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Bayesian integration in sensorimotor learning
If you have a question about this talk, please contact Emli-Mari Nel.
This paper is by Konrad Körding and Daniel Wolpert, Nature (2004). Available here: http://homepages.inf.ed.ac.uk/svijayak/teaching/MLSC/HW2papers/Wolpert.pdf
When we learn a new motor skill, such as playing an approaching tennis ball, both our sensors and the task possess variability. Our sensors provide imperfect information about the ball’s velocity, so we can only estimate it. Combining information from multiple modalities can reduce the error in this estimate1–4. On a longer time scale, not all velocities are a priori equally probable, and over the course of a match there will be a probability distribution of velocities. According to bayesian theory an optimal estimate results from combining information about the distribution of velocities—the prior—with evidence from sensory feedback. As uncertainty increases, when playing in fog or at dusk, the system should increasingly rely on prior knowledge. To use a bayesian strategy, the brain would need to represent the prior distribution and the level of uncertainty in the sensory feedback. Here we control the statistical variations of a new sensorimotor task and manipulate the uncertainty of the sensory feedback. We show that subjects internally represent both the statistical distribution of the task and their sensory uncertainty, combining them in a manner consistent with a performance-optimizing bayesian process. The central nervous system therefore employs probabilistic models during sensorimotor learning.
This talk is part of the Machine Learning Journal Club series.
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