The Binding of Learning to Action in Motor Adaptation

DSpace/Manakin Repository

The Binding of Learning to Action in Motor Adaptation

Citable link to this page

 

 
Title: The Binding of Learning to Action in Motor Adaptation
Author: Monsen, Craig Bryant; Gonzalez Castro, Luis Nicolas; Smith, Maurice A

Note: Order does not necessarily reflect citation order of authors.

Citation: Gonzalez Castro, Luis Nicolas, Craig Bryant Monsen, and Maurice A. Smith. 2011. The Binding of Learning to Action in Motor Adaptation. PLoS Computational Biology 7(6): e1002052.
Full Text & Related Files:
Abstract: In motor tasks, errors between planned and actual movements generally result in adaptive changes which reduce the occurrence of similar errors in the future. It has commonly been assumed that the motor adaptation arising from an error occurring on a particular movement is specifically associated with the motion that was planned. Here we show that this is not the case. Instead, we demonstrate the binding of the adaptation arising from an error on a particular trial to the motion experienced on that same trial. The formation of this association means that future movements planned to resemble the motion experienced on a given trial benefit maximally from the adaptation arising from it. This reflects the idea that actual rather than planned motions are assigned 'credit' for motor errors because, in a computational sense, the maximal adaptive response would be associated with the condition credited with the error. We studied this process by examining the patterns of generalization associated with motor adaptation to novel dynamic environments during reaching arm movements in humans. We found that these patterns consistently matched those predicted by adaptation associated with the actual rather than the planned motion, with maximal generalization observed where actual motions were clustered. We followed up these findings by showing that a novel training procedure designed to leverage this newfound understanding of the binding of learning to action, can improve adaptation rates by greater than 50%. Our results provide a mechanistic framework for understanding the effects of partial assistance and error augmentation during neurologic rehabilitation, and they suggest ways to optimize their use.
Published Version: doi://10.1371/journal.pcbi.1002052
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3121685/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:11213336
Downloads of this work:

Show full Dublin Core record

This item appears in the following Collection(s)

 
 

Search DASH


Advanced Search
 
 

Submitters