The Effects of Motor Modularity on Performance, Learning, and Generalizability in Upper-Extremity Reaching: a Computational Analysis

Abstract

It has been hypothesized that the central nervous system simplifies the production of movement by limiting motor commands to a small set of modules known as muscle synergies. Recently, investigators have questioned whether a low-dimensional controller can produce the rich and flexible behaviors seen in everyday movements. We implemented * Corresponding author: malborno@stanford.edu controllers could also accomplish new tasks-such as reaching to targets on a higher or lower plane, starting from an alternate initial pose, moving at a faster velocity, and holding a light weight-with average errors similar to a full-dimensional controller. We also found that for new tasks, the synergybased controllers converged to good solutions (as defined by a cost-function threshold) with lower computational cost than a full-dimensional controller.Our results show that a modular architecture based on muscle synergies can generate a wide variety of reaching behaviors for a high-dimensional musculoskeletal system without a significant degradation in performance.

Motor synergy generalization framework for new targets in multi-planar and multi-directional reaching task

“…It should be noted that the reaching task in this study is complex even compared with related works that used musculoskeletal systems because we treated a three-dimensional task. Although recent studies have dealt with simulated musculoskeletal systems with many muscles [22,54,55], most studies only handled planar physics, in which the rotational directions of joints are always constant (i.e. perpendicular to the plane).…”

“…There are also some studies that examined the generalization ability of motor synergies. Al Borno et al evaluated reaching tasks using a musculoskeletal model and reported that a single repertory of motor synergies obtained by trajectory optimization could be generalized to different initial poses and targets [22]. Hagio & Kouzaki demonstrated that a hard-wired motor synergy structure could accelerate motor learning against changes in musculoskeletal structures [23].…”