Human Motor Learning Dynamics in High-dimensional Tasks

TL;DR

This paper introduces a computational model of human motor learning in high-dimensional tasks, using motor synergies and internal models to capture multiple learning processes and validate with human data.

Contribution

It presents a novel model combining motor synergies and internal models to better understand and simulate complex human motor learning processes.

Findings

The model converges and captures key learning dynamics.

Model parameters influence trade-offs like speed-accuracy and exploration-exploitation.

Humans tune parameters to optimize learning and performance.

Abstract

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.