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Interacting Adaptive Processes with Different Timescales Underlie Short-Term Motor Learning

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  • Maurice A Smith
  • Ali Ghazizadeh
  • Reza Shadmehr

Abstract

Multiple processes may contribute to motor skill acquisition, but it is thought that many of these processes require sleep or the passage of long periods of time ranging from several hours to many days or weeks. Here we demonstrate that within a timescale of minutes, two distinct fast-acting processes drive motor adaptation. One process responds weakly to error but retains information well, whereas the other responds strongly but has poor retention. This two-state learning system makes the surprising prediction of spontaneous recovery (or adaptation rebound) if error feedback is clamped at zero following an adaptation-extinction training episode. We used a novel paradigm to experimentally confirm this prediction in human motor learning of reaching, and we show that the interaction between the learning processes in this simple two-state system provides a unifying explanation for several different, apparently unrelated, phenomena in motor adaptation including savings, anterograde interference, spontaneous recovery, and rapid unlearning. Our results suggest that motor adaptation depends on at least two distinct neural systems that have different sensitivity to error and retain information at different rates. This study presents evidence for two learning processes with distinct time courses that contribute to motor skill acquisition, and a computational model of the interactions between these processes that unifies much of the literature in motor adaptation.

Suggested Citation

  • Maurice A Smith & Ali Ghazizadeh & Reza Shadmehr, 2006. "Interacting Adaptive Processes with Different Timescales Underlie Short-Term Motor Learning," PLOS Biology, Public Library of Science, vol. 4(6), pages 1-1, May.
  • Handle: RePEc:plo:pbio00:0040179
    DOI: 10.1371/journal.pbio.0040179
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    References listed on IDEAS

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    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.
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    Cited by:

    1. Ajaz Ahmad Bhat & Gaurang Mahajan & Anita Mehta, 2011. "Learning with a Network of Competing Synapses," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-9, September.
    2. Takuya Honda & Masaya Hirashima & Daichi Nozaki, 2012. "Adaptation to Visual Feedback Delay Influences Visuomotor Learning," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-9, May.
    3. Jun Izawa & Reza Shadmehr, 2011. "Learning from Sensory and Reward Prediction Errors during Motor Adaptation," PLOS Computational Biology, Public Library of Science, vol. 7(3), pages 1-11, March.
    4. Orlando Arévalo & Mona A Bornschlegl & Sven Eberhardt & Udo Ernst & Klaus Pawelzik & Manfred Fahle, 2013. "Dynamics of Dual Prism Adaptation: Relating Novel Experimental Results to a Minimalistic Neural Model," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-12, October.
    5. Aaron L Wong & Mark Shelhamer, 2011. "Exploring the Fundamental Dynamics of Error-Based Motor Learning Using a Stationary Predictive-Saccade Task," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-13, September.
    6. Jordan A Taylor & Kurt A Thoroughman, 2008. "Motor Adaptation Scaled by the Difficulty of a Secondary Cognitive Task," PLOS ONE, Public Library of Science, vol. 3(6), pages 1-11, June.
    7. Wilsaan M Joiner & Gary C Sing & Maurice A Smith, 2017. "Temporal specificity of the initial adaptive response in motor adaptation," PLOS Computational Biology, Public Library of Science, vol. 13(7), pages 1-18, July.
    8. Barbara Feulner & Matthew G. Perich & Lee E. Miller & Claudia Clopath & Juan A. Gallego, 2025. "A neural implementation model of feedback-based motor learning," Nature Communications, Nature, vol. 16(1), pages 1-14, December.

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