Dynamic Systems Theory

The dynamic (ecological) systems approach addresses the relationship between the organised and executed skilled movements of a learner and the specific manipulation of boundaries during a practise task

In this educational blog article we will look at the many concepts involved in the dynamic systems approach to movement patterns and ways in which a practitioner may apply the theory to their practical methods. Components of this article include: constraints, self-oragnisation, affordances and perception-action coupling and the practical application of dynamic system approach. 

Dynamic systems theory suggests that movements of a learner can happen due to a response between an individual and their environment (Coker, 2017). The learner can also be described as a dynamical movement system (Araujo, Button, Davids & Shuttleworth, 2003). In contrast, when it comes to the control of complex systems, motor program theory based on cognition is said to fall short (Coker, 2017). When looking at skilled movements, the load on memory is said to be too great for a command center to hold (Coker, 2017). 

Constraints: 

Constraints in this case can be described as the boundaries that either limit or enable a learner, bringing about optimal movement patterns. Constraints in the context of dynamic systems theory are features that help a learner achieve acquisition of movement skills, not negative restrictions (Chow, Davids, Hammond & Renshaw, 2010). Similarly, they influence the shaping of an individual's physical education (Chow et al., 2010). 

Constraints can be/are categorised into three: task, environmental and individual and then further into two: internal (body subsystems) and external (Chow et al., 2010 ; Coker, 2017). Individual constraints also known as organismic constraints can be explained as functional and biological aspects of a learner (Coker, 2017). This can include characteristics such as: height, shape, personality, fitness variables, and perception and decision making skills (Coker, 2017). Environmental constraints can be physical or social and include things such as presence of spectators, gravity, temperature, wind (Coker, 2017). Coker (2017) categorises task constraints into: goals and outcomes, rules that are either primary or secondary, and implements such as equipment or machinery. 

Some research supports an additional constraint- instructional constraint which is non physical and includes instructions and feedback (augmented information) to the learner (Davids, Renshaw & Savelsbergh, 2010; Araujo, et al., 2003). Augmented information leads to more effective learning and facilitates the learner in their search of movements and self-organisation (Davids et al., 2010; Araujo et al., 2003).

Davids (2015) and Coker (2017) both examine the importance of interaction with constraints and therefore success in movement patterns, decision making behaviours and intentions. Learners execute movements through self-organisation when specific constraints are introduced to them. 

Self-Organisation: 

Self-organisation is similar to problem solving where learners are encouraged to seek their own solutions to motor problems, which can be done through exploring in practise (Araujo et al., 2003). The reason that movement occurs is due to self-organisation. Self-organisation, closely linked to complexity theory, refers to the organisation that occurs in movement patterns of an individual (Butler, Hopper & Ovens, 2013). Self-organisation as a process impacts how individuals move according to environments, objects and other people (Bennett, Button, & Davids, 2008). Both constraints and affordances impact and bring about the way one will self-organise the information presented to them (Coker, 2017). An example of how self-organisation can occur is through the changes in speed. When speed is increased the system (individual) self-organises and generates a new behaviour (Coker, 2017). This new behaviour presents itself in functional patterns of coordination to satisfy/compensate for the constraints (Araujo et al., 2003). Therefore, self-organisation can be described as being instinctive and deep-rooted in a dynamical movement system (Davids, 2015).

Perception, Action, Affordances and Attractors: 

In order for one to self-organise according to constraints, there must be perception involved before the execution of the action takes place. Actions depend on the perceptions of constraints, and perception can change as actions are completed. This coupling of action and perception is the way that the individual uses their senses in relation to the constraints that are directly affecting them. Action-perception can therefore be described as being circular (Coker, 2017). The self-organisation and action-perception coupling can be explained as adaptive behaviour between the environment and the performer, and is acquired during the acquisition of a skill (Davids, 2015; Chow et al., 2010). 

Skill acquisition is where the learner self-organises, and seeks a stable state of coordination during the activity (Bennett, et al., 2008). This state is called an attractor region or attractor well, and occurs when practise has taken place with constraints and helps with the self-organising process (Araujo et al., 2003; Coker, 2017). Stable state of an individual’s movement systems can change due to control parameters such as speed, force, directional and perceptual information (Coker, 2017). Self-organisation occurs when a new behaviour emerges and therefore the attractor well becomes shallow and more susceptible to change; changes are called phase shifts (Coker, 2017). As an individual practises the attractor well will become deeper and a stable state of performance will be found. The attractors can be considered as an action-perception landscape necessary for the self-organisation of performance skills (Bennett et al., 2008). 

When performing an action, and self-organisation occurs there are possibilities that an individual can take in relation to their capabilities. These action possibilities are called affordances, can present as direct opportunities for action, and can differ between performers according to their perception (Coker, 2017).

Conclusion: 

All of these specific components of the dynamic systems theory must work together in order to achieve optimal movement processes. Without one component the rest do not work and the dynamic systems theory falls apart. 

Please view my second blog “Showcasing Dynamic Systems Theory” (see here) for ideas of implementation regarding dynamic system theory in practise.

References

Araujo, D., Button, C., Davids, K., & Shuttleworth, R. (2003). Acquiring skill in sport: A constraints-led perspective. International Journal of Computer Science in Sport, 2(2), 31-39. Retrieved from https://www.researchgate.net/publication/292604016_Acquiring_skill_in_sport_A_constraints-led_perspective

Bennett, S., Button, C. & Davids, K., (2008). Dynamics of skill acquisition : a constraints-led approach. Retrieved from https://web-b-ebscohost-com.ezproxy.lib.monash.edu.au/ehost/detail/detail?vid=0&sid=d0d9f876-71c1-4338-8140-f2fedc15dc13%40pdc-v-sessmgr03&bdata=JnNpdGU9ZWhvc3QtbGl2ZSZzY29wZT1zaXRl#AN=697908&db=nlebk

Butler, J., Hopper, T., & Ovens, A. (Eds.). (2013). Complexity Thinking in Physical Education: Reframing Curriculum, Pedagogy and Research. Retrieved from https://books.google.com.au/books?id=maIxh30GakYC&pg=PA30&lpg=PA30&dq=complexity+theory+and+ambiguous+bounding&source=bl&ots=aLkFVTgOJd&sig=ACfU3U38uFCZNzDVWvkVtEl5kethseO2xw&hl=en&sa=X&ved=2ahUKEwjq3fewq4jpAhXJxzgGHViJAmQQ6AEwA3oECAoQAQ#v=onepage&q=complexity%20theory%20and%20ambiguous%20bounding&f=false

Chow, Y.J., Davids, K., Hammond, J., & Renshaw, I. (2010). A constraints-led perspective to understanding skill acquisition and game play: a basis for integration of motor learning theory and physical education praxis. Physical education and sport pedagogy,15(2), 117-137. doi:10.1080/17408980902791586

Coker, C. A. (2017). Motor learning and control for practitioners. Retrieved from https://ebookcentral-proquest-com.ezproxy.lib.monash.edu.au

Davids, K. (2015). Ecological dynamics in analysis of performance in team sport [Video file]. Retrieved from https://www.youtube.com/watch?v=K17791XqNqU

Davids, K., Renshaw, I., & Savelsbergh, G. J. P. (Eds.). (2010). Motor learning in practise: a constraints-led approach. Retrieved from https://ebookcentral-proquest-com.ezproxy.lib.monash.edu.au/lib/monash/detail.action?docID=496290#