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
ii
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.
iii
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.
iv
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
v
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
vi
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
ix
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 13: Familiarization melody 2............................................................................................. 41
Figure 14: Familiarization melody 3............................................................................................. 41
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 26: Distractor Melody 4..................................................................................................... 47
Figure 27: Sequence 2 Correct Melody ........................................................................................ 48
Figure 28: Distractor Melody 1..................................................................................................... 48
Figure 29: Distractor Melody 2..................................................................................................... 48
Figure 30: Distractor Melody 3..................................................................................................... 48
Figure 31: Distractor Melody 4..................................................................................................... 48
Figure 32: Sequence 1 Correct Rhythm ........................................................................................ 49
Figure 33: Distractor 1 .................................................................................................................. 49
Figure 34: Distractor 2 .................................................................................................................. 49
Figure 35: Distractor 3 .................................................................................................................. 49
Figure 36: Distractor 4 .................................................................................................................. 49
Figure 37: Sequence 2 Correct Rhythm ........................................................................................ 49
Figure 38: Distractor 3 .................................................................................................................. 50
Figure 39: Distractor 2 .................................................................................................................. 50
xi
Figure 40: Distractor 3 .................................................................................................................. 50
Figure 41: Distractor 4 .................................................................................................................. 50
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
xii
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.
2
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).
3
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).
4
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).
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
Figure 13: Familiarization melody 2
Figure 14: Familiarization melody 3
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.
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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
standard error of the mean.
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
represent standard error of the mean.
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
represent standard error of the mean.
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
standard error of the mean.
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.
83
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,
85
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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References
1. Mang CS, Snow NJ, Wadden KP, Campbell KL, Boyd LA. High-Intensity Aerobic
Exercise Enhances Motor Memory Retrieval. Med Sci Sports Exerc. 2016;48(12):2477–
86.
2. Mang CS, Snow NJ, Campbell KL, Ross CJD, Boyd LA. A single bout of high-intensity
aerobic exercise facilitates response to paired associative stimulation and promotes
sequence-specific implicit motor learning. J Appl Physiol. 2014;117(117):1325–36.
3. Tipton CM. The history of “Exercise Is Medicine” in ancient civilizations. AJP Adv
Physiol Educ. 2014;38(2):109–17.
4. Haskell WL, Lee IM, Pate RR, Powell KE, Blair SN, Franklin BA, et al. Physical activity
and public health: Updated recommendation for adults from the American College of
Sports Medicine and the American Heart Association. Med Sci Sports Exerc.
2007;39(8):1423–34.
5. Mikkelsen K, Stojanovska L, Polenakovic M, Bosevski M, Apostolopoulos V. Exercise
and mental health. Maturitas. 2017;106(August):48–56.
6. Hillman CH, Erickson KI, Kramer AF. Be smart, exercise your heart: exercise effects on
brain and cognition. Nat Rev Neurosci. 2008;9(1):58–65.
7. Roig M, Skriver K, Lundbye-Jensen J, Kiens B, Nielsen JB. A Single Bout of Exercise
Improves Motor Memory. PLoS One. 2012;7(9):28–32.
8. Thomas R, Flindtgaard M, Skriver K, Geertsen SS, Christiansen L, Korsgaard Johnsen L,
et al. Acute exercise and motor memory consolidation: Does exercise type play a role?
Scand J Med Sci Sports. 2017;1523–32.
9. Skriver K, Roig M, Lundbye-Jensen J, Pingel J, Helge JW, Kiens B, et al. Acute exercise
improves motor memory: Exploring potential biomarkers. Neurobiol Learn Mem.
2014;116:46–58.
89
10. Taubert M, Villringer A, Lehmann N. Endurance Exercise as an “Endogenous” Neuro-
enhancement Strategy to Facilitate Motor Learning. Front Hum Neurosci.
2015;9(December):692.
11. Thomas R, Beck MM, Lind RR, Johnsen LK, Geertsen SS, Christiansen L, et al. Acute
exercise and motor memory consolidation: The role of exercise timing. Neural Plast.
2016;2016:1–25.
12. Chartrand G, Kaneva P, Kolozsvari N, Li C, Petrucci AM, Mutter AF, et al. The effects of
acute aerobic exercise on the acquisition and retention of laparoscopic skills. Surg Endosc.
2015;29(2):474–80.
13. Kvavilashvili L, Ellis J. Ecological validity and the real-life /laboratory controversy in
memory research: A critical and historical review. Hist Philos Psychol. 2004;6:59–80.
14. Ericsson KA, Krampe RT, Tesch-Römer C. The role of deliberate practice in the
acquisition of expert performance. Psychol Rev. 1993;100(3):363–406.
15. Thomas R, Johnsen LK, Geertsen SS, Christiansen L, Ritz C, Roig M, et al. Acute
exercise and motor memory consolidation: The role of exercise intensity. PLoS One.
2016;11(7):1–16.
16. Charalambous CC, Helm EE, Lau KA, Morton SM, Reisman DS. The feasibility of an
acute high-intensity exercise bout to promote locomotor learning after stroke. Top Stroke
Rehabil. 2017;9357:1–7.
17. Schmidt, Lee. Motor Learning. 1987.
18. Squire LR. Memory systems of the brain: A brief history and current perspective.
Neurobiol Learn Mem. 2004;82(3):171–7.
19. Ebbinghaus H. Memory: A Contribution to Experimental Psychology. Ann Neurosci.
2013;20.
20. Bartlett FC. Remembering. Cambridge; 1932.
90
21. Lashley K. In search of the engram. In: Society of Experimental Biology Symposium No
4: Physiological Mechanisms in Animal Behaviour. Cambridge, University Press; 1950.
22. Hebb DO. The organization of behavior: A neurophysiological approach. 1949;
23. Tulving E. How Many Memory Systems Are There? 1985.
24. Robertson EM. From creation to consolidation: A novel framework for memory
processing. PLoS Biol. 2009;7(1).
25. Squire LR, Zola-morgan S. The Medial Temporal Lobe Memory System. 1978;
26. Stanley J, Krakauer JW. Motor skill depends on knowledge of facts. Front Hum Neurosci.
2013;7(August):1–11.
27. Chambaron S, Berberian B, Delbecque L, Ginhac D, Cleeremans A. Implicit motor
learning in discrete and continuous tasks: Toward a possible explanation of discrepant
results. In: LT P, editor. Handbook of Motor Skills: Development, Impairment, and
Therapy. 2009. p. 139–55.
28. Mang CS, Snow NJ, Wadden KP, Campbell KL, Boyd LA. High-Intensity Aerobic
Exercise Enhances Motor Memory Retrieval-Published Ahead of Print. Medicine &
Science in Sports & Exercise. 2016.
29. Dayan E, Cohen LG. Neuroplasticity subserving motor skill learning. Neuron.
2011;72(3):443–54.
30. Robertson EM, Pascual-Leone A, Miall RC. Current concepts in procedural consolidation.
Vol. 5, Nature Reviews Neuroscience. 2004. p. 576–82.
31. Kantak SS, Winstein CJ. Learning-performance distinction and memory processes for
motor skills: A focused review and perspective. Behav Brain Res. 2012;228(1):219–31.
32. Newell KM. Coordination, Control and Skill. Adv Psychol. 1985;27(C):295–317.
33. Newell KM. Constraints on the development of coordination. In: Wade, M & Whiting
HTA, editor. Motor development in children: Aspects of coordination and control.
91
Amsterdam: Martinus Nijhoff Publishers; 1986.
34. Fitts, P. M., & Posner MI. Human performance. 1967.
35. Schmidt, R. A., Lee, T., Winstein, C., Wulf, G., & Zelaznik H. Motor Control and
Learning. 6E ed. Human kinetics; 2018.
36. Schmidt R. A Schema Theory of Discrete Motor Skill Learning. Psychol Rev.
1975;82(4):225–60.
37. Shea CH, Wulf G. Schema Theory: A Critical Appraisal and Reevaluation. Artic J Mot
Behav. 2005;
38. Lai Q, Shea CH, Wulf G, Wright DL. Optimizing Generalized Motor Program and
Parameter Learning. Res Q Exerc Sport. 2000;71(1):10–24.
39. Keele S. Movement Control in Skilled Motor Performance. Am Psychol Assoc.
1968;70(6):387–403.
40. Kelso JS. Dynamic patterns: The self-organization of brain and behavior. MIT press;
1997.
41. Davids K, Glazier P, Araújo D, Bartlett R. Movement Systems as Dynamical Systems.
Sport Med. 2003;33(4):245–60.
42. Janata P, Tomic ST, Haberman JM. Journal of Experimental Psychology: General
Sensorimotor Coupling in Music and the Psychology of the Groove Sensorimotor
Coupling in Music and the Psychology of the Groove. J Exp Psychol Gen. 2011;
43. Fujioka T, Trainor LJ, Large EW, Ross B. Beta and Gamma Rhythms in Human Auditory
Cortex during Musical Beat Processing. 2009;92:89–92.
44. Haken H, Kelso JAS, Bunz H. A Theoretical Model of Phase Transitions in Human Hand
Movements. Vol. 51, Biol. Cybern. 1985.
45. Zatorre RJ, Chen JL, Penhune VB. When the brain plays music: auditory-motor
interactions in music perception and production. Nat Rev Neurosci. 2007;8(7):547–58.
92
46. Lundbye-Jensen J, Skriver K, Nielsen JB, Roig M. Acute Exercise Improves Motor
Memory Consolidation in Preadolescent Children. Front Hum Neurosci.
2017;11(April):1–10.
47. Ostadan F, Centeno C, Daloze JF, Frenn M, Lundbye-Jensen J, Roig M. Changes in
corticospinal excitability during consolidation predict acute exercise-induced off-line
gains in procedural memory. Neurobiol Learn Mem. 2016;136:196–203.
48. Nepveu J-F, Thiel A, Tang A, Fung J, Lundbye-Jensen J, Boyd LA, et al. A Single Bout
of High-Intensity Interval Training Improves Motor Skill Retention in Individuals With
Stroke. Neurorehabil Neural Repair. 2017;31(8):726–35.
49. Dal Maso F, Desormeau B, Boudrias MH, Roig M. Acute cardiovascular exercise
promotes functional changes in cortico-motor networks during the early stages of motor
memory consolidation. Neuroimage. 2018;174(December 2017):380–92.
50. Snow NJ, Mang CS, Roig M, McDonnell MN, Campbell KL, Boyd LA. The effect of an
acute bout of moderate-intensity aerobic exercise on motor learning of a continuous
tracking task. PLoS One. 2016;11(2):1–16.
51. Simons DJ. The Value of Direct Replication. Perspect Psychol Sci. 2014;
52. Caspersen CJ, Powell KE, Christenson GM. Physical activity, exercise, and physical
fitness: definitions and distinctions for health-related research. Public Health Rep.
1985;100(2):126–31.
53. Wasserman K, Hansen J, Sue D, Casaburi R, Whipp B. Principles of Exercise Testing and
Interpretation. 3rd ed. Weinberg R, Reter R, editors. Baltimore: Lippincott Williams &
Wilkins.; 1999.
54. Mackenzie B. Lactic Acid [Internet]. 1999. Available from:
https://www.brianmac.co.uk/lactic.htm
55. Dennis A, Thomas AG, Rawlings NB, Near J, Nichols TE, Clare S, et al. An ultra-high
field magnetic resonance spectroscopy study of post exercise lactate, glutamate and
glutamine change in the human brain. Front Physiol. 2015;6(DEC).
93
56. Quistorff B, Secher N, Lieshout JJ Van, Secher NH. Lactate fuels the human brain during
exercise. FASEB J. 2008;22(10):3443–9.
57. Solberg G, Robstad B, Skjønsberg OH, Borchsenius F. Respiratory Gas Exchange Indices
For Estimating The Anaerobic Threshold. J Sport Sci Med. 2005;4:29–36.
58. Beltz NM, Gibson AL, Janot JM, Kravitz L, Mermier CM, Dalleck LC. Graded Exercise
Testing Protocols for the Determination of VO 2 max: Historical Perspectives, Progress,
and Future Considerations. J Sports Med. 2016;2016:1–12.
59. Buchfuhrer MJ, Hansen JE, Robinson TE, Sue DY, Wasserman K, Whipp BJ. Optimizing
the exercise protocol for cardiopulmonary assessment. J Appl Physiol. 1983
Nov;55(5):1558–64.
60. Shields M, Tremblay MS, Laviolette M, Craig CL, Janssen I, Gorber SC. Fitness of
Canadian adults: results from the 2007-2009 Canadian Health Measures Survey. Heal
reports. 2010;21(1):21–35.
61. Stromme SB, Ingjer F, Meen HD. Assessment of maximal aerobic power in specifically
trained athletes. J Appl Physiol. 1977 Jun;42(6):833–7.
62. Larsen RT, Christensen J, Tang LH, Keller C, Doherty P, Zwisler A-D, et al. A
Systematic Review And Meta-Analysis Comparing Cardiopulmonary Exercise Test
Values Obtained From The Arm Cycle And The Leg Cycle Respectively In Healthy
Adults. Int J Sports Phys Ther. 2016 Dec;11(7):1006–39.
63. Milanović Z, Sporiš G, Weston M. Effectiveness of High-Intensity Interval Training
(HIT) and Continuous Endurance Training for VO2max Improvements: A Systematic
Review and Meta-Analysis of Controlled Trials. Sport Med. 2015 Oct 5;45(10):1469–81.
64. Gillen J, Gibala M. Is high-intensity interval training a time-efficient exercise strategy to
improve health and fitness? Appl Physiol Nutr Metab. 2014;39:409–12.
65. Chang YK, Labban JD, Gapin JI, Etnier JL. The effects of acute exercise on cognitive
performance: A meta-analysis. Brain Res. 2012;1453(250):87–101.
94
66. Lambourne K, Tomporowski P. The effect of exercise-induced arousal on cognitive task
performance: A meta-regression analysis. Vol. 1341, Brain Research. 2010. p. 12–24.
67. Talanian J. Defining Different Types of Interval Training: Do we need to use more
specific terminology? 2015;1(5):161–3.
68. Ekkekakis P, Parfitt G, Petruzzello SJ. The Pleasure and Displeasure People Feel When
they Exercise at Different Intensities. Sport Med. 2011;41(8):641–71.
69. Zenko Z, Ekkekakis P, Ariely D. Can you have your vigorous execise and enjoy it too?
Ramping intensity down increases postexercise, remembered, and forecasted pleasure. J
Sport Exerc Psychol. 2016;In Press:1–30.
70. Saanijoki T, Nummenmaa L, Eskelinen J-J, Savolainen AM, Vahlberg T, Kalliokoski KK,
et al. Affective Responses to Repeated Sessions of High-Intensity Interval Training. Med
Sci Sports Exerc. 2015;47(12):2604–11.
71. Olney N, Wertz T, Laporta Z, Mora A, Serbas J, Todd A. Comparison Of Acute
Physiological And Psychological Responses Between Moderate- Intensity Continuous
Exercise And Three Regimes Of High-Intensity Interval Training. 2010;6(2):2130–8.
72. Warr-di Piero D, Valverde-Esteve T, Redondo-Castán JC, Pablos-Abella C, Sánchez-
Alarcos Díaz-Pintado JV. Effects of work-interval duration and sport specificity on blood
lactate concentration, heart rate and perceptual responses during high intensity interval
training. Sandbakk Ø, editor. PLoS One. 2018 Jul 16;13(7):e0200690.
73. Krusnauskas R, Venckunas T, Snieckus A, Eimantas N, Baranauskiene N, Skurvydas A,
et al. Very Low Volume High-Intensity Interval Exercise Is More Effective in Young
Than Old Women. Biomed Res Int. 2018 May 9;2018:1–9.
74. Gibala MJ, Little JP, Macdonald MJ, Hawley JA. Physiological adaptations to low-
volume, high-intensity interval training in health and disease. J Physiol.
2012;590(5):1077–84.
75. Francois ME, Little JP. Effectiveness and Safety of High-Intensity Interval Training in
Patients With Type 2 Diabetes.
95
76. Ostadan F, Centeno C, Daloze JF, Frenn M, Lundbye-Jensen J, Roig M. Changes in
corticospinal excitability during consolidation predict acute exercise-induced off-line
gains in procedural memory. Neurobiol Learn Mem. 2016;136:196–203.
77. Charalambous CC, Alcantara CC, French MA, Li X, Matt KS, Kim HE, et al. A single
exercise bout and locomotor learning after stroke: physiological, behavioral, and
computational outcomes. J Physiol. 2018;
78. Roig M, Thomas R, Mang CS, Snow NJ, Ostadan F, Boyd LA, et al. Time-Dependent
Effects of Cardiovascular Exercise on Memory. Exerc Sport Sci Rev. 2016;44(2):81–8.
79. Coco M, Perciavalle V, Cavallari P, Perciavalle V. Effects of an Exhaustive Exercise on
Motor Skill Learning and on the Excitability of Primary Motor Cortex and Supplementary
Motor Area. Medicine (Baltimore). 2016;95(11):e2978.
80. Brisswalter J, Collardeau M, René A. Effects of Acute Physical Exercise Characteristics
on Cognitive Performance. Sport Med. 2002;32(9):555–66.
81. Sharma DA, Chevidikunnan MF, Khan FR, Gaowgzeh RA. Effectiveness of knowledge of
result and knowledge of performance in the learning of a skilled motor activity by healthy
young adults. J Phys Ther Sci. 2016;28(5):1482–6.
82. Palmer C, Drake C. Monitoring and planning capacities in the acquisition of music
performance skills. Can J Exp Psychol. 1997;51(4):369–84.
83. Kleynen M, Braun SM, Rasquin SMC, Bleijlevens MHC, Lexis MAS, Halfens J, et al.
Multidisciplinary Views on Applying Explicit and Implicit Motor Learning in Practice:
An International Survey.
84. Ferrer-Uris B, Busquets A, Lopez-Alonso V, Fernandez-Del-Olmo M, Angulo-Barroso R.
Enhancing consolidation of a rotational visuomotor adaptation task through acute
exercise. PLoS One. 2017;12(4):3–9.
85. Nissenandpetekbullemer MJ. Attentional Requirements of Learning: Evidence from
Performance Measures. Cogn Psychol. 1987;19.
96
86. Edwin M. Robertson, 1, * Alvaro Pascual-Leone, 1 2, Press1 and DZ. Awareness
Modifies the Skill-Learning Benefits of Sleep. Curr Biol. 2004;14:206–12.
87. Destrebecqz A, Peigneux P, Laureys S, Degueldre C, Del Fiore G, Aerts J, et al. The
neural correlates of implicit and explicit sequence learning: Interacting networks revealed
by the process dissociation procedure. Learn Mem. 2005;12(5):480–90.
88. Perkins DN. Transfer of learning. International Encyclopedia of Education, Second
Edition, Oxford. 1992. p. 11.
89. Schmidt RA, Young DE. Transfer of movement control in motor skill learning. In:
Transfer of learning: Contemporary research and applications. 1987. p. 47–79.
90. Yokoi A, Bai W, Diedrichsen J. Restricted transfer of learning between unimanual and
bimanual finger sequences. J Neurophysiol. 2016;jn.00387.2016.
91. Müssgens DM, Ullén F. Transfer in Motor Sequence Learning: Effects of Practice
Schedule and Sequence Context. Front Hum Neurosci. 2015;9(November):642.
92. Wan CY, Schlaug G. Music making as a tool for promoting brain plasticity across the life
span. Neuroscientist. 2010 Oct;16(5):566–77.
93. Villeneuve M, Penhune V, Lamontagne A. A piano training program to improve manual
dexterity and upper extremity function in chronic stroke survivors. Front Hum Neurosci.
2014;8(662):1–9.
94. Mcgaugh JL. Memory-a Century of Consolidation.
95. Brashers-Krug T, Shadmehr R, Bizzi E. Consolidation in human motor memory. Nature.
1996;
96. Shadmehr R, Holcomb H. Neural Correlates of Motor Memory Consolidation. Science
(80- ). 1997;277(5327):821–5.
97. Korman M, Doyon J, Doljansky J, Carrier J, Dagan Y, Karni A. Daytime sleep condenses
the time course of motor memory consolidation. Nat Neurosci. 2007;10(9):1206–13.
97
98. Rhee J, Chen J, Riechman SM, Handa A, Bhatia S, Wright DL. An acute bout of aerobic
exercise can protect immediate offline motor sequence gains. Psychol Res.
2016;80(4):518–31.
99. Jo JS, Chen J, Riechman S, Roig M, Wright DL. The protective effects of acute
cardiovascular exercise on the interference of procedural memory. Psychol Res.
2018;(2012):1–13.
100. Heyman E, Gamelin FX, Goekint M, Piscitelli F, Roelands B, Leclair E, et al. Intense
exercise increases circulating endocannabinoid and BDNF levels in humans-Possible
implications for reward and depression. Psychoneuroendocrinology. 2012;37(6):844–51.
101. Mcdonnell MN, Buckley JD, Opie GM, Ridding MC, Semmler JG. A single bout of
aerobic exercise promotes motor cortical neuroplasticity. 2013;1174–82.
102. Rojas Vega S, Strüder HK, Vera Wahrmann B, Schmidt A, Bloch W, Hollmann W. Acute
BDNF and cortisol response to low intensity exercise and following ramp incremental
exercise to exhaustion in humans. Brain Res. 2006;1121(1):59–65.
103. van Hall G, Stømstad M, Rasmussen P, Jans Ø, Zaar M, Gam C, et al. Blood Lactate is an
Important Energy Source for the Human Brain. J Cereb Blood Flow Metab. 2009
Jun;29(6):1121–9.
104. Rasmussen P, Wyss MT, Lundby C. Cerebral glucose and lactate consumption during
cerebral activation by physical activity in humans. FASEB J. 2011 Sep;25(9):2865–73.
105. Suzuki A, Stern SA, Bozdagi O, Huntley GW, Walker RH, Magistretti PJ, et al.
Astrocyte-neuron lactate transport is required for long-term memory formation. Cell.
2011;
106. Yang J. The Role of the Right Hemisphere in Metaphor Comprehension: A Meta-Analysis
of Functional Magnetic Resonance Imaging Studies. Hum Brain Mapp. 2014
Jan;35(1):107–22.
107. Coco M, Alagona G, Rapisarda G, Costanzo E, Calogero RA, Perciavalle V, et al.
Elevated blood lactate is associated with increased motor cortex excitability. Somatosens
98
Mot Res. 2010;27(March):1–8.
108. Knaepen K, Goekint M, Heyman EM, Meeusen R. Neuroplasticity – Exercise-Induced
Response of Peripheral Brain-Derived Neurotrophic Factor. Sport Med. 2010
Sep;40(9):765–801.
109. Mang CS, McEwen LM, MacIsaac JL, Snow NJ, Campbell KL, Kobor MS, et al.
Exploring genetic influences underlying acute aerobic exercise effects on motor learning.
Sci Rep. 2017;7(1):1–10.
110. Noble EP. Addiction and its reward process through polymorphisms of the D2 dopamine
receptor gene: a review. Eur Psychiatry. 2000 Mar;15(2):79–89.
111. Hattori S, Naoit M, Nishinos’ H. Striatal Dopamine Turnover During Treadmill Running
in the Rat: Relation to the Speed of Running. Vol. 35, Brain Research Bulletin. 1994.
112. Wang G, Volkow ND, Fowler JS, Franceschi D, Logan J, Pappas NR, et al. PET Studies
of the Effects of Aerobic Exercise on Human Striatal Dopamine Release. 1999;41(8).
113. Basso JC, Shang A, Elman M, Karmouta R, Suzuki WA. Acute Exercise Improves
Prefrontal Cortex but not Hippocampal Function in Healthy Adults. 2015;791–801.
114. Pearson-Fuhrhop KM, Minton B, Acevedo D, Shahbaba B, Cramer SC. Genetic Variation
in the Human Brain Dopamine System Influences Motor Learning and Its Modulation by
L-Dopa. Sgambato-Faure V, editor. PLoS One. 2013 Apr 17;8(4):e61197.
115. Singh AM, Staines WR. The Effects of Acute Aerobic Exercise on the Primary Motor
Cortex. J Mot Behav. 2015;epub ahead(4):1–12.
116. Mang CS, Brown KE, Neva JL, Snow NJ, Campbell KL, Boyd LA. Promoting Motor
Cortical Plasticity with Acute Aerobic Exercise: A Role for Cerebellar Circuits. Neural
Plast. 2016;2016.
117. Penhune VB, Steele CJ. Parallel contributions of cerebellar , striatal and M1 mechanisms
to motor sequence learning. Behav Brain Res. 2012;226:579–91.
99
118. Stavrinos E, Coxon J. High Intensity Interval Exercise Promotes Motor Cortex
Disinhibition and Early Motor Skill Consolidation. J Cogn Neurosci. 2017;29(4):593–604.
119. MacIntosh BJ, Crane DE, Sage MD, Rajab AS, Donahue MJ, McIlroy WE, et al. Impact
of a single bout of aerobic exercise on regional brain perfusion and activation responses in
healthy young adults. PLoS One. 2014;9(1).
120. Rajab AS, Crane DE, Middleton LE, Robertson AD, Hampson M, Macintosh BJ, et al. A
single session of exercise increases connectivity in sensorimotor-related brain networks: a
resting-state fMRI study in young healthy adults. 2014;
121. Davids K. Ecological validity in understanding sport performance: some problems of
definition. Quest. 1988;40(2):126–36.
122. Bangert M, Altenmüller EO. Mapping perception to action in piano practice: A
longitudinal DC-EEG study. BMC Neurosci. 2003;4.
123. Lahav A, Boulanger A, Schlaug G, Saltzman E. The power of listening: auditory-motor
interactions in musical training. Ann N Y Acad Sci. 2005;1060:189–94.
124. Steele CJ, Penhune VB. Specific Increases within Global Decreases : A Functional
Magnetic Resonance Imaging Investigation of Five Days of Motor Sequence Learning.
2010;30(24):8332–41.
125. Chen JL, Penhune VB, Zatorre RJ. Moving on time: brain network for auditory-motor
synchronization is modulated by rhythm complexity and musical training. J Cogn
Neurosci. 2008;20(2):226–39.
126. Lappe C, Trainor LJ, Herholz SC, Pantev C. Cortical plasticity induced by short-term
multimodal musical rhythm training. PLoS One. 2011;6(6).
127. Brown RM, Penhune VA. Efficacy of auditory versus motor learning for skilled and
novice performers. J Cogn Neurosci. 2018;Accepted.
128. Unsworth N, Engle RW. Individual differences in working memory capacity and learning:
Evidence from the serial reaction time task. Mem Cognit. 2005;
100
129. Talamini F, Carretti B, Grassi M. The Working Memory of Musicians and Nonmusicians.
Music Percept. 2016;34(2):183–91.
130. Bergman Nutley S, Darki F, Klingberg T. Music practice is associated with development
of working memory during childhood and adolescence. Front Hum Neurosci.
2014;7(January):1–9.
131. Swaminathan S, Glenn Schellenberg E. Musical Competence is Predicted by Music
Training, Cognitive Abilities, and Personality. 2018;8:9223.
132. Hove MJ, Fairhurst MT, Kotz SA, Keller PE. Synchronizing with auditory and visual
rhythms: An fMRI assessment of modality differences and modality appropriateness.
Neuroimage. 2013;67:313–21.
133. Sloboda J. The eye-hand span: an approach to the study of sight reading. Psychol Music.
1974;2(2):4–10.
134. Engel A, Bangert M, Horbank D, Hijmans BS, Wilkens K, Keller PE, et al. Learning
piano melodies in visuo-motor or audio-motor training conditions and the neural
correlates of their cross-modal transfer. Neuroimage. 2012;63(2):966–78.
135. Herholz SC, Coffey EBJ, Pantev C, Zatorre RJ. Dissociation of Neural Networks for
Predisposition and for Training-Related Plasticity in Auditory-Motor Learning. Cereb
Cortex. 2016;26:3125–34.
136. Besson M, Faïta F. An event-related potential (ERP) study of musical expectancy:
Comparison of musicians with nonmusicians. J Exp Psychol Hum Percept Perform.
1995;21(6):1278–96.
137. Hund-Georgiadis M, Yves Von Cramon D. Motor-learning-related changes in piano
players and non-musicians revealed by functional magnetic-resonance signals. Exp Brain
Res. 1999;
138. Chen JL, Rae C, Watkins KE. Learning to play a melody: An fMRI study examining the
formation of auditory-motor associations. Neuroimage. 2012;59(2):1200–8.
101
139. Koelsch S, Gunter T, Friederici AD, Schröger E. Brain indices of music processing:
“nonmusicians” are musical. J Cogn Neurosci. 2000;12(3):520–41.
140. Penhune V, Zatorre R, Evans A. Cerebellar Contributions to Motor Timing: A PET Study
of Auditory and Visual Rhythm Reproduction. J Cogn Neurosci. 1998;(1):752–65.
141. Savion-Lemieux T, Penhune V. The effect of practice pattern on the acquisition ,
consolidation , and transfer of visual-motor sequences. 2010;271–81.
142. Romano Bergstrom JC, Howard JH, Howard D V. Enhanced Implicit Sequence Learning
in College‐age Video Game Players and Musicians.
143. Gozli DG, Bavelier D, Pratt J. The effect of action video game playing on sensorimotor
learning: Evidence from a movement tracking task. Hum Mov Sci. 2014;38:152–62.
144. Ayotte J, Peretz I, Hyde K. Congenital amusia: a group study of adults afflicted with a
music-specific disorder. Brain. 2002;125(Pt 2):238–51.
145. Peretz I, Ayotte J, Zatorre RJ, Mehler J, Ahad P, Penhune VB, et al. Congenital Amusia:
A disorder of fine-grained pitch discrimination. Neuron. 2002;33(2):185–91.
146. Winstein CJ, Schmidt R a. Reduced frequency of knowledge of results enhances motor
skill learning. J Exp Psychol Learn Mem Cogn. 1990;16(4):677–91.
147. Weeks DL, Whitney AA, Tindall AG, Carter GT. Pilot randomized trial comparing
intersession scheduling of biofeedback results to individuals with chronic pain: Influence
on psychologic function and pain intensity. Am J Phys Med Rehabil. 2015;94(10):869–78.
148. Wu WFW, Young DE, Schandler SL, Meir G, Judy RLM, Perez J, et al. Contextual
interference and augmented feedback: Is there an additive effect for motor learning? Hum
Mov Sci. 2011 Dec 1;30(6):1092–101.
149. Peretz I, Hyde KL. What is specific to music processing? Insights from congenital amusia.
Trends Cogn Sci. 2003;7(8):362–7.
150. Merchant H, Grahn J, Trainor L, Rohrmeier M, Fitch WT, Merchant H. Finding the beat :
102
a neural perspective across humans and non-human primates. 2015;
151. Iversen JR, Patel AD. The Beat Alignment Test (BAT): Surveying beat processing
abilities in the general population. Proc 10th Int Conf Music Percept Cogn. 2008;(Icmpc
10):465–8.
152. Borota D, Murray E, Keceli G, Chang A, Watabe JM, Ly M, et al. Post-study caffeine
administration enhances memory consolidation in humans. Nat Neurosci. 2014;17(2):201–
3.
153. Attaway CM, Compton DM, Turner MD. The effects of nicotine on learning and memory:
A neuropsychological assessment in young and senescent Fischer 344 rats. Physiol Behav.
1999;67(3):421–31.
154. Lohse KR, Boyd LA, Hodges NJ. Engaging Environments Enhance Motor Skill Learning
in a Computer Gaming Task. J Mot Behav. 2016;48(2):172–82.
155. Wulf G, Lewthwaite R. Optimizing performance through intrinsic motivation and
attention for learning: The OPTIMAL theory of motor learning. Psychon Bull Rev.
2016;1382–414.
156. Greeley B, Seidler RD. Mood induction effects on motor sequence learning and stop
signal reaction time. Exp Brain Res. 2016;
157. Ekkekakis P, Petruzzello SJ. Acute Aerobic Exercise And Affect: Current Status,
Problems And Prospects Regarding Dose Response. Sport Med. 1999;28(May 2016):337–
74.
158. Poolton JM, Masters RSW, Maxwell JP. Passing thoughts on the evolutionary stability of
implicit motor behaviour: Performance retention under physiological fatigue. Conscious
Cogn. 2007;16(2):456–68.
159. Borragán G, Slama H, Destrebecqz A, Peigneux P. Cognitive Fatigue Facilitates
Procedural Sequence Learning. Front Hum Neurosci. 2016;10(March):1–8.
160. Bickford PC, Shukitt-Hale B, Joseph J. Effects of aging on cerebellar noradrenergic
103
function and motor learning: Nutritional interventions. Mech Ageing Dev. 1999;111(2–
3):141–54.
161. Hoddes E, Zarcone V, Smythe H, Phillips R, Dement WC. (Stanford Sleepiness Q)
Quantification of Sleepiness: A New Approach. Vol. 10, Psychophysiology. 1973. p. 431–
6.
162. Litle P, Zuckerman M. Musical Preference Questionnaire. Pers undivid Diff.
1986;7(4):575–7.
163. Rentfrow PJ, Gosling SD. The do re mi’s of everyday life: The structure and personality
correlates of music preferences. J Pers Soc Psychol. 2003;84(6):1236–56.
164. Booth M. Assessment of Physical Activity: An International Perspective. Res Q Exerc
Sport. 2000;71(sup2):114–20.
165. Watson D, Clark LA, Tellegen A. Development and Validation of Brief Measures of
Positive and Negative Affect: The PANAS Scales. J Pers Soc Psychol. 1988;54(6):1063–
70.
166. Borg E, Kaijser L. A comparison between three rating scales for perceived exertion and
two different work tests. Scand J Med Sci Sport. 2006;16(1):57–69.
167. McKerns M, Aivazis M. pathos: a framework for heterogeneous computing. 2010;
168. McKerns MM, Strand L, Sullivan T, Fang A, Aivazis MAG. Building a framework for
predictive science. Proc 10th Python Sci Conf. 2011;
169. Tanaka H, Monahan KD, Seals DR. Age-predicted maximal heart rate revisited. J Am
Coll Cardiol. 2001;37(1):153–6.
170. Gulati M, Shaw LJ, Thisted RA, Black HR, Noel Bairey Merz C, Arnsdorf MF. Heart rate
response to exercise stress testing in asymptomatic women: The St. James women take
heart project. Circulation. 2010;122(2):130–7.
171. Kantak SS, Winstein CJ. Learning-performance distinction and memory processes for
104
motor skills: A focused review and perspective. Behav Brain Res. 2012;228(1):219–31.
172. Roig M, Thomas R, Mang CS, Snow NJ, Ostadan F, Boyd LA, et al. Time-Dependent
Effects of Cardiovascular Exercise on Memory. Exerc Sport Sci Rev. 2016;44(2):81–8.
173. Bender R, Lange S. Adjusting for multiple testing - When and how? J Clin Epidemiol.
2001 Apr 1;54(4):343–9.
174. American College of Sports Medicine. ACSM’s guidelines for exercise testing and
prescription. Lippincott Williams & Wilkins.; 2013. 179 p.
175. Brunner E, Domhf S, Langer F. Nonparametric Analysis of Longitudinal Data in Factorial
Experiments. 1st ed. Wiley-Interscience; 2001.
176. Song S. Consciousness and the consolidation of motor learning. Behav Brain Res. 2009
Jan 23;196(2):180–6.
177. Mooney RA, Coxon JP, Cirillo J, Glenny H, Gant N, Byblow WD. Acute aerobic exercise
modulates primary motor cortex inhibition. Exp Brain Res. 2016;234(12):1–8.
178. Singh AM, Duncan RE, Staines WR. Aerobic exercise abolishes cTBS-induced
suppression of motor cortical excitability. Neurosci Lett. 2016;633:215–9.
179. Winter B, Breitenstein C, Mooren FC, Voelker K, Fobker M, Lechtermann A, et al. High
impact running improves learning. Neurobiol Learn Mem. 2007;87(4):597–609.
180. Hardwick RM, Rottschy C, Miall RC, Eickhoff SB. A quantitative meta-analysis and
review of motor learning in the human brain. Neuroimage. 2013;67:283–97.
181. Lawson RR, Gayle JO, Wheaton LA. Novel behavioral indicator of explicit awareness
reveals temporal course of frontoparietal neural network facilitation during motor
learning. 2017;
182. Goldberg G. Supplementary motor area structure and function: Review and hypotheses.
Behav Brain Sci. 1985;8(4):567–88.
183. Halpern D, Hakel M. Applying the Science of Learning: Teaching for Long-Term
105
Retention and Transfer. 2003;
184. Janacsek K, Nemeth D. Predicting the future: From implicit learning to consolidation. Int
J Psychophysiol. 2012;83:213–21.
185. Dinoff A, Herrmann N, Swardfager W, Lanctôt KL. The effect of acute exercise on blood
concentrations of brain-derived neurotrophic factor in healthy adults: a meta-analysis. Eur
J Neurosci. 2017;46(1):1635–46.
186. Midgley AW, Bentley DJ, Luttikholt H, McNaughton LR, Millet GP. Challenging a
Dogma of Exercise Physiology. Sport Med. 2008;38(6):441–7.
187. Banerjee A, Chitnis UB, Jadhav SL, Bhawalkar JS, Chaudhury S. Hypothesis testing, type
I and type II errors. Ind Psychiatry J. 2009 Jul;18(2):127–31.
188. Karageorghis CI, Priest D-L. Music in the exercise domain: a review and synthesis (Part
II). Int Rev Sport Exerc Psychol. 2012;5(1):44–66.
189. Salimpoor VN, Benovoy M, Larcher K, Dagher A, Zatorre RJ. Anatomically distinct
dopamine release during anticipation and experience of peak emotion to music. Nat
Neurosci. 2011;14(January):257–62.
190. Dinoff A, Herrmann N, Swardfager W, Liu CS, Sherman C, Chan S, et al. The Effect of
exercise training on resting concentrations of peripheral brain-derived neurotrophic factor
(BDNF): A meta-analysis. PLoS One. 2016;11(9):1–21.
191. Yadav G, Mutha PK. Deep Breathing Practice Facilitates Retention of Newly Learned
Motor Skills. Sci Rep. 2016;6(October):37069.
192. Hordacre B, Immink MA, Ridding MC, Hillier S. Perceptual-motor learning benefits from
increased stress and anxiety. Hum Mov Sci. 2016;49:36–46.
193. Immink M, Hordacre B, Hillier S. Preceding motor task learning with exposure to
elevated levels of psychological stress improves short- and long-term performance in
healthy adults. J Sport Exerc Psychol. 2011;33:S78.
106
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?
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_________________________________________________________
4. Do you play any other music video games?
______________________________________________________________________
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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
_____ minutes 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
_____ minutes 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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_____ minutes per day
☐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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