Towards Modeling Human Motor Learning Dynamics in High-Dimensional Spaces

TL;DR

This paper presents a computational model of human motor learning in high-dimensional spaces, utilizing motor synergies to simplify control, and demonstrates its effectiveness in capturing human learning behavior.

Contribution

It introduces a novel synergy-based model grounded in internal model theory for high-DoF motor learning, validated with experimental data.

Findings

Model captures human motor learning behavior effectively

Model converges reliably in high-dimensional control spaces

Fitted model aligns well with experimental data

Abstract

Designing effective rehabilitation strategies for upper extremities, particularly hands and fingers, warrants the need for a computational model of human motor learning. The presence of large degrees of freedom (DoFs) available in these systems makes it difficult to balance the trade-off between learning the full dexterity and accomplishing manipulation goals. The motor learning literature argues that humans use motor synergies to reduce the dimension of control space. Using the low-dimensional space spanned by these synergies, we develop a computational model based on the internal model theory of motor control. We analyze the proposed model in terms of its convergence properties and fit it to the data collected from human experiments. We compare the performance of the fitted model to the experimental data and show that it captures human motor learning behavior well.