United States

Mental attention, a maturational component of working memory, develops significantly during adolescence, yet its neural correlates remain unclear (Arsalidou et al., 2010). This study used fMRI to examine brain activity in adolescents (13–16 years, n = 28) performing a blocked-design Color Matching Task with increasing difficulty. Results revealed consistent activation in frontoparietal regions, including the dorsolateral prefrontal cortex, superior parietal lobule, and cerebellum, across easy and moderate difficulty levels. Higher task demands recruited additional regions, such as the middle frontal gyrus and dorsal anterior cingulate cortex, with lateralization patterns varying by difficulty and age. Whole-brain analyses highlighted distinct recruitment of attentional networks across difficulty levels. Findings align with working memory research, emphasizing the protracted maturation of the prefrontal cortex and functional reorganization of mental-attentional networks during adolescence. This study advances our understanding of cognitive development and contributes to models of working memory and attentional control in developing brains.

CogSci 2025

Neural correlates of mental attention in adolescents: a cross-sectional fMRI study

cognitive development

spatial cognition

cognitive neuroscience

fmri

psychology

memory

### 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.
<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)**.
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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

This study investigates how Large Language Models (LLMs), specifically Baidu’s Ernie Bot, shape personal narratives when users seek emotional support over repeated sessions. Sixteen participants from China engaged in weekly chatbot interactions for four weeks, supplemented by reflective diaries and pre-/post-study interviews. Conversation analysis and quantitative measures (e.g., mood ratings, meaning-making scales) revealed incremental shifts in user language, including increased lexical alignment with AI-generated phrases and more positive emotion words. In-depth interviews highlighted the complex process by which participants alternately embraced or resisted the AI’s framing, with many reporting newfound perspectives and a sense of empathic resonance. However, some voiced skepticism regarding the AI’s genuine capacity for emotional understanding, underscoring ethical dilemmas related to anthropomorphism and data privacy. Overall, the findings suggest that iterative dialogues with an empathic-seeming LLM can facilitate meaningful narrative reframing, albeit with notable variations in user experience and potential risks of over-reliance.

Co-Constructing Meaning with Large Language Models: A Longitudinal Analysis of Human–AI Dialogues in Emotional Support Contexts

This research investigates the feasibility of a communication system using Electroencephalography (EEG) signals mediated by a deep learning model, designed to aid individuals with significant communication challenges. The study builds on prior work in EEG-based image generation, testing the accuracy and efficiency of the system in conveying intended meanings. In the experiment, participants were presented images generated from EEG signals and were asked to give titles according to their interpretation. These titles were analyzed using text embeddings derived from a large language model (LLM) to measure cosine similarity. Results indicate that while sender and receiver interpretations often diverged, consistent patterns emerged within and between receivers. This suggests that repeated communication trials will align interpretations over time, improving mutual understanding. The findings highlight the potential of the method to facilitate adaptive communication, though further research is required to optimize its reliability, scalability, and practical applicability.

From Brainwaves to Understanding: A Study on EEG-Based Communication Systems

This study investigates the way in which participants can distinguish between direct assertions and implied claims with particular emphasis on accuracy and response times. Building on Gardner's (1975) claim-belief interaction theory, which explains advertisements may indirectly influence consumer beliefs through subtle suggestions. The study focuses on examining if participants can accurately identify both types of claims. Using PsychoPy software participants were asked to judge the truthfulness of the claims.
Results showed that subjects could identify direct claims more accurately than they could implied claims (t (95) = -2.197, p < .050). They took more time to respond to implied claims (t (95) = 6.705, p < .001) than direct claims. Thus, these findings highlight cognitive difficulties associated with processing implied claims and that the direct claims are easier to identify and have a quick response time. The study provides valuable insights into how advertisement claims influence consumer understanding and memory.

The Impact of Direct and Implied Claims in Advertising

Thurstone models have been widely applied in wisdom-ofthe-crowd applications to aggregate individual rankings due to their ability to represent individual knowledge and achieve high accuracy. However, they lack the ability to generalize even across highly similar items and cannot leverage external knowledge bases or learned machine representations. In this work, we extend Thurstone models for partial ranking data by introducing a latent construct that maps pretrained vector representations to latent truths. These representations are finetuned through a single neural network layer, enhancing the model’s ability to capture meaningful ranking structures. We evaluate our neural Thurstone model across objective ranking tasks, including animal speeds, material hardness, and the longitudinal positioning of U.S. states from west to east. Our results demonstrate that the extended model improves aggregation accuracy in sparse data settings and generalizes to novel items with moderate predictive accuracy, highlighting its potential to enhance collective intelligence in ranking-based inference.

Neural Thurstone Model: Leveraging Latent Spaces for Collective Intelligence in Ranking Predictions

Electrical status epilepticus during sleep (ESES) is a serious condition that causes notable cognitive decline. It is characterized by distinct spike and slow-wave patterns on electroencephalograms (EEG). Clinical ESES diagnosis is extremely time-consuming and labor-intensive as it demands clinicians to manually interpret and count EEG screens. Existing automated diagnosis algorithms for ESES have major flaws, like struggling to adapt to complex spike-and-wave patterns and not fully exploiting the rich multi-view features of EEG. To overcome these issues, we propose a multi-view feature selection framework integrating reinforcement learning and attention mechanisms for automated ESES diagnosis. A CLEAN reward mechanism is introduced to address complex multi-objective feature selection challenges. Experiments on the clinical data consisting of 36 epilepsy patients prove the proposed method's remarkable spike-and-wave identification ability and high agreement with expert diagnoses. Our approach represents a significant step toward developing automated bedside ESES clinical diagnostic systems.

Multi-view Feature Selection with Reinforcement Learning for EEG-based Automated ESES Diagnosis

The development of numerical cognition is a critical foundation for children’s later mathematics performance, with number ordering as a key predictor of advanced competence. This study examines how children’s number ordering performance varies by number adjacency and size. A sample of 104 kindergartners arranged triplets of numbers in ascending order, with trials featuring adjacent or non-adjacent and small (1–10) or large numbers.  Logistic regressions showed that adjacency, size, and their interaction significantly predicted performance, ps<.05. Children were more accurate with adjacent versus non-adjacent numbers and small versus large numbers. The interaction revealed the adjacency effect was driven by a higher accuracy on small adjacent versus non-adjacent numbers (p<.001); response accuracy was similar for large adjacent versus non-adjacent numbers (p=.27). These results, significant after controlling for verbal counting, ps<.001, underscore the importance of number adjacency and size in designing tasks to measure numerical skills.

The Effect of Task Features on Children’s Number Ordering Performance

Alzheimer's disease (AD) is a neurodegenerative disorder with limited treatment options. While transcranial magnetic stimulation (TMS) has demonstrated therapeutic potential, its underlying mechanisms and personalized applications are not yet fully explored. This study integrates deep learn-ing with neurodynamics, employing a pretrained resting-state AD self-supervised model to constrain dynamic train-ing and simulate TMS-induced neural dynamics in AD pa-tients. The results reveal regional heterogeneity, showing enhanced excitability in stimulated regions, in contrast to reduced global neural dynamics. We identified significant differences in brain responses between AD patients and healthy controls, which provide critical theoretical support for developing TMS treatment strategies. These insights ad-vance the understanding of AD pathophysiology and high-light the potential of TMS interventions, which may lead to more effective therapeutic approaches.

Transcranial Magnetic Stimulation for Modeling Alzheimer's Disease: A Neural Dynamics Approach with Pre-training

This study examined how verbal probability expressions (VPEs) used in group decision-making discussions influence individuals’ decision-making processes. An online experimental task was developed to investigate how biased decisions emerge depending on the type of expression used during discussions. Scripted conversational agents were employed to experimentally manipulate the VPEs used during group discussions. 440 participants took part in the online experiment, which controlled two factors: (1) the type of VPEs used by group members during discussions and (2) the type of anchoring group decision. The results revealed an interaction between these two factors when participants perceived the agents as human-like. Specifically, confirmation bias occurred more quickly when positive VPEs were used by the agents and the anchoring probability of the group decision was low (20\%). These findings provide valuable insights into the influence of VPEs on probability decision-making during group discussions, highlighting the advantages of utilizing multiple conversational agents for investigation.

Influences of Language Expressions in Group Decision Making: Exploring Verbal Probability Expressions in Group Discussions with Conversational Agents

Children express some concepts in their gestures before expressing them in speech (Goldin-Meadow, 2015). This phenomenon has been shown in several domains that are rich in visuospatial information. One other domain that can benefit from gestures is emotion expression (Kelly & Tran, 2023). In this study, we explored monolingual Turkish speaking children (N=23, Mage=8.6) and adults (N=19, Mage=35.6) in emotion recall after watching a silent video demonstrating a range of emotions. We coded participants’ emotion expression either in speech-alone or multimodally (speech plus head/body/hand gestures and/or facial expressions). Overall, children (M=13.8, SD=6.4) recalled significantly more emotions than adults (M=9.5, SD=3.8) (p=.014). They also recalled emotions significantly more multimodally (M=8, SD=7) compared to adults (M=4.9, SD=4) (p=.03). These results corroborate previous research on children’s reliance on gestures, now also extending it to the domain of emotions and by incorporating facial expressions as an alternative expression channel.

Children express emotions multimodally before expressing them in speech

Gestures and silent pauses are integral to the pragmatic, semantic, and temporal organization of speech. However, how gesture inhibition affects silent pauses in emotional context remains unexplored. This study examined how narrative type and gesture conditions affect (a) the distribution of silent pauses [short (250–500 ms), medium (500–1000 ms), and long (>1000 ms)], measured by frequency, average duration, and time ratio, while controlling for speech rate, and (b) self-reported emotional intensity. Thirty participants (Mage=20.61) narrated negative (sadness, fear, anger) and neutral (daily routine) experiences in Hindi-English under gesture-restricted (N=15) and gesture-free (N=15) conditions. A significant main effect of narrative type was found: short pauses increased in neutral narratives under the gesture-free condition, and long pauses increased in negative narratives under the gesture-restricted condition. No significant effect of gesture condition or interaction effect was observed. Gesture restriction also appeared to increase self-reported emotional intensity during negative narratives.

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