United States

Conventional methods in event cognition often focus on identifying boundaries by instructing participants to mark transitions. While effective for detecting shifts, they offer limited insight into how events unfold. This study examines six characteristics—location, people, activities, mood, bodily states, and purposeful actions—and their stability across daily events. Using nightly segmentation, 41 participants captured and reviewed daily images over 14 days, defined events, and described each using the six dimensions. People (0.58) and location (0.55) were most stable, followed by mood (0.38) and bodily states (0.31). Activities (0.07) and purposeful actions (0.18) were highly variable. These findings emphasise that characteristics such as goals and activities not only serve as effective markers for identifying transitions at boundaries but also provide valuable perspectives on how events are distinguished and understood within the broader context of daily life.

Keywords: Daily Events, Event Cognition, Nightly Segmentation.

CogSci 2025

More Than Boundaries: Exploring the Characteristics and Attributes of Daily Life Events

event cognition

psychology

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. 





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### 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.

Sensory processing sensitivity (SPS), a trait marked by heightened reactivity to stimuli, is linked to emotional dysregulation and stress-related problems. Its subscales include Ease of Excitation that reflects emotional sensitivity to internal and external demands, Low Sensory Threshold reflects sensory overload susceptibility, and Aesthetic Sensitivity denotes appreciation for subtleties.  While music can sometimes amplify maladaptive outcomes (rumination, avoidance), SPS’s role in such behaviours, especially in non-Western contexts, remains underexplored.  This study examines how SPS drives maladaptive music listening in 673 Indian adults. Network analysis and structural equation modelling revealed sensitivity to external demands elevated psychological distress which in turn predicted maladaptive music use. Reactivity to internal demands may reduce maladaptive music use but exacerbated it when mediated by external demands, reflecting preference for avoidant coping in Indian context. Findings emphasize SPS’s role in maladaptive behaviours, demonstrating how sensory reactivity interacts with traits to shape distress-driven music use as an emotional regulation mechanism.

Role of Sensory Processing Sensitivity in driving Maladaptive Music Use

Advancements in brain-computer interface (BCI) technology emphasize the need to understand brain signals in emotions and social interactions. Electroencephalograms (EEG) are essential for analyzing brain activity and diagnosing neurological disorders but suffer from low signal-to-noise ratios and high noise levels, hindering accurate interpretation. To address this, we propose a unified information-theoretic framework for optimizing EEG signal representation and fusion learning. Guided by this framework, we developed EEG-FNN, a Fusion Neural Network model that integrates raw EEG data with Gramian Angular Field (GAF) transformations through an innovative attention-driven fusion technique. This approach captures diverse neural activity patterns, significantly improving the ability to distinguish neurological states. Experimental validation on short-duration BCI and long-duration clinical datasets demonstrates that EEG-FNN outperforms existing methods, achieving higher accuracy and robustness, thus confirming its potential as a reliable tool for EEG analysis.

Unified Fusion Network Model for EEG Signals

Today, people make judgments with the help of artificial intelligence (AI) assistance in many situations, such as medical diagnoses. Although many studies have examined the effects of AI assistance, they have mainly focused on aspects of AI (e.g., AI’s accuracy). Here, we emphasize the importance of interactions between AI and human biases. A highly accurate AI may not always be a promising intervention; rather, AI with biases (especially in the direction opposite to individuals’ biases) may work effectively because AI’s biases may cancel out individuals’ biases (e.g., individuals’ overestimation bias may be corrected by AI’s underestimation bias). We investigated these is-sues using a simple perceptual task assuming medical judgments. First, computer simulations showed that appropriate AI assistance would differ depending on individuals’ prior beliefs. Behavioral experiments demonstrated that AI with biases in the direction opposite to participants’ biases could effectively reduce their biases. However, participants tended to evaluate AI with biases in the same direction as their own and considered it more trustworthy. Our theoretical and empirical results raise questions about conventional beliefs that more accurate, trustworthy AI should be better. Our findings will provide practical implications for designing AI as a collaborator of people.

Effective but untrustworthy: How artificial intelligence bias opposing human bias affects judgments

The interaction between hippocampal ripples and cortical slow oscillations (SO) has been proposed to play a critical role in memory consolidation during sleep. However, the neuronal mechanisms underlying the transmission of ripples within cortical regions remain poorly understood. In this study, we used a computational model to investigate how ripple events propagate through cortical networks. We found that sparse and weak inter-areal connections impede ripple propagation, while dense and strong inter-areal connections facilitate it. Notably, our findings reveal that when cortical networks exhibit slow oscillations (SOs), characterized by alternating up and down states, ripples occurring before the SO peak can propagate to distant cortical areas even in the presence of sparse and weak inter-areal connections. These results indicate that the precise coordination between SO and the ripple promotes efficient communication in cortical regions during sleep. This study offers new mechanistic insights into the role of SOs during slow-wave sleep, deepening our understanding of the processes underlying memory consolidation.

Precise SO-ripple coupling facilitates the signal transmission during slow-wave sleep

Audience design, the ability to tailor communication to an interlocutor’s perceived competence and conversational behavior, is a fundamental cognitive process shaping communicative interactions. Robots provide a unique context in which speakers must establish communication strategies with minimal prior experience with their interlocutor, making HRI an effective tool for exploring audience design. This study examines whether people adapt their communication differently when addressing a robot versus a human and how prior conversational success influences these adaptations. In a word-guessing game, participants described word meanings to an audience, who maintained a 50% accuracy rate across randomly ordered correct and incorrect trials. They played one round with each audience separately. We analyzed adaptations in participants’ description strategies (e.g., definitions, semantic categories) and lexical choices (e.g., specificity, word frequency). Findings indicate that adaptation unfolds dynamically over time, shaped by accumulating conversational experience rather than fixed patterns. This study refines theories of language adaptation across diverse audiences and highlights implications for social cognition.

Adapting to the Unfamiliar: Communicative Adjustments in Human-Robot Interaction (HRI)

Alzheimer's disease (AD) is a highly heritable neurodegenerative disease, and brain imaging genetics (BIG) has become a key area for understanding its pathogenesis. However, existing methods often ignore the complex interrelationships between the multiple factors that lead to AD, especially when exploring the intrinsic connection between brain imaging features and gene variation. To address this challenge, we proposed a multimodal association modeling framework (MAM-GAN) based on generative adversarial networks, which aims to deeply reveal the association between genes and brain imaging features and apply it to disease state prediction. To verify the effectiveness of the framework, we conducted experiments using public datasets, and the results showed that MAM-GAN performed well in two classification tasks and successfully identified biomarkers closely related to AD.

MAM-GAN: Multimodal association modeling based on generative adversarial networks for Alzheimer's disease diagnosis

While modern computational models accurately predict free
viewing scanpaths via learned deep neural networks, the in-
vestigation of the brain’s implementation of the modeled be-
havior has been thus far limited to primarily bottom-up vi-
sual features. One of the key question in the investigation in
the early visual system’s implementation of bottom-up visual
saliency is how (and whether) it dynamically adapts its sensi-
tivity to features of different spatial scales. This paper provides
a simple test of whether identical stimuli presented at different
spatial scales produce different gaze behavior. We asked sub-
jects (n=12) to freely view video stimuli twice each session
over two sessions In one session (intervention) the visual scale
changed from large (25 degrees of visual angle) to small (10
degrees) between viewings. In the other session (control) the
video size was the same. Gaze was more strongly correlated
between viewings of the same video size (r=0.265) than differ-
ent sizes (r=0.231), independent of whether there was a long
(> 24 hours) or short (< 10 min) delay between viewings, im-
plying that memory effects are not a strong factor. Although
low, these within-subject correlations are higher than the cor-
relation of gaze between different subjects viewing identical
videos (r=0.195).

Stimulus size influences gaze targets during free viewing of natural video

As global life expectancy increases, cognitive impairment and dementia are becoming increasingly common. With limited effective treatments available, early identification of cognitive decline markers is essential for timely intervention. While many studies focus on predicting cognitive impairment, forecasting the trajectory of cognitive development offers greater foresight, enabling interventions even before diagnostic thresholds are reached. This study identifies 10 key determinants of cognitive trajectories and introduces a novel 2-stage model, CoTTA (Cognitive Trajectory Tracking Algorithm). CoTTA integrates causal inference with predictive modeling to forecast cognitive trajectories using data from 9,345 participants aged 50–80 at baseline year from CHARLS, a nationally representative sample of middle-aged and older Chinese adults. By leveraging causal features, CoTTA predicts the risk of consistently low cognitive function over an 8-year period, outperforming baseline models, particularly in recall and F1 score. This approach offers a scalable solution for early intervention and long-term cognitive health management.

Long-Term Cognitive Trajectory Prediction in a Chinese Cohort of Middle-Aged and Older Adults Using Causal Machine Learning

Large Language Models (LLMs) have been proven useful for various tasks but remain vulnerable to malicious exploitation. Attackers can bypass LLM safety restrictions ("jail") through carefully crafted "jailbreaking" prompts. To evaluate LLMs' security, researchers proposed various jailbreak techniques based on optimization, obfuscation, or persuasive strategies. However, these methods treat LLMs as passive persuasion targets, which overlooks LLMs' ability to reason actively. We propose Persu-Agent, a novel jailbreak framework based on Greenwald's Cognitive Response Theory. We focus more on LLM's internal cognitive processing of a prompt than the prompt itself. Persu-Agent uses the self-persuasion strategy to guide LLMs in generating justifications and rationalizing responses to harmful queries. The experimental results on advanced open-source and commercial LLMs revealed that Persu-Agent achieved an average jailbreak success rate of 84%, surpassing existing SOTA methods. Our work provides valuable insights into understanding LLMs' cognitive traits and contributes to developing safer LLMs.

Self-Persuasion: A Novel Cognitive Approach to Effective LLM Jailbreaking

This study investigates how integrating human eye-tracking data into Large Language Models (LLMs) and Visual Large Language Models (VLLMs) can enhance document comprehension in tasks that require both linguistic understanding and visual attention, specifically Semantic Entity Recognition (SER) and Document Question Answering (DQA). Despite rapid advancements in AI-based document understanding, LLMs still face challenges in replicating the depth of human cognition, particularly in how reading order and visual attention affect comprehension. The results demonstrate that human reading order and the regions they focus on significantly impact performance in both tasks. Additionally, while LLMs do not need to fully mimic human reading sequences, their performance improves when their attention patterns align more closely with human visual strategies. This highlights the importance of incorporating cognitive-inspired attention mechanisms in AI systems, offering a path to better AI models that reflect human cognitive strategies in complex document understanding.

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Role of Sensory Processing Sensitivity in driving Maladaptive Music Use

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