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 Japanese) | (See Japanese page) |
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 |
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Conference Date | 2002/3/13(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (in Japanese) | (See Japanese page) |
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Registration To | Neurocomputing (NC) |
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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 |
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