Dynamical Systems Theory views movement as an emergent property arising from complex interactions. It emphasizes self-organization, attractor states, and the role of variability in motor control. This approach offers a fresh perspective on how we learn and adapt our movements.

Understanding constraints is key in this theory. Organismic, environmental, and task constraints shape our motor behavior. By manipulating these constraints, we can explore different movement solutions and develop more efficient and adaptable motor skills.

Dynamical Systems Theory for Motor Learning

Principles of Dynamical Systems Theory in Motor Control

• Views movement as an emergent property arising from complex interactions between the individual, environment, and task
• Movement patterns are self-organized and understood as stable attractor states in a complex, nonlinear system
  • Attractor states represent preferred or stable movement patterns that the system tends to gravitate towards
  • Examples of attractor states include walking and running gaits, or the coordination patterns in bimanual tasks (in-phase and anti-phase)
• Control parameters, such as movement speed or joint angles, can lead to transitions between different coordinative states or movement patterns
  • Changes in control parameters can cause the system to shift from one attractor state to another
  • For example, increasing walking speed can lead to a transition from a walking gait to a running gait
• Variability in movement is an essential feature of the system, allowing for flexibility and adaptability in response to changing constraints
  • Variability enables the system to explore different movement solutions and adapt to perturbations or changes in the environment
  • Example: Variability in joint angles during walking allows for adjustments to uneven terrain or obstacles
• Learning is viewed as a process of exploring the perceptual-motor workspace and discovering new, stable movement patterns through the interaction of constraints
  • Learners actively explore the range of possible movement solutions within the constraints imposed by their body, the environment, and the task
  • Through this exploration, learners discover and refine stable, efficient movement patterns that meet the task demands

Self-Organization and Emergence of Coordinated Movement

• Self-organization refers to the spontaneous formation of ordered, stable patterns from complex interactions within a system
  • Ordered patterns emerge without the need for a central controller or explicit programming
  • Examples of self-organization in nature include the formation of snowflakes, flocking behavior in birds, or the synchronization of firefly flashing
• In motor control, self-organization occurs through the interplay of the neuromusculoskeletal system, environment, and task demands
  • The complex interactions between muscles, joints, sensory feedback, and environmental factors give rise to coordinated movement patterns
  • For example, the rhythmic coordination of limbs during walking or swimming emerges from the self-organization of the neuromuscular system
• Synergies, or functional groupings of muscles and joints, arise through self-organization to produce efficient and coordinated movements
  • Synergies reduce the dimensionality of the motor control problem by organizing multiple degrees of freedom into functional units
  • Examples of synergies include the co-activation of leg muscles during jumping or the coordination of arm and hand muscles during reaching and grasping
• "Order parameters" are collective variables that capture the essential dynamics of the system and govern the emergence of specific movement patterns
  • Order parameters describe the overall state of the system and can be used to predict or control its behavior
  • Examples of order parameters in motor control include relative phase in bimanual coordination or the center of mass trajectory in postural control

Constraints Shaping Motor Behavior

Types of Constraints

• Organismic constraints refer to the individual's physical and psychological characteristics
  • Body size, strength, flexibility, motivation, and attention are examples of organismic constraints
  • These constraints shape the individual's "intrinsic dynamics" or preferred movement patterns
  • For example, an individual's height and limb lengths influence their natural walking stride length and frequency
• Environmental constraints include factors external to the individual
  • Gravity, surface properties, and the presence of obstacles or other individuals are examples of environmental constraints
  • These constraints can facilitate or hinder specific movement patterns and influence the perception-action coupling
  • For example, walking on a slippery surface requires different movement strategies compared to walking on a stable surface
• Task constraints encompass the goals, rules, and equipment specific to a particular motor task
  • Task constraints define the "task space" or the range of possible movement solutions that can successfully achieve the goal
  • Examples of task constraints include the target size and distance in a reaching task, or the rules and equipment in a sport

Interaction of Constraints and Motor Learning

• The interaction of organismic, environmental, and task constraints determines the "perceptual-motor workspace"
  • The perceptual-motor workspace represents the range of possible movement solutions available to the individual within the given constraints
  • Learners explore this workspace to discover and refine effective movement patterns
• Manipulating constraints can be used as a tool for shaping motor learning and facilitating the acquisition of new movement skills
  • Modifying task constraints, such as increasing or decreasing the size of a target, can challenge learners to adapt their movements and promote learning
  • Altering environmental constraints, such as practicing in different surface conditions, can enhance the transfer of skills to novel contexts
  • Tailoring practice to individual organismic constraints, such as adjusting equipment size or providing specific feedback, can optimize learning for each learner