Dynamic Systems Theory is a theoretical framework that is used to understand and predict self-organizing phenomena in complex systems that are constantly changing, reorganizing, and progressing over time (Connell, 2017). This theory is applicable to both psychology and learned behaviors, specifically movement patterns. The concept of self-organization is crucial when coaching and understanding movement because we are all unique and there is no one specific way to move.
This DTS framework is used to understand and predict self-organizing phenomena in complex systems that are constantly changing, reorganizing, and progressing over time (Connell, 2017). In terms of how we learn to move, efficient and skilled movers are able to produce the same end result for a given task in a slightly different way each repetition (Gray, 2021). A study was conducted by Soviet neurophysiologist, Nikolai Bernstein, on blacksmiths that showed the path of the hammer with each strike. Bernstein pioneered quantitative analysis in human kinetics through the use of cyclography to track the specific movement patterns of the hammersmiths. What Bernstein discovered was experienced blacksmiths were able to hit the same spot of the chisel each repetition despite using a slightly different movement pattern each time. In conjunction with DTS, it was shown that multiple repetitions all produced the same end result despite the hammer taking slightly different paths each strike. This study is contrary to what most people believe in terms of teaching and programming movement. Many people believe there is one specific movement pattern, this study helps display that this is not true.
DTS also states that several attempts at the same task always lead to different patterns of performance including kinematics, kinetics, and patterns of muscle activation (Spina, 2020). Bernstein described the phenomenon within consecutive attempts at solving a given motor task as “repetition without repetition”. Skilled movers are able to repeatedly solve a given motor task and produce the same result each time despite each repetition being unique and different in its execution.
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