No abstract

After more than 20 years since the introduction of ecological and dynamical approaches in sports research, their promising opportunity for interdisciplinary research has not been fulfilled yet. The complexity of the research process and the theoretical and empirical difficulties associated with an integrated ecological-dynamical approach have been the major factors hindering the generalisation of interdisciplinary projects in sports sciences. To facilitate this generalisation, we integrate the major concepts from the ecological and dynamical approaches to study behaviour as a multi-scale process. Our integration gravitates around the distinction between functional (ecological) and execution (organic) scales, and their reciprocal intra- and inter-scale constraints. We propose an (epistemological) scale-based definition of constraints that accounts for the concept of synergies as emergent coordinative structures. To illustrate how we can operationalise the notion of multi-scale synergies we use an interdisciplinary model of locomotor pointing. To conclude, we show the value of this approach for interdisciplinary research in sport sciences, as we discuss two examples of task-specific dimensionality reduction techniques in the context of an ongoing project that aims to unveil the determinants of expertise in basketball free throw shooting. These techniques provide relevant empirical evidence to help bootstrap the challenging modelling efforts required in sport sciences.

In 2017, Di Paolo, Buhrmann, and Barandiarán proposed a list of criteria that post-cognitivist theories of learning should fulfill. In this article, we review the ecological theory of direct learning. We argue that this theory fulfills most of the criteria put forward by Di Paolo et al. and that its tools and concepts can be useful to other post-cognitivist theories of learning. Direct learning holds that improvements with practice are driven by information for learning that can be found in the dynamic organism-environment interaction. The theory formally describes information for learning as a vector field that spans a space with all the perception-action couplings that may be used to perform an action. Being located at a point of such a space means using a specific perception-action coupling. Changes in perception-action couplings due to learning can be represented as paths across the space, and can be explained with the vector field of information for learning. Previous research on direct learning considered actions that were best understood with single perception-action couplings. To conclude the article, and inspired by the criteria of Di Paolo et al., we discuss an extension of the theory to actions that are best understood with multiple perception-action couplings.