Mechanisms within the Parietal Cortex Correlate with the Benefits of Random Practice in Motor Adaptation

The motor learning literature shows an increased retest or transfer performance after practicing under unstable (random) conditions. This random practice effect (also known as contextual interference effect) is frequently investigated on the behavioral level and discussed in the context of mechanism...

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Published inFrontiers in human neuroscience Vol. 11; p. 403
Main Authors Thürer, Benjamin, Stockinger, Christian, Putze, Felix, Schultz, Tanja, Stein, Thorsten
Format Journal Article
LanguageEnglish
Published Switzerland Frontiers Research Foundation 02.08.2017
Frontiers Media S.A
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ISSN1662-5161
1662-5161
DOI10.3389/fnhum.2017.00403

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Summary:The motor learning literature shows an increased retest or transfer performance after practicing under unstable (random) conditions. This random practice effect (also known as contextual interference effect) is frequently investigated on the behavioral level and discussed in the context of mechanisms of the dorsolateral prefrontal cortex and increased cognitive efforts during movement planning. However, there is a lack of studies examining the random practice effect in motor adaptation tasks and, in general, the underlying neural processes of the random practice effect are not fully understood. We tested 24 right-handed human subjects performing a reaching task using a robotic manipulandum. Subjects learned to adapt either to a blocked or a random schedule of different force field perturbations while subjects' electroencephalography (EEG) was recorded. The behavioral results showed a distinct random practice effect in terms of a more stabilized retest performance of the random compared to the blocked practicing group. Further analyses showed that this effect correlates with changes in the alpha band power in electrodes over parietal areas. We conclude that the random practice effect in this study is facilitated by mechanisms within the parietal cortex during movement execution which might reflect online feedback mechanisms.
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Reviewed by: David L. Wright, Texas A&M University, United States; Marika Berchicci, Foro Italico University of Rome, Italy; Bahamn Nasseroleslami, Trinity College, Dublin, Ireland
Edited by: Christopher J. Hasson, Northeastern University, United States
ISSN:1662-5161
1662-5161
DOI:10.3389/fnhum.2017.00403