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Inferring Visuomotor Priors for Sensorimotor Learning

Author

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  • Edward J A Turnham
  • Daniel A Braun
  • Daniel M Wolpert

Abstract

Sensorimotor learning has been shown to depend on both prior expectations and sensory evidence in a way that is consistent with Bayesian integration. Thus, prior beliefs play a key role during the learning process, especially when only ambiguous sensory information is available. Here we develop a novel technique to estimate the covariance structure of the prior over visuomotor transformations – the mapping between actual and visual location of the hand – during a learning task. Subjects performed reaching movements under multiple visuomotor transformations in which they received visual feedback of their hand position only at the end of the movement. After experiencing a particular transformation for one reach, subjects have insufficient information to determine the exact transformation, and so their second reach reflects a combination of their prior over visuomotor transformations and the sensory evidence from the first reach. We developed a Bayesian observer model in order to infer the covariance structure of the subjects' prior, which was found to give high probability to parameter settings consistent with visuomotor rotations. Therefore, although the set of visuomotor transformations experienced had little structure, the subjects had a strong tendency to interpret ambiguous sensory evidence as arising from rotation-like transformations. We then exposed the same subjects to a highly-structured set of visuomotor transformations, designed to be very different from the set of visuomotor rotations. During this exposure the prior was found to have changed significantly to have a covariance structure that no longer favored rotation-like transformations. In summary, we have developed a technique which can estimate the full covariance structure of a prior in a sensorimotor task and have shown that the prior over visuomotor transformations favor a rotation-like structure. Moreover, through experience of a novel task structure, participants can appropriately alter the covariance structure of their prior. Author Summary: When learning a new skill, such as riding a bicycle, we can adjust the commands we send to our muscles based on two sources of information. First, we can use sensory inputs to inform us how the bike is behaving. Second, we can use prior knowledge about the properties of bikes and how they behave in general. This prior knowledge is represented as a probability distribution over the properties of bikes. These two sources of information can then be combined by a process known as Bayes rule to identify optimally the properties of a particular bike. Here, we develop a novel technique to identify the probability distribution of a prior in a visuomotor learning task in which the visual location of the hand is transformed from the actual hand location, similar to when using a computer mouse. We show that subjects have a prior that tends to interpret ambiguous information about the task as arising from a visuomotor rotation but that experience of a particular set of visuomotor transformations can alter the prior.

Suggested Citation

  • Edward J A Turnham & Daniel A Braun & Daniel M Wolpert, 2011. "Inferring Visuomotor Priors for Sensorimotor Learning," PLOS Computational Biology, Public Library of Science, vol. 7(3), pages 1-13, March.
  • Handle: RePEc:plo:pcbi00:1001112
    DOI: 10.1371/journal.pcbi.1001112
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    References listed on IDEAS

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    1. Daniel A Braun & Pedro A Ortega & Daniel M Wolpert, 2009. "Nash Equilibria in Multi-Agent Motor Interactions," PLOS Computational Biology, Public Library of Science, vol. 5(8), pages 1-8, August.
    2. Marc O. Ernst & Martin S. Banks, 2002. "Humans integrate visual and haptic information in a statistically optimal fashion," Nature, Nature, vol. 415(6870), pages 429-433, January.
    3. 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. Tim Genewein & Eduard Hez & Zeynab Razzaghpanah & Daniel A Braun, 2015. "Structure Learning in Bayesian Sensorimotor Integration," PLOS Computational Biology, Public Library of Science, vol. 11(8), pages 1-27, August.
    2. Jordi Grau-Moya & Pedro A Ortega & Daniel A Braun, 2012. "Risk-Sensitivity in Bayesian Sensorimotor Integration," PLOS Computational Biology, Public Library of Science, vol. 8(9), pages 1-7, September.
    3. 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.
    4. Luigi Acerbi & Daniel M Wolpert & Sethu Vijayakumar, 2012. "Internal Representations of Temporal Statistics and Feedback Calibrate Motor-Sensory Interval Timing," PLOS Computational Biology, Public Library of Science, vol. 8(11), pages 1-19, November.

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