We possess a remarkable ability to learn new motor skills and retain memories for those skills throughout life, such as riding a bicycle. The ease with which we perform these skills belies their overwhelming computational complexity. My research focuses on unraveling the different computational processes involved in solving this motor control problem. One area of research aims at understanding how verbally-based strategies interact with implicit motor adaptation during skill acquisition. Specifically, how do novel movement strategies arise, what are the functional consequences of their interaction with learning, and what are their respective neural systems? Insight into these processes may be gained by considering different models for action-selection, such as model-free and model-based reinforcement learning, and combining them with models for sensorimotor adaptation. Another area of my research concerns the role of feedback in motor learning. Motor tasks offer a unique situation where learning may be dependent on both a fine measure of movement performance, in the form of sensory-prediction errors, and a coarse measure of task performance, in the form of reward-prediction errors. Both error-feedback mechanisms appear to follow similar computational principles for learning. However, there are striking differences in their respective neural regions, neurotransmitters, and neural architectures, suggesting that each system provides a unique contribution to learning. Insight into how the motor system coordinates learning by these very different systems can be gained by examining feedback-dependent learning deficits in neurological populations with damage to the cerebellum, basal ganglia, medial temporal lobe, and prefrontal cortex. Ultimately, we hope this research can lead to the development of optimal training protocols that can guide learning towards different, but still functioning learning mechanisms following stroke or disease.