IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0045075.html
   My bibliography  Save this article

Generalization in Adaptation to Stable and Unstable Dynamics

Author

Listed:
  • Abdelhamid Kadiallah
  • David W Franklin
  • Etienne Burdet

Abstract

Humans skillfully manipulate objects and tools despite the inherent instability. In order to succeed at these tasks, the sensorimotor control system must build an internal representation of both the force and mechanical impedance. As it is not practical to either learn or store motor commands for every possible future action, the sensorimotor control system generalizes a control strategy for a range of movements based on learning performed over a set of movements. Here, we introduce a computational model for this learning and generalization, which specifies how to learn feedforward muscle activity in a function of the state space. Specifically, by incorporating co-activation as a function of error into the feedback command, we are able to derive an algorithm from a gradient descent minimization of motion error and effort, subject to maintaining a stability margin. This algorithm can be used to learn to coordinate any of a variety of motor primitives such as force fields, muscle synergies, physical models or artificial neural networks. This model for human learning and generalization is able to adapt to both stable and unstable dynamics, and provides a controller for generating efficient adaptive motor behavior in robots. Simulation results exhibit predictions consistent with all experiments on learning of novel dynamics requiring adaptation of force and impedance, and enable us to re-examine some of the previous interpretations of experiments on generalization.

Suggested Citation

  • Abdelhamid Kadiallah & David W Franklin & Etienne Burdet, 2012. "Generalization in Adaptation to Stable and Unstable Dynamics," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-11, October.
  • Handle: RePEc:plo:pone00:0045075
    DOI: 10.1371/journal.pone.0045075
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0045075
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0045075&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0045075?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. Kurt A. Thoroughman & Reza Shadmehr, 2000. "Learning of action through adaptive combination of motor primitives," Nature, Nature, vol. 407(6805), pages 742-747, October.
    2. Christopher M. Harris & Daniel M. Wolpert, 1998. "Signal-dependent noise determines motor planning," Nature, Nature, vol. 394(6695), pages 780-784, August.
    3. Etienne Burdet & Rieko Osu & David W. Franklin & Theodore E. Milner & Mitsuo Kawato, 2001. "The central nervous system stabilizes unstable dynamics by learning optimal impedance," Nature, Nature, vol. 414(6862), pages 446-449, November.
    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. Ian S Howard & David W Franklin, 2015. "Neural Tuning Functions Underlie Both Generalization and Interference," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-21, June.
    2. Bastien Berret & Adrien Conessa & Nicolas Schweighofer & Etienne Burdet, 2021. "Stochastic optimal feedforward-feedback control determines timing and variability of arm movements with or without vision," PLOS Computational Biology, Public Library of Science, vol. 17(6), pages 1-24, June.

    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. Ian S Howard & David W Franklin, 2015. "Neural Tuning Functions Underlie Both Generalization and Interference," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-21, June.
    2. 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.
    3. Bastien Berret & Frédéric Jean, 2020. "Stochastic optimal open-loop control as a theory of force and impedance planning via muscle co-contraction," PLOS Computational Biology, Public Library of Science, vol. 16(2), pages 1-28, February.
    4. Bastien Berret & Adrien Conessa & Nicolas Schweighofer & Etienne Burdet, 2021. "Stochastic optimal feedforward-feedback control determines timing and variability of arm movements with or without vision," PLOS Computational Biology, Public Library of Science, vol. 17(6), pages 1-24, June.
    5. Joshua G A Cashaback & Heather R McGregor & Ayman Mohatarem & Paul L Gribble, 2017. "Dissociating error-based and reinforcement-based loss functions during sensorimotor learning," PLOS Computational Biology, Public Library of Science, vol. 13(7), pages 1-28, July.
    6. Frédéric Crevecoeur & Stephen H Scott, 2013. "Priors Engaged in Long-Latency Responses to Mechanical Perturbations Suggest a Rapid Update in State Estimation," PLOS Computational Biology, Public Library of Science, vol. 9(8), pages 1-14, August.
    7. 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.
    8. 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.
    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. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. Maxime Teremetz & Isabelle Amado & Narjes Bendjemaa & Marie-Odile Krebs & Pavel G Lindberg & Marc A Maier, 2014. "Deficient Grip Force Control in Schizophrenia: Behavioral and Modeling Evidence for Altered Motor Inhibition and Motor Noise," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-11, November.
    17. Frederic Danion & Raoul M Bongers & Reinoud J Bootsma, 2014. "The Trade-Off between Spatial and Temporal Variabilities in Reciprocal Upper-Limb Aiming Movements of Different Durations," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-10, May.
    18. Wei Zhang & Sasha Reschechtko & Barry Hahn & Cynthia Benson & Elias Youssef, 2019. "Force-stabilizing synergies can be retained by coordinating sensory-blocked and sensory-intact digits," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-17, December.
    19. Julian J Tramper & Bart van den Broek & Wim Wiegerinck & Hilbert J Kappen & Stan Gielen, 2012. "Time-Integrated Position Error Accounts for Sensorimotor Behavior in Time-Constrained Tasks," PLOS ONE, Public Library of Science, vol. 7(3), pages 1-10, March.
    20. Konrad P Körding & Izumi Fukunaga & Ian S Howard & James N Ingram & Daniel M Wolpert, 2004. "A Neuroeconomics Approach to Inferring Utility Functions in Sensorimotor Control," PLOS Biology, Public Library of Science, vol. 2(10), pages 1-1, September.

    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:pone00:0045075. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.