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Fundamental Concepts of Motor Control: Lecture Notes (HLTH2013)

Acknowledgement and Course Basics

  • The University of Notre Dame Australia acknowledges the traditional owners and custodians of the land where campuses sit:
    • Fremantle Campus on Wadjuk Country
    • Broome Campus on Yawuru Country
    • Sydney Campus on Cadigal Country
  • Course: HLTH2013 Motor Control, Development & Learning, Lecture 1: Fundamental Concepts of Motor Control
  • Semester: 2, 2025
  • Instructor: Dr. Khaya Morris-Binelli
    • Email: khaya.morris-binelli@nd.edu.au
    • Background: Bachelor of Psychology (Hons); PhD in Expertise and Skill Learning; Lecturer in Exercise and Sports Science
  • Contact expectations: ~2 business day turnaround for emails; appointment via email

Administration and Housekeeping

  • Labs:
    • On campus; weekly; begin Week 1
    • Location: ND28/101 (some labs at Drill Hall or other venues)
    • Lab slides/worksheets uploaded to Blackboard
    • Print and bring lab sheets; have access during class (laptops essential)
  • Communications and etiquette:
    • Be on time for labs; phone and email etiquette
    • Labs require lab sheets/slides and participation
    • Medical certificate needed if missing a lab
    • No use of artificial intelligence (e.g., ChatGPT) for assessments

Course Structure and Schedule (Overview)

  • Week 1 (21 July): Course Introduction; Fundamental Concepts of Motor Control
  • Week 2 (28 July) & Week 3/4 (4 & 11 Aug): Neuromotor Control – Central influences; Sensory input (Touch, Proprioception, Vision); Action Preparation; Psychological Refractory Period
    • Readings: Magill & Anderson; Ch. 1, 2, 4, 6, 7 (pg. 146-152); Ch. 8
  • Week 4 (11 Aug): Attention, Memory, Forgetting
  • Week 5 (18 Aug): Motor Control Theories (Ch. 9 & 10); Dual Task and Skill Automaticity (Ch. 5)
  • Week 6 (25 Aug): Defining and Assessing Learning; Principles of Practice; Skill Classification and Stage of Learning
  • Week 7 (1 Sept): Mid-Semester Exam
  • Week 8 (8 Sept): Feedback and Learning; Learner Abilities, Individual Differences, Talent Identification
  • Week 9 (15 Sept): Learning to Juggle; Practice Schedules; Part-Whole Practice; Blocked and Random Practice; Skill Learning and Transfer
  • Non-Teaching Week (week of 22 Sept)
  • Week 10 (29 Sept) & Week 11 (6 Oct): Theories of Motor Development; Infancy and Early Childhood Development (Online); Can We Change a Child’s Motor Development? Early motor stimulation; Child rearing; Early swim programs
  • Week 12 (13 Oct): Skill Hierarchy: Reflexes to Skilled Actions; Movement Reinvestment
  • Week 13 (20 Oct): Review; Study Week (week of 27 Oct); Exams (weeks of 3 and 10 Nov)
  • Non-teaching and administrative notes are inserted at specified weeks

Assessments (Outline and Weights)

  • Week 3, Friday (Aug 11): Lab Content Quiz – 0%
    • Purpose: Pre-census formative feedback; 1-2, 4 (AWST) time windows
  • Mid-Semester Exam: 25%
    • Content: Weeks 1–5 readings and lectures; multiple-choice; 2-hour duration; conducted during lecture time under standard exam conditions
    • Policy: If not seated, zero grade unless written special consideration with documentation
    • Group element: Perceptual-Motor Skill Learning Presentation; pre-recorded; students teach a sport skill to a peer; include background, skill learning cues, movement progression, and environment design for perceptual-motor learning outcomes
  • Final Exam: 40%
    • Content: All material from weeks 6–13; no external resources allowed
  • Overall course assessment structure: Fully graded; weight distribution adds to 100%
  • Note: The mid-semester exam includes a standalone written component; the learning presentation contributes to the overall 25%

Textbook and Readings

  • Textbook: Magill, R. A., & Anderson, D. (2021). Motor learning and control: Concepts and applications (12th ed.). McGraw-Hill
    • E-book available via library; 2017 edition available in library reserves
  • Weekly Readings: Access via Leganto and the course LMS; key chapters include:
    • Week 1: Chapters 1 & 2
    • Week 2: Chapters 4, 6, & 7 (pp. 146-152)
    • Week 3: Chapter 8
  • Lectures and labs align with Magill & Anderson chapters (Ch. 1–18 as listed across weeks)

Lecture Objectives (Summary)

  • 1) Introduction to the field
  • 2) Skill classification
    • One-dimensional classification
    • Multi-dimensional (Gentile’s taxonomy)
  • 3) Measuring motor performance
    • Outcome measures
    • Process (production) measures
  • Readings and lectures cover both foundational and applied aspects; emphasis on linking theory to practice

Core Concepts: Motor Behaviour, Control, Learning, and Development

  • Motor Behaviour: the study of movement and processes underlying motor performance
    • Three interrelated components: Motor Control, Motor Learning, Motor Development
  • Definitions:
    • Motor skill: voluntary and coordinated body, head, and/or limb movement to achieve a desired goal
    • Motor Control: how movements are controlled; neuromuscular system function; producing coordinated movement in learning or performance of new or well-learned skills
    • Motor Learning: acquiring and refining skilled movements through practice and related variables; relearning after injury or disease
    • Motor Development: changes in learning and control of movements across the lifespan (infancy to old age)
  • Relevance: these areas inform professional practice in movement contexts (sport science, health, physical education); improvements in teaching and learning environments; understanding development informs skill learning and re-learning
  • Broader aim: explain how motor control relates to development level and learning, and how to optimize interventions and training

Why Study Motor Behaviour? Key Motivations

  • Develop methods to maximize performance in motor tasks (e.g., sport, rehabilitation)
  • Improve understanding of human physiology and motor behavior
  • Enable practitioners to:
    • Identify performance problems
    • Develop and evaluate intervention strategies and training programs
    • Design new interventions and assess effectiveness
  • Performance spectrum: from poor control (e.g., neurological dysfunction) to exceptional control (elite performers)

Performance Spectrum and Constraints (Newell’s Model)

  • Performance spectrum examples: neurological dysfunction (e.g., cerebral palsy, stroke, ageing) to elite performers (athletes, surgeons)
  • Newell’s constraints model: movement emerges from the interaction of three constraints
    • The individual/learner
    • The task (skill demands)
    • The environment (external context)
  • The learner generates movement to meet task demands within a given environment

Classification of Motor Skills: One-Dimensional and Beyond

  • Defining Skills, Actions, Movement, and Abilities:
    • Skills: goal-directed tasks or activities
    • Actions: to kick, strike, throw, etc.
    • Movement: motor techniques/behavioral characteristics of limb movements
    • Abilities: stable, enduring traits that influence performance
  • Why classify skills?
    • Understand the nature of a skill
    • Guide learning and instruction across categories
    • Identify similarities across seemingly different skills
    • Determine what makes a skill difficult/complex and how to simplify or extend it
  • Classification systems:
    • One-dimensional: single continuum; easy to understand
    • Multi-dimensional: Gentile’s Taxonomy (more detailed; considers performance demands)

One-Dimensional Classification (Overview)

  • Based on a single characteristic; placed on a continuum
  • Examples of continua used:
    • Size of primary musculature (Gross vs Fine)
    • Beginning vs end points (Continuous vs Discrete vs Serial)
    • Environmental cue stability (Open vs Closed)

One-Dimensional Details

  • Size of primary musculature / precision of movement
    • Gross motor skills: use large muscles; less precise (e.g., walking, jumping)
    • Fine motor skills: control small muscles; high precision (e.g., knitting, tying shoes, typing, shooting)
  • Organization of skill / timing of actions
    • Serial: series of discrete skills combined (e.g., piano piece, gymnastics routine, writing a paper)
    • Discrete: defined beginning and end points; brief duration (< 1 s)
    • Continuous: arbitrary beginning and end; may last minutes; usually rhythmic (e.g., walking, running)
  • Stability of environmental context
    • Closed: stationary, self-paced; performer controls when to begin (e.g., penalty kick in soccer, walking in a quiet room)
    • Open: environment or cues in motion; external pacing (e.g., catching a pitched ball, walking on a treadmill with others)

Open vs Closed Skills: Continuum and Examples

  • Open skills: performance is influenced by dynamic environment; externally paced
  • Closed skills: stable environment; internally/self-paced
  • Practical exercise: classify real-world activities as open or closed
  • Illustration examples: bowling vs archery; netball vs windsurfing (not identical across tasks; some skills blend between open/closed)
  • Visual/clinical applications: use examples (e.g., putting golf balls at varied locations vs from a fixed tee) to illustrate open vs closed placement

Gentile’s Taxonomy of Motor Skills (Multi-Dimensional)

  • Purpose: organize and group skills by performance demands; more detailed than one-dimensional systems
  • Two main axes plus inter-trial variability:
    • Action Function (body orientation & object manipulation)
    • Environment / Cues (stability of environment; regulatory conditions; variability across trials)
  • Components:
    • Body orientation: stationary vs transport
    • Object manipulation: no object vs object manipulation
    • Regulatory conditions: stationary vs in-motion; variability/no variability vs variability across trials
    • Inter-trial variability: whether performance requirements change across attempts
  • The taxonomy is designed to help therapists, coaches, and teachers design practice progressions and assess skill complexity
  • Practical illustrations (from application slides):
    • Examples across combinations of body orientation, object manipulation, regulatory conditions, and variability (e.g., push-ups, standing on escalator, golf putting, high jump, etc.)
    • Progressive practice scenarios: from stationary/no variability to in-motion/variability
  • Key takeaway: Gentile’s taxonomy provides a framework to analyze and design motor skill practice tailored to the specific demands of a skill

Gentile’s Taxonomy – Environment/Cues Details

  • 1. Stability of environment cues
    • Stationary vs in-motion regulatory conditions
    • Features of the environment: surfaces, objects, people
    • Regulatory conditions specify movements required for a skill
    • Examples:
    • Stationary environment: walk on an empty sidewalk; hit a ball off a tee
    • In-motion environment: step onto escalator; hit a pitched ball; teammate moves into position for a pass
  • 2. Inter-trial variability
    • Whether cues are constant across attempts or vary between trials
    • Examples:
    • No inter-trial variability: walking in an uncluttered hallway
    • Inter-trial variability: golf shots during a round; set shots from varying distances/angles

Gentile’s Taxonomy – Action Function Details

  • 3. Action Function: body orientation and object manipulation
    • Body stationary vs body transport (moving)
    • Object manipulation: holding or manipulating an object
  • Example matrix (stylized):
    • Stationary body, stationary regulatory conditions, no variability
    • Standing on different surfaces (variable surfaces) with stationary regulation/variability
    • In-motion, stationary regulation/variability (e.g., standing on escalator; walking on treadmill at different speeds)
    • Body transport with object manipulation (e.g., walking with a ball, throwing while moving)
  • Practical applications include a list of everyday and sport tasks mapped to the taxonomy (e.g., push-ups, golf putting, high jump, standing on escalator, tennis serves, surfing)

Applying Gentile’s Taxonomy: Practice Progressions

  • Use Gentile’s framework to design practice progressions that gradually increase skill complexity
  • Examples of progression ideas:
    • Stationary/no variability: basic stance and target hitting (e.g., teeing off in golf)
    • Stationary/variability in environment: practice with different distances/angles to target
    • In-motion/no variability: moving a target or platform but consistent speed
    • In-motion/variability: live pitching/variable speeds, varied pitches
  • Goal of progressions: increase skill level, address performance weaknesses, chart individual progress toward more complex performance

Part B: Measurement of Motor Performance

  • Rationale: measuring motor performance is essential to understanding motor learning
  • Why measure?
    • Performance assessment/evaluation
    • Inferring whether learning has occurred
    • Identifying areas for improvement
    • Supporting motor learning and control research

Types of Motor Skill Performance Measurements

  • Two general categories (reaction time is covered in a separate lecture): 1) Performance outcome measures
    • Quantitative indicators of the result (speed, distance, frequency, accuracy, consistency)
    • Limitations: do not reveal the quality of the underlying movement or muscle activity
      2) Performance process/production measures
    • Describe the processes leading to the outcome
    • Examples: EMG, EEG, movement analysis, muscle activation, nervous system measures; movement observation
  • Reaction Time: mentioned as a possible measure but covered in a separate lecture

Error Measures for Target Accuracy

  • Absolute Error (AE):
    • Definition: AE = | actual performance - criterion |
    • Purpose: magnitude of error; general index of accuracy
    • Formula: AE = ig| ext{actual} - ext{criterion} ig|
  • Constant Error (CE):
    • Definition: CE = actual - criterion (signed)
    • Purpose: indicates bias and direction of error (overshoot/undershoot)
    • Formula: CE = ext{actual} - ext{criterion}
  • Variable Error (VE):
    • Definition: VE is the standard deviation of the CE scores across trials
    • Purpose: index of performance variability/consistency
    • Formula (conceptual): VE = ext{SD}(CE)
  • Example: comparing two bowlers with similar AE but different CE and VE can reveal biases or inconsistencies in performance

Spatial and Multidimensional Error Assessment

  • When two-dimensional accuracy is required (e.g., golf putting):
    • Radial error (RE): general 2D accuracy measure
    • RE =
      \sqrt{(xt - xp)^2 + (yt - yp)^2}
    • CE and VE provide biased/consistent tendencies; qualitative assessments are sometimes useful
  • Example visualization: golfers A and B with different patterns of bias and variability in 2D space; RE provides overall distance, while CE/VE reveal directional bias and consistency

Kinematic Measures: Describing Motion

  • Kinematics: description of motion without regard to forces
  • Measures include:
    • Displacement: spatial position of a limb/joint over time
    • Velocity: v = \frac{\Delta x}{\Delta t}
    • Acceleration: a = \frac{\Delta v}{\Delta t}
    • Angular measures (for joints): angular displacement, angular velocity, angular acceleration
  • Practical interpretation: track how movement patterns change over time during skill acquisition

Kinetics: Forces Causing Motion

  • Kinetics: study of forces causing motion
  • Internal and external forces influence movement; force-time profiles illustrate preparation, push-off, ground reaction forces, and take-off
  • Example: a dancer’s ground reaction force during jump illustrates phases A–F in a kinetic sequence (from preparation to take-off to landing)

Electromyography (EMG) and Brain Activity Measures

  • EMG: electrical activity of muscles; used to determine when a muscle activates during a movement
  • Brain activity measures in motor learning/research:
    • EEG: measures electrical activity in the brain; different bands indicate different states (Beta, Alpha, Theta, Delta)
    • PET: measures brain blood flow to infer active regions
    • fMRI: measures blood-oxygenation-level-dependent signals; high-resolution images of brain activity

Inter-Joint Coordination and Observational Methods

  • Inter-joint coordination: quantify relationships between limb segments during movement
    • Use angle-angle diagrams; cross-correlation and relative phase analysis to assess coordination
  • Practical observation: teachers/coaches can use video or real-time observation with checklists or rubrics when full instrumentation is not feasible

Practical Notes and Implications for Practice

  • Measurement practicality: some measures are time-consuming or expensive; use practical process measures like video observation and checklists when feasible
  • Strategy for instructors: combine outcome and process measures to gain a fuller picture of motor performance and learning progress

Review and Study Tools

  • Review questions cover:
    • Skill classifications (one- and multi-dimensional) with examples
    • Types of performance errors (AE, CE, VE) and what they reveal
    • Performance production measures (e.g., displacement, velocity, acceleration, EMG, EEG, PET, fMRI)
    • Distinctions between outcome and process measures
  • Text references: Magill & Anderson; chapters corresponding to topics discussed in Weeks 1–13

Quick Reference Formulas and Concepts (LaTeX)

  • Velocity: v = rac{\Delta x}{\Delta t}
  • Acceleration: a = \frac{\Delta v}{\Delta t}
  • Rate of Force Development (RFD): RFD = \frac{dF}{dt}
  • Absolute Error: AE = \left| \text{actual} - \text{criterion} \right|
  • Constant Error: CE = \text{actual} - \text{criterion}
  • Variable Error: VE = \sigma(CE)
  • Radial Error (2D accuracy): RE = \sqrt{(xt - xp)^2 + (yt - yp)^2}$$
  • Movement time-based representations and graphs (refer to teaching visuals on displacement/velocity/acceleration over time)

Final Remarks

  • The content integrates theoretical foundations with practical assessment and teaching strategies
  • Gentile’s taxonomy provides a robust framework for designing progression in motor skill learning
  • Measurement of motor performance combines quantitative outcomes with qualitative process observations to guide instruction and rehabilitation
  • The course emphasizes ethical considerations (e.g., caution with AI for assessments) and professional applicability to sport science, health, and physical education