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arXiv 2025年

作者： Havin, Miriam Kleinman, Timna Wharton Koren, Moran Dover, Yaniv Goldstein, Ariel Department of Cognitive and Brain Sciences The Hebrew University of Jerusalem Israel The Federmann Center for the Study of Rationality The Hebrew University of Jerusalem Israel School of Computer Science and Engineering The Hebrew University of Jerusalem Israel Industrial Engineering and Management Department Ben Gurion Univeristy Israel The Hebrew University Business School Jerusalem Israel

The increasing integration of large language model (LLM) based conversational agents into everyday life raises critical cognitive and social questions about their potential to influence human opinions. Although previous studies have shown that LLM-based agents can generate persuasive content, these typically involve controlled, English-language settings. Addressing this, our preregistered study explored LLM’s persuasive capabilities in more ecological, unconstrained scenarios, examining both static (written paragraphs) and dynamic (conversations via Telegram) interaction types. Conducted entirely in Hebrew with 200 participants, the study assessed the persuasive effects of both LLM and human interlocutors on controversial civil policy topics. Results indicated that participants adopted LLM and human perspectives similarly, with significant opinion changes evident across all conditions, regardless of interlocutor type or interaction mode. Confidence levels increased significantly in most scenarios, except in static LLM interactions. These findings demonstrate LLM-based agents’ robust persuasive capabilities across diverse sources and settings, highlighting their potential impact on shaping public opinions. © 2025, CC BY.

关键词： Chatbots

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Cross-Subject Decision Confidence Estimation from EEG Signals Using Spectral-Spatial-Temporal Adaptive GCN with Domain Adaptation

Cross-Subject Decision Confidence Estimation from EEG Signal...

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International Joint Conference on Neural Networks (IJCNN)

作者： Rong-Fei Gu Rui Li Wei-Long Zheng Bao-Liang Lu Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai China RuiJin-Mihoyo Laboratory Clinical Neuroscience Center RuiJin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China

The study of the human decision-making process has long been a valuable field for both scientific research and practical application. Towards knowing and taking control of the decision-making process, evaluating the reliability of human decisions objectively plays an important role. Various studies have demonstrated that the confidence level of humans during the decision-making process is an important factor that reflects the correctness of decisions. In literature, several deep learning based methods have been developed to estimate decision confidence using Electroencephalography (EEG). Among these approaches, the spectral-spatial-temporal adaptive graph convolutional neural network (SST-AGCN) stands out. However, SST-AGCN focuses on specific subjects, and may lead to less efficiency in cross-subject situations, which are more common in application scenarios. In this paper, we propose a deep learning model called SST-AGCN with Domain Adaptation (SST-AGCN-DA) for cross-subject decision confidence estimation. To examine the effectiveness of our proposed model, we compare our SST-AGCN-DA with the original SST-AGCN, three typical domain adaption algorithms in the field, and the SST-AGCN with Domain Generalization (SST-AGCN-DG), which is another transfer learning model we developed in this paper. We conduct cross-subject confidence estimation experiments on an EEG dataset collected under a text-based decision-making task. The averaged results of leave-one-out cross-validation come out that the F1-scores of our proposed SST-AGCN-DA and SST-AGCN-DG are 79.45% and 77.04%, respectively, while the original SST-AGCN and the best of the existing domain adaptation algorithms are 74.15% and 74.25%, respectively.

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The Effect of Face-to-Face Interaction on Older Adults’ Attitudes Toward Robots in Human-computer Interaction 25th

The Effect of Face-to-Face Interaction on Older Adults’ Att...

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25th International Conference on Human-computer Interaction, HCII 2023

作者： Yang, Chien-Chun Yeh, Su-Ling Chien, Sung-En Huang, Tsung-Ren Chang, Yu-Ling Goh, Joshua O. S. Chen, Yi-Chuan Fu, Li-Chen Department of Psychology College of Science National Taiwan University Taipei Taiwan Department of Medicine Mackay Medical College New Taipei City Taiwan Center for Artificial Intelligence and Advanced Robotics National Taiwan University Taipei Taiwan Neurobiology and Cognitive Science Center National Taiwan University Taipei Taiwan Graduate Institute of Brain and Mind Sciences College of Medicine National Taiwan University Taipei Taiwan Department of Neurology College of Medicine National Taiwan University Hospital National Taiwan University Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan MOST Joint Research Center for AI Technology and All Vista Healthcare Taipei Taiwan

ISBN: (纸本)9783031359910

Interpersonal interactions are often expected to be polite and follow communicative norms to avoid potential social conflicts by employing strategies such as face work. Although face work has been well-documented in human-human interactions, it is unclear whether people would be polite to the robots and give them higher ratings in face-to-face interactions than in paper questionnaires. In addition, according to the Socioemotional Selectivity Theory, older adults are more likely to be polite to reduce potential interpersonal tensions and maintain emotional harmony, which causes them to respond kindly to others. The current study investigated how face-to-face human-robot interactions influence people’s subjective evaluations of the robot and, more critically, whether older and younger adults perform differently. We recruited older and younger adults and assessed their attitudes toward the robot through face-to-face interaction and a paper questionnaire. The results revealed that the older adults rated the robot more positively than the younger adults. Critically, the older adults rated the robot’s personal association and perceived ease of use higher when responding to the robot directly than when answering the paper questionnaire;however, the younger adults did not show this difference. The findings emphasize the importance of face-to-face interaction and social norms in human-robot interactions, especially for older adults. Our findings have implications for human-computer interaction researchers and practitioners who are interested in developing social robots that are sensitive to social norms for different age groups. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Human robot interaction

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Diagnosis of Pediatrics Epilepsy Based on Graph Analysis of Scalp EEG Applying Mutual Information

Diagnosis of Pediatrics Epilepsy Based on Graph Analysis of ...

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2022 International Conference on Computational science and Computational Intelligence, CSCI 2022

作者： Zarafshan, Elaheh Forouzannezhad, Parisa Rajaei, Hoda Morar, Ulyana Cabrerizo, Mercedes Yaylali, Ilker Pinzon, Alberto Adjouadi, Malek Florida International University Department of Electrical and Computer Engineering FL United States School of Medicine University of Washington Dept. of Radiation Oncology SeattleWA United States Massachusetts Institute of Technology Department of Brain and Cognitive Science MA United States Oregon Health and Science University Department of Neurology PortlandOR United States Neuroscience Center Baptist Hospital of Miami Dept. of Neurology FL United States

ISBN: (纸本)9798350320282

Epilepsy is a brain disorder that causes seizures, affecting nearly half a million children in the US alone. In this study, we aimed to use a nonlinear driven method to characterize scalp EEG recordings of pediatric epilepsy patients (PE: n=7) compared to pediatric control subjects (PC: n=7) in a clinical environment. A time-varying approach was used to construct functional connectivity networks (FCNs) of all subjects. Next, the FCNs are mapped into the form of undirected graphs that are subjected to the extraction of graph theory-based features. An unsupervised clustering technique based on K-mean is used to delineate the PE from the PC group. Our findings show a statistically significant difference in the mean FCNs between PC and PE groups (t(340)=- 15.9899, p © 2022 IEEE.

关键词： Graph theory

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Tracing the emergence of gendered language in childhood 42

Tracing the emergence of gendered language in childhood

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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： Prystawski, Ben Grant, Erin Nematzadeh, Aida Lee, Spike W.S. Stevenson, Suzanne Xu, Yang Computer Science and Cognitive Science Programs University of Toronto Canada Department of Electrical Engineering & Computer Sciences University of California Berkeley United States DeepMind Rotman School of Management Department of Psychology University of Toronto Canada Department of Computer Science University of Toronto Canada Department of Computer Science Cognitive Science Program University of Toronto Canada

Are gender associations in general language reflected in the words spoken to and by children? Previous work has suggested that language reveals gender differences in discourse, speech style, language use and acquisition. Work in artificial intelligence has shown that word embeddings trained on large corpora reflect human gender associations. We connect this work to developmental psychology by exploring whether gender associations in word embeddings are present in the linguistic input and output of children, and if so, how early gendered language emerges. We present a computational method that quantifies the gender associations of words and use a corpus of child-caretaker speech to show that these gender associations correlate significantly with those in word embeddings. We discover that gendered word use emerges in English-speaking children around age 2, and the gender associations cannot be explained solely by variables including word length, frequency, concreteness, and valence. © 2020 The Author(s)

关键词： Embeddings

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A computer-Vision Approach for Testing Developmental Changes in Object Manipulation

A Computer-Vision Approach for Testing Developmental Changes...

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IEEE International Conference on Development and Learning, ICDL

作者： Ajay Krishna Brianna E. Kaplan Juliette Pope Sinisa Todorovic Karen E. Adolph Ori Ossmy School of Electrical Engineering and Computer Science Oregon State University Corvallis OR USA Department of Psychology New York University New York NY USA Depts. of Psychology Neuroscience and Child & Adolescent Psychiatry New York University New York NY USA Centre for Brain and Cognitive Development School of Psychological Science Birkbeck University of London London UK

ISBN: (数字)9798350348552

ISBN: (纸本)9798350348569

Object manipulation is a foundational behavior that emerges in infancy and improves with age. Research on children's interactions with objects is a cornerstone for understanding cognitive and motor development. Traditionally, developmental researchers rely on human video annotation to analyze object interactions by categorizing behavioral events (e.g., “banging,” “constructing”). However, human video annotation cannot provide precise, quantitative details from moment to moment about the location and orientation of objects and the movements of each hand and finger during reach, grasp, and manipulation. To overcome the challenges in acquiring real-time, continuous, quantitative data, researchers turned to high-speed motion tracking and inertial measurement units—which require children to wear markers—and to instrumenting the objects. However, “wearables” present a new set of complications as they disrupt the natural spontaneity of children's movements and sensors may fail to accurately track changes in object position and orientation. Critically, only video data captures the subtleties and complexities of manual behavior and the surrounding context. Consequently, we devised a novel, video-based, marker- and sensor-free approach that enables real-time quantification of children's coordination patterns during object interaction. We demonstrate the power of our approach in a tower-building study with children (2 to 8 years) and adults. This approach marks a paradigm shift in testing the evolving dynamics of object manipulation over development.

关键词： Measurement units Tracking Image annotation Manuals Streaming media Motors Real-time systems

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Predicting Dominant Beat Frequency from brain Responses while Listening to Music

Predicting Dominant Beat Frequency from Brain Responses whil...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Pandey, Pankaj Ahmad, Nashra Miyapuram, Krishna Prasad Lomas, Derek Computer Science and Engineering Indian Institute of Technology Gandhinagar Gujarat India Centre for Cognitive and Brain Sciences Indian Institute of Technology Gandhinagar Gujarat India Delft University of Technology Industrial Design Engineering for Positive AI Delft Netherlands

ISBN: (纸本)9781665401265

Modern neuroscience has shown that the brain is profoundly rhythmic and that frequencies of neural rhythms are responsive to frequencies of musical rhythms. We collected Electroencephalography (EEG) response on 12 naturalistic music stimuli (songs), from 20 participants. We retrieved the tempo and its sub-harmonics from our stimuli (songs), and further used this information to predict the beats in the brain response using Machine Learning techniques. We observed a hierarchy of beats in each of the songs, with a specific beat frequency to be dominant (i.e. higher in magnitude) than others. This led us to form three groups of songs and their brain responses, with each of the groups indicating the frequency of a beat that dominated in the hierarchy of beat structure of that song. We used small segments of 1, 3 and 5 seconds of brain responses, rather than the entire song duration. We further created two sets for classification of the three groups of brain responses and utilized two spatial filtering techniques: Mean across electrodes (ME) and first principal component (PC1), and a Dense method using data from all electrodes. This was followed by feature extraction using band power. We developed univariate and multivariate models for classification to demonstrate the significance of each frequency band which represent beat frequencies. The dense method outperformed ME and PC1. Features related to eighth note generated maximum discrimination between classes. We also observed a positive correlation between window length and rate of correct prediction. Accuracy from one second to five seconds window improved significantly in both the sets. We achieved maximum accuracy of 70% and 56% accuracies for binary and ternary classification respectively, which is 20% above chance-level accuracy. Random Forest and kNN performed better than SVM. This work contributes to the growing body of knowledge to understand the underlying neural mechanism of rhythm processing in the brain. © 202

关键词： Electrodes

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Flexible mapping of abstract domains by grid cells via self-supervised extraction and projection of generalized velocity signals 24

Flexible mapping of abstract domains by grid cells via self-...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Abhiram Iyer Sarthak Chandra Sugandha Sharma Ila Fiete Department of Electrical Engineering and Computer Science MIT Cambridge MA and McGovern Institute for Brain Research MIT Cambridge MA and K. Lisa Yang Integrative Computational Neuroscience (ICoN) MIT Cambridge MA Department of Brain and Cognitive Sciences MIT Cambridge MA and McGovern Institute for Brain Research MIT Cambridge MA Department of Brain and Cognitive Sciences MIT Cambridge MA and McGovern Institute for Brain Research MIT Cambridge MA and K. Lisa Yang Integrative Computational Neuroscience (ICoN) MIT Cambridge MA

ISBN: (纸本)9798331314385

Grid cells in the medial entorhinal cortex create remarkable periodic maps of explored space during navigation. Recent studies show that they form similar maps of abstract cognitive spaces. Examples of such abstract environments include auditory tone sequences in which the pitch is continuously varied or images in which abstract features are continuously deformed (e.g., a cartoon bird whose legs stretch and shrink). Here, we hypothesize that the brain generalizes how it maps spatial domains to mapping abstract spaces. To sidestep the computational cost of learning representations for each high-dimensional sensory input, the brain extracts self-consistent, low-dimensional descriptions of displacements across abstract spaces, leveraging the spatial velocity integration of grid cells to efficiently build maps of different domains. Our neural network model for abstract velocity extraction factorizes the content of these abstract domains from displacements within the domains to generate content-independent and self-consistent, low-dimensional velocity estimates. Crucially, it uses a self-supervised geometric consistency constraint that requires displacements along closed loop trajectories to sum to zero, an integration that is itself performed by the downstream grid cell circuit over learning. This process results in high fidelity estimates of velocities and allowed transitions in abstract domains, a crucial prerequisite for efficient map generation in these high-dimensional environments. We also show how our method outperforms traditional dimensionality reduction and deep-learning based motion extraction networks on the same set of tasks. This is the first neural network model to explain how grid cells can flexibly represent different abstract spaces and makes the novel prediction that they should do so while maintaining their population correlation and manifold structure across domains. Fundamentally, our model sheds light on the mechanistic origins of cognitive flexib

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Depression Severity Identification Based on Shallow 2d Self-Attention-Cnn Using Eeg Functional Connectivity Network

SSRN

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SSRN 2024年

作者： Zhou, Yihan Yu, Xiaokang Lin, Huiping Li, Rihui Liang, Jiuxing Shi, Xue Luo, Yuxi School of Biomedical Engineering Shenzhen Campus of Sun Yat-sen University Guangdong Shenzhen518100 China Center for Cognitive and Brain Sciences Department of Electrical and Computer Engineering University of Macau Taipa China Key Laboratory of Brain Cognition and Education Science Ministry of Education Institute for Brain Research and Rehabilitation Guangdong Key Laboratory of Mental Health and Cognitive Science South China Normal University China Department of Neurology Shenzhen People's Hospital The Second Clinical Medical College Jinan University The First Affiliated Hospital Southern University of Science and Technology Shenzhen China Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province Sun Yat-sen University Guangdong Shenzhen518100 China

Background and objectives: Depression inflicts significant harm on both society and family. Previous studies have indicated that the functional network of EEG signals worked well in recognizing major depression. This study aims to further identify depression severities and characterize their EEG functional network difference by designing a deep learning strategy and a corresponding visualization ***: Rest state EEG recordings from 30 healthy controls, 35 mild depressed and 26 severe depressed patients were included. Weighted phase lag indexes were computed across four frequency sub-bands to delineate the functional connectivity of EEG networks, serving as the input matrix. To better adapt volume conduction effects, a shallow CNN-based incorporated with 2D Self-Attention architecture was designed, enabling the model to capture information across diverse spans and scales within the functional connectivity (FC) matrix. Leveraging the Grad-CAM algorithm, the model highlighted crucial FCs and corresponding EEG pairs for classification. Finally, the changes of EEG FC network across depression severities were statistically analyzed and ***: An accuracy of 89.2% was achieved in tri-classification of 10-second EEG segments using 52 EEG channels, remaining high at 84.1% with 30 selected channels. Notably, the investigation revealed that significant FC changes from mild to severe depression did not exhibit a simple or monotonous ***: This research presented a directly measured methodology to identify depression severity, vital for informing prevention and therapeutic interventions. Furthermore, the findings shed light on the evolving patterns of brain function as depression progresses. The proposed deep learning model and channel selection algorithm offer potential applications beyond this study, promising broader utility in EEG-based research endeavors. © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Adaptive network interactions in cognition after stimulation-induced inferior parietal lobe inhibition

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brain Stimulation 2023年第1期16卷 324-325页

作者： Williams, Kathleen Numssen, Ole Bzdok, Danilo Hartwigsen, Gesa Max Planck Institute for Human Cognitive and Brain Sciences Lise Meitner Research Group Cognition and Plasticity Germany Mila QC Canada Montreal Neurological Institute McConnell Brain Imaging Centre Canada Department of Biomedical Engineering Faculty of Medicine and School of Computer Science McGill University Montréal QC Canada

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