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How Does Our Motor System Determine Its Learning Rate?

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  • Robert J van Beers

Abstract

Motor learning is driven by movement errors. The speed of learning can be quantified by the learning rate, which is the proportion of an error that is corrected for in the planning of the next movement. Previous studies have shown that the learning rate depends on the reliability of the error signal and on the uncertainty of the motor system’s own state. These dependences are in agreement with the predictions of the Kalman filter, which is a state estimator that can be used to determine the optimal learning rate for each movement such that the expected movement error is minimized. Here we test whether not only the average behaviour is optimal, as the previous studies showed, but if the learning rate is chosen optimally in every individual movement. Subjects made repeated movements to visual targets with their unseen hand. They received visual feedback about their endpoint error immediately after each movement. The reliability of these error-signals was varied across three conditions. The results are inconsistent with the predictions of the Kalman filter because correction for large errors in the beginning of a series of movements to a fixed target was not as fast as predicted and the learning rates for the extent and the direction of the movements did not differ in the way predicted by the Kalman filter. Instead, a simpler model that uses the same learning rate for all movements with the same error-signal reliability can explain the data. We conclude that our brain does not apply state estimation to determine the optimal planning correction for every individual movement, but it employs a simpler strategy of using a fixed learning rate for all movements with the same level of error-signal reliability.

Suggested Citation

  • Robert J van Beers, 2012. "How Does Our Motor System Determine Its Learning Rate?," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-17, November.
  • Handle: RePEc:plo:pone00:0049373
    DOI: 10.1371/journal.pone.0049373
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    Cited by:

    1. Vinil T Chackochan & Vittorio Sanguineti, 2019. "Incomplete information about the partner affects the development of collaborative strategies in joint action," PLOS Computational Biology, Public Library of Science, vol. 15(12), pages 1-23, December.
    2. Joby John & Jonathan B Dingwell & Joseph P Cusumano, 2016. "Error Correction and the Structure of Inter-Trial Fluctuations in a Redundant Movement Task," PLOS Computational Biology, Public Library of Science, vol. 12(9), pages 1-30, September.
    3. 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.
    4. Robert J van Beers & Yor van der Meer & Richard M Veerman, 2013. "What Autocorrelation Tells Us about Motor Variability: Insights from Dart Throwing," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-8, May.

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