resource pool still exists and is heavily taxed for the management of multiple ongoing tasks. We use the combination of neural activity and performance to estimate the degree to which the demands of a visual-spatial-manual (VSM) task impedes the performance of an auditory-verbal-vocal (AVV) task, where each taxes independent pools of attentional resources. We found AVV performance decreased when paired with a more difficult VSM task. Using components from group-level event related potentials (ERPs), we draw conclusions to estimate how and why cross-modal task performance changes, and diagnose resource bottlenecks and limitations. Specifically, we find auditory evoked potentials, P300, and Reorienting Negativity serve as fruitful indicators of not only high or low cross-modal load, but are predictive of (in)correct trial performance. Further, we discuss how these indicators provide insight to the underlying mechanisms driving misses, and whether crossmodal bottlenecks may occur at the perceptual, cognitive, or response stage.

This study investigates the role of low-level acoustic cues in relation to increasing number of syllables in two word recognition experiments conducted in a remote field site. Participants are bilingual speakers of French and Drehu (Oceanic), two edge-marking languages. The two languages use grammatical gender, but differ in the number of function words used for it. In two experiments, prosodic cues were manipulated at the edges of Accentual Phrases (AP) of increasing length. APs consisted of an article and a following content word. Results show that the acoustic manipulations had a greater impact on short APs with three syllables while words in APs with more syllables could be retrieved faster in French. In Drehu, results indicate that words in longer APs are recognised later. This shows that despite similarities in the intonational phonology, listeners rely on different strategies during word recognition influenced by the grammatical make up of the language.

Procrastination is a universal phenomenon, with a significant proportion of the population reporting interference and even harm from such delays. Why do people put off tasks despite what are apparently their best intentions, and why do they de- liberately defer in the face of prospective failure? Past research shows that procrastination is a heterogeneous construct with possibly diverse causes. To grapple with the complexity of the topic, we construct a taxonomy of different types of procrasti- nation and potential sources for each type. We simulate com- pletion patterns from three broad model types: exponential or inconsistent temporal discounting, and waiting for interesting tasks; and provide some preliminary evidence, through com- parisons with real-world data, of the plausibility of multiple types of, and pathways for, procrastination.

Does language help make sense of the visual world? How important is it to actually see the world rather than having it described with words? These basic questions about the nature of intelligence have been difficult to answer because we only had one example of an intelligent system -- humans -- and limited access to cases that isolated language or vision. However, the development of sophisticated Vision-Language Models (VLMs) by artificial intelligence researchers offers us new opportunities to explore the contributions that language and vision make to learning about the world. We ablate components from the cognitive architecture of these models to identify their contributions to learning new tasks from limited data. We find that a language model leveraging all components recovers a majority of a VLM's performance, despite its lack of visual input, and that language seems to allow this by providing access to prior knowledge and reasoning.

“Misfortunes never come singly” is a saying common in different languages and historical contexts. Could this proverb reflect more than irrational superstitions? We draw from two frameworks, the fast-and-frugal heuristics approach to decision making, and the rational analysis of cognition. The former prompts us to conceptualize the proverb as a simple but smart heuristic that may be adapted to statistical regularities in decision-making environments, and the latter offers a method for studying such environments. Analyzing the pattern of humanitarian disasters between 2000 and 2022, we find that the probability of observing a new disaster in a country increases with the frequency of new disasters observed in the previous 100 days in that country. We propose a research agenda to study the ecological rationality of proverbs. Our results are also potentially relevant to humanitarian analysts.

Measuring the neural correlates of cognition in freely moving preschoolers presents several challenges. The current article describes a proof-of-principle study assessing brain activation in preschoolers while performing a naturalistic action planning task in the wild. Ninety-two children between 3 and 5 years of age built both a Duplo house and a Duplo spaceship. Both building tasks involve the completion of multiple subgoals within the overall goal. The results revealed an increase in oxyhaemoglobin activation in right DLPFC when planning for the next subgoal, as well as in a standard go/no-go inhibition task, suggesting that inhibition may play a special role in selecting subgoals at these ages. More generally, we demonstrate that fNIRS data can be recorded from moving preschoolers and that a multi-modal set-up including optical motion capture can allow the reconstruction of events of interest. Implications of the approach, as well as recommendations to improve data quality of wireless fNIRS in freely moving toddlers, are discussed.

Interpersonal synchrony is a foundation of social interaction. However, as a form of coordination, synchrony is limited to regular, rhythmic actions. As such, research regarding the relationship between synchrony and social factors may not generalise to other forms of interpersonal behaviour. Here, we explored whether factors known to influence synchrony, also impact a complimentary form of coordination, complexity matching. When people interact, complex patterns of variability inherent to their individual behaviour can become more similar (i.e., more coordinated). In pairs, participants completed four walking trials that manipulated social interdependence while their gait patterns were captured. We also measured subclinical levels of social anxiety. Although data collection is ongoing, the results point to social anxiety having a detrimental effect on individual behavioural variability, and in turn, complexity matching. Effects of the interdependence manipulation were also evident, but await further data. These results are discussed with respect to theories of interpersonal dynamics.

People learn new words in narrative contexts. Little is known about the influence of the emotional valence of the text on word learning. In a pre-registered experiment, we investigated whether emotional narrative context shapes word learning. English adults (N = 76) read 30 novel adjectives embedded in 60 short narratives (20 positive, 20 negative, and 20 neutral valence). Post-tests assessed learning (immediate and 24 hours later) and examined whether the valence of the novel words can be inferred from contextual valence. Compared to the neutral context, emotional contexts (both positive and negative) facilitated word form learning in the immediate post-tests, but only negative emotion words were recognized better 24 hours later. Furthermore, the valence of the context was reflected in the word meanings participants generated for each novel word. These findings are discussed with reference to theories of affective embodiment and its implications for supporting the learning of abstract concepts.

Feedback, when used successfully, supports student learning and motivation. Although various types of feedback are used in the actual classroom, however, most interactive learning systems provide feedback that addresses learner performance only (e.g., correctness feedback). We developed two versions of an Intelligent Tutoring System (ITS) for learning ratio calculations using mathematics number lines that differed in the type of feedback it provides: effort-based and performance-based feedback. We conducted a school-based experiment with 5th graders in Japan to test the effectiveness of the two types of feedback on student learning and motivation. The results indicate a trend that performance-based feedback in the ITS had a positive impact on student learning but no difference was found on learner motivation. This study adds new knowledge of what types of adaptive feedback are effective for student learning in mathematics.

Situation models are known to help structure our experiences in our memory. But what are the ingredients of a situation model and to what degree do abstract event features contribute to updating of situation models? We manipulated abstract event feature dimensions and narrative specific factors in an experiment in which participants actively constructed a narrative from a random order of event descriptions. We investigated the influence of abstract factors (“degree of feature-change”, “event position”) on response speeding during a subsequent oddball task. Participants were faster for oddballs with a different degree of feature change, which interacted with whether the oddball was from within the same story or from another story. When looking at other-story-oddballs only, we found an interaction between position within the event structure and degree of feature change. Our results suggest that people use abstractions of event features which are important for the instantiation of a situation model.

Intelligent systems that record, analyze, and respond to events have become major parts of our lives. They are available as Decision Support (DS) for many tasks and can enhance the information on which decision-makers can base their decisions. Decision makers need to evaluate the available information, and they also have to decide whether to seek information from additional information sources. The information is often costly, and its costs and benefits must be weighted. Also, integrating information from multiple sources can complicate the decision task. Here, we study the combined decision process that chooses information sources and integrates them, if chosen, in a classification decision. In an online experiment with 75 engineering students, we manipulated the redundancy level of information received from DS with already existing information. Participants' task in two between-subjects conditions was to classify binary events with the option to access up to two DS systems. In one of the conditions, the two DSs provided non-redundant information, and in the second condition, one of them provided fully redundant information, and the other provided non-redundant information. We found that the decision to access information was not affected by whether some information was redundant (strongly correlated with already available information). Participants used the information to improve classification performance, and the improvement was significantly higher when they used non-redundant information. However, the benefits gained were smaller than predicted from a normative model. Moreover, the use of information from multiple non-correlated sources can increase mental workload, as was evident in our results, possibly because of conflicting information from different sources.

When making decisions, humans often rely on information from their social networks through a process termed social sampling. Prior work suggests that when drawing social samples, people search through their contacts in a sequential manner based on structured social categories. We examined whether the problem domain impacts how one categorizes their social contacts and which social categories they sample from. In our study, participants answered questions about the relative popularity of national parks or social media platforms, respectively associated with offline activities and online activities. Participants then provided frequency information about the number of their contacts who have engaged in these activities. Adopting a hierarchical Bayesian modeling approach, we compared two social sampling models: one defining social groups based on closeness of social relations, and one defining social groups based on contact mode. Our findings suggest that people sample from different members of their social network depending on the type of decision they are making.

A major goal of cognitive science is to characterize how an individual's past experiences guide their present decisions in a sequential task. Various empirical evidence support a process of incremental learning, well-characterized by the framework of reinforcement learning, whereby repeated exposures to similar situations shape decisions. However, in a complex world with sparse data a more sample-efficient process is needed. Prior work has suggested that episodic memory supports decision-making in such settings. Here, we provide novel behavioral evidence that episodic memory supports decision-making in temporally extended settings. We propose that value-based decision-making and episodic memory share common mechanisms to encode and retrieve past events, which in turn shape option evaluation and ultimately choice. In two experiments, we empirically test hypotheses that relate classic dynamics of sequential episodic memory retrieval to response patterns in novel evaluation and decision tasks. We find subjects’ reported value estimates are subject to biases analogous to classic episodic memory biases (Experiment 1), and their choices are best captured by an episodic recall-based model (Experiment 2). These results suggest a novel link between value-based decision-making and episodic memory, which could reflect a psychologically plausible mechanism for computing decision variables by Monte Carlo sampling.

The human ability to learn and compose conceptual operations is foundational to making flexible generalizations, such as creating new dishes from known cooking processes. Beyond naive chaining of functions, there is evidence from the linguistic literature that people can learn and apply context-sensitive, interactive rules, such that output production depends on context changes induced by different function orderings. Extending the investigation into the visual domain, we developed a function learning paradigm to explore the capacity of humans and neural network models in learning and reasoning with compositional functions under varied interaction conditions. Following brief training on individual functions, human participants were assessed on composing two learned functions, in ways covering four main interaction types, including instances in which the application of the first function creates or removes the context for applying the second function. Our findings indicate that humans can make zero-shot generalizations on novel visual function compositions across interaction conditions, demonstrating sensitivity to contextual changes. A comparison with a neural network model on the same task reveals that, through the meta-learning for compositionality (MLC) approach, a standard sequence-to-sequence Transformer can approximate a strong function learner, and also mimic human error patterns with additional fine-tuning.

What makes an explanation seem insightful? Prior work shows that even circular explanations can seem insightful when they include information from a lower level of explanation (reductive information). Here, we suggest that this impression of insight is not an illusion. Rather, circular explanations with reductive information are pragmatically instructive: they suggest at which level of description the phenomenon should be explained. In Study 1, even single-sentence circular explanations appeared insightful when infused with reductive information. In Study 2, rating circular explanations with reductive information as insightful correlated with rating them as helpful both with searching for explanatory information and with narrowing down which mechanisms an explanation should address. Study 2 also provides preliminary evidence that these ratings were not driven by prior knowledge of these circular explanations’ explicit propositional content.

As children enter elementary school, their understanding of the ordinal structure of numbers transitions from a memorized count list of the first 50-100 numbers to knowing the successor function and understanding the countably infinite. We investigate this developmental change in two neural network models that learn the successor function on the pairs (N, N+1) for N in (0, 98). The first uses a one-hot encoding of the input and output values and corresponds to children memorizing a count list, while the second model uses a place-value encoding and corresponds to children learning the language rules for naming numbers. The place-value model showed a predicted drop in representational similarity across tens boundaries. Analysis of the latent representation shows that counting across a tens boundary can be understood as a vector operation in 2D space, where the numbers with the same tens place are organized in a linearly separable manner, whereas those with the same ones place are grouped together. A curriculum learning simulation shows that, in the expanding numerical environment of the developing child, representations of smaller numbers continue to be sharpened even as larger numbers begin to be learned. These models set the stage for future work using recurrent architectures to move beyond learning the successor function to simulating the counting process more generally, and point towards a deeper understanding of what it means to understand the countably infinite.

The photo-taking-impairment effect refers to the detrimental impact photo taking has on one’s memory for the photographed object. We explored the role of two mechanisms that have been said to underlie the effect, namely offloading and attentional disengagement. In this online study 107 undergraduate students were shown 3x5 paintings and were instructed to simply observe them, take a picture that would be available for later usage, or take a picture and delete it. Afterwards, they were presented with a visual details multiple-choice test. It was expected that if attentional disengagement was the mechanism underlying the photo-taking-impairment effect, then the effect would not be present in the current study. This expectation was due to the non-distracting nature of the photo taking task that was used in the study. The results were in line with the expectations and did not indicate the presence of the photo-taking-impairment effect. Consequentially, it supported the hypothesis that attentional disengagement, rather than offloading, is the mechanism underlying this effect.

Question-asking, an essential yet understudied activity, holds significant implications for fields such as learning, creativity, and cognitive development. The quality, and complexity in particular, of the questions are recognized as crucial factors affecting these fields. Previous research explored question complexity through Bloom's taxonomy, but measurement remains challenging. Recent advancements have enabled automated scoring of psychological tasks but have not been applied to open-ended question complexity. Here, we address this gap by employing large language model (LLM) techniques to predict human ratings of open-ended question complexity. Our results reveal that our LLM-generated complexity scores correlated strongly with human complexity ratings in both the holdout-responses (r = .73) and holdout-item set (r = .77), whilst also exceeding baseline methods tested. The research emphasizes the significance of LLMs in psychological research and their potential in automating question complexity assessment. This study also highlights exciting possibilities for usage of LLMs in education and psychology.

In this pre-registered study, we investigated whether emotional inner speech influences heart rate. Participants were asked to engage in 3-minute sessions of: positive inner speech, negative inner speech, or inner counting while their heart rate was monitored. Participants were lying on a bed and asked to remain still. Motion tracking was applied to control for body movement. Median heart rate across each inner speech session was analyzed and a significant difference was found between emotional inner speech and inner counting. No difference between positive and negative inner speech was observed. Post-hoc analyses investigated the relationship between movement and heart rate and found an effect with a peak lag of approximately 14 seconds. Removing these effects did not change the effect of emotional inner speech. Additional analyses showed that heart rate and respiration rate were linked. Including respiration rate as a covariate did not alter the effect of emotion.

This study examined how individuals would express their preference or distaste for experiences associated with beverages they found to be delicious or unpalatable using facial expressions. We recorded videos where six individuals were asked to drink their preferred or unpreferred beverages and to make “delicious” or “unpalatable” expressions irrespective of what they drank, resulting in four conditions: (1) “delicious” expression with a preferred beverage (genuine delicious), (2) “unpalatable” expression with an unpreferred beverage (genuine unpalatable), (3) “delicious” expression with an unpreferred beverage (fake delicious), and (4) “unpalatable” expression with a preferred beverage (fake unpalatable). A total of 33 participants watched the videos and estimated the level of deliciousness of the beverage and inferred the emotions of happiness, sadness, and disgust conveyed by the actor. The results showed genuine and fake delicious expressions conveyed more deliciousness than genuine and fake unpalatable expressions. The participants interpreted that the drink was more unpalatable when observing fake expressions than when observing genuine unpalatable expressions. There was no difference in the evaluation of deliciousness between the genuine and fake delicious expressions. Furthermore, fake unpalatable expressions were rated as containing more disgust than genuine unpalatable expressions. These results suggest that individuals exaggerate disgust when making fake and unpalatable expressions.

An accurate estimation of driver vigilance is crucial for reducing fatigue-related incidents and traffic accidents. Despite advances in the field of fatigue detection, effective utilization of multimodal information remains a major challenge. Additionally, prevalent methodologies predominantly focus on local features, overlooking the importance of global features in this context. To solve the above problems, we propose the deep channel attention transformer (DCAT) model, which can effectively utilize multimodal information and extract local-global features for fatigue detection regression tasks. We first introduce a novel multimodal approach that integrates electroencephalography (EEG) and electrooculogram (EOG) data, capitalizing on their complementary strengths to enhance the understanding and assessment of fatigue states. Then, the DCAT model utilizes multimodal information by extracting local and global features using channel attention and transformer encoder modules, respectively. Our evaluation of the SEED-VIG and SADT public datasets showcases the model's superior performance compared to that of the state-of-the-art baselines.

Previous studies suggested brain differences in the temporal domain when processing real human faces versus virtual agent faces, starting from 400 ms onward. However, few studies directly compared the early and the late face processing stages within one paradigm. Here we conducted an EEG study utilizing real human faces and high-quality virtual agent faces, examining two event-related potentials; the early N170 and the Late Positive Potential (LPP). Results showed identical N170 responses for both face types. However, the LPP response revealed a nuanced distinction, with real human faces evoking a slightly larger LPP compared to virtual agent faces. These results suggest that although virtual agent faces can approach the level of emotional engagement and higher-order evaluation associated with real human faces, human faces remain the most engaging. These findings shed light on the cognitive processes involved in face perception and the potential for intelligent virtual agents in AI and education.

This study examines three types of vocal iconicity—sound effects, onomatopoeia, and iconic prosody—in Chinese child-directed speech (CDS), adult-directed speech (ADS), and child production. We analyzed a corpus of semi-spontaneous ADS and CDS from forty Chinese mother-child dyads, where the children were 18 and 24 months old. Our findings revealed that (1) mothers used significantly more sound effects and iconic prosody, but not onomatopoeias, in CDS compared to ADS. Interestingly, mothers’ iconic prosody was also acoustically more congruent with lexical meanings; (2) The frequency of sound effects was lower than iconic prosody but higher than onomatopoeias; and (3) Chinese children aged 18 or 24 months seldom produced onomatopoeia or iconic prosody. These findings suggest that iconicity is more prevalent and prosodically marked in CDS than in ADS, which may help children’s word-to-world mapping. Also, iconic prosody is an advanced prosodic skill that is not typically developed by two-year-old children.

This paper presents an approach to model the internal cognitive state of decision-makers when interacting with AI to understand exchanges between agents and improve future interactions. We focus on understanding how AI suggestions are perceived by a human agent using an approach based on the technology acceptance model. The variation in the user’s state is investigated when perceiving the interaction with AI by considering it as a hidden (latent) state. Using human evaluation data collected from two cases of clinical decision-making and software development scenarios, we analyse and explore the user’s perceptional state during interaction. The experiment conducted employs the Bayesian belief network to represent the human perceptional model and provide a prediction of the usefulness of AI model’s suggestions in the considered case. Upon introduction of cognitive states in the model, we observed an increase in predictive performance by 76–77%. Our investigation can be concluded as an attempt to identify implicit static and dynamic cognitive characteristics of users to provide personalized assistance in human-AI interaction (HAI) and collaboration in complex domains of decision-making

How people reach consensus in social networks with locally distributed interactions is relevant to understanding collective group decision-making and problem-solving. However, while the importance of theory of mind in consensus problems has been hypothesized, little work has been done to test it systematically. We present both computational modeling and behavioral experiments designed to test the impact of theory of mind on individual choices within such consensus networks. We test 2,108 computational models informed by theoretical work on a graph-coloring consensus task to compare models using theory of mind to other behavioral parameters. We then use behavioral responses from 107 participants in a similar task to evaluate support for theory of mind in consensus formation. We find that the computational model that best accounts for prior behavioral data uses theory of mind, and our behavioral results likewise support use of theory of mind over other potential decision-making models.

The verbal overshadowing effect, a phenomenon where verbal descriptions of an encoded face hinder subsequent recognition, has been linked to the similarity in facial image sets used in recognition tasks. However, the specific aspects of similarity that influence this effect remained underexplored. This study, therefore, employed the Stable Diffusion image-generation model to create image sets that are similar either verbally or nonverbally. Experimental results using these sets revealed the presence of the verbal overshadowing effect in the verbally-similar set, but it was not evident in the nonverbally-similar set. These findings align with existing explanations of the verbal overshadowing effect and contribute to enhancing its predictability.

All languages have demonstratives—grammatical words like ‘this’ and ‘that’ in English, which are a universal tool to establish joint attention on a referent. Demonstratives are acquired early, but their mature use has a protracted development, with recent studies showing that 10- and 11-year-old children do not yet use demonstratives like adults do. Here we investigated demonstrative use by teenagers (ages 12-17) and adults with a focus on two social parameters affecting demonstrative choice in Spanish: Listener Position and Listener Attention. The results of two experiments using an online demonstrative-choice task revealed that teenagers use Spanish demonstratives comparably to adults in most conditions. However, teenagers seem to still be adjusting the relative weight of the social parameters affecting demonstrative choice in Spanish, supporting the view that acquiring and regularly using demonstratives trains social cognition through communicative interaction.

Humans learn that temporarily occluded objects continue to exist within the first months of their lives. Deep learning mod- els, on the other hand, struggle to generalize such concepts from observations, due to missing proper inductive biases. Here, we introduce the first self-supervised interpretable ma- chine learning model that learns about object permanence di- rectly from video data without supervision. We augment a slot- based autoregressive deep learning system with the ability to adaptively and selectively fuse latent imaginations with pixel- based observations into consistent object-specific ‘what’ and ‘where’ encodings over time. We show that (i) Loci-Looped tracks objects through occlusions and anticipates their reap- pearance while outperforming state-of-the-art baseline models, (ii) Loci-Looped shows signs of surprise when the principle of object permanence is violated, and (iii) Loci-Looped’s internal latent loop is key for learning object permanence.

The steady increase in time spent on smartphone applications and particularly on social networks, raises questions about the environmental and societal sustainability of such a phenomenon. Utility and enjoyment have a key role in such practices, but other factors such as passing time may also contribute. From May to November 2023, 5,028 people took part in a web survey aiming at producing durations prospectively using mobile applications like Facebook, Instagram, TikTok, Reading. The protocol introduces variables known to have an effect on time perception. On average, the produced durations were underestimated. This result is in line with the notion that tracking information and tracking time compete for the brain’s limited attentional resources and, hence, that attention plays a critical role in time estimation. Significant differences emerged between the applications tested. TikTok and Reading tasks appear the most underestimate but with opposite dynamics as the level of satisfaction and familiarity are lower for the first compared to the former. Among the variables studied to explain the difficulties in evaluating time spent, the importance of familiarity with the activity is undoubtedly something worth exploring in the context of the race between new algorithms and cognitive adaptability.

Theory-of-mind (ToM) plays a critical role in early language acquisition. However, whether ToM continues to support word learning in adults is unknown. This study tests whether engaging nonverbal ToM assists novel word encoding and retention. We examined young adults word learning in direct-mapping and pragmatically-inferred contexts. Each word learning block for each context was proceeded by a brief, non-verbal animation that either primed the ToM system or did not engage ToM. We found that initial pragmatically-inferred meaning mapping was assisted specifically by priming ToM prior to word learning. Long-term word retention was strengthened for both pragmatically-inferred and directly-mapped words,when learning was preceded by a ToM video. These results demonstrate that ToM causally interacts with word learning processes and facilitates both encoding and retention. Our findings strengthen previous arguments that ToM plays a critical role in language development and broaden this to encompass lifelong vocabulary learning.

We propose two novel benchmarks for assessing models of probability judgments: the impact of Cognitive Reflection Test (CRT) on probability judgment expressions and 16 ``normality identities" expected to sum to 1 under classical probability theory. We compared three models on these benchmarks – the Probability Plus Noise Model (PPN), the Bayesian Sampler (BS), and the Quantum Sequential Sampler (QSS) – using the largest dataset to date for probability judgments. Our results reveal that higher CRT scores are associated with fewer probabilistic fallacies and identity violations, a trend most accurately captured by the QSS, although we also identified QSS limitations. Regarding the normality identities, the QSS outperformed the PPN and the BS, which had difficulty with both the average values of the normality identities and their dependence on CRT scores. Additionally, we uncovered a unique ``1 crossing" effect for normality identities N8 and N11, an effect PPN and BS cannot capture.

We investigated the processing and interpretation of aspectual coercion in the case of non-culminating accomplishments in English and German. Two offline experiments employing an inference rating task showed that non-culminating accomplishments in both languages actually involve a shift in interpretation. Four self-paced reading experiments furthermore show that this type of coercion isn't costly - neither in German, a language lacking grammatical aspect, nor in English with an aspectual opposition between progressive and perfective forms. This lack of effect in processing coercion was obtained in a first pair of experiments using adverbial modification (sentence-internally) within the verb phrase and in a second pair of experiments in which aspectual coercion was triggered in a subsequent discourse unit. A final stops-making-sense experiment replicates the lack of effect for English and furthermore shows that the processing of non-culminating accomplishments does not incur a processing effect even in a task calling for immediate full interpretation.

Humans can flexibly bind familiar functional roles to novel entities in their environment. For example, children who have the concept of ``goal posts'' can bind this abstract role to two hats placed on the street. In doing so, they can port over existing expectations of ``goal posts" for the duration of the game. In this paper, we seek to explore artificial agents' ability to perform flexible role binding and rebinding. To this end, we designed a Gridworld navigation game and tested a popular CNN-based agent which has had success in other tasks involving visual and spatial state spaces (e.g. Atari or Minigrid). To our surprise, we found that while this architecture was capable of overfitting to the training set, it was not able to learn flexible role binding without intervention. We ultimately show that with carefully engineered data augmentation techniques, our artificial agent is able to learn the task. This suggests that the diversity of the training dataset was a limiting factor.

While a number of disciplines have empirically investigated self-control (e.g., psychology, cognitive science, and sociology), along with philosophy, they have offered differing (although sometimes overlapping) perspectives. A process-based, mechanistic theory explaining empirical self-control data can help integrate these perspectives. A mechanistic (computational) approach through a computational cognitive architecture where simulations can be performed may unify the interpretations of empirical studies based on various (e.g., implicit-explicit) conflicts as well as utility calculation (e.g., from motivational considerations). Such a framework facilitates simulations that account for human data and capture notions of self-control capacity and control fatigue/reduction, facilitating detailed explanations.

Latin American dancing relies heavily on Cuban Motion (CM) to captivate audiences with its powerful, enchanting, and beautiful expressions. We researched how skilled dancers' CM movements influence audience perceptions. We compared CM movement and evaluations between different skill levels. From previous research, we hypothesize that expert’s CM is symmetrical and involves whole-body coordination. Result showed that the heel and other body parts were coordinated in the R-L direction and that the hip trajectory in the horizontal plane was highly circular. However, contrary to the hypothesis, the symmetrical feature of the CM’s hip trajectories of experts was divided into two groups: symmetrical / asymmetrical expert. In the evaluation results, the symmetrical group was evaluated higher for factor of Aesthetics and Dynamism, while the asymmetrical group was lower.

People often display essentialist biases, which can lead them to underestimate within-species variability. This bias is especially pronounced when traits are described as advantageous for survival. However, it is unclear whether this bias is limited to the specified trait or encompasses complex trait interactions. We used Markov Chain Monte Carlo with People (MCMCp) to analyze people’s representations of biological variability, using ladybeetles as a model species. Participants either received contextual information about the benefits of ladybeetle color for survival, or survival-irrelevant information. Overall, participants held consistent beliefs about ladybeetle features, but those with survival-relevant context produced lighter and larger ladybeetles; this difference was consistent with survey responses. However, we found no significant interaction between MCMCp variability and essentialism scores, given our context manipulation. We discuss potential explanations for these results and highlight advantages of MCMCp for assessing biological variability, particularly when studying the development of essentialist biases.

Disparate impact rules are formally neutral but indirectly discriminate against protected groups (i.e., by targeting a characteristic that is more prevalent in a given group). Because these rules are not obviously malicious, they have been widely enacted to circumvent policies against explicit discrimination. In a series of four experiments, we show that adults and children are sensitive to the moral implications of disparate impact rules. However, we also find that they are more accepting of these rules when strong justification is provided, compared to rules with no justification. Crucially, demographic differences also impact people's judgments of disparate impact rules and their creators. We find that conservatives and those from groups not directly affected by the rule tend to be more accepting of it. By studying people's reasoning about disparate impact rules, this work aims to identify the mechanisms by which these rules may evade detection. Finally, we discuss how these insights may inform the development of interventions that highlight the problematic effects of indirectly discriminatory policies.

People sometimes explicitly announce that they are being sarcastic. The announcement appears to be particularly common in text-based conversations where prosodic cues are more difficult to identify. In certain cases, the tone of a comment is sufficient to determine non-literal meaning. However, what happens in the absence of these features, or when context forces us to explicitly caveat our sarcasm? In this study, we examined Reddit comments from r/AskReddit for the features that are present in comments tagged with "/s", a convention on the platform for users to denote sarcasm. We found that a host of cues which mimic prosody, and other aspects of figures of speech, were inconsistent predictors of announced sarcasm. In contrast, when talking about sociomoral topics such as politics or race, users were more likely to tag their comments with "/s". This suggests that users are more likely to announce sarcasm in text-based conversations where misinterpretation would be socially detrimental.

We applied eye tracking and semantically rich four-term analogies with a broad range of distractor types to investigate strategies of analogical reasoning. We adopted the operationalization of strategies proposed in previous eye-tracking studies and introduced an alternative, more fine-grained method of presenting gaze dynamics across a trial in a four-term analogy (A:B::C:D). Our analysis of fixations and transitions between Areas of Interest provided support for existing research findings, suggesting that the primary and most effective strategy when solving four-term analogies is the so-called projection-first strategy, which focuses on the source-domain relation and its generalization to the target domain.

When people make decisions, these often do not stand alone but are made in a sequence of decisions. For instance, a doctor will first decide on a patient's treatment and then about the duration of the treatment, such that later decisions frequently depend on the outcome of the first decision. While there is research on how humans discover inter-relations between sequentially presented information (e.g., grammar), little is known about learning complex inter-category relations in decision sequences. Hence, we present an experiment in which we embedded an Exclusive-Or (Type-II) structure, as known in category learning, in a sequence of three categorization tasks where the outcomes of tasks 1 and 2 predicted the outcome of task 3. We hypothesized that embedded structure would facilitate learning and generalization compared to sequence without regularity. Instead, the evidence favored the Null hypothesis in both cases, contrasting with the findings in the visual categorization domain.

In the remote triad task, participants judge the relatedness between randomly chosen words in a three-alternative choice triadic judgement task. While most word pairs in these triads are weakly related, humans agree on which to choose. This is theoretically interesting as it contradicts previous claims that suggest that the notion of similarity is unconstrained in principle (e.g., Goodman, 1972}. Here, we present new evidence from GPT-4, showing that context-aware LLMs provide excellent predictions of this task. Moreover, the strength of this effect was even larger than that found for basic-level comparisons, which involve highly similar items. Together, this implies that the similarity of human representations is highly structured at every scale, even in tasks with limited context. Follow-up analysis provides insights into how LLMs are successful in this task. Further implications of the ability to compare words at every scale are discussed.

This study extends the Retrieving Effectively from Memory model, a prominent computational model of episodic memory, to the domain of categorization. Our modeling approach begins with the assumption that same-category items share common features representing defining characteristics of their category, and that they are encoded in the same category list context. We then assumed that category judgments occur based on the comparison of an item’s averaged similarity to the exemplars from each category. We use this model to explore how the learning modes of observation and classification might influence category learning and consider several strategies that may emerge during the classification mode. Model simulation results indicate that different strategies which people might adopt during classification can either confer an advantage or pose a disadvantage in category learning. These findings suggest potential avenues for future research, particularly in exploring diverse strategies employed during learning.

Gricean maxims prescribe cooperative speakers to make their utterances maximally informative so that listeners have the highest chance of understanding the utterances. At the same time, speakers are expected to save effort and not produce descriptions that are more explicit than necessary. In this work, we first ask how predictability of the described events affects the choice of anaphoric referring expressions. We show that speakers prefer phonologically overt descriptions, such as definite NPs, when they refer to agents that behave in an unexpected way. We further test how the interpretation of referring expressions changes depending on the listening conditions and prior expectations about the plausibility of an event. Our work shows that the speaker's extra effort in choosing a more phonologically overt referring expression is justified by listeners' behavior: they report having heard an utterance which is more plausible than the originally spoken utterance and which contains additional phonological material.

Having a correct mental model of a technical system facilitates interaction and problem solving. To assess such mental models of system functioning, appropriate methods are needed. We tested whether concept mapping with a focus on means-ends relations leads to valid assessments of participants’ mental models of system functioning. Automotive and utility vehicle apprentices constructed concept maps of two simple, everyday systems (bike, traffic) and one complex, domain-specific system (fuel temperature control). However, only one group of participants had previously covered the complex system in class. Aspects of participants’ concept maps regarding content (correct functional propositions) and structure (intersection over union) were assessed and related to respective reference maps. Results indicated that group differences in knowledge about the complex system were represented by concept map content, but not structure. We argue that the applied structural reference might need to be adapted to match typical requirements of the domain and task.

Peak shifting is a key method to enhance the resilience of water supply infrastructure. Due to limitations related to the costs of smart meters, experimentally investigating the impact of a time-of-use tariff on inducing peak shifting in water usage has been challenging. Having introduced smart meters across 1,890 households, this study investigated behavioral responses to reduced water prices during off-peak hours (i.e., by increasing the relative cost of peak times). This was achieved by implementing a pricing scheme offering a 60% discount from 23:00 to 05:59 hours and a 20% discount from 10:00 to 16:59 hours. The results revealed significant changes in water usage behavior near the boundary between off-peak and peak times, suggesting a shift in water usage behavior towards the off-peak period. Moreover, a time-series analysis demonstrated the peak shifting induced by off-peak price reductions. Furthermore, this study examines cognitive processing and its impact on altering water usage behaviors on an hourly basis. These findings suggest that reducing prices during off-peak periods can effectively induce peak shifting across a broad range of times.

Learning how to make decisions from experience is often studied using probabilistic outcome prediction or choice tasks, as in conditioning, reward learning, or risky gambles (e.g., response A provides reward in 75% of the cases, response B in 25% over repeated trials with feedback). One debated phenomenon in such tasks is that of negative recency, describing that learners expect the rare event after observing a streak of common events (e.g., Gamblers fallacy). Here, we show that this behavior, despite instructing participants to use a visual stimulus, also occurs in probabilistic single-cue conditioning training, where participants predicted whether digging at a specific location on a plane (visual cue) leads to finding a Vase or Nothing (events), when they received reward for correct predictions. We manipulated reward magnitude in three conditions (equal for both common and rare events vs. high for common event vs. high for rare event, between factor). We further manipulated whether the label of the rare event was framed as event (finding a Vase) or non-event (finding Nothing; between factor). The results suggest, that reward magnitude affected the emergence of negative recency, being most prevalent when correctly predicting the rare event yielded a high reward, and least prevalent when the common event yielded a high reward. Interestingly, the event label instead rather affected when the rare event was expected, such that common Vase runs were expected to end earlier than common Nothing runs. We discuss the findings from conditioning and economic perspectives, generally concerning experience-based learning.

Investigating how people evaluate more or less complex causal theories has been a focal point of research. However, previous studies have either focused on token-level causation or restricted themselves to very small sets of explanatory variables. We provide a new approach for modeling theory selection that foregrounds the balance between observed and latent structure in the mechanism being explained. We combine a Bayesian framework with program induction, allowing an unbounded and partially observable model space through sampling, and reflecting how a preference for simplicity emerges naturally in this setting. Through simulation, we identify two rational principles: (1) Simpler explanations should be favored as latent uncertainty (the number of hidden variables) increases; (2) latent structure is attributed a larger role when the observable patterns become less compressible. We conducted a behavioral experiment and found that human judgments tended to reflect these principles, indicating that people are sensitive to latent uncertainty when selecting between explanations.

Connectives like ‘because’ are referred to as ‘processing instructions’ as they facilitate processing of linguistic material directly following the connective. In an expectation-driven account of discourse processing, this can be attributed to predictions that readers make about the upcoming discourse relation, but also to predictions about upcoming discourse content. By modeling these two accounts, termed the relation prediction account and the content prediction account respectively, we show that they make different predictions about when the presence of a connective is most beneficial. In a self-paced reading study, we replicate the facilitative effect of the connective on processing, but do not find any evidence that this effect can be explained by a strong or weak version of either of the two accounts. This suggests that the role of the connective goes above and beyond informing the reader about the upcoming relation and content and possibly triggers a different processing strategy.

This work provides an alternative account for deviations in human causal reasoning from normative predictions based on Causal Bayesian Networks (CBNs). We highlight violations of the Markov condition (Screening Off) and insufficient Explaining Away. Different from other accounts, our model does not assume that people fail to honor normative predictions due to reliance on heuristics, hidden nodes and links or cognitive limitations. Instead, we propose that people are rationally uncertain about the received causal model they are asked to reason with. We fitted the model to published data from two experiments where people were asked to make probability estimates on inferences of interest within a causal model. We find that the model is able to i) reproduce deviations from normative predictions, and ii) predict changes in the magnitude of these deviations across contexts. We conclude that assuming that people, in order to be rational, will always fully believe in the information they receive about a causal model may be too strong an assumption.

Brain breaks are often used during lessons to replenish childrens’ attention, but children may respond differently to the variety of brain breaks they are offered. Therefore, two studies were conducted to identify both teachers’ current use of brain breaks (Study 1) as well as the types of brain breaks children prefer (Study 2). Study 1 consisted of a survey of K-2 teachers (N = 796) across the United States regarding the implementation and types of brain breaks commonly used in their classrooms. The three most common break types reported by teachers were physical activity breaks, videos, and dancing. Study 2 consisted of a forced choice task in which elementary- and middle-school students were asked to pick between two instantiations of six different break types: cognitive engagement breaks, mindfulness exercises, physical activity breaks, nature videos, coloring, and mind wandering. For each break type, children were asked to pick the instantiation they preferred as well as the one they believed would help them focus. Children were then asked to rank the six breaks they selected from most to least preferred and most to least beneficial for focusing. Data collection is ongoing (N = 53). Preliminary results revealed children were more likely to rank cognitive engagement breaks as their most preferred break type. Analyses within break type revealed that students preferred mazes over pattern blocks as a cognitive engagement break, color jump over calisthenics for physical activity breaks, videos of forest scenery over cows grazing for a nature video break, mandala coloring over abstract coloring as a coloring break, and viewing a poster of a starry sky over an abstract poster as a mind wandering break.

The current study examines the interaction between predisposed mood, perceptual processing, and induced mood using music. We conducted an experiment in which participants were asked to identify stimuli at global or local (G/L) perceptual levels with four different background music conditions, which had different valence and arousal ratings. We used BMIS to assess current mood and PHQ-9 and GAD-7 to assess depression and anxiety, and divided the participants into two groups: distress and no distress (encompassing both disorders). We found a main effect of background music on mood. However, the distress group showed an overall low mood. Further, we observed an overarching effect of predisposed mood, encompassing depression and anxiety, on individuals’ transient mood experience and perceptual task performance. Individuals in the non-distress group showed a larger global-precedence effect. The results are discussed in light of emotional reactivity theories and the theory of positive emotion.

This study investigates sentence processing in Standard Arabic (SA) by examining subject- and object-extracted relative clauses (SRCs and ORCs) through eye tracking. We test memory- and expectation-based theories of processing difficulty, and whether good-enough or noisy-channel processing leads to misinterpretations in ORCs. Our results find increased processing difficulty in ORCs, supporting expectation-based theories; however, this processing difficulty is not localized to the disambiguating region (relative clause verb) as predicted, but rather at the integration of the second noun phrase (relative clause NP). The findings support good-enough/noisy-channel processing theories, suggesting that readers may accept a noisy SRC interpretation of an ORC, and thus bypass integration costs at the RC NP.

We investigated the emotion-based modulation in the attentional mechanism by presenting angry and happy faces simultaneously in the extrafoveal vision. In a letter discrimination task at the fixation, pairs of task-irrelevant happy and angry faces were displayed peripherally (≥5° away from the fixation) to study the valence-facilitated attentional capture under mutual competition for processing resources. Selective orienting was assessed using eye movement measures such as the probability of first fixation on these emotional face images. Results revealed a higher probability of first fixation for happy faces than angry ones. Processing of affective stimuli in the extrafoveal indicates early occurring covert orienting of attention followed by overt attention in the foveal vision. The attentional capture advantage by happy faces occurred in the absence of differences in arousal levels. We propose that happy faces have a unique capacity to capture attention when competing with angry faces.