HLTH2013 Motor Control, Development and Learning — Lecture 5: Motor Control Theories

Overview

• The course covers motor control, development, and learning with a focus on theories that explain how we learn, control, and coordinate movement, and how movements adapt to different situations and environments.
• Assessment context (not part of theory): Mid-Semester Exam (Week 7), 40 MCQs, 50 minutes, 25% of course grade; content from Magill & Anderson (2021) weeks 1–5. Weeks 1–5 cover foundational concepts, neuromotor control, action preparation, attention & memory, and motor control theories.

Key Objectives and Concepts from Lecture

• Coordination: patterning of movements to be smooth and matched to the environment.
• Degrees of Freedom (df): many possible ways to perform a skill; the challenge is to control and coordinate multiple joints, muscles, and motor units.
• Open-loop vs. Closed-loop Control of Movement: two fundamental control modes used to plan and execute movement.
• Theoretical Explanations of Motor Control:
  • Cognitive theories (top-down, hierarchical control).
  • Dynamical Systems Theory (DST) (movement emerges from interactions among organism, task, and environment).
• Theories are not just descriptive; they guide practice, therapy actions, exercise prescription, coaching, and teaching by revealing underlying neurophysiology and learning constraints.

The Two Major Approaches to Motor Control

• Cognitive Theories (top-down, hierarchical):
  • A command centre issues actions, plans, and carries commands to execute an action.
• Dynamical Systems Theory (DST):
  • Movement emerges or self-organises from dynamic interaction among the individual, task, and environment.
• Both approaches aim to predict and improve motor skill performance, but they stress different aspects of control and neurophysiological organization.

Cognitive Control Theories: GMP, Schema, and Open/Closed-Loop

• Central idea: movement is controlled by cognitive processes with a mental representation of actions.
• Motor Program Theories (MPT):
  • The movement plan contains all commands for the muscles to carry out a skill.
  • Classic view is hierarchical: higher levels generate motor programs that flow to lower levels to execute.
  • Example: writing uses a stored motor program; practice builds a repertoire that can be retrieved and executed.
• Generalised Motor Program (GMP) and Schema Theory (Schmidt):
  • GMP stores a pattern of movement that can be adapted to different contexts; it represents a class of similar actions rather than a single specific movement.
  • Schema Theory provides rules to modify GMP parameters to produce novel or varied movements.
  • Together, GMP and schema explain how learners adapt known movement patterns to new situations.
• Invariant features of GMP (the components that remain constant across variations):
  • 1) Sequence of movements (order in which components are produced).
  • 2) Relative time (rhythm of the skill; each component takes a similar proportion of total time).
  • 3) Relative force (the contribution from components remains proportionally consistent).
• Parameters of GMP (movable features that can be adjusted without changing the invariant pattern):
  • Overall time/duration (speedup or slow down).
  • Overall force / movement size (increase or decrease in force).
  • Movement direction and the limbs/muscles used.
• How GMP is modified (Schema):
  • The learner stores a GMP as a class of movements and uses a schema to adjust the GMP for different situations.
  • Invariant features stay the same; parameters are adjusted to fit task demands.
• Examples illustrating GMP and Schema:
  • Writing: when you write your name, you use the same sequence of letters (invariant features) but can vary hand used, grip, speed, and force (parameters).
  • Overweight implements in sports (heavier shots, discs, javelins; heavier bats) change parameters but rely on the same underlying GMP pattern.
  • Distinctions between running, walking, and jogging can share the same GMP class but differ in parameters.
• How a GMP is modified: invariant features remain constant while parameters adapt to task demands and context.
• Practical applications: GMP-based instructions emphasize developing stable motor programs and appropriate schemas to adapt to different tasks and contexts. Experience, practice conditions, and transfer of learning influence GMP and schema development.

The Generalised Motor Program (GMP) and Schema Theory Details

• GMP as a memory-based mechanism for adaptive and flexible movement control (Schmidt, 1975).
• Schema: a set of rules that learners use to modify GMP parameters based on past practice and experience.
• Components of GMP (invariant features):
  • Sequence: order of movement components.
  • Relative time: timing of each component relative to total duration.
  • Relative force: proportion of force contributed by each component.
• Components of GMP that can be modified (parameters):
  • Overall time/duration.
  • Movement direction.
  • Limbs or muscles used.
• How a new skill variation is learned: maintain invariant features while manipulating parameters (time, direction, force, limb use) according to the situation.
• Writing example (full-name on paper):
  • Invariant features: sequence of letters (order of letters) remains the same.
  • Parameters: hand used, duration, and force (e.g., writing with dominant/non-dominant hand, in-mouth grip, different sizes, or pressure).
• Sport example: use of overweight equipment alters parameters (duration, force, limb involvement) but not the GMP’s invariant sequence.
• Schema development: practice and experience build flexible rules to adapt GMP for different contexts; improved recall and recognition support adaptation to varying task constraints (e.g., distance to goal, required force).
• Practical takeaways for teaching and coaching: emphasize development of effective GMPs and robust schemas; provide varied practice conditions to promote adaptability and transfer of learning.

Dynamical Systems Theory (DST)

• Core proposition: movement is not controlled by a central motor program; instead, patterns self-organise from the interaction of multiple constraints.
• Constraints are boundaries that shape movement outcomes and can be:
  • Organismic (individual): body structure, motivation, cognitive level, skill, perceptual abilities.
  • Environmental: gravity, temperature, wind, surface, light, etc.
  • Task: rules, goals, equipment, and the nature of the skill (open vs closed).
• Movement patterns emerge as the system self-organises under changing constraints (self-organisation).
• Learning is non-linear: small changes in one sub-system can trigger phase shifts to new movement patterns.
• Constraints-led approach (Newell, 1996; Araújo et al., 2004): movement emerges from the interaction of task-perception-environment with organism constraints.
• Practical implications for coaching and teaching: embrace variation and functional variation to enhance performance; adapt training to reflect real-world task and environmental constraints.
• Perception-action coupling: skillful performance depends on coupling perceptual information with action; detection of invariant environmental information guides the movement that follows.
• Attractors and non-linear changes:
  • Attractors are preferred stable movement patterns the system tends toward.
  • Changes in task or environment can push the system from one attractor state to another (non-linear transition).
  • Control parameters (direction, speed, force, perceptual information) move the system toward different attractor states.
• Example: relearning a gait after injury; increased leg strength as a control parameter may shift gait to a new attractor state through practice under varied constraints.
• Perception-action coupling examples: cricket batting vs bowling-machine task constraints show different initiation timings and step lengths due to perceptual information differences.
• Constraints-led approach in practice: use small games and modified tasks to direct learner discovery; game sense vs traditional isolated skill drills (see Table 8.3).

Open-Loop vs Closed-Loop Control Systems

• Open-loop control:
  • Movement is initiated with pre-planned instructions; execution proceeds without reliance on ongoing feedback.
  • Fast movements with minimal attentional demand.
  • Accurate only if the initial command is correct; once started, the action is difficult to alter.
  • Examples: kicking a football, throwing a dart, clapping hands, high-five.
• Closed-loop control:
  • Feedback is used during movement to detect and correct errors; ongoing adjustments are made based on sensory information (vision, proprioception).
  • Typically longer-duration movements requiring accuracy (balance, precise trajectory).
  • Examples: slalom skiing, walking on a rope, cross-country skiing, driving a car, staying in a lane.
• Combined usage: motor behavior often blends open- and closed-loop processes depending on task demands (e.g., bouncing a tennis ball before serving: initial throw is open-loop; catch and bounce decisions involve closed-loop adjustments).
• Practical implication: during early learning, performers rely more on closed-loop processing; with practice and skill automatization, performance shifts toward open-loop control to allow faster execution.

Speed-Accuracy Trade-Off

• In many sports, increasing movement speed reduces accuracy because feedback-based corrections become less feasible within rapid actions.
• Faster movements rely more on pre-planned commands (open-loop), reducing capacity to adjust using feedback.
• Slower movements allow more time for feedback-based corrections (closed-loop), increasing accuracy.
• This trade-off explains why athletes must balance speed and precision depending on task goals and environmental constraints.

The Role of Degrees of Freedom in Motor Control

• Degrees of Freedom problem (Bernstein, 1967): the body has many redundant components, creating a combinatorial control problem.
• Early learning: performers may freeze some df to simplify control; with practice, they progressively release (free) df to achieve more refined, efficient, and coordinated movement.
• Coordination refers to the patterning of body and limb motions that are smooth and aligned with environmental demands; expertise correlates with higher levels of coordinated control.
• Theories must explain how we coordinate limbs, head, and trunk to produce context-appropriate movements.

Practical Applications and Tabled Concepts

• Key concepts summarized (Spittle, 2017; Table 7.1):
  • Motor equivalence: the same task can be achieved with different movements (flexibility of movement).
  • Uniqueness: movements can be performed with different patterns yet achieve the same outcome.
  • Modifiability: movement can be altered to match environmental demands.
  • Serial order: the elements of a skill are produced in a reliable sequence.
  • Coordination: temporal organization and interaction among body parts to produce a functional action.
• Environmental, organismic, and task constraints (Table 8.1): examples across height, gravity, weight, temperature, surface properties, wind, motivation, confidence, perceptual skills, and game rules.
• Constraint-led approach in practice (Table 8.3): learner-directed discovery learning with game-like activities vs traditional instructor-directed drills; emphasis on perception-action coupling and variable task constraints to facilitate learning and transfer.
• Perception-action coupling examples: tethering perception with action—how environmental information influences the timing and control of movements.

Application to Practice and Training Design

• When teaching or coaching, consider three constraint categories for skill acquisition:
  • Organismic constraints: e.g., strength, flexibility, motivation, perceptual skills.
  • Environmental constraints: surface, wind, lighting, weather, temperature.
  • Task constraints: rules, goals, equipment (e.g., ball shape, court size, number of players).
• Use a constraints-led approach to design practice that fosters functional variation and adaptability, promoting robust skill transfer to real-world contexts.
• Embrace variation as a positive driver of learning; design tasks that reflect the variability encountered in actual performance settings.
• Progressive shift from closed-loop to open-loop control with practice as skills become more automatized; ensure initial learning includes feedback and instruction before promoting autonomous execution.
• Practical examples from the lecture:
  • Gait retraining after injury: leg strength changes act as control parameters driving a phase shift to a new attractor gait.
  • Batting/stroke performance: perception of ball flight and environmental cues informs when and how to initiate movement; task constraints shape timing and direction.
  • Writing task (GMP example): invariant sequence of letters remains constant while motor parameters (hand, duration, force) vary.

Summary of Key Takeaways

• GMP and Schema Theory (cognitive control) propose a central, memory-based mechanism for movement planning with invariant features and adjustable parameters; learning involves developing robust GMPs and adaptable schemas through practice and varied contexts.
• Dynamical Systems Theory posits that movement emerges from the interaction of organismic, environmental, and task constraints; self-organization leads to attractor states, with learning manifested as non-linear transitions between attractors driven by control parameters and perceptual information.
• Open-Loop and Closed-Loop control describe two ends of a spectrum for movement execution; most real-world skills involve a mix of both, depending on speed, accuracy, and task demands.
• Perception-Action Coupling highlights the continuous loop between environmental information and action; skilled performance depends on effectively coupling perception with the appropriate motor response.
• Degrees of Freedom and Coordination explain how complex motor systems manage many possible movements to produce smooth, goal-directed actions; practice tends to release degrees of freedom to optimize coordination.
• Constraint-Led Approaches and Game Sense models advocate learner-centered, discovery-based learning using varied task constraints to promote adaptability and transfer; traditional isolated drills may be less effective for skill transfer.
• In practice, understanding these theories helps practitioners design better learning environments, select appropriate feedback, and tailor interventions to individual needs and real-world contexts.

Review Questions (from Lecture)

1. What is the degrees of freedom problem as it relates to the study of human motor control and learning?
2. What are the key differences between open- and closed-loop control systems?
3. Compare the key differences of Cognitive Control Theories (GMP, Schema) and Dynamical Systems Theory of motor control.