Acquisition and Use of Internal Models for Human Motor Learning
Final rept. 15 Jul 2006-31 Jul 2009
ROCHESTER UNIV NY
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We study decision making in dynamic environments in general, and human motor learning in particular. Our approach focuses on the acquisition and use of libraries of representational primitives. This approach is motivated by computational considerations -- learning new motor plans by linearly combining primitives from a library ameliorates the curse of dimensionality. It is also motivated by evidence from the field of cognitive neuroscience indicating that biological organisms including humans linearly combine motor primitives known as motor synergies when planning and executing motor actions. We have made excellent progress showing that linear combinations of global primitives can achieve near-optimal performance on tasks requiring the control of a simulated two-joint robot arm. We have also shown that new linear combinations for novel tasks can be learned rapidly. In more recent research, we have explored the strengths of libraries of local primitives where primitives are linearly combined using a local additive regression procedure.
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