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arXiv 2024年

作者： Jeon, Yeseul Kim, Jeong-Jae Yu, SuMin Choi, Junggu Han, Sanghoon Department of Statistics and Data Science Yonsei University Seoul Korea Republic of Graduate Program in Cognitive Science Yonsei University Seoul Korea Republic of Department of Psychology and Neuroscience Duke University DurhamNC United States Department of Psychology Yonsei University Seoul Korea Republic of

Functional magnetic resonance imaging (fMRI) data is characterized by its complexity and high-dimensionality, encompassing signals from various regions of interests (ROIs) that exhibit intricate correlations. Analyzing fMRI data directly proves challenging due to its intricate structure. Nevertheless, ROIs convey crucial information about brain activities through their connections, offering insights into distinctive brain activity characteristics between different groups. To address this, we propose a cuttingedge interpretable fusion analytic framework that facilitates the identification and understanding of ROI connectivity disparities between two groups, thereby revealing their unique features. Our novel approach encompasses three key steps. Firstly, we construct ROI functional connectivity networks (FCNs) to effectively manage fMRI data. Secondly, employing the FCNs, we utilize a self-attention deep learning model for binary classification, generating an attention distribution that encodes group differences. Lastly, we employ a latent space item-response model to extract group representative ROI features, visualizing these features on the group summary FCNs. We validate the effectiveness of our framework by analyzing four types of cognitive impairments, showcasing its capability to identify significant ROIs contributing to the differences between the two disease groups. This novel interpretable fusion analytic framework holds immense potential for advancing our understanding of cognitive impairments and could pave the way for more targeted therapeutic interventions. Copyright © 2024, The Authors. All rights reserved.

关键词： Neurophysiology

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Gritty Heart: Improved Heart Rate Variability Markers of Adaptive Physiological Response in Grit

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Psychophysiology 2025年第5期62卷 e70064页

作者： Yoo, Jaehoon Kim, Boyoon Park, Sujin Jeon, Jeewon Yoo, Chaebin Kim, M. Justin Park, Daeun Department of Psychology Sungkyunkwan University Seoul South Korea Department of Cognitive Science University of California San Diego La Jolla CA United States Department of Psychology & Program in Neuroscience and Cognitive Science University of Maryland College Park MD United States Center for Neuroscience Imaging Research Institute for Basic Science Suwon South Korea

Grit is a personality trait, conceptualized as perseverance of effort and consistency of interests in long-term goals. Previous research has shown that grit is associated with various positive outcomes, including well-being. Despite extensive research on grit, most studies relied on self-reported measures rather than objective measures. To address this gap, our study investigated the relationship between grit and physiological responses, focusing on resting-state heart rate variability (HRV)–a physiological marker of well-being and adaptability. Additionally, we examined whether this relationship was unique to grit and not explained by other related psychological constructs (i.e., conscientiousness and self-control). A total of 206 healthy college students participated in this study (M_age = 21.03, SD = 2.48, age range = 18–33;111 women). Results of hierarchical regression analyses showed that grit significantly predicted resting-state HRV measures (i.e., RMSSD, SDNN, and HF) even after controlling for conscientiousness, self-control, age, gender, and respiration rate. These findings suggest that grit may play an important role in maintaining improved biological responses in daily life, beyond the effects of neighboring constructs. © 2025 The Author(s). Psychophysiology published by Wiley Periodicals LLC on behalf of Society for Psychophysiological Research.

关键词： conscientiousness grit heart rate variability self-control

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EARLY-STAGE DETECTION OF cognitive IMPAIRMENT BY HYBRID QUANTUM-CLASSICAL ALGORITHM USING RESTING-STATE FUNCTIONAL MRI TIME-SERIES

arXiv

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arXiv 2024年

作者： Choi, Junggu Park, Daniel K. Lee, Seung-Koo Hur, Tak Shin, Na-Young Lee, Hakbae Han, Sanghoon Yonsei Graduate program in Cognitive science Yonsei University Seoul Korea Republic of Department of Applied Statistics Department of Statistics and Data Science Yonsei University Seoul Korea Republic of Department of Radiology College of Medicine Yonsei University Seoul Korea Republic of Department of Statistics and Data Science Yonsei University Seoul Korea Republic of Department of Psychology Yonsei Graduate program in Cognitive science Yonsei University Seoul Korea Republic of

Following the recent development of quantum machine learning techniques, the literature has reported several quantum machine learning algorithms for disease detection. This study explores the application of a hybrid quantum-classical algorithm for classifying region-of-interest time-series data obtained from resting-state functional magnetic resonance imaging in patients with early-stage cognitive impairment based on the importance of cognitive decline for dementia or aging. Classical one-dimensional convolutional layers are used together with quantum convolutional neural networks in our hybrid algorithm. In the classical simulation, the proposed hybrid algorithms showed higher balanced accuracies than classical convolutional neural networks under the similar training conditions. Moreover, a total of nine brain regions (left precentral gyrus, right superior temporal gyrus, left rolandic operculum, right rolandic operculum, left parahippocampus, right hippocampus, left medial frontal gyrus, right cerebellum crus, and cerebellar vermis) among 116 brain regions were found to be relatively effective brain regions for the classification based on the model performances. The associations of the selected nine regions with cognitive decline, as found in previous studies, were additionally validated through seed-based functional connectivity analysis. We confirmed both the improvement of model performance with the quantum convolutional neural network and neuroscientific validities of brain regions from our hybrid quantum-classical model. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolution

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Over the Quantum Rainbow: Explaining Hybrid Quantum Reinforcement Learning

Over the Quantum Rainbow: Explaining Hybrid Quantum Reinforc...

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Quantum Computing and Engineering (QCE), IEEE International Conference on

作者： Junghoon Park Jiook Cha Samuel Yen-Chi Chen Shinjae Yoo Huan-Hsin Tseng Interdisciplinary Program in Artificial Intelligence Seoul National University Department of Psychology Seoul National University Department of Brain and Cognitive Sciences Seoul National University Graduate School of Data Science Seoul National University Wells Fargo Computational Science Initiative Brookhaven National Laboratory

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

In the realm of artificial intelligence, deep rein-forcement learning (RL) agents struggle with generalizability and require substantial computational resources, unlike humans who easily adapt and generalize across tasks. To address these challenges, we introduce Quantum Rainbow, a hybrid algorithm that leverages the neural mechanisms of human decision-making and the efficiency of quantum computing. Quantum Rainbow combines variational quantum circuits with the Rainbow Deep Q-Network (DQN) model to create a novel approach in rein-forcement learning that integrates quantum principles into deep learning paradigms. We evaluate our model using behavioral experiments through the Iowa Gambling Task and 4-Armed Bandit Task. Our investigations reveal a significant relationship between the architecture of quantum circuits and the performance of quantum RL agents. Specifically, using causal discovery methods, we demonstrate the critical role of quantum entanglement in enhancing model performance. These findings not only show promising results but also pave the way for future explorations into optimizing quantum circuit architectures for reinforcement learning applications. This study underscores the potential of quantum-enhanced algorithms to achieve “quantum advantage” by addressing fundamental limitations in conventional deep RL methods.

关键词： Analytical models Quantum computing Quantum algorithm Quantum advantage Quantum entanglement Computational modeling Reinforcement learning Quantum state Integrated circuit modeling Quantum circuit

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Linking In-context Learning in Transformers to Human Episodic Memory

arXiv

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arXiv 2024年

作者： Ji-An, Li Zhou, Corey Y. Benna, Marcus K. Mattar, Marcelo G. Neurosciences Graduate Program University of California San Diego United States Department of Cognitive Science University of California San Diego United States Department of Neurobiology University of California San Diego United States Department of Psychology New York University United States

Understanding connections between artificial and biological intelligent systems can reveal fundamental principles of general intelligence. While many artificial intelligence models have a neuroscience counterpart, such connections are largely missing in Transformer models and the self-attention mechanism. Here, we examine the relationship between interacting attention heads and human episodic memory. We focus on induction heads, which contribute to in-context learning in Transformer-based large language models (LLMs). We demonstrate that induction heads are behaviorally, functionally, and mechanistically similar to the contextual maintenance and retrieval (CMR) model of human episodic memory. Our analyses of LLMs pre-trained on extensive text data show that CMR-like heads often emerge in the intermediate and late layers, qualitatively mirroring human memory biases. The ablation of CMR-like heads suggests their causal role in in-context learning. Our findings uncover a parallel between the computational mechanisms of LLMs and human memory, offering valuable insights into both research fields. © 2024, CC BY.

关键词： Intelligent systems

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Functional connectivity in resting-state fMRI (rs-fMRI) in opioid use disorder

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The European Physical Journal Special Topics 2025年 1-11页

作者： Atanasova, Neli Todeva-Radneva, Anna Stoyanova, Kristina Psederska, Elena Stoyanov, Drozdstoy Traykova, Nikoleta Vassileva, Jasmin Strategic Research and Innovation Program for the Development of MU-PLOVDIV–(SRIPD-MUP) European Union-NextGeneration EU Plovdiv Bulgaria Department of Psychiatry and Medical Psychology Medical University Plovdiv Plovdiv Bulgaria Research Institute at Medical University of Plovdiv Medical University of Plovdiv Plovdiv Bulgaria Department of Cognitive Science and Psychology New Bulgarian University Sofia Bulgaria Institute for Drug and Alcohol Studies Virginia Commonwealth University Richmond USA Department of Psychiatry Richmond USA

This mini-review examines functional connectivity in resting-state functional magnetic resonance imaging (rs-fMRI) among opioid users. The goal is to summarize existing research data and clarify the implications of altered brain connectivity in this population. The first part of the review addresses the critical question of how opioid addiction influences the functional connectivity of key brain networks, such as the default mode network (DMN), salience network (SN), and executive control network (ECN). It examines the neurological basis of opioid addiction, the principles of rs-fMRI, different methodologies employed in this type of research, and inconsistencies and methodological challenges that complicate the interpretation of findings. The second part of the article presents findings derived from our ongoing research in the field. We tested 42 participants of whom 23 healthy controls and 19 patients with opioid use disorder. Each participant underwent an MRI scanning procedure comprised of structural, resting-state and task sequences. The neuroimaging data was processed using the CONN Toolbox running on MATLAB. Our preliminary rs-fMRI findings reveal significant disruptions in functional connectivity in individuals with opioid addiction within DMN and SN networks involved in cognitive functions such as decision-making and impulse control. The review concludes by emphasizing the importance of standardizing research practices, conducting longitudinal randomized studies, and developing a more holistic approach to understanding the effects of heroin addiction. These efforts would contribute to the development of personalized and effective intervention strategies.

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Human Learners Integrate Visual and Linguistic Information in Cross-Situational Verb Learning 43

Human Learners Integrate Visual and Linguistic Information i...

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43rd Annual Meeting of the cognitive science Society: Comparative Cognition: Animal Minds, CogSci 2021

作者： Zhang, Yayun Amatuni, Andrei Cain, Ellis Wang, Xizi Crandall, David Yu, Chen Department of Psychology University of Texas Austin United States Cognitive Science Program Indiana University Bloomington United States Luddy School of Informatics Computing and Engineering Indiana University Bloomington United States

Learning verbs is challenging because it is difficult to infer the precise meaning of a verb when there are a multitude of relations that one can derive from a single event. To study this verb learning challenge, we used children’s egocentric view collected from naturalistic toy-play interaction as learning materials and investigated how visual and linguistic information provided in individual naming moments as well as cross-situational information provided from multiple learning moments can help learners resolve this mapping problem using the Human Simulation Paradigm. Our results show that learners benefit from seeing children’s egocentric views compared to third-person observations. In addition, linguistic information can help learners identify the correct verb meaning by eliminating possible meanings that do not belong to the linguistic category. Learners are also able to integrate visual and linguistic information both within and across learning situations to reduce the ambiguity in the space of possible verb meanings. © cognitive science Society: Comparative Cognition: Animal Minds, CogSci *** rights reserved.

关键词： Toys

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Evolution of emotion semantics

arXiv

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arXiv 2021年

作者： Xua, Aotao Stellar, Jennifer E. Xua, Yang Department of Computer Science University of Toronto Department of Psychology University of Toronto Cognitive Science Program University of Toronto

Humans possess the unique ability to communicate emotions through language. Although concepts like anger or awe are abstract, there is a shared consensus about what these English emotion words mean. This consensus may give the impression that their meaning is static, but we propose this is not the case. We cannot travel back to earlier periods to study emotion concepts directly, but we can examine text corpora, which have partially preserved the meaning of emotion words. Using natural language processing of historical text, we found evidence for semantic change in emotion words over the past century and that varying rates of change were predicted in part by an emotion concept's prototypicality-how representative it is of the broader category of "emotion". Prototypicality negatively correlated with historical rates of emotion semantic change obtained from text-based word embeddings, beyond more established variables including usage frequency in English and a second comparison language, French. This effect for prototypicality did not consistently extend to the semantic category of birds, suggesting its relevance for predicting semantic change may be category-dependent. Our results suggest emotion semantics are evolving over time, with prototypical emotion words remaining semantically stable, while other emotion words evolve more freely. © 2021, CC BY.

关键词： Semantics

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Early-Stage Detection of cognitive Impairment by Hybrid Quantum-Classical Algorithm Using Resting-State Functional MRI Time-Series

SSRN

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SSRN 2024年

作者： Choi, Junggu Hur, Tak Park, Daniel Kyungdeock Shin, Na-Young Lee, Seung-Koo Lee, Hakbae Han, Sanghoon Yonsei Graduate Program in Cognitive Science Yonsei University Seoul Korea Republic of Department of Statistics and Data Science Yonsei University Seoul Korea Republic of Department of Applied Statistics Yonsei University Seoul Korea Republic of Department of Radiology College of Medicine Yonsei University Seoul Korea Republic of Department of Psychology Yonsei University Seoul Korea Republic of Cleveland Clinic United States

Following recent developments in quantum machine learning techniques, several algorithms have been developed for disease detection. This study explored the application of a hybrid quantum-classical algorithm for classifying region-of-interest time-series data obtained from resting-state functional magnetic resonance imaging (FMRI) in patients with early-stage cognitive impairment. Classical one-dimensional convolutional layers were used in conjunction with quantum convolutional neural networks in our hybrid algorithm. In a classical simulation, the proposed hybrid algorithms in our study exhibited higher balanced accuracies than classical convolutional neural networks under similar training conditions. In addition, in our study, among the 116 brain regions, two brain regions (the right hippocampus and left parahippocampus) were found to be relatively effective for classification based on the model performance. The associations of the two selected regions with cognitive decline, as found in previous studies, were validated using seed-based functional connectivity analysis. Thus, we confirmed both improvement in model performance with the quantum convolutional neural network and neuroscientific validity of brain regions from our hybrid quantum-classical model. © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Prototype theory and emotion semantic change 42

Prototype theory and emotion semantic change

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42nd Annual Meeting of the cognitive science Society: Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020

作者： Xu, Aotao Stellar, Jennifer Xu, Yang Department of Computer Science University of Toronto Canada Department of Psychology University of Toronto Canada Department of Computer Science Cognitive Science Program University of Toronto Canada

An elaborate repertoire of emotions is one feature that distinguishes humans from animals. Language offers a critical form of emotion expression. However, it is unclear whether the meaning of an emotion word remains stable, and what factors may underlie changes in emotion meaning. We hypothesize that emotion word meanings have changed over time and that the prototypicality of an emotion term drives this change beyond general factors such as word frequency. We develop a vector-space representation of emotion and show that this model replicates empirical findings on prototypicality judgments and basic categories of emotion. We provide evidence that more prototypical emotion words have undergone less change in meaning than peripheral emotion words over the past century, and that this trend holds within each family of emotion. Our work extends synchronic theories of emotion to its diachronic development and offers a computational characterization of emotion semantics in natural language use. © 2020 The Author(s)

关键词： Semantics

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