Abstract
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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Wolpert, D.M., Ghahramani, Z. (2000). Maps, Modules, and Internal Models in Human Motor Control. In: Winters, J.M., Crago, P.E. (eds) Biomechanics and Neural Control of Posture and Movement. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2104-3_24
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DOI: https://doi.org/10.1007/978-1-4612-2104-3_24
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