Estimation of Visual Feedback Contribution to Limb Stiffness in Visuomotor Control

  • Yuki Ueyama
  • Eizo Miyashita
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7670)


The purpose of this work was to investigate contribution of a visual feedback system to limb stiffness. It is difficult to differentiate the visual component from others out of measured data obtained by applying a force perturbation, which is required to estimate stiffness,. In this study, we proposed an experimental procedure consisted of a pair of tasks to investigate the visual feedback component, and showed it as end-point stiffness ellipses at several timings of a movement. In addition, we carried out a numerical simulation of the movement with the perturbation in according with a framework of optimal feedback control model. As results, long axes of the stiffness ellipses of the visual component were modulated to the movement directions and the simulation showed that a positional feedback gain was exponentially increased toward a movement end. Consequently, the visual feedback system is supposed to regulate compliance of a movement direction.


Torque Covariance Exter Rane 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Saijo, N., Gomi, H.: Multiple motor learning strategies in visuomotor rotation. PLoS One 5, e9399 (2010)Google Scholar
  2. 2.
    Izawa, J., Shadmehr, R.: On-line processing of uncertain information in visuomotor control. J. Neurosci. 28, 11360–11368 (2008)CrossRefGoogle Scholar
  3. 3.
    Todorov, E., Jordan, M.I.: Optimal feedback control as a theory of motor coordination. Nat. Neurosci. 5, 1226–1235 (2002)CrossRefGoogle Scholar
  4. 4.
    Todorov, E.: Optimality principles in sensorimotor control (review). Nature Neuroscience 7, 907 (2004)CrossRefGoogle Scholar
  5. 5.
    Liu, D., Todorov, E.: Evidence for the flexible sensorimotor strategies predicted by optimal feedback control. J. Neurosci. 27, 9354–9368 (2007)CrossRefGoogle Scholar
  6. 6.
    Izawa, J., Shadmehr, R.: Learning from Sensory and Reward Prediction Errors during Motor Adaptation. PLoS Computational Biology 7, e1002012 (2011)Google Scholar
  7. 7.
    Izawa, J., Rane, T., Donchin, O., Shadmehr, R.: Motor adaptation as a process of reoptimization. J. Neurosci. 28, 2883–2891 (2008)CrossRefGoogle Scholar
  8. 8.
    Nagengast, A.J., Braun, D.A., Wolpert, D.M.: Optimal control predicts human performance on objects with internal degrees of freedom. PLoS Comput. Biol. 5, e1000419 (2009)Google Scholar
  9. 9.
    Ueyama, Y., Miyashita, E.: Cocontraction of Pairs of Muscles around Joints Improve an Accuracy of a Reaching Movement: a Numerical Simulation Study. In: 2011 International Symposium on Computational Models for Life Sciences (CMLS 2011), pp. 73–82. American Institute of Physics (2011)Google Scholar
  10. 10.
    Pruszynski, J.A., Kurtzer, I., Scott, S.H.: Rapid motor responses are appropriately tuned to the metrics of a visuospatial task. J. Neurophysiol. 100, 224–238 (2008)CrossRefGoogle Scholar
  11. 11.
    Pruszynski, J.A., Kurtzer, I., Nashed, J.Y., Omrani, M., Brouwer, B., Scott, S.H.: Primary motor cortex underlies multi-joint integration for fast feedback control. Nature, 387–390 (2011)Google Scholar
  12. 12.
    Ueyama, Y., Miyashita, E.: A Numerical Simulation Using Optimal Control Can Estimate Stiffness Profiles of a Monkey Arm During Reaching Movements. In: Conf. Proc IEEE The 12th International Workshop on Advanced Motion Control, pp. 1–6 (Year)Google Scholar
  13. 13.
    Winter, D.A.: Biomechanics and motor control of human movement. John Wiley & Sons Inc. (2009)Google Scholar
  14. 14.
    Slifkin, A.B., Newell, K.M.: Variability and noise in continuous force production. Journal of Motor Behavior 32, 141–150 (2000)CrossRefGoogle Scholar
  15. 15.
    Harris, C.M., Wolpert, D.M.: Signal-dependent noise determines motor planning. Nature 394, 780–784 (1998)CrossRefGoogle Scholar
  16. 16.
    Crevecoeur, F., McIntyre, J., Thonnard, J.L., Lefevre, P.: Movement stability under uncertain internal models of dynamics. Journal of Neurophysiology 104, 1301–1313 (2010)CrossRefGoogle Scholar
  17. 17.
    Gawthrop, P., Loram, I., Lakie, M., Gollee, H.: Intermittent control: A computational theory of human control. Biological Cybernetics, 1–21 (2011)Google Scholar
  18. 18.
    Nakano, E., Imamizu, H., Osu, R., Uno, Y., Gomi, H., Yoshioka, T., Kawato, M.: Quantitative examinations of internal representations for arm trajectory planning: minimum commanded torque change model. J. Neurophysiol. 81, 2140–2155 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yuki Ueyama
    • 1
  • Eizo Miyashita
    • 1
  1. 1.Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and EngineeringTokyo Institute of TechnologyYokohamaJapan

Personalised recommendations