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Predictors of sensorimotor adaption: insights from over 100,000 reaches

View ORCID ProfileJonathan S. Tsay, Hrach Asmerian, Laura T. Germine, Jeremy Wilmer, View ORCID ProfileRichard B. Ivry, Ken Nakayama
doi: https://doi.org/10.1101/2023.01.18.524634
Jonathan S. Tsay
1Department of Psychology, University of California Berkeley, Berkeley, CA
2Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA
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  • ORCID record for Jonathan S. Tsay
  • For correspondence: xiaotsay2015@gmail.com hrach.asmerian@berkeley.edu
Hrach Asmerian
1Department of Psychology, University of California Berkeley, Berkeley, CA
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  • For correspondence: xiaotsay2015@gmail.com hrach.asmerian@berkeley.edu
Laura T. Germine
3Department of Psychiatry, Harvard Medical School, Boston, MA
4Institute for Technology in Psychiatry, McLean Hospital, Boston, MA
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Jeremy Wilmer
5Department of Psychology, Wellesley College, Wellesley, MA
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Richard B. Ivry
1Department of Psychology, University of California Berkeley, Berkeley, CA
2Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA
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Ken Nakayama
1Department of Psychology, University of California Berkeley, Berkeley, CA
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Abstract

Sensorimotor adaptation is essential for keeping our movements well-calibrated in response to changes in the body and environment. For over a century, we have studied sensorimotor adaptation in highly controlled laboratory settings that typically involve small sample sizes. While this approach has proven useful to characterize different learning processes, laboratory studies are typically very underpowered to generate data suited for exploring the myriad of factors that may modulate motor performance. Here, using a citizen science website (testmybrain.org), we collected over 2000 sessions on a visuomotor rotation task. This unique dataset has allowed us to replicate classic motor findings, reconcile controversial findings in the learning and memory literature, and discover novel constraints underlying dissociable implicit and explicit learning processes supporting sensorimotor adaptation. Taken together, this study suggests that large-scale motor learning studies hold enormous potential to advance sensorimotor neuroscience.

Competing Interest Statement

RI is a co-founder with equity in Magnetic Tides, Inc.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted January 20, 2023.
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Predictors of sensorimotor adaption: insights from over 100,000 reaches
Jonathan S. Tsay, Hrach Asmerian, Laura T. Germine, Jeremy Wilmer, Richard B. Ivry, Ken Nakayama
bioRxiv 2023.01.18.524634; doi: https://doi.org/10.1101/2023.01.18.524634
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Predictors of sensorimotor adaption: insights from over 100,000 reaches
Jonathan S. Tsay, Hrach Asmerian, Laura T. Germine, Jeremy Wilmer, Richard B. Ivry, Ken Nakayama
bioRxiv 2023.01.18.524634; doi: https://doi.org/10.1101/2023.01.18.524634

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