Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Letter
  • Published:

Learning of action through adaptive combination of motor primitives

Nature volume 407, pages 742–747 (2000)Cite this article

Abstract

Understanding how the brain constructs movements remains a fundamental challenge in neuroscience. The brain may control complex movements through flexible combination of motor primitives1, where each primitive is an element of computation in the sensorimotor map that transforms desired limb trajectories into motor commands. Theoretical studies have shown that a system's ability to learn action depends on the shape of its primitives2. Using a time-series analysis of error patterns, here we show that humans learn the dynamics of reaching movements through a flexible combination of primitives that have gaussian-like tuning functions encoding hand velocity. The wide tuning of the inferred primitives predicts limitations on the brain's ability to represent viscous dynamics. We find close agreement between the predicted limitations and the subjects’ adaptation to new force fields. The mathematical properties of the derived primitives resemble the tuning curves of Purkinje cells in the cerebellum. The activity of these cells may encode primitives that underlie the learning of dynamics.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Catch trials induced short-term unlearning.
Figure 2: Sensitivity to movement error across target directions.
Figure 3: Movement characteristics of systems that learn an internal model with velocity encoding gaussians.
Figure 4: Learning with wide gaussians imposes limits on adaptation.

Similar content being viewed by others

References

  1. Mussa-Ivaldi, F. A., Giszter, S. F. & Bizzi, E. Linear combinations of primitives in vertebrate motor control. Proc. Natl Acad. Sci. USA 91, 7534 –7538 (1994).

    Article  ADS  CAS  Google Scholar 

  2. Schaal, S. & Atkeson, C. G. Constructive incremental learning from only local information. Neural Comput. 10, 2047–1084 (1998).

    Article  CAS  Google Scholar 

  3. 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 

  4. Ghez, C., Krakauer, J. W., Sainburg, R. L. & Ghilardi, M. F. in The New Cognitive Neurosciences (ed. Gazzaniga, M. S.) 501–514 (MIT Press, Cambridge, Massachusetts, 2000).

    Google Scholar 

  5. Thoroughman, K. A. & Shadmehr, R. Electromyographic correlates of learning internal models of reaching movements. J. Neurosci. 19, 8573–8588 (1999).

    Article  CAS  Google Scholar 

  6. Gandolfo, F., Mussa-Ivaldi, F. A. & Bizzi, E. Motor learning by field approximation. Proc. Natl Acad. Sci. USA 93, 3843–3846 (1996).

    Article  ADS  CAS  Google Scholar 

  7. 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 

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

    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 (1996).

    Article  ADS  Google Scholar 

  10. Kawato, M. Adaptation and learning in control of voluntary movement by the central nervous system. Adv. Robotics 3, 229– 249 (1989).

    Article  Google Scholar 

  11. Sanner, R. M. & Kosha, M. A mathematical model of the adaptive control of human arm motions. Biol. Cybern. 80, 369–382 (1999).

    Article  CAS  Google Scholar 

  12. Bhushan, N. & Shadmehr, R. Computational architecture of human adaptive control during learning of reaching movements in force fields. Biol. Cybern. 81, 39–60 (1999).

    Article  CAS  Google Scholar 

  13. Shadmehr, R. & Brashers-Krug, T. Functional stages in the formation of human long-term motor memory. J. Neurosci. 17, 409–419 (1997).

    Article  CAS  Google Scholar 

  14. Georgopoulos, A. P., Schwartz, A. B. & Kettner, R. E. Neural population coding of movement direction. Science 233, 1416–1419 ( 1986).

    Article  ADS  CAS  Google Scholar 

  15. Moran, D. W. & Schwartz, A. B. Motor cortical representation of speed and direction during reaching. J. Neurophys. 82, 2676–2692 (1999).

    Article  CAS  Google Scholar 

  16. Conrad, B., Matsunami, K., Meyer-Lohmann, J., Wiesendanger, M. & Brooks, V. B. Cortical load compensation during voluntary elbow movements. Brain Res. 71, 507–514 (1974).

    Article  CAS  Google Scholar 

  17. Goodkin, H. P., Keating, J. G., Martin, T. A. & Thach, W. T. Preserved simple and impaired compound movement after infarction in the territory of the superior cerebellar artery. Can. J. Neurol. Sci. 20, S93–S104 (1993).

    Article  Google Scholar 

  18. 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 

  19. Lang, C. E. & Bastian, A. J. Cerebellar subjects show impaired adaptation of anticipatory EMG during catching. J. Neurophysiol. 82, 2108–2119 ( 1999).

    Article  CAS  Google Scholar 

  20. Nezafat, R., Shadmehr, R. & Holcomb, H. H. Neural correlates of motor memory retention over a 4 week period as quantified through PET. Soc. Neurosci. Abs. 25, 2178 (1999).

    Google Scholar 

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

    Article  ADS  CAS  Google Scholar 

  22. Coltz, J. D., Johnson, M. T. V. & Ebner, T. J. Cerebellar Purkinje cell simple spike discharge encodes movement velocity in primates during visuomotor arm tracking. J. Neurosci. 19, 1782–1803 (1999).

    Article  CAS  Google Scholar 

  23. Matsouka, Y. Models of generalization in motor control. PhD thesis, MIT ( 1998).

  24. Amirikian, B. & Georgopulos, A. P. Directional tuning profiles of motor cortical cells. Neurosci. Res. 36, 73–79 (2000).

    Article  CAS  Google Scholar 

  25. Houk, J. C. & Wise, S. P. Distributed modular architectures linking basal ganglia, cerebellum, and cerebral cortex: their role in planning and controlling action. Cereb. Cortex 5, 95–110 (1995).

    Article  CAS  Google Scholar 

  26. Wolpert, D. M. & Kawato, M. Multiple paired forward and inverse models for motor control. Neural Networks 11, 1317–1329 (1998).

    Article  CAS  Google Scholar 

  27. Spoelstra, J., Schweighofer, N. & Arbib, M. A. Cerebellar learning of accurate predictive control for faster-reaching movements. Biol. Cybern. 82, 321–333 (2000)

    Article  CAS  Google Scholar 

  28. Gandolfo, F., Li, C. S. R., Benda, B. J., Schioppa, C. P. & Bizzi, E. Cortical correlates of learning in monkeys adapting to a new dynamical environment. Proc. Natl Acad. Sci. USA 97, 2259–2263 ( 2000).

    Article  ADS  CAS  Google Scholar 

  29. Holdefer, R. N., Miller, L. E. & Houk, J. C. Functional connectivity between cerebellum and primary motor cortex in the awake monkey. J. Neurophysiol. 84, 585–590 (2000).

    Article  CAS  Google Scholar 

  30. Martin, J. H., Cooper, S. E., Hacking, A. & Ghez, C. Differential effects of deep cerebellar nuclei inactivation on reaching and adaptive control. J. Neurophysiol. 84, 1886– 1899 (2000).

    Article  Google Scholar 

Download references

Acknowledgements

We thank M. A. Smith, O. Donchin, and R. Nezafat. The work was supported by grants from the Office of Naval Research and the National Institutes of Health (to R.S.), and a research traineeship from the National Science Foundation (K.A.T.).

Author information

Author notes

  1. Kurt A. Thoroughman

    Present address: Volen Center of Complex Systems and Department of Biology, Brandeis University, Waltham , Massachusetts, 02454, USA

Authors and Affiliations

  1. Department of Biomedical Engineering Johns Hopkins University, 419 Traylor Building, 720 Rutland Avenue, Baltimore, 21205, Maryland, USA

    Kurt A. Thoroughman & Reza Shadmehr

Authors
  1. Kurt A. Thoroughman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Reza Shadmehr
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Reza Shadmehr.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Thoroughman, K., Shadmehr, R. Learning of action through adaptive combination of motor primitives. Nature 407, 742–747 (2000). https://doi.org/10.1038/35037588

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1038/35037588

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing