The Effects of High-Intensity Interval Training on Piano ... · improved with high-intensity...

The Effects of High-Intensity Interval Training on Piano Learning

by

Dana Swarbrick

A thesis submitted in conformity with the requirements for the degree of Master of Science

Rehabilitation Sciences Institute University of Toronto

© Copyright by Dana Swarbrick 2018

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The Effects of High-Intensity Interval Training on Piano Learning

Dana Swarbrick

Master of Science

Rehabilitation Sciences Institute

University of Toronto

2018

Abstract

High-intensity interval training (HIIT) improves implicit motor sequence learning (1,2).

However, little is known about the impact of HIIT on the learning of explicit ecologically valid

motor skills. We hypothesized that healthy volunteers who performed HIIT after explicit piano

melody training would exhibit better retention of the learned melody, and better transfer to a new

melody, than those who performed low-intensity exercise.

Participants with no musical training underwent a graded maximal exercise test to determine

their cardiorespiratory fitness. Later, participants practiced a piano melody before completing

high- or low-intensity exercise. Participants were tested on the piano melody one hour, one day,

and one week after initial practice. Performance was quantified by pitch and rhythm accuracy.

Contrary to the hypothesis, HIIT did not enhance retention of the piano melody. However, HIIT

did promote modest transfer to a new sequence. We conclude that HIIT may enhance explicit

task-general motor sequence consolidation mechanisms.

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Acknowledgements

This thesis would not have been possible without the help and encouragement I received from so

many incredible people that I encountered throughout my degree, including those I met through

academics, athletics, and music.

First and foremost, my supervisor Dr. Joyce Chen provided consistent encouragement, fostered

self-directed learning, allowed me to pursue my passions, and motivated me in the face of

adversity. My collaborators Dr. Alex Kiss, Dr. Luc Tremblay, and Dr. Catherine Sabiston

demonstrated immense generosity with their expertise, time, and equipment. My committee, Dr.

Sandra Trehub, Dr. David Alter, and Dr. Dina Brooks provided invaluable mentorship and

feedback on study design, interpretation, and communication of results. Dr. Rachel Brown, Dr.

Virginia Penhune, and Joe Thibodeau shared programming scripts and provided data that guided

implementation and analysis of the piano learning task. My examiners Dr. Tim Welsh and Dr.

Richard Staines gave insightful feedback on this final version of the thesis.

My rowing coaches and teammates gave me the opportunity to experience the struggles of motor

learning first-hand, taught me to push myself harder than ever before, and made me fall in love

with sport. I would like to acknowledge S&C coach Josh Downer for furthering my interest in

exercise science, Patrick Okens for his inspirational dedication, and Dr. Ming-Chang Tsai for

blowing me away with his superhuman powers and his ongoing mentorship. I would like to

thank my musical network, especially Onoscatopoeia and Ethan Tilbury, for inspiring me

creatively, for being a source of joy, and for endless musical teachings.

Throughout my Master’s there were several equipment failures, programming hiccups, and

administrative struggles that were overcome through others’ generosity. I would like to thank

Nicholas Piegdon (Synthesia creator), Andrew Robertson (VO2 training & long-lost cousin),

Darren Au and Cathie Kessler (cycle ergometer troubleshooting), Dr. Scott Thomas and Robert

Rupf (locating a replacement cycle ergometer), the PMB lab, Melissa DeJonge (administrative

assistance), my peers in the PULSELab, and Faryn Starrs (for consistent emotional support).

Last but not least, I would like to express my deep gratitude to my family for supporting me in

the pursuit of my dreams. To my Mom, Dad, Granny, Grandad, Uncles, Aunties, cousins, and

brother—thank you for being there when I needed you.

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Table of Contents

Acknowledgements ........................................................................................................................ iii

Table of Contents ........................................................................................................................... iv

List of Tables ............................................................................................................................... viii

List of Figures ................................................................................................................................ ix

List of Appendices ........................................................................................................................ xii

Chapter 1 ..........................................................................................................................................1

Introduction .................................................................................................................................1

Chapter 2 ..........................................................................................................................................2

Literature Review ........................................................................................................................2

2.1 Motor Learning ....................................................................................................................2

Types ........................................................................................................................3

Phases .......................................................................................................................4

Theories....................................................................................................................4

Dynamical Systems Theory .....................................................................................6

Music Production and Theories of Motor Learning ................................................7

2.2 The Effects of Exercise on Motor Learning ........................................................................7

Exercise ....................................................................................................................8

Manipulations to the Interval Exercise Protocol ....................................................11

Task Parameters .....................................................................................................15

Transfer and Interference .......................................................................................22

Exercise and Sleep .................................................................................................23

Proposed Mechanisms ...........................................................................................23

Ecological validity .................................................................................................26

2.3 Music..................................................................................................................................27

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Musical learning.....................................................................................................27

Measuring Musical Learning .................................................................................29

Defining non-musicians .........................................................................................30

2.4 Gap .....................................................................................................................................32

Chapter 3 ........................................................................................................................................33

Objectives and Hypotheses .......................................................................................................33

3.1 Objectives ..........................................................................................................................33

3.2 Hypotheses .........................................................................................................................33

Chapter 4 ........................................................................................................................................34

Methods .....................................................................................................................................34

4.1 Participants .........................................................................................................................34

4.2 Procedure ...........................................................................................................................35

Study Overview .....................................................................................................35

Pre-screening..........................................................................................................36

Questionnaires........................................................................................................36

Graded Exercise Test .............................................................................................37

Piano Learning Task ..............................................................................................38

Interval Exercise Test ............................................................................................45

Retention Tests.......................................................................................................45

Post-Session ...........................................................................................................46

Transfer Test ..........................................................................................................46

Auditory Recognition and Motor Only Test ..........................................................46

4.3 Analysis..............................................................................................................................50

Data Processing ......................................................................................................51

Statistical Analysis .................................................................................................53

Chapter 5 ........................................................................................................................................55

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Results .......................................................................................................................................55

5.1 Demographics data.............................................................................................................55

5.2 Summary Figures of Data ..................................................................................................60

5.3 Mixed Effects Modeling ....................................................................................................61

Melodies: Sequence 1 versus Sequence 2 ..............................................................62

Acquisition .............................................................................................................64

Retention ................................................................................................................68

Transfer ..................................................................................................................70

Auditory Recognition Task ....................................................................................73

Motor Only Task ....................................................................................................75

Subjective Report of Learning Strategies ..............................................................77

Chapter 6 ........................................................................................................................................78

Discussion .................................................................................................................................78

6.1 Discussion of Results .........................................................................................................78

Acquisition .............................................................................................................78

Retention ................................................................................................................79

Transfer ..................................................................................................................80

Auditory Recognition and Motor Only Tasks .......................................................81

Learning Strategies ................................................................................................82

6.2 Strengths and Limitations ..................................................................................................82

Task ........................................................................................................................82

Control Group ........................................................................................................84

Participants’ fitness ................................................................................................84

Sample Size ............................................................................................................85

Summary ................................................................................................................85

6.3 Implications for the Rehabilitation Sciences .....................................................................86

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Music and Exercise for Stroke Motor Rehabilitation ............................................86

6.4 Future Directions ...............................................................................................................86

6.5 Conclusions ........................................................................................................................87

References ......................................................................................................................................88

Appendices ...................................................................................................................................106

Copyright Acknowledgements.....................................................................................................134

viii

List of Tables

Table 1: Summary of studies examining high-intensity exercise on motor learning ................... 21

Table 2: Descriptive data of participant characteristics ................................................................ 56

Table 3: Participant Exercise Characteristics ............................................................................... 57

Table 4: Participant fitness levels and respective ACSM Fitness Category (170) ....................... 59

Table 5: Participants' subjective report of their focus during acquisition and transfer tasks ........ 77

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List of Figures

Figure 1: Stages of motor learning.................................................................................................. 4

Figure 2: Visuomotor accuracy tracking task ............................................................................... 16

Figure 3: Implicit continuous visuomotor tracking task. .............................................................. 17

Figure 4: Time on target task. ....................................................................................................... 17

Figure 5: Discrete implicit serial targeting task ............................................................................ 18

Figure 6: Serial Reaction Time Task ............................................................................................ 19

Figure 7: Visuomotor adaptation task ........................................................................................... 20

Figure 8: Spatial component of the piano learning task ............................................................... 30

Figure 9: Schematic of study overview. ....................................................................................... 35

Figure 11: Test trial (no visual cueing) ......................................................................................... 40

Figure 10: Training trial (visual cueing) ....................................................................................... 40

Figure 12: Familiarization melody 1............................................................................................. 40



Figure 15: Sequence 1 ................................................................................................................... 41

Figure 16: Sequence 2 ................................................................................................................... 42

Figure 17: Piano Acquisition Protocol .......................................................................................... 43

Figure 18: Example of training trial with knowledge of results visual feedback. ........................ 43

Figure 19: Example of test trial during blocks 1-3 with knowledge of results visual feedback. .. 43

x

Figure 20: Synthesia provided visual knowledge of results feedback .......................................... 44

Figure 21: Example of test trial during blocks 4-6 ....................................................................... 45

Figure 22: Sequence 1 Correct melody ......................................................................................... 47

Figure 23: Distractor melody 1 ..................................................................................................... 47

Figure 24: Distractor melody 2 ..................................................................................................... 47

Figure 25: Distractor Melody 3..................................................................................................... 47


Figure 27: Sequence 2 Correct Melody ........................................................................................ 48





Figure 32: Sequence 1 Correct Rhythm ........................................................................................ 49

Figure 33: Distractor 1 .................................................................................................................. 49




Figure 37: Sequence 2 Correct Rhythm ........................................................................................ 49



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Figure 42: Pitch accuracy scores of test trials from each session separated by intensity group ... 60

Figure 43: Rhythm accuracy of test trials from each session separated into intensity group ....... 61

Figure 44: Pitch accuracy separated by sequence number ............................................................ 62

Figure 45: Rhythm accuracy separated by sequence number ....................................................... 63

Figure 46: Pitch accuracy score during acquisition. ..................................................................... 65

Figure 47: Rhythm accuracy score during acquisition ................................................................. 66

Figure 48: Individual variability in pitch scores ........................................................................... 67

Figure 49: Individual variability in rhythm scores. ...................................................................... 67

Figure 50: Pitch accuracy during retention and last 10 acquisition trials. .................................... 68

Figure 51: Rhythm accuracy during retention and last 10 acquisition trials ................................ 69

Figure 52: Transfer sequence learning curves on the measure of pitch accuracy. ....................... 70

Figure 53: Transfer sequence learning curves on the measure of rhythm accuracy. .................... 71

Figure 54: Blocks 1-3 of acquisition and transfer in pitch accuracy. ........................................... 72

Figure 55: Blocks 1-3 of acquisition and transfer in rhythm accuracy. ........................................ 73

Figure 56: Recognition task pitch accuracy abilities. ................................................................... 74

Figure 57: Recognition task rhythm accuracy abilities................................................................. 75

Figure 58: Motor only task pitch accuracy ................................................................................... 76

Figure 59: Motor only task rhythm accuracy ................................................................................ 76

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List of Appendices

Appendix A: Screening Questionnaire ........................................................................................106

Appendix B: Information Letter & Informed Consent Form.......................................................111

Appendix C: Pre-Session 1 Questionnaire ...................................................................................118

Appendix D: Pre- and Post-Exercise Emotional Affect Scale .....................................................126

Appendix E: Pre-Session 2, 3, & 4 Questionnaire .......................................................................127

Appendix F: Sleep & Exercise Log .............................................................................................130

Appendix G: Borg’s Ratings of Perceived Exertion ....................................................................131

Appendix H: Debrief Form ..........................................................................................................132

1

Chapter 1

Introduction

The first evidence of exercise as medicine dates back to 600 BC when an Indian physician

named Susruta prescribed daily moderate exercise to his patients (3). Now it is well known that

regular exercise is a vital component of maintaining physical (4), mental (5), and cognitive (6)

health. Recently, researchers discovered that a single session of high-intensity exercise enhances

motor learning (7,8).

An accumulating body of literature indicates that a single session of high-intensity interval

training (HIIT) promotes motor learning. HIIT causes a host of physiological changes that may

contribute to exercise’s ability to increase neuroplasticity —the brain’s ability to change and

learn (9,10). When HIIT takes place during early consolidation, the physiological changes

caused by exercise enhance the neuroplastic mechanisms involved in consolidation and improve

motor memory as measured by performance at retention (7,11).

To our knowledge, only one study has examined whether exercise can enhance the consolidation

of an ecologically valid skill (12). Ecological validity refers to the extent to which research

findings are generalizable to, and representative of, the real-world (13). Learning to play piano is

an example of an ecologically valid skill. Furthermore, no one has examined whether exercise

can promote transfer and improve learning of a new sequence. Therefore, we aim to conceptually

replicate previous research and examine whether HIIT can enhance consolidation and transfer of

piano learning.

In the present study, healthy non-musicians performed a graded maximal exercise test that

determined their fitness level. At least one day later, participants learned a piano melody and

were pseudorandomized into either a high-intensity experimental group or a low-intensity

control group. Both groups completed exercise at their personalized intensities immediately after

piano learning. Retention was measured at one hour, one day, and one week after learning, and

transfer to a new melody was measured at the end of the study.

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Chapter 2

Literature Review

The elite in athletics, performing arts, and medicine develop their expertise through thousands of

hours of deliberate practice (14). However, by leveraging neuroscientific research, the practice

time required to become an expert may be reduced. The ability to learn a motor skill may be

improved with high-intensity interval training (HIIT).

The effects of exercise on health and cognition have been explored extensively (4–6), but until

2012 the effects of exercise on motor learning were rarely examined. In 2012, Roig and

colleagues showed that HIIT improved retention of a motor skill (7).

To date, several studies have replicated Roig et al.’s findings (9,11,15). Much of this research

has examined lab-based, simple visuomotor tracking tasks (1,2,7,8,11,15) and few have

examined skills that are ecologically valid (i.e. generalizable to the real world) (12,16).

Furthermore, the research on motor sequence learning has focused on implicit motor learning—

learning that takes place without conscious awareness (1,2). The objective of the research study

reported in the present thesis is to examine the effects of exercise on the type of motor sequence

learning involved in the ecologically valid skill of playing the piano. In this literature review,

literature on motor learning, exercise, neuroscience, and music cognition will be synthesized to

justify the hypothesis that HIIT improves piano learning.

2.1 Motor Learning

Schmidt and Lee define motor learning as “a set of processes associated with practice or

experience leading to relatively permanent changes in the capability for movement” (1987) (17).

The scientific study of motor learning aims to understand these processes and how they may be

enhanced or disrupted in healthy individuals, experts, and people with disordered motor learning

abilities (e.g. stroke survivors).

Colloquially, people with “good memory” are those who can memorize facts and figures (18).

An expert athlete or musician is rarely referenced as a person with a sharp memory; however,

these motor learning experts have outstanding abilities to store and recall movements. Many

researchers have developed frameworks for understanding memory and its processes (18–22).

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Most have categorized memory into declarative and nondeclarative, with procedural memory as

a subsystem of nondeclarative memory (18,23,24). Declarative memory is defined as the

conscious recollection of facts and figures (25). Procedural memory is defined as memory for

skills, which includes motor skills (24).

Types

2.1.1.1 Implicit versus Explicit Memory

There are several definitions of the distinction between implicit and explicit learning that vary

across researchers. For example, Squire and Zola-Morgan equate declarative memory to explicit

memory, and nondeclarative memory to implicit memory (25). By the Squire and Zola-Morgan

definition, the explicit/implicit distinction relies on the learner’s awareness of the memory; a

memory with conscious awareness is an explicit memory while a nonconscious memory is

implicit (25). On the contrary, Robertson suggests that the implicit/explicit classification is

independent from the declarative/procedural classification (24). He defines the distinction by

awareness during learning (24). His example of explicit-procedural memory is learning to ride a

bike because the learner is aware that they are learning; whereas his example of implicit-

procedural is a child learning the skill of proper grammar in their language. Stanley and

Krakauer (2013) argue that all motor skill relies on factual knowledge, and therefore explicit

learning is always taking place even if intention or awareness of learning is missing (implicit)

(26). The definition used for the purpose of this thesis is most closely aligned with that used by

Robertson (24). Specifically, implicit learning is operationally defined as learning without

conscious awareness while explicit learning is defined as learning with conscious awareness

(24).

2.1.1.2 Continuous versus Discrete

Motor learning tasks can also be categorized by movement type into 1) continuous and 2)

discrete (1,2). A continuous task involves movement with no clear beginning or end, such as the

bowing action of a violin (1,27). A discrete task involves quick, isolated movements, such as

pressing the keys of a piano (2,27).

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Phases

Motor learning consists of three phases: i) acquisition, ii) consolidation, and iii) retention (17)

(figure 1). Acquisition refers to initial skill practice. Consolidation is the period after acquisition

in which skill improvements continue without skill rehearsal. Retention reflects relatively

permanent changes in the ability to perform a skill and is typically measured by testing

performance after some delay (e.g. days, weeks, or months later) (7,28). Consolidation, also

known as offline learning, is measured by a change in performance from the end of acquisition to

retention (7,11,15). Consolidation is the transition of memory from a fragile state in short-term

memory to a more stable form in long-term memory (10,29,30).

Figure 1: Stages of motor learning

Retention is an important phase of motor learning because it represents relatively permanent

changes in the capability to perform a skill, or long-term motor memory (17). It is important to

measure delayed retention because performance during acquisition or in an immediate test does

not necessarily reflect learning (31). Retention is the focus of this study as improvements in skill

measured at delayed testing, reflect that the skill has been consolidated into long-term memory.

Theories

Several theories have been proposed to explain motor control and learning (e.g. Newell’s, Fitts

and Posner, etc.) (32–34). These theories help conceptualize motor skill learning and are useful

5

to the extent that they permit the generation of testable hypotheses. The two most popular

theories for understanding motor learning are (1) information processing theory and (2)

dynamical systems theory.

2.1.3.1 Information Processing Theory

Information processing theory (IPT) postulates that movement executors function similarly to

computers—input to the system is processed and a program is prepared and performed as output

(35). The processing of the input takes place in some central location—for humans, this would

be in our central nervous system and brain. This processing takes place in a linear fashion which

would be observed with linear changes in behaviour.

Motor programs are central to the IPT and are defined by Keele (1968) as “... a set of muscle

commands that are structured before a movement sequence begins, and that allows the entire

sequence to be carried out uninfluenced by peripheral feedback”. Motor programs, like computer

programs, are written or sequenced prior to execution. Once execution begins, they continue

from start to finish without any feedback required. An example would be throwing a ball.

Information from the environment informs the amount of force required to reach a target, and the

angle of release that will help the ball reach a target. According to IPT, the motor program of

winding the arm back, accelerating forward, and releasing it at the desired angle is executed

without feedback in an open-loop fashion.

Initial iterations of IPT had several problems including the storage problem: the inability to

explain the storage of millions of motor behaviours in the human memory system; and the

novelty problem: the inability to explain how, despite identical intentions, no two movements are

executed in exactly the same way (36).

To account for these problems, Schmidt proposed schema theory (1975), a variation on

information processing theory (36). Central to this theory is the concept of a generalized motor

program (GMP) which is defined as an “abstract representation of a movement plan, stored in

memory, that contains all the motor commands required to carry out the intended action” (36).

For example, to throw a ball 5 feet and to throw the same ball 50 feet, the same ball-throwing

GMP is accessed from memory and executed with different parameters. It includes “invariant

features”, that include the sequence of movements and their relative timing (37), and modifiable

6

“movement parameters”, that change depending on the goal (37,38). In the example of ball

throwing, absolute speed of throw, force, and direction would be parameters that would vary

based on the goal. A schema is a set of rules governing movement, and information from

schemas modify the parameters of the GMPs (36). Motor learning involves the development of

schemas (36,37).

According to schema theory, a single movement pattern has both a recall schema and a

recognition schema (36). A recall schema is the set of rules governing movement production and

the relation between movement parameters and action outcomes. A recognition schema is the set

of rules governing the evaluation of a movement and involves the relation between the sensory

feedback of an action and the parameters and outcome of the movement. Note that while the

definition of a motor program by Keele (1968) (39) omitted any mention of feedback, Schmidt’s

schema theory (36) suggests that feedback is integral to the recognition schema, and therefore,

the evaluation of a movement. Schmidt suggests that a movement relies on a generalized motor

program (36).

According to schema theory, learning a motor sequence, such as learning a piano melody,

involves developing a generalized motor program, a recall schema, and a recognition schema

(36). With accumulating piano practice, these schemas become better defined. The development

of the recall schema would involve learning the sequence and timing of finger movements. The

recognition schema would rely on a person’s ability to perceive the auditory feedback of the

sound of their melody in relation to an internal representation of their intended melody and the

kinesthetic feedback of their movements in relation to what they had learned. The recall schema

would continue to develop as the recognition schema contributed to error detection.

Dynamical Systems Theory

In contrast to information processing theory is the dynamical systems theory (DST) (40,41). DST

posits that movement emerges as a function of the constraints on the movement system (32,41).

The system consists of the environment, individual, task, and their interactions (41). This theory

relies on the interaction of numerous complex systems and suggests that motor behaviour

changes nonlinearly and arises without any central control. According to DST, motor learning

emerges when any of the constraints of the environment, individual, or task change.

7

In piano learning, players are subject to the constraint of the ascension of pitch from left to right

on the instrument, their intended melody, and their attentional focus, in addition to many other

constraints. A person might feel most comfortable playing a sequence with only their index

finger however in our task and in most cases of piano learning, the learners are constrained to use

each finger for only one piano key.

DST relies on several principles. One of these principles is called perception-action coupling and

refers to the way in which sensory cues shape behaviour. For example, music frequently compels

synchronization of movement to the beat (42). The emergent synchronization can be explained

by a coupling between neural auditory rhythm perception and motor systems (43).

In DST, all tasks have control parameters and order parameters. Control parameters are the

independent variables that exert influence on a movement system and catalyze changes to a

system. The order parameters are variables that describe the quantitative changes to the

movement system (35,44). For example, if playing the piano, tempo (control parameter) would

influence the speed of movements (order parameters). Constraints of piano learning may be

components of the environment (e.g. background noise and gravity), task (e.g. tempo, melody,

piano size, and amount and type of feedback), or individual (e.g. finger size and dexterity,

emotional expressivity, and cognitive capacity).

Music Production and Theories of Motor Learning

Each of the theories contribute to a framework for testable predictions. Music production relies

on auditory processing and timing, sequencing, and spatial actions rely on motor control systems

(45). Schema theory explains how learners use auditory feedback in error detection of incorrect

rhythms and pitches. Dynamical systems theory helps explain synchronization to music, and

subsequent rhythm production. Therefore, both theories are helpful when conceptualizing the

process of music learning.

2.2 The Effects of Exercise on Motor Learning

Currently there are 16 original research articles examining theses effects. Most studies have been

conducted by the same three research teams. Six studies have been conducted at the University

of Copenhagen in affiliation with Drs. Jens Bo Nielsen and Jesper Lundbye-Jensen (7–

9,11,15,46), three studies have been conducted in affiliation with Dr. Marc Roig at McGill

8

University (47–49), and three studies having been conducted by Dr. Lara Boyd and colleagues at

the University of British Columbia (2,28,50). The majority of these studies have employed a

visuomotor tracking task that involves manipulating a computer cursor to trace an outline (n =

10) (2,7–9,11,15,46,48–50). Many have exclusively examined males (7–9,11,15). There are still

many questions that can be explored by varying the types of tasks examined (i.e. different

complexities, feedback, practice schedules), perfecting the high-intensity exercise protocol, and

examining different populations of participants. To mitigate the reproducibility crisis observed

throughout psychological science, replication attempts should take place in different laboratories

(51). So far, no studies by different laboratories have directly replicated each other therefore the

reproducibility of this research remains under scrutiny. More research is necessary to understand

the effects of exercise-enhanced motor learning.

Research on high-intensity exercise for motor learning requires several key elements in the

research design including 1) a graded exercise test, 2) a motor learning task, and 3) an exercise

protocol. Participants report to the laboratory to undergo a graded exercise test (GXT) in which

gas exchange measurements determine participants’ cardiorespiratory fitness as measured by

peak oxygen consumption (VO2peak). Participants return in a subsequent session to perform a

motor learning task and to complete a single bout of exercise (experimental condition) or to rest

(control condition). Protocols vary in the presentation order of exercise and motor learning.

Boyd’s group typically places exercise before motor acquisition to “prime” learning. Roig’s

group typically places exercise after motor acquisition to promote consolidation (see section

2.2.2.3 for a discussion on the differences between priming acquisition and promoting

consolidation). The exercise protocol is high-intensity interval training (HIIT). Participants then

perform a combination of an immediate retention test (1-hour) and at least one delayed retention

test (8-hours, 24-hours, and/or 7-days). These studies aim to explore the parameters and

underlying mechanisms of this effect. In the following sections, the differences between these

studies will be explored and the gaps of this field of research will be identified.

Exercise

Exercise is defined as a type of physical activity that is planned, repetitive, and performed with

the intention of improving or maintaining physical fitness (52). There are several different types

of exercise. Moderate intensity, endurance exercise can be sustained for prolonged periods of

9

time and is also known as aerobic exercise because it relies on oxygen consumption. As exercise

intensity increases, the exercise becomes anaerobic when the body is no longer able to supply

oxygen at a sufficient rate.

2.2.1.1 Physiology of Exercise

Performing exercise requires increased energy consumption because of increased muscle usage

(53). Fueling the cells of skeletal muscle relies on sources of adenosine triphosphate (ATP). ATP

is stored in muscular cells; however, when sources become depleted, it can be regenerated via

three mechanisms: 1) anaerobic hydrolysis of phosphocreatine, 2) aerobic glycolysis, and 3)

anaerobic glycolysis (53).

2.2.1.2 Aerobic Exercise

During moderate intensity exercise, the aerobic system is the dominant supply of energy because

it is the most efficient. This energy system relies on the supply of oxygen to the cells. The

amount of oxygen that an individual can use during exercise is directly related to their

cardiorespiratory health. Glycolysis causes the release of energy and production of ATP as

glucose is broken down into pyruvate.

2.2.1.3 Anaerobic Exercise

As exercise intensity increases and the demand for ATP is higher than that which can be supplied

with aerobic glycolysis, the body has reached the anaerobic threshold and begins anaerobic

glycolysis. The anaerobic glycolysis system provides ATP very quickly; however, it is much less

efficient than aerobic glycolysis. Without oxygen, pyruvate is converted to lactate and there is an

accumulation of hydrogen ions. The hydrogen ions lower the pH leading to muscle acidosis

which causes the burning sensation characteristic of high-intensity exercise. Colloquially, this is

referred to as lactic acid build-up however, lactate is not inherently bad. Lactate can be used as a

primary energy source for the heart and brain and is used by the liver to regenerate glucose (54–

56). An acidic environment in the muscles can reduce their proper functioning and contributes to

the fatigue that is quickly experienced while training at this intensity. Therefore, the average

person cannot sustain high-intensity exercise for very long. Interval training protocols have been

developed to allow people to train at high intensities for longer by interleaving high-intensity

bouts with active recovery intervals.

10

2.2.1.4 Gas Exchange and Exercise Testing

To calculate how much oxygen is consumed during exercise, gas exchange equipment compares

the proportion of oxygen and carbon dioxide in air expired during exercise to room levels. The

expired air will have less oxygen and more carbon dioxide than room levels and these

differences will increase as intensity of exercise increases because of the increased demand on

oxygen. Gas exchange measurements indicate fitness and the dominant energy system.

The respiratory exchange ratio (RER) is the ratio of produced carbon dioxide to consumed

oxygen. When the respiratory exchange ratio increases above 1.0, this marks the anaerobic

threshold (57).

Exercise testing is used to assess cardiovascular and pulmonary health and cardiorespiratory

fitness. A measure of cardiopulmonary health is an individual’s maximal aerobic power, VO2max.

VO2max is defined as the maximum amount of oxygen that an individual can use while

exercising. Originally, VO2max was defined as a plateau or levelling off of the volume of oxygen

that is consumed with increasing workloads (53). However, in some cases, there is no plateau

prior to a participant’s volitional exhaustion (53). Therefore, an estimate of maximal aerobic

power (VO2max) is maximum aerobic power (also known as VO2peak). VO2peak is measured in a

graded maximal exercise test (GXT) where participants exercise at gradually increasing

intensities until they have reached their maximum capacity and stop due to volitional exhaustion.

Current recommendations suggest that these tests are most accurate when they last between 8

and 12 minutes (58,59).

According to statistics Canada, for Canadian adults aged 20-39, the average VO2 peak in males

is 44.08 mL/kg/min and in females is 38.45 mL/kg/min (60). Interestingly, despite the

physiological nature of these tests, trained endurance athletes achieve higher VO2peak values

when tested during their specific sport (61). Therefore, a trained cyclist would perform better and

achieve a higher VO2peak on a cycle ergometer than in a maximal test on an untrained sport, such

as rowing.

Most previous research on exercise and motor learning has used cycle ergometry. Previous work

using leg cycle ergometry to measure VO2peak has employed a protocol that begins at 50 W

(range: 30-50 W) and increases by 30 W per minute (range: 20-30 W) (62). A participant’s

11

maximum power output (Wmax) is the power output during the final fully completed stage of the

GXT and is used to prescribe individualized intensities during the interval exercise protocol.

Continuous or endurance exercise is exercise performed at a steady low to moderate workload.

These protocols are at a moderate intensity because it is difficult maintaining high-intensity

exercise continuously. To perform high-intensity exercise, interval protocols that involve

alternating short bouts of high-intensity exercise with active rest intervals have gained

popularity.

Manipulations to the Interval Exercise Protocol

Repeated sessions of high-intensity interval training (HIIT) effectively and efficiently improve

cardiorespiratory fitness (63,64). A meta-analysis on 28 randomized controlled trials that

compared HIIT to endurance exercise identified that training interventions with HIIT are slightly

more beneficial for improving cardiorespiratory fitness than training interventions with

endurance exercise (63). HIIT is more efficient than continuous endurance exercise because the

fitness and health benefits can be achieved in less time (63,64).

HIIT can also be beneficial for cognition (65,66). Intensity moderates the effects of acute

exercise on cognition (65). Specifically, when performance is measured immediately after high-

intensity exercise, performance is not improved (65), however after a delay, high- and moderate-

intensity exercise improve crystallized intelligence and executive functioning (65).

HIIT is not to be mistaken for sprint (or supra-maximal) intensity interval training (SIT) which

consists of 4-10 intervals at >150% of VO2max power for 20-30 seconds (67). HIIT consists of 8-

12 intervals between anaerobic threshold and maximum aerobic capacity for 1-4 minutes and

only HIIT reliably improves cardiovascular fitness because SIT targets anaerobic capacity more

than aerobic fitness (67). As exercise intensity increases within an acute bout, emotional affect

decreases and this may result in poor adherence to high-intensity training interventions (68). A

satisfactory warm-up and cool-down can improve the affective experience of HIIT (69) and with

repeated exposure to high-intensity exercise, displeasure continues to decrease (70).

Many HIIT protocols have been developed, and more research is required to understand which

protocol is best for improving motor consolidation. However, the focus of the present study is

the manipulation of the type of motor learning task. To compare results of this study to previous

12

research on exercise and motor learning, the HIIT protocol employed in this study replicates that

which has been previously employed in this body of literature (2,11,15,28). As will be discussed

in section 2.2.6, it is believed that if the HIIT protocol increases blood lactate above the

threshold of 10 mmol/L, the effects of exercise on motor learning will be observed. Both a low-

volume 12-minute session of HIIT and a high-volume 8-minute session of HIIT required greater

oxygen consumption, and resulted in greater release of blood lactate than 25 minutes of moderate

intensity continuous exercise (71). Longer durations of intervals cause higher increases in blood

lactate, one of the possible mediating neurochemicals, which suggests that HIIT protocols with

longer intervals may support enhanced motor consolidation (72) (see section 2.2.6.1.1 for more

information on lactate). However, shorter duration intervals are more tolerable (73), therefore

population characteristics should be considered when selecting a HIIT protocol. Low-volume

HIIT protocols with shorter interval durations could be more appropriate for deconditioned

populations (73–75).

The exercise intervention that has been most extensively researched within the literature on

motor learning is a high-intensity exercise protocol that involves cycling on a stationary cycle

ergometer at alternating high and low intensities (1,2,7,9,11,15,49,76). Typically, it consists of a

warm-up ranging from 2 to 5 minutes at an intensity between 50 and 75 W followed by 3

repetitions of alternating intervals of 3-minutes at a high-intensity of 90% Wmax and 2-minutes at

a low-intensity, either at 60% Wmax (2,11,15,28), 50 W (1,6), or 25% Wmax (47). This protocol

has consistently resulted in blood lactate levels above 10 mmol/L (7,9,15).

Studies on exercise and motor learning have manipulated the parameters of the interval exercise

protocol such as modality, intensity, and timing to understand the underlying mechanisms and

the limitations of exercise’s benefits.

2.2.2.1 Exercise Modality

In a meta-analysis examining acute bouts of exercise on cognitive performance, cognitive

performance was enhanced both during and after cycling while running impaired performance

during cognitive tasks and improved performance only slightly when running occurred before the

tasks (66). Therefore, leg cycling was chosen as the exercise modality of the current study and as

the modality in several previous studies examining exercise’s effects on motor learning

(1,2,7,11,15,49,76).

13

Some studies have examined the effects of exercise modality on motor learning (8,16,46). One

study examined high-intensity running and floorball compared to a resting condition in children

(46). At 7-day retention test, the floorball group was significantly better, and the running group

was trending towards better performance, than the control group (46). Another study compared

the effects of high-intensity strength training, circuit training, and indoor hockey to a resting

control group and found that all three exercise groups performed better than the control group at

24-hour retention (8). Studies on stroke patients have used other types of exercise including

treadmill walking, seated upper and lower body ergometer, and a whole-body recumbent stepper

to administer high-intensity exercise (48,77). Therefore, it appears that the underlying

neurophysiological effects of exercise which will be discussed in section 2.2.6 are more

important than the type of exercise itself.

2.2.2.2 Exercise Intensity

The effect of exercise intensity has also been explored. A study compared the effects of high-

versus moderate-intensity interval training on motor learning (15). The high-intensity group

alternated three repetitions of 3-min 90% and 60% Wmax and the moderate-intensity group

alternated three repetitions of 3-min 45% and 2-min 25% Wmax. Thomas et al. (2016) found that

the high-intensity group performed better than the resting control group at 1-day and 7-day

retention tests. Interestingly, the moderate-intensity group also performed better than the control

group at 7-day retention.

Moderate intensity exercise appears to be more beneficial for motor acquisition than motor

consolidation (50). Participants either rested (control condition) or cycled moderately for 30-

minutes at 60% of VO2peak. Post-hoc testing revealed that moderate-intensity exercise before

motor learning helped maintain motor performance on a visuomotor accuracy tracking task

during acquisition while simply resting caused a deterioration in performance. This is

corroborated by research examining cognitive tasks—exercising at a moderate intensity before

cognitive tasks improves arousal and attention (65).

2.2.2.3 Exercise Timing

The effects of exercise on motor learning appear to be time-dependent (78). Roig et al. (2012)

demonstrated that exercising after motor acquisition compared to before resulted in enhanced

14

motor retention 7 days later (7). Thomas et al. (2016) aimed to replicate these effects (11).

Performance at retention was compared between the experimental groups who exercised 20

minutes, 1 hour, and 2 hours after learning, and a resting control group. Exercise-induced

enhancements to motor consolidation are greatest when exercise takes place approximately 20

minutes to 1 hour after acquisition, with effects fading if exercise is performed 2 hours after

learning (15).

A few studies administered high-intensity exercise before motor acquisition to prime learning

(1,2,7,9). When comparing exercise before learning to after learning, the effects on consolidation

were modestly better when exercise took place after learning (7). When examining continuous

implicit motor sequence learning, exercising before practice promoted acquisition of the

temporal component (but not the spatial component as observed in other studies (9,78)) of the

implicit sequence, this was maintained at retention (2). When examining discrete implicit motor

sequence learning, the rate of retrieval of the targeting task was better for a group who exercised

before acquisition when compared to a resting control group; however, average performance

between groups did not differ (1). It is possible that priming motor acquisition with exercise

increases arousal, reduces inhibition, and promotes neuroplasticity that persists to the early

consolidation phase. To understand whether priming motor acquisition or promoting motor

consolidation is more effective, more research should explore whether the effects on implicit

motor sequence learning could be stronger if exercise takes place after acquisition.

Interestingly, when exhaustive exercise immediately precedes motor learning, motor task

performance may decrease (79). Negative effects of high-intensity exercise have also been

observed in the literature on cognition (65). Immediately after high-intensity exercise,

participants are continuing to recover and may not perform as well as if there is a recovery

period (65). Therefore, the evidence suggests that high-intensity exercise is best positioned after

learning, during consolidation.

2.2.2.4 Summary

HIIT after motor acquisition, during early consolidation demonstrates the strongest benefits to

motor learning as measured with delayed retention tested at 24-hours and 7-days after initial

acquisition (7,9,11,15). The exercise modality does not seem to affect the effects of HIIT on

motor learning, however research on exercise and cognition suggests that leg cycling may be

15

more beneficial than running for improving performance on cognitive tasks. HIIT protocols

lasting for 20-minutes are short enough to prevent dehydration and excessive fatigue that may

result from exercise at longer durations (7,15,80). Further research is required to determine the

exercise protocol that is most effective for improving motor learning as most research has

employed a protocol with 3 repetitions of 3-minute high-intensity intervals and 2-minute low-

intensity intervals (1,2,7,11,15). It is possible that other HIIT protocols could be more effective

at improving motor learning.

Task Parameters

Research on the effects of exercise on motor learning has also explored manipulations to the

motor learning task to examine the task parameters that affect HIIT’s benefits.

2.2.3.1 Feedback

Feedback is an important component of motor learning. Feedback may be presented in forms of

knowledge of performance or knowledge of results. Knowledge of performance feedback is

information on how a movement is performed, for example, watching a video of one’s own

performance (81). Knowledge of results feedback is information on the performance outcome of

a movement, for example, detecting an incorrect note while performing a melody from memory.

In music, knowledge of results, or error detection, is possible without additional feedback from

the experimenter if a learner has developed a correct memory or recognition schema of a melody

(36,82).

Feedback can be leveraged to manipulate the type of motor learning—by providing feedback,

motor learning becomes more explicit and by limiting feedback, it becomes more implicit (83).

When experts were asked what conditions foster implicit motor learning, 43% agreed that only

knowledge of results feedback should be provided (83). So far, in the literature on the effects of

exercise on motor learning, the tasks have either provided no feedback to motor learners (1,2,47),

or they have provided knowledge of results (7–9,11,15,46,48,49). Most tasks have not limited

the participants abilities to view their movements therefore, knowledge of performance is

intrinsic to the tasks (1,2,7,9,11,15, c.f. 84).

Interestingly, those studies that provided feedback observed stronger effects of exercise on motor

learning (7,8,11,15), while the studies without feedback either observed no significant effects

16

(47), or differences in only one of the performance measures (2) (see table 1). Other reasons for

these differences among results are explored in the following sections, however it is possible

feedback might be necessary to observe the enhanced learning effects of exercise.

2.2.3.2 Task Type

The following tasks have been examined: 1) visuomotor tracking task, 2) discrete serial targeting

task, 3) serial reaction time task, 4) visuomotor adaptation task, and 5) locomotor learning task.

2.2.3.2.1 Visuomotor Tracking Task (see figure 2, 3, & 4)

In a visuomotor tracking task, participants manipulate an on-screen cursor to trace a target

trajectory by following a line (7–9,11,15,46) (figure 2), moving point (1,2) (figure 3), or

rectangular targets (48,49) (figure 4). Participants learn to control their movements from the

visual feedback. In one study, a repeating sequence was embedded within random sequences to

tease apart the differences between implicit sequence learning and motor control (2). Without an

embedded repeating sequence, there is no way to disentangle the effects of exercise on implicit

motor sequence learning and improvements to visuomotor control.

Figure 2: This visuomotor accuracy tracking task was used by Roig et al., 2012 to first

examine the effects of exercise on motor learning.

17

Figure 3: Mang et al. (2014) examine implicit sequence-specific motor learning using a

continuous visuomotor tracking task.

Figure 4: Dal Maso et al. (2018) and Nepveu et al. (2017) used an upper limb visuomotor

tracking task called the time on target task.

18

2.2.3.2.2 Discrete Serial Targeting Task (see figure 5)

In a discrete serial targeting task, participants manipulate a cursor to cued locations on a screen

(1). A repeating sequence was embedded within repeating sequences to examine implicit

sequence learning (dashed line in figure 5).

Figure 5: Mang et al. (2016) employed a serial targeting task to examine the effects of high-

intensity exercise on discrete, implicit motor learning.

2.2.3.2.3 Serial Reaction Time Task (see figure 6)

The serial reaction time task (SRTT) is an implicit task that has been used extensively in the

motor learning literature (85) in which participants view a screen with four black rectangles that

correspond to four buttons at the fingers of their dominant hand (figure 6). When a rectangle is

cued, the participant’s task is to press the corresponding button as quickly as possible. There is a

repeating sequence embedded within random sequences. Learning is measured as the participant

becomes faster at the repeating sequence, but not the random sequences (54).

19

Figure 6: Ostadan et al. (2016) used the SRTT to measure the effects of exercise on motor

learning.

2.2.3.2.4 Visuomotor Adaptation Task (see figure 7)

In contrast to visuomotor learning tasks, a visuomotor adaptation task requires participants to

adapt to perturbations in their environment to return to a previous level of performance (84) (see

figure 7). A common visuomotor adaptation task involves moving a cursor as quickly and as

directly as possible to a target that appears in a random location about a circle. After a baseline

measure of performance, the coordinate axis is rotated, and participants must adjust their

movements to the rotation.

20

Figure 7: Ferrer-Uris et al. (2018) used a visuomotor adaptation task to examine exercise’s

effects on motor learning.

2.2.3.2.5 Locomotor learning task

In a locomotor task, participants learn to adjust their walking on a split-belt treadmill. For

example, in a study examining stroke patients’ motor learning, patients learned to walk on a

split-belt treadmill in a 2:1 speed ratio (77).

2.2.3.3 Summary of Studies

The studies on the effects of motor learning and exercise differ in their types of task and

feedback, and their exercise protocols. High-intensity exercise improves delayed retention of

visuomotor tracking tasks (7,8,11,15). The effects of high-intensity exercise on implicit

visuomotor sequence learning appear to be more nuanced (1,2). Specifically, exercise promoted

better consolidation of the temporal, but not spatial, component of an implicit continuous

visuomotor sequence (2); and exercise enhanced sequence-specific rate of retrieval, but not

absolute performance, at delayed retention of a discrete motor task (1).

None of these studies have specifically examined discrete explicit motor sequence learning and

there is reason to believe that consolidation mechanisms underlying explicit and implicit learning

are different (24,86). Specifically, explicit motor consolidation relies on a period of sleep

between acquisition and retention while implicit motor learning is consolidated over time.

21

Explicit and implicit motor learning rely on partially distinct, but overlapping neural systems,

that both include the striatum with the anterior cingulate cortex/mesial prefrontal cortex exerting

control over activity of the striatum during explicit motor sequence learning (87).

Table 1: Summary of studies examining high-intensity exercise on motor learning

Study Task Feedback Exercise Result at retention

Roig et

al. (2012)

Figure 2

Visuomotor tracking

task

KP &

KR

1) Ex90 before

2) Ex90 after

3) Rest

1d & 7d: (1 & 2) > 3

7d: 2>1

Skriver et al.

(2014)

Figure 2

Visuomotor tracking

task

KP &

KR

1) Ex90 before

2) Rest

1d & 7d: 1 > 2

Dal Maso et al.

(2018)

Figure 4

Visuomotor tracking

task

KP &

KR

1) Ex90 after

2) Rest

8h: No differences

1d: 1 > 2

Thomas et al.

(2016)

Timing

Figure 2

Visuomotor tracking

task

KP &

KR

1) Ex90 after

2) Ex90+1h after

3) Ex90+2h after

4) Rest

1d: 1 > 3 & 4

7d: (1, 2, & 3) > 4 and

1 > 3

Thomas et al.

(2016)

Intensity

Figure 2

Visuomotor tracking

task

KP &

KR

1) Ex90 after

2) Ex45 after

3) Rest

1d: 1 > 2 & 3

7d: 1 > 2 > 3

Thomas et al,

(2017)

Type

Figure 2

Visuomotor tracking

task

KP &

KR

1) Strength training

2) Circuit training

3) Hockey

4) Rest

1d: (1, 2, & 3) > 4

Lundbye-Jensen

et al. (2017)

Figure 2

Visuomotor tracking

task

Children

KP &

KR

1) Running

2) Floorball

3) Rest

7d: (1 & 2) > 3

Nepveu et al.

(2017)

Figure 4

Visuomotor tracking

task

Stroke patients

KP &

KR

1) Ex90 after

2) Rest

7d: 1 > 2

Mang et al.

(2014)

Figure 3

Visuomotor tracking

task

Implicit sequence

learning

KP 1) Ex90 before

2) Rest

1d: 1 > 2 with time

lag of repeated

sequences

Mang et al.

(2016)

Figure 5

Discrete serial

targeting Task

Implicit sequence

learning

KP 1) Ex90 before

2) Rest

1d: 1 > 2 with rate of

retrieval of repeated

sequences

22

Ostadan et al.

(2016)

Figure 6

Serial reaction time

task

Implicit sequence

learning

KP 1) Ex90 after

2) Rest

8h: No differences

Ferrer-Uris et al.

(2017)

Figure 7

Visuomotor

adaptation task

KP 1) Ex85 run before

2) Ex85 run after

3) Rest

1h: (1 & 2) > 3

1d & 7d: No

differences

Charalambous et

al. (2018)

Locomotor learning

task (split-belt

treadmill)

Stroke patients

KP 1) Total body

exercise before

2) High-intensity

treadmill walking

after

3) Low-intensity

treadmill walking

after

1d: No differences

Feedback: KP: knowledge of performance; KR: knowledge of results; Exercise: Ex#: #% of VO2peak or Wmax;

Ex90+#h: # of hours after acquisition that exercise occurred; Result at retention: #h/d: # of hours or days after

acquisition that retention occurred

Transfer and Interference

Transfer is a phenomenon in which learning in one context affects learning in another

context (88). Transfer can be categorized along two continuums: 1) positive and negative; and 2)

near and far (88). Positive transfer is when learning in one context enhances learning in a new

context and negative transfer is when learning in one context deters learning in a new context

(88). Negative transfer may also be referred to as interference. Near transfer is transfer to a task

that is very similar to the learned task and context (88). For example, transferring learning to the

untrained limb or to a new sequence, are examples of near transfer (89–91). Far transfer is when

training on one task affects learning a task that is seemingly distant in context from original

learning (88,92). For example, positive far transfer could be stroke patients trained on piano

playing demonstrate improved performance on clinical measures of functional limb usage (93).

Transfer has been examined in the motor learning research by applying learning in a different

environment or context, to a non-dominant hand, or with a new motor sequence (88,89,91,93).

In the present study, we wanted to understand how exercise might promote transfer to a new

piano melody. To explore whether there would be differences in learning, we used the same

protocol that we used for our acquisition melody to compare changes in learning between

acquisition and transfer.

23

Motor sequence interference (negative transfer) may occur if after one sequence is learned, a

different sequence is learned soon afterwards, while the first sequence is still being consolidated

(94–96). The consolidation of the first sequence is disrupted by learning the second sequence

(91). In previous research, a 90-minute period of sleep between learning two sequences reduced

interference effects (97). Moderate-intensity exercise immediately before learning a second

motor sequence, but not immediately after learning the first motor sequence, reduced

interference effects (98).

Exercise and Sleep

Sleep is important for consolidation, especially for explicit motor sequence learning (86). The

effects of exercise may interact and rely on the presence of sleep during consolidation. No study

that used a delayed retention test that took place before a window of sleep has demonstrated

exercise-induced enhancements to motor consolidation (49,76). Similarly, a study that examined

the effects of moderate-intensity exercise on protection against interference found that exercise

only trended towards protecting against interference and failed to replicate previous findings

with only a 6-hour retention test (prior to a window of sleep) (99). These findings suggest that

sleep may be necessary to observe the benefits of exercise on consolidation.

Proposed Mechanisms

High-intensity exercise causes many physiological changes that might contribute to exercise-

enhanced motor consolidation (10). It is believed that high-intensity exercise enhances motor

learning because it promotes neuroplasticity—the brain’s ability to change—during

consolidation (7). The exact mechanism causing enhanced neuroplasticity is unknown though it

could be one or a sum of different physiological changes. These changes have been examined in

studies using procedures involving 1) measuring the release of neurochemicals, 2) non-invasive

brain stimulation, and 3) neuroimaging.

2.2.6.1 Neurochemicals

High-intensity exercise causes the release of growth factors, hormones, and neurochemicals,

particularly catecholamines (9,100–102). Lactate, brain-derived neurotrophic factor, and

dopamine are three of the chemicals that have been linked with benefits to memory.

24

2.2.6.1.1 Lactate

Lactate (also known as lactic acid) is a by-product of anaerobic respiration which is the dominant

energy system engaged during high-intensity exercise. Higher levels of lactate at the end of

exercise were correlated with higher levels of performance in retention tests at 1 hour, 1 day, and

7 days after learning a visuomotor tracking task (9). As blood lactate levels increase, lactate

becomes the primary energy source for the brain as opposed to glucose (9,103,104). Further,

blocking lactate transport between astrocytes and neurons inhibits long-term memory formation

in mice (105). Other research in mice revealed that lactate is a signaling molecule for synaptic

plasticity and increases activity of NMDA receptors in the sensorimotor cortex (106). This work

has been corroborated in humans since increases in blood lactate were associated with increased

motor cortical excitability—a measure of plasticity (107). Interestingly, levels of lactate are not

correlated with performance if the exercise is of a moderate intensity (99); therefore, it is

possible that learners must exceed an intensity threshold to reap the benefits of elevated lactate

after exercise. Most research on exercise and motor learning has employed an exercise

intervention that reliably elevates blood lactate levels to greater than 10 mmol/L (11,15).

Previous research has failed to show correlations between lactate concentrations and motor

performance; however, it is possible that once a threshold of lactate concentration is passed,

there is no additional benefit for more lactate.

2.2.6.1.2 Brain-derived neurotrophic factor

Brain-derived neurotrophic factor (BDNF) is a chemical involved in the process of long-term

potentiation which is one of the mechanisms underlying the learning process (9). Vigorous

exercise increases circulating BDNF (108). Levels of BDNF after high-intensity exercise were

correlated with retention at 1 hour and 7 days after learning a motor task (9). Interestingly, no

correlation between performance and BDNF levels has been observed at the 24-hour retention

test (9). There are genetic variants of the BDNF molecule and these polymorphisms have

different effects on learning. One study has examined the influence of genetics of motor learning

(109). BDNF polymorphisms did not impact the effects of exercise on a 24-hour retention test;

however, polymorphisms of another neurochemical—dopamine—did influence the effects of

exercise on motor learning (109).

25

2.2.6.1.3 Dopamine

Dopamine is a neurochemical released during high-intensity exercise and dopamine is also

associated with reward, addiction, and learning (110). Consistent with how high-intensity

exercise demonstrates the greatest improvements to consolidation, in rats, dopamine levels are

increased only once an intensity threshold is passed (111) and in humans, neural dopamine

increases were not detected after moderate-intensity exercise (112,113).

A dopamine receptor polymorphism that modifies dopamine transmission mediates the effects of

exercise on 24-hour motor retention (109). Specifically, people with the polymorphism that

allows the greatest dopamine transmission benefitted from the exercise while those with

polymorphisms known to transmit less dopamine demonstrated fewer enhancements from

exercise on motor learning (109,114). Dopamine is a catecholamine along with epinephrine and

norepinephrine. Norepinephrine has been correlated with motor performance in a retention test 7

days post-acquisition.

Further research is needed to understand the contributions of each neurochemical on the

hyperplastic effects of exercise. The singular, additive, or interactive effects of these chemicals

are the probable mechanisms driving the effects of exercise on motor learning.

2.2.6.2 Evidence from non-invasive brain stimulation

Exercise also modulates excitability of neural circuits. High-intensity exercise causes

disinhibition and excitation if the primary motor cortex (M1) (2,115)and disinhibition of

cerebellar circuits (116). Since M1 is engaged in both consolidation and storage of motor

sequences, high-intensity exercise may enhance consolidation of motor sequences (1,2,117,118).

Error-correction or optimization mechanisms controlled via the cerebellum and necessary for

performing music may similarly be enhanced (45,116,117).

2.2.6.3 Evidence from neuroimaging

One study used electroencephalography (EEG) to examine the neural mechanisms underlying the

effects of exercise on motor learning (49). They found that motor skill retention was associated

with beta-band event-related desynchronization in the left sensorimotor areas (contralateral to the

hand used for the visuomotor tracking task). Since beta-band event-related desynchronization is

26

thought to be related to motor planning and execution, the authors posit that the reduction in

desynchronization reflects greater efficiency of neural underpinnings of motor consolidation.

fMRI

Blood oxygen level dependent (BOLD) signal in the left parietal operculum (secondary

somatosensory cortex: S2) decreased in response to acute moderate exercise; however another

study using resting state fMRI suggested increased coactivation of S2 (119,120). A resting state

fMRI study demonstrated that moderate intensity exercise increased co-activation of primary

motor and somatosensory cortices, secondary somatosensory cortex, and the thalamus (120).

Cerebral blood flow increased in white matter and decreased in grey matter which the authors

suggest may reflect changes in functional connectivity that could result in exercise-induced

enhancements to attention (119). Furthermore perfusion was decreased in the hippocampus and

insula (119). More research should explore the effects of high-intensity exercise on

neuroimaging biomarkers.

2.2.6.4 Summary

Consolidation involves several neural regions that are also excited by high-intensity exercise

including the striatum, primary motor cortex, the parietal cortex, and the hippocampus (117).

While the underlying mechanism of exercise’s effects on motor learning remain unknown, there

are many possible mechanisms that could be enhancing activity of the neural regions involved in

consolidation. It is unlikely that only one of the neurochemicals is driving the effects, and instead

it is likely that this coordinated symphony of neural activity all contributes and interacts to cause

the observed behavioural enhancements. Further research will illuminate a better understanding

of the interaction of the neural changes caused by high-intensity exercise and how they affect

motor consolidation.

Ecological validity

Designing research with ecological validity requires examining phenomena in contexts that are

similar to where the phenomena occur outside the laboratory, in the real-world (121).

Ecologically valid research is important for understanding whether a finding can be generalized

to and applied in real-world contexts (13). Strategies that unlock human motor learning potential

are only effective to the extent that they can be applied in the real world—in gyms, classrooms,

27

and rehabilitation clinics. If high-intensity exercise cannot promote motor learning that is

relevant to athletes, musicians, and rehabilitation populations, then the effects of exercise may

not be clinically significant. While the previous literature at the intersection of exercise and

motor learning is necessary, it is not sufficient to demonstrate exercise’s efficacy as an

intervention to enhance everyday motor learning. Much of this research has focused on implicit

sequence learning. Implicit sequence learning is interesting, however most learning is rarely

purely implicit (117). Some researchers argue that no learning is truly exclusively implicit (26).

Some of the tasks examined thus far are similar to activities that are performed in everyday life,

such as modifying grip strength to hold objects or pressing buttons quickly and accurately for

typing. More research on a variety of skills is necessary to demonstrate whether the

physiological effects of exercise can further bolster their consolidation.

To the best of my knowledge, only one study has examined the effects of exercise on motor

learning of an ecologically valid task (12). Participants performed moderate-intensity exercise

and then learned laparoscopic skills. Moderate intensity exercise improved consolidation of

simple skills, as measured 2 months after training; however, exercise did not improve

consolidation of more complex skills (12).

The literature on exercise and motor learning has yet to examine ecologically valid tasks and

explicit motor sequence learning. Piano playing is an example of an ecologically valid explicit

motor sequence learning task and serves as the model task for the present study.

2.3 Music

Musical learning

Training protocols for non-musicians have varied in training duration (e.g. across days or weeks

(122) or single session (123)), presentation modality (e.g. visual (124) or auditory cueing (125)),

and stimuli (e.g. melodies (126) or rhythms (125)). Many of the studies involve multiple training

sessions. These multi-day studies typically measure differences in neuroplastic changes

(122,124,126); however neuroplasticity (122) and behavioural changes (123,127) can be

observed within a single session as well.

Since non-musicians are not able to read musical notation, some researchers train them to play

by ear while others use creative visual cueing. Lahav et al. (2005) trained 15-note sequences by

28

ear (123). They promoted chunking strategies by breaking the piece down into segments and

building up as participants learned the chunks. Learning times ranged from 12 minutes to 70

minutes, which reflects the large inter-individual variability of learning rate.

2.3.1.1 Auditory Working Memory

One participant characteristic that might influence learning time is auditory working memory.

People with larger working memories are better at explicit motor sequence learning (128).

Musicians also tend to have a larger working memories than non-musicians (129) either because

musical training increases working memory (130) or because they were predisposed to succeed

in music (131). Therefore, a larger auditory working memory makes music learning easier. In

our study, participants performed a forward auditory digit span task to assess their auditory

working memory span (see section 3.2.3 for more details).

2.3.1.2 Visually Guiding Musical Learning

A study by Brown and Penhune (127) aimed to distinguish the contribution of perception and

action to motor skill acquisition. They taught non-musicians 8 melodies, 4 easy and 4

challenging, always with visual cueing. One easy and one challenging sequence were

randomized into one of four conditions: visual-only, motor-only, auditory-only, or auditory

motor. Visual cueing consisted of presentation of 5 squares distributed horizontally across a

computer screen, each square representing a finger. The sequence was presented by sequentially

cueing the squares. There were test trials with no cueing interspersed throughout the training

protocol. They analysed pitch accuracy and rhythm accuracy of the test trials. They found that

there was no difference between conditions for participants’ pitch accuracy which suggests that

they can learn a sequence of movements in any condition. For rhythm accuracy, the auditory

group performed better than the motor-only group. This corroborates other research

demonstrating that humans are better at synchronizing to auditory rhythms than visual rhythms

(132).

In a study that examined piano training for patients recovering from stroke, researchers sent

participants home with a musical video game for three weeks and found improvements in fine

and gross dexterity, coordination, and functional use of the paretic hand (93). Synthesia (© 2018

Synthesia, LLC) is a computer game in which blocks representing musical notes descend until

29

they hit an on-screen piano keyboard. It is the participant’s task to press keys on a real-life

keyboard when the on-screen icon first touches the on-screen keyboard. A metronome counts

users into the melody and participants learn the association between the on-screen notes and

keyboard and the movements on the real-life keyboard required to play their target melody. This

training system is more like reading musical notation because musicians read ahead in their

scores to prepare for upcoming musical phrases. This is called the eye-hand span and musicians

read as far as 11 notes ahead when sight-reading a musical score (133). With the computer

program Synthesia, multiple notes descend on the screen in sequence therefore participants can

similarly read ahead.

Measuring Musical Learning

Performance is an indirect measure of learning. The learning-performance distinction is an

important phenomenon to consider when designing studies because a participant’s performance

does not always reflect true learning (31). For example, good performance at the end of learning

might not be reflected in performance at retention a week later. It is also important to measure

multiple performances (trials) because with repeated measurements, any performance

fluctuations will be washed out and an average will approximate true learning.

Researchers have frequently operationally defined a learned melody as one that has been played

correctly three times (134,135). Another study trained rhythm sequences to a criterion of above

80% for three consecutive trials (124).

There are many ways to evaluate a musical performance. At an elite level, judges might evaluate

a performance based on a player’s ability to convey emotion or their expressiveness. At a novice

level, it is more appropriate to assess a player’s ability to accurately perform the melody. Recall

that a melody is composed of pitches and their associated rhythms.

Mang et al. (2014) evaluated spatial and temporal components of their visuomotor task

separately (2). In a piano learning motor task, the spatial component of the piano learning task is

the pitch accuracy because participants are pressing buttons in space along the horizontal

dimension (Figure 8). The temporal component of the piano learning task is the rhythm accuracy,

in the time dimension.

30

Figure 8: The spatial component of the piano learning task is the accuracy of the

performed finger sequence.

As mentioned in section 2.2.3, some studies examining exercise on motor learning have

employed tasks that involve finger sequencing such as the discrete sequence production task

(47,98,99). Ostadan et al. recognized that musicians would be unfairly advantaged in their SRTT

procedure, so they excluded anyone with training on an instrument (76). Despite the lack of

musical component in this study, most instrumentalists have extensive training of fine

movements of their fingers and would therefore be advantaged in a finger-tapping task. To

ensure task sensitivity, it was important that they excluded people who were already experts at

the task as they might not be able to measure learning. Musicians may have much better baseline

abilities than non-musicians. The next section will discuss how previous research has defined

non-musicians.

Defining non-musicians

Previous musical training can influence rate of learning. We chose to recruit non-musicians

because they would begin at approximately the same baseline level of expertise. Any effects of

exercise promoting consolidation might be unmeasurable if participants learn the melody too

easily. It was important to recruit participants with little musical experience, even if their musical

Spatial distribution along the horizontal plane

31

experience was not with piano, because as previously discussed, musical training can improve

sequence learning abilities.

Previous research has defined non-musicians in a variety of ways ranging from the very stringent

to much more lenient. The general trend has been that definitions have become more lenient over

time as musical training has been increasingly implemented into the school system. In Ontario,

elementary school children receive training on a simple instrument like recorder in the public-

school system. In high school, students receive training on a concert band instrument in grade 7

and 8 and in grade 9 students may or may not choose to continue musical education. Training in

these settings is rarely extensive therefore participants still have minimal experience even when

they received instruction in school.

In previous research, non-musician status has been defined as “no musical expertise” (136), “no

musical training” (125), “non-musicians who could not play any instrument or touch-type” (137),

“had no previous musical training (including voice)” (123), “no training (self-directed or

instruction) longer than 6 months” (138), “never learned an instrument or singing, and they did

not have any special musical education besides normal school education" (139), no participants

had “previous musical training (with the exception of music classes at school) or had played the

piano before” (134), and “non-musicians, with an average of 0.4 years of formal training on any

instrument or voice” (127). More lenient definitions have included “selected to have a minimum

of musical training or experience (Avg. = 2.6 years; range = 0–4 years)” (140) , “less than three

years musical experience” (117,124,141).

We defined non-musicians as having received no musical training outside the regular school

system, no musical training greater than four years, and never any self-directed or formal

instruction on the piano. It was also important to exclude video gamers because this population

demonstrates better sensorimotor learning relative to the average population (142,143).

Therefore, we excluded participants with extensive video game training or who had competed in

a video game tournament.

Despite a lack of musical training, some people have greater musical propensity than others and

therefore learn melodies more quickly (123,134). Some people lack all propensity for music

(144,145). Colloquially, we call these people tone-deaf. Interestingly, many self-identified tone-

deaf people are, in fact, not tone-deaf. If a person can recognize the tune of a song without

32

accompanying lyrics, then they are not tone-deaf. If a person is unable to recognize a familiar or

popular song from an instrumental version, then it is possible they have a condition called

amusia, the scientific term for tone-deafness (144).

Another way to help people learn is by employing a faded feedback training protocol (146–148).

We provided more feedback at the beginning of training and reduced feedback in the second half

to facilitate learning.

2.4 Gap

This research project aimed to fill several gaps in the literature on the effects of high-intensity

exercise on motor learning. We used the model task of piano learning to examine explicit motor

sequence learning in an ecologically valid context.

33

Chapter 3

Objectives and Hypotheses

3.1 Objectives

Piano learning is an example of a discrete, explicit motor sequence learning task. We aimed to

examine whether high-intensity exercise after learning a piano melody can enhance (1) motor

consolidation and (2) transfer to a novel piano melody compared to low-intensity exercise.

3.2 Hypotheses

(1) We hypothesized that non-musicians who performed high-intensity exercise after learning a

piano sequence would demonstrate enhanced consolidation as measured by retention of the piano

sequence one day later and seven days later compared to a group who performed low-intensity

exercise.

(2) We hypothesized that high-intensity exercise after learning one piano sequence will promote

transfer to another piano sequence compared to a group that performed low-intensity exercise.

34

Chapter 4

Methods

4.1 Participants

We recruited healthy, able-bodied, right-handed non-musicians between the ages of 18 and 35

from the Greater Toronto Area. Participants were excluded if they had any health condition that

might affect their ability to perform high intensity exercise (i.e. cardiovascular disease,

tachycardia) or learn a motor sequence (e.g. depression, dyspraxia, developmental motor

coordination disorder). Similarly, if participants were taking any medications that might affect

their ability to exercise or learn a motor sequence, they were also excluded (e.g. anti-

depressants). Participants were screened for hearing and vision problems. Furthermore, it was

required that participants be able to perceive musical stimuli. Amusia is a condition in which

fine-tuned pitch discrimination is impaired. Participants were screened for amusia by testing

their ability to name the title of an instrumental version of a popular song (Happy Birthday)

(144,149). People with amusia would have greater difficulties learning the melody because they

would not be able to rely on auditory cues and error detection. Non-musicians were defined as

individuals who did not identify as musicians, who had less than 4 years any musical training

and who were not currently practicing any musical instrument.

Participants with a body mass index greater than 30, an approximation of obesity status, were

excluded from participating to further ensure safety of participants, unless their high BMI was

caused by weightlifting (7). Participants with extensive video game practice, especially on rock

band or guitar hero, or who had competed in a video game tournament were also excluded

because of their enhanced motor control and learning abilities (appendix A). All participants

provided written informed consent before the first session (appendix B). The study was approved

by the University of Toronto Research Ethics Board and the study was conducted in accordance

with the declaration of Helsinki.

Participants 16 and 17 failed to complete the interval exercise test. Both these participants

reported that they did not exercise on a weekly basis. To ensure that participants could compete

the interval exercise test, an exclusion criterion was added: participants who exercised less than

once per week were excluded from the study.

35

Figure 9: Schematic of study overview. Session

1 consisted of a graded exercise test to assess

participants' fitness. At least 24 hours later, in

session 2, participants learned a piano

sequence, exercised at their personalized high

or low intensity, and were tested again and

participants’ acquisition abilities were tested

on a novel sequence in the transfer task.

4.2 Procedure

Study Overview

The study consisted of a pre-screening and

four sessions: 1) graded exercise test, 2)

piano learning, interval exercise test, and 1-

hour retention test, 3) 24-hour retention test,

and 4) 7-day retention test and transfer test

(Figure 9). The pre-screening ensured that

participants met our inclusion criteria. In

session 1, participants filled questionnaires

on their demographics, musical experience,

and physical activity habits (see appendix C)

and performed a graded maximal exercise

test (GXT) on a cycle ergometer. The GXT

was used to determine cardiovascular fitness

(VO2peak) and maximal power output

(maximal watts: Wmax). Participants were

matched by gender and fitness for pseudo-

randomization prior to session 2. (Note:

Some participants were randomized after

baseline measurement of piano playing

abilities in session 2. This will be discussed

further in section 4.2.5.3.) In session 2, at

least 24 hours later, participants learned a piano melody. After learning, one person from each

matched pair performed either a high or low intensity interval exercise protocol (HIIT or LIIT)

that was personalized based on their Wmax. One hour after learning, participants were tested on

the piano melody. Participants performed delayed retention tests in session 3, 24-hours later, and

session 4, 7-days after learning the piano melody. In session 4, participants also learned a new

sequence to examine transfer effects. Finally, to explore which aspects of the melody may be

36

consolidated by exercise, they were tested on their ability to recognize it from other melodies

that varied slightly in either pitch or rhythm (auditory recognition test), and they performed a

retention test with no auditory feedback or cueing (motor only test). Throughout the experiment,

participants tracked the quality and quantity of their sleep and exercise (Appendix F).

Pre-screening

Participants were recruited with flyers and through social media postings on public forums. To

verify participants met inclusion criteria, participants were screened via a phone interview. In the

interview, they were provided with an additional summary to ensure that they were comfortable

with the procedures. See section 4.1 for inclusion and exclusion criteria.

Questionnaires

There are several variables that could impact participants’ ability to perform exercise or learn a

piano melody. To quantify these variables, we asked participants to perform screening tests and

questionnaires.

As discussed in section 2.4.4 of the literature review, musical learning relies on an individual’s

ability to perceive the components of a musical melody, including pitch and rhythm. Beat

perception is a phenomenon in which musical rhythms give rise to the perception of an

isochronous pulse (i.e. the beat) (150). To measure beat perception abilities necessary for

learning musical rhythms, participants completed the Beat Alignment Task (151). In this task,

participants judge whether a superimposed isochronous beep track is ON or OFF the beat of the

underlying music. For efficiency of screening, only five stimuli of varying difficulty were used

from the Beat Alignment Test. Specifically, two of the five stimuli’s beep tracks were ON the

beat of the underlying music; among the OFF stimuli, two beep tracks were phase shifted (+25%

and -30%) and one of the stimuli was slower by 10%.

The ability to learn a melody is also dependent on auditory working memory—the number of

items that can be held in memory at once (128); therefore participants completed an auditory

forward digit span task in which participants tried to remember increasingly long sequences of

verbally presented digits. The maximum number of digits reported in the correct sequence

represents their auditory working memory span (Inquisit, © Millisecond Software) (see section

2.4.2.1 for more information).

37

Motivation, alertness, and stimulant usage are all variables that modulate motor learning abilities

and physical performance (152–160). Therefore, prior to every session, we collected information

on participants’ motivational state, alertness (161), and their recent caffeine, nicotine, and food

consumption (appendix E). To further characterize participants, a questionnaire administered

prior to session 1 gathered detailed information on participants’ musical experiences (151,162),

musical preferences (163), self-reported competitiveness, and their physical activity habits (164)

(appendix C). Furthermore, sleep and exercise are important for motor consolidation therefore

participants filled a daily log to track quantity and quality of sleep and exercise (appendix F).

Emotional state can influence motor learning such that a positive emotional state can enhance

motor learning while negative emotional states reduce performance on motor learning tasks

(156). Additionally, affect experienced during and after exercise can moderate adherence to an

exercise prescription (157). To assess how the time course of emotional affect was influenced by

the intensity of the exercise interval protocol, the emotional affect scale was administered

immediately before exercise, immediately after exercise, 10 minutes after exercise, before the

retention tests, and before participant departure (165) (see appendix D).

Graded Exercise Test

The graded exercise test (GXT) protocol was based on the protocol used by Mang et al. (2016)

because we similarly recruited both males and females who have different physiological

capacities for exercise, and therefore have different GXT protocols (28). Participants’ weight and

height were measured. The handle and saddle height of the cycle ergometer (Ergomedic 839E,

Monark, Sweden) were adjusted to maximize participant comfort. Participants were fitted with a

heart rate (HR) monitor (Polar H7) and a mask to measure expired levels of oxygen and carbon

dioxide. HR, VO2, and respiratory exchange ratio (RER) were monitored throughout the test

using a metabolic cart (ParvoMedics TrueOne 2400, Sandy, UT, USA). Participants were

instructed to remain seated throughout the test and to maintain a cycling cadence between 70 and

90 rotations per minute (RPM). They were instructed to continue as long as they could and to try

to perform their best, but they were asked to stop if they experienced any unusual pain in their

chest, dizziness, or faintness and to inform the experimenter immediately. Men began the test at

100 W while women began at 50 W (1). The power output was increased by 30 W every two

minutes. In the middle of each two-minute interval, participants reported their subjective exertion

38

levels using Borg’s 6-20 ratings of perceived exertion (RPE) scale (166) (see appendix G). The

test ended if the participant reached volitional exhaustion or if the participant was unable to

maintain a cycling cadence above 70 RPM despite verbal encouragement. To ascertain that

participants reached their VO2peak, we replicated previous research and verified that at least one

of the following criteria was met: plateau in O2 uptake and heart rate with further increase in

workload, a respiratory exchange ratio >1.1, an inability to maintain the target cadence, and

volitional exhaustion (1,2,7,9). The participant’s maximal power output was defined as the

power output during the final fully completed stage of the GXT and was used to prescribe the

personalized intensities for the interval exercise test (2).

Piano Learning Task

Since Brown and Penhune (127) previously demonstrated that the two intermediate melodies

they used to train non-musicians were approximately matched for difficulty, we used their

melodies as our stimuli.

4.2.5.1 Setup

In session 2, participants were seated in front of a laptop computer (Asus, UX360UAK Signature

Edition) connected to a MIDI piano keyboard (Yamaha YPT-210) via a USB MIDI Interface

(UM-ONE, © Roland). The participant placed their right hand on 5 stickered keys. A computer

program named Synthesia (© 2018 Synthesia, LLC) guided the participant to learn to play a

piano melody. To represent notes to non-musicians, Synthesia uses blocks descending onto an

on-screen keyboard. The length of each block represents the duration of the note and the

horizontal location represents the pitch of the note (see figure 10). The participant was instructed

to press the corresponding piano key when the descending block hits the on-screen keyboard.

The computer screen, with a resolution of 1920 x 1080p, was adjusted so that the on-screen

keyboard keys lined up with the real-life MIDI piano keyboard. Some instructions were spoken

in real-time however most of the instructions were pre-recorded. Verbal instructions were

presented over speakers (MultiMedia Speaker Model A215, Samsung Electro-Mechanics Co.,

LTD.) and auditory musical stimuli were presented through the keyboard’s speakers at

comfortable levels. Task instructions were recorded with a microphone (Sennheiser e835, ©

39

2017 Sennheiser), through an amp interface (Fender mini Passport), and into a digital audio

workstation (REAPER version 5.40, 2017, Cockos).

A custom Python script (version 3.6, www.python.org) automated the recorded task instructions

and the transitions between trials using several Python libraries including MIDO (version 1.2.8,

© 2014 Bjørndalen, O., MIDI Objects for Python, https://mido.readthedocs.io/en/latest/),

PyAutoGUI (version 0.9.36, © 2014, Sweigart, A., https://pyautogui.readthedocs.io/en/latest/),

and dill (version 0.2.7.1, (167,168), https://pypi.python.org/pypi/dill). Each participants’

performance was recorded using a custom Python script that also used MIDO and dill.

4.2.5.2 Trial Types

Three types of learning trials were presented to participants: (1) listen, (2) training, and (3) test.

During listen trials, participants were instructed not to move their fingers and only to listen as the

piano melody played over the speakers without any accompanying visual cueing. These trials

were designed to help participants create a mental representation of the melody, or melody

schema (36). As participants learned the melody from listening, they would be more likely to

detect their own errors during training and test trials. As their schema developed, they could use

it to obtain knowledge of performance (KP) feedback during training and test trials. Other

feedback will be discussed in the acquisition protocol section. During training trials, participants

played and tried to memorize their visually cued melody (figure 10). During test trials,

participants attempted to play the melody from memory without any visual cueing (figure 11).

https://mido.readthedocs.io/en/latest/

https://pyautogui.readthedocs.io/en/latest/

https://pypi.python.org/pypi/dill

40

Figure 10: Test trial (no visual cueing)

Four metronome beats preceded the training and test trials to allow participants to synchronize to

the auditory beat and to facilitate their rhythm performance. In both training and test trials,

participants received auditory feedback as they were able to hear their performed notes. The

visuomotor training trials were designed to explicitly teach the melody. To ensure that

participants did not rely solely on the visual cueing, a test trial followed every training trial. This

ensured that participants were actively trying to memorize the melody during the training trials.

Rather than training visuomotor integration, this protocol was designed to help non-musicians

efficiently learn the auditory-motor melody. The goal of the training protocol was to train non-

musicians so that they could perform the melody from memory without any visual cueing.

Participants and their matched pair partner were randomly assigned to one of two melodies.

Thus, each pair (high, low intensity group)

learned to play the same melody during

acquisition and the other melody during transfer.

4.2.5.3 Familiarization

Prior to the main experiment, participants were familiarized with the task to ensure that they

understood the listen, training, and test trial types, and to ensure that each participant began with

similar baseline capabilities. Three simple familiarization melodies were used to introduce the

participant to the task. The experimenter introduced the program by demonstrating one trial of

the first familiarization melody and further recorded instructions guided the participant through

learning. Participants demonstrated comprehension of the task by performing each

familiarization melody correctly twice in both training and test trials. The number of extra trials a

participant needed during familiarization prior to performing two correct trials each of training

and test was used as an objective measure of their initial musical abilities.

Figure 12: Familiarization melody 1

Figure 11: Training trial (visual cueing)

41



4.2.5.4 Experimental stimuli

Previous research by Dr. Rachel Brown and Dr. Virginia Penhune involved designing and

developing the melodies (figure 15 and figure 16) (127). They taught non-musicians these

melodies and according to their data, these melodies are approximately matched for difficulty.

The melodies for acquisition and transfer were counterbalanced across participants. The two

melodies consisted of 12 notes with 5 unique pitches (A4, B4, C5, D5, E5) and rhythms

consisting of quarter notes and eighth notes at a tempo of 75 bpm. Participants used their right

hand to perform the melodies and each of the 5 unique pitches was assigned to one digit.

Figure 15: Sequence 1

42

Figure 16: Sequence 2

4.2.5.5 Pilot Testing

Pilot testing was conducted to refine the piano training protocol. The original training protocol

consisted of 4 blocks. Blocks 1 and 2 contained 10 trials each of listen, training, and test trials

and blocks 3 and 4 contained 10 trials each of listen and test trials. This protocol was tested on 4

pilot participants who did not demonstrate ceiling effects; however, when it was implemented

into the full experiment with 4 additional participants, 3/4 participants reached ceiling. To

prevent these ceiling effects, the number of trials per block was halved so that instead of 10

trials, there were 5 trials per trial type. This protocol was tested on 3 pilot participants and 2/3

participants learned the melody; however, when implemented into the full experiment with 2

additional pilot participants, they were unable to learn the melody with this protocol. To satisfy

the wide range of individual learning differences, a protocol in which participants trained to a

criterion was devised and will be described in the following section.

4.2.5.6 Acquisition Protocol

The piano learning task acquisition protocol consisted of 6 blocks. Blocks 1-3 had 5 trials each

of listen, training, and test trials for a total of 15 trials per block (figure 17). Blocks 4-6 consisted

of 5 trials each of listen and test trials for a total of 10 trials per block (figure 17). Every

participant performed a minimum of blocks 1-3, but to account for individual differences

between participants’ musical and learning abilities, and to minimize ceiling effects, each

participant trained up to a criterion of 3 consecutive correct pitch sequences during blocks 4-6

(rhythm accuracy was not considered for this measure) (figure 17).

43

Figure 17: Piano Acquisition Protocol

4.2.5.6.1 Feedback

Participants received auditory KP feedback throughout the acquisition protocol as they heard the

notes they performed. During blocks 1-3, in addition to the auditory feedback, participants

received visual KR feedback during both training and test trials. When they played a correct

note at the correct time, the on-screen keyboard key would illuminate in green (figure 18 and

figure 19).

Figure 18: Example of training trial

with knowledge of results visual

feedback.

Figure 19: Example of test trial

during blocks 1-3 with knowledge of

results visual feedback.

44

When an incorrect note was played, or a correct note at an incorrect time (>200 ms early or late),

the on-screen keyboard illuminated in grey (figure 20).

Figure 20: Synthesia provided visual knowledge of results feedback. This figure shows an

example of knowledge of results feedback during an incorrect note.

In the second half of the experiment, during block 4-6, the on-screen keyboard was removed, and

participants no longer received visual KR feedback (figure 20).

45

Figure 21: Example of test trial during blocks 4-6

Interval Exercise Test

Participants were fitted with a heart rate monitor. For men, maximal heart rate is estimated using

the regression equation 208 − 0.7 * age (169). For women, maximal heart rate is estimated using

the regression equation 206 – 0.88 * age (170). Participants were instructed to maintain a cycling

cadence between 70 and 90 RPM and to do their best to complete the interval exercise protocol,

but were asked to stop if they felt dizzy, faint, or experienced unusual chest pain during any part

of exercise. Borg’s ratings of perceived exertion were recorded in the middle of each interval

(166) (see appendix G). Intensity of each interval was prescribed based on each participants’

randomized condition and their peak power output (Wmax), which was defined by their power

output during the final fully completed stage of the graded exercise test.

The interval exercise test was 19 minutes in duration and the main interval exercise consisted of

3 repetitions of 2-minute low-intensity intervals and 3-minute high-intensity intervals. The

experimental group’s low intensity was 60% Wmax and their high intensity was 90% Wmax (15).

The control group performed low-intensity intervals at 8% Wmax and high-intensity intervals at

12% Wmax. There was a 2-minute warm-up and a 2-minute cool-down at 5% Wmax to ensure that

the intensities during the warm-up and cool-down were less than the control group’s interval

exercise test. Most previous research has employed resting control groups in which participants

simply sit and read provided material. However, since the enhancements appear to be driven by

high-intensity exercise and not low-intensity exercise, a more appropriate active control group

would be a low-intensity exercise condition. Since small positive benefits have been observed for

moderate-intensity interval training, we devised a very low-intensity interval exercise protocol.

The 8% and 12% Wmax intensities were chosen because they are the same ratio as the

experimental group and they are unlikely to cause enhancements to motor consolidation (78,79).

Retention Tests

During retention tests, participants performed 2 listen trials to cue their memory and 10 test trials

(without any visual cueing, hearing their auditory feedback). Retention tests took place one hour

after the acquisition phase (approximately 20 minutes after the end of the interval exercise

46

protocol), 24 hours later (+/- 2 hours) and 7 days later (+/- 2 hours). The three retention tests

were used to examine both immediate (1-hr) and delayed (1-day and 7-days) retention (171).

Post-Session

After each session, prior to participant departure, the participants were asked to report any

strategies or thoughts they had during the graded exercise test, acquisition phase, interval

exercise test, retention tests, and transfer. Additionally, participants filled out their daily sleep

and exercise log. At the end of the experiment, they were provided with debriefing information

notifying them whether they were in the experimental or control group and the true purpose of

the research (see appendix H for the debriefing form).

Transfer Test

Following the 7-day retention test, the participant completed a transfer test. The transfer test

follows the same learning protocol as the acquisition protocol however participants continued to

train even after they reached the criterion of three consecutive correct trials to the ceiling of 45

movement trials.

Auditory Recognition and Motor Only Test

To examine the differential contributions of motor learning and auditory learning, participants

performed two tasks that distinguished these components. In the auditory recognition task,

participants were asked to distinguish the melody they learned during acquisition in session 2

from 4 novel, but similar, melodies. These four new melodies were designed to maintain the

same contour and rhythmic structure as the learned melody; however, they contained in-key

errors. These errors are the most challenging to detect (Trainor and Trehub, 1992).

Participants were asked to listen to all melodies before making their judgements. The correct

melody and its pitch distractors were presented first in a randomized order. Then the correct

melody and its rhythm distractors were presented in a randomized order. These were used to

assess if the participants had learned the auditory components (pitch and rhythm) of the

sequence.

47

4.2.10.1 Sequence 1 – Pitch Distractors

Figure 22: Sequence 1 Correct melody

Figure 23: Distractor melody 1 – Note 5 varies

Figure 24: Distractor melody 2 – Note 3 is different

Figure 25: Distractor Melody 3 - Note 6 & 7 are different. Both were changed to maintain

contour.

Figure 26: Distractor Melody 4 – Note 8 is different

48

4.2.10.2 Sequence 2 – Pitch Distractors

Figure 27: Sequence 2 Correct Melody

Figure 28: Distractor Melody 1 – Note 2 is different

Figure 29: Distractor Melody 2: Note 5 is different

Figure 30: Distractor melody 3: Note 6 is different

Figure 31: Distractor Melody 4 - Note 10 is different

Four additional melodies were designed to test participants rhythm recognition abilities.

49

4.2.10.3 Sequence 1 – Rhythm Distractors

Figure 32: Sequence 1 Correct Rhythm

Figure 33: Distractor 1 - Notes 8 and 10 switched rhythms

Figure 34: Distractor 2 - Note 4 is extended by a 16th note and everything else is pushed

back

Figure 35: Distractor 3 - Final note is longer

Figure 36: Distractor 4 – Note 4 ended and note 5 began a 32nd note early

4.2.10.4 Sequence 2 – Rhythm Distractors

Figure 37: Sequence 2 Correct Rhythm

50

Figure 38: Distractor 3 - 3rd Last Note Shorter and final notes shifted

Figure 39: Distractor 2 – Last note longer

Figure 40: Distractor 3 - Final phrase lengthening

Figure 41: Distractor 4 - 3rd note lengthened and everything else shifted

The motor only task evaluated if participants had learned the motor component of the sequence

independently from the auditory components, participants were asked to perform the sequence

with no auditory count-in and without any auditory feedback—not even hearing their performed

notes.

4.3 Analysis

Preliminary analyses conducted during data collection revealed a trend towards unexpected

intensity group differences in acquisition. The number of familiarization trials a participant

required was a good predictor of their acquisition performance. Therefore, beginning at the 21st

participant, matched randomization was stratified with participants’ number of extra

familiarization trials in addition to gender and fitness. Other studies have also randomized

participants based on their initial task performance to ensure that groups are not different

(8,11,48,172).

51

Data Processing

Participant keypresses were recorded with a custom script in Python. Another script recorded

output from Synthesia while participants were learning. Another custom script used the

recordings from Synthesia to identify the beginning and end of each trial and segment the

participants’ recordings into trials. Data was cleaned, examined, and processed to provide

accuracy scores. Pitch accuracy (spatial component) and rhythm accuracy (temporal component)

were calculated separately for each participant and each trial to determine whether there were

individual contributions of high-intensity exercise to each of these performance measures.

4.3.1.1 Pitch Accuracy

Previous research defined pitch accuracy as the longest correctly performed sequence of notes

(127). However, we decided to redefine pitch accuracy because this definition could introduce

artefacts, or inconsistencies, to the scoring. In our study, participants learned a 12-note melody.

If two participants both learned 11 notes of the melody, then intuitively, they should receive the

same score (11/12). However, if one participant repeatedly made an error in the middle of the

sequence, they would have a score of 6/12, because their longest sequence would only be 6 in

length. If the other repeatedly made an error at the end of the sequence, they would have a score

of 11/12. To prevent introduction of artefacts in this manner, pitch accuracy was redefined as the

sum of the length of the correctly performed sequences that are also in the correct order.

This artefact did not exist for prior calculations of rhythm accuracy. Previously, rhythm accuracy

has been defined as the percentage of correct inter-onset intervals (IOI; i.e. the time between

onsets of two notes) performed in the correct order. In a melody with 12 notes, there are 11 IOIs.

For a song at a tempo of 75 beats per minute (bpm), a quarter note has an IOI of 800

milliseconds while an eighth note has an IOI of 400 milliseconds. Since even the most elite

musicians have natural rhythmic fluctuations, previous research has defined a correct IOI as

being within 30% of its expected value. For example, if a participant slows down while playing a

melody and plays a correct quarter note interval with an IOI of 850 milliseconds, the following

quarter note would be expected to have an IOI of 850 milliseconds. If this expected IOI fell

within 30% of what was actually played, then it would be counted as correct. So, if the

participant played another quarter note with an IOI of 900 milliseconds, this would be counted as

correct since 850 milliseconds is less than 30% faster than 900 milliseconds (> 630

52

milliseconds). In time, these ranges might appear miniscule; however, if converted to beats per

minute, it is easier to conceptualize how large a 30% acceleration or deceleration truly is. This

previously used definition of rhythm accuracy means that in a song with a tempo of 75 bpm, a

quarter note with a tempo ranging from 58 to 107 bpm is acceptable, but the latter is nearly twice

as fast. This is like the difference between a lullaby (or the Beatle’s “Something in the Way”)

versus a pop song (like Finger 11’s “Paralyzer” or “My Girl” by the Temptations).

This range may be too large to detect learning in the rhythmic domain therefore for the purposes

of our study, we redefined rhythm accuracy as the percentage of IOIs within 10% that were

performed in the correct order. With this definition, the quarter note range acceptable in a song

with a tempo of 75 bpm is 68 to 83 bpm or with time, 720 milliseconds to 880 milliseconds.

To identify correct notes, a sliding window with sliding length compared sections of the

performed sequence to sections of the target sequence. If there were multiple correct, non-

overlapping sequence sections, their order was verified in relation to the longest correctly

performed section. If the performed sequence was shorter than the target sequence, the number

of correct notes was divided by the length of the target melody (12). If more than 12 notes were

played, the number of correct notes was divided by the number of performed notes to penalize

participants who played excessive notes. Pitch accuracy was calculated independently from

timing information.

4.3.1.2 Rhythm Accuracy

Rhythm accuracy is the percentage of correct inter-onset intervals within 10% earlier or later

than the expected note. The 10% range is to account for natural rhythmic fluctuations in

performance. Correct IOIs were identified by comparing the performed IOI’s duration to the

expected IOI’s duration. At the beginning of a trial, each IOI was compared to 10% shorter and

longer than the target rhythm’s timing (quarter notes = 0.8 seconds and eighth notes = 0.4s).

However, once the participant played a correct IOI, the following IOIs were compared to the

participant’s timing in case they sped up or slowed down. The number of correctly performed

notes was divided by the target melody’s number of inter-onset intervals (n = 11) to calculate

percentage.

53

4.3.1.3 End of Acquisition Performance

Previous research has used the difference from retention to the last block of acquisition to assess

consolidation and account for individual variability in acquisition performance (8,11,78). Due to

the nature of our acquisition task, in which each participant received a different number of trials,

instead of subtracting the last block of acquisition, the average of the last 10 trials played by the

participant will be used to calculate each participant’s end of acquisition performance.

Statistical Analysis

4.3.2.1 Demographics Data

Independent samples t-tests computed in Excel were used to assess if there were any differences

between groups in demographics (age, weight, height), variables that could affect musical learning

(auditory working memory, number of extra familiarization trials required, competitiveness, years of

formal education) and graded exercise test outcomes (VO2peak, Wmax, HRmax, RERmax, and RPEmax).

4.3.2.2 Mixed Effects Modeling

Previous research examining the effects of exercise on motor learning has relied on linear mixed

effects modeling (LME) to determine group differences (8,11,15,49). LME is an effective type of

modeling because in addition to modeling the effects of variables of interest, it also models and

accounts for variability between subjects. This statistical methodology is also advantageous

because LMEs can handle missing data and do not rely on even spacing of repeated measures,

which is important for our study design. Mixed models are called mixed because they include

both fixed and random effects. Fixed effects are the variables of interest. Random effects model

the variability between subjects and therefore prevent between-subject variability from obscuring

the effects of the variables of interest. Random effects are randomly sampled and are not

important for the hypothesis of interest. Random intercepts model baseline differences in

performance.

Participants performed both training and test trials. The test trials, in which participants did not

receive any visual cueing, are used for the inferential analysis because these allow comparisons

between sessions.

54

To ensure that the melodies were equally challenging, pitch and rhythm accuracy were compared

between sequences. Specifically, separate models were fitted for pitch and rhythm accuracy for

the six blocks of acquisition with fixed effect of sequence-block interactions and a random effect

of subject.

Separate models were fitted to the pitch and rhythm accuracy for acquisition, retention, transfer,

and motor-only data. Fixed effects of group-block interactions, and a random intercept of subject

were included in each model.

Mixed effects modeling was conducted in SAS (SAS Institute Inc.). Normality was tested with a

Kolmogorov-Smirnov test. Type III sum of squares were used to test the contribution of the fixed

effects. Post-hoc model-based comparisons were performed using the least-squares method. All

analyses were performed with two-tailed tests and a significance level of p = 0.05. Multiple

comparison corrections were not performed due to the exploratory nature of the study (173).

55

Chapter 5

Results

5.1 Demographics data

Independent samples t-tests were conducted on participant characteristics (table 4) between

groups in Excel. There were no differences between the intensity groups on the participant

characteristics of age (t(23) = 0.65, p = 0.52), weight (t(23) = 0.004, p = 0.99), height (t(23) = 0.13, p =

0.90) and competitiveness (t(23) = 1.1, p = 0.28). No differences existed in characteristics that could have

influenced musical learning abilities including auditory working memory (AWM) (t(23) = 1.35, p = 0.19),

number of extra familiarization trials (EFT) (t(23) = 0.78 p = 0.45), beat alignment task score (BAT)

(t(23) = 0.88, p = 0.39), or years of formal education (t(23) = 1.01, p = 0.32). There were also no

differences between groups in the graded exercise test (GXT) parameters of VO2peak (t(23) = 0.78, p =

0.45), maximum power output (Wmax) (t(23) = 0.24, p = 0.81), maximum heart rate (HRmax) (t(23) = 0.59,

p = 0.56), maximum respiratory exchange ratio (RERmax) (t(23) = 0.14, p = 0.89), or maximum rating of

perceived exertion (RPEmax) (t(23) = 0.36, p = 0.72). As expected, there were differences between groups

in the interval exercise test, with the high-intensity group having a higher HRmax (t(23) = 14.3, p < 0.001)

and a higher RPEmax (t(23) = 9.6, p < 0.001).

56

Table 2: Descriptive data of participant characteristics

(Group mean ± SD)

Intensity High Low

Characteristics N (Sex) 13 (8F, 5M) 12 (7F, 5M)

Age 22.2 ± 3.2 21.5 ± 2.2

Weight (lbs.) 146.5 ± 22.9 146.4 ± 30

Height (cm) 168.2 ± 9.3 168.6 ± 7.5

AWM 7.8 ± 1.0 7.1 ± 1.5

EFT 5.8 ± 5.4 7.9 ± 7.8

BAT 3.5 ± 1.4 3.0 ± 1.7

Competitive 5.5 ± 1.3 4.8 ± 2.2

Graded Exercise Test

VO2peak (ml/kg/min)

30.5 ± 8 33.1 ± 8.5

Wmax (Watts) 140.8 ± 46.8 145.8 ± 58.2

HRmax (bpm) 180.8 ± 9.5 177.3 ± 18.6

RERmax 1.17 ± 0.05 1.17 ± 0.08

RPEmax 17.6 ± 1.4 17.4 ± 1.4

Interval Exercise Test

HRmax 178.2 ± 11.1 110.9 ± 12.4 *

RPEmax 16.9 ± 2.0 9.5 ± 1.9 *

Legend: AWM: Auditory working memory; EFT: Extra Familiarization Trials; BAT: Beat Alignment

Task Score; VO2peak: cardiovascular fitness; Wmax: maximum power output, HRmax: maximum heart rate;

RERmax: maximum respiratory exchange ratio, RPEmax: maximum rating of perceived exertion; *:

significant difference p < 0.05

57

Table 3: Participant Exercise Characteristics

Graded Exercise Test Interval

Exercise Test

ID Intensity Age Sex Weight

(lbs.)

Height

(cm)

VO2peak

(ml/kg/min)

Wmax HRmax RERmax RPEmax HRmax RPEmax

1 Low 23 F 145 173 47.2 230 182 1.12 17 100 7

2 Low 23 M 162 179 38.3 160 180 1.16 18 94 7

3 Low 21 F 115 159 29.3 110 183 1.20 18 107 10

4 Low 25 F 130 158 28.9 110 165 1.04 15 107 8

5 Low 20 M 151 167 43.3 190 203 1.12 17 119 9

6 Low 24 F 124 159 26.9 110 140 1.38 18 122 10

7 Low 21 M 227 181 31.9 220 181 1.15 19 105 14

8 Low 18 F 116 166 24.8 80 146 1.19 16 105 10

9 Low 18 M 140 172 35.9 160 192 1.19 20 130 10

10 Low 20 F 145 171 23.5 80 186 1.19 18 132 9

11 Low 22 M 165 172 44 220 193 1.17 16 97 9

12 Low 23 F 137 166 22.9 80 177 1.13 17 113 11

Mean

21.5

146.4 168.6 33.1 145.8 177.3 1.17 17.4 110.9 9.5

SD

2.2

30.0 7.5 8.5 58.2 18.6 0.08 1.4 12.4 1.9

13 High 24 M 176 176 41.6 220 196 1.17 19 183 16

14 High 30 F 171 170 25.1 110 164 1.12 16 156 15

15 High 24 M 118 174 24.6 100 171 1.27 15 179 18

16 High 21 F 123 151 19.8 80 189 1.18 17 193 18

17 High 23 F 123 154 27.8 110 173 1.18 18 173 19

18 High 19 F 128 174 27.6 110 178 1.14 17 181 17

19 High 24 F 148 172 38.5 170 178 1.14 18 174 18

20 High 23 F 114 168 28.7 140 189 1.21 19 181 20

21 High 20 F 144 155 25.1 110 182 1.11 17 172 16

22 High 18 F 174 164 22.4 110 182 1.13 20 177 13

23 High 18 M 168 174 46 220 174 1.09 17 176 15

24 High 23 M 157 174 32.6 160 195 1.17 17 202 19

25 High 22 M 160 180 36.9 190 179 1.25 19 170 16

Mean

22.2

146.5 168.2 30.5 140.8 180.8 1.17 17.6 178.2 16.9

SD

3.2

22.9 9.3 8.0 46.8 9.5 0.05 1.4 11.1 2.0

Legend: VO2peak: peak oxygen consumption (ml/kg/min); Wmax: maximum power output (W); HRmax:

maximum heart rate (bpm); RERmax: maximum respiratory exchange ratio; RPEmax: maximum rating of

perceived exertion (Borg’s 6-20 scale)

58

Five participants in the high-intensity condition failed to complete the interval exercise protocol

due to volitional exhaustion (see table 4). According to the American College of Sports

Medicine’s (ACSM) fitness categories of maximal aerobic power, these participants all had very

low fitness (table 4: ACSM fitness category) (174). During recruitment, these participants

reported that they did not exercise on a weekly basis. After participants 16 and 17 failed to

complete the interval exercise protocol, an exclusion criterion was added that excluded anyone

who exercised less than once per week. These participants still exercised to high ratings of

perceived exertion and high maximum heart rates therefore we believe that they may still have

experienced increased neurochemical release as a result of the partial interval exercise.

59

Table 4: Participant fitness levels and respective American College of Sports Medicine

Fitness Category (174)

Highlighted participants are those who did not complete the high-intensity interval exercise test

due to volitional exhaustion.

ID Intensity Age Sex VO2peak

(ml/kg/min)

ACSM Fitness

Category (174)

Interval Exercise

HRmax

Interval Exercise

RPEmax

1 Low 23 F 47.2 Excellent 100 7

2 Low 23 M 38.3 Poor 94 7

3 Low 21 F 29.3 Very Poor 107 10

4 Low 25 F 28.9 Very Poor 107 8

5 Low 20 M 43.3 Fair 119 9

6 Low 24 F 26.9 Very Poor 122 10

7 Low 21 M 31.9 Very Poor 105 14

8 Low 18 F 24.8 Very Poor 105 10

9 Low 18 M 35.9 Very Poor 130 10

10 Low 20 F 23.5 Very Poor 132 9

11 Low 22 M 44 Fair 97 9

12 Low 23 F 22.9 Very Poor 113 11

13 High 24 M 41.6 Fair 183 16

14 High 30 F 25.1 Very Poor 156 15

15 High 24 M 24.6 Very Poor 179 18

16 High 21 F 19.8 Very Poor 193 18

17 High 23 F 27.8 Very Poor 173 19

18 High 19 F 27.6 Very Poor 181 17

19 High 24 F 38.5 Fair 174 18

20 High 23 F 28.7 Very Poor 181 20

21 High 20 F 25.1 Very Poor 172 16

22 High 18 F 22.4 Very Poor 177 13

23 High 18 M 46 Good 176 15

24 High 23 M 32.6 Very Poor 202 19

25 High 22 M 36.9 Very Poor 170 16

60

5.2 Summary Figures of Data

Figure 42: Test trials from each session separated into high-intensity (red) and low-

intensity (blue) groups for pitch accuracy. In acquisition, transfer, and motor only data,

each point represents 5 trials. The retention block points each represent the 10 trials in a

single retention session. There was no difference between groups during acquisition,

retention, or the motor-only task, however there was a difference between groups in block

5 of transfer (* p < 0.05) which suggests that HIIT has minimal effects on sequence-specific

consolidation, yet there may be modest effects of HIIT on general consolidation. The error

bars represent standard error of the mean.

*

61

Figure 43: Test trials from each session separated into intensity groups. The error bars

represent standard error of the mean.

5.3 Mixed Effects Modeling

The Kolmogorov-Smirnov test was used to assess normality of the data. The assumption of

normality of the pitch and rhythm accuracy data were violated (pitch accuracy: D = 0.243, p

<0.01; rhythm accuracy: D = 0.123, p < 0.01). Further examination of the data using residual

plots revealed that parametric testing on the pitch accuracy data would be inappropriate.

Parametric testing would have been robust against slight deviations observed in the residual plots

of rhythm, however for consistency between dependent measures, both models were evaluated

using nonparametric methods. Specifically, separate nonparametric longitudinal models,

following the approach of Brunner, Domhof, and Langer (175) were fitted for both dependent

variables (pitch and rhythm accuracy) in acquisition, retention, transfer, and motor only data,

with all models containing fixed effects of group-time interaction and a random effect of subject

nested within block. All models satisfied the tolerance level of less than 0.4 and no variables

were collinear in any model.

62

Melodies: Sequence 1 versus Sequence 2

5.3.1.1 Pitch

There was a significant interaction of sequence and block (F(5, 578.53) = 2.454, p = 0.032)

(figure 44). Post-hoc testing revealed a significant difference only at block 2 (t(32.1) = 2.16, p =

0.038). There was no difference in block 6 (t(48.73) = 1.138, p = 0.2605). Note that each

participant has a different number of trials because they trained to criterion. Due to this artefact

and the associated problems with comparing between groups in block 6, a Wilcoxon rank sum

test confirmed that there was no significant difference between sequences during the last 10 trials

of acquisition (W = 101, p = 0.157).

Figure 44: Pitch accuracy separated by sequence number. The error bars represent

standard error of the mean.

63

5.3.1.2 Rhythm

There was a main effect of block (F(5, 578.9) = 23.21, p < 0.001), but no interaction between

block and sequence (F(5, 578.9) = 0.40, p = 0.85), nor main effect of sequence (F(1, 25.19) =

0.31, p = 0.58) (figure 45). This means that there was no significant difference between the way

participants learned the rhythms of the melodies.

Figure 45: Rhythm accuracy separated by sequence number. The error bars represent


64

Acquisition

5.3.2.1 Trials to Criterion

Participants were each trained to a criterion of three correct trials. Two participants received

extra trials because of experimenter error: participant 16 received 3 extra trials after reaching

criterion and participant 17 received 1 extra trial after they reached criterion. A Welch two-

sample t-test performed in R did not reveal significant differences between the high (μ = 22.5)

and low (μ = 25.5) intensity groups in the measure of number of trials to criterion (t(22) = 1.38,

p = 0.18).

5.3.2.2 Pitch

There was an interaction between block and intensity (F(5, 569) = 6.13, p <0.001) (figure 46).

There was a main effect of block (F(4.27, 569) = 44.97, p < 0.001) which suggests that when

averaging across groups, participants’ performance improved. There was no main effect of

intensity (F(1, 29.1) = 3.72, p = 0.0536). Post-hoc least-squares comparisons revealed that there

was no significant difference between groups in block 1 (t(91) = 0.18, p = 0.85), but there were

significant differences between groups in block 5 t(91) = 2.73, p = 0.008) and block 6 (t(91) =

2.86, p = 0.005).

Participants all trained during blocks 1-3; however, blocks 4, 5, and 6 have a different number of

data points because of training to criterion. Therefore, the differences between groups in blocks 5

and 6 are an artefact of the missing data. Therefore, a Wilcoxon rank sum test compared

participants’ final 10 trials of acquisition, which were not significantly different between

intensity groups (W = 107, p = 0.121).

65

Figure 46: Pitch accuracy score during acquisition; note that the number of participants in

blocks 4-6 differs between groups and from blocks 1-3. The error bars represent standard

error of the mean.

5.3.2.3 Rhythm

There was a similar result for rhythm accuracy. There was an interaction between block and

intensity (F(4.27, 569) = 5.25, p = 0.002). There was also a main effect of block (F(4.27, 569) =

24.29, p < 0.001) (figure 47). There was no main effect of intensity (F(1, 31.4) = 0.10, p =

0.748). Post-hoc least squares mean testing demonstrated that there was no group difference in

block 1 (t(91) = 1.16, p = 0.250); nor was there a statistically significant difference in block 6

(t(91) = 1.99 p = 0.0501). To examine if groups learned differently, a Wilcoxon rank sum test

compared the last 10 trials of acquisition, which were not significantly different between groups

(W = 86, p = 0.683).

66

Figure 47: Rhythm accuracy score during acquisition. The error bars represent standard

error of the mean.

5.3.2.4 Individual variability

There was large inter-individual variability on pitch and rhythm accuracy measures. Some

participants learned rather quickly and 10 never fully learned the melody. Individual learning

curves can be seen in figure 48 and 49. Importantly, this variability was relatively consistent

across groups and sessions.

67

Figure 48: There was much individual variability in the pitch accuracy scores, with 10

participants failing to learn the pitch sequence in acquisition. This variability is prevalent

in both intensity groups. The error bars represent standard error of the mean.

Figure 49: There was much variability in the rhythm accuracy scores, with no participants

learning the rhythm sequence perfectly. This variability is prevalent in both intensity

groups. The error bars represent standard error of the mean.

68

Retention

Previous research used difference scores to assess retention (8,11,15); however, another way to

control for individual differences between participants’ learning abilities is by using baseline

accuracy scores as fixed effects in the model. These scores act as covariates in the model,

accounting for the variability attributable to individual differences, and allowing for the accurate

interpretation of the effects of exercise. Each participant’s baseline pitch and rhythm score were

calculated as the average accuracy in their final 10 trials of acquisition.

5.3.3.1 Pitch

There was no significant interaction between intensity and session (F(2, 726) = 1.36, p = 0.256),

therefore the interaction was removed and the model was refitted with only the main effects of

intensity, session, and baseline pitch accuracy. There was no main effect of intensity (F(1, 29.1)

= 0.28, p = 0.595) or session (F(2, 729) = 0.11, p = 0.893). This suggests that intensity of

exercise had no effect on consolidation of the melody’s pitch (figure 50). There was a main

effect of baseline pitch score (F(1, 29.1) = 306.65, p < 0.001).

Figure 50: There was no difference between groups in retention when controlling for

differences in the last 10 trials. The error bars represent standard error of the mean.

69

5.3.3.2 Rhythm

There was no significant interaction (F(2, 728) = 0.18, = 0.832), therefore the interaction was

removed from the model and the model was refitted only with its fixed effects of intensity,

session, and baseline rhythm accuracy. There was no main effect of intensity (F(1, 22.7) = 0.40,

p = 0.536) or session (F(2, 730) = 1.87, p = 0.1542). This suggests that intensity of exercise had

no effect on consolidation of the melody’s rhythm (figure 51). There was a main effect of

baseline rhythm accuracy (F(1, 22.7) = 94.93, p < 0.001).

Figure 51: There was no significant difference between the exercise groups when examining

the score differences between retention session and end of acquisition. The error bars


70

Transfer

5.3.4.1 Trials to Criterion

In the transfer task, all participants completed the same number of trials because they were not

stopped once they had performed three consecutive trials correctly. A Welch two samples t-test

was conducted in R. This test revealed no differences between groups in the trials to criterion in

transfer (t(22.3) = 0.42, p = 0.68).

5.3.4.2 Pitch

There was an interaction between block and intensity (F(5, 715) = 4.60, p <0.001) (figure 52).

There was also a main effect of block (F(5, 715) = 110.18, p < 0.001) which suggests that

participants learned the transfer melody’s pitch sequence (figure 52). There was no main effect

of intensity (F(1, 23) = 1.88, p = 0.170). Model-based least squares comparisons revealed that

the high-intensity group is performing better in block 5 (t(115) = 2.08, p = 0.040); however, no

significant difference was observed in block 6 (t(115) = 1.86 p = 0.066).

Figure 52: Transfer sequence learning curves on the measure of pitch accuracy. The error

bars represent standard error of the mean.

71

5.3.4.3 Rhythm

For the measure of rhythm accuracy, there was a significant interaction between block and

intensity (F(5, 715) = 4.25, p < 0.001) (figure 53). There was also a main effect of block (F(5,

715) = 47.1, p < 0.001), but no main effect of intensity (F(1, 23) = 0.73, p = 0.392. Model-based

least squares means comparison revealed a difference between groups at block 5 (t(115) = 2.25,

p = 0.0261).

Figure 53: Transfer sequence learning curves on the measure of rhythm accuracy. The

error bars represent standard error of the mean.

5.3.4.4 Comparing acquisition to transfer

To understand whether the high-intensity group truly performed better than the low-intensity

group during transfer, models were fitted to the first 3 blocks in acquisition and transfer for both

dependent measures of pitch and rhythm accuracy. Since not every participant completed block 5

during acquisition, performance in block 5 cannot truly be compared between acquisition and

transfer. Therefore, it is challenging to know whether the high-intensity group would have

similarly been better than the low-intensity group had they trained to block 5 during acquisition.

72

Fixed effects of the interactions of intensity*block*session, intensity*block, and intensity

*session were included along with the main fixed effects of session, block, and intensity.

5.3.4.4.1 Pitch

Intensity*block was the only significant interaction therefore the model was refit without the

non-significant interaction terms. There was an interaction of intensity * block (F(2, 720) = 7.42,

p < 0.001). There was a main effect of session (F(1, 720) = 30.10, p < 0.001) and block (F(2,

720) = 150.45, p < 0.001); however, there was no main effect of intensity (F(1, 23) = 1.04, p =

0.314). Pairwise comparisons of least squares means indicated that there was no difference

between groups even in block 3 (F(46) = 1.72, p = 0.093) (figure 54).

Figure 54: Blocks 1-3 of acquisition and transfer in pitch accuracy. Pairwise comparisons

revealed that there were no differences between groups at any of the blocks. The error bars


5.3.4.4.2 Rhythm

There was no three-way interaction between intensity*block*session (F(3.99, 715) = 0.24, p =

0.914) however there were two-way interactions between intensity*block and intensity*session.

The non-significant interaction was removed and the model was refitted.

73

There was an interaction between intensity*block (F(2,719) = 4.53, p = 0.011) and

intensity*session (F(1, 719) = 5.13, p = 0.024). There was also a main effect of block (F(2, 719)

= 44.3, p < 0.001), but no main effect of session (F(1, 719) = 0.97, p = 0.324) nor intensity (F(1,

23) = 0.04, p = 0.842).

Pairwise comparisons using least squares means revealed that there was a significant difference

between acquisition and transfer in the high-intensity group (t(23) = 2.35, p = 0.028) and no

corresponding difference in the low-intensity group (t(23) = 0.89, p = 0.385) (figure 55).

Figure 55: Blocks 1-3 of acquisition and transfer in rhythm accuracy. There was an

interaction between intensity and session. Post-hoc testing revealed that the high-intensity

group learned better in transfer than in acquisition and this was not the case for the low-

intensity group. The error bars represent standard error of the mean.

Auditory Recognition Task

After participants completed the transfer task, they were tested on their ability to recognize the

sequence they had learned a week prior and that they were tested in at the beginning of the

session. Most people recognized the sequence from the pitch and rhythm distractors. A few in

the high- and low- intensity groups did not correctly identify the sequence (see table 4 and

figures 56 & 57; High and Low NoRec: Non-recognizers).

74

Peculiarly, by examining figure 56, it appears that the high-intensity participants who did not

recognize the melody’s pitch sequence (red line) performed well on pitch accuracy (figure 55).

As expected, the low-intensity participants who did not recognize the melody did not perform

well on the pitch accuracy of the melody.

As expected, those who recognized the melody amidst distractors that varied in rhythmic content

appear to perform better on the measure of rhythm accuracy than those who did not recognize the

melody (figure 57).

Table 5: Results of auditory recognition task

HIGH NO RECOG

HIGH RECOG

LOW NO RECOG

LOW RECOG

PITCH 2 11 2 10

RHYTHM 2 11 3 9

NO RECOG: failed to recognize melody; RECOG: recognized melody

Figure 56: This figure shows high and low intensity groups split into those participants who

recognized and those who did not recognize the trained melody amidst distractors that

varied slightly in their pitch sequence. The error bars represent standard error of the

mean.

75

Figure 57: This figure shows high and low intensity groups split into those participants who

recognized and those who did not recognize the trained melody amidst distractors that

varied slightly in their rhythmic sequence. The error bars represent standard error of the

mean.

Motor Only Task

A model with fixed effects of the group-time interaction and main effects of intensity, block, and

baseline accuracy with random effect of subject nested in block were fitted to the motor only task

data as well. There was no interaction of intensity and block in either measures of pitch accuracy

(F(1, 222) = 1.83, p = 0.176) (figure 58) or rhythm accuracy (F(1, 222) = 0.03, p = 0.862) (figure

59). The models were refitted without the interaction term and demonstrated no main effects in

the measure of rhythm accuracy (intensity: F(1, 21.8) = 0.66, p = 0.415; block: F(1, 223) = 0.90,

p = 0.344; baseline rhythm accuracy: F(1, 21.8) = 3.26, p = 0.071) or in the fixed effect of

intensity for pitch accuracy (F(1, 21.9) = 2.95, p = 0.086) however there was a main effect of

baseline pitch (F(1, 21.9) = 20.49, p < 0.0001) and block (F(1, 21.9) = 5.32, p = 0.02). Pairwise

comparison of least squares means indicated that both groups performed better in the second

block than in the first block (t(24) = 2.31, p = 0.03).

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Figure 59: Groups performed similarly on

the task in the measure of rhythm

accuracy. The error bars represent


Figure 58: Groups performed similarly

on the motor only task in the measure

of pitch accuracy and both groups

performed better in the second block.

The error bars represent standard

error of the mean.

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Subjective Report of Learning Strategies

After the acquisition and transfer tasks, participants were asked to report whether they were

focusing more on the auditory or visual aspects of the sequence during learning. Most

participants reported that they were focusing more on the visual cueing than the auditory aspects

of the melody. One participant refused to choose because they said they were focusing on both

equally. A few participants are missing responses (NA).

Table 5: Participants' subjective report of their focus during acquisition and transfer tasks

Acquisition Transfer

HIGH HIGH

V: 11 A: 2 NA: 2 A/V: 1

V: 8 A: 3 NA: 2

LOW LOW

V: 7 A:3

V: 5 A: 5 NA:2

Legend: V: visual, A: auditory, A/V: equal attention to auditory and visual, NA: no response

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Chapter 6

Discussion

The primary objective of the present study was to examine the effects of high-intensity exercise

on consolidation of piano learning. Non-musicians who performed high-intensity interval

training (HIIT) after piano acquisition were expected to demonstrate enhanced consolidation 1-

day and 7-days later as compared to a group who exercised at a low-intensity. However, there

was no main effect of intensity or interaction between intensity and retention session for either

pitch or rhythm accuracy, therefore there is no evidence that exercise can improve consolidation

of piano learning.

The secondary objective was to examine whether high-intensity exercise could also enhance

transfer to a novel piano melody. Non-musicians who performed HIIT after piano acquisition

were expected to demonstrate enhanced transfer to a new sequence than a group who exercised

at a low-intensity. There was an interaction between block and intensity, and post-hoc testing

revealed that the high-intensity group performed the pitch sequence better than the low-intensity

group during block 5. A comparison between performance in acquisition to performance in

transfer revealed that the high-intensity group performed better in transfer than acquisition and

this was not observed for the low-intensity exercise group. These results suggest that HIIT after

explicit motor sequence acquisition involved in piano learning may promote general skill

consolidation.

The primary result is inconsistent to that which has previously been demonstrated by other

research. The discussion in the following section will attempt to reconcile the discrepancies

between this study and previous literature.

6.1 Discussion of Results

Acquisition

There were individual differences in ability to acquire the melody during this acquisition phase

despite efforts in piloting to determine the best training protocol. The acquisition protocol trained

participants to three consecutive correct trials, or to the maximum of 30 test trials, prior to them

performing exercise. It was important that participants learned to execute the sequence, but that it

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was not over-learned, which could obscure the nuanced effects of exercise on motor learning.

Previous research has similarly operationally defined a learned melody as one that has been

played correctly three times (134,135). We added the additional constraint that these correct

trials must be consecutive because our training protocol was only a single session as opposed to

training across multiple days (134,135). Despite the previous literature, performance is not a

direct measure of learning (171). Our training protocol was designed to train participants just

enough that their memory of the melody was in a fragile state (96) and could be consolidated

further by exercise. It is challenging to assess from performance whether a memory engram is in

a fragile state or has been consolidated into a more stable form (96). If the memory engram was

already stable at the end of acquisition, any effects of exercise could be obscured. Similarly,

some participants were unable to learn the melody, therefore there would be no additional

benefits of exercise as the wrong motor sequence would be consolidated.

The acquisition protocol was also designed to help participants develop an auditory image of the

melody by frequently providing opportunities to listen to the melody. In some cases, despite

hearing the listening trial right before they performed the test trial, some participants continued

to repeat their mistakes, perhaps because they could not detect their own error. Providing

additional knowledge of results feedback during the second half of the acquisition protocol might

have helped bring awareness to their mistakes and might have yielded better learning.

Retention

There was no evidence to indicate that high-intensity exercise enhanced retention. One

interpretation of the data is that exercise does not enhance explicit, discrete motor sequence

learning. This finding would contribute to a better understanding of the differences between

explicit and implicit sequence learning. Some research suggests that consolidation of explicit and

implicit memories relies on distinct mechanisms (24,86,176). High-intensity exercise may

enhance implicit motor consolidation through its ability to reduce inhibition of the primary motor

cortex and cerebellar circuits (101,118,177,178), and increase the release of neurochemicals

including brain-derived neurotrophic factor (9,108), dopamine (109,179), and lactate (9,55).

However these mechanisms may not similarly enhance explicit motor consolidation which may

rely on neural regions other than the primary motor cortex such as the supplementary motor area

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(180,181) that is involved in the intentional control of movements (182), and whose excitability

is decreased after high-intensity exercise (79).

However, it is also important to note, that due to the nature of the task, some participants likely

reached ceiling as they attained perfect performance (100%), which was maintained at retention.

Since 100% was the maximum score, participants could not improve further on the pitch

accuracy score. Prior studies used dependent measures in which ceiling was more difficult to

achieve such as root mean square error and time lag (2,7).

In contrast, participants did not reach ceiling for the measure of rhythm accuracy. It might be

expected that if exercise could enhance explicit motor sequence consolidation, it might be

observable in the measure of rhythm accuracy. However, the rhythm accuracy measure is still

not as sensitive as time lag measures because the rhythm accuracy score is computed as a

proportion of correct inter-onset intervals divided by the length of the sequence and time lag is

computed in milliseconds (2). This effect on time lag observed by Mang et al. (2014) suggests

that participants who exercise at a high-intensity are less inhibited and therefore faster

responders to visual cues. However, when performing rhythms, a musician needs inhibition to

ensure that they do not play a note too early. It is possible that the mechanisms that promote

improved reaction time in an implicit visuomotor sequence learning task do not offer the same

benefits for a complex musical task in which participants must synchronize movements to an

isochronous beat.

Transfer

The high-intensity group demonstrated better pitch accuracy in block 5 of transfer than the low-

intensity group (p = 0.04). The high-intensity group also demonstrated better rhythm accuracy in

transfer than in acquisition, while the low-intensity group did not demonstrate this improvement.

These results collectively suggest that HIIT may promote general consolidation mechanisms.

HIIT after explicit motor sequence acquisition, during early consolidation of a piano melody,

promoted transfer to learning a new piano melody a week later.

Previous research examined whether exercise could protect against interference caused by

learning a new sequence while the first sequence was still being consolidated (98). Participants

exercised either immediately after learning the first sequence, immediately before learning the

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second sequence, or rested between sequences in the case of the control group. The authors

reported that they did not observe any transfer to the new sequence. Their second sequence took

place only 2 hours after learning the first sequence. Testing, or repeated retrieval of information,

is important for long-term retention in pedagogical contexts (183). It is possible that the repeated

retention testing in addition to the high-intensity exercise assisted with consolidation of the

general information that improved the transfer observed in our study.

Alternatively, other research suggests that sequence-specific consolidation is more rapid than

general skill consolidation (184). Specifically, sequence-specific memories stabilize over a

period of hours, but general skill memory continues to stabilize across days or at least up to one

week (184).

Auditory Recognition and Motor Only Tasks

To tease apart what participants consolidated, the auditory-only and motor-only tasks were

devised. Participants were exceptional at the auditory recognition task with every person

recognizing the correct melody in at least one of the pitch or the rhythm version of the task.

Interestingly, by examining figure 56, the participants (n = 2) who did not recognize the melody

and who were in the high-intensity condition appear to be performing better than those who did

recognize the melody. This is not the case—recall that some participants achieved the criterion

and therefore did not complete all trials. The participants who did not recognize the sequence

were continuously making one or two errors which is why they continued until the end of block

6. Despite the listening trials, these participants did not detect their own errors, learned an

incorrect melody, and were unable to recognize the correct melody during the auditory

recognition task. This suggests that at least for these two people, they consolidated the sound of

their own erroneous melody better than the melody they heard repeatedly in the listening trials.

Participants in both intensity groups had better pitch accuracy in the second block of the motor

only task. This is possibly because participants may have been disoriented by the new parameters

of this task where they needed to count themselves in and remember to perform their melody

accurately with note and timing without receiving auditory feedback. As participants became

more accustomed to the new task demands, this may have allowed them to demonstrate their

motor sequence learning.

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Learning Strategies

Most participants reported that they relied on visual cueing. A few participants reported that the

cueing from the video game was distracting. Many participants needed additional coaching

during the familiarization phase. Feedback included encouraging participants to listen to the

metronome count-in and to anticipate when the first on-screen note would reach the on-screen

keyboard.

Four participants reported that they changed their strategy from a focus on the visual elements of

the sequence during acquisition to the auditory elements in the transfer task. It is possible that

their increased familiarity with the musical stimuli and the piano keys helped them rely more on

the auditory cueing, as opposed to the visual cueing from Synthesia. No participants switched

focus from the auditory elements in acquisition to the visual elements in transfer.

6.2 Strengths and Limitations

Our study failed to replicate previous research that shows that exercise promotes motor

consolidation as measured at delayed retention. Instead, we observed the novel finding that

exercise may promote transfer to a new sequence.

There are several possible explanations for why our protocol failed to replicate previous

research. This could be due to the nature of the explicit task, the differences between our

performance measures and previous research, the difference between our active control group

and other studies’ resting control groups, or differences in participant sample characteristics.

Task

As discussed in the literature review, several different task types have been explored. So far, the

task that shows the greatest enhancements to consolidation is a visuomotor tracking task. This

visuomotor tracking task involves coordinating signals from the visual system to fine tune

control of the hands (8,11,15,172). While the participants trace the same sequence repeatedly,

there is no way to know if they are truly learning the sequence because there is no comparison to

their ability to trace random sequences. Therefore, it is possible that participants are learning

enhanced motor control as opposed to the sequence.

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In contrast, two studies have specifically examined motor sequence learning (MSL) in implicit

conditions by comparing performance on a repeated sequence to performance on random

sequences. Exercise before a continuous MSL task improved participants’ ability to anticipate

sequence-specific timing during acquisition (i.e. reduced time lag), and this was maintained at

retention (2). Exercise before a discrete MSL task improved participants’ sequence-specific rate

of retrieval (1). This study used response time as their dependent measure, therefore performance

improvements were quantified as quicker responses.

Our task also examined sequence learning specifically. As opposed to comparing learning to

performance of random sequences, we facilitated motor learning by training participants at first

with visual and auditory cueing, and then removed the visual cueing to force participants to

explicitly memorize the auditory-motor sequence, i.e. melody. During testing of the sequence,

participants received no visual cueing and their sequence-specific memory was tested.

Musical learning has the added challenge of maintaining synchrony with an external rhythm.

Therefore, as opposed to quicker response times being advantageous, such as in the discrete

MSL task used in previous literature (1), this can be detrimental to performance on the piano

learning task.

6.2.1.1 Performance measures

The performance measures of the piano learning task may not have been as sensitive as those

used in previous research. Previous research used measures such as root mean square error, time

lag, and response time which are continuous measures that are sensitive to continuing

improvements in performance (1,2,7,11). In contrast, the pitch accuracy analysis identifies

whether each note within a trial is correct or incorrect in a binary fashion and then assigns a

score to the trial. There is no accuracy score for each note therefore this measurement is not as

nuanced as the performance measures used in other research. Similarly, for rhythm accuracy, the

analysis assigns a score to each note and then evaluates the trial-level accuracy. It is possible that

an analysis that assessed accuracy without a binary model might detect further performance

improvements past a ceiling of percentage accuracy.

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Control Group

In previous literature, the control groups are frequently resting control groups who receive

reading material and simply sit and wait. This control group might experience decreased arousal

and worse performance than if they had not rested and instead immediately performed the

activity or performed another activity that maintained arousal.

One study compared between intensity of exercise and observed that a moderate-intensity group

did demonstrate enhanced retention at 1-day and 7-days compared to the resting control group

(15). Their moderate-intensity group reported an average rating of perceived exertion (RPE) of

12.7 ± 1.1 and their high-intensity group reported an average RPE of 17 ± 1.8.

In our study, our high-intensity group reported a very similar average RPE of 16.9 ± 2.6. Our

low-intensity group reported an average RPE of 9.5 ±1.9. While our exercise protocol was at a

low-intensity and is confirmed by our participants’ subjective report of very light exertion,

cycling cadence (i.e. speed) was matched between groups. For untrained individuals, maintaining

between 70 and 90 rotations per minute may be challenging, despite the minimal exertion

required and reported. The active control condition likely maintained arousal more than a resting

control. It is possible that if we had used a resting control, we might have observed similar

results to previous research.

Participants’ fitness

When compared to other studies, the fitness of our participants is much lower. The average

VO2peak of the high-intensity group is 30.5 ± 8 ml/kg/min and of the low-intensity group was

33.1 ± 8.5 ml/kg/min. This is in contrast to previous research in which participants’ fitness was

much better with an average of 43.3 ml/kg/min (1). It is possible that good fitness is required to

observe the benefits of exercise on motor learning. In fact, a recent meta-analysis found that

better fitness was related to greater release of BDNF after an acute exercise (185). If BDNF is

the mechanism underlying exercise’s effects on motor learning, it is possible that the majority of

participants recruited to this study would not benefit from the high-intensity exercise

intervention.

One participant suggested that if they had had a longer warm-up, they believe they could have

achieved a higher VO2peak score. Research suggests that an adequate warm-up is important,

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especially for participants with low fitness or for short exercise protocols (186). Our graded

exercise test was identical to those used in previous research; however, it is possible that our

sample, with lower fitness than other studies, could have benefited from a longer warm-up prior

to the graded exercise test.

Sample Size

While the present study had a small sample size, other studies within the motor learning and

exercise domain have observed significant results with samples of n = ~ 12 participants per

group (11,15,49) in between-subjects designs. The small sample size makes a type 1 error of

falsely rejecting the null hypothesis more likely, therefore the results demonstrating that high-

intensity exercise promoted improvements to transfer should be interpreted with caution (187).

A sample size calculation performed with the first 22 participants (11 participants per group)

determined that 160 participants per group would be required to observe a 10% difference in

pitch accuracy at 7-day retention with 80% power.

Summary

The findings of this research are limited by a number of factors and future research examining

the effects of exercise on motor learning should attempt to mitigate their impact. There is great

individual variability in musical learning abilities therefore the amount of possible training, or

difference between the designated floor and ceiling of training should be expanded in future

tasks. Additionally, providing more feedback through scoring could have encouraged

participants’ learning. Including matched low-intensity and basic no treatment control groups in

future research can help tease apart whether there is truly no benefit of simply an interfering

exercise test compared to no activity. By recruiting participants with a variety of fitness levels

could illuminate whether fitness mediates the effects of exercise on motor learning and could

indicate whether the null finding in the present study might be attributable in part to most

participants’ low fitness.

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6.3 Implications for the Rehabilitation Sciences

Music and Exercise for Stroke Motor Rehabilitation

The effects of exercise on stroke motor learning have been explored. Stroke motor recovery

relies on motor learning so if exercise could promote motor learning, it might be possible to

expedite recovery. One study demonstrated that high-intensity exercise enhanced consolidation

of an upper-limb motor task compared to rest in chronic stroke patients (48). Another study

examined the effects of exercise on lower-limb locomotor learning; however high-intensity

treadmill walking nor full-body exercise resulted in improvements to consolidation (16). Both

these studies used laboratory tasks that are not used in rehabilitation settings. Future research

should examine if pairing acute exercise with motor rehabilitation sessions over a long-term

period (i.e. weeks to months) can improve therapeutic motor outcomes in stroke patients.

Music and exercise could both be used as adjuncts to rehabilitation, each benefiting each other.

Music promotes endurance and synchronization in exercise (188) and highly pleasurable music

causes the release of dopamine (189). Future research should focus on other aspects of their

interaction and how their effects might interact synergistically.

6.4 Future Directions

Future research should examine other explicit tasks in ecologically valid conditions that are not

constrained by measures with maximum values. Using motion capture to examine movement

smoothness and other kinematic measures might be another way to examine motor learning with

more sensitivity.

Using active control groups might help reveal whether the effects of exercise on motor learning

are driven in part by the reduced arousal of the control group. Given the leading hypotheses of

the underlying mechanism, an active control group who exercises at a low-intensity should not

demonstrate enhanced consolidation. Inclusion of an active control group is important for future

research examining questions related to understanding the underlying mechanisms of exercise on

motor learning.

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Future research should also consider examining the effects of participant fitness on acute

exercise for motor consolidation. It is possible that a baseline level of fitness, and the brain’s

ability to produce BDNF, may mediate the effects of exercise on motor learning (185,190).

If the effects of exercise on explicit motor sequence learning are negligible, the effects of

exercise on ecologically valid implicit motor sequence learning can still be examined. An

example of a real-world task that involved implicit motor sequence learning is memorization of

combination locks or padlocks. Anecdotally, one might fail to be able to verbalize the code of a

well-rehearsed motor sequence used to unlock a padlock; however, once provided with the

opportunity to perform the movements, performance is restored. By examining the effects of

exercise on an ecologically valid motor sequence task such as opening a padlock, one can gain a

better understanding of whether the effects of exercise transfer to real-world contexts.

Aside from high-intensity interval training, other interventions have also demonstrated

enhancements to motor learning, including alternate nostril breathing (191), cognitive fatigue

(159), psychological stress (192,193), and non-invasive brain stimulation. Since exercise seems

to facilitate performance through a reduction in inhibition and increase in certain

neurotransmitters, it is possible it does not enhance the type of consolidation required to learn

piano melodies. Instead, perhaps other interventions may be more effective at improving

consolidation and transfer of piano learning.

6.5 Conclusions

This study examined the effects of high-intensity exercise on consolidation of an ecologically

valid explicit discrete motor sequence learning task. Contrary to the hypothesis, HIIT did not

promote enhanced consolidation and subsequent retention on a piano melody. However, HIIT

did promote enhanced transfer to performance during acquisition of a new sequence. HIIT may

promote some explicit task general consolidation mechanisms, however more research is

required to generalize this finding to other tasks.

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Appendices

Appendix A: Screening Questionnaire

Date: ___________________ Time: _______________ Participant ID: __________________

PULSELab Experiment: Screening Questionnaire

Characteristics

How old are you? _____________________________________________

What is your weight? ___________________________________________

What is your height? ____________________________________________

What hand do you write with? _____________________________________

Health History

Do you have a history of health (physical or mental) conditions that could impact your

ability to learn a motor sequence?

☐ YES ☐ NO

If yes, and if you feel comfortable doing so, could you share what condition you

have/had?

________________________________________________________________________

_________

107

Do you have a history of health (physical or mental) conditions that could impact your

ability to perform high intensity exercise (e.g. cardiovascular diseases)? Does your family

have a history of cardiovascular diseases? Please note that cardiovascular disease makes

sudden death during exercise more likely. For more information refer to Corrado et al.

(2003).

☐ YES ☐ NO

If yes, and if you feel comfortable doing so, could you share what condition you

have/had?

______________________________________________________________________

Are you currently taking any medications (recreational or prescription)?

Do you have any hearing (sensitivity, ringing in your ears, other) or vision problems? If

yes, list them here:

______________________________________________________________________

Athletics

1. Are you an athlete? ☐ YES ☐ NO

2. What sports do you play?

____________________________________________________________

3. In what capacity do you play? (recreational, competitive, varsity)

_________________________________

4. How frequently do you exercise?

___________________________________________________________

5. Have you ever played competitive sports?

____________________________________________________

Music

1. Are you a musician?

☐ YES ☐ NO

108

2. Have you ever played an instrument?

☐ YES ☐ NO

If you answered yes, please answer questions 3, 4, and 5.

3. Have you ever played piano?

☐ YES ☐ NO

4. In what context did you play piano?

______________________________________________________________________

5. What other instruments do you play?

______________________________________________________________________

109

Answer the following questions for all of the instruments you previously listed.

In what context did

you play the

instrument and how

old were you (e.g. at

school in music

class, in band, in

private lessons)?

How often did you

practice?

How much time was

each session on

average?

How long did you

play for (# of

weeks, months, or

years)

Gaming

1. How often do you play video games? _______________________

2. Have you ever competed in a video game tournament? ☐ YES ☐ NO

a. If yes, which game?

_______________________________________________________________

3. Have you ever played guitar hero?

a. How old were you?

_______________________________________________________________

b. How often did you play when you played the most?

_____________________________________

c. When was the last time you played?

_________________________________________________

d. What level did you reach?

110

_________________________________________________________

4. Do you play any other music video games?

______________________________________________________________________

111

Appendix B: Information Letter & Informed Consent Form

PIANO LEARNING STUDY: INFORMATION LETTER & CONSENT FORM

Thank you for considering participation in the Piano Learning Study. The purpose of the study is

to better understand the effects that exercising on a bike will have on motor learning—your

ability to acquire a skill. Motor learning is an activity that we all engage in. In particular,

following neural injury, patients often struggle to relearn skills they knew prior to their injury. In

order to better understand how we can help people with neural injury, we are examining motor

learning in healthy participants. You have been asked to participate because you meet our

inclusion criteria: healthy, right-handed, non-musician, with no competitive sport or gaming

experience.

The study will be conducted in the University of Toronto Athletic Centre (55 Harbord St.) and

the time commitment of the study is outlined in the following chart:

Day Time Commitment Details

Day 1 (Session 1) 45 minutes Questionnaires & Graded

Exercise Test

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Day 2 (Session 2) 1.75 hours Piano Learning Task &

Interval Exercise Test & Piano

Test & Emotional State

Questionnaires

Day 3 (Session 3) 15 minutes Piano Test

Day 8 (Session 4) 30 minutes Piano Test

Day 1-8 (every day) 2 minutes Online Sleep & Exercise Log

Prior to experiment: You will be asked to maintain your usual routine throughout the course of

this experiment. This includes any sleeping, eating, and exercising routines you may have. Please

refrain from changing any of these aspects throughout the experiment.

Questionnaires: Questionnaires will collect information on your past musical experiences,

emotional state, alertness, nutrition, physical activity habits, and competitiveness.

Graded Exercise Test (GXT): The GXT will be used to evaluate your fitness and will require you

to exercise to your maximum capacity on a stationary exercise bicycle. Your weight and height

will be measured to ensure accuracy of the VO2 peak calculation. You will be fitted with a mask

that will measure your oxygen consumption and with a heart rate monitor. You will begin

cycling on a stationary bike at an easy resistance and the resistance will be increased once every

2 minutes until you reach your maximum. Try your best to continue with the test until you feel

like you have worked as hard as you can; however you are free to stop at any time. The test will

end when you stop or when the experimenter stops you. You will be asked to report ratings of

your perceived exertion.

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Piano Learning Task: You will learn a short piano melody using a computer program that will

visually guide your learning.

Interval Exercise Test: You will be fitted with a heart rate monitor. You will alternate between

cycling at two different resistance levels. You will begin with a two-minute warm-up, followed

by two minutes at the lower resistance and three minutes at the high resistance. You will repeat

the 2-min low x 3-min high three times.

Emotional State Questionnaires: You will report your emotional state immediately before the

interval exercise test, immediately after the exercise test, 10 minutes after the exercise test, and

before your departure.

Piano Test: You will be tested on the sequence learned during the piano learning task.

Online Sleep & Exercise Log: Over the course of the 8 days of the study, you will complete a

daily log that indicates the amount and quality of your sleep and the amount and type of your

physical activity.

Confidentiality: Your participation is entirely voluntary and confidential. You may refuse to

participate or withdraw at any time during the study without negative consequences and you will

still receive the compensation that you have earned up to the point of withdrawal. This consent

form with your name will be stored separately from the other questionnaires and data in a secure

cabinet. Your data will be de-identified and stored on a secure server. Your data will be de-

identified by replacing your name with a study-specific identification code. In the interests of

conducting good science, we will be publishing the de-identified data online. Other researchers

will be able to use the data after publication. Identifying information (name and any contact

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information) will not be published online and will be destroyed ten years after study data

collection completion. You may withdraw your data up to 30 days after data collection. After the

30 days and publication on a poster or in a journal, there will be no way to completely withdraw

your data; however, there will be no way for your data to be connected to you.

Publication: We will be attempting to publish this research in a high impact journal. We will be

attempting to share results from this research locally, nationally, and internationally at

conferences.

Quality Assurance: The research study that you are participating in may be reviewed for quality

assurance to make sure that the required laws and guidelines are followed If chosen, (a)

representative (s) of the Human Research Ethics Program (HREP) may access study-related data

and/or consent materials as part of the review. All information accessed by the HREP will be

upheld to the same level of confidentiality that has been stated by the research team.

Risks: You may experience discomfort during the exercise test. The graded exercise test is

dangerous for people with a history of cardiovascular illnesses. If you or your family has a

history of cardiovascular illnesses, please tell the experimenter. In the case that a cardiovascular

incident occurs, emergency responders will be contacted and the experimenter has been trained

in emergency first aid. The experiment would stop, you would rest until emergency responders

arrived, and your vital signs would be monitored by the experimenter. Refer to Corrado et al.

(2003) for further explanation on the risks of maximal exercise tests. You may experience

frustration during the piano learning task, however this is completely natural. Alternatively,

some research suggests that learning to play and creating music is pleasurable.

Benefits: You will learn your predicted VO2 peak value from the graded exercise test. You will

be contributing to a better understanding of how exercise affects motor learning.

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Compensation: You will be paid set amounts per session that sums to approximately $12 per

hour for your time for a total of maximum $42 by the end of the experiment. The experiment

consists of four sessions. Session 1 will take 45 minutes, session 2 will take 1 hour and 45

minutes, session 3 will take 15 minutes, session 4 will take 30 minutes, and you will be asked to

spend 2 minutes per day of the study filling out an online sleep and exercise log. Additionally,

you will be reimbursed for any reasonable public transit costs.

The researchers that are conducting this research are:

Dana Swarbrick Dr. Joyce Chen Dr. Luc Tremblay

Rehabilitation Sciences

Institute

Sunnybrook Research Institute Faculty of Kinesiology &

Physical Education

Dr. Dina Brooks Dr. Sandra Trehub Dr. David Alter

Rehabilitation Sciences

Institute

Department of Psychology Toronto Rehabilitation

Institute

Contacts: The study has been explained to you and you have the right to ask any questions. If

you have any other questions or concerns, you can address them to the experimenter or to

principal investigator: Dr. Joyce Chen, tel: 416-480-6100, ext. 85410, or you may contact the

Research Oversight and Compliance Office: Human Research Ethics Program at

[email protected] or 416.946.3273 if you have questions about your rights as a

participant.

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Debriefing: Upon completion of your participation, you will receive a full written explanation

about the rationale and predictions underlying this experiment.

Please initial the following statements if you agree with them. You may choose to leave #4 & #5

un-initialed and continue to participate in the research:

1. I understand that I will need to refrain from exercising 24

hours prior to the graded exercise test and the interval exercise

protocol.

__________

2. I understand that I will need to refrain from consuming food,

caffeine, nicotine, and other substances other than water 2

hours prior to the experiment.

__________

3. I understand that I will need to refrain from consuming

caffeine for 2 hours after the end of the experiment.

__________

4. I consent to have pictures, video, and audio recorded for

educational purposes. __________

5. If you would like to be contacted to participate in future

research opportunities, please provide your email.

_______________________

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6. If you would like to receive a manuscript once this study has

been published, please initial or leave your email. ______________________

Participant’s Printed Name Participant’s Signature Date

Experimenter Name Participant Number

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Appendix C: Pre-Session 1 Questionnaire

Participant ID: _____________________ Session # : _____________ Date : ______________

Pre-Session 1 (Graded Exercise Test) Questionnaire

Personal Information

Number of years of formal education you have completed ______________

Less than 12

years

High school

graduate

Some

college/university

College/University

Graduate

Graduate or

professional

school

Musical Experiences

Do you listen to music?

Never Rarely Sometimes Often Very Often

If yes, primarily what genres: _____________________________________________

Do you dance?

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Never Rarely Sometimes Often Very Often

If yes, what styles: _____________________________________________________

How would you rate your overall sense of rhythm compared to the general population?

Poor Below average Average Good Excellent

How would you rate your overall sense of pitch compared to the general population?

Poor Below average Average Good Excellent

Can you usually tell when someone is singing out of tune?

1 (Never) 2 3 4 5 (Always)

In general, how would you rate your physical coordination?

Clumsy Below average Average Good Excellent

On average, how many hours per day do you actually spend listening to music, either while

doing something else or as your main activity?

0 1-2 3-4 5-8 9 or more

What is your usual level of attention or involvement when you listen to music?

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1 2 3 4 5

Background Only Total

Concentration

Physical Activity Questionnaire

We are interested in finding out about the kinds of physical activities that people do as

part of their everyday lives. The questions will ask you about the time you spent being physically

active in the last 7 days. Please answer each question even if you do not consider yourself to be

an active person. Please think about the activities you do at work, as part of your house and yard

work, to get from place to place, and in your spare time for recreation, exercise or sport.

Think about all the vigorous activities that you did in the last 7 days. Vigorous physical

activities refer to activities that take hard physical effort and make you breathe much harder than

normal. Think only about those physical activities that you did for at least 10 minutes at a time.

1. During the last 7 days, on how many days did you do vigorous physical activities like heavy

lifting, digging, aerobics, or fast bicycling?

_____ days per week

☐ No vigorous physical activities Skip to question 3

2. How much time did you usually spend doing vigorous physical activities on one of those

days?

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_____ hours per day

_____ minutes per day

☐ Don’t know/Not sure

Think about all the moderate activities that you did in the last 7 days. Moderate activities refer

to activities that take moderate physical effort and make you breathe somewhat harder than

normal. Think only about those physical activities that you did for at least 10 minutes at a time.

3. During the last 7 days, on how many days did you do moderate physical activities like

carrying light loads, bicycling at a regular pace, or doubles tennis? Do not include walking.

_____ days per week

☐ No moderate physical activities Skip to question 5

4. How much time did you usually spend doing moderate physical activities on one of those

days?

_____ hours per day


☐ Don’t know/Not sure

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Think about the time you spent walking in the last 7 days. This includes at work and at home,

walking to travel from place to place, and any other walking that you have done solely for

recreation, sport, exercise, or leisure.

5. During the last 7 days, on how many days did you walk for at least 10 minutes at a time?

_____ days per week

☐ No walking Skip to question 7

6. How much time did you usually spend walking on one of those days?

_____ hours per day


☐Don’t know/Not sure

The last question is about the time you spent sitting on weekdays during the last 7 days. Include

time spent at work, at home, while doing course work and during leisure time. This may include

time spent sitting at a desk, visiting friends, reading, or sitting or lying down to watch television.

7. During the last 7 days, how much time did you spend sitting on a week day?

_____ hours per day

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☐Don’t know/Not sure

Motivational State

How motivated are you to work your hardest in the exercise test today? Circle one.

1 2 3 4 5 6 7

Not at all

motivated

Highly

motivated

Caffeine Consumption

Do you usually consume caffeine before this time of day? ☐ YES ☐ NO

If yes, when? ________________

How do you consume caffeine? _________________

Did you consume caffeine today? __________________

Did this differ from your usual schedule? ________________

Nicotine Consumption

Do you smoke? ☐ YES ☐ NO

How often? __________________________________________

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Are you craving to smoke right now? ☐ YES ☐ NO

Did today’s smoking routine differ from usual? ☐ YES ☐ NO

Food Consumption

When was the last time you ate?_______________________

What did you eat?___________________________________

Has today’s feeding schedule been the same as your usual routine? ☐ YES ☐ NO

Are you currently hungry? ☐ YES ☐ NO

Are you usually hungry now? ☐ YES ☐ NO

Sleepiness

Please report the scale value of the statement that best describes your current state of sleepiness.

___________

1 - Feeling active and vital; alert; wide awake.

2 - Functioning at a high level, but not at peak; able to concentrate..

3 - Relaxed; awake; not at full alertness; responsive.

4 - A little foggy; not at peak; let down.

5 - Fogginess; beginning to lose interest in remaining awake; slowed down.

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6 - Sleepiness; prefer to be lying down; fighting sleep; woozy.

7 - Almost in reverie; sleep onset soon; lost struggle to remain awake.

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Appendix D: Pre- and Post-Exercise Emotional Affect Scale

This scale will be collected immediately before exercise, immediately after exercise, 10 minutes

after exercise, before the retention tests, and before participant departure.

Emotional Affect Scale

This scale consists of a number of words that describe different feelings and emotions. Read each

item and then mark the appropriate answer in the space next to that word. Indicate to what extent

you feel this way right now, that is, at the present moment. Use the following scale to record

your answers.

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Appendix E: Pre-Session 2, 3, & 4 Questionnaire

Participant ID : _____________________ Session # : ___________________ Date :

___________________

Pre-Session 2 (Piano Learning and Interval Exercise) Questionnaire

[For session 2 only]:

How motivated are you to work your hardest in the exercise test today? Circle one.

1 2 3 4 5 6 7

Not at all

motivated

Highly

motivated

How motivated are you to perform to the best of your ability on the piano playing task today?

Circle one.

1 2 3 4 5 6 7

Not at all

motivated

Highly

motivated

Caffeine Consumption

Do you usually consume caffeine before this time of day? ☐ YES ☐ NO

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If yes, when? ________________

How do you consume caffeine? _________________

Did you consume caffeine today? __________________

Did this differ from your usual schedule? ________________

Nicotine Consumption

Do you smoke? ☐ YES ☐ NO

How often? __________________________________________

Are you craving to smoke right now? ☐ YES ☐ NO

Did today’s smoking routine differ from usual? ☐ YES ☐ NO

Food Consumption

When was the last time you ate?_______________________

Has today’s feeding schedule been the same as your usual routine? ☐ YES ☐ NO

Are you currently hungry? ☐ YES ☐ NO

Are you usually hungry now? ☐ YES ☐ NO

Emotional Affect Scale

This scale consists of a number of words that describe different feelings and emotions. Read each

item and then mark the appropriate answer in the space next to that word. Indicate to what extent

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you feel this way right now, that is, at the present moment. Use the following scale to record

your answers.

Sleepiness

Please report the scale value of the statement that best describes your state of sleepiness.

___________

1 - Feeling active and vital; alert; wide awake.

2 - Functioning at a high level, but not at peak; able to concentrate.

3 - Relaxed; awake; not at full alertness; responsive.

4 - A little foggy; not at peak; let down.

5 - Fogginess; beginning to lose interest in remaining awake; slowed down.

6 - Sleepiness; prefer to be lying down; fighting sleep; woozy.

7 - Almost in reverie; sleep onset soon; lost struggle to remain awake.

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Appendix F: Sleep & Exercise Log

Hosted online as a google spreadsheet to be filled out days 1-8 of the study

Day Date

Hours of

Sleep

Quality of

Sleep

(1:poor,

7:good)

Exercise

Activity

Exercise

Time

Exercise Ratings of Perceived

Exertion (Use Borg Chart)

1 Meeting 1: Graded

Exercise Test

2 Meeting 2: Interval

Exercise Protocol +

Test 1

3 Meeting 3: Test 2

4

5

6

7

8

9 Meeting 4: Test 3

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Appendix G: Borg’s Ratings of Perceived Exertion

Borg rating Explanation

6 Zero exertion

7 Very easy

8 Minimal recognition of effort

9 Very light (comfortable walking pace)

10 Can just start to hear your breathing

11 Conversation is easy, and you feel like you could run for a while at this pace

12 Light exertion

13 Somewhat hard

14 You can hear your breathing, but you are not struggling

15 You can talk, but not in full sentences

16 Hard work

17 Very hard, starting to get uncomfortable and you are getting tired

18 You can no longer talk because your breathing is heavy

19 Extremely hard – Your body is screaming at you to stop

20 Max exertion

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Appendix H: Debrief Form

Thank you for your participation in the piano learning study! Please do not discuss the following details with any other participants.

Recent research shows that motor learning, the type of learning involved in learning a new motor skill such as learning an instrument, may be enhanced by exercise. Specifically, high intensity aerobic exercise after initial skill practice enhanced skill performance a day later and seven days later compared to exercise at a lower intensity or no exercise at all (1–4). Prior research has examined implicit learning of motor sequences, where the participants have no conscious awareness of the sequence to be acquired; however, most practical motor learning is explicit, where the participants are aware of the sequence they are learning. Piano playing is a real-world task that requires explicit motor sequence learning and that serves as the model task for this research on the effects of high intensity exercise on motor learning.

The purpose of this study was to determine if high intensity interval exercise after piano learning can enhance performance on a piano sequence a day later and a week later. There were two groups of participants: a high intensity interval exercise group and a low intensity interval exercise group. Both groups performed identical experiments except that during the interval exercise test, their prescribed resistances differed. Peak power output was determined in the graded exercise test and was used to determine each participant’s individualized power. The high intensity exercise group exercised at 90% of their peak power during the high intensity intervals and 60% of peak power during the low intensity intervals. The low intensity exercise group exercised at 30% of their peak power during the high intensity intervals and 20% of peak power during the low intensity intervals. We expect that the high intensity group will perform the piano sequence better one day and seven days after initial practice.

Each person has their own individual fitness, and this is determined by a number of factors. Similarly, motor learning ability is highly variable across participants and is moderated by a number of factors. It should be noted that learning rate on one task is not representative of learning rate on all tasks.

For further information on the research discussed, please refer to the following articles:

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1. Thomas R, et al. (2016) Acute exercise and motor memory consolidation: The role of exercise intensity. PLoS One 11(7):1–16.

2. Roig M, Skriver K, Lundbye-Jensen J, Kiens B, Nielsen JB (2012) A Single Bout of Exercise Improves Motor Memory. PLoS One 7(9):28–32.

3. Roig M, et al. (2016) Time-Dependent Effects of Cardiovascular Exercise on Memory. Exerc Sport Sci Rev 44(2):81–88.

4. Thomas R, et al. (2016) Acute exercise and motor memory consolidation: The role of exercise timing. Neural Plast 2016:1–25.

1. Would you like to be contacted when this research is published? Yes ☐ No ☐

2. Would like to hear about future opportunities for research? Yes ☐ No ☐

If you answered yes to either question above, please provide your email:________________

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Copyright Acknowledgements

We would like to acknowledge Dr. Lara Boyd and Dr. Marc Roig who provided permission to

use figures from several articles including Mang et al. (2014 & 2016), Ostadan et al. (2016) and

Dal Maso et al. (2018).

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