2020 Fiscal Year Final Research Report
Elucidation of anticipation ability of ball direction in skilled athletes based on Bayesian integrated models
| Project/Area Number |
18K10890
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 59020:Sports sciences-related
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| Research Institution | Tokyo Metropolitan University |
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Principal Investigator |
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| Project Period (FY) |
2018-04-01 – 2021-03-31
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| Keywords | 予測 / 文脈情報 / 運動学的情報 / 動作誇張 / 熟達 / テニス |
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| Outline of Final Research Achievements |
The aim of this study is to clarify whether the superior anticipation ability of skilled tennis players is underpinned by a Bayesian integration model (Kording & Wolpert, 2004), which uses information weighted by both past contextual and opponent kinematic information. We used virtual reality (VR) technology to arbitrarily manipulate the opponent's kinematic information (movement exaggeration) and the player's contextual information (court position) to predict the hitting course of a tennis forehand shot. The results showed that the superior anticipation ability of skilled players was influenced by the manipulation of kinematic and contextual information manipulated in this study. The results also indicated that there may be differences in the integrated information that can be used for prediction depending on expertise.
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| Free Research Field |
スポーツ心理学
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| Academic Significance and Societal Importance of the Research Achievements |
テニスや野球などの球技では，熟練者の優れた予測能力は相手の動作だけでなく，試合での文脈も重要な先行情報となります．またこうした両者の情報は，信頼性の高い情報を選択するという「脳の確率論的な情報処理（ベイズ統合）」に支えられている可能性が近年指摘されています．
本研究ではテニスの予測場面をVR環境にて作り上げ、熟練者のベイズ推定に基づく予測能力を評価する新たな手法を確立しました．この手法の応用により、熟達によって統合された情報利用に違いがある可能性が見られました．このような成果は，一流選手の素早く的確な運動能力の解明だけでなく、対戦競技の理解やトレーニングへの発展に寄与すると考えられます．
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