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A motor learning strategy reflects neural circuitry for limb control

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Abstract

During motor skill acquisition, the brain learns a mapping between intended limb motion and requisite muscular forces. We propose that regions where sensory and motor representations overlap are crucial for motor learning. In primary motor cortex, for example, cells that modulate their activity for motor actions at a joint tend to receive input from that same portion of the periphery. We predict that this correspondence reflects a default strategy—a Bayesian prior—in which subjects tend to associate loads at a joint with motion at that joint (local sensorimotor association) when there is ambiguity regarding the nature of the load. As predicted, we found that in the presence of uncertainty, humans inappropriately generalized elbow loads as though they were based on elbow velocity. Generalization improved when we reduced uncertainty by decreasing coupling between elbow velocity and load during training. These results illustrate a key link between motor learning and the underlying neural circuitry.

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Figure 1: The experimental apparatus, target locations and load types.
Figure 2: Reaching movements to the training target with the viscous (left, blue) and interaction (right, red) loads.
Figure 3: Results from the generalization phase of experiment 1.
Figure 4: Changes in elbow motion during training influences generalization of interaction loads.

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References

  1. Shadmehr, R. & Mussa-Ivaldi, F.A. Adaptive representation of dynamics during learning of a motor task. J. Neurosci. 14, 3208–3224 (1994).

    Article  CAS  Google Scholar 

  2. Lackner, J.R. & DiZio, P. Rapid adaptation to Coriolis force perturbations of arm trajectory. J. Neurophysiol. 72, 299–313 (1994).

    Article  CAS  Google Scholar 

  3. Goodbody, S.J. & Wolpert, D.M. Temporal and amplitude generalization in motor learning. J. Neurophysiol. 79, 1825–1838 (1998).

    Article  CAS  Google Scholar 

  4. Sainburg, R.L., Ghez, C. & Kalakanis, D. Intersegmental dynamics are controlled by sequential anticipatory, error correction, and postural mechanisms. J. Neurophysiol. 81, 1045–1056 (1999).

    Article  CAS  Google Scholar 

  5. Imamizu, H. et al. Human cerebellar activity reflecting an acquired internal model of a new tool. Nature 403, 192–195 (2000).

    Article  CAS  Google Scholar 

  6. Conditt, M.A., Gandolfo, F. & Mussa-Ivaldi, F.A. The motor system does not learn the dynamics of the arm by rote memorization of past experience. J. Neurophysiol. 78, 554–560 (1997).

    Article  CAS  Google Scholar 

  7. Wolpert, D.M. & Ghahramani, Z. Computational principles of movement neuroscience. Nat. Neurosci. 3 (Suppl.), 1212–1217 (2000).

    Article  CAS  Google Scholar 

  8. Vetter, P. & Wolpert, D.M. The CNS updates its context estimate in the absence of feedback. Neuroreport 11, 3783–3786 (2000).

    Article  CAS  Google Scholar 

  9. Conditt, M.A. & Mussa-Ivaldi, F.A. Central representation of time during motor learning. Proc. Natl. Acad. Sci. USA 96, 11625–11630 (1999).

    Article  CAS  Google Scholar 

  10. Murphy, J.T., Kwan, H.C., MacKay, W.A. & Wong, Y.C. Activity of primate precentral neurons during voluntary movements triggered by visual signals. Brain Res. 236, 429–449 (1982).

    Article  CAS  Google Scholar 

  11. Scott, S.H. Comparison of onset time and magnitude of activity for proximal arm muscles and motor cortical cells before reaching movements. J. Neurophysiol. 77, 1016–1022 (1997).

    Article  CAS  Google Scholar 

  12. Porter, R. & Lemon, R.N. Corticospinal Function & Voluntary Movement (Oxford Univ. Press, New York, 1993).

    Google Scholar 

  13. Asanuma, H. Functional role of sensory inputs to the motor cortex. Prog. Neurobiol. 16, 241–262 (1981).

    Article  CAS  Google Scholar 

  14. Gibson, A.R., Robinson, F.R., Alam, J. & Houk, J.C. Somatotopic alignment between climbing fiber input and nuclear output of the cat intermediate cerebellum. J. Comp. Neurol. 260, 362–377 (1987).

    Article  CAS  Google Scholar 

  15. Ghez, C. Input–output relations of the red nucleus in the cat. Brain Res. 98, 93–108 (1975).

    Article  CAS  Google Scholar 

  16. Knill, D.C. & Richards, W. Perception as Bayesian Inference (Cambridge Univ. Press, New York, 1996).

    Book  Google Scholar 

  17. Thoroughman, K.A. & Shadmehr, R. Learning of action through adaptive combination of motor primitives. Nature 407, 742–747 (2000).

    Article  CAS  Google Scholar 

  18. Fitts, P.M. The information capacity of the human motor system in controlling the amplitude of movement. J. Exp. Psychol. 47, 381–391 (1954).

    Article  CAS  Google Scholar 

  19. Ernst, M.O. & Banks, M.S. Humans integrate visual and haptic information in a statistically optimal fashion. Nature 415, 429–433 (2002).

    Article  CAS  Google Scholar 

  20. Weiss, Y., Simoncelli, E.P. & Adelson, E.H. Motion illusions as optimal percepts. Nat. Neurosci. 5, 598–604 (2002).

    Article  CAS  Google Scholar 

  21. Todorov, E. & Jordan, M.I. Optimal feedback control as a theory of motor coordination. Nat. Neurosci. 5, 1226–1235 (2002).

    Article  CAS  Google Scholar 

  22. Drew, T. Motor cortical activity during voluntary gait modifications in the cat. I. Cells related to the forelimbs. J. Neurophysiol. 70, 179–199 (1993).

    Article  CAS  Google Scholar 

  23. Scott, S.H. & Loeb, G.E. The computation of position sense from spindles in mono- and multi-articular muscles. J. Neurosci. 14, 7529–7540 (1994).

    Article  CAS  Google Scholar 

  24. Hollerbach, M.J. & Flash, T. Dynamic interactions between limb segments during planar arm movement. Biol. Cybern. 44, 67–77 (1982).

    Article  CAS  Google Scholar 

  25. Sainburg, R.L. & Kalakanis, D. Differences in control of limb dynamics during dominant and nondominant arm reaching. J. Neurophysiol. 83, 2661–2675 (2000).

    Article  CAS  Google Scholar 

  26. Bastian, A.J., Martin, T.A., Keating, J.G. & Thach, W.T. Cerebellar ataxia: abnormal control of interaction torques across multiple joints. J. Neurophysiol. 76, 492–509 (1996).

    Article  CAS  Google Scholar 

  27. Scott, S.H. Apparatus for measuring and perturbing shoulder and elbow joint positions and torques during reaching. J. Neurosci. Methods 89, 119–127 (1999).

    Article  CAS  Google Scholar 

  28. Gribble, P.L. & Scott, S.H. Overlap of internal models in motor cortex for mechanical loads during reaching. Nature 417, 938–941 (2002).

    Article  CAS  Google Scholar 

  29. Winter, D.A. Biomechanics and Motor Control of Human Movement (Wiley, New York, 1990).

    Google Scholar 

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Acknowledgements

The authors would like to thank D.M. Wolpert and J.R. Flanagan for valuable comments on the manuscript. Financial support was provided by the Natural Sciences and Engineering Research Council and start-up funds from the Faculty of Health Sciences at Queen's University. Salary funding was provided by a Canadian Institutes of Health Research (CIHR) Doctoral Award to K.S. and a CIHR Scholar Award to S.H.S.

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Authors and Affiliations

  1. Department of Anatomy & Cell Biology, CIHR Group in Sensory-Motor Systems, Centre for Neuroscience Studies, Queen's University, Kingston, K7L 3N6, Ontario, Canada

    Kan Singh & Stephen H. Scott

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  1. Kan Singh
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  2. Stephen H. Scott
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Correspondence to Stephen H. Scott.

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S.H.S. holds a US patent for the robotic device used in these experiments.

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Singh, K., Scott, S. A motor learning strategy reflects neural circuitry for limb control. Nat Neurosci 6, 399–403 (2003). https://doi.org/10.1038/nn1026

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