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From Efficient Coding to Information Gain: Information-Theoretic Principles in Models of Human Decision Making 42

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From Efficient Coding to Information Gain: Information-Theor...

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

作者： Akrenius, Mikaela Maloney, Laurence T. Nelson, Jonathan D. Cognitive Science Program Indiana University Bloomington United States Department of Psychology Center for Neural Science New York University United States School of Psychology University of Surrey Max Planck Institute for Human Development Berlin Germany

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

Automating the Practice of science – Opportunities, Challenges, and Implications

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arXiv 2024年

作者： Musslick, Sebastian Bartlett, Laura K. Chandramouli, Suyog H. Dubova, Marina Gobet, Fernand Griffiths, Thomas L. Hullman, Jessica King, Ross D. Nathan Kutz, J. Lucas, Christopher G. Mahesh, Suhas Pestilli, Franco Sloman, Sabina J. Holmes, William R. Institute of Cognitive Science Osnabrück University Osnabrück49090 Germany Department of Cognitive Linguistic & Psychological Sciences Brown University ProvidenceRI02912 United States Centre for Philosophy of Natural and Social Science London School of Economics Lakatos Building Houghton Street LondonWC2A 2AE United Kingdom AaltoFI-00076 Finland Department of Computing Science University of Alberta 8900 114 St NW EdmontonABT6G 2S4 Canada Cognitive Science Program Indiana University 1101 E 10th St BloomingtonIN47405 United States School of Psychology University of Roehampton LondonSW15 4JD United Kingdom Departments of Psychology and Computer Science Princeton University PrincetonNJ United States Department of Computer Science Northwestern University IL United States Department of Chemical Engineering and Biotechnology University of Cambridge CambridgeCB3 0AS United Kingdom Department of Computer Science and Engineering Chalmers University of Technology Gothenburg412 96 Sweden Department of Applied Mathematics and Electrical and Computer Engineering University of Washington Seattle98195 United States School of Informatics University of Edinburgh 10 Crichton St. EH8 9AB United Kingdom Department of Materials Science and Engineering University of Toronto Canada Department of Psychology Department of Neuroscience The University of Texas AustinTX United States Department of Computer Science University of Manchester M13 9PL United Kingdom

Automation transformed various aspects of our human civilization, revolutionizing industries and streamlining processes. In the domain of scientific inquiry, automated approaches emerged as powerful tools, holding promise for accelerating discovery, enhancing reproducibility, and overcoming the traditional impediments to scientific progress. This article evaluates the scope of automation within scientific practice and assesses recent approaches. Furthermore, it discusses different perspectives to the following questions: Where do the greatest opportunities lie for automation in scientific practice?;What are the current bottlenecks of automating scientific practice?;and What are significant ethical and practical consequences of automating scientific practice? By discussing the motivations behind automated science, analyzing the hurdles encountered, and examining its implications, this article invites researchers, policymakers, and stakeholders to navigate the rapidly evolving frontier of automated scientific practice. © 2024, CC BY.

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Maps by Which We May Not Steer: Why Psychologists Should Expect Low Belief-Behavior Correspondence

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Psychological Inquiry 2025年第1期36卷 60-66页

作者： Joseph Sommer Kerem Oktar a University Center for Human Values/Program in Cognitive Science Princeton University Princeton New Jersey b Department of Psychology Princeton University Princeton New Jersey

ENIGMA-Meditation: Worldwide Consortium for Neuroscientific Investigations of Meditation Practices

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Biological Psychiatry: cognitive Neuroscience and Neuroimaging 2025年第4期10卷 425-436页

作者： Ganesan, Saampras Barrios, Fernando A. Batta, Ishaan Bauer, Clemens C.C. Braver, Todd S. Brewer, Judson A. Brown, Kirk Warren Cahn, Rael Cain, Joshua A. Calhoun, Vince D. Cao, Lei Chetelat, Gaël Ching, Christopher R.K. Creswell, J. David Dagnino, Paulina Clara Davanger, Svend Davidson, Richard J. Deco, Gustavo Dutcher, Janine M. Escrichs, Anira Eyler, Lisa T. Fani, Negar Farb, Norman A.S. Fialoke, Suruchi Fresco, David M. Garg, Rahul Garland, Eric L. Goldin, Philippe Hafeman, Danella M. Jahanshad, Neda Kang, Yoona Khalsa, Sahib S. Kirlic, Namik Lazar, Sara W. Lutz, Antoine McDermott, Timothy J. Pagnoni, Giuseppe Piguet, Camille Prakash, Ruchika S. Rahrig, Hadley Reggente, Nicco Saccaro, Luigi F. Sacchet, Matthew D. Siegle, Greg J. Tang, Yi-Yuan Thomopoulos, Sophia I. Thompson, Paul M. Torske, Alyssa Treves, Isaac N. Tripathi, Vaibhav Tsuchiyagaito, Aki Turner, Matthew D. Vago, David R. Valk, Sofie Zeidan, Fadel Zalesky, Andrew Turner, Jessica A. King, Anthony P. Department of Biomedical Engineering The University of Melbourne Carlton VIC Australia Contemplative Studies Centre Melbourne School of Psychological Sciences The University of Melbourne Melbourne VIC Australia Systems Lab of Neuroscience Neuropsychiatry and Neuroengineering The University of Melbourne Parkville VIC Australia Universidad Nacional Autónoma de México Instituto de Neurobiolgía México Querétaro Mexico Center for Translational Research in Neuroimaging and Data Science: Georgia State University Georgia Institute of Technology and Emory University Atlanta GA United States Department of Psychology Northeastern University Boston Massachusetts Brain and Cognitive Science McGovern Institute for Brain Research Massachusetts Institute of Technology Cambridge Massachusetts Department of Psychological and Brain Sciences Washington University St. Louis Missouri Department of Behavioral and Social Sciences Brown University School of Public Health Providence Rhode Island Department of Psychology Carnegie Mellon University Pittsburgh PA United States University of Southern California Department of Psychiatry & Behavioral Sciences Los Angeles California University of Southern California Center for Mindfulness Science Los Angeles California Institute for Advanced Consciousness Studies Santa Monica California Department of Psychiatry and Behavioral Health The Ohio State University College of Medicine Columbus OH United States Normandie University Université de Caen Normandie INSERM U1237 Neuropresage Team Cyceron Caen France Imaging Genetics Center Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of the University of Southern California Los Angeles California Desert-Pacific Mental Illness Research Education and Clinical Center VA San Diego Healthcare System San Diego California Computational Neuroscience Group Center for Brain and Cognition Department of Information and Communication Technologies Universitat Pompeu Fa

Meditation is a family of ancient and contemporary contemplative mind-body practices that can modulate psychological processes, awareness, and mental states. Over the last 40 years, clinical science has manualized meditation practices and designed various meditation interventions that have shown therapeutic efficacy for disorders including depression, pain, addiction, and anxiety. Over the past decade, neuroimaging has been used to examine the neuroscientific basis of meditation practices, effects, states, and outcomes for clinical and nonclinical populations. However, the generalizability and replicability of current neuroscientific models of meditation have not yet been established, because they are largely based on small datasets entrenched with heterogeneity along several domains of meditation (e.g., practice types, meditation experience, clinical disorder targeted), experimental design, and neuroimaging methods (e.g., preprocessing, analysis, task-based, resting-state, structural magnetic resonance imaging). These limitations have precluded a nuanced and rigorous neuroscientific phenotyping of meditation practices and their potential benefits. Here, we present ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis)–Meditation, the first worldwide collaborative consortium for neuroscientific investigations of meditation practices. ENIGMA-Meditation will enable systematic meta- and mega-analyses of globally distributed neuroimaging datasets of meditation using shared, standardized neuroimaging methods and tools to improve statistical power and generalizability. Through this powerful collaborative framework, existing neuroscientific accounts of meditation practices can be extended to generate novel and rigorous neuroscientific insights that account for multidomain heterogeneity. ENIGMA-Meditation will inform neuroscientific mechanisms that underlie therapeutic action of meditation practices on psychological and cognitive attributes, thereby advancing the f

关键词： Consortium ENIGMA Magnetic resonance imaging (MRI) Meditation Mindfulness Neuroimaging

Investigation of factors regarding the effects of COVID-19 pandemic on college students’ depression by quantum annealer

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arXiv 2023年

作者： Choi, Junggu Kim, Kion Park, Soohyun Hur, Juyoen Yang, Hyunjung Kim, Younghoon Lee, Hakbae Han, Sanghoon Yonsei Graduate program in Cognitive Science Yonsei University Seoul03722 Korea Republic of Korea Quantum Computing© Seoul06164 Korea Republic of Department of Psychology Yonsei University Seoul03722 Korea Republic of University College Yonsei University Seoul03722 Korea Republic of Deparment of Applied Statistics Yonsei University Seoul03722 Korea Republic of Department of Statistics and Data science Yonsei University Seoul03722 Korea Republic of

Diverse cases regarding the impact, with its related factors, of the COVID-19 pandemic on mental health have been reported in previous studies. College student groups have been frequently selected as the target population in previous studies because they are easily affected by pandemics. In this study, multivariable datasets were collected from 751 college students based on the complex relationships between various mental health factors. We utilized quantum annealing (QA)-based feature selection algorithms that were executed by commercial D-Wave quantum computers to determine the changes in the relative importance of the associated factors before and after the pandemic. Multivariable linear regression (MLR) and XGBoost models were also applied to validate the QA-based algorithms. Based on the experimental results, we confirm that QA-based algorithms have comparable capabilities in factor analysis research to the MLR models that have been widely used in previous studies. Furthermore, the performance of the QA-based algorithms was validated through the important factor results from the algorithms. Pandemic-related factors (e.g., confidence in the social system) and psychological factors (e.g., decision-making in uncertain situations) were more important in post-pandemic conditions. We believe that our study will serve as a reference for researchers studying similar topics. Copyright © 2023, The Authors. All rights reserved.

关键词： Multiple linear regression

Effect of a single session of sensorimotor rhythm neurofeedback training on the putting performance of professional golfers

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Scandinavian Journal of Medicine and science in Sports 2024年第1期34卷 e14540-e14540页

作者： Wu, Jia-Hao Chueh, Ting-Yu Yu, Chien-Lin Wang, Kuo-Pin Kao, Shih-Chun Gentili, Rodolphe J. Hatfield, Bradley D. Hung, Tsung-Min Department of Physical Education and Sport Sciences National Taiwan Normal University Taipei Taiwan Department of Kinesiology University of Maryland College Park MD United States Center of Excellence “Cognitive Interaction Technology” (CITEC) Bielefeld University Bielefeld Germany Neurocognition and Action – Biomechanics Research Group Faculty of Psychology and Sports Science Bielefeld University Bielefeld Germany Department of Health and Kinesiology Purdue University West Lafayette IN United States Program in Neuroscience and Cognitive Science University of Maryland College Park MD United States Institute for Research Excellence and Learning Sciences National Taiwan Normal University Taipei Taiwan

Sensorimotor rhythm (SMR) activity has been associated with automaticity and flow in motor execution. Studies have revealed that neurofeedback training (NFT) of the SMR can improve sports performance;however, few studies have adequately explored the effects of a single session of such NFT or examined the possible mechanisms underlying these effects on sports performance. This study recruited 44 professional golfers to address these gaps in the literature. A crossover design was employed to determine the order of the participation in the NFT and no-training control conditions. The participants were asked to perform 60 10-foot putts while electroencephalograms (EEGs) were recorded before and after the tasks. In pre-and post-tests, visual analog scales were used to assess the psychological states associated with SMR activities including the levels of attention engagement, conscious motor control, and physical relaxation. The results revealed that a single NFT session effectively increased SMR power and improved putting performance compared with the control condition. The subjective assessments also revealed that the participants reported lower attention engagement, less conscious control of the motor details and were more relaxed in the putting task, suggesting that SMR NFT promoted effortless and quiescent mental states during motor preparation for a putting task. This study aligns with theoretical hypotheses and extends current knowledge by revealing that a single session of SMR NFT can effectively enhance SMR power and improve putting performance in professional golfers. It also provides preliminary evidence of the possible underlying mechanisms that drive the effect of SMR NFT on putting performances. © 2023 The Authors. Scandinavian Journal of Medicine & science In Sports published by John Wiley & Sons Ltd.

关键词： EEG neurofeedback training professional golf putting sensorimotor rhythm sports performance

Visual representations in the human brain are aligned with large language models

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arXiv 2022年

作者： Doerig, Adrien Kietzmann, Tim C. Allen, Emily Wu, Yihan Naselaris, Thomas Kay, Kendrick Charest, Ian Institute of Cognitive Science University of Osnabrück Germany Donders Institute for Brain Cognition & Behaviour Nijmegen Netherlands Department of Radiology University of Minnesota MinneapolisMN United States Department of Psychology University of Minnesota MinneapolisMN United States Graduate Program in Cognitive Science University of Minnesota MinneapolisMN United States Department of Neuroscience University of Minnesota MinneapolisMN United States cerebrUM Département de Psychologie Université de Montréal Montréal Canada Center for Human Brain Health School of Psychology University of Birmingham Birmingham United Kingdom

The human brain extracts complex information from visual inputs, including objects, their spatial and semantic interrelations, and their interactions with the environment. However, a quantitative approach for studying this information remains elusive. Here, we test whether the contextual information encoded in large language models (LLMs) is beneficial for modelling the complex visual information extracted by the brain from natural scenes. We show that LLM embeddings of scene captions successfully characterise brain activity evoked by viewing the natural scenes. This mapping captures selectivities of different brain areas, and is sufficiently robust that accurate scene captions can be reconstructed from brain activity. Using carefully controlled model comparisons, we then proceed to show that the accuracy with which LLM representations match brain representations derives from the ability of LLMs to integrate complex information contained in scene captions beyond that conveyed by individual words. Finally, we train deep neural network models to transform image inputs into LLM representations. Remarkably, these networks learn representations that are better aligned with brain representations than a large number of state-of-the-art alternative models, despite being trained on orders-of-magnitude less data. Overall, our results suggest that LLM embeddings of scene captions provide a representational format that accounts for complex information extracted by the brain from visual inputs. Copyright © 2022, The Authors. All rights reserved.

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Improving the efficacy of deep learning models for heart beat detection on heterogeneous datasets

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arXiv 2021年

作者： Bizzego, Andrea Gabrieli, Giulio Neoh, Michelle Jin-Yee Esposito, Gianluca Department of Psychology and Cognitive Science University of Trento Trento38068 Italy Psychology Program Nanyang Technological University Singapore639818 Singapore Lee Kong Chian School of Medicine Nanyang Technological University Singapore308232 Singapore

Deep Learning (DL) have greatly contributed to bioelectric signals processing, in particular to extract physiological markers. However, the efficacy and applicability of the results proposed in the literature is often constrained to the population represented by the data used to train the models. In this study, we investigate the issues related to applying a DL model on heterogeneous datasets. In particular, by focusing on heart beat detection from Electrocardiogram signals (ECG), we show that the performance of a model trained on data from healthy subjects decreases when applied to patients with cardiac conditions and to signals collected with different devices. We then evaluate the use of Transfer Learning (TL) to adapt the model to the different datasets. In particular, we show that the classification performance is improved, even with datasets with a small sample size. These results suggest that a greater effort should be made towards generalizability of DL models applied on bioelectric signals, in particular by retrieving more representative datasets. © 2021, CC BY.

关键词： Deep neural networks