Measuring people’s internal model of motor error distribution
A good decision maker in perception and action (e.g. a good surgeon, a good basketball player) needs to compensate for the irreducible errors in her own movement. In mathematics, the distribution of motor errors can be specified by a probability density function, or by a series of moments, but how about the human brain? We have been developing behavioral and computational techniques to measure how people’s internal model of their motor error may deviate from the true distribution.