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17th International Conference on cognitive Modeling, ICCM 2019

作者： Cho, Sungjae Lim, Jaeseo Hickey, Chris Park, Jung Ae Zhang, Byoung-Tak Interdisciplinary Program in Cognitive Science Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of Department of Psychology Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of Department of Computer Science and Engineering Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of

ISBN: (纸本)9780998508238

The present study aims to investigate similarities between how humans and connectionist models experience difficulty in arithmetic problems. Problem difficulty was operationalized by the number of carries involved in solving a given problem. Problem difficulty was measured in humans by response time, and in models by computational steps. The present study found that both humans and connectionist models experience difficulty similarly when solving binary addition and subtraction. Specifically, both agents found difficulty to be strictly increasing with respect to the number of carries. Furthermore, the models mimicked the increasing standard deviation of response time seen in humans. Another notable similarity is that problem difficulty increases more steeply in subtraction than in addition, for both humans and connectionist models. Further investigation on two model hyperparameters - confidence threshold and hidden dimension - shows higher confidence thresholds cause the model to take more computational steps to arrive at the correct answer. Likewise, larger hidden dimensions cause the model to take more computational steps to correctly answer arithmetic problems;however, this effect by hidden dimensions is negligible. © ICCM *** rights reserved.

关键词： Recurrent neural networks

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Corrigendum to: "Neural mechanisms of inhibitory control in preadolescent irritability: Insights from the ABCD study" [Biological psychology 192 (2024) 108856]

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Biological psychology 2024年 192卷 108880页

作者： Alyssa J Parker Johanna C Walker Leslie S Jordan Yukari Takarae Jillian Lee Wiggins Lea R Dougherty Department of Psychology University of Maryland College Park United States. Electronic address: ajparker@umd.edu. Joint Doctoral Program in Clinical Psychology San Diego State University/University of California San Diego United States. Department of Psychology University of Maryland College Park United States Program in Neuroscience and Cognitive Science University of Maryland College Park MD United States Institute for Clinical & Translational Research (ICTR) University of Maryland Baltimore United States. Department of Psychology San Diego State University United States. Joint Doctoral Program in Clinical Psychology San Diego State University/University of California San Diego United States Department of Psychology University of Maryland College Park United States.

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THE TEMPORAL STRUCTURE OF LANGUAGE PROCESSING IN THE HUMAN BRAIN CORRESPONDS TO THE LAYERED HIERARCHY OF DEEP LANGUAGE MODELS

arXiv

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arXiv 2023年

作者： Goldstein, Ariel Ham, Eric Schain, Mariano Nastase, Samuel A. Zada, Zaid Grinstein-Dabus, Avigail Aubrey, Bobbi Gazula, Harshvardhan Feder, Amir Doyle, Werner Devore, Sasha Dugan, Patricia Friedman, Daniel Reichart, Roi Brenner, Michael Hassidim, Avinatan Devinsky, Orrin Flinker, Adeen Levy, Omer Hasson, Uri Department of Psychology The Neuroscience Institute Princeton University PrincetonNJ United States Google Research United States New York University Grossman School of Medicine New YorkNY United States School of Engineering and Applied Science Harvard University CambridgeMA United States New York University Tandon School of Engineering BrooklynNY United States Blavatnik School of Computer Science Tel-Aviv University Israel Department of Cognitive and Brain Sciences Business School Hebrew University of Jerusalem Israel Bioinformatics Interdepartmental Program University of California Los Angeles Los AngelesCA United States Technion - Israel Institute of Technology Haifa Israel

Deep Language Models (DLMs) provide a novel computational paradigm for understanding the mechanisms of natural language processing in the human brain. Unlike traditional psycholinguistic models, DLMs use layered sequences of continuous numerical vectors to represent words and context, allowing a plethora of emerging applications such as human-like text generation. In this paper we show evidence that the layered hierarchy of DLMs may be used to model the temporal dynamics of language comprehension in the brain by demonstrating a strong correlation between DLM layer depth and the time at which layers are most predictive of the human brain. Our ability to temporally resolve individual layers benefits from our use of electrocorticography (ECoG) data, which has a much higher temporal resolution than noninvasive methods like fMRI. Using ECoG, we record neural activity from participants listening to a 30-minute narrative while also feeding the same narrative to a high-performing DLM (GPT2-XL). We then extract contextual embeddings from the different layers of the DLM and use linear encoding models to predict neural activity. We first focus on the Inferior Frontal Gyrus (IFG, or Broca’s area) and then extend our model to track the increasing temporal receptive window along the linguistic processing hierarchy from auditory to syntactic and semantic areas. Our results reveal a connection between human language processing and DLMs, with the DLM’s layer-by-layer accumulation of contextual information mirroring the timing of neural activity in high-order language areas. © 2023, CC BY-NC-ND.

关键词： Brain

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Addressing the Current Challenges of Quantum Machine Learning through Multi-Chip Ensembles

arXiv

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arXiv 2025年

作者： Park, Junghoon Justin Cha, Jiook Chen, Samuel Yen-Chi Tseng, Huan-Hsin Yoo, Shinjae Interdisciplinary Program in Artificial Intelligence Seoul National University Gwanakgu Seoul08826 Korea Republic of Department of Psychology Department of Brain and Cognitive Sciences Interdisciplinary Program in Artificial Intelligence Seoul National University Gwanakgu Seoul08826 Korea Republic of Computational Science Initiative Brookhaven National Laboratory New York United States Wells Fargo New York United States

Practical Quantum Machine Learning (QML) is challenged by noise, limited scalability, and poor trainability in Variational Quantum Circuits (VQCs) on current hardware. We propose a multi-chip ensemble VQC framework that systematically overcomes these hurdles. By partitioning high-dimensional computations across ensembles of smaller, independently operating quantum chips and leveraging controlled inter-chip entanglement boundaries, our approach demonstrably mitigates barren plateaus, enhances generalization, and uniquely reduces both quantum error bias and variance simultaneously without additional mitigation overhead. This allows for robust processing of large-scale data, as validated on standard benchmarks (MNIST, FashionMNIST, CIFAR-10) and a real-world PhysioNet EEG dataset, aligning with emerging modular quantum hardware and paving the way for more scalable QML. © 2025, CC BY.

关键词： Qubits

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Mind the dad–A review on the biopsychosocial influences of drug abuse on father-infant interaction

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Emerging Trends in Drugs, Addictions, and Health 2021年 1卷 100015-100015页

作者： Lee, Jovin Jie Ning Cataldo, Ilaria Coppola, Aurora Corazza, Ornella Esposito, Gianluca Psychology Program School of Social Sciences Nanyang Technological University Singapore Singapore Department of Psychology and Cognitive Science University of Trento Rovereto Italy Psychology Unit Azienda Provinciale per i Servizi Sanitari Trento Italy Department of Clinical and Pharmaceutical Sciences University of Hertfordshire Hatfield United Kingdom Lee Kong Chian School of Medicine Nanyang Technological University Singapore Singapore

Substance use disorder (SUD) is an issue of concern that can have inter- generational impacts. Fathers affected by this disorder can exhibit atypical parenting that leaves pronounced, adverse consequences for the child, espe cially during a critical window for development, such as neonatal life and infancy. However, factors sustaining paternal drug use and its associated health outcomes remain elusive. The present review provides a systematic literature search of the scientific evidence published until February 2021 on PubMed Central, Scopus, PsycInfo, and PubMed databases. Adopting a biopsychosocial model, this review provides comprehensive insights into the issue, detailing: (i) the neurobiological correlates of paternal substance use and atypical parenting mechanisms, (ii) influence of drug consumption on paternal psychological state, and (iii) the social environment modulating the social dynamics central to fathers with SUD. Attention is also paid to the bidirectional relationships between paternal drug abuse and fatherhood, which has been severely neglected so far. Findings shed new light on the importance of paternal contributions to the father-child interaction, supporting the formulation of more targeted multidisciplinary interventions aimed at restoring such a crucial and overlooked relationship. © 2021

关键词： Drug abuse Father-infant Father-infant interaction Fatherhood Fathers Parenting Substance use disorder

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Learning physical graph representations from visual scenes 20

Learning physical graph representations from visual scenes

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Proceedings of the 34th International Conference on Neural Information Processing Systems

作者： Daniel M. Bear Chaofei Fan Damian Mrowca Yunzhu Li Seth Alter Aran Nayebi Jeremy Schwartz Li Fei-Fei Jiajun Wu Joshua B. Tenenbaum Daniel L.K. Yamins Department of Psychology Stanford University and Wu Tsai Neurosciences Institute Stanford University Department of Psychology Stanford University and Department of Computer Science Stanford University Department of Computer Science Stanford University MIT CSAIL MIT Brain and Cognitive Sciences Neurosciences Ph.D. Program Stanford University Department of Computer Science Stanford University and Department of Psychology Stanford University MIT CSAIL and MIT Brain and Cognitive Sciences Department of Psychology Stanford University and Department of Computer Science Stanford University and Wu Tsai Neurosciences Institute Stanford University

ISBN: (纸本)9781713829546

Convolutional Neural Networks (CNNs) have proved exceptional at learning representations for visual object categorization. However, CNNs do not explicitly encode objects, parts, and their physical properties, which has limited CNNs' success on tasks that require structured understanding of visual scenes. To overcome these limitations, we introduce the idea of "Physical Scene Graphs" (PSGs), which represent scenes as hierarchical graphs, with nodes in the hierarchy corresponding intuitively to object parts at different scales, and edges to physical connections between parts. Bound to each node is a vector of latent attributes that intuitively represent object properties such as surface shape and texture. We also describe PSGNet, a network architecture that learns to extract PSGs by reconstructing scenes through a PSG-structured bottleneck. PSGNet augments standard CNNs by including: recurrent feedback connections to combine low and high-level image information; graph pooling and vectorization operations that convert spatially-uniform feature maps into object-centric graph structures; and perceptual grouping principles to encourage the identification of meaningful scene elements. We show that PSGNet outperforms alternative self-supervised scene representation algorithms at scene segmentation tasks, especially on complex real-world images, and generalizes well to unseen object types and scene arrangements. PSGNet is also able learn from physical motion, enhancing scene estimates even for static images. We present a series of ablation studies illustrating the importance of each component of the PS-GNet architecture, analyses showing that learned latent attributes capture intuitive scene properties, and illustrate the use of PSGs for compositional scene inference.

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Consciousness in Artificial Intelligence: Insights from the science of Consciousness

arXiv

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arXiv 2023年

作者： Butlin, Patrick Long, Robert Elmoznino, Eric Bengio, Yoshua Birch, Jonathan Constant, Axel Deane, George Fleming, Stephen M. Frith, Chris Ji, Xu Kanai, Ryota Klein, Colin Lindsay, Grace Michel, Matthias Mudrik, Liad Peters, Megan A.K. Schwitzgebel, Eric Simon, Jonathan VanRullen, Rufin Future of Humanity Institute University of Oxford United Kingdom Center for AI Safety United States University of Montreal MILA - Quebec AI Institute Canada Centre for Philosophy of Natural and Social Science London School of Economics and Political Science United Kingdom School of Engineering and Informatics The University of Sussex Centre de Recherche en Éthique University of Montreal United Kingdom Department of Philosophy University of Montreal Canada Department of Experimental Psychology Wellcome Centre for Human Neuroimaging University College London United Kingdom Wellcome Centre for Human Neuroimaging University College London Institute of Philosophy University of London United Kingdom Araya Inc. School of Philosophy The Australian National University Australia Department of Psychology Center for Data Science New York University United States Center for Mind Brain and Consciousness New York University United States School of Psychological Sciences and Sagol School of Neuroscience Tel-Aviv University CIFAR Program in Brain Mind and Consciousness Israel Department of Cognitive Sciences University of California Irvine and CIFAR Program in Brain Mind and Consciousness United States Department of Philosophy University of California Riverside United States Centre de Recherche Cerveau et Cognition CNRS Université de Toulouse France

Whether current or near-term AI systems could be conscious is a topic of scientific interest and increasing public concern. This report argues for, and exemplifies, a rigorous and empirically grounded approach to AI consciousness: assessing existing AI systems in detail, in light of our best-supported neuroscientific theories of consciousness. We survey several prominent scientific theories of consciousness, including recurrent processing theory, global workspace theory, higher-order theories, predictive processing, and attention schema theory. From these theories we derive"indicator properties" of consciousness, elucidated in computational terms that allow us to assess AI systems for these properties. We use these indicator properties to assess several recent AI systems, and we discuss how future systems might implement them. Our analysis suggests that no current AI systems are conscious, but also suggests that there are no obvious technical barriers to building AI systems which satisfy these indicators.1 © 2023, CC BY-NC-SA.

关键词： Neurophysiology

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Magnetic seizure therapy and electroconvulsive therapy increase aperiodic activity

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Translational Psychiatry 2023年第1期13卷 347页

作者： Smith, Sydney E. Kosik, Eena L. van Engen, Quirine Kohn, Jordan Hill, Aron T. Zomorrodi, Reza Blumberger, Daniel M. Daskalakis, Zafiris J. Hadas, Itay Voytek, Bradley Neurosciences Graduate Program University of California San Diego La Jolla CA United States Department of Cognitive Science University of California San Diego La Jolla CA United States Herbert Wertheim School of Public Health and Human Longevity Science University of California San Diego La Jolla CA United States Department of Psychiatry School of Medicine University of California San Diego La Jolla CA United States Cognitive Neuroscience Unit School of Psychology Deakin University Geelong VIC Australia Department of Psychiatry Central Clinical School Monash University Melbourne VIC Australia Temerty Centre for Therapeutic Brain Intervention Centre for Addiction and Mental Health University of Toronto Toronto ON Canada Halıcıoğlu Data Science Institute University of California San Diego La Jolla CA United States Kavli Institute for Brain and Mind University of California San Diego La Jolla CA United States

Major depressive disorder (MDD) is a leading cause of disability worldwide. One of the most efficacious treatments for treatment-resistant MDD is electroconvulsive therapy (ECT). Recently, magnetic seizure therapy (MST) was developed as an alternative to ECT due to its more favorable side effect profile. While these approaches have been very successful clinically, the neural mechanisms underlying their therapeutic effects are unknown. For example, clinical “slowing” of the electroencephalogram beginning in the postictal state and extending days to weeks post-treatment has been observed in both treatment modalities. However, a recent longitudinal study of a small cohort of ECT patients revealed that, rather than delta oscillations, clinical slowing was better explained by increases in aperiodic activity, an emerging EEG signal linked to neural inhibition. Here we investigate the role of aperiodic activity in a cohort of patients who received ECT and a cohort of patients who received MST treatment. We find that aperiodic neural activity increases significantly in patients receiving either ECT or MST. Although not directly related to clinical efficacy in this dataset, increased aperiodic activity is linked to greater amounts of neural inhibition, which is suggestive of a potential shared neural mechanism of action across ECT and MST. © 2023, The Author(s).

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Gambling at the time of COVID-19: Results from interviews in an Italian sample of gamblers

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Emerging Trends in Drugs, Addictions, and Health 2022年 2卷 100032-100032页

作者： Cataldo, Ilaria Casonato, Eleonora Levari, Ermelinda Negri, Attilio Simonato, Pierluigi Tomasi, Giulia Branz, Giovanna Coppola, Aurora Gianfranceschi, Pietro Leoni, Eva Mistretta, Pietro Stefani, Martina Vanzetta, Miriam Franceschini, Anna Esposito, Gianluca Corazza, Ornella Department of Psychology and Cognitive Science University of Trento Rovereto Italy Local Public Health Authority Addiction Treatment Unit (SERD) Trento Italy School of Life and Medical Sciences University of Hertfordshire Hatfield United Kingdom Clinic “Parco dei Tigli” Teolo Padova Italy Self-help mutual aid association "AMA" Trento Italy Psychology Program School of Social Sciences Nanyang Technological University Singapore Singapore Lee Kong Chian School of Medicine Nanyang Technological University Singapore Singapore

The coronavirus pandemic affected the life of those suffering from addictive behaviors often confined to prolonged periods of self-isolation. To explore the variation of symptoms related to gambling, 46 outpatients of the mental health services in the Trento Province were invited to take part in a phone interview at the start of the national lockdown. Although only 2.17% increased gambling activity during this period, half of the sample (50.00%) experienced irritability, mood fluctuation (43.48%) and anxiety (39.13%). Follow-up studies should assess modifications in their behaviors that occurred after the reopening of gambling venues. © 2022 The Author(s)

关键词： Behavioral addiction COVID-19 Depression Gambling Internet addiction Mental health Online gambling

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Correction to: Reproducible Inter-Personal Brain Coupling Measurements in Hyperscanning Settings With functional Near Infra-Red Spectroscopy

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Neuroinformatics 2022年第3期20卷 677页

作者： Andrea Bizzego Atiqah Azhari Gianluca Esposito Department of Psychology and Cognitive Science University of Trento Trento Italy. Psychology Program School of Social Sciences Nanyang Technological University Singapore Singapore. Department of Psychology and Cognitive Science University of Trento Trento Italy. gianluca.esposito@unitn.it. Psychology Program School of Social Sciences Nanyang Technological University Singapore Singapore. gianluca.esposito@unitn.it. Lee Kong Chian School of Medicine Nanyang Technological University Singapore Singapore. gianluca.esposito@unitn.it.

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