Probabilistic Inference in Human Sensorimotor Processing

TL;DR: The results show that the CNS employs probabilistic models during sensorimotor learning even when the priors are multimodal, and subjects internally represent both the distribution of the task as well as their sensory uncertainty.

Abstract: When we learn a new motor skill, we have to contend with both the variability inherent in our sensors and the task The sensory uncertainty can be reduced by using information about the distribution of previously experienced tasks Here we impose a distribution on a novel sensorimotor task and manipulate the variability of the sensory feedback We show that subjects internally represent both the distribution of the task as well as their sensory uncertainty Moreover, they combine these two sources of information in a way that is qualitatively predicted by optimal Bayesian processing We further analyze if the subjects can represent multimodal distributions such as mixtures of Gaussians The results show that the CNS employs probabilistic models during sensorimotor learning even when the priors are multimodal