Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Reinforcement meta-learning optimizes visuomotor learning

Taisei Sugiyama, Nicolas Schweighofer, Jun Izawa
doi: https://doi.org/10.1101/2020.01.19.912048
Taisei Sugiyama
1Empowerment Informatics, University of Tsukuba, Tsukuba, Ibaraki, 305-8573, Japan
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Nicolas Schweighofer
2Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, CA, 90089-9006 USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jun Izawa
3Engineering, Information, and Systems, University of Tsukuba, Ibaraki 305-8573, Japan
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: izawa@emp.tsukuba.ac.jp
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Reinforcement learning enables the brain to learn optimal action selection, such as go or not go, by forming state-action and action-outcome associations. Does this mechanism also optimize the brain’s willingness to learn, such as learn or not learn? Learning to learn by rewards, i.e., reinforcement meta-learning, is a crucial mechanism for machines to develop flexibility in learning, which is also considered in the brain without empirical examinations. Here, we show that humans learn to learn or not learn to maximize rewards in visuomotor learning tasks. We also show that this regulation of learning is not a motivational bias but is a result of an instrumental, active process, which takes into account the learning-outcome structure. Our results thus demonstrate the existence of reinforcement meta-learning in the human brain. Because motor learning is a process of minimizing sensory errors, our findings uncover an essential mechanism of interaction between reward and error.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted January 20, 2020.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Reinforcement meta-learning optimizes visuomotor learning
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Reinforcement meta-learning optimizes visuomotor learning
Taisei Sugiyama, Nicolas Schweighofer, Jun Izawa
bioRxiv 2020.01.19.912048; doi: https://doi.org/10.1101/2020.01.19.912048
Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
Citation Tools
Reinforcement meta-learning optimizes visuomotor learning
Taisei Sugiyama, Nicolas Schweighofer, Jun Izawa
bioRxiv 2020.01.19.912048; doi: https://doi.org/10.1101/2020.01.19.912048

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Neuroscience
Subject Areas
All Articles
  • Animal Behavior and Cognition (2633)
  • Biochemistry (5220)
  • Bioengineering (3643)
  • Bioinformatics (15706)
  • Biophysics (7210)
  • Cancer Biology (5590)
  • Cell Biology (8039)
  • Clinical Trials (138)
  • Developmental Biology (4731)
  • Ecology (7458)
  • Epidemiology (2059)
  • Evolutionary Biology (10518)
  • Genetics (7695)
  • Genomics (10079)
  • Immunology (5144)
  • Microbiology (13819)
  • Molecular Biology (5350)
  • Neuroscience (30570)
  • Paleontology (211)
  • Pathology (870)
  • Pharmacology and Toxicology (1519)
  • Physiology (2233)
  • Plant Biology (4980)
  • Scientific Communication and Education (1036)
  • Synthetic Biology (1379)
  • Systems Biology (4129)
  • Zoology (802)