@article{Renshaw_Davids_O’Sullivan_2022, title={Learning and performing: What can theory offer high performance sports practitioners?}, volume={16}, url={https://www.socibracom.com/bjmb/index.php/bjmb/article/view/280}, DOI={10.20338/bjmb.v16i2.280}, abstractNote={<p style="font-weight: 400;">Currently, the most prominent motor control theories that underpin the pedagogy of coaches in high performance sport are derived from the discipline of psychology with a dominant focus on internalised control processes for learning and performance. In contrast, ecological dynamics is a contemporary meta-theory focused on the person-environment scale of analysis for understanding human behavior, exemplified by strengthening the<em> relations between each learner and their environment</em>. In this tutorial, we outline key concepts in ecological dynamics that considers learning and performance as being distinct, yet inextricably linked. In our considerations, we raise questions on long-held assumptions about control process theories on learning and performance for practice designs in high performance sports. For example, how useful is inferring learning by describing improved <em>performance</em> as showing more relative permanence, greater stability and consistency, with commensurate lower levels of attention and movement variability? How relevant are traditional ways of measuring learning using retention and transfer tests in high performance sports? What is actually attained in an ecological view of learning, focussed on education of attention and calibration of actions to specifying information present in performance environments? An implication of these issues for high performance sport is that learning needs to be assessed by how well a learner adapts to the specific constraints and demands of a performance context. This key idea has important implications for performance analysis and evaluation in sport.</p>}, number={2}, journal={Brazilian Journal of Motor Behavior}, author={Renshaw, Ian and Davids, Keith and O’Sullivan, Mark}, year={2022}, month={Jun.}, pages={162–178} }