Adaptive Representation
of Dynamics
During Learning
of a Motor Task
Reza Shadmehr and Ferdinando
A. Mussa-Ivaldi
Department of Brain and Cognitive
Sciences
Massachusetts Institute of
Technology
Cambridge, MA 02139 USA
Published in: Journal of
Neuroscience, May 1994, 14:3208-3224.
Abstract We investigated how the CNS
learns to control movements in different dynamical conditions, and how this learned
behavior is represented. In particular, we considered the task of making reaching
movements in the presence of externally imposed forces from a mechanical environ-
ment. This environment was a force field produced by a robot manipulandum, and
the subjects made reaching movements while holding the end effector of this manipulandum.
Since the force field significantly changed the dynamics of the task, subjects'
initial movements in the force field were grossly distorted compared to their
movements in free space. However, with practice, hand trajectories in the force
field converged to a path very similar to that observed in free space. This indicated
that for reaching movements, there was a kinematic plan independent of dynamical
conditions.
The recovery of performance
within the changed mechanical environment is motor adapt- tation. In order to
investigate the mechanism underlying this adaptation, we considered the response
to the sudden removal of the field after a training phase. The resulting trajectories,
named after-effects, were approximately mirror images of those which were observed
when the subjects were initially exposed to the field. This suggested that the
motor controller was gradu- ally composing a model of the force field, a model
which the nervous system used to predict and compensate for the forces imposed
by the environment. In order to explore the structure of the model, we investigated
whether adaptation to a force field, as presented in a small region, led to after-effects
in other regions of the workspace. We found that indeed there were after-effects
in workspace regions where no exposure to the field had taken place, i.e., there
was transfer beyond the boundary of the training data. This observation rules
out the hypothesis that the subject's model of the force field was constructed
as a narrow association between visited states and experienced forces, i.e. adaptation
was not via composition of a look{up table. In contrast, subjects modeled the
force field by a combination of computational elements whose output was broadly
tuned across the motor state space. These elements formed a model, which extrapolated
to outside the training region in a coordinate system similar to that of the joints
and muscles rather than endpoint forces. This geometric property suggests that
the elements of the adaptive process represent dynamics of a motor task in terms
of the intrinsic coordinate system of the sensors and actuators.