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Human decision making anticipates future performance in motor learning

Published version
Peer-reviewed

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Abstract

It is well-established that people can factor into account the distribution of their errors in motor performance so as to optimize reward. Here we asked whether, in the context of motor learning where errors decrease across trials, people take into account their future, improved performance so as to make optimal decisions to maximize reward. One group of participants performed a virtual throwing task in which, periodically, they were given the opportunity to select from a set of smaller targets of increasing value. A second group of participants performed a reaching task under a visuomotor rotation in which, after performing a initial set of trials, they selected a reward structure (ratio of points for target hits and misses) for different exploitation horizons (i.e., numbers of trials they might be asked to perform). Because movement errors decreased exponentially across trials in both learning tasks, optimal target selection (task 1) and optimal reward structure selection (task 2) required taking into account future performance. The results from both tasks indicate that people anticipate their future motor performance so as to make decisions that will improve their expected future reward.

Description

Funder: Royal Society

Keywords

Research Article, Biology and life sciences, Social sciences, Medicine and health sciences

Journal Title

PLOS Computational Biology

Conference Name

Journal ISSN

1553-734X
1553-7358

Volume Title

16

Publisher

Public Library of Science
Sponsorship
Natural Sciences and Engineering Research Council of Canada (RGPIN/04837-2014)
Canadian Institute of Health Research (126158)
Wellcome Trust (GB) (WT097803MA)