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Neuromotor Noise Is Malleable by Amplifying Perceived Errors

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  • Christopher J Hasson
  • Zhaoran Zhang
  • Masaki O Abe
  • Dagmar Sternad

Abstract

Variability in motor performance results from the interplay of error correction and neuromotor noise. This study examined whether visual amplification of error, previously shown to improve performance, affects not only error correction, but also neuromotor noise, typically regarded as inaccessible to intervention. Seven groups of healthy individuals, with six participants in each group, practiced a virtual throwing task for three days until reaching a performance plateau. Over three more days of practice, six of the groups received different magnitudes of visual error amplification; three of these groups also had noise added. An additional control group was not subjected to any manipulations for all six practice days. The results showed that the control group did not improve further after the first three practice days, but the error amplification groups continued to decrease their error under the manipulations. Analysis of the temporal structure of participants’ corrective actions based on stochastic learning models revealed that these performance gains were attained by reducing neuromotor noise and, to a considerably lesser degree, by increasing the size of corrective actions. Based on these results, error amplification presents a promising intervention to improve motor function by decreasing neuromotor noise after performance has reached an asymptote. These results are relevant for patients with neurological disorders and the elderly. More fundamentally, these results suggest that neuromotor noise may be accessible to practice interventions.Author Summary: It is widely recognized that neuromotor noise limits human motor performance, generating errors and variability even in highly skilled performers. Arising from many spatiotemporal scales within the physiological system, the intrinsic noise component is commonly assumed to be invariant by most computational models of human neuromotor control. We challenge this assumption and show that after an individual has reached a performance plateau, amplifying perceived errors elicits continued reductions in observed variability. Model-based analyses show that the main driver of this effect is a reduction in the variance of neuromotor noise. Thus, error amplification has the potential to become a key intervention for individuals with increased movement variability due to high levels of neuromotor noise, ranging from children with dystonia, through patients with stroke, to healthy elders.

Suggested Citation

  • Christopher J Hasson & Zhaoran Zhang & Masaki O Abe & Dagmar Sternad, 2016. "Neuromotor Noise Is Malleable by Amplifying Perceived Errors," PLOS Computational Biology, Public Library of Science, vol. 12(8), pages 1-28, August.
  • Handle: RePEc:plo:pcbi00:1005044
    DOI: 10.1371/journal.pcbi.1005044
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    References listed on IDEAS

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    1. Jérémy Bluteau & Sabine Coquillart & Yohan Payan & Edouard Gentaz, 2008. "Haptic Guidance Improves the Visuo-Manual Tracking of Trajectories," PLOS ONE, Public Library of Science, vol. 3(3), pages 1-7, March.
    2. Jooeun Ahn & Zhaoran Zhang & Dagmar Sternad, 2016. "Noise Induces Biased Estimation of the Correction Gain," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-18, July.
    3. Dagmar Sternad & Masaki O Abe & Xiaogang Hu & Hermann Müller, 2011. "Neuromotor Noise, Error Tolerance and Velocity-Dependent Costs in Skilled Performance," PLOS Computational Biology, Public Library of Science, vol. 7(9), pages 1-15, September.
    4. Christopher M. Harris & Daniel M. Wolpert, 1998. "Signal-dependent noise determines motor planning," Nature, Nature, vol. 394(6695), pages 780-784, August.
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    Cited by:

    1. Jack Brookes & Faisal Mushtaq & Earle Jamieson & Aaron J Fath & Geoffrey Bingham & Peter Culmer & Richard M Wilkie & Mark Mon-Williams, 2020. "Exploring disturbance as a force for good in motor learning," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-21, May.
    2. Wolfgang I. Schöllhorn & Nikolas Rizzi & Agnė Slapšinskaitė-Dackevičienė & Nuno Leite, 2022. "Always Pay Attention to Which Model of Motor Learning You Are Using," IJERPH, MDPI, vol. 19(2), pages 1-36, January.

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