Presentation 2007-03-15
Spatio-temporal Decomposition of Internal Models in Motor Learning under Mixed Dynamic Environments
Naoki TOMI, Manabu GOUKO, Koji ITO, Toshiyuki KONDO,
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Abstract(in English) Humans can behave adaptively in the different dynamical conditions. In order to make adaptive behaviors, it is required to adjust the kinematic and dynamic relations among the brain and environment according to the task. Recent studies have shown that humans can acquire a neural representation of the relation between motor command and sensory feedback, i.e. learn an internal model of the environment dynamics. Then, we can compensate for the dynamical perturbation in a feedforward manner. The present paper discusses whether humans can identify simple physical characteristics of dynamics from the mixed
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Keyword(in English) Motor adaptation / Internal model / Reaching movement / Decomposition of internal models
Paper # NC2006-168
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Committee NC
Conference Date 2007/3/8(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) Spatio-temporal Decomposition of Internal Models in Motor Learning under Mixed Dynamic Environments
Sub Title (in English)
Keyword(1) Motor adaptation
Keyword(2) Internal model
Keyword(3) Reaching movement
Keyword(4) Decomposition of internal models
1st Author's Name Naoki TOMI
1st Author's Affiliation Department of Computational Intelligence and System Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology()
2nd Author's Name Manabu GOUKO
2nd Author's Affiliation Department of Computational Intelligence and System Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology
3rd Author's Name Koji ITO
3rd Author's Affiliation Department of Computational Intelligence and System Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology
4th Author's Name Toshiyuki KONDO
4th Author's Affiliation Department of Computer, Information and Communication Sciences, Tokyo University of Agriculture and Technology
Date 2007-03-15
Paper # NC2006-168
Volume (vol) vol.106
Number (no) 589
Page pp.pp.-
#Pages 6
Date of Issue