United States

On the Reddit forum ChangeMyView, users post beliefs and invite others to challenge them. In this study, we aimed to determine whether a GPT-4-based analytical pipeline could accurately recover belief structures from a subset of posts on predefined topics, identified through covariation statistics from a lab sample. This approach would enable us, in a second stage, to extract novel insights from naturalistic data on belief structures that have not been directly elicited in lab studies, providing a bottom-up examination at scale. Our findings suggest that the pipeline captures meaningful belief patterns, aligning moderately with human responses in structured surveys. Analyzing 3082 posts from 346 users revealed distinct ideological clusters and belief patterns that mirrored well-established political divisions. This method offers a scalable way to study belief networks, shedding light on their role in shaping societal attitudes.

CogSci 2025

Recovering belief structures using a language model on a naturalistic dataset of attitude change

cognitive architectures

big data

learning

social media analysis

psychology

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

The AAMAS conference series was initiated in 2002 in Bologna, Italy as a joint event comprising the 6th International Conference on Autonomous Agents (AA), the 5th International Conference on Multiagent Systems (ICMAS), and the 9th International Workshop on Agent Theories, Architectures, and Languages (ATAL).

Subsequent AAMAS conferences have been held in Melbourne, Australia (July 2003), New York City, NY, USA (July 2004), Utrecht, The Netherlands (July 2005), Hakodate, Japan (May 2006), Honolulu, Hawaii, USA (May 2007), Estoril, Portugal (May 2008), Budapest, Hungary (May 2009), Toronto, Canada (May 2010), Taipei, Taiwan (May 2011), Valencia, Spain (June 2012), Minnesota, USA (May 2013), Paris, France (May 2014), Istanbul, Turkey (May 2015), Singapore (May 2016), São Paulo (2017) and Stockholm, Sweden (2018), Montreal (May 2019), Auckland (May 2020, Virtual), London (May 2021, Virtual).
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AAMAS 2022

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

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.

Object knowledge comprises a virtually limitless feature space. When cued to generate attributes for an exemplar (dog), any feature is possible (has molecules). Yet, some features are definitively favored (tail). We hypothesize a sensorimotor bias in feature generation wherein perceptually salient features upon first pass are more cognitively accessible than abstract, verbally mediated knowledge. We examined the role of sensorimotor dominance in semantic feature generation by yoking concreteness values to cues (N=4436) and features (N=69,284) within the Buchanan et al. (2019) norms. We predict that cues regardless of their concreteness evoke relatively more concrete features (e.g., dogs evoke tails, justice evokes lawyers). The data moderately supported this hypothesis. Feature concreteness increased linearly with cue concreteness (R=.83) but the y-intercept (2.78) indicates that overall, features were more concrete than their cues. We discuss alternate factors (e.g., frequency, familiarity) that may moderate the likelihood that people retrieve tail when cued with dog.

  Investigating the Role of Sensorimotor Dominance in Semantic Feature Listings:  When I Say Dog, Will You Say Tail?

Joint attention (JA) is a crucial form of early communication strongly associated with word learning and vocabulary development. However, limited research has examined JA in children older than 36 months, despite its potential role in classroom learning. This study employed head-mounted eye-tracking in a social interactive context to investigate the relationship between JA and word recall. During the study, JA was measured as parent-child pairs engaged in play with unfamiliar objects, with parents actively naming the objects. Subsequently, children were tested on their ability to recall the names of these objects. The results suggest that JA significantly predicted the recall of unfamiliar words but was not related to vocabulary development. These results contribute to the growing body of research on JA by providing insights into the potential mechanisms supporting word learning and vocabulary development.

Examining the Relationship Between Joint Attention and Word Recall in Preschool-Aged Children

Regret is a common emotion that might either catalyze or impair decision-making. What determines whether regret will be helpful or harmful in a given situation? We test the hypothesis that regret is more likely to hinder decision-making during the early stages of learning, when information is limited, but help during later stages of learning, when the learner has a better understanding of the environment. We introduce a Bayesian model of
learning from regret, in which the “counterfactual weight” parameter – reflecting how strongly individuals update their beliefs about foregone outcomes – predicts both learning
outcomes and the intensity of subjective regret. We find that probing regret early in the learning phase leads to worse performance than probing regret later or not at all. This work has important implications for both cognitive and affective science, shedding light on the appraisal mechanisms by which regret influences decision-making.

Learning From ‘What Might Have Been': A Bayesian Model of Learning from Regret

Recent empirical results have sparked a debate about whether or not Large Language Models (LLMs) are capable of Theory of Mind (ToM). While some have found LLMs to be successful on ToM evaluations such as the False Belief task, others have shown that their performance is not robust against trivial alterations to stimuli.
In this paper, we introduce SCALPEL---a technique to  incrementally modify stimuli to test different specific hypotheses about why LLMs fail---and apply this method to the `transparent-access' modification of the unexpected contents task. 
Our results suggest that LLMs often do poorly because they fail to make essential common-sense inferences, such as that seeing a transparent container implies recognizing its contents. We conclude that while modern LLMs go beyond mere pattern matching, they still fall short of robust human-like ToM.
We argue that SCALPEL can help cognitive scientists examine LLMs' capabilities in finer detail and provide insight into alternative mechanisms by which tasks that are used to assess human cognition might be completed.

Dissecting the Ullman Variations with a SCALPEL: Why do LLMs fail at Trivial Alterations to the False Belief Task?

While extrinsic and intrinsic motivation have been well studied, the effects of altruistic motivation on motor performance remain largely unexplored. This study investigates the influence of altruistic motivation on brain–muscle coherence and its effect on improving time to task failure. Thirty-one participants performed two high-intensity isometric grip tasks to failure. The first trial was conducted without any extra motivational incentive, while the second trial was performed under one of three conditions: altruistic, extrinsic, or control. Electroencephalogram (EEG) and electromyogram (EMG) signals were recorded during both trials. Our results demonstrate that only the altruistic group improved their performance from the first to the second trial (68%; p = 0.004). The altruistic group exhibited increased EEG–EMG coherence in the alpha and beta bands and reduced delta coherence. These findings suggest that prosocial motivation restructures neural oscillatory activity, optimizing force control and endurance.

How Altruistic Motivation Synchronizes Brain-Muscle Coherence to Enhance Motor Performance

Learning perception-action contingencies from the environment depends on attention, to efficiently control cognition and selectively process sensory information. Shifting attention between sensory modalities incurs a cost in humans, other primates, and rodents, resulting in slower learning in the new modality. Previous set-shifting work in rats showed that increased difficulty of perceptual discrimination in the preceding modality increases the following shift cost. We studied this in humans by manipulating perceptual attention in one sensory modality, titrating task demand by staircase design, to test the effect on perceptual contingency learning in another modality. To accommodate the complexity of human learning, we introduce a Bayesian method to decompose and estimate learning characteristics from performance data. This method identifies the completion of rule acquisition and consolidation, accounting for individual variation in learning. Results show the expected modality shift cost, and offer new evidence in humans that shift cost is exacerbated by prior demands on attention.

Higher perceptual attention cost slows contingency learning after a modality shift

The Rational Speech Act (RSA) model has been employed to explain word learning and inferences based on the costs of forms. Here, we focus on a hitherto untested and counterintuitive cost-based effect predicted by RSA: In learning a lexicon with two forms and meanings, learners should prefer an ambiguous costly form over an ambiguous cheap form. We demonstrate this prediction in an RSA model. We then measure reaction times and lexicon ratings in a novel word learning task to test whether a lexicon with an ambiguous costly form is less surprising than one with an ambiguous cheap form. We found no clear evidence for this effect in either measure. We discuss alternative explanations for documented cost-based effects, and the possibility that cost-based inferences may not occur during learning.

Testing counterintuitive predictions about cost-based inferences in learning from the Rational Speech Act model

Two experiments investigated how task demands influence sentence processing mechanisms, as reflected in neural responses at the disambiguating word. Participants read garden-path sentences with late-closure ambiguity (e.g., While the man hunted the deer ran into the woods) and answered comprehension questions while their brainwaves were recorded. Experiment 1 used standard questions (e.g., Did the man hunt the deer?), while Experiment 2 used "explicit" questions (e.g., Did the sentence explicitly say that the man hunted the deer?) to reduce inference-based responses. Results showed a typical P600 effect for ambiguous sentences in Experiment 1, indicating syntactic reanalysis. In Experiment 2, responses showed an N400 followed by Sustained Frontal Negativity, suggesting a shift toward plausibility evaluation and increased processing load. The explicit questions appear to alter underlying sentence processing mechanisms, prompting more effortful resolution of interpretive conflict beyond initial syntactic reanalysis.

What You Ask Affects What You Get: Task-Dependent ERPs in the Processing of Syntactically Ambiguous Sentences

Collaborative multi-brain motor imagery is an innovative brain-computer interface (BCI) paradigm that records and decodes brain signals from multiple individuals to collectively complete motor imagery tasks. However, existing decoding methods often rely on techniques such as averaging, concatenating, or cross-brain coupling of data or features, and lack coordination between single-brain and multi-brain decision-making in this context. To address this, we propose a dual-branch electroencephalogram (EEG) decoding method that jointly learns private and shared domain information. The method employs a Siamese network for private common spatial pattern (CSP) learning and a feature-sharing network for shared features, then combines the outputs for classification. Experiments with EEG data demonstrated a 10.27% improvement over the single-brain scenario and a 9% improvement over state-of-the-art methods. This approach effectively integrates private and shared domain learning, advancing collaborative BCI technology.

Dual-Branch EEG Decoding Method for Collaborative Multi-Brain Motor Imagery

Number words follow a set of principles that some have proposed to be universal. According to Hurford (1975), these principles include phrase-structure rules along with additional arithmetic constraints. However, languages vary in how transparently their number systems reflect these principles; for example, the English teens (e.g., twelve) and Turkish decades (e.g., yirmi) deviate from them. How do such irregularities influence speakers’ understanding of number word structure? To find out, we examined grammaticality judgments for novel number words among speakers of Chinese (with a transparent number system) and English and Turkish (with less transparent systems). While all groups demonstrated a better understanding of the phrase-structure rules than the arithmetic constraints, Chinese speakers showed more sensitivity to both the rules and the constraints than English or Turkish speakers. The results pinpoint ways in which the structure of number words is available to all speakers and ways in which it varies.

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Investigating the Role of Sensorimotor Dominance in Semantic Feature Listings: When I Say Dog, Will You Say Tail?

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