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Perspectives and problems in motor learning.

Daniel M. Wolpert1, Zoubin Ghahramani, J Randall Flanagan

  • 1Sobell Dept of Neurophysiology, Institute of Neurology, Queen Square, WC1N 3BG, London, UK

Trends in Cognitive Sciences
|October 31, 2001
PubMed
Summary
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Motor learning enables adaptation to new environments and social conventions. This review explores computational approaches to understanding how we learn and represent movements.

Area of Science:

  • Neuroscience
  • Cognitive Science
  • Robotics

Background:

  • Human interaction with the world relies on movement, which can be innate or learned.
  • Motor learning facilitates adaptation to dynamic physical surroundings and evolving societal norms.

Purpose of the Study:

  • To provide a computational perspective on motor learning.
  • To explore the necessity, content, representation, and mechanisms of motor learning.

Main Methods:

  • Review of computational theories of motor learning.
  • Integration of empirical studies on human motor learning.

Main Results:

  • Movement is essential for interaction and adaptation.
  • Motor learning involves understanding what is learned, how it's represented, and the underlying mechanisms.

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Conclusions:

  • Computational approaches offer valuable insights into motor learning.
  • Understanding motor learning is crucial for fields ranging from neuroscience to robotics.