United States

Fraction arithmetic is an essential skill for students to advance to higher level math, but it is also important for daily life. The current study examines how sixth grade students with math learning difficulties perform on symbolic arithmetic problems and analogous word problems to determine what makes some fraction arithmetic easier for students to solve when given symbolic notation, and others easier to solve when problems are embedded in word problem contexts. We found that students make similar errors on both symbolic arithmetic problems and word problems, and they also struggle to identify the correct operation for a word problem.

CogSci 2025

Computation in Context: A Comparison of Fraction Story Problems and Symbolic Arithmetic

classroom studies

education

Join us for CogSci2022! Our 44th Annual Conference will be centered around the theme of diversity, with an accompanying range of keynotes and invited speakers who themselves highlight the diversity of our field. Our three symposia are Cognition across Diverse Populations, Cognition within Diverse Societies, and Global Challenges for Cognitive Science. The conference is being offered in a fully hybrid environment for onsite and online attendees.
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<br>[**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2022/07/Cogsci-2022-PAAG-Detailed-July-12final-1.pdf)
<br>[**The Detailed Program can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2022/08/DeArmond-CogSci2022-program-v113-final.pdf)
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CogSci 2022

The 44th Annual Meeting of the Cognitive Science Society highlighted research on the theme Cognitive Diversity in addition to the full breadth and diversity of research topics offered by the society’s membership. 





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.

Neurodevelopmental disorders (NDDs) in preschoolers often involve social and communication deficits, contributing to heightened anger, sadness, and anxiety. This study examined whether acoustic features of speech could detect emotional dysregulation in 65 French-speaking children (age 4) diagnosed with ADHD, developmental language disorder, psychosocial issues, or cognitive impairments. Using standardized assessments, participants were grouped by emotional/psychological, speech/language, or cognitive/motor difficulties. Audio recordings from structured and unstructured tasks were processed via openSMILE, generating 153 features capturing spectral, prosodic, and energy parameters linked to emotion. A random forest classifier compared these acoustic profiles to EmoDB samples labeled with negative emotions. Results showed that children with NDDs exhibited unique acoustic markers of negative emotions, though differences among subgroups were minimal. Attempts to pinpoint anxiety as a diagnostic feature were inconclusive. Overall, machine learning–based acoustic analysis holds promise for identifying emotional dysregulation, encouraging further multimodal approaches in clinical assessments, and more robust early interventions overall.

Leveraging Machine Learning for Acoustic Feature Analysis in Neurodevelopmental Disorders: Insights into Emotional Profiles

How do languages structure word classes, and is this organization arbitrary? This study explores groundedness--the degree of association between a word in an utterance and the meaning which the utterance describes--as a potential organizing factor. In particular, we look at visual groundededness, where meaning is approximated with an image, allowing tractable estimation with neural vision-and-language models. Prior work showed that nouns, adjectives, and verbs differ in groundedness cross-linguistically. We test whether groundedness describes the atypical word class structure in Japanese, where adjectives are split split into i-adjectives (formally verb-like) and na-adjectives (noun-like). Analyzing the Crossmodal-3600 dataset with the PaliGemma model, we find that na-adjectives exhibit significantly (p=0.029) higher visual groundedness, suggesting that the formal similarities of these classes to nouns and verbs reflect their semantics. This challenges the idea that linguistic categories are purely conventional or innate and arbitrary, and supports a view where language structure emerges from human perception.

Visual groundedness as an organizing principle for word class: Evidence from Japanese

Previous work suggests that non-visual object properties like
weight are automatically integrated into event models during
language comprehension. Horchak and Garrido (2021) found
that Portuguese speakers’ response times were faster when the
state of a presented object (e.g., a smashed tomato) matched
the event implied by the preceding sentence (e.g., You drop a
bowling ball on a tomato). In an exact replication in English
(Experiment 1), we failed to replicate this weight-state match
effect. In Experiment 2, we examined the potential role of
sentence focus, manipulating whether the target item served as
the subject or direct object in the sentence. Response times
revealed a weight-state match effect, but only when the target
object was the focus (i.e., subject) of the sentence. Overall,
these findings suggest that the representation of object state
changes during language comprehension may depend on the
interaction of object properties and language-specific syntactic   
constraints.

Are object state changes represented during language comprehension? A non-replication and extension

Large language models segment words into chunks called tokens, using compression algorithms that ignore semantics. We investigated whether tokenization corrupts representations of word meanings in 17 languages. We found that GPT-4o and Llama 3 inflate the similarity of words that share tokens. However, tokens turned out to be good predictors of orthographic priming, such that people recognize a target word faster after reading a prime that ends with the same token. This boost in priming far exceeds what other overlapping strings of letters explain, which suggests that tokenization selectively identifies functional subword units. The pattern extends to the production of word associates in English: Tokens capture phonologically motivated associations, while other strings of letters do not. So, tokenization does influence semantic representations, but because tokens correspond to psychologically salient orthographic and/or phonological constituents, they may endow large language models with human-like language networks and facilitate alignment with human word processing.

Large language model tokens are psychologically salient

One explanation for how people can plan efficiently despite limited cognitive resources is that we possess a set of adaptive planning strategies and know when and how to use them. But how are these strategies acquired? While previous research has studied how individuals learn to choose among existing strategies, little is known about the process of forming new planning strategies. In this work, we propose that new planning strategies are discovered through metacognitive reinforcement learning. To test this, we designed an experiment to investigate the discovery of new planning strategies. We then presented metacognitive reinforcement learning models and demonstrated their capability for strategy discovery as well as show that they provided a better explanation of human strategy discovery than alternative learning mechanisms. However, when fitted to human data, these models exhibited a slower discovery rate than humans, leaving room for improvement.

Experience-driven discovery of planning strategies

Previous studies of predictive mechanisms in computer-mediated communication (CMC) suggested that native speakers (L1) rely on auditory cues and emotion in conversation processing. To understand how the prediction mechanism differs for non-native speakers (L2) in CMC, this study assessed how the loss of multi-modal cues affects word predictability in turn-taking, considering language background and social factors. L2 watched videos, listened to audio, or read a transcript of conversations, and predicted the same set of omitted words with different levels of predictability and semantic relatedness in different CMC. Results showed that, similar to the L1 study in He et al. (2025), higher response similarity but longer response time were observed in conditions with richer cues in L2 predictive processing. Semantic relatedness, self-emotion, attention, and language proficiency did not affect predictability. Participants reporting negative emotions and more limited L2 exposure demonstrated reduced prediction accuracy, particularly in cue-rich environments. These findings expand our understanding of L2 predictive processing in CMC by highlighting how multimodal cue integration operates differently for L2. The results have implications for developing communication technologies and language pedagogies tailored to L2 across various mediated communication contexts.

Difference in the Cognitive Mechanism of Predictive Processing in Computer-Mediated Communication: A Comparison Study of L2 Speakers

Written in Stone: Lay intuitions about the emergence of formal rules

Many languages exhibit Differential Argument Marking (DAM), where nouns are marked for their grammatical role only in certain contexts. DAM is subdivided into Differential Object Marking (DOM) and Differential Subject Marking (DSM), both of which are conditioned by factors such as animacy across languages and hypothesized to arise via ambiguity avoidance and tendencies to mark atypical situations. While previous studies have primarily focused on production, this study employs artificial language learning to investigate comprehension in both DOM and DSM, examining whether learners rely on typicality biases for grammatical role assignment in unmarked sentences, and whether the type of marking learned affects their interpretation. Results indicate a strong givenness effect and a smaller animacy effect, with no significant differences between DOM and DSM conditions. These findings suggest that typicality biases play a key role in shaping DAM systems and that their emergence is best understood as a communicative phenomenon.

Typicality biases in interpreting unmarked sentences: an artificial language learning experiment on differential argument marking

Humans can categorize with only a few samples despite the numerous features. To mimic this ability, we propose a novel mixture of probabilistic principal component analyzers (mPPCA) model with dimension-reduced category representations, along with a theoretical analysis of rational dimensionality choices in categorization. Tests on the {\tt CIFAR-10H} natural image categorization dataset show that introducing a single principal component for each category effectively improves predictions of human categorization patterns. We further use mPPCA to account for human category generalization with very few samples. In our experiments with visual patterns of varying size and color, combining principal components and the hierarchical prior leads to significantly better predictions of human generalization within and beyond previously learned categories.

A Rational Model of Dimension-reduced Human Categorization

Many states now provide access to computer science (CS) courses across multiple grade bands. Younger students are often introduced to block-based programming languages before transitioning to more conventional text-based languages (Kao et al., 2022). However, students often struggle to see connections between previously learned programming languages and novel ones (e.g., transitioning from block-based to text-based programming). To better understand how knowledge transfer occurs in CS classrooms, we interviewed teachers regarding the examples of knowledge transfer they observe in their classrooms, including whether and how their CS curriculum supports such knowledge transfer between programming languages. Qualitative analyses revealed emerging themes related to syntactical affordances and challenges of programming languages, whether and how teachers make direct or indirect comparisons between languages in their instruction, and the types of transfer teachers observe most frequently in their classrooms. Our findings will guide the development of curricular materials that support programming language knowledge transfer.

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Next from CogSci 2025

Leveraging Machine Learning for Acoustic Feature Analysis in Neurodevelopmental Disorders: Insights into Emotional Profiles

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Inhibition and Fraction Arithmetic: Insights from Heat-map Strategy Reports

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PRESENTATIONS

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