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Separate and shared low-dimensional neural architectures for error-based and reinforcement motor learning

View ORCID ProfileCorson N. Areshenkoff, Anouk de Brouwer, View ORCID ProfileDaniel J. Gale, View ORCID ProfileJoseph Y. Nashed, View ORCID ProfileJason P. Gallivan
doi: https://doi.org/10.1101/2022.08.16.504134
Corson N. Areshenkoff
1Centre for Neuroscience Studies, Department of Psychology, Queens University, Kingston ON, Canada
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  • For correspondence: c.areshenkoff@queensu.ca
Anouk de Brouwer
2Department of Psychology, University of British Columbia, Vancouver BC, Canada
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Daniel J. Gale
3Centre for Neuroscience Studies, Queens University, Kingston ON, Canada
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Joseph Y. Nashed
4Centre for Neuroscience Studies, Queens University, Kingston ON, Canada
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Jason P. Gallivan
5Centre for Neuroscience Studies, Department of Psychology, Queens University, Kingston ON, Canada
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Abstract

Motor learning is supported by multiple systems adapted to processing different forms of sensory information (e.g., reward versus error feedback), and by higher-order systems supporting strategic processes. Yet, the extent to which these systems recruit shared versus separate neural pathways is poorly understood. To elucidate these pathways, we separately studied error-based (EL) and reinforcement-based (RL) motor learning in two functional MRI experiments in the same human subjects. We find that EL and RL occupy opposite ends of neural axis broadly separating cerebellar and striatal connectivity, respectively, with somatomotor cortex, and that alignment of this axis to each task is related to performance. Further, we identify a separate neural axis that is associated with strategy use during EL, and show that the expression of this same axis during RL predicts better performance. Together, these results offer a macroscale view of the common versus distinct neural architectures supporting different learning systems.

Competing Interest Statement

The authors have declared no competing interest.

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-ND 4.0 International license.
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Posted August 16, 2022.
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Separate and shared low-dimensional neural architectures for error-based and reinforcement motor learning
Corson N. Areshenkoff, Anouk de Brouwer, Daniel J. Gale, Joseph Y. Nashed, Jason P. Gallivan
bioRxiv 2022.08.16.504134; doi: https://doi.org/10.1101/2022.08.16.504134
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Separate and shared low-dimensional neural architectures for error-based and reinforcement motor learning
Corson N. Areshenkoff, Anouk de Brouwer, Daniel J. Gale, Joseph Y. Nashed, Jason P. Gallivan
bioRxiv 2022.08.16.504134; doi: https://doi.org/10.1101/2022.08.16.504134

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