United States

What factors of our learning experiences enable us to best acquire complex skills? Recent ideas from artificial intelligence point to two such factors: (1) a balance of real experience with simulated experience acquired during planning itself, and (2) appropriate diversity in training examples. To test whether these factors influence the development of human expertise, we analyzed data from 1,873 chess players on the online platform Lichess, each of whom played hundreds to thousands of games over months to years. We found that both the time spent planning before moves and the diversity of opening positions encountered predict skill improvement over time. These findings suggest that principles shaping the development of expertise in artificial intelligence systems may also apply to human learning.

CogSci 2025

Learning in online chess increases with more time spent thinking and diversity of experience

big data

learning

psychology

artificial intelligence

### Welcome to CogSci Conference 2025!

The 47th Annual Meeting of the Cognitive Science Society was a hybrid meeting held in San Francisco. 

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#### 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. 
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* **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.
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* **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.
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#### 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.

poster

Efficiency informs perceptions and expectations of people’s actions from early in life. We examined whether young children aged three and four consider efficiency when evaluating how well agents completed goals. In three experiments, we showed children scenarios where two characters each walked to target objects, and then asked children which character did a better job. In the first experiment, children appeared to consider efficiency. They more often chose a character who took a direct path over one who took an indirect path, but only when the latter character could have taken a shorter path. Two follow-up experiments, though, failed to replicate this pattern and the success in the initial experiment could be explained in terms of the features of the paths (not strictly related to efficiency) used in that experiment. The findings suggest, then, that three- and four-year-olds do not yet use efficiency to normatively evaluate actions. We consider two alternative explanations for this.

Evaluating actions: Do young children prefer actions completed efficiently over those completed inefficiently?

How can an AI teammate implicitly coordinate with a human? We address this question by integrating Instance-Based Learning (IBL), a cognitive theory of learning and decision making, with the level-k Theory of Mind framework. We hypothesize that coordination emerges when partners adopt complementary k-levels and when the higher k-level agent has an accurate model of their partner's cognitive processes. To test this hypothesis, we introduce a simultaneous-choice, multi-attribute task, where outcomes depend on interactions between choice features and agent decisions. Simulations of pairs of IBL-based agents at different k-levels support the hypothesis that complementary k-levels enhance coordination. However, empirical results from an experiment reveal no advantage of [human, IBL-L2] pairs over [human, IBL-L1] pairs, even when participants are restricted to operate as L1 agents. Post-hoc simulations show that model fitting recovers some advantage for [human, IBL-L2] teams by enabling the IBL-L2 agent to more accurately predict their human partner's actions.

Facilitating Human-AI Coordination through Computational Theory of Mind

This study was the first to examine whether the volume of one’s voice serves as an embodied cue for assessing information credibility. Eighty U.S. undergraduate students were randomly assigned to one of three conditions: loud, soft, or control. They read aloud in their assigned loudness condition while rating the truthfulness of trivia statements, followed by silently rating additional statements. The results revealed no significant effect of voice loudness on truthfulness ratings. When examining confidence levels reflected in the ratings, an interaction effect between reading status and loudness condition emerged. Participants who controlled their volume (either loud or soft) rated statements with higher confidence compared to when rating statements silently. These findings suggest that speakers do not associate their own voice loudness with the truthfulness of information.

Amplifying Truth? Vocal Volume and Speakers’ Self-Perceived Truthfulness

Prediction is essential in language processing and is often studied as an isolated task. However, in real life, listening often occurs alongside other cognitively demanding activities. We investigated how cognitive demand affects prediction in older adults and whether hearing impairment impacts this process. Eighty-four older adults with hearing impairment (HI) or normal hearing (NH), matched in working memory scores, participated in a visual world paradigm study with semantically predictable and unpredictable sentences. The experiment featured two conditions: listening without a memory task and listening while concurrently retaining a three-item visuo-spatial memory load to simulate competing cognitive demands. Divergence point analyses showed that memory load delayed prediction timing in NH participants, and an interaction revealed differences occurring with hearing loss. As prediction timing was impacted both by load and hearing loss, our work suggests that prediction requires executive resources.

One Thing at a Time: Investigating the Impact of Increased Cognitive Demand on Semantic Prediction in Older Adults with and without Hearing Impairment

Prediction is central in human language processing, as the brain continuously predicts upcoming words using prior knowledge and context. Surprisal theory quantifies predictability using word surprisal. While previous studies link neural activity to surprisal during passive listening or reading, we investigate how surprisal is tracked in dynamic face-to-face conversations. Two key challenges arise: estimating surprisal as well as identifying predictions in EEG data in natural conversation. We address the first challenge by adapting a pre-trained large language model to a dataset of spontaneous conversation capturing features like hesitations and repetitions. We then relate the surprisal estimated by the adapted model to EEG data using temporal response functions. Our experimental results show neural tracking of surprisal at different time lags after word onset, supporting the surprisal theory in face-to-face conversation. To the best of our knowledge, we are the first to address the application of surprisal theory in such interactive settings.

Exploring Neural Correlates of Predictability in Natural Face-to-Face Conversation

Ownership affords different rights and privileges to owners than non-owners. We investigated whether children view transgressions that impose a large or permanent cost to the owner as less acceptable than (1) actions that impose small or temporary costs, and (2) actions that do not impose any costs to owners. Children aged three to eight years (N=72) and adults (N=72) were shown vignettes in which an agent interacts with someone else’s property without permission. Both adults and children judged actions that imposed severe costs to owners as less acceptable than minor transgressions that imposed temporary costs and actions that did not involve physical contact. These findings reveal that children and adults consider the costs imposed on owners when judging the acceptability of people’s interactions with others’ property. Critically, these findings also provide preliminary evidence that children’s concept of ownership may be embedded into their broader social cognitive framework of intuitive psychology.

Children consider costs to owners when reasoning about ownership transgressions

The sequencing of training trials can significantly influence learning outcomes in humans and neural networks. However, studies comparing the effects of training curricula between the two have typically focused on the acquisition of multiple tasks. Here, we investigate curriculum learning in a single perceptual decision-making task, examining whether the behavior of a parsimonious network trained on different curricula would be replicated in human participants. Our results show that progressively increasing task difficulty during training facilitates learning compared to training at a fixed level of difficulty or at random. Furthermore, a sequences designed to hamper learning in a parsimonious neural network network impair learning in humans. As such, our findings indicate strong qualitative similarities between neural networks and humans in curriculum learning for perceptual decision-making, suggesting the former can serve as a viable computational model of the latter.

Curriculum learning in humans and neural networks

As conversational AI systems like ChatGPT become increasingly adept at socially engaging interactions, users are more likely to form emotional attachments to these technologies. This study explores the relationship between autistic traits and emotional attachment to ChatGPT, emphasizing the mediating roles of Theory of Mind and social anxiety. A sample of 286 participants completed the study. The structural equation modeling analysis revealed that Theory of Mind partially mediated the relationship between autistic traits and emotional attachment to the chatbot, while social anxiety did not show a significant mediating effect. These findings underscore the critical role of individual differences in shaping attachment to AI, suggesting opportunities for personalized designs and raising questions about the psychological implications of such bonds.

Theory of Mind and Social Anxiety in Emotional Attachment to AI Chatbots in Individuals with Autistic Traits

When talking about the world in front of us, humans are remarkably efficient communicators. Our referential expressions help listeners efficiently find what we’re talking about by strategically adding color or material words. But most conversations involve things not physically in front of us. In these cases, do we also use language to efficiently help a listener retrieve an item from memory? Across two experiments, we asked participants to describe images to help a listener recall them. In Experiment 1 (n = 600), participants spontaneously incorporated expectations about memorability by providing more description for images they expected to be less memorable. People’s descriptions aligned more with subjective memorability estimates rather than objective, empirically-derived metrics. In Experiment 2 (n = 300), we replicated this pattern even when participants had no access to their listener’s prior experience. Together, this work provides evidence that speakers spontaneously guide listeners’ mental processes to effectively facilitate memory recall.

Speakers strategically adjust their descriptions based on perceived memorability

We demonstrate that inducing the construction of a coherent, biodiversity-conserving moral narrative about one’s place in the world can have a lasting impact on pro-biodiversity behaviors. Across two studies (n=447 and n= 509), one-time under-40-minute interventions leveraging two basic cognitive constraints — coherence and causal invariance — led to increased intentions to take biodiversity-conserving actions (Phase 1) and subsequent self-reports of engagement in these actions assessed a year later (Study 2 Phase 2, n=344). This sustained impact contrasts sharply with the typically short-lived (< 2 weeks) effects of pro-environmental messaging. Participants completed exercises implementing the constraints to foster an expanded sense of self. Results show that the expanded self (e.g., agreement with “I imagine myself to be part of a larger cyclical process of living”) mediated reports of engagement in biodiversity-supporting actions (e.g., donating to biodiversity organizations). These effects held across political ideologies, suggesting the approach’s broader applicability to other persuasion topics.

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Evaluating actions: Do young children prefer actions completed efficiently over those completed inefficiently?

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