United States

CogSci 2025

Task Resolution Time Estimation through Cognitive Load: An EEG Study of Chess Players

electroencephalography (eeg)

skill acquisition and learning

cognitive neuroscience

problem solving

psychology

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.

poster

### 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.
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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.
<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.

Participants (N = 74) learned a predefined route from a digital map, during which gaze patterns were recorded. Subsequently, participants navigated the route from memory in a virtual environment, and navigation errors were measured. This task was performed twice, with half of the participants receiving specific map reading instructions between the pretest and posttest. Based on sequences of distances between fixations and the indicated destination of the route, gaze patterns were categorized as systematic (following the route with their gaze) or unsystematic. In the pretest, navigation performance of systematic readers was significantly better, and navigation performance was positively associated with repetitions of systematic reading following the route. Moreover, more systematic reading was found in the posttest, and navigation errors decreased from pretest to posttest. No effect was found for instruction. Results show that effective map reading can be predicted by gaze patterns.

Gaze Patterns during Map Reading as Predictors of Route Learning

Examining motion event descriptions allows us to evaluate what information individuals deem to be salient when communicating about events. Systematic variations between languages regarding how they encode details about motion events have given rise to theories of typological classifications of languages. In this study we evaluate the classification of Bulgarian, a South Slavic language, along Talmy’s typology of verb-framed and satellite-framed languages, and relate this to Slobin’s concept of manner salience. Based on behavioural evidence from an experiment using free-form descriptions in Bulgarian and English, we show that path encoding in Bulgarian is distinct from that of English (a satellite-framed language), but the use of complex path expressions in Bulgarian means it cannot be easily captured by Talmy’s two-way classification system. We show that Bulgarian displays a lower rate of manner salience than English and patterns similarly to verb-framed languages in its treatment of the default manner of motion (walking).

Path encoding and manner salience in motion event descriptions: the case of Bulgarian and English

Word meaning extension refers to the process by which a single word form develops multiple related meanings. Young children exhibit the capacity to extend word meaning, and previous research shows that such word overextension relies on multimodal semantic knowledge. We explore the evolutionary trace of word meaning extension by asking whether nonhuman animals might have this shared capacity. We compare meaning extension in children with the attested cases of overextension collected from the YouTube channel of a Grey Parrot, Apollo, who has acquired some English words. Our results show that parrot overextension can be predicted by a multimodal child overextension model better than baselines, which demonstrates that Grey Parrot may be using semantic knowledge similar to children for choosing words to express new referents. Our finding suggests that meaning extension is a cognitive ability identifiable in species about 320 million years apart from humans.

Modeling word overextension in a Grey Parrot

Exploratory learning before instruction has been effectively employed in STEM education to promote deeper conceptual understanding. However, its application to sociopolitical reasoning is underexplored. This research investigated whether exploratory learning can mitigate biased information processing, fostering more reflective evaluation of counter-attitudinal sociopolitical information. We examined how the order in which participants engaged with exploratory (data table) versus directly persuasive (verbal message) stimuli influenced the strength and confidence in sociopolitical beliefs. Participants reported increased support for positions they had initially opposed, and to large effect, regardless of stimuli order. However, an interaction of time and order on confidence levels hinted at potential metacognitive benefits for participants who explored first. Exploratory learning may be less beneficial in the context of sociopolitical decision making—at least when individuals are likely to update their beliefs anyway. However, exploratory learning might impact individuals’ metacognition when the messenger contradicts their political position.

Using Exploratory Learning Methods to Challenge Sociopolitical Beliefs

In this work, we outline a methodological analogy between cognitive sciences and physics regarding the use of models and the synthetic method. Beginning with brief historical remarks on ‘the discovery of the artificial’ (as defined by Roberto Cordeschi) in early 20th century behavioral sciences and the methodological turning point in statistical physics at the same time, we demonstrate that the ‘envy’ for the use of analytical theories in the exact sciences—often referred to as physics envy—which has significantly influenced the development of psychology and the sciences of human behavior, is ultimately unfounded.
Finally, we use this ‘overcoming’ of the physics envy, along with some brief considerations on notable 20th century theoretical results related to ‘limitation’ of computability and complexity, to demonstrate how cognitive sciences —like the natural sciences— must necessarily rely on models and simulations. This necessity arises from the inherent complexity of the systems under study, which precludes their treatment in analytical and exact terms.

The Discovery of the Artificial and the Use of Synthetic Method between Physics and Cognitive Science

Large Language Models (LLMs) have shown surprising capabilities in reasoning tasks despite lacking direct physical experience with the world. We examine LLMs' ability to reason about object affordances through a tool innovation task where one must select unconventional objects to replace typical tools. In a study comparing GPT-3.5-turbo and GPT-4o with human participants (N=100), we found that while GPT-3.5 performed significantly worse than humans (38.7% vs. 85.8%), GPT-4o with chain-of-thought prompting achieved human-level performance (85.0%). Qualitative analysis revealed that both models could identify causally relevant object properties, but GPT-4o was superior in flexibly applying these properties in novel contexts. We argue that this success relies on compositional reasoning—the ability to decompose objects into abstract properties and recombine them for novel uses. Our findings suggest that LLMs’ ability to reason about object affordances has progressed substantially, highlighting the need for further mechanistic research to characterise LLMs’ underlying abilities.

Exploring Causal and Compositional Reasoning in Large Language Models

When making decisions from experience, people rely on memories of past outcomes, which can be influenced by extreme outcomes (best and worst). These memories, however, can be forgotten. In a pre-registered experiment, we evaluated risk preference with delays of up to 7 days between initial learning and later consequential decisions. Participants (N=277 total) learned to choose between safe and risky options (e.g., 25 points vs 50/50 chance of 5 or 45) both in gain and loss domains. After a delay, they made decisions without feedback. All groups were more risk seeking for gains than losses by the end of learning, contrary to the typical pattern with explicit descriptions. This effect was long-lasting and persisted across the delays. Additionally, risk aversion slightly increased for both gains and losses with any delay. These results provide novel evidence that the influence of extreme outcomes on risky decisions persists over long-term delays.

People Consistently Overweight Extreme Outcomes in Risky Choices, Even after Long Delays

Interfering with inner speech during action encoding impacts their execution

The pseudosciences often bear a striking resemblance to the sciences. Using a mimicry account as a framework, this paper investigates how the appearance of social media posts influences people’s perception of the content of such posts as scientific. We present the results of two empirical studies. The first, preparatory study, identifies typical characteristics of “scientificness” in social media posts to inform feature manipulation within the main study. The main study then examines what happens if the features are systematically manipulated. The findings support the hypothesis that pseudoscientific digital content benefits from using features of scientificness. We discuss implications for understanding the appeal and persistence of pseudoscience.

The illusion of credibility: How the pseudosciences appear scientific

The impact of hormone contraceptives on cognitive function in young women remains a topic of ongoing debate, with inconsistent findings across studies. While some research suggests a beneficial effect on cognitive performance, others report no effect. The variability in these reports may be due to the different types of hormone contraceptives available, each potentially influencing memory differently. The present study examined the effects of progesterone-only contraceptives and combined hormone contraceptives (consisting of estrogen and progesterone) on spatial memory performance using the landmark memory task in young women (18-25 years old). Results indicate no significant differences in landmark memory task scores between naturally cycling women and those using hormone contraceptives. There were also no differences in spatial task scores between women using progesterone-only contraceptives and those using combined hormone contraceptives. These findings suggest that the use of hormone contraceptives does not significantly impact spatial memory performance in young women.

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Gaze Patterns during Map Reading as Predictors of Route Learning

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