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Abstract
A number of internal model concepts are now widespread in neuroscience and cognitive
science. These concepts are supported by behavioral, neurophysiological, and imaging
data; furthermore, these models have had their structures and functions revealed by
such data. In particular, a specific theory on inverse dynamics model learning is
directly supported by unit recordings from cerebellar Purkinje cells. Multiple paired
forward inverse models describing how diverse objects and environments can be controlled
and learned separately have recently been proposed. The 'minimum variance model' is
another major recent advance in the computational theory of motor control. This model
integrates two furiously disputed approaches on trajectory planning, strongly suggesting
that both kinematic and dynamic internal models are utilized in movement planning
and control.