THE SPECIFICITY AND GENERALIZABILITY OF MOTOR LEARNING 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



  

 

 

 

THE SPECIFICITY AND/OR GENERALIZABILITY OF MOTOR LEARNING: 

A SCOPING REVIEW, A CHECKLIST, AND A FRAMEWORK FORWARD 

 

By 

Claire Marie Tuckey, BPhEd, MSc 

 

 

A Thesis Submitted to the School of Graduate Studies in Partial Fulfilment of the 

Requirements for the Degree Doctor of Philosophy 

 

 

 

 

 

McMaster University © Copyright by Claire M. Tuckey, March 2024 

 



 ii 

McMaster University DOCTOR OF PHILOSOPHY (2024) 

Hamilton, Ontario (Kinesiology) 

 

TITLE: The Specificity and/or Generalizability of Motor Learning: A Scoping Review, a 

Checklist, and a Framework Forward 

 

AUTHOR: Claire Marie Tuckey, BPhEd, MSc  

 

SUPERVISOR: Jim Lyons, PhD 

 

NUMBER OF PAGES: xxiv, 283 

 

 

 

 

 

 

 

 

 

 

 

 



 iii 

LAY ABSTRACT 

 Our previous movement experiences can impact our capability to learn new motor 

tasks. These previous movement experiences can be either beneficial or detrimental (or 

have no effect) on our learning of that task depending on many different things with no 

real definitive answers to why the outcomes differ and when. The purpose of this thesis is 

to review how prior motor skill practice may be beneficial to future motor skill learning 

(generalizability), detrimental to learning, or no effect (specificity) and to organize these 

findings into a new ‘types of transfer’ taxonomy, create a framework to help guide future 

motor learning research and conduct an experiment that follows this framework. By 

considering and organizing this large motor learning literature into a review, creating this 

taxonomy and outlining an empirical investigative framework, this thesis will help us to 

better understand motor learning history and provide a pathway forward for future 

researchers.  



 iv 

ABSTRACT 

Humans are constantly faced with learning motor tasks throughout their lifespan 

(e.g., children learning how to throw a ball overhand, elite athletes learning how to 

become more even more efficient at their sports performance, and an older adult 

relearning how to walk post-stroke recovery). With such variety in the types of motor 

tasks that humans try to learn across the lifespan, little is known about the impact of a 

learner’s previous motor skill experience. Thus, the purpose of this thesis was to 

investigate when motor learning generalizability or specificity are more likely to occur, 

respectively. An in-depth background of motor learning generalizability and specificity 

was provided in chapter one. The scope of the motor learning literature including 

generalizability and/or specificity was investigated in chapter two. At the end of chapter 

two, certain limitations of the motor learning literature are addressed and framed into a 

useable checklist for future motor learning experiments. Chapter three serves as a 

bridging chapter to connect the scoping review and checklist in chapter two, to the 

framework implemented in chapter four. In chapter four, the checklist was employed to 

assess its usefulness in future motor learning experiments. Collectively, this thesis 

provides organization to the previous motor learning generalizability and specificity 

literature, as well as recommendations for future motor learning researchers based on a 

tested framework protocol.  



 v 

ACKNOWLEDGEMENTS 

I would like to start by thanking my supervisor, Dr. Jim Lyons. I will never forget 

our first-time meeting was at my very first conference as a master’s student and you 

asked my supervisor at the time, Dr. Jae Patterson, if he had any prospective PhD 

students, and he said, “probably Claire”. I defended my MSc at Brock in December of 

2017, began my PhD journey at McMaster in January of 2018, and the rest is history. I 

want to thank you for the potential that you saw in me, and continue to see in me; this has 

always been clear. I have learned a lot from you, one of my favourites is from our lab 

motto “quaerere intelligere non ludicare”, translating from Latin to mean “seek to 

understand, not judge”. This is a motto that I will continue to keep in mind, and it is 

applicable beyond research. I am grateful for your dedication as my supervisor and for 

your support moving forward into my teaching career.  

Next, I would like to thank my supervisory committee members Dr. Michael 

Carter, Dr. Lawrence Grierson, and Dr. Lori Ann Vallis. I appreciate all of your feedback 

and contributions to this work. This work was made better thanks to your contributions 

and your expertise. I appreciate your time and efforts, and it is an honour to have your 

input on my dissertation.  

To my lab mates over the years Jess C., Jess S., Stevie, Noah, Jackie, Anthony, 

Jim B., Kristen, colleagues/friends/family Michelle, Sheereen, Laura, Jem, Giulia, Lara, 

Matt, Jess M., Justine, thank you for all of your support because it truly ‘takes a village’.  

To my mom and dad, I always strive to make you proud. Dad, growing up, you 

always said “stay in school”, but I think we can both agree this is enough. Momma, thank 



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you for your exceptional patience and understanding you always know how to cheer me 

up when I need it. Thank you both for all of your support, you’re the best. 

Thank you to my partner, Steve, your unconditional love and sense of humour 

have been more support throughout this degree than I can express. I am so lucky to have 

your support through the highs and lows of PhD life, including agreeing to adopt our 

adorable emotional support kitty, Stanley.  



 vii 

TABLE OF CONTENTS 

LAY ABSTRACT iii 

ABSTRACT iv 

ACKNOWLEDGEMENTS v 

TABLE OF CONTENTS vii 

LIST OF FIGURES xv 

LIST OF TABLES xviii 

LIST OF ABBREVIATIONS xviii 

GLOSSARY xxi 

DECLARATION OF ACADEMIC ACHIEVEMENT xxiv 

 

CHAPTER 1 - GENERAL INTRODUCTION TO THE HISTORICAL 

PERSPECTIVES OF MOTOR LEARNING 

1.1 General Introduction 1 

1.2 Defining Motor Learning 3 

1.2.1 Motor Adaptation vs. Motor Development vs. Motor Learning 3 

1.3 Classifications of Movements, Actions, and Skills 5 

1.3.1 Fine vs. Gross Motor Skills 5 

1.3.2 Continuous vs. Discrete vs. Serial Motor Skills 5 

1.3.3 Open vs. Closed Environments 7 

1.3.3.1 Open vs. Closed Motor Skills 7 



 viii 

1.4 The Performance-Learning Distinction 8 

1.4.1 The Power Law of Practice 9 

1.5 Distinctions Between Types of Feedback 11 

1.6 Requirements of Motor Learning 12 

1.7 Neural Correlates of Motor Learning 14 

1.8 Theoretical Perspectives on Motor Learning 18 

1.8.1 Information Processing Perspective 18 

1.8.1.1 Transfer Appropriate Processing 20 

1.8.1.2 Models from an Information Processing Perspective 23 

1.8.2 Dynamical Systems Perspective 30 

1.8.2.1 Ecological and Systems Models of Motor Learning 32 

1.9 Conditions of Practice 34 

1.9.1 Deliberate Practice 34 

1.9.2 Massed vs. Distributed Practice 35 

1.9.3 Blocked vs. Random Practice 36 

1.9.4 Part vs. Whole Practice 40 

1.10 The Generalizability and/or Specificity of Motor Learning 41 

1.10.1 Generalizability of Motor Learning 41 

1.10.1.1 Theoretical Perspectives in Generalizability of Motor Learning 43 

1.10.1.2 Pros of Generalizability 48 

1.10.1.3 Cons of Generalizability 49 



 ix 

1.10.2 The Specificity of Practice Hypothesis 49 

1.10.2.1 Theoretical Perspectives in Specificity of Motor Learning 50 

1.10.2.2 Pros of Specificity 61 

1.10.2.3 Cons of Specificity 61 

 

CHAPTER 2: A SCOPING REVIEW AND TAXONOMY OF THE 

SPECIFICITY OR GENERALIZABILITY OF LEARNING A MOTOR TASK 

2.1 Abstract 63 

2.2 Introduction 64 

2.2.1 Definitions of Terminology 64 

2.2.2 Generalizability of Motor Learning 66 

2.2.3 Specificity of Motor Learning 67 

2.2.4 The Gap in the Literature 68 

2.3 Research Questions 68 

2.4 Methodology 69 

2.4.1 Study Design 69 

2.4.2 Search Strategy 70 

2.4.3 Inclusion/Exclusion Criteria 70 

2.4.4 Sources of Evidence Selection 71 

2.4.5 Data Extraction 72 



 x 

2.5 Results 72 

2.5.1 Objective 1: Survey of Generalizability and Specificity 72 

2.5.2 Objective 2: Categorize into Positive Transfer/ Negative Transfer/ Neutral 

Transfer/ Mixed Results 74 

2.5.3 Objective 3: Taxonomy 74 

2.5.4 Objective 4: Any Commonalities 84 

2.5.4.1 Target/Task (N = 22) 85 

2.5.4.2 Conditions of Practice (N = 20) 87 

2.5.4.3 Expertise (N = 20) 89 

2.5.4.4 Feedback Modality (N = 20) 90 

2.5.4.5 Anthropometrical (N = 18) 92 

2.5.4.6 Equipment (N = 11) 94 

2.5.4.7 Ecological Validity (N = 7) 96 

2.5.4.8 Attention (N = 7) 96 

2.5.4.9 State (N = 6) 98 

2.5.4.10 Virtual Environment (N = 4) 99 

2.6 Discussion 101 

2.6.1 Objective 1: Survey of Generalizability and Specificity 101 

2.6.2 Objective 2: Categorization into Positive Transfer/ Negative Transfer/ 

Neutral transfer/ Mixed Results 101 

2.6.3 Objective 3: Tuckey’s Ten Transfer Taxonomy 103 

2.6.4 Objective 4: Any Commonalities 103 

2.7 Conclusions 104 



 xi 

2.7.1 Limitations in the motor learning literature 106 

2.7.1.1 Duration of Retention Interval 106 

2.7.1.2 Format of Transfer Test Protocol 107 

2.7.1.3 Development of Independent and Dependent Variables 108 

2.7.2 Checklist for transfer test studies 109 

2.8 Funding 110 

2.9 Appendix A: Reporting Items for Scoping Reviews (PRISMA-ScR) 

Checklist 111 

2.10 Appendix B: Full Title and Abstract Search Strategy for Medline as an 

Example Database (03/20/2020) 114 

2.11 Appendix C: Positive Transfer 115 

2.12 Appendix D: Neutral Transfer 132 

2.13 Appendix E: Negative Transfer 143 

2.14 Appendix F: Mixed Results 150 

 

CHAPTER 3: BRIDGING CHAPTER 

3.1 Where we are now 162 

3.2 What we can do moving forward 162 

3.3 Connecting Chapter 2 and Chapter 4 166 



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CHAPTER 4: THE SPECIFICITY OF LEARNING A MOTOR TASK UNDER 

PERIPHERAL FATIGUE: AN INVESTIGATIVE FRAMEWORK 

4.1 Abstract 167 

4.2 Introduction 168 

4.2.1 Preamble 168 

4.2.2 Background 169 

4.2.3 Positive Transfer 170 

4.2.4 Neutral Transfer 171 

4.2.5 Negative Transfer 171 

4.2.6 State Transfer Taxonomy: Neutral Transfer 173 

4.2.7 State Transfer Taxonomy: Negative Transfer 176 

4.2.8 State Transfer Taxonomy: Mixed Results 178 

4.3 Method 180 

4.3.1 Participants 180 

4.3.2 Procedure 181 

4.3.2.1 Physical Fatigue Group 182 

4.3.2.1.1 MVCs (Maximal Voluntary Contractions) 183 

4.1.1.1.1 60% MVC Physical Fatigue Maintenance 185 

4.1.1.1.2 Subjective Perceptions of Physical Fatigue 187 

4.3.2.2 No-Physical Fatigue Group 188 

4.3.2.3 Motor Task 188 

4.3.3 Instrumentation 189 



 xiii 

4.3.4 Procedure Day One 190 

4.3.4.1 Acquisition 190 

4.3.4.2 Immediate Retention 192 

4.3.5 Procedure Day Two 193 

4.3.5.1 Delayed Retention 193 

4.3.5.2 Transfer 193 

4.3.6 Dependent Variables 194 

4.3.7 Statistical Analyses 195 

4.4 Results 196 

4.4.1 Movement Time (in milliseconds) Across all blocks (not collapsed) 196 

4.4.1.1 Collapsed blocks (EA, IR, DR, T) Movement Time (in milliseconds)199 

4.4.2 RMSE (Root Mean Square Error) Across all blocks (not collapsed) 200 

4.4.2.1 RMSE (Root Mean Square Error) Collapsed blocks (EA, IR, DR, T) 202 

4.4.3 Correlation 204 

4.5 Discussion 211 

4.5.1 Movement Time (MT) 212 

4.5.2 Root mean square error 215 

4.5.3 Correlation 218 

4.5.4 Limitations 219 

4.6 Conclusions 220 

4.6.1 Investigative Framework 221 



 xiv 

4.7 Appendix G – Participant recruitment poster 224 

4.8 Appendix H – Participant criteria 225 

4.9 Appendix I – Letter of information and consent 226 

4.10 Appendix J – Pocock alpha spending function calculation 230 

4.11 Appendix K – Participant set up instructions 231 

4.12 Appendix L – Participant reimbursement form 232 

 

CHAPTER 5: GENERAL DISCUSSION 

5.1 Summary of research aims 233 

5.1.1 Summary from Chapter Two (Scoping Review) 233 

5.1.3 Summary from Chapter Four (Protocol) 235 

5.2 Application 235 

5.3 Limitations and future directions 238 

5.4 Conclusions 240 

 



 xv 

LIST OF FIGURES 

Figure 1.1: Figure demonstrating the power law of practice (figure 

adapted from McLaughlin et al., 2010). 

 

10 

Figure 1.2: Difference between exponential and logarithmic function 

curves (figure adapted from Jones, 2023). 

 

11 

Figure 1.3: Illustrative representation of the locations of brain areas 

involved in performing a new motor task compared to 

performing a well-learned motor task (figure adapted from 

Dahms et al., 2020). 

 

17 

Figure 2.1: Flow Diagram PRISMA-ScR 

Haddaway, N. R., Page, M. J., Pritchard, C. C., & 

McGuinness, L. A. (2022). PRISMA2020: An R package 

and Shiny app for producing PRISMA 2020-compliant 

flow diagrams, with interactivity for optimized digital 

transparency and Open Synthesis Campbell Systematic 

Reviews, 18, e1230.  

 

73 

Figure 2.2: Amount of generalizability or specificity included in this 

scoping review represented by four possible outcomes 

including positive transfer, neutral transfer, negative 

transfer, or mixed results. 

 

74 

Figure 2.3: Tuckey’s Ten Transfer Taxonomies 

 

78 

Figure 2.4: Tuckey’s Ten Transfer Taxonomies including the visual 

representation of how Virtual Environment Transfer is a 

subset of Ecological Validity Transfer, but remains its 

own category. 

 

83 

Figure 2.5: Number of articles (n = 135) represented as a percentage 

in each taxonomy 

 

84 

Figure 2.6: Pie charts to represent each taxonomy containing the 

proportion of studies that represent positive change 

85 



 xvi 

(green, “+”), neutral transfer (yellow, “0”), negative 

change (red, “-”), and mixed results (purple, “x”). 

 

Figure 2.7: Motor Learning Transfer Test Study Checklist 

 

109 

Figure 4.1: Results from the only state transfer study with ‘negative 

transfer’ results (adapted from Movahedi et al., 2007) 

 

177 

Figure 4.2: Physical fatigue levels indicated by percentage (%) of 

change from baseline to end of day maximal voluntary 

contractions (MVCs) 

 

184 

Figure 4.3: Average hold (in seconds) for the Physical Fatigue group 

and the No-Physical Fatigue group as a function of time 

 

186 

Figure 4.4: Subjective physical fatigue as a function of group and 

time 

 

187 

Figure 4.5: Representation of the motor task using the mouse 

trackpad. The participants’ hand were to be in a 

‘handshake’ position, and they were instructed to use the 

pad of their fifth digit to draw on the trackpad using 

primarily wrist flexion and extension movements 

 

189 

Figure 4.6: Sinusoidal waveform images, presented in random order,  

that participants would see on the screen in front of them 

 

192 

Figure 4.7: Movement time in milliseconds as a function of group 

 

197 

Figure 4.8: Movement time in milliseconds as a function of block 

 

197 

Figure 4.9: Movement time in milliseconds as a function of group and 

all block 

 

198 

Figure 4.10: Movement time in milliseconds as a function of collapsed 

blocks (test type)  

 

199 



 xvii 

Figure 4.11: Movement time in milliseconds as a function of group and 

collapsed blocks (test type) 

 

199 

Figure 4.12: Root mean square error in pixels as a function of group 

 

200 

Figure 4.13: Root mean square error in pixels as a function of block  

 

201 

Figure 4.14: Resultant root mean square error in pixels as a function of 

group and block  

 

201 

Figure 4.15: Root mean square error in pixels as a function of blocks 

collapsed (test type) 

 

202 

Figure 4.16: Root mean square error in pixels as a function of group 

and blocks collapsed (test type) 

 

203 

Figure 4.17: Pearson r correlation between movement time in 

milliseconds and root mean square error in pixels during 

acquisition for the physical fatigue group 

 

204 

Figure 4.18: Pearson r correlation between movement time in 

milliseconds and root mean square in pixels error during 

acquisition for the no-physical fatigue group 

 

205 

Figure 4.19: Pearson r correlation between movement time in 

milliseconds and root mean square error in pixels during 

immediate retention for the physical fatigue group 

 

206 

Figure 4.20: Pearson r correlation between movement time in 

milliseconds and root mean square error in pixels during 

immediate retention for the no-physical fatigue group 

 

207 

Figure 4.21: Pearson r correlation between movement time in 

milliseconds and root mean square error in pixels during 

delayed retention for the physical fatigue group 

 

208 



 xviii 

Figure 4.22: Pearson r correlation between movement time in 

milliseconds and root mean square error in pixels during 

delayed retention for the no-physical fatigue group 

 

208 

Figure 4.23: Pearson r correlation between movement time in 

milliseconds and root mean square in pixels error during 

transfer for the physical fatigue group 

 

209 

Figure 4.24: Pearson r correlation between movement time in 

milliseconds and root mean square in pixels error during 

transfer for the no-physical fatigue group 

 

210 

 

LIST OF TABLES 

Table 2.1: Initial 12 categories created the basis of the scoping 

review taxonomies that were later paired down to only 10 

categories 

75-76 

Table 2.2: Combination of transfer test outcome (positive transfer, 

neutral transfer, negative transfer and mixed results) with 

the number of studies in each taxonomy category 

100 

 

LIST OF ABBREVIATIONS 

VR Virtual reality 

KP Knowledge of performance 

KR Knowledge of results 

TOTE Test-operate-test-exit 

CINAHL Cumulated index to nursing and allied health literature 

QCRI Qatar Computing Research Institute 

PRISMA Preferred reporting items for systematic review and meta-analyses 



 xix 

NSERC Natural Sciences and Engineering Research Council of Canada 

EV Ecological validity 

CP Conditions of practice 

AN Anthropometrical 

EQ Equipment 

EX Expertise 

FB Feedback modality 

MI Attention 

TT Target/Task 

ST State 

VE Virtual environment 

MT Movement time 

AE Absolute error 

CE Constant error 

VE Variable error 

RMSE Root mean square error 

RT Reaction time 

COM Center of mass 

EMG Electromyography 

CI Contextual interference  

FLS Fundamentals of Laparoscopic Surgery 

VBLaST Virtual Basic Laparoscopic Skill Trainer 



 xx 

PD Parkinson’s disease 

Ex. 1 Experiment one 

Ex. 2 Experiment two 

CSR Choice stimulus-response 

ACE Absolute constant error 

ABSE Absolute error of relative phase 

BDNF Brain-derived neurotrophic factor 

M Mean 

SD Standard deviation 

SEM Standard error of the mean 

MREB McMaster Research Ethics Board 

MVC Maximum voluntary contraction 

COVID-19 Coronavirus disease 2019 

ANOVA Analysis of variance  

EA Early acquisition 

IR Immediate retention 

DR Delayed retention 

T Transfer 

HSD Honestly significant difference 

ms milliseconds 



 xxi 

 

GLOSSARY 

Term Operational Definition 

Acquisition 

The initial or early-stage practice or performance of a new or novel 

motor skill. May also refer to the practice of a new type of 

movement control for a previously learned motor skill. 

Retention 

The preservation of a movement skill after a period of rest where 

no overt practice of the skill takes place. 

Transfer 

The attempt to apply a learned skill in a new task or context. At this 

time, there are no parameters to neither which elements of the skill 

are changed, nor the magnitude to which the skill is changed. 

Transfer can include attempting an entirely new task, on the 

premise that previous experience on another task will be applied. 

The term ‘transfer’ does not indicate the success level of the 

application to the new task.   

Generalizability 

The ability to apply what has been learned in one context to other 

contexts with motor performance success. This refers to a positive 

gain from a previous motor task to the transfer of a new motor task.  

Specificity of Practice 

A principle that rationalizes how some motor skills are very 

specific and uncorrelated with one another and leaves the learner 

with motor skills that are not generalizable. This refers to a negative 



 xxii 

decrement from a previous motor task to the transfer of a new 

motor task.  

Motor Learning 

Changes in an organism’s movements that reflect changes in the 

structure and function of the nervous system. This is a process that 

demonstrates a relatively permanent change in the ability to execute 

a motor skill as a result of practice or experience.  

Motor Adaptation 

The process of acquiring and restoring movement patterns through 

an error-driven learning process. 

Motor Development 

The change in motor behaviour over the life span, and the 

sequential, continuous, age-related process of change. 

Ecological Perspective 

In an attempt to generalize a motor skill, an ecological perspective 

is not limited to ‘real world’ transfer tasks. An ecologically valid 

transfer task replicates an environment, setting, or conditions to 

better make inferences towards its generalizability. 

Affordances 

As described by (Gibson, 1979), affordances define objects as a 

fact of the environment as well as a fact of behaviour. Sensorimotor 

capabilities of the individual constrain the kind of information that 

is accessed regarding an object and the meanings associated with it. 

Virtual Reality (VR) 

An artificially created environment typically consisting of 

computer-generated information made available to the human 

sensory systems (e.g., visual, auditory, tactile, etc.)  that appear to 



 xxiii 

be “real”. The effect is to make the user experience sensory 

immersion in their surroundings.   

 

 



 xxiv 

DECLARATION OF ACADEMIC ACHIEVEMENT  

 

I, Claire Tuckey, declare this thesis to be my own work. I am the sole author of this document. No 

part of this work has been published or submitted for publication for a degree at another institution.  

 

To the best of my knowledge, this work does not infringe on anyone’s copyright. 

 

My supervisor, Dr. Jim Lyons, and the members of my supervisory committee, Dr. 

Michael Carter, Dr. Lawrence Grierson, and Dr. Lori Ann Vallis, have provided feedback 

and guidance at all stages of this thesis. My secondary readers provided feedback on titles 

and abstracts per scoping review procedures, and McMaster Health Sciences librarians 

provided support through the creation of the scoping review search trains across search 

engines. I completed all of the research work.



Ph.D. Thesis – C. Tuckey; McMaster University - Kinesiology 

 

 1 

CHAPTER 1: GENERAL INTRODUCTION TO THE HISTORICAL PERSPECTIVES 

OF MOTOR LEARNING 

1.1 General Introduction 

Learning is fundamental to humans at every developmental level. The first 

evidence of learning as a concept dates back to Ancient Greece and the perspectives of 

the early philosophers. At this time, how knowledge comes to humans was divided into 

two general ideas: rationalism and empiricism. Rationalism suggests that knowledge 

comes from an innate place and may happen without external stimuli. Conversely, 

empiricism relates to knowledge coming from experience and does not exist without 

external stimuli. Learning has been researched for hundreds of years, yet its definitions 

are still evolving. The definition of learning has evolved and one of the more recent 

definitions for learning is “the process by which relatively stable modification in 

stimulus-response relations is developed as a consequence of functional environmental 

interaction via the senses” (Lachman, 1997, p. 477). This definition distinguishes 

learning from other phenomena such as sensory adaptation, and the effects of maturation. 

To fully understand motor skill learning, it is important to acknowledge its connection to 

the broader field of learning. Within this broader field of learning, motor skill learning 

isolates any learning wherein goal-directed, usually observable, movements or actions are 

performed by the motor system. While much is known and understood about motor skill 

learning, and how new skills can be acquired and retained, there are still gaps in the 

literature relating to broader concepts of which motor skills are generalizable, and those 

which motor skills may be specific to the context in which they have been practiced. One 



Ph.D. Thesis – C. Tuckey; McMaster University - Kinesiology 

 

 2 

of the greatest challenges with understanding motor learning is the breadth of the topic 

itself, making the creation of new definitions and new ways to organize the literature 

difficult. This thesis, therefore, provides an extensive examination of the generalizability 

and specificity of motor skill learning. In this research, I intend to determine the extent to 

which motor skill learning can be generalizable or specific and to formalize an 

organizational solution to the breadth of research included in this literature. This thesis is 

composed of five themed chapters. The first chapter provided an overview of motor 

learning and the key concepts and theories that will be used in subsequent chapters. 

Chapter two will assess the scope of all motor learning literature with motor skill 

generalizability or specificity with an attempt to find commonalities between them as 

well as presenting a taxonomy to organize the motor learning generalizability and 

specificity literature, and a checklist to aid in overcoming common methodological 

limitations identified in the motor learning literature. Chapter three bridges the main 

outcomes of chapter two and explains how they will be implemented into an investigative 

framework in chapter four. Chapter four builds on the results and discussions of the prior 

three chapters by reporting a protocol, developed as a proof of concept, for an experiment 

to demonstrate the most salient form of specificity of motor learning. Chapter five 

focuses on a discussion of the main points of this thesis relating to motor skill 

generalizability, motor skill specificity, a taxonomy to use in future motor skill learning 

research, and the protocol design presented to prove a concept for salient specificity of 

learning results.  



Ph.D. Thesis – C. Tuckey; McMaster University - Kinesiology 

 

 3 

1.2 Defining Motor Learning 

1.2.1 Motor Adaptation vs. Motor Development vs. Motor Learning 

Motor adaptation, motor development, and motor learning share a commonality; 

changes in the way individuals move over time (Newell et al., 2001). While the focus of 

this thesis is on motor learning, this term is interrelated with motor adaptation and motor 

development thus requiring some clarification as to how these definitions coincide and 

how they differ. From a human evolutionary perspective, motor adaptation exists as a 

means of survival and ecological fitness of the sensorimotor system (Babič et al., 2016).  

Motor adaptation refers to the trial-and-error process of adjusting movements to 

new demands and involves predictive calibrations associated with new task demands 

(Bastian, 2008). Adaptations are made to help minimize movement ‘costs’ associated 

with energy demands, fatigue, and movement inefficiencies (Bastian, 2008). Repeated 

adaptations can lead to learning a new motor calibration (Bastian, 2008). 

Motor development is a term used to describe the changes in motor behaviours 

that occur throughout a human life span. Motor development reflects humans’ 

interactions among the maturing organism, the environment, and the task (Newell, 1986). 

One influential explanation of this developmental process is Newell’s model of 

constraints (1986) which describes an evolving three-way interaction of systems among 

the individual, the environment and the task, which results in the movements of which 

humans are capable at any given point across the lifespan. Furthermore, each of these 

factors is characterized as a ‘constraint’ that can impact movement outcomes. The 

individual system constraints can be further subdivided into functional or structural 



Ph.D. Thesis – C. Tuckey; McMaster University - Kinesiology 

 

 4 

constraints. A functional constraint relates to the behaviour of the individual (e.g., fear, 

motivation, attentional focus). A structural constraint relates to the individual’s 

anatomical structure that changes as people grow or age (e.g., height, weight, strength). 

Environmental system constraints are external to the body and involve the world around 

each person (e.g., temperature, floor surfaces, humidity). Lastly, the task system 

constraints relate to the goals and rules involved in the motor activity (e.g., in basketball, 

it would be faster to run carrying the ball, but the rules state that you must dribble the 

ball). Movement is a product of the constraints constantly interacting and modifying one 

another based on the moment-to-moment demands of the task (Garcia & Garcia, 2006).  

Motor learning, as a general concept, is a complex phenomenon involving both 

motor adaptation and motor development. Motor learning is generally defined as a 

relatively permanent and stable gain in motor skill capability that is associated with 

practice or experience (e.g., Adams, 1964; Fitts, 1964; Newell & Rosenbloom, 1982). 

According to this definition, practice is fundamental for motor learning to occur and, 

without practice, the action would be a cross-sectional display of in-the-moment motor 

performance (see Section 1.2 for a more detailed delineation of the differences between 

motor learning and motor performance). Skill capability refers to the potential of an 

individual, which can be developed with practice, but depends on the presence of a subset 

of abilities (Nagarajan & Prabhu, 2015). Take, for example, the skill of successfully 

flipping a pancake in a frying pan without using a spatula. This requires both ability (e.g., 

the dexterity to grasp the handle of the pan and the forearm strength to hold the frying 

pan) and capability (i.e., the wrist motion to successfully flip the pancake that can be 



Ph.D. Thesis – C. Tuckey; McMaster University - Kinesiology 

 

 5 

learned with practice). Motor learning also involves a set of processes aimed at learning 

and refining new skills through practice (Nieuwboer et al., 2009).  

1.3 Classifications of Movements, Actions, and Skills 

1.3.1 Fine vs. Gross Motor Skills 

In any discussion of motor skill learning, it is important to note that not all motor 

skills, or the movements that provide the foundation for those skills, are “created equal”. 

In other words, the term motor skill as a catch-all phrase implies a misleading 

homogeneity to complex situations. Indeed, how motor skills are learned, and how 

successful the practice conditions are in the acquisition of those skills, can vary greatly 

depending on how the skilled movements are classified. Therefore, how movements, 

actions, and skills are classified is important for the generalizations we can, and cannot, 

make about them. Motor skills can generally be classified on a continuum from fine to 

gross (Davis, 2000). Fine motor skills involve smaller muscles and can be used for more 

precision movements (e.g., writing, grasping), whereas gross motor skills involve skills 

containing larger muscle groups required for movements. Gross motor skills can be 

further categorized into locomotor activities (e.g., walking, running, hopping, skipping), 

non-locomotor activities (e.g., bending, stretching, twisting, turning), and manipulative 

skills (e.g., throwing, kicking, striking, catching).  

1.3.2 Continuous vs. Discrete vs. Serial Motor Skills 

Another way to classify movements, actions, and skills is through how the motor 

task is sequenced. The sequence of a motor skill can fall on a continuum, with one end 



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representing discrete skills that have a clear start and end. These skills can be repeated, 

but the performer would essentially be starting over each time (an example of a discrete 

skill is a vertical jump, Davis, 2000). A serial motor task is in the middle of this 

continuum and is composed of several discrete motor tasks being strung together to 

create an integrated movement (e.g., a triple jump including the hop, skip, and jump 

phases) (Davis, 2000). On the other end of the continuum are continuous skills, with no 

obvious start or endpoints, and could be performed in theory for as long as the individual 

wishes (e.g., walking, running, Davis, 2000). Understanding the classification of a motor 

skill in terms of being continuous, discrete, or serial is important for measuring the motor 

task. A researcher/coach/teacher will need to know how to identify the start and end of a 

movement, from one movement to the next. To simplify the motor task being examined, 

motor learning researchers will often use discrete motor tasks to initially test their 

research question for this reason of clear measurements. Discrete skills are generally a 

rapid task with little time to apply intrinsic feedback corrections during the movement. 

When motor tasks are carried out over a longer period of time as in a more continuous 

task, then feedback can be used throughout the task to monitor and correct movements. 

For example, tracking tasks, such as a rotary pursuit, would be considered a continuous 

task and would be a closed-loop control as it can be monitored via sensory feedback 

(Schmidt & Wrisberg, 2008). These are all important factors in motor learning that 

change the dynamics of not only the duration of the task, and musculature used in the 

task but also the sensory information that is involved. 



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1.3.3 Open vs. Closed Environments 

The ability of an individual to complete a movement depends on the context of 

the environment around them. Closed environments refer to a stationary environmental 

context, and open environments refer to complex and non-regulatory events in the 

external environment. With these binary labels, it is important to note that within a 

“closed” environment context, an individual is still free to execute a variety of 

movements. An open motor environment has a variable and unpredictable setting, where 

the performer cannot evaluate all the environmental demands or fully prepare their motor 

actions in advance. In between predictable (closed) (e.g., typing on a keyboard) and 

unpredictable (open) (e.g., playing whack-a-mole) motor skills, there can be semi-

predictable environments (e.g., playing chess). In the typing on a keyboard example, the 

keys on the keyboard never change, creating a predictable environment for the individual 

typing. In the playing whack-a-mole example, the player cannot plan their next move. 

Their movements that take place depend on the environment.  The chess example is a 

semi-predictable environment where there are a limited number of options available to an 

opponent to move, allowing the player to plan with some movement possibilities.  

1.3.3.1 Open vs. Closed Motor Skills 

Open and closed motor skills differ from open and closed environments. An open 

environment can make a typically closed motor skill more challenging to perform. For 

example, swinging a baseball bat to hit a ball off a tee in a non-competitive nature is a 

closed motor skill in a closed environment. The batter can choose when they swing the 

bat, and there is certainty about the ball's location. The same motor skill can be placed in 



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an open environment in a game scenario with opponents. Now the batter needs to 

strategize where to hit the ball depending on fielder locations and other base runners.  

The context of the environment can change whether the motor skill is open or closed. 

These open and closed motor skills fit on a continuum rather than as a binary. Preparing 

for a ball that is thrown from a pitcher on the opposing team is a more challenging task 

compared to hitting the ball off of a tee in a baseball game, making the former a more 

open environmental task than the latter.  

1.4 The Performance-Learning Distinction 

From a philosophical perspective, it has been stated that “No one has ever 

measured learning or memory. They can be only inferred from behavior.” (Cahill et al., 

2001, p. 578). Motor learning cannot be confirmed from a single quantitative measure, 

but rather behavioural tests that allow for logical inferences based on the comparison of 

prior behaviours. To make any inferences as to whether motor learning has occurred, 

retention and transfer tasks can be used to assess motor skill performance after a period 

and under a common context. Retention tests assess the learner’s performance of the 

same skill from acquisition following a period of time where no overt practice on that 

skill has taken place. The purpose of the retention task is to evaluate the extent to which 

the skill has been retained by the learner. Retention tasks call on the individual to 

reproduce later what they’ve previously acquired. A transfer task, in comparison, is 

designed to test the learner on a new variation of the skill, a different testing situation, or 

context with the intention to test the generalizability of what was acquired during practice 

(Kantak & Winstein, 2012).  



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An important consideration when implementing retention and transfer tasks is the 

timing of when they are executed after the acquisition period. Kantak and Winstein 

(2012) reviewed motor learning studies that have both immediate (from 10 seconds to a 

couple of hours) and delayed (24 hours or more) retention/transfer tests. Observations 

from Kantak and Winstein (2012) suggest that in at least 63% of the studies included in 

their review, motor performance in an immediate retention/transfer test was not a good 

predictor of relatively permanent motor learning. This review by Kantak and Winstein 

(2012) provides evidence in support of the learning-performance distinction and supports 

utilizing a delayed retention/transfer test of at least 24 hours after the acquisition as a 

more reflective measure of the relative permanence of motor learning.  

1.4.1 The Power Law of Practice 

Across many motor tasks, there will be a stereotypical pattern of performance, 

which can be described by a graphed curve, represented by an initially steep motor 

acquisition period, followed by a plateau of performance. Snoddy (1926) was the first to 

formalize the observation that the rate of improvement in the performance of a motor task 

can be characterized by a power function. This “power law of practice” states that: 1) the 

time it takes to perform a task decreases with the number of repetitions of that task, and 

2) the decrease follows the shape of the power law (See Figure 1.1). 

 



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Figure 1.1: Figure demonstrating the power law of practice (figure adapted from 

McLaughlin et al., 2010). 

The power law of practice describes that average performance for a particular task 

is likely to improve logarithmically with the number of practice trials performed 

(Snoddy, 1926). This power function is based on the idea that average learning occurs at 

a rate where information at the start can be acquired quickly, then results may slow with 

what is left to be acquired. Heathcote and colleagues (2000) provide evidence that 

individuals, learn at more of an exponential law of practice. Exponential function curves 

begin with a gentle curve and become steeper, while logarithmic function curves are the 

inverse, starting steep and then levelling off. Nonetheless, practice on a motor task is not 

exactly exponential or even averaged to exactly a power function. The importance of 

these theories is to understand that practice occurs over the course of a curve (i.e., non-

Power Law of Practice



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linear function) and can provide a useful constraint for theories of motor skill acquisition 

(Heathcote et al., 2000) (see Figure 1.2).  

 

Figure 1.2: Difference between exponential and logarithmic function curves (figure 

adapted from Jones, 2023). 

1.5 Distinctions Between Types of Feedback 

Feedback is an important variable in motor learning. Feedback can be further 

separated into intrinsic and extrinsic feedback. The distinction between intrinsic and 

extrinsic feedback is important as it lends to the learner’s experience and the takeaway 

from their performance. Intrinsic feedback is what the learner feels during their motor 

performance (e.g., a gymnast feeling off-balance). Intrinsic feedback comes from the 

learner’s proprioception and somatosensory system. This feedback type can also be 

referred to as inherent, task-intrinsic, and response-produced feedback that is inherently 

available to the learner from sources (e.g., vision, proprioception).  



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Extrinsic feedback is provided by an external source such as a coach or watching 

their performance on a video and can be used in addition to intrinsic feedback. Extrinsic 

feedback can be further classified into knowledge of performance (KP) or knowledge of 

results (KR). KP refers to information about the individual’s movement characteristics 

that resulted in a specific outcome. KR relates to the information regarding the accuracy 

of the individual’s movement relative to the task goal (Schmidt & Young, 1991). An 

example of KP is a gymnast’s coach telling the gymnast to point their toes, and an 

example of KR is the gymnast seeing the judges' scores for their routine. When learning a 

motor skill, it is important to consider the feedback that an individual will be 

experiencing, and how to control feedback experiences. It is also important to use 

appropriate language when describing feedback experiences. Many retention tests in 

motor learning research will claim that there was a removal of feedback, when really 

there was a removal of extrinsic feedback only, as the learner can still experience 

intrinsic feedback through proprioception.  

1.6 Requirements of Motor Learning 

To make any inferences about motor learning, and to differentiate between it and 

motor performance, certain requirements must be met to conclude that motor learning has 

occurred. As motor learning is not directly observable, motor learning can only be 

inferred from recognizable changes in overt motor behaviours. A critical feature of motor 

learning is that changes to a learner’s capabilities relevant to the learned skill are 

relatively permanent such that the learning does not dissipate after practice ceases. Thus, 

experimental settings must be carefully constructed in order to have confidence that the 



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observed changes are a result of motor learning and are not simply temporary 

performance gains. Given that motor learning is not directly observable, it is important to 

understand the most common methods of measuring motor learning processes. In a 

typical motor learning setting, learners will practice a motor task where performance may 

be measured as a function of trials. This results in the performance curve (e.g., 

Dubrowski, 2005). This practice of a motor skill is referred to as motor skill acquisition. 

Conditions and variables can be modified to assess their influence on motor skill 

acquisition and motor learning. As practice alone does not guarantee learning (Newell, 

1991), retention and transfer tasks are used to demonstrate the permanence of motor skill 

acquisition, from which motor learning can then be inferred (Pinder et al., 2011; 

Shewokis, 1997). While a retention test has the potential to demonstrate the presence of 

motor learning, the depth of learning may be shallow if it can only be applied to a hyper-

specific movement. Therefore, a transfer task can be used to assess the relative degree of 

generalizability of learning, or lack thereof, to novel (previously unpracticed) tasks or 

performance environments. Generally, the generalizability of motor learning can be 

thought of as an indication of the flexibility and/or adaptability of the previously acquired 

mechanisms that led to the learning permanence of the original skill. For example, 

repeated free throws in an empty gym from a stationary spot are likely to improve over 

repeated shots (motor skill acquisition). When performance in a subsequent session is 

demonstrated the next day (retention task), and if the performance levels demonstrated 

following acquisition are maintained, motor learning can be inferred to have occurred. 

However, the depth of this learning may be shallow if it is only applied to those specific 



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practice conditions (i.e., empty gym, stationary shot, the location from the net). 

Therefore, a deeper understanding of true learning can be obtained with a transfer task, to 

determine the generalizability of motor learning (e.g., from various angles to the basket, 

in a crowded gym, or during a game). 

1.7 Neural Correlates of Motor Learning 

 Humans have a complex multisensory process that is constantly receiving 

information from each sense (i.e., vision, audition, olfaction, gustation, and tactition). For 

this dissertation, the focus will primarily be on visual and proprioceptive sensory 

information. When a visual stimulus associated with a movement initiation cue is 

presented to the eye, activity in the occipital lobe is seen 100 ms after its presentation, 

with activity 260 ms later seen in the parietal, frontal, and motor regions as secondary 

processes influenced by earlier perception (Pins & Ffytche, 2003). From a movement and 

touch perspective, once humans receive sensory information from the surrounding 

environment, this information travels from the skin and proprioceptors to the spinal cord 

before reaching the brain (Thau et al., 2022). The neural processes responsible for motor 

learning are complex, as during each phase, and depending on the motor task, different 

cortical structures are involved. During the early phases of motor learning, for example, 

where high attentional demands are required, frontal, striatal, and parietal areas are 

activated (Marinelli et al., 2017; Poldrack & Gabrieli, 2001). The frontal lobe is 

responsible for executive functions, thinking, planning, problem-solving, emotions, and 

behavioural control, it also contains the motor cortex responsible for movement, and the 

sensory cortex responsible for sensations. The striatum is responsible for the preparation, 



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initiation, and execution of movements (Báez-Mendoza & Schultz, 2013). The parietal 

area is involved in the understanding of the external environment to help process 

sensations. During the learning of a new motor task, more specifically, the prefrontal 

cortex and striatum (caudate nucleus and anterior putamen) are activated (Jueptner, 1998; 

Nakahara et al., 2001). The motor cortex is divided into the primary motor cortex, the 

premotor cortex, and the supplementary motor area. The primary motor cortex is the 

main contributor to the execution of movement (Bhattacharjee et al., 2021). The premotor 

cortex is responsible for the preparation of a movement, and motor control including 

spatial guidance. The supplementary motor area helps with planning sequences of 

movements and coordination of bimanual movements. A review by Jueptner and Weiller 

(1998) consolidates the results of studies demonstrating the brain areas activated during 

the various stages of motor learning by reducing the results from new motor tasks 

compared to well-trained motor tasks. The brain areas involved in the learning of new 

motor sequences were subtracted from the activation seen in well-trained motor tasks, to 

reveal activations in the striatum, globus pallidus, and cerebellum (Jueptner & Weiller, 

1998). The role of the striatum in performing a new motor task is necessary for voluntary 

motor control (Mendoza & Schultz, 2013). The role of the globus pallidus in a new motor 

task is to control conscious and proprioceptive movements and helps to send information 

to the thalamus. The thalamus is an egg-shaped structure in the centre of the brain that 

relays motor and sensory information from the body to the brain (Sommer, 2003). Novice 

motor skill performance requires effortful cognitive control, and differences in neural 

activity are seen in well-learned motor skills.  



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During well-learned motor sequences, the sensorimotor cortex and posterior 

putamen are activated (Jueptner & Weiller, 1998). Once the motor learning phase 

switches to more automatism, there is optimizing activity of cortical and subcortical 

motor areas and a lesser reliance on the attention-executive networks (Cacciola et al., 

2017; Nakahara et al., 2001). It is this attentive-to-automatic process, and the storage of 

learned procedures to be combined in the formation of new motor skills that permits such 

variety in behavioural repertoires (Hikosaka et al., 1995). Jueptner and Weiller (1998) 

describe that once a motor task becomes automatic, the prefrontal area of the motor 

system is no longer engaged, which allows for the motor system to take over and permits 

the prefrontal cortex to be engaged in another task. The prefrontal area of the motor 

cortex plays a role in cognitive control which includes attention, impulse inhibition, and 

cognitive flexibility There is flexibility depending on our task requirements where the 

prefrontal area (i.e., dorsolateral prefrontal cortex and striatum) are re-engaged when 

participants attend to their performance of an automatic task (Jueptner & Weiller, 1998) 

(See Figure 1.3, adapted from Dahms et al., 2020) 



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Figure 1.3: Illustrative representation of the locations of brain areas involved in 

performing a new motor task compared to performing a well-learned motor task (figure 

adapted from Dahms et al., 2020). 

Thus, from a neurobiological perspective, motor learning can be considered in 

terms of neuroplasticity (i.e., the change in neural firing patterns and strength of neural 

connections in the motor cortex and striatum) that can be directly observed in overt 

changes to movement parameters with time (typically demonstrated by improved 

performance) (Hwang et al., 2022). Hwang and colleagues (2022) trained mice on a 

motor learning task and used cranial window surgery with in vivo imaging 1-2 hours after 

each motor training session to identify the neurons related to their behaviour changes. 

Immunostaining was used to identify which neurons were activated during the motor 

training, and which neurons were activated or reactivated in the training session 1 week 



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later when mice still successfully performed the task. The motor task involved 30 reaches 

for 20 minutes using the preferred paw to grasp a food pellet through a plexiglass box 

with a vertical and bring the pellet to its mouth. This experiment found that motor 

learning recruits engram neurons in the motor cortex that are reactivated during motor 

performance. This motor learning increases dendritic spine density and strengthens the 

outputs to the striatum of primary motor cortex engram neurons (Hwang et al., 2022). 

These results indicate highly specific synaptic plasticity in the formation of long-lasting 

motor learning.  

1.8 Theoretical Perspectives on Motor Learning 

1.8.1 Information Processing Perspective 

In studying motor learning, it is important to understand the various theoretical 

perspectives that are fundamental to the learning of motor tasks. There are two major 

theories for how learning occurs. The first major theory is based on the information 

processing perspective first described in a two-part paper from an engineering 

perspective entitled A Mathematical Theory of Communication (Shannon, 1948; Shannon 

& Weaver, 1949). Within these papers, a mathematical definition of information was 

used to conceptualize the abstract notion of how information is processed, which was 

later extrapolated to an application to humans. This theory proposes that information is 

processed in a two-part fashion wherein each piece of information received, serves to 

reduce remaining uncertainty. This theory suggests that the main goal of information is to 

decrease uncertainty. According to the information processing perspective, people 

receive information from the environment, process it, and then output a movement. For 



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example, a baseball player is up to bat and watches the pitch come toward them. The 

information they are receiving is visual information about the ball which is transformed 

into motor information to swing the bat. Adapted from Shannon (1948) and Shannon and 

Weaver (1949), Fitts and Posner (1967) compares the information processing perspective 

to a computer; where there is an input of signals, processing of information, and output. 

To translate the computer analogy to human movements, the human receives information, 

processes the information, and creates a motor response (Fitts & Posner, 1967). Similar to 

computers, humans can solve problems by linking new information with previously 

stored information. For example, humans receive sensory information about a motor task 

which is converted into neural activity, where sensory memory is created. Sensory 

memory filters out irrelevant information and only sends necessary information to the 

next stage. When this information is attended to, this sensory memory moves into short-

term memory. How much information can be processed into short-term memory depends 

on several factors and can vary from person to person (e.g., cognitive load, amount of 

information being processed, one’s focus, one’s attention, one’s perception of the task's 

importance, etc.). With the practice of the motor task, an internal representation of the 

sensorimotor information can be encoded and retrieved as needed into long-term 

memory. The information processing model by Schmidt and colleagues (2018) divides 

the whole process into distinctive stages. The model begins with the onset of a stimulus, 

where the individual identifies the stimulus (e.g., incoming baseball), selects a response 

(e.g., to swing the bat), the response is programmed (e.g., when there is an incoming 

baseball, initiate bat swing), and there is a response output (e.g., swing the bat to hit the 



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baseball) (Schmidt et al., 2018). This model considers the three stages of stimulus 

identification, response selection, and response programming to be the information 

processing model. The information processing perspective is a main theory into how 

human motor learning and control are a result of a complex interplay between 

information received from the environment around us and use cognitive processes to 

execute a movement. While the representation of how humans process information is 

similar to that of a modern computer, this metaphor has its limitations. Computers are not 

faced with emotions, and motivations like humans are, which has a large impact on 

human motor performance. It may also be naive to consider that each piece of additional 

information brought to the system will always result in a reduction of remaining 

uncertainty. It is possible for additional sensory information to create an increase in 

uncertainty and lead to movement performance decrements.  

1.8.1.1 Transfer Appropriate Processing 

One important aspect of an information processing theoretical framework is that it 

allows for reasonable inferences to be drawn with respect to if, how and the degree to 

which the learning that results in the acquisition of a motor skill can be transferred to a 

new skill or a novel condition. Stated differently, the underlying processes of learning 

that develop during the acquisition of a skill, as intrinsic and extrinsic information is 

processed, may be more or less generalizable (see section 1.10 of this thesis) to new 

situations (or, conversely, may remain specific to the original skill). Specifically, the 

more generalizable the learning is, the more appropriate the information processing is to 

transfer conditions or situations. 



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This concept of transfer-appropriate processing  (e.g., Lee, 1988; Schmidt & 

Bjork, 1992) was initially applied to memory studies where researchers examined the 

amount of overlap between processes engaged from a first study exposure to the 

processes engaged in a second test exposure (Bransford et al., 1979; Morris et al., 1977).  

The theory has since been applied to motor learning and is a common perspective in this 

field of research. In the context of motor learning, transfer-appropriate processing holds 

that motor learning can be optimized when the processing activities in a transfer test are 

similar to the processing activities undertaken during acquisition. Previous experience 

with a similar task will typically be beneficial in this situation. Edwards and Lee (1984) 

examined blocked versus random practice conditions (see section 1.9.3 in this thesis) 

with children and special populations and found positive transfer with their random 

practice interventions. These random practice conditions are a classic example of 

contextual interference (see section 1.9.3 in this thesis) and can better represent the 

unplanned or random elements in activities of daily living in these rehabilitation motor 

learning studies. Similar appropriate transferring is seen with Rajan and colleagues 

(2019), using a transfer of motor learning from one body part, to another in what is 

operationally defined and termed ‘anthropometrical’ transfer later in this thesis (see 

section 2.5.3). Their study examined the whole arm and transferred the motor task to just 

the hand on a robotic exoskeleton device to control an on-screen cursor task. This makes 

sense that there would be transfer-appropriate processes, as motor skills could generalize 

from proximal to distal effectors and distal to proximal effectors (Rajan et al., 2019).  



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With a transfer test, researchers can determine if the learning was task-specific, or if 

the learning can be applied more broadly to tasks that are different from those originally 

acquired. For the purposes of this thesis, ‘generalizability’ differs from ‘transfer’ where 

transfer refers to attempting a new motor skill that can either result in motor skill 

generalizability or motor skill specificity (see section 1.10 of this thesis). The term 

transfer is typically associated with a transfer task or a transfer test without directionality 

of the results (i.e., generalizability or specificity). There are essentially only three 

possible outcomes of a motor skill transfer test (i.e., positive transfer, negative transfer, 

and neutral transfer). To determine if there was a positive transfer (or generalizability of 

the learned motor task) the experiment would result in transfer performance that is better 

than it would have been had the original task not been acquired. In these situations of 

positive transfer, it has been suggested that the process of learning the original skill likely 

provided the learner with a “head-start” on the novel task. Second, if an experiment is 

showing motor learning specificity the results, while demonstrating performance 

improvements in retention testing, would reveal no performance benefits on the transfer 

task. Such a result would suggest that there was no beneficial effect of practicing the 

previous skill on the novel skill. In this situation, the processing activities involved in 

practicing the original task are specific to that task. The third potential outcome of a 

motor task transfer protocol is considerably more rare than the first two: Negative 

transfer wherein the practicing of the original task has a detrimental effect on the learning 

of the novel task. Research revealing true negative transfer effects (non-transient, 

disadvantageous influences of prior practice) is scarce and our understanding of its causes 



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is incomplete however theoretical accounts of negative transfer typically involve the idea 

that previously developed knowledge structures acquired during the original practice are 

maladaptive to change and will result in poor learning outcomes in tasks with changed 

motor demands (e.g., Woltz et al., 2000). Such accounts suggest that the learner must, in 

effect, “unlearn” the initial motor task and relearn the novel test under its new context.  

1.8.1.2 Models from an Information Processing Perspective 

Several models have been developed from an information processing perspective 

that describe how motor learning occurs (e.g., Fitts and Posner’s three-stage model 

[1967], Adams’ closed-loop model [1971], Gentile’s two-stage model [1972], and 

Schmidt’s Schema theory [1975]). For the purposes of this thesis, the models outlined 

below will begin with Fitts and Posner’s three-stage model (1967).  

Fitts and Posner's three-stage model of motor learning includes the previously 

mentioned cognitive, associative, and autonomous stages (Fitts & Posner, 1967). The 

cognitive phase entails the individual receiving information about how to perform a 

movement and continuously integrating extrinsic (i.e., from an external source) and 

intrinsic (i.e., from an internal source) feedback they are receiving. Cognitive phase 

movements may be slower, inconsistent, and inefficient, all requiring significant 

cognitive activity (Fitts & Posner, 1967). Fitts and Posner (1967) suggest that this first 

cognitive stage requires attention to the specific body parts required to make the desirable 

movement, making these movements under conscious control. During this first phase, the 

individual typically is experiencing high variability of motor performance, and the 

duration of this phase will depend on task complexity (Anderson et al., 2021). The 



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second phase, the associative phase, involves movements becoming more fluid and 

reliable, requiring less cognitive activity than the first phase (Fitts & Posner, 1967). 

During this phase, some movements will still be under conscious control, whereas others 

will be more automatic (Fitts & Posner, 1967). The final stage is the autonomous stage, 

where movements are more accurate and consistent with less cognitive activity being 

required (Fitts & Posner, 1967). When motor learning enters the autonomous stage, 

attention can be focused on other aspects of the motor task such as tactical choices, the 

strategy of movement, greater range of motion, or increased speed and acceleration (Fitts 

& Posner, 1967). In the context of this thesis, it should be noted that ‘automaticity’ does 

not necessarily equate to expertise. It may serve as a foundation for specificity or impede 

the broader application of motor skills.  

Adams’ (1971) closed-loop theory proposes that motor learning occurs through 

the refinement of perceptual-motor feedback loops. The motor system relies on sensory 

feedback to continually execute skilled movements. This theory consists of two parts: a 

memory trace and a perceptual trace. The memory trace is responsible for selecting and 

initiating the movement, while the perceptual trace develops during practice and serves as 

the reference for correctness. A practical example of the closed-loop theory is drawing a 

5-centimetre (cm) line with a pen. This model would suggest that though you can draw a 

5 cm line, you will need a new memory and perceptual trace to draw a 7 cm line. This is 

because this model has such a heavy reliance on sensory feedback to develop and 

strengthen memory and perceptual traces. This model mainly applies to discrete, closed 

motor tasks. This closed-loop model allows performers to use sensory feedback to 



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improve their movements. However, this model has limitations in being able to extend to 

practical applications to continuous and open motor skills. This model suggests that a 

motor program is created for every single motor movement and that they each must be 

created, stored, and recalled when needed.  

As a response to Fitts and Posner's (1967) model, Gentile (1972) developed a 

model to address the previous model’s limitations. Gentile (1972) created a model 

seeking to refine Fitts and Posner’s (1967) original Stages of Learning work by taking 

this open vs. closed skill distinction into account. Comparable to the Fitts and Posner 

(1967) cognitive stage, Gentile (1972, p. 5) refers to the initial phase of learning as 

“getting the idea of the movement”. This stems from an interaction with the external 

environment to solve a problem that has emerged, creating a movement goal. The 

individual must learn how to release the specific movement pattern required to achieve 

that movement goal. Generally, the number of environmental events related to that same 

goal will increase, with spatial characteristics also changing over time, allowing the 

degree of spatial/temporal movement control that an individual must also increase over 

time. To untangle the range of motor patterns best suited to yield the appropriate 

movement goal, the individual must identify an effective motor plan, which becomes 

more difficult in complex stimulus environments (Gentile, 1972). Gentile uses terms by 

Poulton (1957) of “open” and “closed” environments to explain the options that the 

individual has before outputting their movement. According to Gentile (1972), 

movements are on a continuum of “open” to “closed” environments but there is value in 

dichotomizing the nature of the environmental context as either closed or open.  



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Gentile (1972) describes the initial stage of acquisition to be similar for both 

environments, where the learner tries to find a general motor organization that works to 

produce the desired outcome. The main task for the individual at this early stage is to 

attend to information about the environmental conditions (i.e., open or closed) that are 

controlling their movements. Gentile (1972) adopts concepts from the “Test-Operate-

Test-Exit” (“TOTE”) theory, developed by Miller and colleagues (1960), which describes 

the motor plan (i.e., a preconceived image or general plan of action) used to direct the 

motor output. “TOTE” matches intentions with movement outcomes, where “Test” is the 

initial image or plan of the intended movement, “Operate” involves the musculature 

contractions involved with producing the movement, the second “Test” is comparing the 

movement feedback against the initial image of the desired movement, and “Exit” is 

interpreting the match or mismatch of the feedback, ending in a termination of the 

operations, or a modification of the operation. “TOTE” is then used as a hierarchical 

system where the ”Operate” phase of a movement plan can serve as a test for subroutines, 

allowing for additional movement organizations to be created. Gentile (1972) adapts this 

concept to suggest that first there is an image or movement plan before output, second, 

that movement output information is fed back to the individual and to be matched against 

the initial plan, and third, having an evaluation of the feedback to determine at which 

point to terminate or amend the action. After movement outcome information (i.e., 

intrinsic and/or extrinsic) is fed back to the individual, there is a decision-making process 

that occurs for the learner to formulate their next response. Gentile (1972) outlines four 

possible outcomes during the evaluation phase of the decision process surrounding “Was 



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the movement executed as planned? Yes/No” and “Was the goal accomplished? Yes/No”. 

If “Yes” the movement was executed as planned, and “Yes” the goal was accomplished, 

then the individual got the idea of the movement. If “No” the movement was not 

executed as planned, but “Yes” the goal was accomplished, then a “surprise” experience 

occurs where the learned experiences the goal even though their movement did not go as 

planned. If “Yes” the movement was executed as planned, but “No” the goal was not 

accomplished, then the “something’s wrong” outcome results in the individual needing to 

re-evaluate whether the environment or movement matches the initial evaluations of the 

identification process. In the last scenario, “No” the movement was not executed as 

planned, and “No” the goal was not accomplished, then, “everything’s wrong” and can 

lead to several alternate strategies or quitting.  

Stage two of Gentile’s (1972) model is “Fixation/Diversification” which occurs 

after the individual has acquired a general idea of the motor pattern that seems to work 

well, then the individual will progress into this second stage to increase the consistency 

or to refine some of the movement characteristics. During stage two, the individual is 

now progressing into attaining a particular level of skill with their motor task. The 

experience in stage two will vary per instance depending on whether the motor skill is 

occurring in an open or closed environment. In a closed environment, the environmental 

conditions are fixed, allowing the individual to predict in advance what the context of 

their next environmental conditions will be. During stage two in an open environment 

motor task (i.e., diversification), the experience can be quite different from that of a 

closed environmental condition. An individual in an open environment context must learn 



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an array of motor patterns. Each motor output from one attempt to the next may involve a 

slight modification to the movement.  In these open environments, no single movement 

pattern will be the solution to all possible outcomes. Thus, the individual must have a 

repertoire of possible movement outcomes to be able to move in accordance with other 

moving objects or other individuals, making the demands of motor learning in open 

environments more complex.  

As an argument against Adam's (1971) closed-loop model, and taking into 

account the information processing requirements outlined by both Fitts and Posner (1967) 

and Gentile (1972), Schmidt, (1975) developed a schema based theory that essentially 

proposes that humans do not learn specific movements (based on a multitude of 

individual motor programs). Rather, Schmidt's (1975) schema theory argues that learned 

movement patterns involve the development of far few motor programs that can be 

“generalized” across many different movement parameters. A generalized motor 

program, as conceptualized by Schmidt (1975) is a smaller pre-set set of motor 

commands that can be retrieved from memory and customized for a specific situation 

before initiating movement. This theory is open-loop in nature wherein augmented 

feedback (i.e., information regarding movement execution and outcome from an external 

source resulting respectively in knowledge of result or knowledge of performance) may 

or may not be available but it does not, in and of itself, control the action. This theory 

works best for fast, ballistic, and more automatic movements (e.g., a golf swing) where 

there is little time to change the movement mid-swing. Schmidt’s motivation in 

developing this schema-driven explanation of motor learning was to account for two 



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fundamental problems with Adams’ (1971) closed-loop theory: Novelty of movement 

(i.e., how can new movements be executed if there has been no prior opportunity to 

develop the perceptual and memory traces needed to perform them?) and storage (i.e., the 

inherent inefficiency in the requirement to develop and retain for future use a seemingly 

infinite number of individual motor programs). Although it has been suggested that 

humans are capable of storing about 100,000 programs for speech, this number would 

increase to countless outcomes for the storage of human motor movement possibilities 

(MacNeilage, 1970). Schmidt’s (1975) model suggests that humans must have a more 

efficient way to store motor programs. The next concern with Adam’s (1971) closed-loop 

model is the novelty problem and the lack of explanation for how an individual can 

produce a novel movement or many variations of a particular motor skill. For example, 

Adams (1971) would not be able to explain how to throw a bean bag 7 meters and then 

7.5 meters. Based on Adam’s (1971) closed-loop model, a perceptual trace is created for 

each movement, for example, the 7-meter bean bag throw. With practice, the perceptual 

trace for the 7-meter throw gets thicker and thicker. When called on, the memory trace 

for the 7-meter throw can be used, but there are no traces for 7.5-meter throws, the 

individual would not be able to accurately throw shorter or farther.  

Thus, closed-loop learning models such as Adams’ cannot adequately explain 

how the thrower can have a repertoire of different throw distances that are slightly 

different, yet characteristic of all the previous ones. In Schmidt’s (1975) model, however, 

learners can produce different movements within a class of movements by adjusting 

certain parameters that will change various movement outcomes. This explanation is a 



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solution to the previous model’s limitations where the individual would specify the 

appropriate parameters for the movement. When patterns of similar movements are 

examined, there may be elements of the movement that are easy to change, while other 

aspects are fixed from movement to movement, termed invariant (Schmidt, 1985). These 

parameters are features of a movement (e.g., the amount of force from the muscles to 

contribute to the movement). By scaling a parameter of a movement, people can produce 

variations of the movement within the class of movements. For example, as a performer 

practices a movement such as throwing the bean bag at various distances, they will learn 

the relationship between the amount of force required and the outcome of the throw, not 

the individual distances themselves. By practicing throwing the bean bag at these 

different distances, the performer will improve their understanding of the relationship 

between their control of the parameters and the throw outcome. In Schmidt’s (1975) 

theory, this relationship between parameters and the movement outcome is collected in 

two schemata: recall schema and recognition schema. The recall schema relates the 

movement outcome to parameters such as the amount of force in an overhand baseball 

throw. The recognition schema connects the expected sensory results of a movement to 

the actual outcome of that movement. 

1.8.2 Dynamical Systems Perspective 

Not all models of motor learning involve a top-down processing of information, 

however. One influential model of learning that downplays such resource-heavy 

cognitive processing requirements suggests that movement in general, and motor learning 

in particular, rely less on cognitive processing and more on the dynamic physical 



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constraints of the mover/learner. This dynamical systems perspective is also a 

multifaceted and complex perspective to explain motor learning but it is more focused on 

an ever-changing interaction between the individual, the task, and the environment. 

Bernstein (1967) describes dynamical systems with a focus on the progression in solving 

the problem of degrees of freedom. In terms of human movements, degrees of freedom 

relate to the number of independent variables (e.g., joints, muscles) that need to be 

controlled while executing a movement (Bernstein, 1967). For example, an elbow joint 

has two degrees of freedom as it can afford only a flexion and an extension movement. 

To learn new movements, individuals must learn to coordinate their actions with the 

number of associated degrees of freedom. Bernstein (1967) breaks down this model into 

three stages. Stage one consists of ‘freezing degrees of freedom’ where individuals utilize 

control or limit the number of joints and muscles that move independently. The second 

stage involves ‘releasing degrees of freedom’ where individuals no longer need to isolate 

the body segments after they can successfully perform the basic movements of the motor 

skill (Bernstein, 1967). The third stage of the Bernstein (1967) model is ‘exploiting 

degrees of freedom’ where individuals can begin to exploit reactive forces and passive 

dynamics of the body and environment, allowing for more efficient and effective 

movements.  

The dynamical systems perspective on motor learning suggests that motor skills 

will emerge naturally as practice occurs or experience with a movement develops. This 

theory states that any movement outcome depends on the individual’s body (system) as 

well as their interaction with the environmental conditions (dynamics). Through the 



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dynamical systems perspective, motor development is seen as probabilistic, with different 

factors in the environment and individual that can affect these probabilities. The body is 

composed of a complex system with many interacting parts, which lends to the 

probabilistic approach to movement outcomes.  

The main takeaway regarding the dynamic systems perspective is that humans are 

complex systems that have an inherent capacity to self-organize. Given various tasks, 

environments, and individual situations, many great movement solutions may arise, and 

these factors can be quite pivotal in an individual’s dynamic motor behaviour. For 

example, the dynamical systems perspective would be a snowboarder learning to use 

gravity to their advantage down the slopes. According to this perspective, the 

snowboarder self-organizes to emerge the necessary next movements.  

1.8.2.1 Ecological and Systems Models of Motor Learning 

Motor learning can also be explained through ecological theory and systems 

models. The ecological theory finds its origin in the earlier work of Bernstein (1967) on 

the control and coordination of movement and in Gibson’s (1979) theory of direct 

perception. The ecological theory suggests that humans perceive their environments 

directly and without mediation by cognitive processes. This approach focuses on how a 

person's surroundings shape their perception and behaviour based on the opportunities 

and limitations they afford. The individual, the task, and the environment will interact to 

provide perceptual information used to control movement. The stimulus to accomplish a 

desired movement task goal is what facilitates motor learning according to this approach.  



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A systems model described by Shumway-Cook and colleagues (2007) builds on 

these concepts by positing a framework of multiple body systems overlapping to activate 

synergies for movements to occur that are driven by functional movement goals. Similar 

to the ecological theory, the systems model also considers the interaction of the 

individual with the environment, but with a more goal-directed behaviour that is task 

oriented. The movement results from an interaction of multiple systems working in 

synchrony to solve a motor problem while accounting for the adaptability of motor 

behaviour depending on the environmental contexts. The theories mentioned previously, 

including information processing, dynamical systems, ecological, and systems models, 

each offer insights into motor learning and, to varying degrees, attempt to account for the 

phenomenon of transfer. In this thesis, the focus will primarily lean towards employing 

concepts from the information processing perspective, especially in relation to transfer-

appropriate processing. Both the information processing perspective and the concept of 

transfer-appropriate processing underscore the significance of the ways in which we 

encode, store, and recall information. The information processing theory lays the 

groundwork for understanding how sensory inputs and memory processes interact, 

whereas transfer-appropriate processing theory emphasizes the crucial influence of 

context and the congruence between the encoding of information and its later retrieval in 

the context of motor learning. With these theoretical perspectives in mind relating to the 

broad outcomes of human motor learning, we can better strategize how to structure the 

practice of a motor task. 



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1.9 Conditions of Practice 

1.9.1 Deliberate Practice 

An individual can inadvertently practice without self-investment in the motor 

task, which is why it is important to highlight deliberate practice where activities have 

been explicitly designed to improve the current level of motor performance (Ericsson et 

al., 1993). Deliberate practice is when an individual is putting effort into the task with the 

goal of personal improvement in motor performance. Ericsson and Harwell (2019) 

outline how deliberate practice differs from other forms of practice (i.e., purposeful 

practice, structured practice, and naïve practice).  

Alternatively, when individuals are practicing in the absence of, or with limited 

exposure to individualized evaluation and guidance by a teacher or coach, this is referred 

to as ‘purposeful practice’ (Ericsson & Harwell, 2019). When practice is in a group or 

team setting guided by a coach or teacher, also without individualized feedback, this is 

referred to as ‘structured practice’ (Ericsson & Harwell, 2019). ‘Naïve practice’ are 

activities that are motivated by other factors than the goal of improvement such as 

playing games with friends or executing a job in response to a demand from an external 

factor (Ericsson et al., 1993; Ericsson & Harwell, 2019). 

When it comes to these various types of practice, there are positives and negatives 

associated with each of these types of practice. Depending on the desired outcome of the 

individual, each type of practice has space in the practice space. If an individual is serious 

about their movement goals to improve, then the individualized feedback style of 

deliberate practice may be best. If a coach or teacher's resources are limited, perhaps 



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group settings or limited meetings with coaches and teachers are the best approaches in 

purposeful or structured practice. If an individual is practicing a task for the experience, 

or only wants to try it once and isn’t concerned with ideal motor performance, then naïve 

practice best fits this situation. In terms of motor learning research, deliberate practice is 

ideal as it contains tailored feedback for the individual to improve toward the movement 

goal.   

1.9.2 Massed vs. Distributed Practice 

Distributed practice is a learning strategy that involves breaking up the practice 

into multiple sessions spaced out over time (e.g., 20 trials per day over 3 days), compared 

to a massed practice involving longer practice sessions (e.g., 60 trials performed on a 

single day). While massed practice can be seen as an efficient use of time to perform all 

the practice at once, this type of practice can be fatiguing. When practice is dispersed 

over time in distributed practice, this can allow for recovery, and give the individual time 

for mental rehearsal and feedback, with a disadvantage being that it can be time-

consuming. It is important to consider in motor learning research when using massed and 

distributed practice, or comparing studies that use different forms of practice, to ensure 

the same number of trials are occurring between groups. Massed practice is continuously 

repeating a movement without taking breaks which can be beneficial for short time 

frames, and immediate performance improvements. Massed practice does have 

limitations with its short time frame, as it may not have time for augmented feedback, 

and the repetitions may be fatiguing for the learner. Distributed practice will allow time 



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for recovery and time for augmented feedback. One of the drawbacks of distributed 

practice is its time-consuming nature. 

1.9.3 Blocked vs. Random Practice 

How practice is structured can also be categorized into blocked and random 

practice. Blocked practice refers to practicing the same skill under the same conditions, 

repeatedly, before moving on to the next skill. Random practice refers to practicing the 

motor skill with variability between each attempt. Blocked practice will have a low 

contextual interference which is described in a review by Magill and Hall (1990) as a 

learning phenomenon where interference during practice is beneficial to skill learning. 

Practice performance tends to be worse with higher levels of contextual interference, but 

retention and transfer performance are generally better. Lower levels of contextual 

interference, instead, can result in better practice performance but lower retention and 

transfer performance (Magill & Hall, 1990). This contextual interference originated from 

verbal learning research with evidence of ‘intratask interference’ (Battig, 1972). Prior to 

this research, the prevailing idea was that interference would lead to a decrease in 

performance. This research was able to demonstrate that under certain circumstances, 

interference could be beneficial to performance. The intratask interference principle was 

expanded to represent more general ‘contextual interference’ including intrinsic and 

extrinsic factors to the task being learned (Battig, 1972). Shea and Morgan (1979) found 

that this contextual interference effect could be applied to motor skill learning contexts as 

well. Shea and Morgan (1979) define blocked practice as practicing the same skill under 

the same conditions and leads to more rapid gains in motor performance, due to its low 



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contextual interference, but limited generalizability when variability is introduced. 

Random practice is the adding of variable task requirements into practice which slows 

performance but can improve retention and generalizability to other contexts of the motor 

skill, due to its high contextual interference (Shea & Morgan, 1979). In the Shea and 

Morgan (1979) experiments, the participant was tasked with responding to a light 

stimulus as quickly as possible by knocking down a series of hinged barriers in an order 

specific to the colour of the signal to respond. With the random condition, three different 

possible signals would be illuminated, making the task primarily a choice-reaction 

paradigm. Under the blocked condition, only one signal would illuminate, making the 

participant’s response a simple-reaction paradigm. Lee and Magill (1983) replicated the 

procedures of Shea and Morgan (1979) and altered the procedures such that the 

contextual variety and reaction paradigm could be controlled. In experiment one, Lee and 

Magill (1983) created factors of cued vs. uncued to denote whether a warning light was 

provided and blocked vs. random referring to the contextual variety in the forms of the 

following groups: cued-blocked, uncued-random, uncued-blocked, cued-random. The 

retention test involved all groups performing the motor task in random order. The 

findings of Lee and Magill's (1983) experiment one support Shea and Morgan's (1979) 

contention that random contextual variety conditions facilitate the retention of motor 

skills relative to blocked practice, and this effect is not due to an interaction of the 

practice schedule with a reaction paradigm. Lee and Magill added an explanation for the 

process of using active regeneration for a new movement plan in a chapter (Lee and 

Magill, 1985) that synthesized some of the conceptual findings of their work. The chapter 



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described how forgetting the specifics of a previously generated action plan will force the 

learner to reconstruct an action plan on a subsequent repetition of the movement goal 

(Lee & Magill, 1985). By having the practice of a particular movement goal spaced, the 

individual learns more about the process of developing and implementing an action plan 

(Lee & Magill, 1985). This effort-related learning can be explained by using a math 

example. For example, if a child is trying to remember math problems, they can practice 

5 x 4 = 20, and go through the effort of counting by fives on their fingers. When they 

practice 5 x 4 again and again, they will eventually skip over the effort of counting on 

their fingers, and have the answer 20 memorized to reiterate. By adding in different math 

problems such as 5 x 5, now the child can be effortful again in counting by fives. Moving 

on to 5 x 5 forces the child to forget about 20. When 5 x 4 is revisited, rather than 

reacting with memorization, the act of forgetting forces this active regeneration of 

effortful counting. This process of forgetting and reconstructing the solution by Lee and 

Magill (1985) is what leads to improved learning and is in line with the contextual variety 

effect (Battig, 1972). 

The elaborative-distinctiveness hypothesis (Shea & Morgan, 1979; Shea & Titzer, 

1993; Shea & Zimny, 1983) and the forgetting-reconstruction hypothesis (Lee & Magill, 

1983; Lee & Weeks, 1987) are both explanations for the contextual interference effect. 

They differ where the elaborative-distinctiveness hypothesis uses intertask comparisons 

and embellishment of task-relevant information to create more elaborate information 

processing. More elaborate information processing is thought to result in a more 

comprehensive memory trace (Lin et al., 2008). Alternatively, the forgetting-



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reconstruction hypothesis suggests that a previously constructed action plan is more 

likely to be available in working memory when the same task is practiced repeatedly. 

When practice is random, however, this forces the learner to abandon the action plan 

previously constructed because they have a different task (Lin et al, 2008). Compared to a 

blocked practice schedule, a random practice schedule engages the learner in deeper 

cognitive processes which can lead to a stronger motor memory representation for 

retention (Kantak & Winstein, 2012). Allowing for more inter-task comparisons in a 

random practice schedule leads the learner to a stronger and more elaborate memory 

representation (Wright, 1991). 

In summary, the use of scheduling random task requirements in practice during 

acquisition is thought to induce a contextual interference effect. When the learner is given 

blocked practice scheduling in the acquisition, there may be evidence of more immediate 

performance gains, but the learner may have limited motor skill generalizability when 

variability to the motor task is introduced in the future (i.e., a lower degree of learning). 

Conversely, when the learner experiences random practice, or trial-to-trial practice 

variability during acquisition, immediate motor performance may be lower but the future 

retention and generalizability performance is increased. With the distinction between 

motor performance and motor learning, the contextual interference effect is a great 

example of how one snapshot of a learner’s performance in acquisition can be deemed 

localized and specific to that time and place (Kantak & Winstein, 2012). To go beyond 

immediate motor performance and determine whether motor learning has occurred, the 



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contextual interference effect stresses the importance of using a retention and transfer test 

to assess the relative permanence of motor learning.  

1.9.4 Part vs. Whole Practice 

Whole practice is when the motor skill is practiced in its entirety, compared to 

part practice is when the motor skill is broken into smaller parts to be practiced in 

isolation before joining the segments together (McGuigan & MacCaslin, 1955). Part 

practice can help link sequential movements into a single movement pattern over time. 

With practice, the parts can be “chunked” together into a single, cohesive movement. The 

decision of whether to break a motor task into parts versus keeping the motor task whole 

can depend on the needs and skill level of the learner. When the motor task is low in 

complexity, and the learner has high interdependence, whole practice may be more 

suitable. When a motor task has high complexity, and the learner has low 

interdependence, part practice may be more appropriate. Skill complexity has been 

defined in a taxonomy of human perceptual-motor abilities by Fleishman (1972) to be 

used as a classification system to underlie any complex motor task. Within the review by 

Fleishman (1972), there is the understanding that the skills involved in complex activities 

can be described in terms of more basic abilities (Fleishman, 1972). “Motor skill 

complexity is defined as the number of parts or components of a skill; meaning the more 

parts or components a sill has, the higher it is in complexity” (Kiefer et al., 2014, p. 2).  



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1.10 The Generalizability and/or Specificity of Motor Learning 

Whether a motor skill has transfer-appropriate or specific properties is a key 

theme of this thesis. It is known that sometimes motor skills are generalizable to other 

motor tasks, and in rarer instances, learned motor skills are specific to one motor skill and 

can be detrimental to performance on other motor skills. Where this line between 

generalizability and specificity is drawn is unclear. “Unfortunately for psychologists, the 

human organism was not designed for the convenience of researchers” (Miller, 1956, p. 

136). This thesis aims to further explore how many accounts of true motor learning 

specificity are there and to examine whether there are commonalities between these types 

of movements using a scoping review. The scoping review will pose key areas of inquiry 

for future researchers looking to implement motor learning experiments with true transfer 

tests (Chapter 2). Further, the experimental work in this thesis aims to create an 

experimental protocol to promote a true transfer test (Chapter 4). With a true transfer test 

implemented, any robust findings of motor skill generalizability or specificity will have 

the opportunity to present themselves cleanly and clearly. With these theoretical 

perspectives in mind as to why specificity of practice exists (i.e., sensorimotor 

representations, movement patterns, and incompatible knowledge structures), these 

concepts represent the foundation of this thesis.  

1.10.1 Generalizability of Motor Learning 

 Everyday activities of daily living suggest that we can learn and have a repertoire 

of multiple motor skills. How a repertoire of motor skills aids the learning of new motor 

skills is referred to as motor skill generalizability. In previous motor learning literature, 



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‘generalizability’ has been used to describe our ability to apply what has been learned in 

one context to another context (Krakauer & Shadmehr, 2006). This refers to the extent to 

which practice on one task contributes to the performance of other similar skills, 

sometimes in other contexts. Seeing motor task generalizability in a transfer task is 

typically seen as an extension and confirmation of having learned the initial motor task. 

Currently, retention and transfer tests are motor learning researchers’ gold standard to 

best assess the success of motor learning (Pinder et al., 2011; Shewokis, 1997).  

In traditional motor learning experiences, the series of events typically include 

acquisition (i.e., where the learner is practicing the motor skill), retention (i.e., where the 

learner is assessed on the relative permanence of the learned motor skill), and transfer 

(i.e., where the learner is assessed on an additional degree of learning to demonstrate 

whether the skill acquired from the acquisition is generalizable to a similar motor task). 

Transferring to a new motor task has been widely used in motor learning to permit 

making motor learning claims. The idea is that if the motor skill has been retained based 

on the retention task, then learning has occurred. To solidify this claim, if learning has 

been generalized to a similar task in a transfer task, that this also advocates motor 

learning has occurred. Work by Sigmundsson and colleagues (2017) describes the 

learning process to occur in four phases: starting with understanding the skill, acquiring, 

and refining the skill, automatization of the skill, and ending with a generalization of the 

skill. The final stage in this model is suggested to only be achieved if the skill has been 

well learned and maintained (Sigmundsson et al., 2017). It is also suggested that some 



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individuals may have difficulty reaching the generalizability stage if they have not 

automated the skill due to a lack of practice (Sigmundsson et al., 2017).   

1.10.1.1 Theoretical Perspectives in Generalizability of Motor Learning