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.

Variability is actively regulated in speech

“…Early evidence shows that skilled performers are able to upregulate the level of motor variability in each joint of the upper arm to meet the change of task demands/constraints, while less skilled performers, in comparison, tend to have rigidly fixed motor variability that is not fine-tuned with task constraints (Arutyunyan et al, 1968, see Newell & Vaillancourt, 2001 for a review). More recent work using computational models also found that force variability and the resulting kinematic variability are not generated primarily by random "motor noise", and emphasize the importance of other sources of force variability which can be tuned as needed by distributed sensorimotor systems (Nagamori et al, 2021). The results from the current study extend previous work and provide support for this perspective by showing the motor system closely monitors sensory variability and uses such information to actively regulate the motor variability, even in complex and well-practiced behaviors such as natural speech.…”

Numerical instability of Hill-type muscle models

“…OpenSim, Anybody or MuJoCo), while there are othernormally much more sophisticated-versions of the HMM such as Virtual Muscle [10][11][12] or other variations that aimed to include more detailed contractile mechanisms [12][13][14][15][16]. Also, it is worthwhile to point out more recent efforts made toward muscle simulation models that are not purely phenomenological, by integrating detailed models of cross-bridge mechanics or motor unit recruitment, such as populationbased models [12,[17][18][19] or implementation of phosphate kinetics [15]. An overview can be found in reviews [7][8][9].…”

“…These neural regulations allow muscles to run in versatile modes, such as impedance regulator, energy absorber, or instant stabilizersee Nishikawa et al [36] for a review. In order to simulate these higher-level control behaviours, recent computational models that implemented physiologically realistic closedloop models of neuro-muscular mechanics can be considered [18,19,[37][38][39][40].…”

Measuring complexity of muscle force control: Theoretical principles and clinical relevance in musculoskeletal research and practice

“…For example, in people with anterior cruciate ligament (ACL) injury, complexity measures of knee extensor muscle force control have uncovered injured versus uninjured limb differences whereas variability measures of knee extensor muscle force control have not (Hollman et al, 2021;Skurvydas et al, 2011); specifically, measures revealed lower complexity in the ACL-injured limb versus the uninjured contralateral limb (Hollman et al, 2021;Skurvydas et al, 2011). Therefore, time-based fluctuations ('complex fluctuations') in muscle force signals are now of special interest to researchers because they act as a biomechanical (kinetic) surrogate measurement that gives novel insight into peripheral joint sensorimotor control (neurophysiological) strategies (Nowak et al, 2013;Nagamori et al, 2021;Tracy, 2007).…”