Computations in human motor learning
Sensory and motor uncertainty form a fundamental constraint on human motor control. I first describe how signal-dependent noise on the motor output places constraints on performance. Given these constraints the features of goal-directed movement arise from a model in which the statistics of our actions are optimized. I will then describe how prediction of the consequences of our actions can be used to reduce uncertainty, and present experiments on tickling which elucidate this predictive mechanism. Finally, I will present experiments on interference between motor learning tasks, which shed light on the way motor skills are represented in motor working memory.