Statistical Learning in Human Sensorimotor Control
Abstract: Humans spend a lifetime learning, storing and refining a repertoire of motor memories appropriate for the multitude of tasks we perform. However, it is unknown what principle underlies the way our continuous stream of sensorimotor experience is segmented into separate memories and how we adapt and use this growing repertoire. I will review our recent work on how humans learn to make skilled movements focussing on how statistical learning can lead to multimodal object representations, how we represent the dynamics of objects, the role of context in the expression, updating and creation of motor memories and how families of objects are learned.
Bio: Daniel Wolpert FMedSci FRS. Daniel qualified as a medical doctor in 1989. He worked with John Stein and Chris Miall in the Physiology Department of Oxford University where he received his D.Phil. in 1992. He worked as a postdoctoral fellow in the Department of Brain and Cognitive Sciences at MIT in Mike Jordan's group and in 1995 joined the Sobell Department of Motor Neuroscience, Institute of Neurology as a Lecturer. In 2005 moved to the University of Cambridge where he was Professor of Engineering (1875) and a fellow of Trinity College and from 2013 the Royal Society Noreen Murray Research Professorship in Neurobiology. In 2018 Daniel joined the Zuckerman Mind Brain and Behavior Institute at Columbia University as Professor of Neuroscience and is vice-chair of the Department of Neuroscience. Daniel retains a part-time position as Director of Research at the Department of Engineering, University of Cambridge.