ABSTRACT
Individual differences are observed in motor learning and development; however, studies typically analyze the averaged data over groups of participants. Based on the ergodic theorem of mathematics, it is clear that the processes of human motor learning and development are non-ergodic as reflected in non-stationarity and heterogeneity. Given this, it is necessary to analyze the intra-individual data to unravel the characteristics of the change processes. We present a landscape model of multiple timescales as a framework to describe the individual pathways of change in motor learning and development from a dynamical systems perspective. Examples of individual differences, including those in the context of sport skills, are provided from the evolving attractor landscape of multiple timescales.
