Date: October 15, 2020. 12:00
Location: Online (webinar)
To attend this webinar please register here.
Title: Contextual inference underlies the learning of sensorimotor repertoires
Affiliation: Columbia University
Humans spend a lifetime learning, storing and refining a repertoire of motor memories. However, it is unknown what principle underlies the way our continuous stream of sensori-motor experience is segmented into separate memories and how we adapt and use this growing repertoire. Here we develop a principled theory of motor learning based on the key insight that memory creation, updating, and expression are all controlled by a single computation – contextual inference. Unlike dominant theories of single-context learning, our repertoire-learning model accounts for key features of motor learning that had no unified explanation and predicts novel phenomena, which we confirm experimentally. These results suggest that contextual inference is the key principle underlying how a diverse set of experiences is reflected in motor behavior.
Bio: Daniel 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. In 2018 he joined the Zuckerman Mind Brain Behavior Institute at Columbia University as Professor of Neuroscience.