The motor system is continuously monitoring our performance, ensuring that our actions are occurring as planned. Sensory prediction errors, which arise from a discrepancy between the expected and actual sensory consequence of a motor command (i.e., a planned action), are assumed to drive sensorimotor adaptation. Sensorimotor adaptation is thought to involve changes in motor output that allow the motor system to regain its former level of performance in perturbed circumstances. We employed experimental paradigms that involved both mechanical and visual perturbations to evoke sensory prediction errors while participants performed planar reaching movements. Movement error activates learning processes in the brain, which alter our behaviour in the future. A prominent model of short-term adaptation is built upon the theory that there appear to be at least two processes of varying timescales operating together as humans learn to counteract sensorimotor disturbances: a fast process that learns to reduce errors quickly but also quickly forgets, and a slow process that learns to reduce errors slowly but slowly forgets. The purpose of this dissertation was to track the mechanisms of short-term motor adaptation within the framework of a two-state model. Collectively, our three studies reinforce the hypothesis that short-term sensorimotor adaptation, occurring over short time scales (e.g., over a period of minutes), is supported by at least two underlying processes. Substantiated by our first and third study, we have shown that both the fast and slow adaptation processes are responsive to a history of error and both contribute to savings. The motor system receives sensory feedback about both the environment and the body on a continual basis, in addition to predictive feedforward commands. How feedback gains are changed can vary greatly, based on the state of the body and environment, as well as the behavioural context of learning. It has been routinely suggested that adaptation in response to a perturbation, results in a gradual shift over the course of error-reduction from a feedback-driven mode of control to more predictive, feedforward control. Based on the results of our second study, we demonstrate that the fast process of feedforward adaptation parallels the modulation in gain of the feedback response over the course of learning to counter a force field perturbation. We propose that the fast process, estimated from overall learning, may alternatively be an identification of the feedback controller, while the slow process is the recalibrated forward model. And lastly, while unpacking the result of our third study we further suggest that it is the slow process which stores a memory component from prior training which is then later accessed by both processes during subsequent learning
