Force variability is mostly not motor noise: Theoretical implications for motor control

Abstract

Variability in muscle force is a hallmark of healthy and pathological human behavior. Predominant theories of sensorimotor control assume ‘motor noise’ leads to force variability and its ‘signal dependence’ (variability in muscle force whose amplitude increases with intensity of neural drive). Here, we demonstrate that the two proposed mechanisms for motor noise (i.e. the stochastic nature of motor unit discharge and unfused tetanic contraction) cannot account for the majority of force variability nor for its signal dependence. We do so by considering three previously underappreciated but physiologically important features of a population of motor units: 1) fusion of motor unit twitches, 2) coupling among motoneuron discharge rate, cross-bridge dynamics, and muscle mechanics, and 3) a series-elastic element to account for the aponeurosis and tendon. These results argue strongly against the idea that force variability and the resulting kinematic variability are generated primarily by ‘motor noise.’ Rather, they underscore the importance of variability arising from properties of control strategies embodied through distributed sensorimotor systems. As such, our study provides a critical path toward developing theories and models of sensorimotor control that provide a physiologically valid and clinically useful understanding of healthy and pathologic force variability.

Author summary

Variability in our movements is thought to arise predominantly from ‘noise’ in the processes that convert central neural drive into muscle force. Constant variance of such noise has been a theoretical basis from which to explain various aspects of motor behavior. However, the physiological basis for such an assumption has not been tested rigorously. Our new computational model of a population of motor units demonstrates that non-physiological assumptions in previous models have led to erroneous interpretations of the role and significance of motor unit properties in the generation of force variability. Our results provide a clear path forward for future efforts using computational modeling to build theories of how altered neuromuscular systems emerge in aging or neurological disorders.