Movement is essential to human life because it provides us with the freedom of mobility and the power to affect our surroundings. Moreover, movements are vital to communication: from hand and finger movements when writing, mouth and throat movements when speaking, to painting, dancing, and other forms of artistic self expression. As people grow and experience new environments, adaptively maintaining the accuracy of movements is a critical function of the motor system. In this dissertation, I explore the key mechanisms that underlie the adaptability of simple visually guided reaching movements. I specifically focus on two key facets of this adaptability: how motor learning rate can be predicted by motor variability and how motor learning affects the mechanisms which underlie movement planning. Inspired by reinforcement learning, I hypothesized that greater amounts of motor variability aligned with a task will produce more effective exploration, leading to faster learning rates. I discovered that this relationship predicts person-to-person and task-to-task differences in learning rate for both reward-based and error-based learning tasks. Moreover, I found that the motor system actively and enduringly reshapes motor output variability, aligning it with a task to improve learning. These results indicate that the structure of motor variability is an activelyregulated, critical feature of the motor system which plays a fundamental role in determining motor learning ability. Combining prominent theories in motor control, I created a model which describes the planning of visually guided reaching movements. This model computes a weighted average of two independent feature-based motor plans: one based on the goal location of a movement, and the other based on an intended movement vector. Employing this model to characterize the generalization of adaptation to movements and movement sequences, I find that both features, movement vector and goal location, contribute significantly to movement planning, and that each feature is remapped by motor adaptation. My results show that multiple features contribute to the planning of both point-to-point and sequential reaching movements. Moreover, a computational model which is based on the remapping of multiple features accurately predicts how visuomotor adaptation affects the planning of movement sequences. / Engineering and Applied Sciences

Understanding the neural processes underlying motor learning in humans is important to facilitate the acquisition of new motor skills and to aid the relearning of skills lost after neurologic injury. Although it is known that the learning of a new movement is guided by the error feedback received after each repeated attempt to produce the movement, how the central nervous system (CNS) processes individual errors and how it modulates its learning rate in response to the history of errors experienced are issues that remain to be elucidated. To address these issues we studied the generalization of learning and learning decay – the transfer of what has been learned, or unlearned, in a particular movement condition to new movement conditions. Generalization offers a window into the process of error credit assignment during motor learning, since it allows us to measure which actions benefit the most in terms of learning after experiencing an error. We found that the distributions that describe generalization after learning are unimodal and biased towards the motion directions experienced during training, a finding that suggests that the credit for the learning experienced after a particular trial is assigned to the actual motion (motion-referenced learning) and not to the planned motion (plan-referenced learning) as it had previously been assumed in the motor learning literature. In addition, after training the same action along multiple directions, we found that the pattern of learning decay has two distinct components: one that is time-dependent and affects all trained directions, and one that is trial-dependent and affects mostly the direction where decay was induced, generalizing narrowly with a unimodal pattern similar to the one observed for learning generalization. We finally studied the effect that the consistency of the error perturbations in the training environment has on the learning rate adopted by the CNS. We found that learning rate increases when the perturbations experienced in training are consistent, and decreases when these perturbations are inconsistent. Besides increasing our understanding of the mechanisms underlying motor learning, the findings described in the present dissertation will enable the principled design of skill training and rehabilitation protocols that accelerate learning. / Engineering and Applied Sciences

Human sensorimotor control is remarkably adept at utilising contextual information to learn and recall systematic sensorimotor transformations. Here, we investigate the motor representations that underlie such learning, and examine how motor memories acquired based on different contextual information interact. Using a novel three-dimensional robotic manipulandum, the 3BOT, we examined the spatial transfer of learning across various movement directions in a 3D environment, while human subjects performed reaching movements under velocity-dependent force field. The obtained pattern of generalisation suggested that the representation of dynamic learning was most likely defined in a target-based, rather than an extrinsic, coordinate system. We further examined how motor memories interact when subjects adapt to force fields applied in orthogonal dimensions. We found that, unlike opposing fields, learning two spatially orthogonal force fields led to the formation of separate motor memories, which neither interfered with nor facilitated each other. Moreover, we demonstrated a novel, more general aspect of the spontaneous recovery phenomenon using a two-dimensional force field task: when subjects learned two orthogonal force fields consecutively, in the following phase of clamped error feedback, the expression of adaptation spontaneously rotated from the direction of the second force field, towards the direction of the first force field. Finally, we examined the interaction of sensorimotor memories formed based on separate contextual information. Subjects performed reciprocating reaching and object manipulation tasks under two alternating contexts (movement directions), while we manipulated the dynamics of the task in each context separately. The results suggested that separate motor memories were formed for the dynamics of the task in different contexts, and that these motor memories interacted by sharing error signals to enhance learning. Importantly, the extent of interaction was not fixed between the context-dependent motor memories, but adaptively changed according to the task dynamics to potentially improve overall performance. Together, our experimental and theoretical results add to the understanding of mechanisms that underlie sensorimotor learning, and the way these mechanisms interact under various tasks and different dynamics.