United States

CogSci 2025

Passive Behavioral Sensing: Using Within-Person Variability Features from Mobile Sensing to Assess Self-Regulated Learning

learning

psychology

education

### 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.

poster

People conceptualize space using different spatial reference frames, based either in the body or the environment. Many studies attribute this cognitive diversity to spatial language, but their effects are confounded by differences across cultures and experimental tasks. Here we tested this hypothesis in people and tasks that are directly comparable. Indigenous BaYaka adults in the Congo basin reconstructed simple object arrays from memory and later described the same arrays aloud. Reference frames diverged across modalities in three primary ways. First, they used body-based frames seven times more often in their spatial memory than in their spatial descriptions of the same stimuli. Second, linguistic and non-linguistic responses were uncorrelated across participants, despite substantial variation. Third, each of two factors – spatial aspect and axis – influenced spatial language and memory differently, even in the same individuals. The results show that this fundamental feature of spatial thinking does not reflect patterns of spatial language.

Spatial language and memory diverge in BaYaka hunter-gatherers

Effective connectivity (EC) is crucial for elucidating causal interactions between brain regions, providing valuable insights into cognitive function. Traditional methods infer EC indirectly through temporal predictions, but unmeasured confounding factors lead to temporal delays, resulting in spurious causal inferences. Compared to the indirect inference of EC using traditional methods, neurostimulation experiments directly infer EC, yet their invasive nature and ethical constraints limit their applicability in assessing whole-brain EC. To address this, we propose a data-theory-driven virtual neurostimulation (VNS) model for directly estimating EC between brain regions. This model constructs a large-scale brain network model as a surrogate for the brain, applying perturbations to specific brain regions based on the physiological mechanism of membrane potential polarization induced by current stimulation. Causal relationships between brain regions are then inferred directly by performing statistical analysis on the responses in blood oxygenation level dependent (BOLD) signals. The model’s accuracy and stability were validated on macaque and human medial temporal lobe (MTL) datasets with known ground-truth EC, demonstrating superior performance over baseline methods. Application to a disease specific dataset further highlights its potential as a personalized biomarker for neurodegenerative diseases, providing a novel
pathway for early diagnosis in brain disorders.

A Computational Model for Estimating Effective Connectivity Using Virtual Neurostimulation

Music evokes profound emotions, yet the universality of emotional descriptors across languages remains debated. A key challenge in cross-cultural research on music emotion is biased stimulus selection and manual curation of taxonomies, predominantly relying on Western music and languages. To address this, we propose a balanced experimental design with nine online experiments in Brazil, the US, and South Korea, involving N=672 participants. First, we sample a balanced set of popular music from these countries. Using an open-ended tagging pipeline, we then gather emotion terms to create culture-specific taxonomies. Finally, using these bottom-up taxonomies, participants rate emotions of each song. This allows us to map emotional similarities within and across cultures. Results show consistency in high arousal, high valence emotions but greater variability in others. Notably, machine translations were often inadequate to capture music-specific meanings. These findings together highlight the need for a domain-sensitive, open-ended, bottom-up emotion elicitation approach to reduce cultural biases in emotion research.

Are Expressions for Music Emotions the Same Across Cultures?

This paper examines how LLMs handle false presuppositions and whether certain linguistic factors influence their responses to falsely presupposed content.
Presuppositions subtly introduce information as given, making them highly effective at embedding disputable or false information. This raises concerns about whether LLMs, like humans, may fail to detect and correct misleading assumptions introduced as false presuppositions, even when the stakes of misinformation are high.
Using a systematic approach based on linguistic presupposition analysis, we investigate the conditions under which LLMs are more or less sensitive to adopt or reject false presuppositions. Focusing on political contexts, we examine how factors like linguistic construction, political party, and scenario probability impact the recognition of false presuppositions. We conduct experiments with a newly created dataset and examine three LLMs: OpenAI's GPT-4-o, Meta's LLama-3-8B, and MistralAI's Mistral-7B-v03. 
Our results show that the models struggle to recognize false presuppositions, with performance varying by condition. 
This study highlights that linguistic presupposition analysis is a valuable tool for uncovering the reinforcement of political misinformation in LLM responses.

LLMs Struggle to Reject False Presuppositions when Misinformation Stakes are High

The network science perspective on vocabulary development emphasizes the structured relationships between words and how they can influence learning. Words that appear in many contexts and thus develop associations with many other words tend to be learned earlier---a growth model called preferential acquisition. Likewise, children appear to have a bias towards learning new words associated with many known words---a growth model called lure of the associates. Although both are statistically related to age of acquisition estimates, much variance remains unexplained, and it is unknown what structures these models promote within the developing vocabulary. We simulated vocabulary growth strictly adhering to preferential acquisition and lure of the associates and found that they promote similar structures: more connectivity, more clustering, and much shorter path lengths than random growth would achieve. They clearly promote small-world structure, consistent with that seen in young children’s vocabularies.

What if child vocabulary development followed network acquisition models exactly?

A brain-computer interface (BCI) allows direct communication between the human brain and external devices. Despite impressive performance, previous EEG decoding algorithms still face several challenges: 1) how to shorten or eliminate the calibration process in cross-subject BCI scenarios; 2) how to capture more characteristic features from different scales in EEG data; and 3) how to extract subject-independent EEG features more effectively. To address these problems, we propose a cross-subject EEG decoding algorithm based on a multiscale convolutional neural network (MSCNN) and domain adaptation for P300-based BCIs. The MSCNN was trained on a large-scale EEG dataset to extract subject-independent features. Subsequently, we fine-tune the MSCNN through adversarial discriminative domain adaptation (ADDA) to reduce the differences among cross-subject EEG data. In offline analysis, we achieved a cross-subject average accuracy exceeding 83\%, indicating that we successfully established a cross-subject EEG decoding algorithm, which can eliminate the subject-specific calibration process for new subjects.

MSCNN-ADDA: A Cross-Subject P300 EEG Decoding Algorithm Based on a Multi-Scale Convolutional Neural Network and Adversarial Discriminative Domain Adaptation

By understanding the anatomical and functional organization of the visual system at birth we can gain critical insights into the mechanisms that support early developing perceptual and cognitive abilities. In the current study, we used resting-state fMRI and diffusion tensor imaging (DTI) to understand the cortical and subcortical organization of the visual system in newborn human infants (37-42 weeks gestation). We used an adult probabilistic atlas of retinotopic maps (Wang et al. 2015) to identify putative visual areas in neonates. To achieve this, each neonate’s cortical surface was registered to an adult cortical surface template and the adult probabilistic atlas was projected onto each neonate’s cortical surface. Functional correlations between cortical areas revealed that the infant visual system exhibits an adult-like hierarchical organization, with distinct clusters for regions of the occipital cortex, as well as ventral, lateral, and dorsal visual pathways. Direct comparisons between neonates and adults revealed that correlation patterns within occipital and dorsal areas were more adult-like than ventral and lateral areas. We then examined the degree to which developmental motifs discovered in the connectivity between cortical visual areas were mirrored in the organization of structural connections between each cortical area and the pulvinar – a subcortical structure that is extensively interconnected with the entire visual cortex in adults and plays a crucial role in visual processing (Arcaro et al., 2018). Probabilistic tractography analyses reliably identified white matter pathways between the pulvinar and each cortical visual area. These connections showed region-level specificity and overlapped with homologous pathways of adults. However, we found developmental differences within the fine-grained connectivity pattern of the pulvinar. Although the coarse connectivity organization for all neonate networks were qualitatively similar to those of adults, the connectivity maps for ventral visual areas were immature and did not show strong specificity within the pulvinar. Altogether, our findings indicate that the large-scale anatomical and functional organization of the visual system is established by birth. However, they also revealed developmental differences in the maturity of different pathways with the dorsal pathway maturing earlier than the ventral pathway. Thus, by understanding the initial organization of the human visual system, we may shed light on the biological constraints that shape visual development.

The building blocks of vision: Cortical and subcortical organization of the newborn visual system

The ability to generalize when experiencing new situations while retaining specific memories structures our knowledge about the world and guides our forecasts. From an early age, infants and toddlers excel at acquiring generalizable knowledge, amassing an expressive vocabulary, categorizing objects, and following (some) social rules. In contrast, the ability to recollect specific events develops more slowly. Neurocomputational accounts of memory offer insights into how seemingly contradictory goals are met without a trade-off via labor-division schemes in the adult brain, conceptualizing memory as a set of component processes. These processes include (i) pattern separation, which aids the discrimination of similar memories, (ii) pattern completion, which reactivates an episode cohesively from its partial cue, and (iii) pattern categorization, which extracts shared features from related experiences in the service of generalization. My research investigates whether these processes develop at different rates throughout childhood by testing the hypothesis that generalization is prioritized over memory specificity in early life. This talk will present preliminary findings from a longitudinal study, showing that relative to generalization, processes that support memory specificity indeed develop more slowly across childhood.

Developmental trajectories of generalization and memory specificity in childhood

Humans have the remarkable ability to construct commonsense theories about the world through perception. From inferring the physical relationships between objects to understanding social interactions and predicting future events, perception plays a foundational role in shaping how we learn and reason. But how do systems for perception and higher level cognitive theories interact? Are perception and common sense knowledge distinct, such that perception simply provides information for other cognitive systems to act on (Firestone & Scholl, 2016)? Or are they interlinked in ways that allow common-sense theories to affect what we perceive, or represent the world for other commonsense domains (Levin, Baker, & Banaji, 2016)?

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