Motor Control Theory – ESPS 3002 Lecture 1

Overview of the Session

  • First content lecture for ESPS 3002 – Human Motor Learning & Performance.

  • Presenter: Dr. Rachel Ward (unit convener; delivers lectures + practical classes).

  • Primary focus: Motor Control Theory – what theories are, why they matter, and an introduction to the two dominant theoretical families.

  • Lecture aligns with Unit Learning Outcome 4: “Explain the common theoretical models used to explain motor control, motor learning & skill acquisition.”

Scientific Theory in the Research Process

  • Definition: A scientific theory unifies many related observations into a single coherent framework and is repeatedly confirmed by experiment/observation.

    • Must describe a large number of observations with only a few core propositions.

    • Must predict future outcomes/events.

  • Theory vs. Law:

    • Theory ⇒ explains why something happens (causal mechanism).

    • Law ⇒ describes what happens (empirical regularity).

    • Example used later: Fitts’ Law (descriptive) vs. theoretical accounts that explain why the speed–accuracy trade-off emerges.

Why Motor Control Theory Matters to Exercise & Sport Science Practitioners

  • Clarifies why humans behave/move the way they do, how skills are learned, and how control is exerted.

  • Functions for practitioners:

    • Identify performance problems.

    • Design interventions to solve those problems.

    • Predict effectiveness of chosen interventions.

    • Systematically enhance performance capabilities.

    • Invent new motor-skill strategies.

    • Evaluate effectiveness → close the practitioner “feedback loop.”

  • Ultimately guides evidence-based coaching, rehab, PE, and skill-acquisition programs.

Two Core Behavioural Questions Motor Control Theories Must Answer

  1. Coordination

    • Definition: “Patterning of head, body, and limb movements relative to environmental objects & events.”

    • Two nested relationships:

      • Inter-segmental: arrangement of body parts relative to each other at a given time.

      • Task/Environment coupling: movement of the performer relative to objects, surfaces, opponents, implements, etc.

    • Theory must account for how such patterns emerge and are regulated across contexts.

  2. Degrees of Freedom (DoF) Problem

    • Coined by Nikolai Bernstein.

    • System contains many independent components (≈ 600 muscles, ≈ 360 joints, multi-level neural elements).

    • DoF for a single joint = number of independent movement planes it permits (e.g., shoulder ≈ 3, finger IP joint ≈ 1).

    • Core challenge: How does the CNS reduce or exploit these DoF to achieve a stable, goal-directed movement?

    • “Solution” = constraining or coordinatively structuring DoF so that effective, efficient movement emerges.

Control System Typology: Open-Loop vs. Closed-Loop

Open-Loop Control
  • One-way information flow: Control Center → Movement Effectors; no feedback utilized during the execution.

  • Ideal for:

    • Very fast, discrete, ballistic skills (e.g., golf drive, throw, kick).

    • Situations requiring minimal attentional load once initiated.

  • Limitations:

    • Poor for unpractised or unpredictable environments.

    • Accuracy suffers when the performer is not highly trained (no online corrections possible).

Closed-Loop Control
  • Circular flow: Control Center → Effectors → Feedback → Control Center … (loop continuously updates command).

  • Advantages:

    • Suitable for unpractised, slow, or precision tasks (e.g., tracing a line, balancing, aiming archery shot).

    • Allows ongoing error‐correction; higher ultimate accuracy.

  • Disadvantages:

    • Greater attentional demand; slower overall due to processing time of feedback.

  • Continuum Concept: Skills sit between the poles depending on speed requirement & necessity for online sensory feedback.

Comparative Table (Advantages/Disadvantages)
  • Summarised in lecture slide; key points captured above.

Speed–Accuracy Trade-Off & Fitts’ Law

  • Everyday observation: increasing speed usually ↓ accuracy and vice-versa.

  • Formalised by Paul Fitts (1954). Classic reciprocal-tapping task:

    • Two targets separated by distance A (amplitude) with width W.

    • Index of Difficulty: ID = \log_2!\left(\dfrac{2A}{W}\right).

    • Movement Time: MT = a + b \times ID (constants a & b empirically fitted).

  • Practical tie-in: Lab class will replicate this tapping paradigm; students observe open- vs. closed-loop tendencies as target conditions change.

Two Major 20ᵗʰ/21ˢᵗ-Century Motor Control Theories

1. Motor Program-Based (Hierarchical) Theory
  • Assumes pre-structured commands (motor programs) stored in CNS.

  • Hierarchical organisation: higher centers issue generalized programs, lower centers handle execution specifics.

  • Cognitive, top-down orientation.

  • Strengths:

    • Explains rapid movements where feedback is too slow.

    • Accounts for invariance across different effectors (e.g., writing signature with hand or foot).

  • Challenges:

    • Storage problem (infinite programs?).

    • Novelty problem (how generate never-before-executed movement?).

2. Dynamical Systems (Ecological / Self-Organising) Theory
  • Movement patterns emerge from real-time interaction among:

    1. Individual (intrinsic dynamics, morphology, fatigue state…)

    2. Task (goal, rules, implements…)

    3. Environment (surface, temperature, obstacles, social context…)

  • No single executive; control is distributed and emergent.

  • Key constructs: attractors, phase transitions, stability, self-organisation.

  • Strengths:

    • Naturally solves DoF via self-organisation.

    • Explains sudden “aha!” transitions in skill acquisition.

  • Challenges:

    • Hard to specify neural implementation explicitly.

    • Predictive precision sometimes lower without quantitative models.

Ethical, Philosophical & Practical Implications

  • Understanding control models guides safe & effective coaching (avoid overload, unnecessary constraints).

  • Shapes rehab protocols: e.g., closed-loop emphasised early post-injury for safe precision, then transition to open-loop speed work.

  • Affects human–machine interface design (wearables, exoskeletons, VR rehab systems): must respect speed–accuracy trade-offs and DoF constraints.

  • Highlights ethical duty to provide evidence-based interventions rather than intuition-only coaching.

Connections to Previous/Foundation Units

  • Functional Anatomy: joint DoF count, muscle lines of action underpin movement possibilities.

  • Neuroscience / Motor Physiology: motor unit recruitment & feedback pathways underpin theoretical constructs.

  • Biomechanics: kinematic chains & constraints directly relate to coordination patterns.

Example Scenarios (Elaborated from Lecture)

  • Throwing a javelin (high-speed, ballistic): predominantly open-loop; training builds internal motor program.

  • Archery aiming: closed-loop utilisation of visual & proprioceptive feedback for micro-corrections.

  • Novice vs. expert typist: novice relies on closed-loop (looking at keys), expert shifts to open-loop feed-forward control.

Lecture-End Summary (Consolidated)

  • Motor control theories explain why and how people produce skilled movement.

  • Two essential behavioural constructs: coordination & degrees of freedom problem.

  • Two foundational control systems: open-loop (no feedback) vs. closed-loop (feedback-based).

  • Fitts’ Law quantifies speed–accuracy trade-off; illustrates role of feedback demands.

  • Two dominant theoretical families:

    1. Motor Program-Based (hierarchical, top-down).

    2. Dynamical Systems (self-organising, interaction-driven).

  • Mastery of these ideas enables practitioners to diagnose, intervene, predict, and evaluate motor-skill performance.


Next steps: Future lectures will deep-dive into motor program hierarchy (e.g., GMP, invariant features, parameters) and dynamical principles (attractors, order parameters, control parameters). Practical classes will experimentally test Fitts’ Law and explore open- vs. closed-loop dominance in selected tasks.