|Speaker||Joint IPAB/ILCC Laurence T Maloney|
|Date||Apr 16, 2012|
|Location||S1.7, George Square|
|Title||Bayesian decision theory and the planning of actions: learning, representing and making use of information about motor uncertainty|
The movement we plan is not always the movement we execute. Any discrepancy is the consequence of own intrinsic motor uncertainty. I will first describe a model of movement planning based on Bayesian decision theory that takes our own visual and motor uncertainty into account in selecting movement strategies and present experimental evidence suggesting that human movement planners deviate slightly but systematically from optimal (Wu et al, 2009, 2011; Zhang et al, 2012; Zhang & Maloney, 2012). I will next present a recent study of how humans learn to predict their own probability of success in a simple motor task (Schüür et al, in preparation) comparing human performance to optimal Bayesian updating. While human performance is impressive in all of the tasks considered, it is not optimal, and deviations from optimality are potentially a valuable source of information concerning how humans learn, represent and make use of information about uncertainty.
Laurence T. Maloney is currently Professor in Psychology and Neural Science at New York University. He has been at New York University since 1988 and was previously Assistant Professor at the University of Michigan, Ann Arbor in Psychology and in Electrical Engineering & Computer Science from 1985 until 1988. He received his PhD in Psychology from Stanford University in 1985, an MS in Mathematical Statistics, also from Stanford, in 1982, and a BA in Mathematics from Yale University, in 1973.