United States

Previous studies have found that front vowels facilitate finger movement better than back vowels. The previous experiments were conducted with human voices. The aim of this preregistered experiment was to see if the same results as in the previous studies could be achieved with artificially generated vowels. In the experiment, the task was to press the key on the keyboard that corresponded to the vowel sound heard. The results showed that there was no difference in the percentage of correct responses, but the response times were relatively short for the front vowels (especially for /i/), compared with the back vowels (i.e., /u/ and /o/). Even when we conducted another experiment in which we used long vowels to make pronunciation clearer, we also obtained the similar results. The results suggest that artificially generated voices can facilitate finger movements similar to listening to human voices.

CogSci 2025

Facilitating effect of finger movements on artificially generated front vowels

language and thought

semantics of language

motor control

psychology

linguistics

The 27th International Conference on Systems, Signals and Image Processing.

IWSSIP 2020

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.

Cognitive science was founded on the idea that an understanding of mind requires interdisciplinary approaches among specialist fields. However, after more than half a century since the cognitive revolution, the study of mind is as specialized as ever, which raises an existential question: What lies on the horizon for a theory of cognition—unification or dissolution? This question is approached by way of a parallel with the (meta-)mathematical field of category theory. A core principle is construction by universal (mapping) property: every instance of a structure is uniquely composed of a common constituent (map). This principle applies at the level of categories and at the meta-level of categories of other categories. The analogous view of cognitive science presented here is as a meta-theory employing this unifying principle: in the form of a slogan, Cognitive science is to mind as category theory is to mathematics.

Funding: JSPS KAKENHI Grant Number JP23K11797

What theoretical horizon for cognition? Towards a categorical cognitive science

With the growth of the metaverse and XR technologies, BCIs are expanding from medical applications to various consumer industries. However, this broader adoption has raised concerns about the privacy and security of EEG data. To address this challenge, we propose the RPW-EEG framework, which embeds copyright information as perturbations to enhance the security and traceability of data, while ensuring the robustness and usability of the data. The framework adopts an encoder-decoder architecture for end-to-end training, incorporating a noise layer to enhance the stability and anti-attack capabilities of the watermark data. Additionally, to prevent the loss of task-related features in EEG data, we introduce a plug-and-play fine-tuning module that restores these features within the watermark-embedded signals. Experimental results show that RPW-EEG outperforms baseline models in watermark quality, with extraction accuracy exceeding 61% under various attacks, and achieves 88.5% classification accuracy for task paradigms, effectively balancing copyright protection and EEG data usability.

RPW-EEG:An Unified Framework for Robust and Practical Watermark of EEG

While prediction has previously been considered a strong primitive in Subject-Object-Verb (SOV) languages, it is relatively unknown how prediction might manifest in morphologically complex languages with verbs that potentially undergo morphological decomposition into their constituent verb stems and affixes during online sentence comprehension. In this reading study, we looked at how such top-down (prediction) and bottom-up processes (decomposition) interact in an agglutinating language like Malayalam. We investigate two questions simultaneously (i) whether predicting a suffix actually confers any processing advantage during lexical access in real-time, similar to predicting a verb stem, and (ii) whether this can reveal something about the representations of verb stems and suffixes of words within the mental lexicon. We find that when predicted correctly, suffixes pattern similarly to verb stems, which seems to suggest that suffixes can have independent representations alongside verb stems, thereby aiding visual word recognition by acting as access points to activate lexical entries.

Leveraging prediction to investigate the mental lexicon: Evidence from an agglutinating language

Based on the concept that ventral visual stream (VVS) mainly functions for object recognition, current unsupervised task-driven methods model VVS by contrastive learning, and have achieved good brain similarity. However, we believe functions of VVS extend beyond just object recognition. In this paper, we introduce an additional function involving VVS, named relative position (RP) prediction. We first theoretically explain contrastive learning may be unable to yield the model capability of RP prediction. Motivated by this, we subsequently integrate RP learning with contrastive learning, and propose a new unsupervised task-driven method to model VVS, which is more inline with biological reality. We conduct extensive experiments, demonstrating that: (i) our method significantly improves downstream performance of object recognition while enhancing RP predictivity; (ii) RP predictivity generally improves the model brain similarity. Our results provide strong evidence for the involvement of VVS in location perception (especially RP prediction) from a computational perspective.

Improving Unsupervised Task-driven Models of Ventral Visual Stream via Relative Position Predictivity

The current study investigates how (dis)fluency, manipulated by the (in)congruence between speech and gesture, can impact metamemory and memory. In two experiments (N=36 for each), we employed a mismatch paradigm where participants were presented with short videos of an actor simultaneously verbalizing a verb-object pair (e.g., distributing cards) while performing the congruent (e.g., distributing cards) or incongruent iconic hand gesture (e.g., drawing cards). After each video, participants evaluated speech-gesture compatibility on a 5-point Likert scale and provided a JOL rating (0-100). In Experiment 1, they were administered a free recall test, writing only the speech, while Experiment 2 utilized a recognition test to identify the exact video from the encoding phase. Results indicate higher memory performance and higher JOL ratings for congruent videos than incongruent videos. In contrast, no significant differences in memory or metamemory were observed in the second experiment.

Gestures on Memory: How Speech-Gesture Congruency Influences Memory and Metamemory Across Test Types

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.

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

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.

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What theoretical horizon for cognition? Towards a categorical cognitive science

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