Daniel Wolpert "Probabilistic Models of Human Sensorimotor Control"
Daniel Wolpert: Probabilistic Models of Human Sensorimotor Control.
Vision Sciences Society Annual Meeting, Keynote 2011.
The effortless ease with which humans move our arms, our eyes, even our lips when we speak masks the true complexity of the control processes involved. This is evident when we try to build machines to perform human control tasks. While computers can now beat grandmasters at chess, no computer can yet control a robot to manipulate a chess piece with the dexterity of a six-year-old child. I will review our recent work on how the humans learn to make skilled movements covering probabilistic models of learning, including Bayesian and structural learning, how the brain makes and uses motor predictions, and the interaction between decision making and sensorimotor control.
Vision Sciences Society Annual Meeting, Keynote 2011.
The effortless ease with which humans move our arms, our eyes, even our lips when we speak masks the true complexity of the control processes involved. This is evident when we try to build machines to perform human control tasks. While computers can now beat grandmasters at chess, no computer can yet control a robot to manipulate a chess piece with the dexterity of a six-year-old child. I will review our recent work on how the humans learn to make skilled movements covering probabilistic models of learning, including Bayesian and structural learning, how the brain makes and uses motor predictions, and the interaction between decision making and sensorimotor control.