Computations underlying sensorimotor learning

- Sensorimotor learning consists of processes acting on multiple timescales.
- Skill learning and adaptation are distinct components of sensorimotor learning.
- Reward affects sensorimotor learning and retention.
- Variability can be both beneficial and detrimental to motor learning.
- Probabilistic models underlie many sensorimotor processes.

The study of sensorimotor learning has a long history. With the advent of innovative techniques for studying learning at the behavioral and computational levels new insights have been gained in recent years into how the sensorimotor system acquires, retains, represents, retrieves and forgets sensorimotor tasks. In this review we highlight recent advances in the field of sensorimotor learning from a computational perspective. We focus on studies in which computational models are used to elucidate basic mechanisms underlying adaptation and skill acquisition in human behavior.