A Multilayer Network Model for Motor Competence from the View of the Science of Complexity.

Motor competence is related to a large number of correlates of different natures, forming together a system with flexible parts that are synergically and cooperatively connected to produce a wide range of motor outcomes that cannot be explained from a predetermined linear view or a unique mechanism. The diversity of interacting correlates, the various connections between them, and the fast changes between assessments at different time points are clear barriers to the study of motor competence. In this manuscript, we present a multilayer framework that accounts for the theoretical background and the potential mathematical procedures necessary to represent the non-linear, complex, and dynamic relationships between several underlying correlates that emerge as a motor competence network. Exploring motor competence from a new perspective that could be operationalized through multilayer networks seems promising, and allows more accurate inspection and representation of its topology and dynamics. This new perspective might also improve the understanding of motor competence structure and functionality over the developmental course. The use of the proposed approach could open up new horizons for the broad literature comprising motor competence.