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タイトル: Cooperative update of beliefs and state-transition functions in human reinforcement learning
著者: Higashi, Hiroshi  KAKEN_id  orcid https://orcid.org/0000-0001-8880-3411 (unconfirmed)
Minami, Tetsuto
Nakauchi, Shigeki
著者名の別形: 東, 広志
南, 哲人
中内, 茂樹
キーワード: Decision
Human behaviour
Learning algorithms
発行日: 27-Nov-2019
出版者: Springer Nature
誌名: Scientific Reports
巻: 9
論文番号: 17704
抄録: It is widely known that reinforcement learning systems in the brain contribute to learning via interactions with the environment. These systems are capable of solving multidimensional problems, in which some dimensions are relevant to a reward, while others are not. To solve these problems, computational models use Bayesian learning, a strategy supported by behavioral and neural evidence in human. Bayesian learning takes into account beliefs, which represent a learner’s confidence in a particular dimension being relevant to the reward. Beliefs are given as a posterior probability of the state-transition (reward) function that maps the optimal actions to the states in each dimension. However, when it comes to implementing this learning strategy, the order in which beliefs and state-transition functions update remains unclear. The present study investigates this update order using a trial-by-trial analysis of human behavior and electroencephalography signals during a task in which learners have to identify the reward-relevant dimension. Our behavioral and neural results reveal a cooperative update—within 300 ms after the outcome feedback, the state-transition functions are updated, followed by the beliefs for each dimension.
著作権等: © The Author(s) 2019. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
URI: http://hdl.handle.net/2433/245045
DOI(出版社版): 10.1038/s41598-019-53600-9
PubMed ID: 31776353
出現コレクション:学術雑誌掲載論文等

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