Presentation 2002/3/13
How Does Our Brain Reduce the Variance of Movements by Learning?
Naohiko IGUCHI, Yutaka SAKAGUCHI,
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Abstract(in English) Though the trajectory and endpoint of our movements vary trial by trial, the amount of variance can be reduced by repeating the movement. The present report discusses what makes out movement variant and how our brain reduces this variance by learning. The authors propose four working hypotheses, and discuss their principles and properties. Through this discussion, it is clarified that these hypotheses can be classified into two distinct theories, each which has a potential possiblity to explain the stabilization of human movements.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) motor learning / movement stabilization / minimum end-point variance theory / reinforcement learning / unsupervised learning / central limit theorem
Paper # NC2001-202
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Committee NC
Conference Date 2002/3/13(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) How Does Our Brain Reduce the Variance of Movements by Learning?
Sub Title (in English)
Keyword(1) motor learning
Keyword(2) movement stabilization
Keyword(3) minimum end-point variance theory
Keyword(4) reinforcement learning
Keyword(5) unsupervised learning
Keyword(6) central limit theorem
1st Author's Name Naohiko IGUCHI
1st Author's Affiliation Graduate School of Information Systems, University of Electro-Communications()
2nd Author's Name Yutaka SAKAGUCHI
2nd Author's Affiliation Graduate School of Information Systems, University of Electro-Communications
Date 2002/3/13
Paper # NC2001-202
Volume (vol) vol.101
Number (no) 737
Page pp.pp.-
#Pages 6
Date of Issue