United States

The question of whether mental disorders are brain disorders has sparked curiosity in cognitive science for years. But does framing mental disorders as brain disorders actually help the public better understand and engage with mental health? What do people understand when we call something a brain disorder, and why does it matter if mental illnesses are described as brain-based? To explore these questions, we conducted three quantitative vignette studies with a UK-based general public sample, focusing on perceptions of seven mental disorders: ADHD, ASD, OCD, major depressive disorder, anorexia nervosa, schizophrenia, and bipolar disorder (as defined in the DSM-5-TR). Our findings show that seeing mental disorders as brain disorders is linked to beliefs about greater severity, earlier onset, longer duration, lower chances of recovery, and higher effectiveness of medication. These results highlight how public perceptions might impact reasoning and decision-making about mental health.

CogSci 2025

The (side) effects of medicalization: How viewing mental disorders as brain disorders shapes perceptions of onset, recovery, severity, and treatment efficacy in the general public

behavioral science

psychology

perception

survey

philosophy

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.

The typicality effect is the finding that some members of a category are more ``central'' and others more ``peripheral''. This effect is seminal for understanding the mental representation of concepts. Recently, researchers have looked for typicality effects in the representations learned by machine learning models as evidence of their cognitive alignment. Studies of the typicality effect in Large Language Models (LLMs) have focused on models trained on English corpora and category norms collected from English speakers. Here, we use existing norms to investigate the typicality effect across five languages: English, French, Portuguese, German, and Spanish. We focused on eight categories common across these norms, and asked whether a multilingual LLM, GPT-4o-mini, shows human-like typicality effects across these languages. The results show variation in typicality gradients across languages. Importantly, GPT-4o-mini's typicality judgments show strong alignment with human norms for some languages: English and French. The strong performance for French, in particular, cannot simply be attributed to the representation of that language in the training corpus. We discuss the implications of these findings for future studies exploring alternative model prompting approaches, different languages, and the modeling of new category norms collected using uniform methods.

Cross-Language Typicality Effects in a Multilingual Large Language Model

This study examined the effects of regularity, frequency, and phonological complexity on morphological production in Turkish-speaking children with developmental language disorder (DLD) and phonological disorder (PD) compared to typically developing (TD) peers. Thirty children (ages 4–6) completed elicited production tasks using real and nonce words with regular and irregular noun and verb suffixes. DLD children showed lower accuracy in tasks involving consonant voicing and epenthesis and performed significantly worse on irregular suffixation, often substituting irregular forms with familiar ones. Nonce word production confirmed these challenges. Random Forest analyses indicated that phonotactic probability best predicted TD performance, while lemma frequency and phonological neighborhood density were more influential for DLD and PD groups, respectively. These findings suggest that DLD children rely on familiar, regular forms to manage morphological complexity, reflecting distinct processing strategies compared to PD and TD peers.

Exploring the Impact of Regularity, Frequency and Phonological Complexity on Morphological Production in Children with DLD and Phonological Disorders

Semantic distinctions are encoded variably in kinship terminology, the set of words that denotes family members. Nonetheless, it has been suggested that kinship terminology, like other linguistic domains, is constrained by opposing pressures to be simple yet expressive. Here, we use this insight to explore how the meaning space for kinship is structured cross-linguistically. Under the assumption that kinship systems map forms to meanings in a compressible, structure-preserving manner, we designed a metric for identifying which semantic features are most important for distinguishing individuals in a kinship system. For 1229 kinship systems, we calculated the correlation between semantic similarity (the weighted sum of shared semantic features between individuals) and wordform similarity (the edit similarity between terms).  We then identified the optimal weight for each semantic feature in each language, confirming that kinship systems vary in which semantic features they encode, and that the features themselves vary in the extent to which they are encoded. Additionally, we identified that semantic features are encoded hierarchically; more simple and more informative features are weighted highest in general. By identifying this constraint on the distribution of forms and meanings in kinship terminology, our results provide new insights on how kin terms are structured for efficient communication.

Efficient communication drives the semantic structure of kinship terminology

Do large language models (LLMs) learn like people do? We investigate this question with a simple task that compares human learning and LLM finetuning on the same set of novel inputs. We find that while humans learn and generalize robustly, finetuned LLMs largely fail to generalize from what they learned and are more influenced by prior expectations than humans are. We then analyze human solutions of our task and find that stronger performance is characterized by the proactive formation of efficient representations that aid learning and generalization. Although LLMs can use in-context learning to match the performance of humans who do not form these representations, and can use similar representations provided in-context to match the performance of those who, they do not form these representations on their own. Given these findings, we then consider how future theories of human learning might be built in the age of LLMs

Humans learn proactively in ways that language models don't

Learning about complex, scientific topics often involves reading competing explanations posited by multiple disagreeing sources. This necessitates comprehending both explanations, understanding the extent of their disagreement, and determining which is more likely. In a series of three experiments, we investigated the role of features of explanations and their sources in readers’ evaluations of the explanations. Specifically, we presented participants with pairs of disagreeing explanations that varied in their simplicity, the expertise of their source, and the salience of each feature. We examined the extent to which these features individually and interactively affected readers’ evaluation of explanations, the causes they attributed to the disagreement, and curiosity about the topic of disagreement. We also examined the role of individual differences between readers, namely their prior topic knowledge and trust in science, in these outcomes. The findings inform theory about how people evaluate explanations and learn about science.

The contributions of explanation simplicity and source expertise to evaluations of disagreeing explanations

What and how do people learn about visual appearance from language? We test the hypothesis that in the absence of sensory evidence, people born blind use abstract causal knowledge to infer object appearance. Congenitally blind (n=19) and sighted adults (n=59) reported how many colors two types of artifacts were likely to have: artifacts for which having many colors is intended to facilitate function (n=30, e.g., fairytale book, fruit candies), and artifacts for which colorfulness is irrelevant or distracting (n=30, e.g., instructional manual, painkillers). The number of colors estimated per object was highly correlated across groups. Blind and sighted people assigned more colors to artifacts for which colorfulness facilitates function and appealed to makers’ intentions in open-ended explanations. A text-only version of GPT-4 generated similar but non-identical colorfulness estimates compared to humans. Our findings suggest that people infer the appearance of unseen objects using causal ‘intuitive theories’ informed by linguistic evidence.

Learning visual appearance from language is mediated by causal intuitive theories

We explore how people “bootstrap”, or reuse chunked action sequences, to tackle complex problems, in a novel puzzle task. In this task, participants perform sequences of actions to recreate target shapes. In our experimental condition, participants are trained on problems whose best solutions share a distinct abstract action sequence, or schema. Meanwhile, a control group trained on tasks of commensurate difficulty whose solutions did not conform to this pattern. We find experimental-condition participants outperform controls in a set of more difficult test puzzles whose solutions are compositional generalizations of experimental group’s training tasks. Notably, the experimental group outperformed controls even in “far transfer” tasks that lacked surface similarity to training in both their target shape and solution sequence. Our results provide a compelling demonstration of the human ability to cache and reuse abstract patterns, offering new insights into how humans approach complex problems that, naively, seem to demand a prohibitive amounts of planning or trial and error.

Bootstrapping in Geometric Puzzle Solving

During conversation, individual speakers become part of a larger conversational system. This is observable in interpersonal coordination, variously described as alignment, synchrony, convergence, and complexity matching. While this coordination is ubiquitous, the degree of coordination varies greatly between conversations, interlocutors, and group-level goals. There is growing interest in understanding the constraints that operate at the level of the conversational system. We explore how social distance, the status and demographic differences of conversation partners, constrain linguistic alignment. To do this, we leverage the CANDOR dataset (Reece et al., 2022), which contains 1656 approximately half-hour ZOOM conversations between strangers and a large battery of follow-up questions regarding their conversation, personality, and demographic information including perceptions of status. In this exploratory study, we use recurrence analysis to examine how various coordinative behaviors, particularly lexical and syntactic alignment, are influenced by the social distance between conversation partners. Experimental follow-ups are discussed.

Social position as a constraint on linguistic alignment

The human brain supports complex behaviors through diverse functional connectivity patterns. We propose Pareto optimality as a novel framework to understand this functional organization. According to Pareto theory, systems optimizing multiple competing goals do so by balancing trade-offs along a low-dimensional "Pareto front" defined by archetypes that each optimize a single goal. Applying Pareto analysis to resting-state fMRI data (HCP, N=1200), we found that individual connectomes lie on a low-dimensional triangle. The three archetypes represent core computational goals: minimizing energetic cost, supporting cognitive control and goal-directed behavior, and enabling internal processing and memory. These goals are reflected in connectivity patterns, network topology, information flow, behavioral and clinical associations. The framework generalizes beyond rest to task-based brain states, and a simple neural model illustrates the trade-offs’ computational basis. Pareto optimality offers a principled approach to decompose brain function into core computations across conditions, populations, and stages of life.

Pareto optimality reveals the core computations of the human brain

Empathy is an emotion that plays a key role in emotional understanding and perspective-taking, and has been identified as a strong motivator for prosocial behavior. We explore people's intuitive theory of empathy, focusing more specifically on the role that the concept of empathy plays in people's causal model of prosocial behavior. We suggest that people implicitly think of empathy as indexing the weight that the actor puts on the welfare of the recipient when deciding whether to help. We test this proposal by asking participants (N=150) to read a series of vignettes in which an actor has the opportunity to help a recipient in need. We find that participants have a robust expectation that actors who feel empathy for the recipient are more likely to help. Furthermore, participants seem to expect that actors who feel empathy are more sensitive to the potential benefits of an action when deciding whether to help. We also test if people can `invert' this intuitive theory to make inferences about an actor's empathy, given their observable behavior. We find only weak evidence that they can do so, although this might be due to limitations in our experimental design. Overall, our work is a first step toward elucidating the computational principles underlying laypeople's conception of empathy.

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Cross-Language Typicality Effects in a Multilingual Large Language Model

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Cross-Language Typicality Effects in a Multilingual Large Language Model

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