Measuring people’s internal models of motor error distribution
A good decision maker in perception and action (e.g. a good surgeon) needs to allow for the irreducible errors in her own movement. In simple reaching tasks, the distribution of end points can be specified by a probability density function. We developed behavioral and computational techniques to measure people’s internal models of their motor error and examine how people’s subjective distributions deviate from the true distribution (Zhang, Daw, & Maloney, 2013). In a second paper we consider how deviations may result from heuristics and assumptions that the visuo-motor system employs to approximate optimal performance while reducing computational load (Zhang, Daw, & Maloney, in prep).