Computational Elements of the Adaptive Controller of the Human Arm

We consider the problem of how the CNS learns to control dynam(cid:173) ics of a mechanical system. By using a paradigm where a subject's hand interacts with a virtual mechanical environment, we show that learning control is via composition of a model of the imposed dynamics. Some properties of the computational elements with which the CNS composes this model are inferred through the gen(cid:173) eralization capabilities of the subject outside the training data.