From Modules to Movement: Deconstructing the Modular Architecture of the Motor System

TL;DR

This review argues that modularity is a fundamental organizational principle of the human motor system, providing computational advantages for robust movement control in complex, uncertain environments.

Contribution

It synthesizes evidence for brain modularity, discusses factors driving modular architecture, and compares computational frameworks, highlighting the central role of modularity in motor control theories.

Findings

Modularity underpins the organization of the motor system.

Major neuroanatomical modules correspond to different motor functions.

All major computational models rely on modular principles.

Abstract

Coordinating multi-articulated bodies to generate purposeful movement is a formidable computational challenge. Yet the human motor system performs this task robustly in dynamic, uncertain environments, despite noisy and delayed feedback, slow actuators, and strict energetic constraints. A central question is what organizational principles underlie this efficiency. One widely recognized principle of neural organization is modularity, which enables complex problems to be decomposed into simpler subproblems that specialized modules are optimized to solve. In this review, we argue that modularity is a fundamental organizing principle of the motor system. We first summarize evidence for brain modularity, ranging from classical lesion studies to contemporary graph-theoretical analyses. We next discuss the main factors underlying the emergence and evolutionary selection of modular architectures, highlighting the computational advantages they provide. We then review the major neuroanatomical modules that structure current descriptions of the motor system and compare three prominent computational frameworks of motor control$-$optimal feedback control theory, muscle synergy theory, and dynamical systems approaches$-$showing that all implicitly or explicitly rely on specialized computational modules. We conclude by contrasting the key strengths and limitations of existing frameworks and by proposing promising directions toward more comprehensive theories.