De novo learning versus adaptation of continuous control in a manual tracking task

  1. Christopher S Yang  Is a corresponding author
  2. Noah J Cowan
  3. Adrian M Haith
  1. Johns Hopkins University, United States
  2. Johns Hopkins University School of Medicine, United States

Abstract

How do people learn to perform tasks that require continuous adjustments of motor output, like riding a bicycle? People rely heavily on cognitive strategies when learning discrete movement tasks, but such time-consuming strategies are infeasible in continuous control tasks that demand rapid responses to ongoing sensory feedback. To understand how people can learn to perform such tasks without the benefit of cognitive strategies, we imposed a rotation/mirror reversal of visual feedback while participants performed a continuous tracking task. We analyzed behavior using a system identification approach which revealed two qualitatively different components of learning: adaptation of a baseline controller and formation of a new, task-specific continuous controller. These components exhibited different signatures in the frequency domain and were differentially engaged under the rotation/mirror reversal. Our results demonstrate that people can rapidly build a new continuous controller de novo and can simultaneously deploy this process with adaptation of an existing controller.

Data availability

The data and code used to produce the results in this study can be found in the Johns Hopkins University Data Archive (https://doi.org/10.7281/T1/87PH8T).

The following data sets were generated

Article and author information

Author details

  1. Christopher S Yang

    Department of Neuroscience, Johns Hopkins University, Baltimore, United States
    For correspondence
    christopher.yang@jhmi.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7645-3861
  2. Noah J Cowan

    Mechanical Engineering, Johns Hopkins University, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2502-3770
  3. Adrian M Haith

    Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5658-8654

Funding

Link Foundation

  • Christopher S Yang

National Institutes of Health (5T32NS091018-17)

  • Christopher S Yang

National Institutes of Health (5T32NS091018-18)

  • Christopher S Yang

National Science Foundation (1825489)

  • Noah J Cowan

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: Informed consent and consent to publish was obtained from all participants included in this work. All methods were approved by the Johns Hopkins School of Medicine Institutional Review Board under NA_00048918.

Reviewing Editor

  1. Timothy Verstynen, Carnegie Mellon University, United States

Version history

  1. Received: August 29, 2020
  2. Accepted: June 22, 2021
  3. Accepted Manuscript published: June 25, 2021 (version 1)
  4. Version of Record published: July 8, 2021 (version 2)

Copyright

© 2021, Yang et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Christopher S Yang
  2. Noah J Cowan
  3. Adrian M Haith
(2021)
De novo learning versus adaptation of continuous control in a manual tracking task
eLife 10:e62578.
https://doi.org/10.7554/eLife.62578

Share this article

https://doi.org/10.7554/eLife.62578

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