Neuromotor Noise, Error Tolerance and Velocity-Dependent Costs in Skilled Performance

Abstract

In motor tasks with redundancy neuromotor noise can lead to variations in execution while achieving relative invariance in the result. The present study examined whether humans find solutions that are tolerant to intrinsic noise. Using a throwing task in a virtual set-up where an infinite set of angle and velocity combinations at ball release yield throwing accuracy, our computational approach permitted quantitative predictions about solution strategies that are tolerant to noise. Based on a mathematical model of the task expected results were computed and provided predictions about error-tolerant strategies (Hypothesis 1). As strategies can take on a large range of velocities, a second hypothesis was that subjects select strategies that minimize velocity at release to avoid costs associated with signal- or velocity-dependent noise or higher energy demands (Hypothesis 2). Two experiments with different target constellations tested these two hypotheses. Results of Experiment 1 showed that subjects chose solutions with high error-tolerance, although these solutions also had relatively low velocity. These two benefits seemed to outweigh that for many subjects these solutions were close to a high-penalty area, i.e. they were risky. Experiment 2 dissociated the two hypotheses. Results showed that individuals were consistent with Hypothesis 1 although their solutions were distributed over a range of velocities. Additional analyses revealed that a velocity-dependent increase in variability was absent, probably due to the presence of a solution manifold that channeled variability in a task-specific manner. Hence, the general acceptance of signal-dependent noise may need some qualification. These findings have significance for the fundamental understanding of how the central nervous system deals with its inherent neuromotor noise.

A preferred pattern of joint coordination during arm movements with redundant degrees of freedom

“…DOF redundancy is used to further reduce active control of the trailing joints. This benefit of the leadingtrailing joint control and of the preferred joint control pattern in particular links the LJH to the minimum intervention principle that emphasizes the minimization of the effort for control of errors during movement execution (Todorov and Jordan 2002) and, specifically, to theories suggesting that this effort is reduced yet at the stage of movement planning (Sternad et al 2011). …”

Individual differences in error tolerance in humans: Neurophysiological evidences

“…This concept has been important in the design of computer modeling of operating systems (Fields, Wright, & Harrison, 1995) and even in understanding the degree of tolerance against errors in simple living organisms (Jeong, Tombor, Albert, Oltvai, & Barabasi, 2001). In human performance, error-tolerance has been investigated in low-level sensoriomotor learning, in which a large degree of noise, variability, and redundancy exist in movement execution (Jeong et al, 2001;Sternad, Abe, Hu, & Muller, 2011) as well as in speech motor control (Houde & Nagarajan, 2011).…”

Trial-to-trial dynamics and learning in a generalized, redundant reaching task

“…Two geometry-based approaches that address these issues experimentally are the uncontrolled manifold (UCM) analysis (Latash et al 2002;Schöner and Scholz 2007) and the tolerance-noise covariation (TNC) analysis (Müller and Sternad 2004;Sternad et al 2011). Both methods postulate that the nervous system only corrects deviations orthogonal to some proposed subsurface (i.e., a "manifold") within a larger space of relevant variables.…”

“…In UCM, this manifold is defined at each instant in time along an experimentally recorded average movement trajectory, based on the hypothesis that this trajectory then determines "what task-level variables are 'most important' for the nervous system" (Latash et al 2010). In contrast, the TNC approach (Müller and Sternad 2004;Sternad et al 2011) analyzes data relative to a task manifold defined by some minimal subset of body-level variables required to achieve the external goal of the task itself (e.g., when throwing a ball, the position and velocity of the ball at release define exactly where the ball will land relative to some external target). In both methods, ratios of the variances orthogonal to and along the defined manifold are then analyzed and interpreted.…”