IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1001112.html
   My bibliography  Save this article

Inferring Visuomotor Priors for Sensorimotor Learning

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1001112
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1001112&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1001112?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Konrad P. Körding & Daniel M. Wolpert, 2004. "Bayesian integration in sensorimotor learning," Nature, Nature, vol. 427(6971), pages 244-247, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wen-Hao Zhang & Si Wu & Krešimir Josić & Brent Doiron, 2023. "Sampling-based Bayesian inference in recurrent circuits of stochastic spiking neurons," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    2. Adam N Sanborn & Ulrik R Beierholm, 2016. "Fast and Accurate Learning When Making Discrete Numerical Estimates," PLOS Computational Biology, Public Library of Science, vol. 12(4), pages 1-28, April.
    3. Seth W. Egger & Stephen G. Lisberger, 2022. "Neural structure of a sensory decoder for motor control," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    4. 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.
    5. Brocas, Isabelle & Carrillo, Juan D., 2012. "From perception to action: An economic model of brain processes," Games and Economic Behavior, Elsevier, vol. 75(1), pages 81-103.
    6. Carrillo, Juan & Brocas, Isabelle, 2007. "Reason, Emotion and Information Processing in the Brain," CEPR Discussion Papers 6535, C.E.P.R. Discussion Papers.
    7. Jannes Jegminat & Maya A Jastrzębowska & Matthew V Pachai & Michael H Herzog & Jean-Pascal Pfister, 2020. "Bayesian regression explains how human participants handle parameter uncertainty," PLOS Computational Biology, Public Library of Science, vol. 16(5), pages 1-23, May.
    8. Guido Marco Cicchini & Giovanni D’Errico & David Charles Burr, 2022. "Crowding results from optimal integration of visual targets with contextual information," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    9. Udo A Ernst & Sunita Mandon & Nadja Schinkel–Bielefeld & Simon D Neitzel & Andreas K Kreiter & Klaus R Pawelzik, 2012. "Optimality of Human Contour Integration," PLOS Computational Biology, Public Library of Science, vol. 8(5), pages 1-17, May.
    10. Philipp Schustek & Rubén Moreno-Bote, 2018. "Instance-based generalization for human judgments about uncertainty," PLOS Computational Biology, Public Library of Science, vol. 14(6), pages 1-27, June.
    11. Jingwei Sun & Jian Li & Hang Zhang, 2019. "Human representation of multimodal distributions as clusters of samples," PLOS Computational Biology, Public Library of Science, vol. 15(5), pages 1-29, May.
    12. 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.
    13. Laurence Aitchison & Máté Lengyel, 2016. "The Hamiltonian Brain: Efficient Probabilistic Inference with Excitatory-Inhibitory Neural Circuit Dynamics," PLOS Computational Biology, Public Library of Science, vol. 12(12), pages 1-24, December.
    14. Joseph G Makin & Matthew R Fellows & Philip N Sabes, 2013. "Learning Multisensory Integration and Coordinate Transformation via Density Estimation," PLOS Computational Biology, Public Library of Science, vol. 9(4), pages 1-17, April.
    15. 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.
    16. Shih-Wei Wu & Maria F Dal Martello & Laurence T Maloney, 2009. "Sub-Optimal Allocation of Time in Sequential Movements," PLOS ONE, Public Library of Science, vol. 4(12), pages 1-13, December.
    17. Simon Weiler & Vahid Rahmati & Marcel Isstas & Johann Wutke & Andreas Walter Stark & Christian Franke & Jürgen Graf & Christian Geis & Otto W. Witte & Mark Hübener & Jürgen Bolz & Troy W. Margrie & Kn, 2024. "A primary sensory cortical interareal feedforward inhibitory circuit for tacto-visual integration," Nature Communications, Nature, vol. 15(1), pages 1-24, December.
    18. 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.
    19. Leopold Zizlsperger & Thomas Sauvigny & Thomas Haarmeier, 2012. "Selective Attention Increases Choice Certainty in Human Decision Making," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-9, July.
    20. Catarina Mendonça & Pietro Mandelli & Ville Pulkki, 2016. "Modeling the Perception of Audiovisual Distance: Bayesian Causal Inference and Other Models," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-18, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1001112. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.