United States

How people reason about the mechanics of the physical world is an important question for several different cognitive sciences. Education, cognitive psychology, and developmental psychology have each conducted large numbers of studies over the last several decades, largely in isolation from one another (especially in the last quarter century). The results have suggested that cognitive mechanics may be subserved by a number of mechanisms that are differentially involved in different tasks. Here, we report converging results from factor analysis of a large compendium of mechanics questions.

CogSci 2025

How many factors underlie cognitive mechanics?

psychophysics

concepts and categories

problem solving

psychology

education

poster

### 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.

Recent studies have used complex regularization procedures for whole-cortex neural decoding, with results suggesting that neural representations may be much more widely distributed and variable than previously suspected. Such work typically requires extensive parallel compute infrastructure, bespoke regularizers, and complicated workflows. We considered whether comparable results can be obtained from the iterated LASSO, a simple algorithm using standard L1 regularization to conduct an iterative voxel-selection scheme. We applied the procedure to decode stimulus class (face, place, or object) from whole-brain 3T-fMRI data individually in each of 8 participants, achieving a remarkable 98% classification accuracy on held-out images. The algorithm found signals in about 8% of voxels across cortex, many appearing outside the traditional occipito-temporal regions thought to support visual object representation. Moreover, the model weights revealed wide variation across participants in how and where stimulus information is neurally encoded—results consistent with prior work that deployed much more complex workflows.

Iterated LASSO reveals highly distributed and variable representations of faces, places, and objects.

Social robots increasingly mimic human traits. When a human-like robot (an android) seems to engage with music, a universal human behavior, how do people judge its social attributes? Musical engagement can enhance the android’s perceived human-likeness, which may increase affinity but also trigger discomfort (the uncanny valley effect). In Experiment 1 (N =192), an android showed apparent musicality through movement-music synchronization (vs uncoordinated; or without music). In Experiment 2 (N=160), we manipulated musicality by adding (vs. not adding) headphones during movement – implying the presence of music participants could not hear. In both, participants rated the android with higher apparent musicality as warmer, more competent, and eliciting less discomfort (all p’s<0.01, measured by the RoSAS scale; Carpinella et al., 2017). These findings show that human perception of androids is impacted by cognitive schemas about and attribution of musicality, which can be inferred even without hearing music directly.

Perceived musicality in an android increases positive social attributions

Humans exhibit a remarkable ability to acquire, generalize, and compose strategies for object manipulation, yet the underlying mechanisms of this flexible strategy learning and reuse remain poorly understood. In this paper, we extend the Virtual Tool Game (Allen, Smith, & Tenenbaum, 2020), where humans solve complex physical puzzles in just a few attempts. Through two behavioral experiments, we show that humans acquire abstract strategy representations and can flexibly chain multiple strategies for novel tasks in both forward and backward directions. To formalize this process computationally, we introduce a probabilistic framework that models physical events, actions, and high-level manipulation strategies. Our approach represents strategies as amortized sequences of physical events and integrates them into a bi-level search mechanism that combines simulation and planning. These findings advance our understanding of human physical reasoning and contribute to the development of AI systems with human-like physical problem-solving capabilities.

Flexible Physical Problem Solving with Strategy Acquisition and Composition

People often infer that those who actively switch away from a default option have stronger preferences than those who passively accept it (termed asymmetric preference inferences). We test whether this classic effect reflects rational causal inference about how defaults provide alternative explanations for others’ mental states. This account predicts that asymmetric inferences should occur only when accepting the default provides an alternative explanation for choice (e.g., following a recommendation), and that asymmetry should diminish or disappear when it does not (e.g., a default licensing indulgence in a preferable option). In a pre-registered study (N=120), participants showed this effect: They made asymmetric inferences only when the default provided an alternative explanation for preference, and made symmetrical inferences when it did not. These findings suggest this classic effect reflects rational causal inference, providing a framework for predicting when people make asymmetric preference inferences from defaults.

When Default Options Explain Away Preferences:  A Causal Reasoning Account of Mental State Reasoning from Default Options

Creativity is essential for success in both social and professional settings, and science fiction has emerged as a potential tool for enhancing this ability. The present meta-analysis aimed to examine the effect of science fiction on creativity. A systematic literature search was conducted across diverse electronic platforms and databases, resulting in a final sample of five studies with robust methodological quality. The meta-analytic estimate of the overall effect size indicated a medium effect that was not statistically significant. However, the substantial heterogeneity observed among studies suggests that the influence of science fiction on creativity may vary depending on the context or study characteristics. These findings indicate that while science fiction shows promise for enhancing creativity, further research is needed to clarify the conditions and mechanisms that optimize its impact.

Effects of Science Fiction on Creativity: A Meta-Analysis

How do we communicate with others to achieve our goals? We use our prior experience or advice from others, or construct a candidate utterance by predicting how it will be received. However, our experiences are limited and biased, and reasoning about potential outcomes can be difficult and cognitively challenging. In this paper, we explore how we can leverage Large Language Model (LLM) simulations to help us communicate better. Based on ideas from cognitive science such as the Rational Speech Act model, we propose the Explore-Generate-Simulate (EGS) framework, which takes as input any scenario where an individual is communicating to an audience with a goal they want to achieve. EGS (1) explores the solution space by producing a diverse set of advice relevant to the scenario, (2) generates communication candidates conditioned on subsets of the advice, and (3) simulates the reactions from various audiences to determine both the best candidate and advice to use. We evaluate this framework on eight scenarios spanning a range of interpersonal communication settings. For each scenario, we collect a dataset of human evaluations across candidates and baselines, and show that our framework’s chosen candidate is significantly preferred over popular generation mechanisms for LLMs. Finally, we demonstrate the generality of our framework by applying it to real-world scenarios described by users on web forums.

Improving Interpersonal Communication by Simulating Audiences with Large Language Models

According to reconsolidation hypothesis, consolidated memories can be reactivated and become labile for a period of time, followed by a re-stabilization process that allows them to be strengthened, weakened, or updated. Novel research has begun to investigate if music-based interventions (MBI) could modulate memory reconsolidation. The present study aimed to address this issue through two experiments. First, Experiment 1 aimed to test a paradigm for reactivating episodic emotional memories by comparing different reactivation tasks. It was found that a reactivation task with incomplete reminders was able to reactivate emotional memories and to strengthen them by reconsolidation. Experiment 2 assessed if a MBI after the reactivation of such memories was able to modulate its reconsolidation. We found that listening to arousing music after memory reactivation interfered the reconsolidation process, reducing retrieval. Possible underlying mechanisms are discussed to continue the path toward application of the findings in clinical and educational settings.

Memory reconsolidation: Exploring how to reactivate and modulate episodic memories

Early numeracy is foundational for children’s future mathematics achievement (Duncan et al., 2007), and research shows that math-related language plays a role in its development (Purpura et al., 2017). However, the specific ways in which different types of math-related language contribute to numeracy remain unclear. This study examined if preschoolers’ spontaneous use of math language during picture description predicts numeracy skills. A sample of 321 preschoolers (ages 3-5) completed picture description, print awareness, and numeracy tasks, including a measure of cardinality. Children’s picture descriptions were coded for cardinal labels (e.g., “three birds”), spatial prepositions (e.g., “in”, “on”), and other math-related language (e.g., “more”, “some”). Results showed general numeracy and cardinality were significantly predicted by math language use (p=.002) and print awareness (p<.001). Spatial prepositions and cardinal labels were not consistent predictors. These findings provide further evidence for the link between numeracy and language.

Preschoolers’ Use of Quantitative Math Language in Picture Descriptions Predicts Early Numeracy Skills

Previous research has effectively employed the fast-paced action puzzle video-game Tetris for understanding the acquisition of extreme expertise in complex, dynamic environments. A common approach when contrasting expert to novice performance has been the dissection of their interactions with the environment into disjoint sub-tasks – such as reaction time (RT), measured by the input latency to new events on screen. The crucial, underlying assumption to this paradigm is task consistency at all levels of expertise. Using data collected from participants of the Tetris World Championship 2019 and from novices in our lab, we show that this assumption does not hold. While for novices the RT task type remains the same across all conditions, for experts - depending on environmental parameters - RT task type undergoes a shift and under specific conditions does not represent an RT task anymore. Thus, expert vs. novice sub-task comparison may not be a valid paradigm.

Apples and Oranges - Evaluating Reaction Time Measures as a Paradigm to Contrast Expert vs. Novice Performance in Complex, Dynamic Task Environments

Language encodes societal beliefs about social groups through word patterns. While computational methods like word embeddings enable quantitative analysis of these patterns, studies have primarily examined gradual shifts in Western contexts. We present the first large-scale computational analysis of Chinese state-controlled media (1950-2019) to examine how revolutionary social transformations are reflected in official linguistic representations of social groups. Using diachronic word embeddings at multiple temporal resolutions, we find that Chinese representations differ significantly from Western counterparts, particularly regarding economic status, ethnicity, and gender. These representations show distinct evolutionary dynamics: while stereotypes of ethnicity, age, and body type remain remarkably stable across political upheavals, representations of gender and economic classes undergo dramatic shifts tracking historical transformations. This work advances our understanding of how officially sanctioned discourse encodes social structure through language while highlighting the importance of non-Western perspectives in computational social science.

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Iterated LASSO reveals highly distributed and variable representations of faces, places, and objects.

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