Probabilistic Inference in Human Sensorimotor Processing

Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)

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Konrad Körding, Daniel M. Wolpert


When we learn a new motor skill, we have to contend with both the vari- ability inherent in our sensors and the task. The sensory uncertainty can be reduced by using information about the distribution of previously ex- perienced 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.