United States

We investigated how the decision environment influences choice and confidence in a binary decisions-from-experience (DfE) task. We manipulated four aspects of the decision environment: option composition (safe vs. risky; SR, or risky vs. risky; RR), the riskiness of the EV-maximizing choice, feedback type (partial or full), and problem order (SR before or after RR). We found that participants made more EV-maximizing choices and were more confident in SR environments and when the EV-maximizing choice was the safer option. Participants&#39; behavior was influenced by their understanding of the payoff structure: confidence dropped sharply after discovering a previously unexpected outcome, and experiencing RR first led to over-sampling and reduced rewards in later SR environments. A computational model incorporating choice and reward history provided a unifying explanation for these patterns. Our results shed light on how decision environment features interact to shape sampling biases, subsequently altering learning and decision-making outcomes.

CogSci 2025

The Decision Environment and Confidence in Experiential Risky Choice

decision making

learning

We investigated how the decision environment influences choice and confidence in a binary decisions-from-experience (DfE) task. We manipulated four aspects of the decision environment: option composition (safe vs. risky; SR, or risky vs. risky; RR), the riskiness of the EV-maximizing choice, feedback type (partial or full), and problem order (SR before or after RR). We found that participants made more EV-maximizing choices and were more confident in SR environments and when the EV-maximizing choice was the safer option. Participants' behavior was influenced by their understanding of the payoff structure: confidence dropped sharply after discovering a previously unexpected outcome, and experiencing RR first led to over-sampling and reduced rewards in later SR environments. A computational model incorporating choice and reward history provided a unifying explanation for these patterns. Our results shed light on how decision environment features interact to shape sampling biases, subsequently altering learning and decision-making outcomes.

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.

Sometimes, stimulus preparation is the most resource-consuming stage of experiments on communication. We present an open ready-to-use database of mimed expressive short stories (150 .mp4 files, 03:34:56 of footage) funded by the National Science Centre of Poland under the agreement UMO 2021/43/D/HS2/01866. It can be used as a stimulus in experiments or for annotation or rating. We describe how the database was created: (1) preparation: we used Chat GPT 4.0. to create rich stories, with varying age, gender, number of characters, and themes; (2) recording: we worked with professional actors for maximum expressiveness; (3) editing: we synced front and side shots for maximum visibility; (4) annotating: we analysed representational strategies to argue that the database showcases the potential of pantomime for storysharing and discuss its implications for the pantomimic scenarios of language origins (e.g., Arbib, 2018, 2024; Zlatev et al., 2020).

MESS (Mimed Expressive Short Stories) Database: Showcasing the potential of pantomime for story transmission

People often share their personal data online despite reporting that they should not. They also show surprise and distress when data is used in ways they authorize despite giving consent. But, what underlies this inconsistency in people’s thinking? In the present study, we investigated the proposal that thinking about control over information, or informational autonomy, likely underlies variability in thinking about privacy and data ownership. Namely, we propose that threats to one’s autonomy might account for the aforementioned changes in people’s concern for their data. To test this account, we used a survey-style design to measure how a hypothetical threat to the self, and thereby control, influenced adults’ (N = 51) judgments about the ownership and privacy of their personal data. The threat was police lawfully obtaining their data with a warrant. We found that privacy and ownership judgments significantly increased over time. We also found that the variability in participants’ ownership and privacy judgments was related. Together, our findings suggest that privacy and ownership likely have a shared psychological basis, and this shared psychology can likely explain the variability in people’s judgments about personal data across time.

Data Ownership and Privacy: Investigating a Shared Psychological Basis

Studies in novel word learning show that comparison settings (i.e., several stimuli introduced simultaneously) favor taxonomically-based generalization. Most comparison studies have been done with forced-choice designs. Here, we investigated, in a free-choice comparison design the type of stimuli four and five-year-old children chose in a novel noun generalization task, either from the same basic level category, or a near superordinate category, or a distant superordinate category, but also perceptual, thematic, and unrelated lures. Same basic level category items were more chosen than other taxonomically related items. Perceptual lures and near superordinate items did not differ, suggesting that children did not arbitrate between perception and taxonomy. Results are discussed in terms of different theoretical perspectives on stimulus generalization, lexical constraints, stimulus comparison and finally Bayesian approaches. They suggest that children integrate the results of their comparison rather than sampling probabilistic regularities

Novel Noun Generalization And Stimulus Comparisons In Children: Manipulating The Number And Structure Of Learning Stimuli. 

The meaning of being healthy is widely debated, with many suggesting it is a multidimensional concept encompassing key dimensions such as the absence of disease, the presence of well-being, and a healthy lifestyle. While recent studies indicate that lifestyle may be a dominant dimension, it remains unclear whether this holds true across populations or if significant differences exist, particularly between laypeople and healthcare professionals. Our studies reveal a difference, but surprisingly, in the opposite direction of what the literature would predict: medical professionals are substantially more likely than laypeople to frame discussions of "healthy" and "unhealthy" in lifestyle contexts. This result challenges prevailing assumptions about the biomedical focus of healthcare professionals and has implications for improving health communication.

A Common Language? Analyzing the Use of Health-Related Vocabulary Between Laypeople and Medical Professionals

The present work is dedicated to modeling and analyzing processes of vibro-acoustic comfort perception among passengers in a civil aviation propeller aircraft. The experimental data analyzed in this study were collected as part of the European project IDEA PACI (IDEntification of an Aircraft PAssenger Comfort Index) and encompass both vibro-acoustic and psychometric characteristics.
We introduce a computational model of the perceptual processes of passenger comfort for this vehicle class based on an automatic classification system with cognitive plausibility. This system incorporates both prototype theory and exemplar theory of categorization. The study has two primary objectives: first, to develop an artificial classification system with high performance, serving as a valuable support tool for designing more comfortable aircraft; second, to investigate which of the considered cognitive theories most accurately represents the categorization process of human comfort.
The experimental results, including other instance-based systems, demonstrate that the computational model used (the Prototype Exemplar Learning - Classifier) effectively predicts human passenger comfort, and the type of representative instances inferred by the system indicates a clear predominance of exemplar theory over prototype theory in modeling the perception of comfort.

A Cognitive-Computational Model of Comfort Categorization in Civil Aviation Propeller Aircraft

Researchers have proposed that people set thresholds to decide when to stop searching in optimal stopping tasks with full information, where option values are known. Most models assume that individuals set internal thresholds to guide their stopping decisions. However, whether humans actually set and adjust thresholds with experience remains unexamined. This experiment investigates how people set and adjust thresholds and whether this affects search behavior and learning over time. We designed an optimal stopping task where participants either report a threshold before seeing the option’s value or proceed without setting one. In addition, we varied whether the set threshold was binding for stopping decisions. Our findings, based on model predictions and empirical data, suggest that setting thresholds leads to more errors and lower accuracy. Accuracy is lowest when thresholds are non-binding. Participants often deviate from their set thresholds and perform better for doing so. These results challenge the assumption that people rely on thresholds for stopping decisions. Instead, they seem to learn from experience to improve accuracy and reduce errors, offering new insights into sequential decision making.

Setting and Adjusting Thresholds in an Optimal Stopping Task: Model Predictions and Empirical Results

Theories of reading are mostly based on English, that is rather atypical among alphabetic orthographies due to its inconsistent orthography-phonology mappings. Differences in length effects have been observed between languages. Psycholinguistic characteristics of the orthography, such as orthographic depth, seem to have an impact on reading strategies and could be correlated with different visual-attentional profiles.  However, no computational model has yet demonstrated the impact of language-dependent visual-attentional mechanisms on reading. This study explores these effects using BRAID-Acq, a probabilistic reading model with a visual-attentional module. We simulated word and pseudoword reading in English, German, and French to examine how orthographic depth and visual attention shape processing. Our simulation results suggest an effect of the orthography on processing time. In particular, English requires a larger attentional quantity for efficient processing of words and pseudowords, offering a novel interpretation of difficulties in reading acquisition in English.

Visual attention and cross-linguistic effects in reading: Simulations with BRAID-Acq, a probabilistic model of reading

The brain utilizes corollary discharge signals to anticipate the visual consequences of saccadic eye movements and provide a coherent visual perception. However, discrepancies between a saccade’s predicted and actual sensory outcomes challenge the brain’s capacity to maintain visual stability. In this work, we introduce a comprehensive computational framework for visual perception incorporating a feedback corrective mechanism that dynamically adjusts predictions based on sensory discrepancies. We show that this feedback mechanism refines internal world models, and provides robust performance with an increasing number of saccades. Our results highlight the delicate balance between the benefits and vulnerabilities of predictive feedback systems supporting and extending current theories of sensory prediction and visual stability.

Feedback-correcting ConvLSTM-driven Neural Model for Stable Saccadic Visual Perception

Humans can perceive causality when viewing collision-like interactions between plain, two-dimensional shapes. To what extent subjective causality reports arise from the physical plausibility of events as predicted by Newtonian physics remains an open question. Here we measured each participant’s perceptual judgments about the contact point angle ($\alpha$) and their causality judgments about the target ball's trajectory offset ($\beta$) using Michotte’s launching paradigm. Our results showed that subjective causality reports decreased as the target’s trajectory deviated further from the physically plausible angle, confirming the important role of physical plausibility in causal perception. Moreover, as uncertainty about $\alpha$ increased, participants were more likely to report events with larger $\beta$ as causal, and their causality reports were more sensitive to changes of $\beta$, consistent with a mental physics model that incorporates $\alpha$. These findings align with predictions of causality perception under a Noisy Newton framework, and provide experimental evidence using individualized uncertainty estimates.

Contact angle uncertainty influences perceived causality in launching events

Rapport is a central component in fostering effective and meaningful interactions between humans and AI agents. It forms the foundation of trust, mutual understanding, and collaboration, all of which are essential for achieving shared goals in cooperative tasks. However, a significant factor influencing rapport development is the diversity of individual differences among human users. This diversity complicates the process of rapport building, as AI agents must adapt dynamically to the unique needs and intentions of individual users. The ability of AI systems to detect and respond to such differences remains a pressing research challenge, as mismatched responses can lead to frustration and disengagement.Theory of mind capabilities enable AI agents to infer user intentions, preferences, and emotional states, while advanced emotion-processing systems allow for nuanced and context-sensitive responses. This study examines how integrating these capabilities into an LLM and RAG enhanced AI Tutor can help sustain engagement and alignment, fostering rapport in Human-AI collaborative learning environments.

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MESS (Mimed Expressive Short Stories) Database: Showcasing the potential of pantomime for story transmission

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