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Abstract:
Reaching movements require the brain to generate motor
commands that rely on an internal model of the task's dynamics.
Here we consider the errors that subjects make early in their
reaching trajectories to various targets as they learn an
internal model. Using a framework from function approximation, we
argue that the sequence of errors should reflect the process of
gradient descent. If so, then the sequence of errors should obey
hidden state transitions of a simple dynamical system. Fitting
the system to human data, we find a surprisingly good fit
accounting for 98% of the variance. This allows us to draw
tentative conclusions about the basis elements used by the brain
in transforming sensory space to motor commands. To test the
robustness of the results, we estimate the shape of the basis
elements under two conditions: in a traditional learning paradigm
with a consistent force field, and in a random sequence of force
fields where learning is not possible. Remarkably, we find that
the basis remains invariant.
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