IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1000629.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000629
    Download Restriction: no
    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1000629&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pcbi.1000629?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. 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.
    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, 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.

    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. 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.
    2. 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.
    3. Vassilios N Christopoulos & Paul R Schrater, 2009. "Grasping Objects with Environmentally Induced Position Uncertainty," PLOS Computational Biology, Public Library of Science, vol. 5(10), pages 1-11, October.
    4. Matteo Bertucco & Nasir H Bhanpuri & Terence D Sanger, 2015. "Perceived Cost and Intrinsic Motor Variability Modulate the Speed-Accuracy Trade-Off," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-18, October.
    5. 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.
    6. Kang He & You Liang & Farnaz Abdollahi & Moria Fisher Bittmann & Konrad Kording & Kunlin Wei, 2016. "The Statistical Determinants of the Speed of Motor Learning," PLOS Computational Biology, Public Library of Science, vol. 12(9), pages 1-20, September.
    7. Todd E Hudson & Laurence T Maloney & Michael S Landy, 2008. "Optimal Compensation for Temporal Uncertainty in Movement Planning," PLOS Computational Biology, Public Library of Science, vol. 4(7), pages 1-9, July.
    8. Shogo Yonekura & Yasuo Kuniyoshi, 2017. "Bodily motion fluctuation improves reaching success rate in a neurophysical agent via geometric-stochastic resonance," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-16, December.
    9. Max Berniker & Megan K O’Brien & Konrad P Kording & Alaa A Ahmed, 2013. "An Examination of the Generalizability of Motor Costs," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-11, January.
    10. 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.
    11. Geonhui Lee & Woong Choi & Hanjin Jo & Wookhyun Park & Jaehyo Kim, 2020. "Analysis of motor control strategy for frontal and sagittal planes of circular tracking movements using visual feedback noise from velocity change and depth information," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-22, November.
    12. 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.
    13. Lionel Rigoux & Emmanuel Guigon, 2012. "A Model of Reward- and Effort-Based Optimal Decision Making and Motor Control," PLOS Computational Biology, Public Library of Science, vol. 8(10), pages 1-13, October.
    14. Yanhao Ren & Qiang Luo & Wenlian Lu, 2023. "Synchronization Analysis of Linearly Coupled Systems with Signal-Dependent Noises," Mathematics, MDPI, vol. 11(10), pages 1-15, May.
    15. 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.
    16. 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.
    17. Ashesh Vasalya & Gowrishankar Ganesh & Abderrahmane Kheddar, 2018. "More than just co-workers: Presence of humanoid robot co-worker influences human performance," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-19, November.
    18. 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.
    19. Josh Merel & Donald M Pianto & John P Cunningham & Liam Paninski, 2015. "Encoder-Decoder Optimization for Brain-Computer Interfaces," PLOS Computational Biology, Public Library of Science, vol. 11(6), pages 1-25, June.
    20. Nidhi Seethapathi & Barrett C. Clark & Manoj Srinivasan, 2024. "Exploration-based learning of a stabilizing controller predicts locomotor adaptation," Nature Communications, Nature, vol. 15(1), pages 1-23, 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:pcbi00:1000629. 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.