United States

In daily life, humans selectively search for information about options to make decisions. The metalevel MDP framework, proposed to understand this information search process, has so far been evaluated only for its predictive performance regarding group differences in summary measures under unrealistic scenarios. This study examined whether metalevel MDP can explain information search processes in more realistic decision-making situations. We proposed a chatbot-based experiment that enables diverse information search actions, as well as a novel analysis method that extends metalevel MDP as a probabilistic model, allowing for the estimation of latent variables and more rigorous evaluation of statistical model fit. The experimental results showed that the model partially explains the information search process, and we discussed potential directions for model improvement.

CogSci 2025

Stochastic Metalevel Markov Decision Processes: Proposal and Validation through Ecologically Valid Experiments

computer-based experiment

computational modeling

statistics

decision making

psychology

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.

Humans can quickly infer social relationships from minimal cues, such as where people choose to sit in a meeting room. We investigated how people make graded, context-sensitive judgments about social attitudes beyond simple proximity-based heuristics. Using controlled seating scenarios, we compared participants' judgments to the predictions of Bayesian models: the interaction-probability model, which captures how one person's seat choice affects the probability that another person will initiate the conversation, and the interaction-cost model, which accounts for the effort required based on how far apart they sit from each other. Results showed that participants' inferences aligned best with the interaction-cost model, indicating sensitivity to effort and moving trajectory, rather than relying solely on proximity. Our findings suggest that higher-order cognition refines perceptual cues, enabling nuanced, graded social reasoning essential for complex social interactions.

When Seating Matters: Modeling Graded Social Attitudes as Bayesian Inference

Computational modeling has been a crucial tool in cognitive science to understand human cognitive functions and impairments in neurocognitive disorders. Convolutional Neural Networks (CNNs) exhibit striking similarities to human visual processing systems for object recognition, making them a powerful tool for studying visual processes. In this study, we examined the neurobiological theories, namely, the Excitation/Inhibition (E/I) Imbalance and Internal Noise (IN) in explaining face recognition challenges in autism spectrum disorder (ASD) using CNNs, and revealed that over-excitation and increased noises in the CNNs led to compromised performance on face recognition and atypical patterns of internal representations of face stimuli. This approach enables systematic comparisons between typical and atypical cognition, offering a theory-driven perspective to investigate cognitive challenges and their neurocognitive mechanisms with a computational approach.

Modeling Face Recognition Challenges in Autism Spectrum Disorder: A CNN-Based Approach

Data visualizations often display averages without raw data to simplify communication and enhance understanding, especially for lay audiences. However, the theory that such simplification improves understanding remains untested. Here, we test this theory’s most basic prediction—that at minimum, the average itself is conveyed better by plotted averages than by plotted raw data. Remarkably, we find the opposite: under a wide range of conditions, overall accuracy of average estimation is higher with raw data. This is due to frequent, severe misinterpretations of both bar and line graphs depicting averages. In contrast, raw data yields some variability but few outright errors; notably, the observed variability is comparable to the uncertainty captured by confidence intervals. We conclude that plotted raw data provides valuable context that helps prevent misunderstandings of the average. Our findings challenge the notion that plotted averages alone yield enhanced understanding and emphasize the value of raw data in communicating evidence.

The Paradox of Certainty: When Graphed Ensembles Convey Averages Better than Graphed Averages

One of the main objectives in developing large vision-language models (LVLMs) is to engineer systems that can assist humans with multimodal tasks, including interpreting descriptions of perceptual experiences. A central phenomenon in this context is amodal completion, in which people perceive objects even when parts of those objects are hidden. Although numerous studies have assessed whether computer-vision algorithms can detect or reconstruct occluded regions, the inferential abilities of LVLMs on texts related to amodal completion remain unexplored. To address this gap, we constructed a benchmark grounded in Basic Formal Ontology to achieve a systematic classification of amodal completion. Our results indicate that while many LVLMs achieve human-comparable performance overall, their accuracy diverges for certain types of objects being completed. Notably, in certain categories, some LLaVA-NeXT variants and Claude 3.5 Sonnet exhibit lower accuracy on original images compared to blank stimuli lacking visual content. Intriguingly, this disparity emerges only under Japanese prompting, suggesting a deficiency in Japanese-specific linguistic competence among these models.

Bridging Perception and Language: A Systematic Benchmark for LVLMs’ Understanding of Amodal Completion Reports

Prominent U.S. legal cases have turned on the resolution of linguistic ambiguities that arise from interactions between negation and coordination. Negative disjunction sentences (ND, “John didn’t buy cake or cookies”) are ambiguous between two readings: a neither-nor reading and a not-both reading. Negative conjunction (NC, “John didn’t buy cake and cookies”) is similarly ambiguous. Experimental findings on such scope ambiguities (Jasbi et al. 2023, Tobia et al. 2023) suggest that in general, listeners favor the neither-nor reading for ND but are less biased in their interpretations of NC. We present a corpus analysis with researcher annotations of several hundred tokens of these constructions in English treebank data. Initial results are consistent with previous conclusions based on controlled experimental data, suggesting that an attested tendency in linguistic comprehension is also reflected in naturalistic production. Our dataset can further be used to explore factors that modulate the disambiguation in context.

Scope Ambiguity Resolution of Negated Connectives in English Corpora

Understanding speech demands integration of various kinds of information over time. Previous research has shown that listeners can use both semantic and acoustic cues to understand spoken words (Bushong, 2024) and that listeners can dynamically reweight different acoustic cues relative to each other (Kapatsinski, 2024); but it is unclear if listeners are able to readjust the relative contributions of semantic and acoustic cues depending on the current context (e.g. adverse listening conditions). The current study manipulates the acoustics of a target word, a semantic cue (sentential context), and the location of the semantic cue relative to the target word. Additionally, we manipulate the level of noise in the utterance and the location of that noise (full utterance vs. target word). Noise reduces listeners’ ability to use acoustic cues for spoken word recognition and as local noise increases, semantic context is up-weighted. However, as global noise increases, semantic context is also down-weighted.

Listener Trade-Offs between Acoustics and Semantics in Noisy Speech

Models of events represent the interactions of the entities involved. In the event “The chef chopped an onion," a chef and an onion are explicitly involved, and the event results in a chopped onion. However, it is also implied that an instrument, e.g., a knife, must interact with the chef and the onion. In this study, we investigate the extent to which people model different implicit instruments in event representations. We find that people's representations of events reliably include instruments that are implied to be involved even when they are not explicitly stated in the event description. These findings are consistent across different sentence constructions of events, suggesting that implicit instrument representation is robust in comprehension of events. We also show that implicit instrument representation persists despite lexical priming of other items, and that the representations provide evidence for the disambiguation of the Instrument semantic role from other semantic role categories.

Experimentally extracting implicit instruments

The word-frequency distributions infants hear during language learning are highly skewed (Zipfian). Previous work suggests that such skewed distributions facilitate speech segmentation, a crucial milestone in language acquisition. However, the experimental studies supporting this have only examined individuals aged 10 years or older, and it is not clear whether this effect arises from accumulated linguistic experience or is already present in the early stages of learning. To address this, we ran a word-segmentation study with 8-month-old infants (N=60) by exposing them to a continuous speech stream with a skewed or uniform frequency distribution of artificial words. At test, infants in the skewed condition exhibited a larger looking time difference between heard and unheard words than infants in the uniform condition. These findings suggest that Zipfian distributions can facilitate word segmentation during early linguistic development, and highlight the importance of the distributional characteristics of linguistic input in natural language learning

Zipfian distributions facilitate word segmentation in infants

This study aimed to explore the relationship between ESL learners' cognitive styles and personality types, focusing on how personality influences perceptual learning preferences and language learning strategies. Participants included 20 English majors and 58 Nursing majors in a Year 1 ESL course (62 females and 16 males). Data were collected using a structured questionnaire, including the Myers-Briggs Type Indicators (MBTI) test, and 40 items on learning styles and strategies. Results indicated that the most common personality types among English majors were INFP and ISFP, while Nursing majors frequently exhibited ISFJ and ENFP types. The least common types were INTJ and ENTP. Learning style preferences showed 37% favored reading/writing, 33% visual, 26% auditory, and 4% kinesthetic. Significant relationships were found between language learning strategies and introverted/extroverted personality types, though no significant gender differences were observed. The findings suggest that ESL instructors should consider these differences to enhance classroom dynamics and effectiveness.

The Impact of Personality Types and Cognitive Learning Styles on ESL Learners in Art and Science Programmes

Humans’ capacity for Theory of Mind (ToM) allows us to reason about and infer others’ mental states, including their emotions. While ToM has been extensively studied in interpersonal contexts, how people attribute mental states–particularly emotions–to collective entities (e.g., corporations) remains underexplored. The current work examines whether and how people ascribe emotions to collectives using the appraisal theory framework. Participants were randomly assigned to scenarios designed to elicit a specific emotional inference about either an individual (e.g., a lawyer) or a collective (e.g., a law firm). We then collected and compared emotion attributions and appraisal judgments of the situations across both conditions. Our results suggest that people attribute emotions to individuals and collectives in remarkably similar ways, with subtle differences in event appraisals. The results pave the way for a deeper understanding of collective ToM, with implications for studying moral judgments and decision-making in societal contexts.

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When Seating Matters: Modeling Graded Social Attitudes as Bayesian Inference

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When Seating Matters: Modeling Graded Social Attitudes as Bayesian Inference

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