Human Representation of Visuo-Motor Uncertainty as Mixtures of Orthogonal Basis Distributions

In many visuo-motor decision tasks subjects compensate for their own visuo-motor error, earning close to the maximum reward possible. To do so they must combine information about the distribution of possible error with values associated with different movement outcomes. The optimal solution is a potentially difficult computation that presupposes knowledge of the probability density function (pdf) of visuo-motor error associated with each possible planned movement. It is unclear how the brain represents such pdfs or computes with them. In three experiments, we used a forced-choice method to reveal subjects’ internal representations of their spatial visuo-motor error in a speeded reaching movement. While subjects’ objective distributions were unimodal, close to Gaussian, their estimated internal pdfs were typically multimodal, better described as mixtures of a small number of distributions differing only in location and scale. Mixtures of a small number of uniform distributions outperformed other mixture distributions including mixtures of Gaussians.