IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0228083.html
   My bibliography  Save this article

Asymmetry in kinematic generalization between visual and passive lead-in movements are consistent with a forward model in the sensorimotor system

Author

Listed:
  • Ian S Howard
  • Sae Franklin
  • David W Franklin

Abstract

In our daily life we often make complex actions comprised of linked movements, such as reaching for a cup of coffee and bringing it to our mouth to drink. Recent work has highlighted the role of such linked movements in the formation of independent motor memories, affecting the learning rate and ability to learn opposing force fields. In these studies, distinct prior movements (lead-in movements) allow adaptation of opposing dynamics on the following movement. Purely visual or purely passive lead-in movements exhibit different angular generalization functions of this motor memory as the lead-in movements are modified, suggesting different neural representations. However, we currently have no understanding of how different movement kinematics (distance, speed or duration) affect this recall process and the formation of independent motor memories. Here we investigate such kinematic generalization for both passive and visual lead-in movements to probe their individual characteristics. After participants adapted to opposing force fields using training lead-in movements, the lead-in kinematics were modified on random trials to test generalization. For both visual and passive modalities, recalled compensation was sensitive to lead-in duration and peak speed, falling off away from the training condition. However, little reduction in force was found with increasing lead-in distance. Interestingly, asymmetric transfer between lead-in movement modalities was also observed, with partial transfer from passive to visual, but very little vice versa. Overall these tuning effects were stronger for passive compared to visual lead-ins demonstrating the difference in these sensory inputs in regulating motor memories. Our results suggest these effects are a consequence of state estimation, with differences across modalities reflecting their different levels of sensory uncertainty arising as a consequence of dissimilar feedback delays.

Suggested Citation

  • Ian S Howard & Sae Franklin & David W Franklin, 2020. "Asymmetry in kinematic generalization between visual and passive lead-in movements are consistent with a forward model in the sensorimotor system," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-21, January.
    • Handle: RePEc:plo:pone00:0228083
    DOI: 10.1371/journal.pone.0228083
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0228083
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0228083&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0228083?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
    ---><---

    References listed on IDEAS

    as
    1. Kurt A. Thoroughman & Reza Shadmehr, 2000. "Learning of action through adaptive combination of motor primitives," Nature, Nature, vol. 407(6805), pages 742-747, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ian S Howard & David W Franklin, 2015. "Neural Tuning Functions Underlie Both Generalization and Interference," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-21, June.
    2. Barbara Feulner & Matthew G. Perich & Raeed H. Chowdhury & Lee E. Miller & Juan A. Gallego & Claudia Clopath, 2022. "Small, correlated changes in synaptic connectivity may facilitate rapid motor learning," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    3. Simon P Orozco & Scott T Albert & Reza Shadmehr, 2021. "Adaptive control of movement deceleration during saccades," PLOS Computational Biology, Public Library of Science, vol. 17(7), pages 1-30, July.
    4. Yael Mandelblat-Cerf & Itai Novick & Eilon Vaadia, 2011. "Expressions of Multiple Neuronal Dynamics during Sensorimotor Learning in the Motor Cortex of Behaving Monkeys," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-14, July.
    5. Joshua G A Cashaback & Heather R McGregor & Ayman Mohatarem & Paul L Gribble, 2017. "Dissociating error-based and reinforcement-based loss functions during sensorimotor learning," PLOS Computational Biology, Public Library of Science, vol. 13(7), pages 1-28, July.
    6. Frédéric Crevecoeur & Stephen H Scott, 2013. "Priors Engaged in Long-Latency Responses to Mechanical Perturbations Suggest a Rapid Update in State Estimation," PLOS Computational Biology, Public Library of Science, vol. 9(8), pages 1-14, August.
    7. 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.
    8. Abdelhamid Kadiallah & David W Franklin & Etienne Burdet, 2012. "Generalization in Adaptation to Stable and Unstable Dynamics," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-11, October.
    9. Daniel Blustein & Ahmed Shehata & Kevin Englehart & Jonathon Sensinger, 2018. "Conventional analysis of trial-by-trial adaptation is biased: Empirical and theoretical support using a Bayesian estimator," PLOS Computational Biology, Public Library of Science, vol. 14(12), pages 1-15, December.
    10. Yoshiyuki Sato & Kazuyuki Aihara, 2011. "A Bayesian Model of Sensory Adaptation," PLOS ONE, Public Library of Science, vol. 6(4), pages 1-7, April.
    11. Luis Nicolas Gonzalez Castro & Craig Bryant Monsen & Maurice A Smith, 2011. "The Binding of Learning to Action in Motor Adaptation," PLOS Computational Biology, Public Library of Science, vol. 7(6), pages 1-14, June.

    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:plo:pone00:0228083. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.