Optimal Process Modeling for Assessment and Enhancement of Error-Based Motor Learning

As emerging human-machine interfaces enable new opportunities for enhanced performance, users must acquire appropriate motor skills to operate the technology through lengthy, intensive, and elaborate training. Model-based frameworks can enable optimal training through computational modeling to better understand the process of motor learning and simulations to explore the training conditions that facilitate skill acquisitions. In this dissertation, we conducted reaching experiments on 15 healthy individuals and explored the role of error-based feedback and its augmentations (error-augmentation or EA) to shape the time course of motor learning. Computational models tested several learning mechanisms including: 1) constant versus error-dependent learning rates, 2) first-order versus higher-order processes, and 3) generalization across movement directions. We first determined the sample size of trial performances needed to best assess the time course of motor learning, and we found that the number of samples required increased linearly with the time constant of transient signals and decreased exponentially with signal-to noise ratio. Next, we found that the second-order proxy process model with trial-to-trial update method best predicted the influence of EA on changes in movement errors from sparse, intermittent no-vision (catch) trials. Next, our simulations revealed that the optimal EA (gain and offset) schedules should be held constant near 2 to minimize the final error and maximize the rate of learning, and the training duration of at least 16 trials were necessary. We then identified generative model structures to predict motor learning through randomized reaching practice across movement directions, and we found that iterative update of the initial ballistic launch to reaching targets was predictable using the direction-specific, locally generalizing first-order model with constant learning rate. Finally, we investigated generative model structures that can predict motor learning through corrective submovements, and we found that iterative update of the submovements during rapid reaching was predictable using the first-order affine model with Gaussian-weighted learning rate. This work illuminates computational frameworks to better understand and to enhance motor learning process that would dramatically impact neurorehabilitation, sports, piloting, and other forms of performance training.