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Social Decision-making and Theoretical Neuroscience: Prospects for Human Sciences and Computational Psychiatry Hiroyuki Nakahara 1 , Shinsuke Suzuki 1,2,3,4 1Laboratory for Integrated Theoretical Neuroscience, RIKEN Brain Research Institute 2Division of the Humanities and Social Sciences, California Institute of Technology 3Graduate School of Letters, Hokkaido University 4JSPS Keyword: reward prediction , reinforcement learning , social cognition , decision making pp.973-982
Published Date 2013/8/1
DOI https://doi.org/10.11477/mf.1416101573
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Abstract

 Learning to predict others' minds is critical for social cognition, but the underlying computation and neural mechanisms remains largely unknown. According to theories in social cognition, a simple conception is that humans simulate others' mental processes by directly recruiting one's own process to model others' minds. In this review, we first describe our recent finding and discuss its possible implications. Using human fMRI with model-based analysis on frameworks of reinforcement learning and value-based decision making, we found that simulation involves two hierarchical learning signals: a reward prediction error, generated by simulation of direct recruitment to model others' valuation, and an action prediction error, based on simulation and observation of the other's choices to track others' variability. These findings show that humans can learn to predict others' minds from simulation, using a scaffold of mentalizing signals. Then, we discuss prospects that theoretical neuroscience and computational approaches will play significant roles in understanding human behavior and neural mechanisms, leading to the so-called computational psychiatry as well as synthesis over different disciplines to study human.


Copyright © 2013, Igaku-Shoin Ltd. All rights reserved.

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電子版ISSN 1344-8129 印刷版ISSN 1881-6096 医学書院

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