Abstract
The sensorimotor system is a product of evolution, development, learning
and adaptationwhich work on different time scales to improve behavioral
performance. Consequently, many theories of motor function are based
on optimal performance: they quantify task goals as cost functions,
and apply the sophisticated tools of optimal control theory to obtain
detailed behavioral predictions. The resulting models, although not
without limitations, have explained more empirical phenomena than
any other class. Traditional emphasis has been on optimizing desired
movement trajectories while ignoring sensory feedback. Recent work
has redefined optimality in terms of feedback control laws, and focused
on the mechanisms that generate behavior online. This approach has
allowed researchers to fit previously unrelated concepts and observations
into what may become a unified theoretical framework for interpreting
motor function. At the heart of the framework is the relationship
between high-level goals, and the real-time sensorimotor control
strategies most suitable for accomplishing those goals.
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