How is somatosensory information used to adapt to changes in the mechanical environment?

Milner, Theodore E., Hinder, Mark and Franklin, David W. (2007). How is somatosensory information used to adapt to changes in the mechanical environment?. In Paul Cisek, Trevor Drew and John F. Kalaska (Ed.), Computational neuroscience: Theoretical insights into brain function (pp. 363-372) Amsterdam, Netherlands: Elsevier. doi:10.1016/S0079-6123(06)65022-X


Author Milner, Theodore E.
Hinder, Mark
Franklin, David W.
Title of chapter How is somatosensory information used to adapt to changes in the mechanical environment?
Title of book Computational neuroscience: Theoretical insights into brain function
Place of Publication Amsterdam, Netherlands
Publisher Elsevier
Publication Year 2007
Sub-type Research book chapter (original research)
DOI 10.1016/S0079-6123(06)65022-X
Open Access Status
Series Progress in Brain Research
ISBN 9780444528230
ISSN 0079-6123
Editor Paul Cisek
Trevor Drew
John F. Kalaska
Volume number 165
Chapter number 22
Start page 363
End page 372
Total pages 10
Collection year 2008
Language eng
Subjects 321403 Motor Control
C1
780108 Behavioural and cognitive sciences
Abstract/Summary Recent studies examining adaptation to unexpected changes in the mechanical environment highlight the use of position error in the adaptation process. However, force information is also available. In this chapter, we examine adaptation processes in three separate studies where the mechanical environment was changed intermittently. We compare the expected consequences of using position error and force information in the changes to motor commands following a change in the mechanical environment. In general, our results support the use of position error over force information and are consistent with current computational models of motor learning. However, in situations where the change in the mechanical environment eliminates position error the central nervous system does not necessarily respond as would be predicted by these models. We suggest that it is necessary to take into account the statistics of prior experience to account for our observations. Another deficiency in these models is the absence of a mechanism for modulating limb mechanical impedance during adaptation. We propose a relatively simple computational model based on reflex responses to perturbations which is capable of accounting for iterative changes in temporal patterns of muscle co-activation.
Keyword Motor learning
Error feedback
Internal model
Mechanical impedance
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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Created: Wed, 16 Apr 2008, 14:23:09 EST by Deborah Noon on behalf of School of Human Movement and Nutrition Sciences