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Learning of action through adaptive combination of motor primitives

Author

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  • Kurt A. Thoroughman

    (419 Traylor Building
    Brandeis University)

  • Reza Shadmehr

    (419 Traylor Building)

Abstract

Understanding how the brain constructs movements remains a fundamental challenge in neuroscience. The brain may control complex movements through flexible combination of motor primitives1, where each primitive is an element of computation in the sensorimotor map that transforms desired limb trajectories into motor commands. Theoretical studies have shown that a system's ability to learn action depends on the shape of its primitives2. Using a time-series analysis of error patterns, here we show that humans learn the dynamics of reaching movements through a flexible combination of primitives that have gaussian-like tuning functions encoding hand velocity. The wide tuning of the inferred primitives predicts limitations on the brain's ability to represent viscous dynamics. We find close agreement between the predicted limitations and the subjects’ adaptation to new force fields. The mathematical properties of the derived primitives resemble the tuning curves of Purkinje cells in the cerebellum. The activity of these cells may encode primitives that underlie the learning of dynamics.

Suggested Citation

  • Kurt A. Thoroughman & Reza Shadmehr, 2000. "Learning of action through adaptive combination of motor primitives," Nature, Nature, vol. 407(6805), pages 742-747, October.
    • Handle: RePEc:nat:nature:v:407:y:2000:i:6805:d:10.1038_35037588
    DOI: 10.1038/35037588
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    Citations

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    Cited by:

    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. 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.
    3. 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.
    4. 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.
    5. Yoshiyuki Sato & Kazuyuki Aihara, 2011. "A Bayesian Model of Sensory Adaptation," PLOS ONE, Public Library of Science, vol. 6(4), pages 1-7, April.
    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. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.

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