Decomposition of Variability in the Execution of Goal-Oriented Tasks: Three Components of Skill Improvement.

TLDR

Motor control reliably achieves goals, yet variability is never zero; practice reduces this variability through stochastic noise reduction, exploitation of task tolerance, and covariation between central variables. The study introduces a method to decompose motor variability into stochastic noise, task tolerance, and covariation components relative to task space. The method partitions variance by mapping execution variables onto task space and separating the variance into N, T, and C components. In a virtual skittles task, participants’ accuracy improved over repetitions, with the relative contributions of N, T, and C shifting across practice and task variations.

Abstract

A central ability of the motor system is to achieve goals with great reliability, although never with zero variability. It is argued that variability is reduced with practice by 3 separate means: reduction of stochastic noise (N), exploitation of task tolerance (T), and covariation (C) between central variables. A method is presented that decomposes variability into these components in relation to task space that is defined by the execution variables. Successful variable combinations form the solution manifold. In a virtual skittles task, it is demonstrated that participants' improvement over repetitions, indicated by increasing accuracy, is accounted for by N, T, and, to a lesser degree, C. The relative contribution of these components changes over the course of practice and task variations.