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Bayesian Integration and Non-Linear Feedback Control in a Full-Body Motor Task

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  • Ian H Stevenson
  • Hugo L Fernandes
  • Iris Vilares
  • Kunlin Wei
  • Konrad P Körding

Abstract

A large number of experiments have asked to what degree human reaching movements can be understood as being close to optimal in a statistical sense. However, little is known about whether these principles are relevant for other classes of movements. Here we analyzed movement in a task that is similar to surfing or snowboarding. Human subjects stand on a force plate that measures their center of pressure. This center of pressure affects the acceleration of a cursor that is displayed in a noisy fashion (as a cloud of dots) on a projection screen while the subject is incentivized to keep the cursor close to a fixed position. We find that salient aspects of observed behavior are well-described by optimal control models where a Bayesian estimation model (Kalman filter) is combined with an optimal controller (either a Linear-Quadratic-Regulator or Bang-bang controller). We find evidence that subjects integrate information over time taking into account uncertainty. However, behavior in this continuous steering task appears to be a highly non-linear function of the visual feedback. While the nervous system appears to implement Bayes-like mechanisms for a full-body, dynamic task, it may additionally take into account the specific costs and constraints of the task.Author Summary: There is a growing body of work demonstrating that humans are close to statistically optimal in both their perception of the world and their actions on it. That is, we seem to combine information from our sensors with the constraints and costs of moving to minimize our errors and effort. Most of the evidence for this type of behavior comes from tasks such as reaching in a small workspace or standing on a force plate passively viewing a stimulus. Although humans appear to be near-optimal for these tasks, it is not clear whether the theory holds for other tasks. Here we introduce a full-body, goal-directed task similar to surfing or snowboarding where subjects steer a cursor with their center of pressure. We find that subjects respond to sensory uncertainty near-optimally in this task, but their behavior is highly non-linear. This suggests that the computations performed by the nervous system may take into account a more complicated set of costs and constraints than previously supposed.

Suggested Citation

  • Ian H Stevenson & Hugo L Fernandes & Iris Vilares & Kunlin Wei & Konrad P Körding, 2009. "Bayesian Integration and Non-Linear Feedback Control in a Full-Body Motor Task," PLOS Computational Biology, Public Library of Science, vol. 5(12), pages 1-9, December.
    • Handle: RePEc:plo:pcbi00:1000629
    DOI: 10.1371/journal.pcbi.1000629
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    References listed on IDEAS

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    1. Christopher M. Harris & Daniel M. Wolpert, 1998. "Signal-dependent noise determines motor planning," Nature, Nature, vol. 394(6695), pages 780-784, August.
    2. Konrad P. Körding & Daniel M. Wolpert, 2004. "Bayesian integration in sensorimotor learning," Nature, Nature, vol. 427(6971), pages 244-247, January.
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    Cited by:

    1. Jordi Grau-Moya & Pedro A Ortega & Daniel A Braun, 2016. "Decision-Making under Ambiguity Is Modulated by Visual Framing, but Not by Motor vs. Non-Motor Context. Experiments and an Information-Theoretic Ambiguity Model," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-21, April.
    2. Luigi Acerbi & Sethu Vijayakumar & Daniel M Wolpert, 2017. "Target Uncertainty Mediates Sensorimotor Error Correction," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-21, January.

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