An Ecological Dynamics Conceptualization of Human Behaviour: Implications for Use of AI Systems in Sport

In Chapter 1, we discussed how an ecological dynamics framework for sport scientists, educators, and practitioners can support the interpretation of large datasets collected from athlete behaviours in practice and performance, which can help coaches and trainers design better practice tasks and sustain evidence-based interpretations of athlete performance in sport. The aim of enriching participatory experiences is fundamentally an embedded process of using technological support to continuously promote individual interactions with sport, exercise, and physical activity environments (Reed, 1993; Renshaw et al., 2019; Stone et al., 2018; Woods, McKeown, Shuttleworth, Davids, & Robertson, 2019). The ontology of ecological realism underpins ecological dynamics as an important framework for understanding learning, practice, education, and development, from a perspective that emphasizes search exploration, discovery, re-organization, stabilization, and exploitation of knowledge of a performance environment for regulating actions. New technologies can support provision of real-time or short-term feedback and monitoring, which could help the regulation of actions, favouring behavioural adaptability in performance. For instance, use of miniaturized inertial measurement units allows continuous data collection during ecological contexts of performance that enables sport practitioners to detect perturbations (performance and/or behavioural destabilizations) developing flexibility and adaptive behaviours in athletes (for an example, see Guignard et al., 2017b).

Technology Implementation Should Drive Knowledge of the Environment in Athletes

Chapter 1 is fundamentally important to understanding key concepts of psychological theories of human behaviour that underpin implementation of Al technologies for use in sport, physical activity, and exercise contexts. That chapter outlined the role of these technologies in the evolution and development of the ability to use affordances of the environment that are fundamental to successful performance in the human econiche. According to James Gibson, perception is a fundamental type of cognition, and aligned with these ideas, Turvey and Carello (1981) pointed out that cognition, from the ontological perspective of ecological realism, may be considered as the coordination of an individual’s intentional interactions with the affordances of a performance environment. Reed (1993) elegantly explained how psychological processes underpinning cognition could support knowledge of an environment, describing how it is provided by perception allowing an organism (sport performer) to be aware of events, objects, and significant others that exist (attention, perception), have existed (memories), may come to exist (anticipation), and ought to exist (prediction) during interactions. Reed’s (1993) insights can be taken to imply that actions, perception, and cognition of athletes during performance and practice are ‘knowledge-yielding processes’ (p. 47). In the many chapters of this book, we have outlined how AI systems can be implemented to provide augmented information in support of the knowledge-based functioning of athletes during their interactions with practice and performance environments.

 
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