Daniel Wolpert is Professor of Engineering at the University of Cambridge and a Fellow of Trinity College. Daniel's research focuses on computational and experimental approaches to human sensorimotor control. Daniel read medical sciences at Cambridge and clinical medicine at Oxford. After working as a medical doctor for a year he completed a D. Phil. in the Physiology Department in Oxford. He then worked as a postdoctoral fellow and Fulbright Scholar at MIT, before moving to the Institute of Neurology, UCL. In 2005 he took up his current post in Cambridge. He was elected a Fellow of the Academy of Medical Sciences in 2004 and was awarded the Royal Society Francis Crick Prize Lecture (2005) and has given the Fred Kavli Distinguished International Scientist Lecture at the Society for Neuroscience (2009). Further details can be found on www.wolpertlab.com.
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.