Impedance Control and Internal Model Formation When Reaching in a Randomly Varying Dynamical Environment

• Published in: Journal of neurophysiology Vol. 86; no. 2; pp. 1047 - 1051
• Main Authors: Takahashi, C. D, Scheidt, R. A, Reinkensmeyer, D. J
• Format: Journal Article
• Language: English
• Published: United States Am Phys Soc 01.08.2001
• Subjects: Adult; Arm - physiology; Biomechanical Phenomena; Electric Impedance; Environment; Female; Humans; Male; Middle Aged; Movement - physiology; Psychomotor Performance - physiology; Space life sciences; Stochastic Processes
• ISSN: 0022-3077; 1522-1598
• DOI: 10.1152/jn.2001.86.2.1047

  1 Department of Mechanical and Aerospace Engineering and Center for Biomedical Engineering, University of California, Irvine, California 92697-3975; and   2 Department of Biomedical Engineering, Marquette University, Milwaukee, Wisconsin 53201 Takahashi, C. D., R. A. Scheidt, and D. J. Reinkensmeyer. Impedance Control and Internal Model Formation When Reaching in a Randomly Varying Dynamical Environment. J. Neurophysiol. 86: 1047-1051, 2001. We investigated the effects of trial-to-trial, random variation in environmental forces on the motor adaptation of human subjects during reaching. Novel sequences of dynamic environments were applied to subjects' hands by a robot. Subjects reached first in a "mean field" having a constant gain relating force and velocity, then in a "noise field," having a gain that varied randomly between reaches according to a normal distribution with a mean identical to that of the mean field. The unpredictable nature of the noise field did not degrade adaptation as quantified by final kinematic error and rate of adaptation. To achieve this performance, the nervous system used a dual strategy. It increased the impedance of the arm as evidenced by a significant reduction in aftereffect size following removal of the noise field. Simultaneously, it formed an internal model of the mean of the random environment, as evidenced by a minimization of trajectory error on trials for which the noise field gain was close to the mean field gain. We conclude that the human motor system is capable of predicting and compensating for the dynamics of an environment that varies substantially and randomly from trial to trial, while simultaneously increasing the arm's impedance to minimize the consequence of errors in the prediction.