Lecture Title: Probabilistic models of sensorimotor control and decision making

ABSTRACT: 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. I will review our work on how humans learn to make skilled movements covering probabilistic models of learning, including Bayesian models as well as the role of context in activating motor memories. I will also review our work showing the intimate interactions between decision making and sensorimotor control processes. Taken together these studies show that probabilistic models play a fundamental role in human sensorimotor control.

BIOGRAPHY: Daniel Wolpert read medicine at Cambridge before completing an Oxford Physiology DPhil and a postdoctoral fellowship at MIT. He joined the faculty at the Institute of Neurology, UCL in 1995 and moved to Cambridge University in 2005 where he was Professor of Engineering and a Royal Society Research Professor. In 2012 he was elected a Fellow of the Royal Society (FRS). In 2018 he joined the Zuckerman Mind Brain Behavior Institute at Columbia University as Professor of Neuroscience. His research interests are computational and experimental approaches to human movement (www.wolpertlab.com).