2022 Fiscal Year Final Research Report

Neural mechanisms of concurrent learning of multiple prior distributions in human timing behavior

Research Project

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Project/Area Number	19H01087
Research Category	Grant-in-Aid for Scientific Research (A)
Allocation Type	Single-year Grants
Section	一般
Review Section	Medium-sized Section 59:Sports sciences, physical education, health sciences, and related fields
Research Institution	Shizuoka University
Principal Investigator	Miyazaki Makoto 静岡大学, 情報学部, 教授 (30392202)
Co-Investigator(Kenkyū-buntansha)	竹内成生上武大学, ビジネス情報学部, 教授 (10329162) 関口浩文上武大学, ビジネス情報学部, 教授 (20392201) 板口典弘慶應義塾大学, 文学部(三田), 助教 (50706637) 中澤公孝東京大学, 大学院総合文化研究科, 教授 (90360677)
Project Period (FY)	2019-04-01 – 2023-03-31
Keywords	ベイズ推定 / タイミング / 事前分布 / 学習 / 脳 / 身体 / 日常環境
Outline of Final Research Achievements	Previous studies, including those of the principal investigator, showed that the brain exerts Bayesian estimation in timing tasks. In our daily life, multiple events occur, and each event can have unique statistics. Effective Bayesian estimation in real environments relies on the ability to learn multiple prior distributions. As one of the representative results of this study, we found that when short and long (i.e., fastball and slowball in baseball batting) prior distributions are assigned to two different body parts to make timing responses, participants concurrently learned the two priors (“body-part specificity”). Moreover, this study revealed various conditions that enabled concurrent learning of multiple prior distributions. In addition, this study provided findings that can contribute to explore basic mechanisms or to develop the application of Bayesian estimation in the human sensorimotor system.
Free Research Field	身体教育学，認知神経科学
Academic Significance and Societal Importance of the Research Achievements	タイミング行動におけるベイズ推定の研究は，国際的な重要研究課題の一つとなった．従来の研究の多くが単一の事前分布の学習を対象としてきたが，実環境でベイズ推定が有効に機能するためには「複数の事前分布の学び分け」が必要である．本研究の成果は，それを可能とする条件を複数明らかにした．これらの成果は，有力な神経基盤の候補を矛盾なく示し，さらにスポーツのスキル解析やスキルアップ法の提言に繋がることも期待される．加えて，先行研究課題から継続・発展させてきた研究や本研究課題から派生した研究から，医学・産業応用への寄与が期待される成果も得られた．