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Error Correction and the Structure of Inter-Trial Fluctuations in a Redundant Movement Task

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  • Joby John
  • Jonathan B Dingwell
  • Joseph P Cusumano

Abstract

We study inter-trial movement fluctuations exhibited by human participants during the repeated execution of a virtual shuffleboard task. Focusing on skilled performance, theoretical analysis of a previously-developed general model of inter-trial error correction is used to predict the temporal and geometric structure of variability near a goal equivalent manifold (GEM). The theory also predicts that the goal-level error scales linearly with intrinsic body-level noise via the total body-goal sensitivity, a new derived quantity that illustrates how task performance arises from the interaction of active error correction and passive sensitivity properties along the GEM. Linear models estimated from observed fluctuations, together with a novel application of bootstrapping to the estimation of dynamical and correlation properties of the inter-trial dynamics, are used to experimentally confirm all predictions, thus validating our model. In addition, we show that, unlike “static” variability analyses, our dynamical approach yields results that are independent of the coordinates used to measure task execution and, in so doing, provides a new set of task coordinates that are intrinsic to the error-regulation process itself.Author Summary: During the repeated execution of precision movement tasks, humans face two formidable challenges from the motor system itself: dimensionality and noise. Human motor performance involves biomechanical, neuromotor, and perceptual degrees of freedom far in excess of those theoretically needed to prescribe typical goal-directed tasks. At the same time, noise is present in the human body across multiple scales of observation. This high-dimensional and stochastic character of biological movement is the fundamental source of variability ubiquitously observed during task execution. However, it is becoming clear that these two challenges are not merely impediments to be overcome, but rather hold a key to understanding how humans maintain motor performance under changing circumstances, such as those caused by fatigue, injury, or aging. In this work, by studying skilled human participants as they play a virtual shuffleboard game, we demonstrate the fundamental importance of adopting a dynamical perspective when analyzing the motor variability observed over many trials. Using this dynamical approach, we can not only study the geometry of observed inter-trial variability, but can also theoretically describe and experimentally characterize how it is temporally generated and regulated. Furthermore, our theoretical framework and model-based data analysis approach helps to unify previous variability analysis approaches based on stability, correlation, control theory, or task manifolds alone. This conceptual unification supports the idea that such seemingly disparate features of motor variability arise from a single, relatively simple underlying neurophysiological process of motor regulation.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pcbi00:1005118
    DOI: 10.1371/journal.pcbi.1005118
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    References listed on IDEAS

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    1. 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.
    2. Julius Verrel & Didier Pradon & Nicolas Vuillerme, 2012. "Persistence of Motor-Equivalent Postural Fluctuations during Bipedal Quiet Standing," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-8, October.
    3. Avigdor Eldar & Michael B. Elowitz, 2010. "Functional roles for noise in genetic circuits," Nature, Nature, vol. 467(7312), pages 167-173, September.
    4. Jonathan B Dingwell & Joby John & Joseph P Cusumano, 2010. "Do Humans Optimally Exploit Redundancy to Control Step Variability in Walking?," PLOS Computational Biology, Public Library of Science, vol. 6(7), pages 1-15, July.
    5. Leslie C. Osborne & Stephen G. Lisberger & William Bialek, 2005. "A sensory source for motor variation," Nature, Nature, vol. 437(7057), pages 412-416, September.
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    Cited by:

    1. Jonathan B Dingwell & Joseph P Cusumano, 2019. "Humans use multi-objective control to regulate lateral foot placement when walking," PLOS Computational Biology, Public Library of Science, vol. 15(3), pages 1-28, March.
    2. Paolo Tommasino & Antonella Maselli & Domenico Campolo & Francesco Lacquaniti & Andrea d’Avella, 2021. "A Hessian-based decomposition characterizes how performance in complex motor skills depends on individual strategy and variability," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-32, June.
    3. Joshua G A Cashaback & Christopher K Lao & Dimitrios J Palidis & Susan K Coltman & Heather R McGregor & Paul L Gribble, 2019. "The gradient of the reinforcement landscape influences sensorimotor learning," PLOS Computational Biology, Public Library of Science, vol. 15(3), pages 1-27, March.

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