Directionality in distribution and temporal structure of variability in skill acquisition

Abstract

Observable structure of variability presents a window into the underlying processes of skill acquisition, especially when the task affords a manifold of solutions to the desired task result. This study examined skill acquisition by analyzing variability in both its distributional and temporal structure. Using a virtual throwing task, data distributions were analyzed by the TNCmethod (Tolerance, Noise, Covariation); the temporal structure was quantified by autocorrelation and detrended fluctuation analysis. We tested four hypotheses: 1) Tolerance and Covariation, not Noise, are major factors underlying long-term performance improvement. 2) Trial-to-trial dynamics in execution space exhibits preferred directions in execution space. 3) The directiondependent organization of variability becomes more pronounced with practice. 4) The anisotropy is in directions orthogonal and parallel to the solution manifold. Results from 13 subjects practicing for six days revealed that performance improvement correlated with increasing Tolerance and Covariation; Noise remained relatively constant. Temporal fluctuations and their directional modulation were identified by a novel rotation method that was a priori ignorant about orthogonality. Results showed a modulation of time-dependent characteristics that became enhanced with practice. However, this directionality was not coincident with orthogonal and parallel directions of the solution manifold. A state-space model with two sources of noise replicated not only this temporal structure but also its deviations from orthogonality. Simulations suggested that practice-induced changes were associated with an increase in the feedback gain and a subtle weighting of the two noise sources. The directionality in the structure of variability depended on the scaling of the coordinates, a result that highlights that analysis of variability sensitively depends on the chosen coordinates. © 2013 Abe and Sternad.