Visual feedback of the finger position was only provided briefly, We refer to this distribution as the true prior. Shift of 1 cm to the right and a standard deviation of 0.5 cm Randomly drawn from a prior distribution that was gaussian with a mean Of their finger laterally relative to its actual location ( Fig. Virtual-reality set-up that allowed us to displace the visual feedback Reached to a visual target with their right index finger in a The central nervous system therefore employs probabilistic models during sensorimotor learning. Performance-optimizing bayesian process 4, 5. Sensory uncertainty, combining them in a manner consistent with a Represent both the statistical distribution of the task and their Statistical variations of a new sensorimotor task and manipulate the The level of uncertainty in the sensory feedback. Strategy, the brain would need to represent the prior distribution and System should increasingly rely on prior knowledge. As uncertainty increases, when playing in fog or at dusk, the According to bayesian theory 5, 6,Īn optimal estimate results from combining information about theĭistribution of velocities-the prior-with evidence from sensoryįeedback. The course of a match there will be a probability distribution of Time scale, not all velocities are a priori equally probable, and over Combining information from multiple modalities can reduce Imperfect information about the ball's velocity, so we can onlyĮstimate it. Learn a new motor skill, such as playing an approaching tennis ball,īoth our sensors and the task possess variability. Sobell Department of Motor Neuroscience, Institute of Neurology, University College London, Queen Square, London WC1N 3BG, UKĬorrespondence and requests for materials should be addressed to K.P.K. Bayesian integration in sensorimotor learning