United States

CogSci 2025

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

developmental analysis

cognitive development

social cognition

development

statistics

psychology

representation

survey

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

The conference program is packed with insightful presentations, thought-provoking workshops, and engaging networking opportunities. In addition to the technical sessions, we have also planned several social events that will provide you with the opportunity to connect with your colleagues.

The conference is held in person at the Resorts World Convention Centre in Singapore, and available on-line with the help of Underline.

For those in Singapore, I hope that you will find time to explore the exciting Sentosa Island, Gardens by the Bay, Singapore Botanic Gardens and the many other attractions unique to Singapore.

Of course, we are grateful to our sponsors and partners for their generous support of EMNLP 2023, whose contributions make it possible for us to host this world-class event.

Yuji Matsumoto (RIKEN AIP)<br>
EMNLP 2023 General Chair<br><br><br><br>

To access the **EMNLP 2023** event page on Underline, you need to register for the Conference.<br>
Please follow **[this link](https://2023.emnlp.org/registration/)** for more details. 

EMNLP 2023

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

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. 
<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.
<br><br>
* **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)**.
<br><br>


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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.

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.

Supporting Knowledge Transfer in Programming: Insights from K-12 Computer Science Teachers

This study explores neural speech tracking of local and foreign accents. Studies have found neuro-cognitive differences for foreign accent processing in lower-level acoustic extraction and higher-level predictive mechanisms. However, how these mechanisms are recruited in speech tracking for different accents remains unclear. We explored neural speech tracking while 24 native English speakers listened to local and foreign accents in an EEG experiment. We examined the decoder accuracy of predicted speech envelopes using the Temporal Response Function to the speech envelope of our stimuli. Results showed stronger tracking for the local accent, and for accents participants rated more familiar. Findings suggest that participants utilized available cognitive resources to recruit predictive mechanisms during local accent processing, allowing them to attend to speech cues more efficiently. This top-down benefit was less available for foreign accents as listeners could not effectively access pre-stored sound variations for predictions.

Neural Speech Tracking and Accents: Are You Familiar with My Accent?

Spatial memory is evolutionarily important for many animals. Boundaries can systematically distort spatial memory in line with hierarchical models, while other evidence supports more metric path-integration theories. To test these competing theories, we developed a novel, highly immersive Object Placement Task (OPT) in virtual reality to examine the effect of boundaries on human spatial memory. Our task decomposes spatial memory into 3 components: 1) object-placement errors, 2) distance judgments, and 3) angular judgments (i.e., our single task provides multidimensional information about spatial memory). Thirty participants used the OPT to recall object positions in environments with or without a central boundary. Bayesian analyses showed that distance influenced memory performance, but boundary presence had no significant effect (Bayes factors favored the null models). These findings suggest a set of conditions in which boundaries may not impact memory. We discuss potential modulators of boundary effects and present our open-source task for future research.

Another Brick in the Wall? A Null Effect of Boundaries on Spatial Memory Judgments in a Novel, Highly Immersive, First-Person Object-Placement Task

Ideophones are characterized by sound symbolism, a non-arbitrary relationship between speech sounds and meaning, which is observed across languages worldwide. Despite the increasing interest in sound symbolism and ideophones in cognitive science, Japanese ideophone research has lacked a robust quantitative approach. This paper proposes a dictionary-based method to quantify the sound-symbolic system of Japanese ideophones and presents statistical results that support and extend previous findings. The proposed method aims to advance the objective and comprehensive analysis of ideophones, sound symbolism, and iconicity, offering valuable contributions to ongoing discussions in the cognitive sciences.

A Dictionary-based Quantitative Analysis of the Sound-symbolic System of Japanese Ideophones

In this study, we developed a computational implementation for a model of metaphor comprehension based on the theory of indeterminate natural transformation (TINT) proposed by Fuyama et al. We simplified the algorithms implementing the model to be closer to the original theory and verified it through data fitting and simulations. For details, we proposed a method to replace the deterministic operation of existing model with a probabilistic (softmax) operation. The outputs of the algorithms are evaluated with three measures: data-fitting with experimental data, the size of the correspondence of the metaphor comprehension result, and the novelty of the comprehension (i.e. the correspondence of the associative structure of the source and target of the metaphor). The improved algorithm outperformed the existing one in all the three measures. We suggest that the metaphor comprehension process in humans is based on more probabilistic procedure.

Computational Implementation of a Model of Category-Theroretic Metaphor Comprehension

This study examines scaffolding techniques to support students’ analogical comparisons with science images. Analogical comparison involves identifying deep relational structure over superficial similarities, which can be challenging without guidance. Across experiments, participants compared analogous evolutionary science images with or without various scaffolds: describing the relation, completing a mapping table, and spatial support for alignment. Results demonstrated that describing the relation and completing a mapping table, especially in combination, significantly enhanced scientific interpretations of the images. However, spatial support was ineffective. The findings highlight practical strategies for improving conceptual learning from science visuals.

Scaffolding to Support Analogical Comparisons with Science Images

Detecting dynamic spatial relationships, such as pursuit, can be cognitively demanding (Scholl & Gao, 2013). Research has identified visual cues that influence pursuit detection, including the distance between the pursuer and the target (Meyerhoff, Schwan & Huff, 2014) and the number of objects in a scene (Gao, Baker, et al., 2019; Kon, Khemlani & Lovett, 2024). We turn to data visualization research to explore techniques to improve pursuit detection in dynamic scenes. For example, displaying trajectory histories can aid processing by reducing cognitive load (Heer & Robertson, 2007). We conducted a study in which participants viewed visualizations of six moving dots, either with or without trace lines, and determined whether one dot was chasing another. Preliminary findings suggest that trace lines improve the speed and accuracy of pursuit detection. Our results bridge visualization and vision science, suggesting that trace lines might enhance pursuit detection by providing less transient shape-based visual cues.

Visualizing Motion Traces Enhances Pursuit Detection in Dynamic Scenes

Children with Developmental Language Disorder (DLD) exhibit longer reaction times (RTs) than age-matched neurotypical children. Drift diffusion models estimate parameters influencing RT distributions: drift-rate represents speed of information accumulation, non-decision time represents other factors contributing to longer RTs, e.g., poor attention or motor coordination. Using a hierarchical Bayesian framework, we modeled RT data from visual search and mental rotation tasks completed by 3rd graders (N = 248). Children with impaired verbal abilities without accompanying nonverbal impairment (DLD) and those with global verbal/nonverbal impairments were compared to neurotypical children. Across tasks, children with DLD exhibited a lower drift rate than neurotypical children, indicating slower information accumulation, and no difference in non-decision time. Children with global impairments showed lower drift rates and higher non-decision times than neurotypical children. Results suggest directions for nuanced tests of the generalized slowing hypothesis of DLD. Clinical implications for diagnosis and treatment of DLD are discussed.

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Next from CogSci 2025

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