Imperfectly optimal animals: Bayesian decision theory and the planning of action

25 October 2012
Begin time: 


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 in many tasks (Schüür et al, under review; Wu et al, 2009, 2011; Zhang et al, under review; Zhang & Maloney, 2012). While human performance is impressive in all of the tasks considered, it is not optimal, and patterned deviations from optimality are potentially a valuable source of information concerning how humans systematically distort probability and frequency information (Zhang & Maloney, 2012). I’ll outline an alternative to Bayesian decision theory that provides a better account of what humans actually do in planning movement.