United States

Dance is more than physical exercise; it integrates cognitive, emotional, and motor skills. This study investigated the role of dance in modulating response inhibition in emotional and non-emotional contexts. We compared dancers (N = 15) and non-dancers (N = 21) on two response inhibition tasks: the non-emotional stop-signal task (NESST) and the emotional stop-signal task ESST (examined inhibition in the presence of emotional distractors). Inhibitory control was similar between the dancers and non-dancers in the non-emotional stop-signal task. However, a significant interaction between group and emotion was observed in the ESST, which may indicate that irrelevant emotional information modulates inhibitory control differently in both groups. More specifically, stop signals with irrelevant emotional happy faces (compared to angry and neural) facilitated inhibitory control in dancers only. These findings suggest that dance training is associated with enhanced cognitive control in emotionally salient contexts, particularly when processing positive emotional stimuli.

CogSci 2025

Happy Faces, Faster Stops: The Cognitive Benefits of Dance in Emotional Contexts

comparative analysis

cognitive neuroscience

dance

psychology

emotion

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.<br><br>

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AAAI 2026

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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### Welcome to CogSci Conference 2025!

The 47th Annual Meeting of the Cognitive Science Society was a hybrid meeting held in San Francisco. 

<div style="position:relative;padding-top:0;width:900px;height:500px;"><iframe style="position:absolute;border:none;width:100%;height:100%;left:0;top:0;" src="https://online.fliphtml5.com/ebtyf/amvr/"  seamless="seamless" scrolling="no" frameborder="0" allowtransparency="true" allowfullscreen="true" ></iframe></div>

#### About

The Cognitive Science Society brings together researchers from around the world who hold a common goal: understanding the nature of the human mind. The mission of the Society is to promote Cognitive Science as a discipline, and to foster scientific interchange among researchers in various areas of study, including Artificial Intelligence, Linguistics, Anthropology, Psychology, Neuroscience, Philosophy, and Education.

The Society is a non-profit professional organization and its activities include sponsoring an annual conference and publishing the journals Cognitive Science and TopiCS.

#### Our History 

* **Society Creation**<br>
The Society was incorporated as a 501(c)(3) non-profit professional organization in Massachusetts in 1979. The organizing committee included Roger Schank, Allan Collins, Donald Norman, and a number of other scholars from psychology, linguistics, computer science, and philosophy. 
<br><br>
* **Conference Creation**<br>
The first conference on cognitive science was held at La Jolla, California in August, 1979, and has occurred annually since then. The proceedings of each conference are published, and those from most years are available through Lawrence Erlbaum Associates, Inc. The annual proceedings of the Cognitive Science Conference represent a major source of information on new work and new ideas in the scientific study of thinking. In 1990, the Society, with help from an anonymous donor, established the David Marr Prize for the best student paper at each annual meeting.
<br><br>
* **Journal Creation**<br>
The Journal, Cognitive Science, began publication in 1976, and is now published by Wiley-Blackwell. The Executive Editor is currently Richard P. Cooper of Birkbeck, University of London, and there are 18 Associate Editors and a 30-member editorial board. It serves as the premier outlet for research reports that intersect two or more disciplines. Copyrights for articles published in the journal are held by the Society. The Governing Board of the Cognitive Science Society voted in late 2006 to found a new journal, Topics in Cognitive Science (topiCS). The Editor in Chief is Wayne Gray, Cognitive Science Department, Rensselaer Polytechnic Institute. The journal seeks to fill a niche not occupied by Cognitive Science Journal or other cognitive science journals. Membership in the Society includes a subscription to Cognitive Science and TopiCS. Copyrights for articles published in the journal are held by the Society.
<br><br>

#### Code of Conduct

By attending the CogSci 2025 Conference, you are required to adhere to the society’s **[Code of Conduct](https://drive.google.com/file/d/1ChPuihLy6jE_BWqfO7J2KKgX35JW2zsM/view?usp=sharing)**.
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The 47th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

Physically folding paper is a common activity performed by many children, but it is not mastered until middle-childhood. Paper folding ability has been the focus of studies motor development. There has been a long history in cognitive science of assessing spatial skills through mental paper folding tests. Despite the similarities between physical and mental paper folding, it is currently unknown whether there is a relation between physically and mentally folding paper. This study examined 107, 3- to 8-year-old children in both skills. Our results show that children of all ages were able to physically fold paper, but became more accurate with age. Additionally, we found that there is a significant relation between physical and mental paper folding, and that this relation was robust to different statistical controls and statistical specifications. We discuss how these results influence our understanding of the co-development of cognitive and motor skills.

Cognition in Action: The relation between physical and mental paper folding in young children

Recent work has proposed that there are symbolic beliefs. These beliefs do not serve primarily to track the facts in the world but rather to express the believer’s own identity. On this view, several disparate features of belief – from whether a belief is important to identity to whether it is sensitive to evidence – would be related to an underlying “symbolicness” dimension. We converted the features potentially related to symbolicness into items and asked people to rate their own beliefs on them. Study 1 found that beliefs which were high on one feature (importance) were rated higher on all the items, except for insensitivity to evidence. Study 2 found that ratings of any beliefs on almost all the items loaded onto a single, symbolicness factor, except again for evidence insensitivity. Study 3 asked participants to rate their own beliefs on all the items in Study 2 and several additional items designed to measure whether a belief was subjective. We recovered the symbolicness factor, but found it was largely orthogonal to the subjectivity and evidence insensitivity items. These findings suggest that most of the features we tested relate to an underlying symbolicness factor, which corresponds to whether a belief represents identity. But, surprisingly, this factor was not related to items that get at whether a belief represents facts about the world. It would seem that the degree to which a belief aims to express the believer’s identity and the degree to which a belief aims at accurately tracking facts in the world are two orthogonal dimensions, which can vary independently.

Dimensions of Identity-Representing Belief

A key challenge of theory of mind, or the ability to reason about others' mental states, is understanding the process by which others' perceptions influence their beliefs. While specific tasks---like competitive feeding---benchmark participants' ability to infer beliefs, it remains unclear how such capabilities can be learned. In this work, we introduce a modular framework that solves a computational, competitive-feeding-like game in which two agents compete. By systematically replacing modules of a successful rule-based framework with neural networks, we identify which capabilities can be learned from narrow sets of experiences, and which are critical for robust generalization. Using feature extraction techniques, we analyze how different architectures process task-relevant information. Finally, we describe and compare three novel approaches to improving generalization via first-person exposure to uncertainty: role reversal with the opponent, artificial observation masking, and synthesizing beliefs from conflicting information.

A Modular Framework for Analyzing Theory of Mind Learning in Competitive Tasks

Theory of mind (ToM) refers to the ability to reason about the behaviour of others and oneself by attributing internal mental states, such as knowledge, desires and intentions. ToM can be applied recursively - for example, “Amy thinks that Bernard knows that it is raining” is said to be a second-order ToM statement from the reader's perspective. Past research suggests that there is a limit to the number of times humans can apply ToM recursively - for example, they tend to use up to second-order ToM reasoning in strategic games. In the present study, we propose and conduct a novel human experimental design, in which different orders of ToM reasoning in the logic puzzle “Cheryl’s Birthday” can be distinguished. Results show that higher-order ToM reasoning is associated with longer times to solve the puzzle(s) and a higher rate of mistakes.

How Well Do People Perform on Novel Logic Puzzles Requiring Higher-Order Theory of Mind?

How do labels affect people’s interpretation of symbols? The preferred label for “=” is debated amongst mathematics educators. In Study 1, U.S. adults defined the “=” symbol and rated the “smartness” of definitions when the label “equal” or “equals” sign was used. As predicted, the “equal” label activated a relational understanding of the equal(s) sign more than the “equals” label. Study 2 utilized a 2x2 factorial design and incorporated an activation phase before the definition tasks. Participants were randomly assigned to operational activation, where they solved problems with operations to the left of the equal(s) sign (8 + 5 = __), or to relational activation, where they solved problems with operations to the right of the equal(s) sign (8 = 5 + ___). Results suggest the terms “equal” and “equals” are not equivalent. The term “equal” may enhance students’ conceptual understanding of equations.

Equal > Equals: Labels Affect How Math Symbols Are Interpreted

This study hypothesized an underlying similarity between mental simulation, the automatic activation of multimodal features during language processing, and mental imagery, the voluntary effortful process of deliberate imagination. The mechanisms involved in both processes are underspecified and may be shared. To test this, participants performed a sentence-picture verification task with two blocks. In both blocks, participants read a sentence that implied the visual features of an item, then saw a (mis)matched picture of the item and were asked to verify if the item in the sentence appeared in the image. In one block, participants performed the same task but were instructed to form a mental image corresponding to the sentence. Predicted common patterns of results between these conditions will indicate similar processing with differences in time courses suggesting similar mechanisms. Distinct patterns will suggest these processes are entirely different. Results and implications for language processing will be discussed.

Investigating Mental Simulation and Mental Imagery Using Sentence-Picture Verification

Algorithms generate input-output mappings through operations on representations. In cognitive science, we use algorithms to explain cognition. For example, we use tree-search algorithms to explain planning, reinforcement learning algorithms to explain exploration, and Bayesian algorithms to explain categorization. There are often many cognitive science algorithms consistent with a subject's performance on a task. How are we supposed to choose? It is natural to think of algorithms as causal models of brain processes. Thus, a natural method for choosing an algorithm is to look for parts in the brain corresponding to the steps of the algorithm. However, we haven't found many cognitive science algorithms using this method. This has led some to view cognitive science algorithms as merely normative, indicating the ideal input-output mapping without attributing any particular operation to the brain. It has led others to view cognitive science algorithms as merely useful fictions; useful insofar as they allow us to predict behavior, but fictional insofar as they inaccurately describe the causes of that behavior. They recommend explaining cognitive processes using other frameworks, such as dynamical systems theory. As an alternative, we suggest identifying a neural system's algorithm by assessing how quickly it learns alternative input-output mappings, that is, its transfer learning profile. The basic idea is that, depending on which algorithm is being used, different input-output mappings will be easier to learn, allowing us to recover its original algorithm from its transfer learning profile. We use artificial neural networks to demonstrate that this proposal productively applies to multiple networks and tasks. We conclude that transfer learning is a promising approach for integrating algorithms with neural networks and thus for integrating cognitive science with systems neuroscience and machine learning.

Using transfer learning to identify a neural system's algorithm

Do Large Language Models (LLMs) possess a Theory of Mind (ToM)? Research into this question has found that LLMs succeed on a range of benchmark tasks. However, these evaluations do not test for the actual representations posited by ToM: namely, a causal model of mental states and behavior. Here, we use a cognitively-grounded definition of ToM to develop and test a new evaluation framework. Specifically, our approach probes whether LLMs have a coherent, abstract, and consistent model of how mental states cause behavior—regardless of whether that model matches a human-like ToM. We test our evaluation against GPT-4o and find that even though it succeeds in approximating human judgments in a simple ToM paradigm, GPT-4o fails at a logically-equivalent task and exhibits low consistency between its action predictions and corresponding mental state inferences. As such, these findings suggest that GPT-4o’s social proficiency is not the result of a ToM.

GPT-4o Lacks Core Features of Theory of Mind

Recent studies revealed that the brain’s system for representing physical space is also recruited to represent relations that are not inherently spatial. We investigated the relationship between the ability to form these abstract spatial representations and other spatial abilities. In four experiments, participants created spatial representations of a series of premises relating objects on two dimensions (e.g., A is faster than B, B is louder than C) and answered inference questions based on these relations. The type of information, i.e., spatial information (A is above B) versus abstract, non-spatial information (A is smarter than B), did not affect task performance. Individual differences in how ‘grid-like’ the created representations were, as well as reasoning consistency, correlated with some spatial abilities, including path integration. These findings are consistent with the view that a common spatial mental representation system underlies our ability to form mental representations of physical and abstract spaces.

A Shared Spatial Mental Representation System for Navigation and Reasoning

What is the value of probability? Keren and Teigen (2001) demonstrated that people prefer extreme probability (“10%” or “90%”) to medium probability (“50%”) and high probability (“90%”) to low probability (“10%”), and proposed that people’s perception of the value of probability phrases follows the principle of searching for definitive predictions. The present study proposes that this principle aligns with information theory and predicts that people’s judgments of informativeness will vary according to their prior beliefs. Additionally, this study also proposes that surprisingness judgment also obey the prediction from the information theory. To examine these propositions, this study required participants to estimate the valuableness and surprisingness for probability phrases expressing the winning probabilities of gambles. To manipulate prior beliefs about winning a gamble, the study created four conditions where the winning amounts varied. Results indicated that participants' estimations of the informativeness of the probability phrases changed in accordance with predictions from the information theoretic analysis.

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Cognition in Action: The relation between physical and mental paper folding in young children

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Detecting Emotional Dynamic Trajectories: An Evaluation Framework for Emotional Support in Language Models

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