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翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Bayesian inference in motor learning ： ウィキペディア英語版 Bayesian inference in motor learning Bayesian inference is a statistical tool that can be applied to motor learning, specifically to adaptation. Adaptation is a short-term learning process involving gradual improvement in performance in response to a change in sensory information. Bayesian inference is used to describe the way the nervous system combines this sensory information with prior knowledge to estimate the position or other characteristics of something in the environment. Bayesian inference can also be used to show how information from multiple senses (e.g. visual and proprioception) can be combine for the same purpose. In either case, Bayesian inference dictates that the estimate is most influenced by whichever information is most certain. ==Example: Integrating Prior Knowledge with Sensory Information in Tennis== A person uses Bayesian inference to create an estimate that is a weighted combination of his current sensory information and his previous knowledge, or prior. This can be illustrated by decisions made in a tennis match.〔Körding, K. P., & Wolpert, Daniel M. (2006). Bayesian decision theory in sensorimotor control. Trends in Cognitive Sciences (Vol. 10, pp. 319–326).〕 If someone plays against a familiar opponent who likes to serve such that the ball strikes on the sideline, one's prior would lead one to place the racket above the sideline to return the serve. However, when one sees the ball moving, it may appear that it will land closer to the middle of the court. Rather than completely following this sensory information or completely following the prior, one would move the racket to a location between the sideline (suggested by the prior) and the point where her eyes indicate the ball will land. Another key part of Bayesian inference is that the estimate will be closer to the physical state suggested by sensory information if the senses are more accurate and will be closer to the state of the prior if the sensory information is more uncertain than the prior. Extending this to the tennis example, a player facing an opponent for the first time would have little certainty in his/her previous knowledge of the opponent and would therefore have an estimate weighted more heavily on visual information concerning ball position. Alternatively, if one were familiar with one's opponent but were playing in foggy or dark conditions that would hamper sight, sensory information would be less certain and one's estimate would rely more heavily on previous knowledge. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Bayesian inference in motor learning」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.