a paper on human adaptive control

[Reza Shadmehr]

Dear Connectionists:

An abridged version of the following paper on human adaptive
control will be presented at NIPS this year.  It is available from  

http://www.bme.jhu.edu/~reza/nb_paper.pdf


   Computational Nature of Human Adaptive Control During
       Learning of Reaching Movements in Force Fields

                Nikhil Bhushan and Reza Shadmehr

Learning to make reaching movements in force fields was used as a
paradigm to explore the system architecture of the biological adaptive
controller.  We compared the performance of a number of candidate
control systems that acted on a model of the neuromuscular system of
the human arm and asked how well the dynamics of the candidate system
compared with the behavior of the biological controller.  We found
that control via a supra-spinal system that utilized an adaptive
inverse model resulted in dynamics that were similar to that observed
in our subjects, but lacked essential characteristics.  These
characteristics pointed to a different architecture where descending
commands were influenced by an adaptive forward model.  However, we
found that control via a forward model alone also resulted in dynamics
that did not match the behavior of the human arm.  We considered a
third control architecture where a forward model was used in
conjunction with an inverse model and found that the resulting
dynamics were remarkably similar to that observed in the experimental
data.  The essential property of this control architecture was that it
predicted a complex pattern of near-discontinuities in hand trajectory
in the novel force field. A nearly identical pattern was observed in
our subjects, suggesting that generation of descending motor commands
was likely through a control system architecture that included both
adaptive forward and inverse models.  We further demonstrate that as
subjects learned to make reaching movements, adaptation rates for the
forward and inverse models could be independently estimated and the
resulting changes in performance of subjects from movement to movement
could be accurately accounted for.  It appeared that in learning to
make reaching movements, adaptation of the forward model played a very
significant role in reducing the errors in performance.  Finally, we
found that after a period of consolidation, the rates of adaptation in
the models were significantly larger than those observed before the
memory had consolidated.  This suggested that consolidation of motor
memory may have coincided with freeing of certain computational
resources for subsequent learning.