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Flexible Cognitive Strategies during Motor Learning

Overview of attention for article published in PLoS Computational Biology, March 2011
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Title
Flexible Cognitive Strategies during Motor Learning
Published in
PLoS Computational Biology, March 2011
DOI 10.1371/journal.pcbi.1001096
Pubmed ID
Authors

Jordan A. Taylor, Richard B. Ivry

Abstract

Visuomotor rotation tasks have proven to be a powerful tool to study adaptation of the motor system. While adaptation in such tasks is seemingly automatic and incremental, participants may gain knowledge of the perturbation and invoke a compensatory strategy. When provided with an explicit strategy to counteract a rotation, participants are initially very accurate, even without on-line feedback. Surprisingly, with further testing, the angle of their reaching movements drifts in the direction of the strategy, producing an increase in endpoint errors. This drift is attributed to the gradual adaptation of an internal model that operates independently from the strategy, even at the cost of task accuracy. Here we identify constraints that influence this process, allowing us to explore models of the interaction between strategic and implicit changes during visuomotor adaptation. When the adaptation phase was extended, participants eventually modified their strategy to offset the rise in endpoint errors. Moreover, when we removed visual markers that provided external landmarks to support a strategy, the degree of drift was sharply attenuated. These effects are accounted for by a setpoint state-space model in which a strategy is flexibly adjusted to offset performance errors arising from the implicit adaptation of an internal model. More generally, these results suggest that strategic processes may operate in many studies of visuomotor adaptation, with participants arriving at a synergy between a strategic plan and the effects of sensorimotor adaptation.

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Geographical breakdown

Country Count As %
United States 9 3%
Canada 3 <1%
Australia 1 <1%
Brazil 1 <1%
Israel 1 <1%
Germany 1 <1%
France 1 <1%
India 1 <1%
Japan 1 <1%
Other 1 <1%
Unknown 312 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 99 30%
Researcher 46 14%
Student > Master 45 14%
Student > Bachelor 26 8%
Student > Doctoral Student 21 6%
Other 47 14%
Unknown 48 14%
Readers by discipline Count As %
Psychology 72 22%
Neuroscience 66 20%
Agricultural and Biological Sciences 28 8%
Engineering 28 8%
Medicine and Dentistry 21 6%
Other 56 17%
Unknown 61 18%