Human motor learning dynamics in high-dimensional tasks.

Conventional approaches to enhance 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. Author summary: Examining the learning and acquisition of motor skills in humans when facing complex, high-dimensional tasks is vital for understanding human motor learning, optimizing the performance of human-in-the-loop systems, improving learning outcomes, and facilitating rehabilitation. Toward this goal, we develop a normative model of human motor learning in high-dimensional novel motor tasks and show that it explains experimental data reasonably well. Further, through a model-based investigation, we examine various motor learning trade-offs, such as exploration-exploitation, speed-accuracy, satisficing, and flexibility-performance. These findings provide a foundational insight into how the human brain may balance these trade-offs during learning. [ABSTRACT FROM AUTHOR]