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The Journal of Neurophysiology Vol. 86 No. 2 August 2001, pp. 971-985
Copyright ©2001 by the American Physiological Society
1Department of Biomedical Engineering, Marquette University, Milwaukee, Wisconsin 53201; and 2Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, Illinois 60611
Scheidt, Robert A.,
Jonathan B. Dingwell, and
Ferdinando A. Mussa-Ivaldi.
Learning to Move Amid Uncertainty. J. Neurophysiol. 86: 971-985, 2001. We studied how subjects
learned to make movements against unpredictable perturbations. Twelve
healthy human subjects made goal-directed reaching movements in the
horizontal plane while holding the handle of a two-joint robotic
manipulator. The robot generated viscous force fields that perturbed
the limb perpendicular to the desired direction of movement. The
amplitude (but not the direction) of the viscous field varied randomly
from trial to trial. Systems identification techniques were employed to
characterize how subjects adapted to these random perturbations.
Subject performance was quantified primarily using the peak deviation
from a straight-line hand path. Subjects adapted their arm movements to
the sequence of random force-field amplitudes. This adaptive response
compensated for the approximate mean from the random sequence of
perturbations and did not depend on the statistical distribution of
that sequence. Subjects did not adapt by directly counteracting the
mean field strength itself on each trial but rather by using
information about perturbations and movement errors from a limited
number of previous trials to adjust motor commands on subsequent
trials. This strategy permitted subjects to achieve near-optimal
performance (defined as minimizing movement errors in a least-squares
sense) while maintaining computational efficiency. A simple model using information about movement errors and perturbation amplitudes from a
single previous trial predicted subject performance in stochastic
environments with a high degree of fidelity and further predicted key
performance features observed in nonstochastic environments. This
suggests that the neural structures modified during motor adaptation
require only short-term memory. Explicit representations regarding
movements made more than a few trials in the past are not used in
generating optimal motor responses on any given trial.
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