期刊
ELIFE
卷 10, 期 -, 页码 -出版社
ELIFE SCIENCES PUBLICATIONS LTD
DOI: 10.7554/eLife.67019
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类别
资金
- National Institute on Aging [R01 AG041878]
- Smith National Institute of Neurological Disorders and Stroke [R01 NS105839]
When faced with uncertainty, humans generate a motor plan that optimizes task performance rather than averaging potential motor plans. A novel computational model based on this performance-optimization theory can account for a majority of the variance in individual differences between participants, accurately predicting population-averaged changes in motor output.
Actions often require the selection of a specific goal amongst a range of possibilities, like when a softball player must precisely position her glove to field a fast-approaching ground ball. Previous studies have suggested that during goal uncertainty the brain prepares for all potential goals in parallel and averages the corresponding motor plans to command an intermediate movement that is progressively refined as additional information becomes available. Although intermediate movements are widely observed, they could instead reflect a neural decision about the single best action choice given the uncertainty present. Here we systematically dissociate these possibilities using novel experimental manipulations and find that when confronted with uncertainty, humans generate a motor plan that optimizes task performance rather than averaging potential motor plans. In addition to accurate predictions of population-averaged changes in motor output, a novel computational model based on this performance-optimization theory accounted for a majority of the variance in individual differences between participants. Our findings resolve a long-standing question about how the brain selects an action to execute during goal uncertainty, providing fundamental insight into motor planning in the nervous system.
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