 Interaction between decision-making and motor learning when selecting reach targets in the presence of bias and noiseTianyao Zhu, Jason P Gallivan, Daniel M Wolpert and J Randall Flanagan PLOS Computational Biology, 2023, vol. 19, issue 11, 1-26 Abstract: Motor errors can have both bias and noise components. Bias can be compensated for by adaptation and, in tasks in which the magnitude of noise varies across the environment, noise can be reduced by identifying and then acting in less noisy regions of the environment. Here we examine how these two processes interact when participants reach under a combination of an externally imposed visuomotor bias and noise. In a center-out reaching task, participants experienced noise (zero-mean random visuomotor rotations) that was target-direction dependent with a standard deviation that increased linearly from a least-noisy direction. They also experienced a constant bias, a visuomotor rotation that varied (across groups) from 0 to 40 degrees. Critically, on each trial, participants could select one of three targets to reach to, thereby allowing them to potentially select targets close to the least-noisy direction. The group who experienced no bias (0 degrees) quickly learned to select targets close to the least-noisy direction. However, groups who experienced a bias often failed to identify the least-noisy direction, even though they did partially adapt to the bias. When noise was introduced after participants experienced and adapted to a 40 degrees bias (without noise) in all directions, they exhibited an improved ability to find the least-noisy direction. We developed two models—one for reach adaptation and one for target selection—that could explain participants’ adaptation and target-selection behavior. Our data and simulations indicate that there is a trade-off between adaptation and selection. Specifically, because bias learning is local, participants can improve performance, through adaptation, by always selecting targets that are closest to a chosen direction. However, this comes at the expense of improving performance, through selection, by reaching toward targets in different directions to find the least-noisy direction.Author summary: In the real world, movement errors can result from both bias (systematic error) and noise (random error). A fundamental challenge for the sensorimotor system is to make appropriate changes in behavior when facing these two sources of errors. To examine this issue, we developed a center-out reaching task in which participants could select one of three randomly positioned targets. An angular displacement was added between viewed and actual reach endpoint position. The displacement included a constant bias equal in all directions and a noise of which the amplitude depended on the selected target direction. For different participant groups, we applied different bias angles (0 to 40 degrees). Participants who experienced larger bias angles were less successful at finding the least-noisy target direction. In a second experiment, when participants adapted to a 40 degrees bias in all directions before experiencing target-dependent noise, they were more successful at finding the least-noisy direction than participants—in the first experiment—who experienced bias and noise simultaneously. Our results indicate that participants cannot disentangle the contribution of bias and noise to their movement errors, such that adaptation to bias limits their ability to select less-noisy movements. Date: 2023 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1011596 DOI: 10.1371/journal.pcbi.1011596 Access Statistics for this article More articles in PLOS Computational Biology from Public Library of Science |
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