Our cognitive abilities can only be expressed on the world through our actions. Here we review the computations underlying the way that the sensorimotor system converts both low-level sensory signals and high-level decisions into action, focusing on the behavioral evidence for the theoretical frameworks. We review recent work that determines how motor memories underlying sensorimotor learning are activated and protected from interference, the role of Bayesian decision theory in sensorimotor control including sources of suboptimality, the role of risk sensitivity in guiding action, and how rapid motor responses may underlie the robustness of the motor system to the vagaries of the world. Although sensorimotor learning feels to us like a uni-tary process, its study is often broken down into a number of interacting processes. Here we review several aspects of sensorimotor learning that have seen progress in recent years and focus on computational approaches that have been studied behaviorally in humans. We start by exam-ining how motor memories for different skills are orga-nized, focusing on the rules by which different motor memories are parcellated and accessed. We then focu
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