Maps, Modules, and Internal Models in Human Motor Control

Neural network models of computation have recently provided a strong foundation from which to formulate computational theories of learning, planning and action (Kawato et al. 1987; Jordan 1995; see also Chapter 34). Here we consider three computational ideas—the generalization properties of function approximators, self-organized modularity, and optimal estimation—and show how these can be used to design and interpret psychophysical experiments which explore the computations involved in motor control.

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