Dynamical Systems Theory (DST) & Nikolai Bernstein

Nikolai Bernstein’s work laid the foundation for the application of dynamical systems theory (DST). DST is a theoretical framework used to understand complex systems, such as human movement. It highlights the idea that movement emerges from the interactions between the constraints within a system, including the body, the environment, and the task.

One of Bernstein’s key insights was the recognition of the “degrees of freedom problem” in motor control. This refers to the abundance of ways the human body can organize its movement to achieve a goal or complete a task. Bernstein proposed the concept of “synergies” to address the degrees of freedom problem. These are coordinated patterns of the respiratory, postural and locomotion synergies that work together to produce a specific movement. Instead of controlling each muscle or joint individually, the nervous system organizes them into functional solutions.

Also, Bernstein emphasized the need for variability in practice. He argued that true motor learning doesn’t occur through mindless repetition of the exact same movement, but rather through a variety of experiences that challenge the system to adapt and improve its coordination.

Bernstein’s approach recognizes that motor learning is context-dependent. The optimal movement pattern for a specific task will depend on constraints like the environment, the equipment used, and the intent of the movement.

Bernstein believed that motor learning involves a process of refining and optimizing movement patterns. Through practice and feedback, individuals gradually learn to search and discover the most efficient and effective movement solutions for a given task.

In the context of athletic development, Bernstein’s ideas have significant implications. Coaches and athletes can apply principles originated from DST to design training programs that emphasize variability, adaptability, and contextual learning. DST views the human movement system as a complex, adaptive system, influenced by various internal and external constraints. It highlights the nonlinear and unpredictable nature of motor learning and control.
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