There are many causes for variation in the responses of the motor apparatus to neural commands. Muscles fatique, joints change their stiffness and we gain or loose weight. To maintain performance, motor commands need to adapt. Computing the best adaptation in response to any performance error results in a credit assignment problem: which potential cause is responsible for this disturbance? Here we show that a Bayesian solution to this problem accounts for numerous behaviors of humans and animals during motor performance training. We suggest that the best way of learning from motor errors is to interpret them in terms of potential causes.
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