A computational theory for the production of limb movements

TL;DR

This paper introduces a unified computational theory for limb movement production based on optimal feedback control, receding horizon control, and via-point task representation, explaining diverse movement types and properties without free parameters.

Contribution

It proposes a novel, unified theory of motor control that addresses the degrees-of-freedom problem and explains various movement phenomena through minimal assumptions.

Findings

Simulations show the theory explains discrete, continuous, rhythmic, and constrained movements.

The model reproduces observed scaling laws, power laws, and speed/accuracy tradeoffs.

The theory operates with no free parameters and limited implementation variations.

Abstract

Motor control is a fundamental process that underlies all voluntary behavioral responses. Several different theories based on different principles (task dynamics, equilibrium-point theory, passive-motion paradigm, active inference, optimal control) account for specific aspects of how actions are produced, but fail to provide a unified view on this problem. Here we propose a concise theory of motor control based on three principles: optimal feedback control, control with a receding time horizon, and task representation by a series of via-points updated at fixed frequency. By construction, the theory provides a suitable solution to the degrees-of-freedom problem, i.e. trajectory formation in the presence of redundancies and noise. We show through computer simulations that the theory also explains the production of discrete, continuous, rhythmic and temporally-constrained movements, and their parametric and statistical properties (scaling laws, power laws, speed/accuracy tradeoffs). The theory has no free parameters and only limited variations in its implementation details and in the nature of noise are necessary to guarantee its explanatory power.