title: 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.
authors.affiliation: Departments of Biology, Electrical and Computer Engineering, and Physics, Northeastern University, Boston, Massachusetts, United States of America. dagmar@neu.edu
authors.forename: Dagmar
authors.initials: D
authors.lastname: Sternad
authors.forename: Masaki O
authors.initials: MO
authors.lastname: Abe
authors.forename: Xiaogang
authors.initials: X
authors.lastname: Hu
authors.forename: Hermann
authors.initials: H
authors.lastname: Müller
mesh_terms: D000328:Adult; D001132:Arm / Q000502:physiology; D005260:Female; D006801:Humans; D007858:Learning / Q000502:physiology; D016014:Linear Models; D008297:Male; D008954:Models, Biological; D009048:Motor Skills / Q000502:physiology*; D013647:Task Performance and Analysis; D014584:User-Computer Interface