Motor learning is optimally tuned to the properties of motor noise.

In motor learning, our brain uses movement errors to adjust planning of future movements. This process has traditionally been studied by examining how motor planning is adjusted in response to visuomotor or dynamic perturbations. Here, I show that the learning strategy can be better identified from the statistics of movements made in the absence of perturbations. The strategy identified this way differs from the learning mechanism assumed in mainstream models for motor learning. Crucial for this strategy is that motor noise arises partly centrally, in movement planning, and partly peripherally, in movement execution. Corrections are made by modification of central planning signals from the previous movement, which include the effects of planning but not execution noise. The size of the corrections is such that the movement variability is minimized. This physiologically plausible strategy is optimally tuned to the properties of motor noise, and likely underlies learning in many motor tasks.