Motor adaptation as a greedy optimization of error and effort
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Document typeArticle
Defense date2007-03-31
PublisherThe American Physiological Society
Rights accessRestricted access - publisher's policy
Abstract
thought of as a process in which the nervous
system learns to anticipate the environmental forces to eliminate
kinematic error. Here we show that motor adaptation can more
generally be modeled as a process in which the motor system greedily
minimizes a cost function that is the weighted sum of kinematic error
and effort. The learning dynamics predicted by this minimization
process are a linear, auto-regressive equation with only one state,
which has been identified previously as providing a good fit to data
from force-field-type experiments. Thus we provide a new theoretical
result that shows how these previously identified learning dynamics
can be viewed as arising from an optimization of error and effort. We
also show that the coefficients of the learning dynamics must fall
within a specific range for the optimization model to be valid and
verify with experimental data from walking in a force field that they
indeed fall in this range. Finally, we attempted to falsify the model by
performing experiments in two conditions (repeated exposure to a
force field, exposure to force fields of different strengths) for which
the single-state, auto-regressive equation might be expected to not fit
the data well. We found however that the equation adequately captured
the pattern of errors and thus conclude that motor adaptation to
a force field can be approximated as an optimization of effort and
error for a range of experimental conditions.
CitationEmken,J.L.; Benitez,R.; Sideris,A.; Bobrow, J.E.; Reinkensmeyer, D.J.(2007). Motor adaptation as a greedy optimization of error and effort. Journal of Neurophysiology, 97(6): 3997-4006. ISSN:0022-3077
ISSN0022-3077
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