  Conventional approaches to enhancing movement coordination, such as providing
instructions and visual feedback, are often inadequate in complex motor tasks
with multiple degrees of freedom (DoFs). To effectively address coordination
deficits in such complex motor systems, it becomes imperative to develop
interventions grounded in a model of human motor learning; however, modeling
such learning processes is challenging due to the large DoFs. In this paper, we
present a computational motor learning model that leverages the concept of
motor synergies to extract low-dimensional learning representations in the
high-dimensional motor space and the internal model theory of motor control to
capture both fast and slow motor learning processes. We establish the model's
convergence properties and validate it using data from a target capture game
played by human participants. We study the influence of model parameters on
several motor learning trade-offs such as speed-accuracy,
exploration-exploitation, satisficing, and flexibility-performance, and show
that the human motor learning system tunes these parameters to optimize
learning and various output performance metrics.
