Impedance control and internal model formation when reaching in a randomlyvarying dynamical environment

We investigated the effects of trial-to-trial, random variation in environm ental forces on the motor adaptation of human subjects during reaching. Nov el sequences of dynamic environments were applied to subjects' hands by a r obot. Subjects reached first in a "mean field" having a constant gain relat ing force and velocity, then in a "noise field," having a gain that varied randomly between reaches according to a normal distribution with a mean ide ntical to that of the mean field. The unpredictable nature of the noise fie ld did not degrade adaptation as quantified by final kinematic error and ra te of adaptation. To achieve this performance, the nervous system used a du al strategy. It increased the impedance of the arm as evidenced by a signif icant reduction in aftereffect size following removal of the noise field. S imultaneously, it formed an internal model of the mean of the random enviro nment, as evidenced by a minimization of trajectory error on trials for whi ch 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 dy namics of an environment that varies substantially and randomly from trial to trial, while simultaneously increasing the arm's impedance to minimize t he consequence of errors in the prediction.