IDEAS home Printed from https://ideas.repec.org/a/nat/nature/v600y2021i7889d10.1038_s41586-021-04129-3.html
   My bibliography  Save this article

Contextual inference underlies the learning of sensorimotor repertoires

Author

Listed:
  • James B. Heald

    (Columbia University
    University of Cambridge
    Columbia University)

  • Máté Lengyel

    (University of Cambridge
    Central European University)

  • Daniel M. Wolpert

    (Columbia University
    University of Cambridge
    Columbia University)

Abstract

Asbtract Humans spend a lifetime learning, storing and refining a repertoire of motor memories. For example, through experience, we become proficient at manipulating a large range of objects with distinct dynamical properties. However, it is unknown what principle underlies how our continuous stream of sensorimotor experience is segmented into separate memories and how we adapt and use this growing repertoire. Here we develop a theory of motor learning based on the key principle that memory creation, updating and expression are all controlled by a single computation—contextual inference. Our theory reveals that adaptation can arise both by creating and updating memories (proper learning) and by changing how existing memories are differentially expressed (apparent learning). This insight enables us to account for key features of motor learning that had no unified explanation: spontaneous recovery1, savings2, anterograde interference3, how environmental consistency affects learning rate4,5 and the distinction between explicit and implicit learning6. Critically, our theory also predicts new phenomena—evoked recovery and context-dependent single-trial learning—which we confirm experimentally. These results suggest that contextual inference, rather than classical single-context mechanisms1,4,7–9, is the key principle underlying how a diverse set of experiences is reflected in our motor behaviour.

Suggested Citation

  • James B. Heald & Máté Lengyel & Daniel M. Wolpert, 2021. "Contextual inference underlies the learning of sensorimotor repertoires," Nature, Nature, vol. 600(7889), pages 489-493, December.
  • Handle: RePEc:nat:nature:v:600:y:2021:i:7889:d:10.1038_s41586-021-04129-3
    DOI: 10.1038/s41586-021-04129-3
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41586-021-04129-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1038/s41586-021-04129-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kisho Ogasa & Atsushi Yokoi & Gouki Okazawa & Morimichi Nishigaki & Masaya Hirashima & Nobuhiro Hagura, 2024. "Decision uncertainty as a context for motor memory," Nature Human Behaviour, Nature, vol. 8(9), pages 1738-1751, September.
    2. Joanna C. Chang & Matthew G. Perich & Lee E. Miller & Juan A. Gallego & Claudia Clopath, 2024. "De novo motor learning creates structure in neural activity that shapes adaptation," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    3. Wei-Long Zheng & Zhongxuan Wu & Ali Hummos & Guangyu Robert Yang & Michael M. Halassa, 2024. "Rapid context inference in a thalamocortical model using recurrent neural networks," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    4. Nidhi Seethapathi & Barrett C. Clark & Manoj Srinivasan, 2024. "Exploration-based learning of a stabilizing controller predicts locomotor adaptation," Nature Communications, Nature, vol. 15(1), pages 1-23, December.
    5. Márton Albert Hajnal & Duy Tran & Michael Einstein & Mauricio Vallejo Martelo & Karen Safaryan & Pierre-Olivier Polack & Peyman Golshani & Gergő Orbán, 2023. "Continuous multiplexed population representations of task context in the mouse primary visual cortex," Nature Communications, Nature, vol. 14(1), pages 1-20, December.
    6. Taisei Sugiyama & Nicolas Schweighofer & Jun Izawa, 2023. "Reinforcement learning establishes a minimal metacognitive process to monitor and control motor learning performance," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    7. Márton Albert Hajnal & Duy Tran & Zsombor Szabó & Andrea Albert & Karen Safaryan & Michael Einstein & Mauricio Vallejo Martelo & Pierre-Olivier Polack & Peyman Golshani & Gergő Orbán, 2024. "Shifts in attention drive context-dependent subspace encoding in anterior cingulate cortex in mice during decision making," Nature Communications, Nature, vol. 15(1), pages 1-17, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:nature:v:600:y:2021:i:7889:d:10.1038_s41586-021-04129-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.