Andante, Allegro o Silenzio: An Examination of Background Music Tempo on Facial Emotions, Electrodermal Responses, and Reading Task

Performance

by

Matthew Moreno

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy

Department of Curriculum, Teaching and Learning University of Toronto

© Copyright by Matthew Moreno, 2020

ii

Andante, Allegro o Silenzio: An Examination of Background

Music Tempo on Facial Emotions, Electrodermal

Responses, and Reading Task Performance

Matthew Moreno

Doctor of Philosophy

Department of Curriculum, Teaching and Learning/ Ontario Institute for Studies in

Education

University of Toronto

2020

Abstract

Current literature has established that learner’s emotions are an integral part of the

learning experience (Pekrun & Perry, 2014) and have significant effects on learning

processes that optimize performance (Cunningham, Dunfield, & Stillman, 2013), and

attentional responses (Kärner & Kögler, 2016). This present study examines the psycho-

emotional and psychophysiological effects that variations in the tempo of background

music have on learners who are completing reading comprehension tasks. To accomplish

this, the present study examines how learning performance is modulated through the

expressed emotions and bodily responses of participants and how our understanding of

the relationship between the emotional experience and cognitive functions in learning

tasks.

A total of seventy-four (N= 74) participants studied in this project indicated that

the tempo condition that participants were exposed to while competing their reading

comprehension task did have a significant effect of predicting their performance

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outcome, emotional expressions, and psychophysiological responses. Results indicated

that participants were more likely to have lower performance scores accompanied with

the likelihood of greater expressions of fear, joy and contempt, along with greater skin

conductance responses when listening to fast tempo music (150bpm). These results can

suggest that a combined regulatory mechanism may be at play that helps indicate the

combined effect that music may have on cognitive performance, attention allocation, and

emotion regulation.

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Acknowledgments

I would like to begin by expressing my deepest, most heartfelt thanks to my family,

friends and loved ones for your continuous support. I would especially like to thank my

father, Luis Carlos. My father’s lifelong passion for learning and instruction were the

impetus for me to pursue my doctorate in education, and hope this document is a

testament to unwavering pursuits that should never be stopped to understand the human

mind as we learning and assemble knowledge about the world around us.

Having the gift of music, and a high-quality music education, has allowed me to

pursue a career that I could have never dreamed of. I have had the chance to learn from

many amazing musicians and music educators who have inspired me to set high goals

and expectations for myself. I would like to thank Pino Boni, who has been my lifelong

music teacher and perhaps the greatest influence on my career path. His unbelievable

musicianship and teaching style have forever left an impression on me as a musician,

teacher and person. I would also like to thank my high school music teachers at St.

Aloysius Gonzaga CSS: Fabio Biagiarelli, Vic Frasson, and Mark Spisic. Without their

guidance, I would have never chosen to become a professional teacher and music

educator.

My time as an undergraduate student at York University helped transform me into

the person I am today through a solid foundation as a musician and educator. I would like

to thank Dr. Catherine Wilson, Dr. Mark Chambers, Dr. Arthur (Art) Levine, Prof. Karen

Burke, and Prof. William (Bill) Thomas. I would also like to thank the many individuals

who have helped make my time at the University of Toronto so memorable. I would like

to thank Dr. Leslie Stewart Rose, Dr. Cameron (Cam) Walter, Dr. Katie Tremblay-

Beaton, Dr. Heather Birch, and Andres Valencia Malfa for their time, help and guidance

throughout my time at UofT. Finally, I would like to thank my lab mates in the Emotion

and Learning Optimization (ELO) Lab. Without your constant support, motivation and

friendship, this would have been a radically different doctoral experience and I am

eternally thankful for you being there with me and for the chance to lend a hand

whenever possible. I would also like to sincerely thank my research assistant, Suzanne

Blainey, who has helped me immensely throughout my dissertation study.

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My mentors and supervisory committee have played a key role in helping define

my research, guide me through this research process, and mentor me in my formative

steps as an academic. Special thanks to my committee members, Dr. Roger Azevedo, Dr.

Charlene Ryan, Dr. James (Jim) Slotta, and supervisor, Dr. Earl Woodruff. Dr. Woodruff

has been a seminal figure in my journey at OISE starting as a Masters student in his CTL

1923 Mobile & Ubiquitous Instruction class all the way through to this present point in

my career. Thank you so much for giving me a chance to pursue my doctorate and learn

in this great environment! Thank you to everyone in this committee, you have grown my

interest in research and have expanded my understanding of your areas of expertise.

This dissertation is dedicated to the late Dr. Michael David Marcuzzi (1966-2012).

Table of Contents

Acknowledgments ....................................................................................................... iv

List of Tables .............................................................................................................. vii

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

List of Appendices ....................................................................................................... ix

Chapter 1 Introduction .................................................................................................. 1

1.1 Researcher Background and Impetus for Study ............................................... 1

1.2 Focus of Research and Gap in Literature ......................................................... 3

1.3 Research Questions ........................................................................................... 6

1.4 Contribution of Research .................................................................................. 7

Chapter 2 Literature Review ......................................................................................... 8

2.1 Emotions: Theories and Role in Learning ........................................................ 8

2.1.1 Definitions and Theories....................................................................... 8

2.1.2 Emotions and Cognition ..................................................................... 12

2.1.3 Learning and Emotions ....................................................................... 14

2.2 States of Stimulation and Performance........................................................... 17

2.2.1 Theories and Application .................................................................... 17

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2.2.2 Facial Emotion Detection ................................................................... 19

2.2.3 Electrodermal Response ..................................................................... 22

2.3 The Nature of Understanding ......................................................................... 24

2.3.1 Understanding and Comprehension .................................................... 24

2.3.2 Strata of Understanding ...................................................................... 25

2.3.3 The Place of Understanding in Learning ............................................ 27

2.4 Music: Stimulation and Affects ...................................................................... 28

2.4.1 Music Expression and Induction ........................................................ 28

2.4.2 Models of Musical Emotion ............................................................... 31

2.4.3 Physiological Indicators of Emotion Induction .................................. 35

2.4.4 Applications of Background Music .................................................... 37

2.4.5 The Impact of Tempo in Background Music ...................................... 40

Chapter 3 Methodology .............................................................................................. 42

3.1 Philosophical Assumptions and Framework .................................................. 42

3.2 Research Design ............................................................................................. 44

3.2.1 Ethical Clearance ................................................................................ 44

3.2.2 Participants ......................................................................................... 44

3.3 Data Collection ............................................................................................... 45

3.3.1 Tools ................................................................................................... 45

3.3.2 Laboratory Space ................................................................................ 46

3.3.3 Trial Overview .................................................................................... 47

3.4 Data Analysis .................................................................................................. 49

3.4.1 Marking and Cleaning of Data ........................................................... 49

Chapter 4 Results ........................................................................................................ 51

4.1 Demographics ................................................................................................. 51

4.2 Research Question #1 ..................................................................................... 54

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4.3 Research Question #2 ..................................................................................... 60

4.4 Research Question #3 ..................................................................................... 65

4.5 Research Question #4 ..................................................................................... 68

4.6 Results Summary ............................................................................................ 69

Chapter 5 Discussion .................................................................................................. 70

5.1 Chapter Overview ........................................................................................... 70

5.1.1 Performance and Effects of Tempo .................................................... 70

5.1.2 Emotions and Cause for Performance ................................................ 72

Chapter 6 Conclusions ................................................................................................ 92

6.1 Significance of this study................................................................................ 92

6.2 Implications of Research ................................................................................ 93

6.2.1 Implications for Education and Learning Science .............................. 93

6.2.2 Implications for Music Cognition ....................................................... 93

6.3 Limitations ...................................................................................................... 97

6.4 Areas for Future Research ............................................................................ 100

Bibliography ............................................................................................................. 102

Appendices ............................................................................................................... 126

List of Tables

Table 1 .............................................................................................................................. 52

Table 2 .............................................................................................................................. 52

Table 3 .............................................................................................................................. 52

Table 4 .............................................................................................................................. 53

Table 5 .............................................................................................................................. 53

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Table 6 ............................................................................................................................ 53

Table 7 .............................................................................................................................. 54

Table 8 .............................................................................................................................. 55

Table 9 .............................................................................................................................. 56

Table 10 ............................................................................................................................ 59

Table 11 ............................................................................................................................ 59

Table 12 ............................................................................................................................ 60

Table 13 ............................................................................................................................ 61

Table 14 ............................................................................................................................ 61

Table 15 ............................................................................................................................ 61

Table 16 ............................................................................................................................ 62

Table 17 ............................................................................................................................ 62

Table 18 ............................................................................................................................ 63

Table 19 ............................................................................................................................ 63

Table 20 ............................................................................................................................ 64

Table 21 ............................................................................................................................ 64

Table 22 ............................................................................................................................ 64

Table 23 ............................................................................................................................ 65

Table 24 ............................................................................................................................ 66

Table 25 ............................................................................................................................ 66

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Table 26 .......................................................................................................................... 67

Table 27 ............................................................................................................................ 67

Table 28 ............................................................................................................................ 68

Table 29 ............................................................................................................................ 69

Table 30 ............................................................................................................................ 69

List of Figures

Figure 1. Summary of Findings ....................................................................................... 70

List of Appendices

Appendix 1. Informed Consent Letter ............................................................................ 126

Appendix 2. Demographic Survey ................................................................................. 128

Appendix 3. Gold-MSI ................................................................................................... 129

Appendix 4. Nelson Denny H ......................................................................................... 130

Appendix 5. Wolfe Post-Task Questions........................................................................ 137

Appendix 6. Recruitment Ad .......................................................................................... 138

Appendix 7. Recruitment Email Message ...................................................................... 139

Appendix 8. Pairwise Comparisons for the Effect of Passage and Condition ............... 139

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Chapter 1 Introduction

1.1 Researcher Background and Impetus for Study

To understand the impetus for this dissertation, it is necessary to explore my position

within music and education. As a young child, I had the privilege of exposure to high-

quality music education. Although neither of my parents were ‘musical’ in the traditional

definition of performers or amateur connoisseurs, they insisted on providing their

children with a quality music education. This education, like many, began with piano

lessons and moved onto guitar, with a focus in Western art-music to help best prepare me

to develop general musicianship. Like many young students, I did not particularly enjoy

these lessons and practicing, but over time I began to enjoy them more and more.

Throughout my high school years, I developed a greater understanding of music,

appreciating how powerful it was not only for my own enjoyment, but as a future

profession.

These early experiences led me to undergraduate degrees in music and education

in order to become a certified teacher. My desire to learn about music also worked in

tandem to propel me to learn how music shapes learners on both affective and technical

levels. The first step towards helping me realize how powerful music could be on its

affective dimension was studying the works of Bennett Reimer. When Reimer released A

Philosophy of Music Education (1970), his work quickly rose to a role of prominence as

an emerging ‘voice’ for modern music educators and theorists to understand the process

of listening to and learning music, and was based upon the emotional/aesthetic power

that was conveyed to the listener. In his subsequent works, Reimer (2003) sought to

provide a philosophical base for the creation and propagation of music education,

arguing that it is music’s aesthetic value and experience that one draws meaning from to

validate music education. Central to Reimer’s philosophical argument is understanding

the role that feeling and affective response plays within one’s perception of music.

Reimer (2003, p. 275) argued,

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“How do these sounds become transformed into felt experience…in all of them,

the sounds of music are arranged to “make sense” as each culture, style, and type

regulates. These sounds capture and exhibit intricacies of feeling as only musical

sounds can do.”

Through reading these works, I began to assemble an understanding that music

has an imminent ‘force’ in the human experience that we need to organize in order to

make sense of its value. Reimer’s philosophy of aesthetic engagement in music

curriculum revolves around this notion of emotional response and the meaning that

humans make out of this stimulation. The aesthetic philosophy to the music curriculum is

one that searches to discover emotional states that can only be unlocked through music;

therefore, music must be active and should flourish to help all students achieve these

feelings and educational goals. As a young music teacher, I began to explore the

limitations and strengths of examining music from an emotional and aesthetic

perspective. In opposition to the aesthetic philosophy of curriculum that Reimer outlined,

others have suggested that music was built around the authentic practice of ‘doing’

music, by consuming and making it (Elliott & Silverman, 2014). This alternative

perspective to the aesthetic curriculum sought to address the perceived ‘weaknesses’ that

theorists and practitioners saw in the philosophical search for aesthetic experience. These

two counter perspectives to music and the value that music has on the long-term role of

emotional experience were critical in establishing the early questions that would drive

future research.

Working as a middle-school music teacher further led to my interest in exploring

graduate studies in education. My need to understand the complex processes in music

curriculum naturally led to understanding psycho-emotional processes and the role that

emotions play in regulating the learner. Exploring the expanded capacity of learners

through curriculum design led to valuing pedagogy that placed an emphasis on learner

agency feedback and direct instruction. The work of Knowledge Building theory

(Scardamalia, 2004; Scardamalia & Bereiter, 2006) and the pedagogy that revolves

around the individual and collective group development of thought illustrated the need to

understand the learning process as a building of complex ideas that take advantage of the

learner’s environment to help accomplish tasks. As I discovered through examining

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curriculum theory and educational psychology, emotions play a pivotal role in the

engagement, sustainment, and long-term developing attitudes of learners as they move

from basic to advanced levels of understanding in their search to develop knowledge. To

understand the process of learning, it became necessary to understand how emotions

work to modulate the learner’s response to their learning environment.

Theories of how emotions play a role in education have a long history within the

fields of cognitive and educational psychology. Through taking classes and embarking on

individualized study, I learned foundational theories, including dimensional, discrete

models, and a plethora of novel models of emotion, which served to provide my

foundational knowledge of where this field stands. These theories have defined emotions

through various classifiers that seek to explain what an emotion is exactly, what

generates emotional responses within learners, and how do we as educators see emotions

impacting the learning process? This began a search to understand how emotions

function in the learning process. As a researcher in an emotion-focused lab studying how

to measure and study the function of emotions in various learning settings, I was afforded

various opportunities to be exposed to cutting-edge technologies and the latest theoretical

models to measure and mobilize knowledge of emotions in learning. What became

evident at this time is that new technologies in the measurement of emotions were

permitting researchers to analyze emotional states as they occurred in learners, without

removing learners from a task.

1.2 Focus of Research and Gap in Literature

As my exploration of emotions in learning and the application of music became more

defined through my analysis of existing literature in both fields, several questions began

to emerge. Current literature in learning sciences has established that learner’s emotions

are an integral part of the learning experience (Pekrun & Perry, 2014) and have

significant effects on learning processes and states that optimize performance

(Cunningham, Dunfield, & Stillman, 2013), and attentional responses (Kärner & Kögler,

2016). There has been a keen focus surrounding the study of Achievement Emotions,

which researchers have identified as the most iconoclastic emotions that are associated

with academic experiences and success (Goetz et al., 2016; Pekrun et al., 2014, 2017)

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across a broad range of academic tasks. Achievement emotions operate through appraisal

processes to generate an emotional response during a learning situation to maximize

learning capacity and successful performance (Scherer & Moors, 2019). The generation

and impetus for these emotions are, therefore, critical to understanding how emotions

operate and facilitate cognitive, behavioral and metacognitive processes within learning

settings (Azevedo et al., 2017). The available literature indicates that varying emotional

responses may be associated with the ability to serve multiple, dual-scaled roles, such as

confusion (D’Mello et al., 2014), which can serve as an inhibitor of positive

performance, yet can also act as a catalyst into sustained engagement that promotes deep-

learning. Research into prominent models of emotional regulation including the control-

value theory (CVT; Pekrun, 2006), the circumplex model of emotion (Russell, 1980), and

the emotion regulation in achievement situations (ERAS; Harley, Pekrun, Taxer, &

Gross, 2019), describes the impetus for these emotions as the learner makes judgements,

in the form of appraisals, to their environmental circumstances and learning situation,

resulting in altered affective states that are expressed to adaptively meet both internal

(e.g., knowledge gaps) and external demands (e.g., task demands). In doing so, there is

space in which a degree of ‘modification’ may occur when the learner might be able to

alter the perceived valence (i.e., the positive or negative feelings associated with the task

or learning environment) or activation (i.e., the degree of agency brought on by the task)

of emotions, resulting in varied emotional responses to a learning task. To put this

succinctly, emotions are generated through appraisals that require judgement, and if one

requires judgement to generate response, there is the opportunity for environmental

factors to modulate that decision-making process.

Furthermore, music cognition research has identified music’s function as an

expressive tool on emotional judgements and more complex cognitive systems (Ochsner

& Gross, 2005; Pearce & Rohrmeier, 2012). Numerous studies have examined how

listeners use background music (as defined as listening to music that is outside of the

context rather than explicit, focused listening of music) during various cognitively

engaging tasks. Researchers have examined the positive effects of listening to

background music on fine motor tasks (Koolidge & Holmes, 2018; Rauscher, Shaw, &

Ky, 1993, 1995), creative writing (Doyle & Furnham, 2012; Hallam & Godwin, 2015),

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and learning performance (Lesiuk, 2005; Schellenberg & Winner, 2001; Sahebdel &

Khodadust, 2014; Su et al., 2017; Thompson, Schellenberg, & Hussain, 2001). More

poignantly, research has identified tempo (the speed of music) as an important

evolutionary musical component that humans develop great sensitivity to beginning at

early ages and developing throughout the listener’s lifetime (Dalla Bella, Peretz,

Rousseau, & Gosselin, 2001). Literature on the effects of musical tempo indicate that

tempo has an effect on arousal (Bramley, Dibben, & Rowe, 2016; Ünal, de Waard,

Epstude, & Steg, 2013), the perception of musical emotions (Kamenetsky, Hill, &

Trehub, 1997), and motivational sustainment in psychomotor tasks (Feng, Suri, & Bell,

2014). Research has further has homed in on the effects of tempo during learning tasks

(Thompson, Schellenberg, & Letnic, 2012), and suggests that there may be more

complex cognitive mechanisms that help to mediate how perceived tempo alters learning

performance and success. What was missing from this literature was a more detailed

examination of how tempo affects cognition, and a deeper exploration into the psycho-

emotional and regulatory effects of music on cognitive performance while participants

engage in cognitively demanding tasks, such as learning-like tasks.

By reviewing this literature, I began to establish questions that pertained to how

researchers envision the use of music in the learning process, which emotions might be

closely linked to performance and musical stimulation, and how could measuring these

emotional expressions of learners listening to music while performing add to our

collective understanding of music’s application in learning? Within these broad questions

began the formation of my research questions, which were intended to explore a small

fragment of these larger questions. Beginning with a literature review in the following

chapter, I realized that there was an immediate gap in the study of: 1) the application of

real-time and multi-modal analysis of expressed emotions during learning tasks, 2) an

understanding of the perceptual effects that tempo modulation has on expressed

emotions, and 3) how the embodied measurement of affect in music has a role in learning

performance and outcome.

This dissertation outlines my initial steps to study emotions, how we measure

them in learners, and how those emotions help to shape our understanding of the use of

music during the learning process. Emotional states and the impact that those measurable

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feelings have on our behaviours and cognitive performance will be explored through

their relationship to the learning process. An important part of that learning process is the

way by which learners cultivate understanding in relationship to the content and means

by which they learn. It is through understanding that learners move beyond superficial

thought and into a complex, rich network that allows for optimized learning. The power

of music through its explicit and implicit emotionality is used by humans to create

meaning in their lives.

In the following chapters, this present study will be outlined and broken down

into smaller units of study. Chapter 2 will examine existing literature that pertains to our

current scheme of emotion, music and learning to provide a depth of understanding from

where I am inserting my own contribution. Chapter 3 provides details regarding my

methodological imperatives as well as an overview of the research design and procedures

that were part of data collection. Chapter 4 explains my data analysis, including the

results and analytic methods that allowed me to support my research questions. Chapters

5 and 6 will contain a discussion and implications that can be drawn from this data. This

will involve a general discussion regarding the contributions of this line of research for

both the broader academic community and my personal program of research. Critical to

this will be the proposition of future application of these findings that may be mobilized

and explored in continuing study.

1.3 Research Questions

To undertake this research, it became evident that a more detailed exploration into the

perceptual effects of tempo and its relationship to expressed emotion would be necessary.

To accomplish this initial research, it was necessary to frame it into the context of a

single question that would encapsulate the parameters of this exploratory analysis. The

overarching question for this research is:

What psycho-emotional and psychophysiological effects does the variation of

tempo in background music have on learners who are completing reading

comprehension tasks? How do these expressed emotions and bodily responses

inform our understanding of the relationship between the emotional experience

and cognitive functions in learning tasks?

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In order to deconstruct this question, 4 sub-questions were used to analyze this research

question:

1. How does background music of varying tempi effect learner performance during

a reading comprehension task?

2. How does background music of varying tempi affect a learner's psycho-emotional

state and real-time expressed emotions during a reading comprehension task?

3. How does background music of varying tempi affect psychophysiological

indicators of response during a reading comprehension task?

4. How does background music of varying tempi affect perception and control of

responses during a reading comprehension task?

1.4 Contribution of Research

This research makes three critical contributions. Firstly, this study provides empirical

data as to the nature of expressed emotions and their role in regulating the learning

process. The use of real-time facial emotion and psychophysiological data collection

tools continues to develop the literature of their practical and methodological application

within learning sciences and educational psychology literature. Secondly, this work

advances the emerging awareness and maturity of the measurement of musical emotions

that are studied in music cognition. By making this contribution, this study will add to

music cognition literature and crossover with learning sciences & educational

psychology to describe how music can be used from a psycho-emotional engagement

perspective. Thirdly, this study will propose how researchers in the aforementioned fields

can employ and conceptualize the use of music as a tool to aid in the regulation of

emotional performance.

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Chapter 2 Literature Review

2.1 Emotions: Theories and Role in Learning

2.1.1 Definitions and Theories

Emotions have been an identified component of human identity for millennia as a means

of effective social communication and interaction. Emotions can affect a host of different

aspects in life including motivational processes, cognitive decision making, learning

processes, innovative thoughts that lead to creativity and divergent thought patterns, and

social interactions across varying domains (Canento et al., 2011). These feelings, also

referred to as affects, encompass a variety of feelings, moods and emotional states

(Boekaerts, 2007; Carver, 2003; Forgas, 1992; Russell & Carroll, 1999) that help form

the emotional makeup of an individual. Researchers theorize that three broad categories

of basic affect exist, defined by their form and social-relationship functions (Aureli &

Schaffner 2002; Evers, de Vries, Spruijt, & Sterck, 2014; Gervais & Fessler, 2017).

Firstly, attitudes are identified as enduring affective valuations that represent broader,

relational values in our lives (e.g. attitudes about life, liberty, happiness, etc.). Emotions

are occurrent affective reactions that mobilize relational behaviours between the

individual and the broader world around them, including people, places, or objects.

Emotions are often labeled with titles such as happiness, joy, frustration, anger, etc. that

humans express as part of their daily lives. The qualities that differentiate emotions from

moods are their intensity and duration (Rosenberg, 1998). Moods tend to have a longer

duration and lack a direct referent that causes them. Emotions refer to a direct action or

interaction with a point of reference and do not simply appear without cause (Pekrun,

2006), and they are tied to specific stimulus-appraisal processes (Fiedler & Beier, 2014).

Finally, sentiments are higher-level networks of attitudes and emotions that function as

‘critical bookkeeping’ tools of affective modulation. All three components to affect exist

within every human, and all affective responses require the activation of all components

in some form or another.

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Researchers have proposed theories that emotions go beyond primitive reflexes in

the human brain (Cacioppo & Gardner, 1999); they are complex and rich responses that

humans have made to externalize feelings in response to their environment. Some

researchers have described the origin of emotions as emerging out of an evolutionary

need to automate human responses to hostile and in-hostile circumstances (Hunt &

Campbell, 1997). To others, emotions can best be described as the psychological

registration of a significant event that creates, maintains or terminates important

relationships between a human and their environment (Campos, Walle, Dahl, & Main,

2011). Our response through emotions are complex, multifaceted streams of information

that can be used to help ensure our safety and ability to function. What makes these

complex responses significant is that they act to articulate our individual circumstances

and environment, and touch upon the physical aspects of our lives (Cacioppo & Gardner,

1999). Within predominating theories of affect, researchers are continually exploring the

tensions between the origins of emotions as both psychological responses between the

self and our environment, and also as biological patterns that help mediate our presence

in a physical space. This bi-modal understanding of emotions and the need to describe

emotion/affect represents a long-felt human necessity. As ontological objectives,

emotions are perceiver-dependent objects (Feldman Barrett, 2012) that do not exist in an

organic space outside the human mind. As Feldman Barrett elaborates, the human mind

assembles and labels emotions into varying categories in order to better define their

purpose and necessity within the social aspect of our lives. In this regard, the ‘reality’

emotions are ‘real’ because they sit as active objects in the human mind and are therefore

as valid as the verbal, representational and reasoning abilities that are hallmarks of

humans as evolutionary beings.

As far back as Darwin (1872) there has been an acknowledgement that the data

that we take in with our senses are one component of the emotional experience. Taking

that data and transforming it through a lens of social evolution helped to take emotions

from primitive responses to rich and complex feelings (Keltner, Oakley, & Jenkins,

2014). Without social interaction that creates the need to fashion emotions as a means of

communicating and responding to others, the human ability to articulate oneself would

have been impaired. The setting and contextualization of emotion is equally as important.

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As noted by Feldman Barrett, Mesquita and Gendron (2011), emotions exist within a

defined context of emotional responses that are expressed and felt and that are

normalized with a setting. The cultural (Elfenbein & Embady, 2003; Feldman Barrett &

Kensinger, 2010; Kang et al., 2019), stimulant, and perceiver (Feldman Barrett,

Mesquita, & Gendron, 2011) contexts help provide varying contextual underpinnings to

emotion perception. The context of emotions helps define the time, place and factors that

elicit emotional responses. By understanding the context of an emotion, we can help

frame the parameters of the emotional response and how to best describe the antecedents

of its origin. There are numerous ways to describe the parametric origins of emotions.

As previously discussed, emotions are ontological objects that have a place within

human interaction. Yet, there are numerous ways to codify the original and categorical

descriptors of emotions. Two of the most commonly referenced models for emotions are:

1) dimensional, and 2) discrete models of emotions.

The dimensional models of emotion describe the generation of emotions as the

byproduct of an interaction across two or more dimensions resulting in the mapping of

these affects on a grid. One of the most prevalent dimensional models is the circumplex

model (Russell, 1980), which describes emotions as being generated by the individual

appraising: 1) the valence (positive versus negative emotions) and 2) the arousal (high-

stimulation versus low-stimulation) of a situation. The affective response that is

generated through this model is a mixture of these two factors across a plane where both

interact with each other. As changes to valence and arousal vary across response, so do

the emotions that an individual will experience. These models are constructivist theories

because individuals’ variances in perception help to create these emotions via the

assimilation of emotional experiences that require prerequisite experiences to draw on.

Due to the nature of these models, it is possible to suggest that there are a varied number

of emotions that might be experienced due to the interactivity of these two factors and

the situations that the individual might be in. Variations of these models from Scherer

(2001, 2009), Pekrun (2006) describe the generation of emotions as resulting from

cognitive appraisals on varying dimensions that result in the generation of affective

responses based on the active cognitive appraisal of a particular setting. As the needs of

the context vary, an individual makes a series of varied appraisals, the categorical

11

dimensions of the appraisals vary on the model, and that in turn elicits emotional

responses. Russell, Weiss and Mendelsohn’s (1989) Affect Grid introduced high and low

agency ratings to his previous work, and Killgore (1999) added Dominance as a third

dimension in order to describe dominating and subjugating emotions. These dimensions

can vary and encompass a wide variety of criteria, yet all of them place an emphasis on

cognitive mediation in appraisal of environmental and contextual cues that elicit

emotional responses. Within these models, it is necessary to understand how the human

appraisal mechanisms function, whether there are universal rules that govern these

responses, and how external factors might help mediate the perception of these

contributing factors. Feldman Barrett (2006) further proposes that emotion is constructed

and then continuously developed through continued exposure to similar affective stimuli.

Humans develop more complex mechanisms to make appraisals and learn to fit these

more acute and developed perceptual processing skills into our affect responses.

The second model can be referred to as the discrete ‘basic’ model (Ekman, 1992)

of emotion. This model ascertains that there are a set of ‘basic’, finite emotional states

that exist, and that more complex emotions exist as a combination of these basic

emotions. The basic emotions, including interest, joy, anger, sadness, and fear (Izard,

2011), are the building-blocks for all emotional responses. This describes emotions as

feelings and expressions of our environment and its contents (Dolan, 2002) that act as

building blocks for further experience. Izard (2009, 2011) proposes that emotions can be

triggered through perceptual, appraisal, conceptual, and noncognitive processes. The

triggers for emotional response can come from a variety of sources, but they must be

generated through an interaction with the environment and do not appear without a cause.

These basic emotions are critical to forming the most rudimentary responses to our

environment through evolutionary necessity and biological needs. This model is useful

because it provides researchers with a finite set of emotions that provide the building

blocks to describe all emotional responses. As affect changes, the number of ‘building

blocks’ to that response change accordingly. One perceived limitation of the discrete

model is the perceived limitation and definition of a ‘basic emotion’. Such labels are still

being contested and debated as to how they function across cultural, contextual and

biological frameworks.

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2.1.2 Emotions and Cognition

Throughout various developments in the field of psychology, researchers have tried to

describe how our emotions engage with our cognitive mechanisms (Oatley, Keltner, &

Jenkins, 2014; Jensen, 2003). It is through this cognitive complex that humans come to

understand and make meaning of the world around them. The research presented has

demonstrated that humans develop their affective learning states and emotions as a

construct in their mind. The use of emotions is the summation of a complete view of a

situation a human is in. Jensen (2003) stresses the importance of memories as they

interact with our consciousness to create an emotional evaluation of a situation via the

appraisal process. Appraisal involves the conscious or subconscious judgement of the

perceivers’ environment and the emotions that might be inferred through it.

Psychology research, both within and outside of educational settings, can describe

how the mind builds, holds and uses emotions. Cognitive psychology describes causal

reasoning, deliberation, goal appraisal, and planning processes and can describe how

humans operate continually throughout the experience of emotions (D’Mello & Graesser,

2012). Emotions interact with human cognition to manifest learning states and emotions

into measurable human actions. Learning is the gradual process of the acquisition and

development of knowledge in the human mind. To have this process occur, the mind

needs to hold multiple ideas simultaneously and then have tools to synthesise that

information. The constant back-and-forth process of emotional judgement and tuning, as

Scherer (2009) discusses, involves a constant process of cognitive judgements. These

past responses as well as our own reflections on those responses, help create a framework

of feelings and emotions that are possible for articulation by an individual. Appraisals

that are made by learners help to link past experiences about affective states with

judgements of the world and allow for an active comparison between the learning goals

that the learner has previously established with those they wish to establish (Schutz,

2002). Understanding this network is important in a classroom setting where emotions

are activated in the learning process and it is necessary for learners to develop mastery

over their emotions to enhance their learning and achievement. The cognitive structures

in our mind help to transform the appraisal and action of a situation into a meaningful

emotion that can be externally exhibited (Shuman & Scherer, 2014) in the form of a

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physical indicator (an action, reaction, physical gestures, movement, etc.). This

stimulation that emotions provide to the user conveys the information needed to produce

an autonomic response or focus the individual’s senses to aid in decision making

(Hanoch & Vitouch, 2016). The arousal that emotions produce must be balanced between

the ability to provide data for a decision as well as ready the mind and body to produce a

response.

Our mind acts as an important mediator between our raw feelings and physical

actions. It helps to collect information from our senses and computes that information to

our brains to make decisions and act upon that information. We can view the mind as an

accommodator or assimilator of ideas (Fiedler & Beier, 2014; Piaget, 1954). External

stimuli can be assimilated to fit the psychological structures of the individual or they can

be accommodated into the mind and transform its nature and predispositions. Both

techniques are not used independently, and most learners will use both techniques in

order to accomplish learning tasks. What is most important to notice about this is that the

mind is highly adaptive to the information that it takes in and how it comes to use that

information. The stimuli is processed and turned into a judgement, and from that point it

can be used. Some theories of how this happens are the emotional congruence theory

(Bower, 1981), which states that our ability to learn must be in agreement with our

emotions for our memory to allow the effective recall of the learning task to occur. In

other words, the affective state that someone is in has to agree with the task they are

conducting. Another theory can be described as the ‘affect as information’ theory (Clore

& Palmer, 2009; Schwarz, 2005), which posits that emotions provide information to

make judgements because most tasks happen too fast for conscious thought; therefore,

emotions provide prerequisite information to produce a response. In this theory, the

unconscious appraisals that generate emotions are themselves the vehicle for thoughts

and work in tandem with memories to produce judgements. Both theories suggest that

our cognitive apparatus is connected to memories, or at the very least our subconscious

mind, to help act as the computational component to an emotional judgement. The data

gained from our senses pass through these stages in order to render an emotional

judgement that can produce emotions that impact our learning state.

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2.1.3 Learning and Emotions

Research has argued that emotions are an integral part of the daily schooling and learning

experience (Pekrun, 2000) as preconditions for learning and performance. This has led to

the study of achievement emotions, the emotions that are associated with experiences of

academic achievement (Goetz, Zirngibl, Pekrun, & Hall, 2003). These emotions can be

seen in test taking, studying, completing assignments and in other settings where the

mind is occupied by completing learning-related tasks. The Control-Value Theory (CVT;

Pekrun, 2006; Pekrun & Perry, 2014) argues that emotions in academic settings are the

by-product of an appraisal that functions across 2 dimensions: 1) subject control, and 2)

subjective value of a learning situation. The subjective control that a learner has refers to

the degree to which they have control over their learning situation, for example, their

ability to modify the present task or the affordances that are offered to them by the

environment. The value that the participant places on the learning task also impacts the

emotional appraisal. The higher value the learner places on the task, the more they will

feel activated to engage with it. The CVT is a valuable model to understand emotions

within learning because it integrates a variety of conceptual underpinnings from

appraisal, expectancy-value, transactional, attribution, as well as learning and

performance theories, all into a malleable design that can be recontextualized in a

multitude of settings (Loderer, Pekrun, & Lester, 2018). Through this theory, researchers

can contextualize learning settings, simulants and performance results and dissect how

emotions come to alter learning through these settings.

The continued need to identify achievement emotions necessitates study of such

theories across all aspects of learning performance. Existing research has studied this

construct to mean the emotions surrounding achievement outcomes, for example the

emotions associated with test anxiety or general failure states in learning (Pekrun, Elliot,

Maier, 2009). Achievement emotions are not just concerned with learning; they work in

conjunction with the process of stimulus and human appraisal of a learning situation to

determine how that learning situation and information interact with our values and

control mechanisms. Research has explored the emotions that surround successful

performance in achievement of a learning task, for example the emotions associated with

positive math performance, yet, alternate avenues of study argue that there needs to be a

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focus on the emotions associated with how individuals achieve those emotions towards

goal attainment (Pekrun, Elliot, Maier, 2009). Performance is a component of the

learning experience, but the states that surround that performance play an important role

in understanding how emotions shape and alter the decision-making process.

Understanding the emotions that enhance or prohibit learning provides educators

with an avenue to explore how these emotions interact within the learning environment.

Individual emotions, their past responses, and memories come together to construct an

affective state for learning. Cunningham, Dunfield and Stillman (2013) articulate that our

current affective state is dependent on previous experiences and our ability to recall and

interpret that data while learning. As we are exposed to new content and methods while

learning, we automatically assess the valence and intensity of the environment,

synthesize it, and make a prediction in the form of an emotional response to a learning

situation. Framing the precondition for emotional response becomes important in

characterizing a learner. Matthews, Deary and Whiteman (2003) presents two paradigms

for describing how learners come to make an emotional judgement: 1) a situationalist

paradigm, which emphasizes that the small changes and nuances within our physical

environment or behaviours from the people around us influence the emotions that we use,

and 2) the dispositionist paradigm that argues humans have natural predispositions

towards certain types of trained emotional responses that impact how we use our

emotions. For educators and researchers to understand how emotions function as part of

the learning process, it is necessary for us to conceptualize how learners enter

emotional/affective states.

An often-critical component of the value of affective study in learning surrounds

emotion regulation (ER) in academic settings. The study of modifying emotions in order

to aid in learning tasks has been viewed from many different perspectives. The control

mechanisms that exist in the learning process, described by Jacobs and Gross (2014),

explore how we use various modifiers in our learning process to achieve maximum

impact and efficiency. Emotional modification to the setting and perception of incoming

stimuli allows us to engage in a degree of regulation to help us control our emotional

response and provide the most useful input of information and response while learning.

The authors go on to suggest that there are procedures and interventions that can allow

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individuals to regulate and enhance their emotional control while learning to help attain

maximum efficiency and learning potential. Kärner and Kögler (2016) concur that

emotional regulation in learning does have an impact on how certain emotions emerge

and what happens to those emotions as they are externalized and turned into measurable

learning behaviours. The need to understand emotion modification necessitates the

identification of strata and categorical dimensions to how these processes may emerge.

Gross (1998, 2015a) have suggested five categories of strategies that humans use to

regulate their emotions, including: 1) situation selection, 2) situation modification, 3)

cognitive change, 4) attention deployment, and 5) modulation of response. These

strategies can be deployed to modify the intrinsic and extrinsic factors that help to

regulate affective response (Harley, Pekrun, Taxer, & Gross, 2019). The emergence of

emotions as powerful forces to move, alter and regulate the learning process forms in the

mind of the learner. In order to understand affective states and how emotions shape the

interactions of learning, one must explore how the mind forms and holds the emotions of

learning.

Contemporary theories of emotion regulation, such as the ERAS (emotion

regulation in achievement situations; Harley, Pekrun, Taxer, & Gross, 2019), and CPM

(component process model; Scherer, 2009) are working to hybridize existing models of

affect and use advancements in theory to worth through perceived shortcomings of these

theoretical models. ERAS applies the work of CVT and discrete emotion theories to

describe how emotions metamorphosize based on the parameters and contextual cues of a

learning task/and or environment. CPM proposes that affective states emerge as a result

of multiple appraisals that occur across different domains, including cognitive and

psychophysiological, in a set sequence. These appraisals involve judgements regarding:

1) the relevance of the setting, 2) the implications for the task, 3) the coping

modifications that can be afforded, and 4) the significance of the experience (Roseman,

2011). This computational model takes into account a linear, yet branching, series of

judgements that have reaching impacts onto further judgements. Contemporary theories

are examining the appraisal processes that occur during learning tasks and continuing to

explore integrated ways of studying the affective process as a complete process that takes

environmental, bodily and cognitive mechanisms into account.

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The theory of emotional intelligence (EI) from Goleman (2005) describes

emotions as playing an ongoing role in how we perceive the value of affective feelings in

our daily lives, how these feelings can become evident through our interactions, and how

we can use an understanding of emotions to better enhance our response to the world

around us. This theory suggests that our deepening understanding of how to analyze our

emotions can lead to more productive reactions and success in our lives, including as

learners. Literature has explored the relationship between emotions as a part of the

regulatory process for effective learning (Artino & Jones, 2012; Davis & Levine, 2013)

and academic performance. As the awareness of the role that emotions play in the

learning process grows, it is imperative that researchers continue to develop theories for

how emotions work under these critical learning actions and performance tasks.

The connection and power of emotions in learning has been explored and

research has demonstrated the power of affect on learners and the learning process. It is

through our cognitive apparatus that the learner passes emotional judgements that in turn

have an affect on how the student can execute learning tasks. Through this literature, it is

apparent that emotions can impact the learner on a variety of levels in the learning

environment. To explore how the learner’s emotions modulate performance, it is

necessary to explore the tools and methods used to gain empirical data on the mind and

body while experiencing these changes in situ.

2.2 States of Stimulation and Performance

2.2.1 Theories and Application

When a learner is engaged in a learning task, they are challenged to push the limits of

their own knowledge. To facilitates this, a learner must dedicate cognitive resources to

accomplish the sub-tasks necessary to learn. An altered state of stimulation, relative to a

rested state, provides the stimulation to engage in these learning tasks. The Yerkes-

Dodson Law (YDL) (Yerkes & Dodson, 1908), as an archetypal theory used to articulate

the connection between stimulation and response, describes the role that emotions play in

facilitating and describing performance across varying situations. According to the law,

for humans to be at their optimal levels of performance, they must be within a zone

where emotions are able to arouse our senses and enhance our ability to collect and

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process data for decision making, yet avoid a state where our emotions limit our ability to

collect that data. The bell curve of the YDL posits that performance increases as we

become more stimulated, until we reach a point that overstimulation occurs, and we

revert to lower levels of performance. The Cognitive Load Theory (CLT) (Sweller, 1988)

can support and describe how cognitive stimulation alters the learner’s reactions and

decision-making process. When various forms of cognitive load are placed on an

individual, the space to make judgements becomes taxed and requires increased

dedication of resources, or else the individual will not be able to satisfactorily complete

the tasks. The CLT informs the YDL by suggesting that as load increases, an individual

can dedicate the needed resources to a task – up until the point where they can no longer

increase resources, and performance falters on the downside of the inverted-U curve.

Likewise, when an individual does not have enough cognitive load placed on them, they

can not recognize the intensity of their task and performance may decrease.

The YDL can provide an overarching roadmap to study interactions and the

effects of stimulation. According to Mendl (1999), the YDL is not a definitive law across

all human activities, it is merely a roadmap for engaging in further study to understand

the specific effects of stimulation given a desired application. Furthermore, there is a

need to explore the effects of stressors on performance to capture subtle nuances that

exist in order to isolate how those stressors can produce specific results. Bennion, Ford,

Murray and Kensinger (2013) hypothesise that the emotional events that occur as a result

of an altered state of stimulation become tied to memories of that event – that is,

emotional events become tied to the stimuli surrounding them. Our memory of the

information around us allows an individual to store information, assess its value and then

make a judgement on it. Easterbrook (1959) described how heightened states of

stimulation gradually lead to a decreased ability to observe cues and contextual

information. This in turn decreases memory capacity and the amount of time the brain

has to recall events that may provide data to inform emotional decisions. While

Easterbrook observed these changes in high stimulation states, no such inferences were

attributed to lower-stimulation states.

Altered states of stimulation activate both autonomic and cognitive responses to

produce a desired response (Imbir & Gołąb, 2017). The tendency to automate responses,

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as well as cognitive assessment of a situation, is believed to be exploited to extract the

most amount of data possible from a situation. They go on to suggest that existing

literature has identified a relationship between language and visual images as they relate

to emotional intensity and performance. That is, extremes in emotional intensity are

negatively related to performance and affective stimulation that humans perceive. No

such inferences or studies are referenced to music or other auditory stimuli. Findings

from Gültepe and Coskun (2016) suggest that music acts, in accordance with YDL, as an

intermediary force, lowering cognitive load and increasing memory capacity as

participants engaged in an open-form, creative writing task.

2.2.2 Facial Emotion Detection

The capturing and measuring of emotions as a functional part of human expression can

be challenging. Having an effective tool to measure the presence of these emotions is

vital. Surveys that have been designed to capture emotions, including those by

Spielberger (1983) and Spielberger (1996), have focused on using self-report measures to

allow an individual to rate the likelihood of exhibiting an emotion. These surveys,

amongst other reviewed literature on emotion recognition tools, highlight the limitations

of self-report and self-rating of emotion expressions. Understanding the limitations of

these methods, especially in the midst of an increase in the presence of technology-based

learning settings (Loderer, Pekrun, & Lester, 2018) in the 21st century, provides a

continued impetus for applying technological interventions to assess emotional

expressions.

To help understand the nature of and derive objective value from emotional

expression, advances in the past decades have led to the proliferation of tools to measure

human expression via facial musculature movement. Among the most prevalent of these

theories is the Facial Action Coding System (FACS) developed by Ekman and Friesen

(1978) to measure the musculature movement in the face at 19 different Action Units

(AUs). Through these AUs, codes are developed to score various basic emotions

including anger, sadness, frustration, confusion, joy, surprise, fear, disgust, and

contempt. In total, 46 combinations of AUs combine together into what researchers

describe with reasonable certainty as ‘emotions’. The advent of this coding system

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required videos to be manually observed and coded to examine the AUs that were then

compiled into these emotions. Although tedious, this methodology allowed researchers in

the early stages to examine the collection of emotional expression data regardless of the

participant breaking their concentration or engagement in a task.

Use of facial emotion detection technology has led to expanded work on the

theories regarding the measurement and codification of expression schemes. An area of

continual debate amongst users of these systems arose from then-preconceived notions of

the emotions that they were measuring. The original FACS was premised on accurately

measuring a series of basic emotions that were understood to be expressed universally.

The emergence of appraisal theories (Vallverdu, 2014), which took the perspective that

emotions are derived from contextual judgements, marked a shift that brought the

contextual nature of emotions and recognition (Ellsworth & Scherer, 2003) into question

over a search to understand the nature of environment and the body as it relates to the

measurement of expression (Aviezer, Trope, & Todorov, 2012).

The most significant advances in the field of facial emotion detection can be seen

in the advent of automated systems to expedite this process. By using computer

technology to monitor, extract and code facial expressions, it has become possible to

increase speed and ecological validity by replacing human coding with computer coding

of emotional expressions. Through understanding more constituent components of the

face and how they provide data on the affective state of the subject, more information can

be extracted to have a clearer, deeper understanding of affect. Within this realm of

technology-assisted facial expression systems, there are two dominating techniques: 1)

facial electromyography activity, and 2) video classification algorithm software, such as

AFFDEX, FACET, Openface or FaceReader (Stöckli et al., 2018). The facial

electromyography activity (fEMG) measures the electrical changes in facial muscle

movement and is therefore able to record subtle facial muscle activities; however, these

systems require specialized hardware and can be obstructive (Schulte-Mecklenbeck et al.,

2017) and not conducive to ecological adaptation to research in the field. Moreover, this

method does not have the ability to classify and discern between individual emotions,

therefore requiring additional information to help with that identification (Wolf, 2015).

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The emergence of a modern generation of video-based, automated facial emotion

detection tools has brought about continuous development in the field with regards to

speed, accuracy and reliability. These systems automatically classify static and dynamic

facial expressions and code the AUs into basic emotions. Among the most prevalent and

widespread of these tools include FACET and Affectivia by iMotions, Noldus’s

FaceReader (den Uyl & van Kuilenburg, 2005), and Openface by Microsoft. The

iMotions softwares use the FACS while employing the Computer Expression

Recognition Toolbox (CERT; Littlewort et al. 2011) to automate the emotion detection

process. These software packages allow researchers to collect images and automatically

code them to distinguish, with generally high-degrees of accuracy, between multiple

systems in identifying basic emotions (Magdin, Benko, & Koprda, 2019; Lewinski et al.,

2014; Stöckli et al., 2018). This reinforces the validity of emotional expressions across

these differing algorithms, indicating while all different, this current generation of

software is able to reliably predict the presence of a basic emotion in excess of high-80th

to 90th percentile.

The development of these technologies, continued criticisms are leveraged

against the use of proprietary algorithms used to track the automated coding process that

are not fully described to the populous in general, due to the patented technology that are

coveted by their developers. Methodological concerns over a variety of areas are still at

the forefront of researcher’s finds as they approach the use of facial-emotion detection

software. Amongst these concerns include the contextualization of emotional expression

(Feldman Barrett et al., 2019). According to Feldman Barrett et al. (2019), researchers

are still challenged with understanding the role that the learner’s context plays in shaping

their emotional expressions, especially as they continue to make more expansive

statements regarding the generalizability of these emotional expressions across tasks and

domains. More importantly, the cache of naturalistic faces that this proprietary software

utilizes are expanding and the clustering of these faces to understand the context and

setting that they emerged in are only starting to be understood (Benitez-Quiroz,

Srinivasan, & Martinez, 2016). Without a host of facial units to draw from, this software

will be able to deliver high accuracy to an emotional expression (i.e.; being about detect

a physical change in a participant), yet their reliability to detect and infer an individual’s

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emotional state will continue to be challenged. Feldman Barrett et al. (2019)

acknowledge in the summary of their findings, that the field of emotion and the

application of emotion-detection software is becoming more contextual to help

researchers understand how software and methodological considerations are intertwining

to help us understand the complexity of human response.

2.2.3 Electrodermal Response

The collection of bodily response data has long provided a lens into human response.

Studying the psychophysiological and autonomic bodily responses of participants gives

researchers a new series of tools to describe the interconnection between human response

systems. Amongst the varying categories of psychophysiological measurement tools, the

study of participants’ skin responses is described as exodermal activity (EDA). With this

area of study, Galvanic Skin Response (GSR) is amongst the most common analyses and

is a measurement of the body’s skin resistance that varies as a result of sub-dermal sweat

glands (Kim & Andre, 2008). More specifically, the recorded activity of sweat glands is

triggered by the postganglionic sudomotor fibers that are located within human skin.

Each sweat gland is innervated by many different sudomotor fibers (Benedek, &

Kaernbach, 2010). These glands are linked to the body’s sympathetic nervous system and

respond to stimuli surrounding the individual. When an individual is aroused, their

sympathetic nervous system subconsciously responds to the changes in stimulation that

an individual is experiencing. This stimulation is recorded over time to provide an

understanding of the changes that may manifest themselves in an individual without

direct knowledge. These sudomotor nervous responses correspond to observable skin

conductance response (SCR), which is the base unit for measurement of response. There

are several variations on the measurement of SCRs, but most systems use a standardized

form of measurement called classic trough-to-peak (CTTP), which involves the

automated detection of significant peak from within areas of increased stimulation

(Benedek & Kaernbach, 2010). Using this method, the spike in the measurable density

of responses, along with the amplitude of the nerve burst, is linearly related to the

number of sweat glands that respond to an event (Nishiyama et al., 2001), and to the

amplitude of the associated SCR. In this mechanical principle, the SCR amplitude can be

considered a measure of sympathetic activity that an individual is responding to. This

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data provides an indicator of changes that are occurring as a subconscious response to the

stimulus appraisal that is occurring. In this sense, the SCRs can be seen as accurate

measurements of the sympathetic nervous responses that individuals experience.

To further understand the interaction with SCRs, there exist two distinct and

parallels measures of psychophysiological response patterns. Skin conductance level

(SCL) is characterized by slow-moving and varying ‘tonic’ waves that are smooth and

move in relation to the individual’s physiological baseline (Braithwaite, Watson, Jones,

& Rowe, 2013). The second category of responses are skin conductance level (SCL).

These faster types of ‘phasic’ responses reflect a measurement of stimulus-specific

responses, such as the measurement of a response to an external stimulus, activity, or

other form of human function, or they can be used to identify non-specific responses that

may be broader and not activity dependent, for example, clinical depression

(Mestanikova, et al., 2016), calmed mental activity (Zangróniz et al., 2017), as well as

sleep patterns (Sano, Pickard, & Stickgold, 2014). These two aspects of physiological

activation are thought to rely on different neurological mechanisms (Nagai et al. 2004)

which lead to differences in how their perceived patterns and relationships are analyzed

to chart their effects on the mind as it responds to stimuli.

Physiological response data has been identified as a reliable indicator of human

emotional and cognitive states across varying modes and testing environments (Feng,

Golshan, & Mahoor (2018). The application of GSR has also been used to explore larger,

broader response patterns that have long been a central component in exploring affective

states (Boucsein, 2012). As individuals pass judgments and register emotional responses

to events, the sympathetic nervous system responds to these messages and produces

SCRs as a by-product to these events. These SCR responses, in turn, can be interpreted as

effective and reproducible psychophysiological data streams to investigate sympathetic

nervous system function in these tasks (Kwon, Kim, Park, & Kim, 2016; Stagg, Davis, &

Heaton, 2013). Researchers have indicated that the measurement of SCL and SCRs

indicate healthy, affective response to emotional states (Luauté et al., 2018), and are a

key identifier to healthy psycho-emotional wellness in individuals (Al Machot et al.,

2019; Greco et al., 2014; Ooi et al., 2016). It is seen that there exists a relationship

between skin response, as an indicator of stimulation, and affective states, although a

24

causal relationship has been difficult to assert. Electrodermal activity is one of the most

researched physiological channels used for measuring emotion and changes in affective

states in learning-based tasks (Mauss & Robinson 2009; Picard et al. 2016). These tools

have been reliable measures in the prediction of frustration of learners (Kapoor et al.

2007), the presence of dual affective states of contrasting emotional valences (Kreibig,

Samson, & Gross, 2015), and to explain the relationship between stimulation and

collaborative versus individualistic learning tasks as well as the need for multimodal data

streams to help add depth to our contextual understanding of stimulation (Villanueva et

al., 2018). More importantly to this present study, EDA has been a prominent data

channel within studies that incorporate computer-based learning environments (Calvo &

D’Mello 2010; Harley et al., 2015; Harley, Jarrell, & Lajoie, 2019).

2.3 The Nature of Understanding

2.3.1 Understanding and Comprehension

The learning process involves the building of ideas, of developing more complex

networks of thoughts to facilitate the acquisition of more complex networks of thoughts.

A requirement for the learning process is understanding. Understanding is differentiated

from comprehension (Nickerson, 1985) in that understanding involves a learner moving

beyond superficial interactions with the mind and an object of knowledge. To

comprehend something is to understand the characteristic of something using our senses,

allowing us to make judgements and analyse the components of what makes up this

object of knowledge. Understanding involves a learner moving ‘into’ an object of

knowledge and developing a complex, interconnected web of thoughts about how an

object of knowledge can be used and fit into different contexts. It is therefore inherently

contextual where an object of knowledge fits into a certain context that must be

understood and in turn informs the context of the learner. In this regard, understanding is

the driven and impact by a particular situation and stimuli that emerge from it. From a

taxonometric perspective, comprehension is the essential first stage towards developing

understanding (Adams, 2015) within the mind of the learner. To ‘educate’ requires

developing the skills to be able to comprehend learning tasks and assimilate them into an

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information network, leading to the goal of developing a learner’s authentic

understanding of that learning.

2.3.2 Strata of Understanding

The movement of thought in the mind of individuals has strata and differentiation in

thought. According to Bereiter (2005), Bloom et al.’s (1956) taxonomy was designed to

assess understanding and the progression of thought. Understanding denotes the ability of

an individual to make judgements on a subject based upon their cognitive ability to

collect, synthesize and process data. Depth of knowledge appears to relate to an

individual’s ability to draw on greater fields of knowledge and extrapolate the

connections, within a context, in order to demonstrate understanding. The taxonomy does

not make affordances for the ‘depth’ of knowledge (Bereiter, 2005, p. 97) as a

component in categorizing understanding. Understanding is not a two-dimensional

concept, but instead runs contrary to what Bloom tried to accomplish; the ability to

generate new forms of knowledge instead of transmitting it to demonstrate mastery of it

is a critical step (Bereiter & Scardamalia, 2012) towards understanding. While there is a

taxonomy and progression of understanding, a learner does not need to climb rungs in

order to achieve it.

To understand something, a learner must have a complete analysis of an object.

This involves analyzing an object from multiple perspectives in order to collect enough

data to pass an informed judgement on it. This understanding can have varying degrees

of completeness and is not bound to limiting factors (Nickerson, 1985, p. 219) that could

act as a sort of ‘ceiling’ to understanding. The completeness of a learner’s understanding

functions across a scale that is ever growing and deepening. The differentiation between

a novice and expert level of understanding is their ability to move deeper into their object

of understanding and find new ways of bringing new connections together. The ‘schema’

(Bartlett, 1932; Oatley, Keltner, & Jenkins, 2014) that learners develop is the web of

ideas that inform understanding, which is richer and more developed in experts in

comparison to novices. This web of ideas follows the learner and is exercised, expanded,

and contracted as the learner reshapes the meaning of this schema through experiences.

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The variability of understanding in a learner allows for varying levels of maturity

in understanding. Differentiating between expert and novice levels of understanding can

be categorized in several ways. Firstly, experts and novices vary in the degree of

abstractness that they bring to their understanding (Nickerson, 1985, p. 222). Experts

look deeper, beyond the surface components of an object that can be observed by the

senses, and instead focus on bringing new relationships together for items and ideas that

may appear to be unrelated. Consequently, novices focus on lower levels of observation

and remain stuck on the structural components of an object. Experts are also great

hypothesis checkers; they can hold multiple ideas simultaneously and are able to

synthesize concepts with greater facility. Experts can provide insightful solutions to

problems that are formed by articulating a deep relationship with that object (Bereiter,

2005, p. 100). In contrast, novices are not proficient in formulating hypotheses and do

not yet have the ability to synthesize abstract concepts and merge them together. Expert

understanding does not focus solely on one’s ability to hold fact, but it is about using

information to inform decision making and thought processes.

This move towards understanding places an emphasis on an individual having the

ability to bring together multiple threads of data, experiences and feelings to inform how

they construct understanding. Gardner’s (2011) theory of multiple intelligences

deconstructs intelligence from multiple perspectives to develop understanding. Learners

perceive the world from multiple angles, and understandings, in order to collect enough

data to make judgements that best inform the way they learn. Gardner focuses on

learning and intelligent action, informed through exposure to different ways of

understanding; in other words, there are multiple points of entry to begin forming

connections. These ‘frames’ shape how we construct our learning schema and what we

use to frame our individualized learning experiences. To develop understanding, we use

this frame as a way to begin the deconstruction and examination of an object of

knowledge, which in turn adds to the richness and complexity that we add to our

deepening of understanding. In order for more complex learning to happen, the

individual is required to develop a complex, deeper frame for where they will learn to

assemble knowledge and permit deeper relationships to happen. Once this has occurred,

deeper levels of understanding can be constructed (Scardamalia & Bereiter, 2006).

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2.3.3 The Place of Understanding in Learning

To learn, a relationship must be established in the mind of the learner wherein they

construct an understanding of the object they are trying to learn, which may be a skill,

knowledge or attitude. Once this relationship has been identified, the individual can

begin a deconstruction of the components of that object of knowledge and assimilate that

understanding into their frame of learning. Understanding has been identified as a

condition associated with intelligent response (Bereiter, 2005). Without understanding,

an individual can only engage in superficial response and relational building with an

object of knowledge. The focus, therefore, turns to developing strategies and methods to

develop deeper levels of understanding to advance or heighten the learning process.

Carey and Smith (1993) describe the three stages of understanding that exist within

scientific learning. The three levels of understanding move from more novice levels, built

on factual recall and a search for absolute-truths, towards a mature, introspective view of

information that informs new theory-building. This emphasis on progressive inquiry into

a subject, combined with the ability to test ideas and not accept definitive facts, indicates

that a learner has matured their understanding of a subject by moving away from

immature observation towards developing their own desires to synthesize information

and make a unique contribution to the knowledge base.

Bereiter (2005) indicates that there are 3 common strategies for developing

understanding, including: 1) direct instruction, i.e. when someone tells you what you

need to ‘know’, 2) a process approach, concentrated on developing understanding

through a developmental process, and 3) creating a conceptual change, by determining

what the student knows and then creating a way to explain it relative to their current

understanding of that object. To develop understanding through instruction, can there be

a way to enhance how teachers develop and measure understanding in learners? A

possible solution that is discussed is finding a way to link emotions or feelings to

understanding (Bereiter, 2005, p. 113). If there are anecdotal relationships between

emotions and developing understanding, the challenge for learning comes in studying a

stimulus that allow emotions to alter understanding. The nature of human understanding

is central to developing learners who are able to hold and construct complex relationships

in their mind, but it is also essential for developing learners who are capable of being

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self-sufficient and independent thinkers in life. It therefore becomes necessary for

learners to utilize tools and stimuli to help develop effective thoughts and emotions to

elicit greater degrees of understanding for performance. A stimulus, such as music, may

be the type of media needed to explore this response.

2.4 Music: Stimulation and Affects

2.4.1 Music Expression and Induction

The art of music is a ubiquitous human experience that is unique to the human species

(Perlovsky, 2012). The practice of making sound in an organized manner and applying

varying degrees of meaning to that act is a universal human experience. Current research

has indicated three domains of the musical experience: 1) experience (the initial stimuli),

2) expression (the affective response), and 3) physiology (the physiological signs of that

experience) (Lundqvist, Carlsson, Hilmersson, & Juslin, 2009). These three domains

work in tandem to create the phenomena of musical perception. These theories suggest

that the impact of music lies in its ability to act as a reflective tool that is used to express

qualities that cannot be expressed in a verbal-linguistic manner outside of a singular

domain. Neuroscience literature continues to develop our understanding of a causal

relationship between the brain and music response, indicating that the brain activates

certain components in response to stimuli that are then transformed into externalized

responses, including emotions (Juslin, Harmat, & Eerola, 2014). The power of the

auditory system to induce emotion suggests that our autonomic nervous system, the part

of our brain designed for automated and fast response, is activated when musical

emotions are induced (Khalfa, Peretz, Blondin, & Robert, 2002; Reybrouck & Eerola,

2017). Our brain appears to form a codification scheme for these auditory messages and

uses it to produce automated responses, including emotions, when this auditory

stimulation is received. Musical emotions can be powerful, but they are one component

to the perception of music. Koelsch (2015) identifies seven features of the musical

experience, with the most explicit being the affective response. He argues that, “musical

information with symbolic sign quality (due to semantic memory) might evoke a concept

with emotional valence, which in turn might also lead to an emotional response” (p. 195).

The power of music is its ability to establish a relationship in the mind of the listener,

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such that they can form instantaneous associations between a stimulus and an emotion,

and then relate that to a cue in their environment.

The ability for music to produce an emotional response is contingent on the

brain’s ability to formulate and perceive that response. Cognition is a vital part of this

meaning-making, as humans enhance this skill through the creation and consumption of

music (Schulkin, 2013). For the human mind to work, it must establish a relationship

between a mental object and a concept in the world (which can be a noun, equation,

theory, etc.). The language that we use to articulate these concepts provides concrete

parameters for how these concepts function for humans. This language allows humans to

describe abstract ideas, and to articulate emotions and feeling with a high degree of

semantic precision. Music, in the human mind, initially evolved concurrently to

language, but it then deviated in order to allow for varying degrees of abstraction and

representation (Perlovsky, 2012). The emotions of music and language both function to

engage the human senses but differ in the degree of abstractness they can represent.

Human cognition takes these emotional patterns and stimuli and produces meaning out of

them to act on the world. Despite this, some researchers have suggested that it is as

inappropriate to be emotionally swayed by music as it would be to be moved emotionally

by “a dandelion or a doorknob” (Kivy, 2002, p. 24). Despite these criticisms, music

presents listeners with a broad palate of emotional landscapes that allow for divergent

and varying experiences from listener to listener (Davies, 2003), which encourage

affective reflection and development.

Contemporary theories of musical-emotional cognition are predicted around what

Ochsner and Gross (2005) describe as “responses to external stimuli and/or internal

mental representations that...are distinct from moods...can be learned and un-

learned...and can have multiple appraisal processes” (p. 242). Pearce and Rohrmeier

(2012) support this relationship between objects, and the understanding that while

musical structures may refer to objects in our world, compared to language – which is

finite – they do not have explicit referential semantics. While language forms a 1-to-1

relationship between an object and the meaning/thought behind it, music forms a

relationship that does not function on a 1-to-1 basis. The abstractness of music allows

music to refer to a broader array of emotions. The emotions that music induces are

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genuine emotions unto themselves; they are independent and unique, and are generally

perceived stronger than they are experienced in listeners (Schubert, 2013). These

emotions are perceived by the listener as they make a judgement of the stimuli around

them (which is processed through cognition). To Fiske (1996), this ambiguous

relationship and judgement of music by the cognitive complex is precisely the point of

music; the stimulation we receive through music generates responses that produce

emotions and a plethora of interpretations that feedback into our decision-making

systems.

The nature of musical emotions brings up a longstanding question: are musical

emotions real? Researchers have debated the emotional validity of affects by arguing

whether emotions are expressed or induced (Juslin & Sloboda, 2011; Scherer, 2004).

This distinction has implications for the degree of agency by which emotions function as

representations in the mind. By arguing that music emotions are expressed, it is possible

to say that emotions are genuine and have a distinct path that begins with an appraisal

and leads through varying mechanisms that produce emotional responses. The argument

for emotional induction states that emotions induce the listener to produce affective

responses that already exist within the listener’s mind as some form of conditioned

response, and therefore that piece of music is merely the trigger to ‘activate’ those

emotions. Central to the argument for emotional induction are the circumstances

surrounding the appraisal process. Both arguments have long histories and continue to

shape arguments in music cognition literature.

What makes these emotions rather interesting to study is the variability that

occurs across individuals (Sloboda, 1996), as well as the lack of a biological survival

impetus for generating these emotions (Aubé et al., 2015; Juslin & Sloboda, 2011;

Vuilleumier & Trost, 2015). A commonly held perspective is that music is the

evolutionary by-product of more advanced, symbolic developments that occurred

through the use of spoken and, later, written language, and that music evolved as a

substandard evolutionary mechanism (Barrow, 1995). The examination of emotion

models in music cognition is therefore open to multiple levels of interpretation as

researchers in several adjoining fields continue to make sense of this art.

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2.4.2 Models of Musical Emotion

Not dissimilar to general theories of emotion, it is important to locate discrete and

general models of affect in music. These two models have emerged from general

psychology literature and have become two of the most dominant theories in present

music cognition literature (Eerola & Vuoskoski, 2011). The discrete/basic model of

music emotions emerged from Ekman’s (1992) work, but states that there are a basic

series of emotions that music can express. Literature from Balkwill and Thompson

(1999), as well as Vieillard et al. (2008), suggests that it is necessary to modify the

palette of music emotions in order to align with the aesthetic capabilities of music. This

has led researchers to develop the Geneva Emotion Music Scale (GEMS; Zentner,

Grandjean, & Scherer, 2008), which focuses on the ability of music to express a finite set

of emotions including wonder, transcendence, tenderness, nostalgia, peacefulness, power,

joyful activation, tension and sadness. Nevertheless, many sources are quick to point out

some shortcomings of discrete models. Many of the emotional descriptors in these

studies can be rather limited; conversely, some research features greatly varying

emotional descriptors that may be inconsistent from one study to the next. The perceived

limited palette of emotional descriptors present within the literature has led some to be

critical of the application of prototypical discrete emotions to describe the rich potential

of the musical experience (Scherer, 2004). There is also great debate about the ecological

differentiation between emotions that may be closely linked together, for example

sadness or mourning (Deng, Leung, Milani, & Chen, 2015; Scherer, 2004), which may

cause problems when attempting to identify these emotions and to properly prove causal

links between stimulation and the onset of these emotions. These limitations suggest that

discrete models, and their understanding that neural mechanisms trigger responses to

basic emotions, can describe emotional states, but there are continued challenges in

accurately identifying and contextualizing the presence and triggers for these emotions.

The dimensional models of music affect have emerged from the work of Russell

(1980) and others, situating musical emotions as emerging from the independent systems

of valence and arousal that generate emotional responses. Within music literature, work

from Vieillard et al. (2008) has explored the interaction of these two planes on affective

generation. Researchers have also explored alternative models that argue affect emerges

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from a combination of tense and energetic arousal (Thayer, 1991). Thayer’s model has

taken Russell’s and accounted for arousal that can come in multiple forms, suggesting

that motivating factors move individuals to act across planes that cannot simply be

categorized in a singular way. A criticism of dimensional models for musical affect is the

inability to account for differentiations between closely related emotions, as well as their

ability to differentiate personal selection-dimensions (Eerola & Vuoskoski, 2013) within

selections. Similarly, the debate over differentiating the activating-component of the

arousal has led to the suggestion that arousal occurs over 2 different dimensions: energy

and tension (Ilie & Thompson, 2006). A critique that Scherer (2004) has addressed is the

ability of dimensional models to account for the feeling of an emotion, but not the core

constructs that elicit it. In describing this, he argues that the qualitative feeling of an

emotion is described through the activating valence and arousal, yet those two

dimensions do not account for the primitive mental representations of the emotion that

have deeper roots in human evolutionary behaviour. Due to this limitation, the feeling of

emotions can be described, but there are shortcomings to describing more complex

behaviours. These two-dimensional models are advantageous for researchers to use as

mental models because they are accessible, provide leverage to describe human response

and agency within emotional theory, and describe the activation processes that elicit

emotion (Scherer, 2004). Despite these criticisms of dimensional and discrete models,

they provide ample grounding for describing affective generation on a psycho-emotional

dimension.

Concurrent to the dichotomous selection of discrete and dimensional models of

music emotions, there are researchers who are seeking to bridge the gaps and

shortcomings by proposing novel models for music emotion. Deng, Leung, Milani, &

Chen (2015) have proposed a resonance-arousal-valence (RAV) model of emotion

recognition that proposes an active filtering system of basic emotions that works in

tandem with appraisals to activate and valence a situation. This model suggests that

activity appraisal works alongside a listener’s memories and factors in resonance-

dissonance appraisals of music into the valence-arousal dimensions. Another model by

Konečni (2008) proposes the Prototypical Emotion-Episode Model (PEEM) for

emotional response, which consists of an event, its perception by the mind, its arousal,

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and then an emotional labelling of what has just occurred. The PEEM contains an active

form of discrete labeling that is individualized based on fixed emotions that resonate in

the ecological memory of the listener. This model suggests that musical emotions are

representations of past real-world events that have been linked to musical patterns that do

involve a degree of memory recall but are more complex and branch into human

experiences. The BREVCUM (brainstem reflexes, rhythmic entrainment, evaluative

conditioning, contagion, visual imagery, episodic memory, and musical expectancy)

model examines the appraisal system of the dimensional model, while incorporating new

and novel ways of holistically examining the aesthetic experience of music (Juslin, 2013,

Juslin & Västfjäll, 2008). This model argues that the induction of musical emotion occurs

as the active appraisal of emotional experience is combined with mental representations

of discrete and aesthetic emotions. The discrete set of emotions combine with unique,

aesthetic emotions that are drawn from past autobiographical musical experiences, and

that transform when combined with discrete emotions. This multilevel model factors in a

wide variety of psycho-emotional, psychophysiological and contextual stimuli into a

model that the authors believe provides greater leverage to account for multi-sensory

experiences while listening to music. Alternative models of emotion suggest

modifications that can be made to both discrete and dimensional models in order to

leverage their strengths and mitigate perceived methodological weaknesses.

Incorporating multiple and eclectic dimensions of emotion regulation in order to

categorize experiences and move deeper into describing the impetus for emotions

continues to provide new tools to describe complex affect.

It is worth noting that work suggesting that musical emotions emerge as a by-

product of extra-musical associations that are tied to a place and time (Kivy, 1990;

Konečni, 2008, Swaminathan & Schellenberg, 2015) places greater emphasis on the

relationship that memory and contextual knowledge may have with the types of emotions

that music can express. The exploration of autobiographical memories in the creation of

present emotions is an area of continued study (Scherer, 2004). As has been suggested by

researchers, the role of memory provides a great amount of richness to explore how

musical affect functions across different experiences. Further challenging existing

models of music emotion, Cespedes-Guevara and Eerola (2018) suggest that, although

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discrete models discuss the expression of basic emotions, they remain partially critical of

the idea that music expresses basic emotions at all. Instead, they suggest that music

portrays a series of affects, not basic emotions. Leaning on definitions discussed earlier,

affect, as defined by Cespedes-Guevara and Eerola, suggests a complete series of

physiological and psychological changes that emerge within the mind. The basic

emotions are valuable tools, but they suggest that individualized expressions of basic

emotions should be thought of as guidelines for generalized states of emotional-being,

rather than firm categorical responses.

Further exploration into the nature of emotional response to music necessitates an

examination of the differentiation between the felt and perceived emotions (Evans &

Schubert, 2008; Gabrielsson, 2002; Juslin & Sloboda, 2011). The emotions that one feels

as a result of music are internalized emotions that are connected to intrinsic systems of

affective expression. In comparison, the perceived emotions that are expressed through

music have deeper, social connections to norms of expression in social settings. The

distinction between felt and perceived emotion necessitates specific categorization of the

types of experiences they have on the mind and body. The perception of musical emotion

implies that an individual is capable of interpreting the affective content without actually

experiencing perceptual changes as a result of the music; for example, if an individual is

listening to a track of Swedish death metal featuring a tempo of 210 beats-per-minute

(bpm) and consisting of heavily distorted guitars, loud drums and screaming vocals, they

would be correct in their perception that this music is attempting to portray aggressive,

even angry emotions. Induction, on the other hand, involves a process where emotions

arise and are ‘felt’ by an individual. This process can occur once an individual listens and

produces physiological responses to that particular affect, similar to the appraisal of a

discrete emotion. It has been suggested that the perception of musical emotion is more

closely linked with dimensional models, while discrete models are attributed to induction

of emotions (Aubé et al., 2015). Despite these differences in the categorization of

emotional stimuli, the processes of induction and perception are believed to work in a

reciprocal manner (Gabrielsson, 2011), wherein perception of emotion leads to a series of

mechanisms that induce the listener to feel an emotion. Not only can these expressions

work in a reciprocal manner, but it is possible to have musical emotions that reflect both

35

positive and negative valences (Kallinen & Ravaja, 2006), suggesting a more complex

mechanism on a felt level.

As an associated component of the musical experience, the ‘location’ of emotion

in music is equally as important as the expression of it. There are two sources of

emotions in music: 1) intrinsic emotions, and 2) extrinsic emotions. Intrinsic musical

emotions refer to emotions that arise within the music due to aesthetic qualities (Juslin &

Sloboda, 2011). These musical emotions are linked closely with the setting and delivery,

or denial, of expectations within the musical structures of the music. Extrinsic emotions

are those that arise from extra-musical associations, for example, linking a sound with an

important life event. These are iconic relationships because they require little formal

musical knowledge, and their meaning is not connected to a specific musical feature;

instead, meaning is variable based on the listener’s interpretation and meaning-making.

Theories of musical-emotional categorization acknowledge that humans have the power

to analyze music and categorize its features based upon musical and non-musical

elements. This is a distinct feature of humans and is connected to our cognition and

ability to make judgements of the stimuli that we experience. These emotions that are felt

by the listener can be in the form of basic/discrete emotions, including anger, fear,

surprise, happiness, and sadness (Zentner, Grandjean & Scherer, 2008). The formation

and feeling of these emotions are some of the foundational emotions that researchers

have identified as musical affects, or the most explicit emotions that are experienced by

listeners. The second group of emotions that can be felt include boredom, alertness,

hopelessness, energy, sleepiness, and satisfaction, and these can build upon those discrete

emotions to produce broader effects in listeners beyond basic emotions. The use of these

emotions serves as a foundation for building more complex emotions and responses.

2.4.3 Physiological Indicators of Emotion Induction

Research from Zentner, Grandjean and Scherer (2008) found that negative emotions are

less likely to be experienced compared to positive emotions, but real-time findings are

still mixed. The range of emotions is also being studied, and research indicates that the

emotions of happiness and sadness appear to be the strongest emotions felt cross-

culturally (Argstatter, 2016). In describing how the mind is affected by music,

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researchers have suggested that music works on the human mind across 2 dimensions to

induce emotions: tempo (fast versus slow), and mode (major or minor keys), much in the

same way that valence and intensity work in emotions (Stalinski & Schellenberg, 2012).

It is an interesting theory that works in conjunction with similar models of emotions,

such as the discrete model. Research from Schmidt (1984) studied the effects of jazz

standards played to participants at four different tempi and found that there was a

relationship between GSR conductance and perceived arousal. Yet, there may be more

complete ways of conceptualizing cognitive control and emotions, given that research

has suggested that mode is perhaps not a universal musical experience (Kreutz et al.,

2008).

The emotions that individuals feel from music can have varied effects on them,

such as enhancing memory (Schulkind, Hennis, & Rubin, 1999; Vieillard & Gilet, 2013).

Developments in measurement tools have allowed researchers to chart changes in how

listeners perceive and feel music. GSR provides a useful tool in helping researchers

discriminate affective states in listeners. Work from Gomez and Danuser (2007) suggests

that across varying musical elements, the tempo of music is most associated with

stimulation and physiological response to varying musical conditions. The authors go on

to suggest that physiological measures of music may provide strong and reliable

information to further explore the affective conditions that drive us to consume music.

The work of Goshvarpour, Abbasi, Goshvarpour and Daneshvar (2016) suggests that

GSR might be an effective tool to evaluate the emotional state of an individual listening

to music, and that it may be an effective tool in helping researchers understand the

relationship between affective judgements and sympathetic response systems. The view

that music perception is based on a system of cognitive judgements allows us to examine

music perceptions from the perspective that stimuli have the possibility of altering those

judgements and therefore impacting affective states. Literature has also discussed the use

of GSR as an effective tool for psychophysiological measurement of response to musical

stimuli. Findings from Goshvarpour, Abbasi, Goshvarpour and Daneshvar (2017) have

validated the use of GSR as an effective tool to accurately measure changes in the

emotional state of individuals while listening to music.

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Findings have indicated that GSR shares a linear relationship with changes to

musical tempo (Gomez & Danuser, 2007). This indicates that subjects who share a

similar musical culture to the music they are listening to will experience linear increases

and decreases in GSR as tempo increases and decreases. Findings on the use of GSR to

understand the effect of music on listeners have indicated a variety of results. Differences

between the GSR values in men and women listening to musical samples of different

tempos suggested that women and men experience changes to tempo in different ways

(Goshvarpour, Abbasi, & Goshvarpour, 2014). Men experiencing the effects of musical

tempo showed a decrease in GSR stimulation, compared to women, who demonstrated

increase in GSR stimulation while listening to similar excerpts.

2.4.4 Applications of Background Music

The messages that music conveys through emotions can be varied experiences. Music is

used in a variety of ways to alter individuals’ emotional experiences. Researchers across

varying fields have studied the application of background music (defined as music that is

played outside the explicit intent of listening) in various settings. Perhaps one of the

single largest contributors to this awareness of music’s intervening powers on cognitive

performance came from the proposition of the Mozart Effect. This theory originates from

the published findings of Rauscher, Shaw and Ky (1993, 1995), which indicated that

listening to music improved fine motor performance in participants. This research has

spawned numerous reviews (Jones, West, & Estell, 2006) of literature as well as studies

that support or challenge the findings as they pertain to the empirical value of music to

enhance performance, learning, or other cognitively demanding tasks. This foundational

theory has been refuted by various studies (Nantais & Schellenberg, 1999; Thompson,

Schellenberg, & Hussain, 2001), but it is still discussed as an analogue of the continued

ways in which researchers can construct a discussion around the empirical value of music

(beyond the anecdotal perception of its effects) for the greater populace. Researchers

such as Anyanwu (2015) reported more positive feelings amongst students in a biology

classroom while being exposed to background music. These feelings carried across

laboratory dissection classes as well as tutorials that were of a highly sensitive nature,

wherein the researchers hypothesized that the Mozart Effect might be helping individuals

deal with the emotionally demanding circumstances of medical dissections. The use of

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music within similarly high-skill, stressful nurse training by Gosselin, Holland, Mulcahy,

Williamson and Widacki (2016) suggested that particularly stressful periods of

performance and evaluation can be mitigated by controlled listening of background

music. The strong effects of music on self-efficacy and anxiety reduction suggest links

between emotional perception of music and self-regulation mechanisms on the cognitive

complex. More research investigating how attentional modification works through music

stimulation might provide some clarity as to how music is creating this effect within the

mind and is worthy of continued study.

Findings from Cho (2015) reported that listening to background music while

engaging in language learning has marginal effects on performance, even amongst

smaller processing tasks nested within larger tasks. Nevertheless, they noted that,

contrary to existing findings, there was not the same cognitive load placed on learners

who experienced musical conditions than previously thought, suggesting that music may

not be inducing excessive load resulting in lower task performance. Studies into the use

of music while engaging in language learning indicate that musical stimuli help to

encourage recall and phonic sounds that help in the acquisition of certain language skills

(Kang & Williamson, 2014). Similar studies on the translation skills of learners

(Ghasemzade & Modarresi, 2014; Karimnia & Lari, 2012) noted the positive effect that

students’ translation speeds were faster in the musical test condition, and learners

demonstrated increased translation speeds. The authors go on to suggest that the

background music may be inducing some form of a relaxation state, thereby lessening

cognitive load and permitting greater performance.

A study from Doyle and Furnham (2012) noted the effects of background music

were only apparent between creative and non-creative individuals; creative/divergent

thinkers tended to study with music, and performance was higher in a comprehension

task, especially when those individuals were tested in off-line, pen-paper conditions. The

authors suggest that there may be some effect of ‘visual thinking’, but that it may be

necessary to explore the effects on online versus offline tasks, to study how music may

interact with an individual’s special processing ability. In a similar analysis of writers in

creative writing tasks, Hallam and Godwin (2015) postulated that background music may

be detrimental to elementary students’ performance. Yet, that weaker performance was

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most strongly exhibited in components of creative writing that required complex

operations and strategic planning. From this, the authors propose that the lower cognitive

malleability of young learners may play a role in how they distribute cognitive resources

given the limited experiences they have in learning. Koolidge and Holmes (2018)

concluded that young children were able to assemble puzzles faster when exposed to

music without lyrics, in comparison to those without music stimulation or with music

that included lyrics. The authors conclude that fine motor control and dexterity may be

reduced by the distracting effect of lyrics that require cognitive resources to decipher for

young learners. Within broader learning settings, Dosseville, Laborde and Scelles (2012)

explored the application of music within large-scale university lectures. Their findings

suggest that listening to music during these types of large-intake learning settings could

alter a student’s perception of the value of this type of task, and may be correlated with

positive emotional affective engagement. The researchers suggest that there is more work

needed to understand the affective dimension to background music’s application across

lecturing.

Research has confirmed that background music can have a positive impact on the

affective state of individuals as they engage in varying types of work-related tasks

(Fassbender et al., 2012; Lesuik, 2005; Schellenberg & Winner, 2001; Sahebdel &

Khodadust, 2014; Su et al., 2017; Thompson, Schellenberg, & Hussain, 2001), leading to

the suggestion that music may have some measurable impact on performance.

Swaminathan and Schellenberg (2015) highlight the work of Balkwill et al. (2004), Fritz

et al. (2009) and Laukka et al. (2013), in understanding that the emotions that individuals

perceive can have universal qualities across cultures, but the emotions that music

portrays are strongest when articulated by an individual from a musical culture that

aligns with that music selection. More practical concerns are suggested by Lehmann and

Seufert (2017), who suggest that background music may not offer detailed cognitive

benefits, and that we must take into account the control of numerous conditions in order

to replicate these findings across tasks. Despite these criticisms, music has an important

role in the propagation of cultural values, normative behaviours and environmental

response skills (such as the societal need for rhythmic coordination abilities) that are

necessary for enculturated individuals (Juslin, 2013). In addition, the richness that arises

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from this variability and how those emotions shape meaning and interact with cognition

provide a broad range of possibilities to explore. Furthermore, the need to study the

combination of emotional and psychophysiological effects of music within learning tasks

has been well established (Jones, West, & Estell, 2006), in order to develop a

comprehensive understanding of possible mechanisms that help moderate and regulate

human response under these settings.

2.4.5 The Impact of Tempo in Background Music

The effects that changes in tempo may have to participant perception of music have been

identified and offer a vehicle to analyze how tempo interacts with human psychology. In

young children, mode (major keys versus minor keys) is a unique quality of Western

musical tastes, and develops much later (about the age of 6), whereas an awareness and

perception to tempo is recognizable almost immediately (Dalla Bella et al., 2001) in

infants as they listen to music. Previous literature on the effects of altered tempo in music

throughout a variety of tasks has indicated that tempo has an effect on arousal (Ünal, de

Waard, Epstude, & Steg, 2013; Bramley, Dibben, & Rowe, 2016) that is felt by listener.

Similarly, changes in tempo have a measurable impact on the perceived emotions that

were intended and expressed between musical samples (Kamenetsky, Hill, & Trehub,

1997). Fast tempi were also associated with increased arousal and decreased sustainment

when participants were faced with simple computational math problems (Feng, Suri, &

Bell, 2014). Almeida et al. (2015) identified that musical tempo had a positive impact on

the stimulation of walkers, especially as they reached greater levels of strain on their

performance. Fernández-Sotos, Fernández-Caballero and Latorre (2016) established a

dimensional grid adapting the Circumplex model from Russell (1980) to examine the

interaction between tempo and felt musical emotions across two dimensions consisting of

high versus low-valence, and high versus low arousal. The researchers concluded that

arousal and valence of emotions are related to the tempo at which musical passages are

presented. In other words, our perception and rating of a musical passage is linked to the

tempo and mode of its presentation. Their conclusions on the interaction between tempo,

mode and musical emotion are demonstrated in a chart (p. 12), as they describe the

interaction between all these factors.

41

Husain, Thompson and Schellenberg (2002) understood that faster tempi

correlated to greater arousal and stimulation, whereas slower tempi correlated to lower

stimulation and arousal. Literature also suggests that faster tempi of music is associated

with heightened levels of stimulation and positive emotional feelings while completing

tasks (Gagnon & Peretz, 2003; Thompson, Schellenberg, & Husain, 2016). Day et al.

(2009) examined the impact of background music on cognitive tasks and determined that

individuals made better decisions when exposed to music at higher tempi. That said, the

benefits of that background music diminished as tempo exceeded a certain threshold,

after which music ceased to be a distraction to lessen cognitive load, and instead became

a stressor. Musical stimuli cannot generate distinct emotional expressions, they can only

reinforce an entrenched contrast between relaxing and stimulating states in listeners

(Khalfa et al., 2008). This could suggest that tempo can unlock more primitive,

autonomic responses when presented to a listener. The impact of background music did

have a measurable impact on perceived stimulation by listeners while completing tasks

(Linek, Marte, & Albert, 2011), but variable tempo was not isolated as a variable.

Performance on a cognitively demanding comprehension task can decrease when

participants listen to music at a fast tempo (150bpm) compared to a slower tempo

(110bpm) (Thompson, Schellenberg, & Letnic, 2012). The authors go on to suggest that

this could be the result of cognitive load and the over-stimulation of the listener as they

become drawn away from the demands of a task, and towards the feature of music.

Findings from Kuribayashi and Nittono (2015) suggested that optimal cognitive

performance in a task was achieved when participants were exposed to music at

approximately 100 bpm. This research falls in line with other findings (McAuley, 2010;

McAuley, Henry, & Tkach, 2012) that indicate optimal cognitive performance with

induced background music happens around 100 to 120 bpm. Findings from this work

suggested that as perception of music from tempi beyond 120bpm occurs, ambiguity

emerges for the listener and has a negative effect on cognitive performance and acts.

The causal relationship between auditory stimulation and measurable effects on

human performance has been understood to be strong across different domains (Pronin &

Jacobs 2008). This stimulation can be effective in arousing higher degrees of engagement

and performance in participants, yet, if that tempo is too high, participants can begin to

42

demonstrate negative signs of performance due to overstimulation and a negative

redirection of the participant’s attention. An argument for the relationship between the

study of tempo across various test settings and the YDL can be made, but as researchers

have indicated, the YDL can provide only a broad overview of possible outcomes as

context-specific study is necessary. It should be noted that in many of these studies,

researchers acknowledge the relationship between the cognitive difficulty of the study

task and the effects of music. More importantly, the studied effects of background music

on performance must be tempered by the suggestion that background stimulation may

elicit negative reactions from listeners who may construe audio sounds as ‘white noise’

that may interfere with performance (Gabrielsson & Juslin, 2003). Nevertheless,

empirical study of such behavioural or emotional effects must be contextualized within

their unique setting. Researchers acknowledge that the studied tasks have been of ‘lower’

cognitive load or strain, including driving, walking, busy-work, etc., and lack the

cognitive demands of learning tasks.

Through this diverse literature, we can see that music has an enormous impact on

the human affective state. These diverse theories of the science of music perception offer

several avenues into describing how music elicits human emotional responses. Moreover,

the study of music’s effect on a variety of human tasks in the form of background music

necessitates continued study into the nature of the appraisal and generation mechanisms

that help humans use this affective media.

Chapter 3 Methodology

3.1 Philosophical Assumptions and Framework

The present research undertaken involves an initial exploration to address how the affect

of music can elicit deeper levels of understanding while learning. Reviewed literature has

shown that emotions are present in all aspects of the learning process and are the

manifestation of judgements that are made by a learner regarding the stimuli around

them. The role of achievement emotions in learning can positively enhance learning

performance through increased processing capacity and inhibitory responses in learning.

If educators can elicit an emotional response in learners and move them closer towards

43

reaching those achievement emotions, learners may be able to increase their learning

performance and output. Reaching this heightened emotional performance may lead to

greater levels of understanding, which facilitates achievement and performance.

Moreover, the affective quality of music has been studied to indicate that this art offers a

rich medium for interpretive qualities and emotional responses to the world around the

listener.

The objective of this study is to begin to explore how music elicits emotions to

facilitate and alter the learning process. To understand this learning state, it will be

necessary to examine how the emotions of music can deliver a measurable effect to a

student, and how that effect can be measured in a learning outcome. The framework for

emotions in this study will define emotions as both a combination of dimensional and

discrete theories. Both theories contribute to an understanding of the qualities that work

in the human mind to generate emotions. This work will also assume that emotions are

actively constructed in the human mind via the stimuli that are collected and processed,

and via the judgements that are made. For this study to be most effective, it will be

necessary to study comprehension of learners as a result of musical exposure. Given the

limited literature available on the real-time measurement of affect, I will develop an

understanding of how music alters emotions and the possible achievement of learners.

Understanding is the foundation of learning. To develop this understanding, it is

necessary to capture how a learner comprehends information they are presented. To do

this, the initial step that this research will take is to study how comprehension occurs and

what stimulates that occurrence. Using this, further research can develop theories as to

how music arouses learning emotions and how those emotions can be effectively elicited

during the learning process.

To understand how emotions function, it is necessary to isolate them as variables

and measure their effect on the learner. To do so, it would be wise to employ a post-

positivist methodology (Creswell, 2014) to guide this research. I have made the decision

to ground this worldview in the scientific method and the notion that I am searching for

observable ‘truths’ as I am researching human beings. To this extent, Phillips and

Burbules (2000) can concur that an absolute truth can never be found; yet, it is important

to continually develop literature and theories to progress our understanding of this area of

44

research. If there are changes that occur in the learner’s state, these changes will have to

be visible and observable to an outside observer. The area of affective/emotional learning

is expanding, and it is impossible to make absolute statements regarding the absolute

power of emotions. By conducting my research with this view, the aim is to fill a gap in

literature through scientific observation and measurement that can be statistically used to

create generalizable statements. In doing so, there is the potential that such work will

inform a more complex and thorough construction of theories that exist in this area of

research.

To contribute new empirical research into this area, it is necessary to understand

and analyze the marked changes in the human body as it experiences emotional changes;

therefore, it is essential to hold the position that this data will provide evidence of such

changes. In doing this, it is necessary to question the validity of scientific tools and

measurements and whether or not they are producing the type of feedback needed to

inform a research question. This must be a continuing focus of research, in order to

ensure that the tools at my disposal are the best available to address the questions in hand

and to understand the strengths and limitations that particular tools can offer.

3.2 Research Design

3.2.1 Ethical Clearance

Prior to commencing this study, ethical clearance from the Social Sciences, Humanities,

and Education Research Ethics Board at the University of Toronto was obtained in

August of 2018. Recruitment of participants continued through the Fall term of 2018, and

ethical clearance for human participants was given in January 2019 from the Research &

Innovation office at Ryerson University.

3.2.2 Participants

Once ethical approval was given, recruitment of participants began in the Fall 2018 term.

Recruitment was conducted by distributing recruitment flyers to a total of 17

departments/faculties at 2 universities. A second round of recruitment was made in

Winter 2019 to help bolster participant numbers and diversify the sample population.

Flyers were distributed in accordance with university policies for recruiting research

45

participants. Recruiting through these avenues was done in order to capture the widest

and most diverse sample possible. Participants were offered a $10 Tim Hortons gift card

as compensation for their time and in order to incentivize participation. Prior to

participation, the researcher had no contact with participants that would be a conflict of

interest.

Participants for this study were adolescents enrolled as first-year undergraduate

students, and who were at least 18 years or older. This sample was selected due to their

availability to easily participate in research while on the university campus. The broad

cross-section of the population present in the metropolitan city where the university is

located helped to ensure an appropriate distribution within the population to observe

results. Research has identified adolescence as being a critically important time for

language skill development (Reed, Petscher, & Foorman, 2016). It is during this age that

development of many of the critical skills that we use to decode, decipher and

comprehend written texts will take place (Denton, 2015). Through this, we see a need to

understand how reading occurs in adolescent learners, and how those critical skills can be

enhanced to facilitate more complex forms of learning.

3.3 Data Collection

3.3.1 Tools

3.3.1.1 Facial Emotion Recognition Software

This study utilized modern facial-recognition technology to collect real-time data about

participant emotions and provide a quantitative rating of emotions based on facio-

muscular movements using iMotions Emotient (FACET) 7.1 software. The software

recorded an 8-second baseline that was used to set participant responses for the entire

study. This data was collected using a Logitech 1080p HD web camera for processing

and analysis.

3.3.1.2 Psychophysiological Measurement

GSR was measured using a Biopac™ MP160 system with 2 wet sensors that were

attached to the palm of the participant’s non-dominant hand. Once the sensors were

placed and attached to the central unit, the researchers ensured that the participant had

46

adequate range of motion so that their hand was not impeded. These sensors measure

changes to arousal to provide a view into the psychophysiological impulses and changes

that may occur as a result of internal response mechanisms. Prior to commencing the

trial, the researcher ran a brief period of testing to ensure that the sensors were offering

reliable data, a clean signal and no discomfort to the participant. All data was recorded in

a temperature-controlled environment that had an ambient temperature of 22o Celsius

throughout the task.

3.3.1.3 Demographic, Self-Rating Scales, and Comprehension task

The demographic survey used was designed to collect basic, identifying information

from participants (e.g. age, sex, past education, etc.). The second scale is the Gold-MSI

scale (Müllensiefen, Gingras, Musil, & Stewart, 2014). This scale is designed to assess

the listener’s sophistication and sensitivity to music. Such information will be valuable in

providing a baseline for an individual’s general sensitivity to music that may be

presented to them over the course of this research. The final scale used was a music

awareness scale derived from Wolfe (1983) and adapted from Gillis (2010). This scale

rated participants’ perception of musical stimuli in order to understand the effects that

such a stimulus has on their performance in cognitive learning tasks. This Likert scale

asked participants to rate a series of statements to help explore how music alters

perceptual awareness, performance on learning tasks, and a participant’s perception.

The comprehension task selected for this study was drawn from the Nelson-

Denny Form H (Brown, Fishco, & Hanna, 1993). This test offers standardized questions

to measure performance and is employed to rate comprehension ability. This task has

been internationally tested with participants from late-middle school into 4th-year

undergraduate degrees and can function as a reliable measure of adolescent reading

comprehension abilities. This, combined with the broad cross-section of participants

recruited for this study, helps to strengthen the ecological validity of this study.

3.3.2 Laboratory Space

Trials were conducted in a lab within the university’s faculty of education. This lab

setting was a neutral-coloured room approximately 7m by 3m, set in a quiet office space

47

in order to minimize environmental noises that might distract the participant. Once

participants entered the lab, they were greeted by the principal investigator, or a highly

trained research assistant, to begin their trial. The principal researcher employed a

volunteer research assistant throughout this process. The research assistant employed

holds a Bachelor of Psychology (Honours) degree from an accredited research university

in Canada, and has experience working with psychophysiological data collection in a

laboratory setting. Prior to beginning this work, the research assistant was trained in the

scripting of the data collection and had ample opportunity to practice the procedure

numerous times to ensure their confidence in the setup, data collection procedures, and

data extraction/analysis. Each trial lasted approximately 40 minutes, including 30

minutes to complete the task, with the remaining 10 minutes being used to sign consent

forms, attach sensors, and clean up.

Once data collection was complete, all data was stored in a password-protected

and electronically encrypted hard drive, in accordance with university ethical policies.

Participant consent sheets were retained by the researcher and stored in a locked cabinet

that would only be accessible to the researcher, the supervisor and any committee

members, upon request.

3.3.3 Trial Overview

This study required the creation of an environment and test condition to explore how

emotions act in real-time during the learning process. A repeated measures procedure

conducted in a lab environment allowed the researcher to study each individual

participant’s performance to address the primary research questions. The use of a

repeated measures design in this study allowed for data to be collected from a participant

in both a control and stimulus condition, therefore allowing the researcher to observe

changes in the participant’s state with greater certainty. The procedure was administered

to participants via PSTnet’s E-Prime 3.0 software to ensure replicability of results and

minimal interaction on the part of the researcher in collecting participant data. The E-

Prime 3.0 trial software was linked to the Biopac™ MP160 system through a series of

digital signals and markers that were embedded throughout the task, and that provided

48

the researcher with accurate, zero-latency indicators of the onset and termination of

stimulants in order to act as timepoints for the coordination of participant data.

Once participants contacted the researcher to indicate their interest and confirm

their eligibility to participate, they were invited into the lab for their trial. Participants

were given an informed consent letter that outlined the rationale for the study, an

overview of the task, the types of data being collected, and information regarding their

rights to withdraw participation. Once participants read the document and signed their

consent, sensors were attached to the participant and the trial began. The task began with

a demographic questionnaire followed by the Gold-MSI survey. Participants then

completed a baseline analysis of their face and body that lasted for 2 minutes. This task

fulfilled 2 functions: 1) it provided the Emotient (FACET) software with the necessary

baseline to analyze the contours and individual variances of a participant’s face, and 2) it

provided a ‘rest-state’ for the participant to begin the collection of physiological data

after baselines were collected. Participants were asked to complete a standardized

comprehension task as music was introduced into the background. Participants were

given 2 minutes to read each passage, of approximately 250 words each, and 15 seconds

to read and answer each of the 6 accompanying multiple-choice questions per passage.

Once a participant had read the question and responded with the option that they felt best

answered the question, the E-Prime system logged their response and automatically

moved onto presenting the participant with their next question. After all questions were

answered, a 60-second rest period occurred before the next passage was presented.

Two test conditions will be exhibited: a musical accompaniment condition, and

one without music accompaniment to act as a control. Participants will be asked to read a

series of selections and complete an adjoining series of questions that are designed to test

their perception and understanding of the text. During this, participant’s facial-emotional

expressions and physiological feedback will be captured. For participants in the music-

conditions, there will be two tempi offered for accompanying musical selections: 1) fast

(150bpm), and 2) slow (110 bpm). The musical selection was drawn from Mozart’s

(1781) Sonata for Two Pianos in D major, K 375a (K 448)- Allegro con spirito. This

piece was selected because research (Husain, Thompson, & Schellenberg, 2002;

Schellenberg, Nakata, Hunter, & Tamoto, 2007; Thompson et al., 2001) has indicated

49

this it is known to enhance a listener’s arousal as well as their mood. To isolate the

impact of tempo and eliminate the effect of dynamic variation, a MIDI file of this piece

was brought into Finale 2012, a professional quality music notation software, in order to

eliminate dynamics and compress the file to eliminate dynamic simulation. Once these

edits were made to the music, the file was loaded into ProTools audio recording software

and sampled through a studio-grade emulation of a concert grand piano with no reverb or

compression added. The music presented was played through a set of stereo Logitech

speakers placed equidistant from the screen at ear-level. The audio was set at 72.3

decibels and measured with a sound level meter. Tempo acts as an independent variable

in this study to isolate the effects that it may have on participants’ response.

After completing the comprehension task, participants were asked to complete the

Wolfe (1983) music perception test adapted from Gillis (2010). This test was used to

understand how the participants responded to the musical stimuli they were given, their

perceived control over their learning task, and their perceived performance on the task

while being presented with various conditions. Once this was completed, the computer

software logged the completion of the trial, ended the software, and saved the file. The

participant’s sensors were removed, a gift card was issued, and the trial was over.

3.4 Data Analysis

3.4.1 Marking and Cleaning of Data

Once participants had completed their trials, 2 data sources emerged.

Psychophysiological data, from GSRs, was recorded in the form of a raw AcqKnowledge

5.0 file. This data contained 3 elements: firstly, it contained a participant baseline,

secondly it contained the GSR values for skin stimulation, and finally, it contained digital

markers that correspond with the E-Prime 3.0 files where participants completed their

tasks and inputted data. These digital signals were critical in order to establish reliable,

real-time markers to indicate when particular stimuli or tasks were presented to

participants.

Once these digital markers and baseline were logged to indicate their duration, it

was then necessary to begin coordinating those digital signals with the facial-emotion

50

data collected. Participant facial-emotion data files were post-processed using the

iMotions Emotient (FACET) software. Once these videos were processed, the baseline

values from the AcqKnowledge digital signals were placed within the facial-emotion

data file to indicate the baseline and other key areas of interest to extract, based on the

coordinated times. This process allowed for a high-degree of reliability to coordinate the

video and psychophysiological data streams. The baseline, as well as the six 2-minute

segments wherein the participant was reading the passage, were recorded and isolated

within the videos. These areas would be analyzed in order to address the research

questions. Once this facial baseline was applied to the video, the researcher and research

assistant began cleaning both sets of data.

The data cleaning process involved examining the six 2-minute reading segments

and identifying anomalies that might skew the data being extracted. This was a back-and-

forth process that involved looking at facial-emotion video segments, and identifying

responses that might be outside the parameters of the study, for example, participants

looking away from the screen, distractions that may have drawn the participants away

from their task, etc. This cleaning was important to conduct because the facial-muscle

data is extremely sensitive and slight changes may have detrimentally impacted the

quality of output when it came time to mark the videos. If anomalies were identified

within the videos, their time points were noted for omission when it came time to mark

the videos. At the same time, the GSR data was examined to explore similar anomalies

that could inaccurately impact the data output. These included excessive eye-blinking,

unwarranted bodily movement that could not be explained through the task the

participant was performing, and other such movements that may not represent an

accurate picture of the participant’s stimulation. If such fragments did emerge in the GSR

data, that segment of data was highlighted and normalized using AcqKnowledge 5.0’s

ability to remove a segment of data and blend 2 data points together, creating a seamless

stream of data.

Once the data cleaning was done, the Emotient (FACET) files were marked and

extracted to produce raw, mean-values for the 9-basic emotions and 19 AUs. These

values were placed into a spreadsheet for analysis. The GSR data was extracted into 15-

second segments for each of the 6 accompanying reading passages. Within the GSR data,

51

3 types of values were extracted: 1) the number of Skin Conductance Responses (SCRs),

that indicate significant and noteworthy changes in the directional value of skin

responses, 2) value, indicating the mean value (relative to a baseline of 0), of the

stimulation during a segment, 3) amplitude, indicating the intensity of the SCRs. Once

these segments were cleaned, they were extracted and placed into a spreadsheet for

analysis. Data cleaning was also conducted at the variable level by creating and screening

distributions using stem and leaf and boxplots generated with IBM SPSS 25, in order to

limit the influence of outlying scores on the means of comprehension scores, emotion

outputs, and psychophysiological responses (Tabachnick & Fidell, 2013). Outlying

scores were eliminated so that values did not represent outliers, and screening for

skewness and kurtosis took place (Tabachnick & Fidell 2013).

Once participants had completed their trials, data extraction began with removing

demographic information and scores from participants’ reading comprehension tasks and

post-task questionnaires. The raw data output from each participant’s E-Prime files

(known as E-Data), containing scores from these data sources, were extracted and placed

into a data spreadsheet. The E-Prime 3.0 system scored participants’ reading

comprehension tasks and rated each question as either correct or incorrect, and then

produced a cumulative rating (out of a maximum of 5 points) to indicate their overall

success in the passage.

Chapter 4 Results

4.1 Demographics

In total, 75 participants completed their trial. In final analysis, a total of seventy-four (N=

74) participants were included in the final sample, with 1 participant being omitted

because their facial-emotional and psychophysiological data was deemed unusable due to

errors in its recording.

Of the sample, 76% (N= 56) identified as ‘female’, 23% (N= 17) identified as

‘male’, and 1% (N= 1) identified as ‘other’. This sample does skew towards a

proportionately high percentage of women.

52

Table 1

Demographic of Sex

F %

Female 56 75.7

Male 17 23

Other 1 1.4

Total 74 100

Within this sample, 77% (N= 57) of participants were 18-years old, 8% (N= 6)

were 19-years old, 3% were 20-years old (N= 2), and 12% (N= 9) were 21-years old or

older.

Table 2

Demographic of Age

F %

18 years old 57 77

19 years old 6 8.1

20 years old 2 2.7

21 years or older 9 12.2

Total 74 100

Examining their education, 89.2% (N= 66) of participants listed a “high school diploma”

as the highest level of education that they had attained. A total of 3% (N= 2) indicated

that they had attained a college diploma, 7% (N= 5) indicated they had attained an

undergraduate degree, and 1% (N= 1) indicated they had attained another form of

educational qualification.

Table 3

Demographic of Education

F %

High school diploma 66 89.2

College diploma 2 2.7

Undergraduate degree 5 6.8

Other 1 1.4

Total 74 100

53

The demographic questionnaire also asked participants to self-identify as to

whether they had a learning disability, and whether they fit on the autism spectrum. In

total, a lone participant (N= 1) identified as fitting within the autism spectrum, and 3

participants (N= 3) identified as having a diagnosed learning disability.

Table 4

Demographic of Autism Spectrum

F %

Yes 1 1.4

No 72 97.3

Prefer not to answer 1 1.4

Total 74 100

Table 5

Demographic of Learning Disability

F %

Yes 3 4.1

No 71 95.9

Total 74 100

The final 2 questions asked participants: have you ever taken formal music

instruction, and how often do you listen to music? A total of 92 % (N= 68) of participants

said that they listened to music “every day of the week”, with 4% listening to music “3-4

days a week” (N= 3), or “5-6 days a week” (N= 3).

Table 6

Demographic of Music Listening in Sample

F %

3-4 days a week 3 4.1

5-6 days a week 3 4.1

every day of the week 68 91.9

Total 74 100

When asked about formal music training, 7% (N= 5) of participants indicated that

they had never had formal music instruction. Of the remaining participants, 70% (N= 52)

indicated that they had music instruction at some point, but not at present. The remaining

23% of participants (N= 17) indicated they are still actively taking music lessons.

54

Table 7

Demographic of Music Training in Sample

F %

No training 5 6.8

No longer, 1-3 years 25 33.8

No longer, 4 or more years 27 36.5

Yes, 1-3 years 4 5.4

Yes, 4 or more years 13 17.6

Total 74 100

The sample is diverse and contains a diverse representation of adolescents drawn from

the population.

4.2 Research Question #1

To begin the analysis, it was necessary to explore the effect of musical condition on

performance. To accommodate this, a statistical method would need to consider the

effects of the multiple samples presented to participants, and the randomization of

passage as well as all the conditions delivered when conducting this analysis. Based on

these parameters, Generalized Estimating Equations (GEEs) were selected in order to

accomplish this analysis (Zeger & Liang, 1986; Zeger, Liang, & Albert, 1988). The

GEEs are used to correlate this type of data with binary, discrete, or continuous outcomes

(Zeger et al., 1988) in order to determine the degree of effect that a variable has on the

predicted outcome of another. The effects of a model are indicated as well as the

regressive predictability that was indicated through this analyses’ design. Based upon

these assumptions, it was determined that this was the best method of analysis to follow

to answer these questions. All GEE analyses were performed using IBM SPSS 25. This

analysis began by assembling all data in a long-form sheet, where each reading passage

represented its own line of data, amounting to 6 lines of data per participant.

The first test was to determine if each of the 6 passages presented had significant

differences in score against each other. The results of the Parameter Estimates in Table 8

indicated that the passage had no significant impact (p > .050) when presented, and

therefore all passage scores could be treated equally to each other. These fall in line with

55

assumptions of the Nelson-Denny Form H (Brown, Fishco, & Hanna, 1993) in assuming

that all passages are to be considered equally challenging to each other.

Table 8

Parameter Estimates for the Effects of Passage

(I) Passage (J) Passage MD (I-J) Std.

Error df

Bonferroni

Sig.

95% CI

[LL, UL]

2

3 0.24 0.18 1 1.00 -0.30, 0.78

4 -0.10 0.15 1 1.00 -0.54, 0.35

5 0.00 0.17 1 1.00 -0.49, 0.49

6 0.22 0.17 1 1.00 -0.27, 0.72

7 -0.05 0.16 1 1.00 -0.51, 0.42

3

2 -0.24 0.18 1 1.00 -0.78, 0.30

4 -0.33 0.17 1 0.73 -0.83, 0.16

5 -0.24 0.16 1 1.00 -0.70, 0.23

6 -0.01 0.17 1 1.00 -0.52, 0.49

7 -0.28 0.15 1 0.80 -0.71, 0.15

4

2 0.10 0.15 1 1.00 -0.35, 0.54

3 0.33 0.17 1 0.73 -0.16, 0.83

5 0.10 0.14 1 1.00 -0.33, 0.52

6 0.32 0.15 1 0.56 -0.13, 0.77

7 0.05 0.13 1 1.00 -0.34, 0.44

5

2 0.00 0.17 1 1.00 -0.49, 0.49

3 0.24 0.16 1 1.00 -0.23, 0.70

4 -0.10 0.14 1 1.00 -0.52, 0.33

6 0.22 0.15 1 1.00 -0.22, 0.67

7 -0.05 0.16 1 1.00 -0.53, 0.43

6

2 -0.22 0.17 1 1.00 -0.72, 0.27

3 0.01 0.17 1 1.00 -0.49, 0.52

4 -0.32 0.15 1 0.56 -0.77, 0.13

5 -0.22 0.15 1 1.00 -0.67, 0.22

7 -0.27 0.17 1 1.00 -0.75, 0.22

7

2 0.05 0.16 1 1.00 -0.42, 0.51

3 0.28 0.15 1 0.80 -0.15, 0.71

4 -0.05 0.13 1 1.00 -0.44, 0.34

5 0.05 0.16 1 1.00 -0.43, 0.53

6 0.27 0.17 1 1.00 -0.22, 0.75

Pairwise comparisons of estimated marginal means based on the original scale of

dependent variable score in the reading comprehension task. MD represents the mean

difference between the score of the test and dependent passage. LL and UL represent

the lower and upper limits of the confidence interval.

56

The next test conducted was to determine if there were significant differences between

the condition and passage that was presented, which could potentially skew results.

Results of Pairwise Comparisons in Appendix 8 indicated that there was not a significant

difference (p= 1.000) between passages and condition. Therefore, it can be assumed that

the effect of a condition was working across all passages.

The final test conducted was to determine if there were significant differences

between each of the 6 passages presented and the musical condition to determine if a

condition worked equally across all possible passages that were presented. Results in

Table 9 determined that there were no significant differences (p= 1.000) that emerged

and that the condition that a passage was presented in worked equally throughout all

passages presented.

Table 9

Pairwise Comparisons for the Effects of Condition on Passages

Condition (I)

Passage

(J)

Passage

MD

(I-J)

Std.

Error df

Bonferroni

Sig.

95% CI

[LL, UL]

No music

2

3 0.33 0.44 1 1.00 -0.96, 1.62

4 -0.02 0.36 1 1.00 -1.09, 1.04

5 -0.16 0.33 1 1.00 -1.12, 0.80

6 0.26 0.35 1 1.00 -0.78, 1.29

7 0 0.33 1 1.00 -0.97, 0.98

3

2 -0.33 0.44 1 1.00 -1.62, 0.96

4 -0.35 0.41 1 1.00 -1.56, 0.86

5 -0.49 0.36 1 1.00 -1.53, 0.56

6 -0.07 0.42 1 1.00 -1.31, 1.17

7 -0.33 0.43 1 1.00 -1.59, 0.94

4

2 0.02 0.36 1 1.00 -1.04, 1.09

3 0.35 0.41 1 1.00 -0.86, 1.56

5 -0.13 0.27 1 1.00 -0.91, 0.64

6 0.28 0.33 1 1.00 -0.69, 1.25

7 0.03 0.28 1 1.00 -0.80, 0.85

5

2 0.16 0.33 1 1.00 -0.80, 1.12

3 0.49 0.36 1 1.00 -0.56, 1.53

4 0.13 0.27 1 1.00 -0.64, 0.91

6 0.41 0.25 1 1.00 -0.33, 1.16

57

7 0.16 0.28 1 1.00 -0.65, 0.97

6

2 -0.26 0.35 1 1.00 -1.29, 0.78

3 0.07 0.42 1 1.00 -1.17, 1.31

4 -0.28 0.33 1 1.00 -1.25, 0.69

5 -0.41 0.25 1 1.00 -1.16, 0.33

7 -0.25 0.35 1 1.00 -1.27, 0.76

7

2 0 0.33 1 1.00 -0.98, 0.97

3 0.33 0.43 1 1.00 -0.94, 1.59

4 -0.03 0.28 1 1.00 -0.85, 0.80

5 -0.16 0.28 1 1.00 -0.97, 0.65

6 0.25 0.35 1 1.00 -0.76, 1.27

Slow music

2

3 0.21 0.28 1 1.00 -0.60, 1.01

4 0.07 0.27 1 1.00 -0.72, 0.86

5 0.18 0.27 1 1.00 -0.62, 0.97

6 0.22 0.28 1 1.00 -0.61, 1.04

7 0.08 0.29 1 1.00 -0.78, 0.93

3

2 -0.21 0.28 1 1.00 -1.01, 0.60

4 -0.14 0.24 1 1.00 -0.85, 0.57

5 -0.03 0.22 1 1.00 -0.66, 0.60

6 0.01 0.25 1 1.00 -0.73, 0.76

7 -0.13 0.27 1 1.00 -0.91, 0.65

4

2 -0.07 0.27 1 1.00 -0.86, 0.72

3 0.14 0.24 1 1.00 -0.57, 0.85

5 0.11 0.23 1 1.00 -0.56, 0.78

6 0.15 0.23 1 1.00 -0.51, 0.81

7 0.01 0.26 1 1.00 -0.75, 0.77

5

2 -0.18 0.27 1 1.00 -0.97, 0.62

3 0.03 0.22 1 1.00 -0.60, 0.66

4 -0.11 0.23 1 1.00 -0.78, 0.56

6 0.04 0.23 1 1.00 -0.63, 0.72

7 -0.1 0.27 1 1.00 -0.90, 0.70

6

2 -0.22 0.28 1 1.00 -1.04, 0.61

3 -0.01 0.25 1 1.00 -0.76, 0.73

4 -0.15 0.23 1 1.00 -0.81, 0.51

5 -0.04 0.23 1 1.00 -0.72, 0.63

7 -0.14 0.27 1 1.00 -0.94, 0.66

7

2 -0.08 0.29 1 1.00 -0.93, 0.78

3 0.13 0.27 1 1.00 -0.65, 0.91

4 -0.01 0.26 1 1.00 -0.77, 0.75

5 0.1 0.27 1 1.00 -0.70, 0.90

6 0.14 0.27 1 1.00 -0.66, 0.94

Fast music 2 3 0.17 0.25 1 1.00 -0.55, 0.89

58

4 -0.34 0.24 1 1.00 -1.04, 0.37

5 -0.02 0.35 1 1.00 -1.04, 1.00

6 0.19 0.29 1 1.00 -0.66, 1.05

7 -0.22 0.29 1 1.00 -1.07, 0.63

3

2 -0.17 0.25 1 1.00 -0.89, 0.55

4 -0.51 0.26 1 0.73 -1.26, 0.25

5 -0.19 0.33 1 1.00 -1.16, 0.78

6 0.02 0.28 1 1.00 -0.81, 0.85

7 -0.39 0.27 1 1.00 -1.19, 0.40

4

2 0.34 0.24 1 1.00 -0.37, 1.04

3 0.51 0.26 1 0.73 -0.25, 1.26

5 0.32 0.35 1 1.00 -0.72, 1.35

6 0.53 0.31 1 1.00 -0.37, 1.43

7 0.12 0.28 1 1.00 -0.70, 0.93

5

2 0.02 0.35 1 1.00 -1.00, 1.04

3 0.19 0.33 1 1.00 -0.78, 1.16

4 -0.32 0.35 1 1.00 -1.35, 0.72

6 0.21 0.37 1 1.00 -0.87, 1.30

7 -0.2 0.39 1 1.00 -1.33, 0.93

6

2 -0.19 0.29 1 1.00 -1.05, 0.66

3 -0.02 0.28 1 1.00 -0.85, 0.81

4 -0.53 0.31 1 1.00 -1.43, 0.37

5 -0.21 0.37 1 1.00 -1.30, 0.87

7 -0.41 0.34 1 1.00 -1.42, 0.60

7

2 0.22 0.29 1 1.00 -0.63, 1.07

3 0.39 0.27 1 1.00 -0.40, 1.19

4 -0.12 0.28 1 1.00 -0.93, 0.70

5 0.2 0.39 1 1.00 -0.93, 1.33

6 0.41 0.34 1 1.00 -0.60, 1.42

Pairwise comparisons of estimated marginal means based on the original scale of

dependent variable score in the reading comprehension task

These findings all suggest that there is enough randomization between condition

and passage that either will not adversely affect the results of any further tests while

looking at the effect of condition on performance and that the effect of condition was

universal across all possible passages that a participant could be presented.

The next series of GEEs were conducted to determine if the condition of a

passage had an effect on a participant’s score on their reading comprehension task. Table

59

10 shows Parameter Estimates indicated that there was a significant effect (p= .00) in

scoring between fast and slow music, as well as fast and no music conditions. Pairwise

comparisons in Table 11 indicated that there were significant differences (p= .00), 95%

CI [.22, .64] between slow and fast conditions. Between these two conditions, individual

estimates indicated that participants were more likely to score higher (M= 4.04), 95% CI

[3.88, 4.20] when given a slow-condition passage, compared to being given a fast-

condition passage (M= 3.61), 95% CI [3.43, 3.49].

Table 10

Parameter Estimates for the Effects of Condition on Passage Scores

Parameter B Std.

Error

95% CI Hypothesis Test

[LL, UL] Wald χ2 df Sig.

(Intercept) 3.61 0.09 3.43, 3.79 1528.65 1 0.00

No music 0.24 0.12 0.01, 0.47 4.34 1 0.04

Slow music 0.43 0.09 0.26, 0.60 24.58 1 0.00

Fast music 0a . . . . .

(Scale) 1.14

Dependent Variable: Score in the reading comprehension task

Model: (Intercept), Condition of the trial

a. Set to zero because this parameter is redundant.

Table 11

Pairwise Comparisons for the Differences in Scoring between Conditions

(I) Condition

of the trial

(J) Condition

of the trial

MD

(I-J)

Std.

Error df

Bonferroni

Sig.

95% CI

[LL, UL]

No music slow music -0.19 0.11 1 0.28 -0.46, 0.08

fast music 0.24 0.12 1 0.11 -0.04, 0.52

Slow music no music 0.19 0.11 1 0.28 -0.08, 0.46

fast music .43a 0.09 1 0.00 0.22, 0.64

Fast music no music -0.24 0.12 1 0.11 -0.52, 0.04

slow music -.43a 0.09 1 0.00 -0.64, -0.22

Pairwise comparisons of estimated marginal means based on the original scale of

dependent variable score in the reading comprehension task

a. The mean difference is significant at the .05 level.

60

Table 12

Estimates for the Mean Scores between Conditions

Condition of the trial M Std. Error 95% CI

[LL, UL]

No music 3.85 0.11 3.63, 4.07

Slow music 4.04 0.08 3.88, 4.20

Fast music 3.61 0.09 3.43, 3.79

4.3 Research Question #2

To address the next research question, it was necessary to explore the effects of condition

on the expressed emotions that the participant displayed. In order to do this, GEEs were

used to explore the effect of the three-conditions on the expression of nine basic

emotions that were detected by Emotient (FACET) software. The emotions included:

anger, sadness, frustration, confusion, joy, surprise, fear, disgust, and contempt. Of the

GEEs that were conducted with every expressed emotion acting as a dependent variable,

3 emotions emerged as having significance within models.

4.3.1.1 Joy

Parameter Estimates indicated that there was a significant effect (p= .00) in expressions

of joy between fast and no music conditions (Table 13). Pairwise comparisons indicated

that there were significant differences (p= .04), 95% CI [-.39, .00] between slow and no

conditions, and between fast and no conditions (p= .01), 95% CI [-.54, -.07] (Table 14).

Individual estimates indicated that participants were more likely to experience higher

levels of expressed joy (M= .02), 95% CI [-.25, .29] in fast conditions, compared to slow

(M= -.08), 95% CI [-.34, .17) or no-conditions (M= -.28), 95% CI [-.48, -.08] (Table 15).

61

Table 13

Parameter Estimates for the Expression of Joy Between Conditions

Parameter B Std.

Error

95% CI Hypothesis Test

[LL, UL] Wald χ2 df Sig.

(Intercept) 0.02 0.14 -0.25, 0.29 0.03 1 0.87

No music -0.3 0.1 -0.50, -0.11 9.56 1 0.00

Slow music -0.11 0.1 -0.30, 0.09 1.15 1 0.28

Fast music 0a . . . . .

(Scale) 1.33

Dependent Variable: Joy

Model: (Intercept), Condition of the trial

a. Set to zero because this parameter is redundant.

Table 14

Pairwise Comparisons for the Expression of Joy Between Conditions

(I)

Condition

of the trial

(J)

Condition

of the trial

MD (I-J) Std.

Error df

Bonferroni

Sig.

95% CI

[LL, UL]

No music slow music -.19782740387a 0.08 1 0.04 -0.39, 0.00

fast music -.30283747249a 0.10 1 0.01 -0.54, -0.07

Slow music no music .19782740387a 0.08 1 0.04 0.00, 0.39

fast music -0.105010069 0.10 1 0.85 -0.34, 0.13

Fast music no music .30283747249a 0.10 1 0.01 0.07, 0.54

slow music 0.105010069 0.10 1 0.85 -0.13, 0.34

Pairwise comparisons of estimated marginal means based on the original scale of

dependent variable Joy

a. The mean difference is significant at the .05 level.

Table 15

Estimates for the Mean-Level Expressions of Joy Between Conditions

Condition of the trial M Std. Error 95% CI

[LL, UL]

No music -0.28 0.1 -0.48, -0.08

Slow music -0.08 0.13 -0.34, 0.17

Fast music 0.02 0.14 -0.25, 0.29

62

4.3.1.2 Fear

Parameter Estimates indicated that there was a significant effect (p= .000) in expressions

of fear between fast and no music conditions (Table 16). Pairwise comparisons indicated

that there were significant differences (p= .002), 95% CI [-.18, -.03] between fast and no

conditions (Table 17). Individual estimates indicated that participants were more likely to

experience higher levels of expressed fear (M= .17) 95% CI [-.04, .29] when given fast

conditions, compared to slow (M= .12), 95% CI [-0.01, 0.24] or no-conditions (M= .06),

95% CI [-0.06, 0.17] (Table 18).

Table 16

Parameter Estimates for the Expression of Fear Between Conditions

Parameter B Std.

Error

95% CI Hypothesis Test

[LL, UL] Wald χ2 df Sig.

(Intercept) 0.17 0.06 0.04, 0.29 6.53 1 0.01

No music -0.11 0.03 -0.17, -0.05 12.09 1 0.00

Slow music -0.05 0.03 -0.11, 0.01 2.62 1 0.11

Fast music 0a . . . . .

(Scale) 0.32

Dependent Variable: Fear

Model: (Intercept), Condition of the trial

a. Set to zero because this parameter is redundant.

Table 17

Pairwise Comparisons for the Expression of Fear Between Conditions

(I)

Condition

of the trial

(J)

Condition

of the trial

MD (I-J) Std.

Error df

Bonferroni

Sig.

95% CI

[LL, UL]

No music slow music -0.057734745 0.03 1 0.19 -0.13, 0.02

fast music -.10693679993a 0.03 1 0.00 -0.18, -0.03

Slow music no music 0.057734745 0.03 1 0.19 -0.02, 0.13

fast music -0.049202055 0.03 1 0.32 -0.12, 0.02

Fast music no music .10693679993a 0.03 1 0.00 0.03, 0.18

slow music 0.049202055 0.03 1 0.32 -0.02, 0.12

Pairwise comparisons of estimated marginal means based on the original scale of

dependent variable Fear

a. The mean difference is significant at the .05 level.

63

Table 18

Estimates for the Mean-Level Expressions of Fear Between Conditions

Condition of the trial M Std. Error 95% CI

[LL, UL]

No music 0.06 0.06 -0.06, 0.17

Slow music 0.12 0.06 -0.01, 0.24

Fast music 0.17 0.06 0.04, 0.29

4.3.1.3 Contempt

Parameter Estimates indicated that there was a significant effect (p= .00) in expressions

of contempt between fast and no music conditions (Table 19). Pairwise comparisons

indicated that there were significant differences (p= .012) 95% CI [-.19, -.02] between

fast and no conditions (Table 20). Individual estimates indicated that participants were

more likely to experience higher levels of expressed contempt (M= -.03) 95% CI [-0.14,

0.08) when given fast conditions, compared to slow (M= -.09), 95% CI [-0.19, 0.02] or

no-conditions (M= -.13), 95% CI [-0.23, -0.04] (Table 21) .

Table 19

Parameter Estimates for the Expression of Contempt Between Conditions

Parameter B Std.

Error

95% CI Hypothesis Test

[LL, UL] Wald χ2 df Sig.

(Intercept) -0.03 0.06 -0.14, 0.08 0.27 1 0.61

No music -0.11 0.04 -0.18, -0.03 8.31 1 0.00

Slow music -0.06 0.03 -0.12, 0.00 3.66 1 0.06

Fast music 0a . . . . .

(Scale) 0.22

Dependent Variable: Contempt

Model: (Intercept), Condition of the trial

a. Set to zero because this parameter is redundant.

64

Table 20

Pairwise Comparisons for the Expression of Contempt Between Conditions

(I)

Condition

of the trial

(J)

Condition

of the trial

MD (I-J) Std.

Error df

Bonferroni

Sig.

95% CI

[LL, UL]

No music slow music -0.047593777 0.03 1 0.24 -0.11, 0.02

fast music -.10471981162a 0.04 1 0.01 -0.19, -0.02

Slow music no music 0.047593777 0.03 1 0.24 -0.02, 0.11

fast music -0.057126034 0.03 1 0.17 -0.13, 0.01

Fast music no music .10471981162a 0.04 1 0.01 0.02, 0.19

slow music 0.057126034 0.03 1 0.17 -0.01, 0.13

Pairwise comparisons of estimated marginal means based on the original scale of

dependent variable Contempt

a. The mean difference is significant at the .05 level.

Table 21

Estimates for the Mean-Level Expressions of Contempt Between Conditions

Condition of the trial M Std. Error 95% CI

[LL, UL]

No music -0.13 0.05 -0.23, -0.04

Slow music -0.09 0.05 -0.19, 0.02

Fast music -0.03 0.06 -0.14, 0.08

To further explore the relationship between these 3 expressed emotions and their

interaction with each other within the fast conditions, a correlation model was created.

Results indicate that there are significant positive associations between joy and fear,

r(148) = .65, p = .00), joy and contempt, r(148) = .80, p = .000), and fear, r(148) = .53, p

= .00) (Table 21).

Table 22

Correlations for the Mean-Level Expressions of Joy, Contempt, and Fear in the Fast

Condition

1 2 3

1. Joy --

2. Fear .65** --

3. Contempt .80** .53** --

**. Correlation is significant at the 0.01 level (2-tailed).

65

4.4 Research Question #3

To address the third research question, it was necessary to explore the effects of

condition on the psychophysiological GSR responses that participants had during their

comprehension task. This model was done in order to see the interaction that Skin

Conductance Responses had on the condition that was presented to a participant.

Parameter Estimates indicated that there was a significant effect of condition on the mean

number of SCRs (p =.00) of condition between fast and no conditions (Table 23).

Pairwise comparisons indicated that there were significant differences between fast and

no conditions (p = .00), 95%CI [.11- .31], and between slow and no conditions (p= .00),

95% CI [.08- .27] (Table 24). Individual estimates indicated that participants were more

likely to experience a greater number of SCRs (M= .74), 95% CI [.62, .85] in fast

conditions, compared to slow (M= .70), 95% CI [.59, .81] or no-conditions (M= .53),

95% CI [.43, .62] (Table 25).

Table 23

Parameter Estimates for the Mean Number of Skin Conductance Responses Between

Conditions

Parameter B Std.

Error

95% CI Hypothesis Test

[LL, UL] Wald χ2 df Sig.

(Intercept) 0.74 0.06 0.62, 0.85 161.97 1 0.00

No music -0.21 0.04 -0.30, -0.13 23.24 1 0.00

Slow music -0.04 0.04 -0.11, 0.04 0.90 1 0.34

Fast music 0a . . . . .

(Scale) 0.60

Dependent Variable: SCRs

Model: (Intercept), Condition of the trial

a. Set to zero because this parameter is redundant.

66

Table 24

Pairwise Comparisons for the Mean Number of Skin Conductance Responses Between

Conditions

(I) Condition

of the trial

(J) Condition

of the trial MD (I-J)

Std.

Error df

Bonferroni

Sig.

95% CI

[LL, UL]

No music slow music -.18a 0.04 1 0.00 -0.27, -0.08

fast music -.21a 0.04 1 0.00 -0.31, -0.11

Slow music no music .18a 0.04 1 0.00 0.08, 0.27

fast music -0.03 0.04 1 1.00 -0.12, 0.05

Fast music no music .21a 0.04 1 0.00 0.11, 0.31

slow music 0.03 0.04 1 1.00 -0.05, 0.12

Pairwise comparisons of estimated marginal means based on the original scale of

dependent variable SCRs

a. The mean difference is significant at the .05 level.

Table 25

Estimates for the Mean Number of Skin Conductance Responses Between Conditions

Condition of the trial M Std. Error 95% CI

[LL, UL]

No music 0.53 0.05 0.43, 0.62

Slow music 0.70 0.06 0.59, 0.81

Fast music 0.74 0.06 0.62, 0.85

To further explore this construct, GEEs were used to explore the effect of

condition on the number of SCRs, while factoring in the amplitude (or intensity of the

responses) as a covariate to this model. Parameter Estimates indicated that there was a

significant effect (p = .04) of SCRs on the amplitude of responses in this model, as well

as significant differences (p = .000) of condition between fast and no conditions (Table

26). Pairwise comparisons indicated that there were significant differences between fast

and no conditions (p = .00), 95% CI [.84, 3.21] (Table 27). Individual estimates indicated

that participants were more likely to experience greater amplitudes of GSR (M = 8.08),

95% CI [7.25, 8.90] in fast conditions, compared to slow (M = 7.42), 95% CI [6.40, 8.44)

or no-conditions (M = 7.03), 95% CI [6.29- 7.77].

67

Table 26

Parameter Estimates for the Amplitude of GSRs between Conditions with the number of

Skin Conductance Responses as a Covariate

Parameter B Std.

Error

95% CI Hypothesis Test

[LL, UL] Wald χ2 df Sig.

(Intercept) 14.31 1.14 12.08 158.05 1 0.00

No music -2.03 0.50 -3.00 16.79 1 0.00

Slow music -0.91 0.65 -2.18 1.96 1 0.16

Fast music 0a . . . . .

(Scale) 1.43 0.69 0.08 4.28 1 0.04

SCRs 51.7

Dependent Variable: Amplitude

Model: (Intercept), Condition, SCRs

a. Set to zero because this parameter is redundant.

Table 27

Pairwise Comparisons for the Amplitude of GSRs between Conditions with the number of

Skin Conductance Responses as a Covariate

(I)

Condition

of the trial

(J)

Condition

of the

trial

MD (I-J) Std.

Error df

Bonferroni

Sig.

95% CI

[LL, UL]

No music

slow

music -1.117762091 0.67 1 0.29 -2.72, 0.49

fast

music -2.028193260a 0.5 1 0.00

-3.21, -

0.84

Slow

music

no music 1.117762091 0.67 1 0.29 -0.49, 2.72

fast

music -0.910431169 0.65 1 0.48 -2.47, 0.65

Fast

music

no music 2.028193260a 0.495 1 0.00 0.84, 3.21

slow

music 0.910431169 0.65 1 0.48 -0.65, 2.47

Pairwise comparisons of estimated marginal means based on the original scale of

dependent variable Amplitude

a. The mean difference is significant at the .05 level.

68

Table 28

Estimates for the Amplitude of GSRs Between Conditions with the number of SCRs as a

Covariate

Condition of the trial M Std. Error 95% CI

[LL, UL]

No music 7.03 0.38 6.29, 7.77

Slow music 7.42 0.52 6.40, 8.44

Fast music 8.08 0.42 7.25, 8.90

4.5 Research Question #4

To address the fourth research question, it was necessary to explore the post-task

questionnaire results to understand the participant’s perception and control that varying

musical conditions may have had on their performance during their reading

comprehension task. In order to do this, Pearson correlations were run to determine the

relationship that statements had to each other, as well as how those appraisal statements

related to performance scores in varying conditions.

The first series of correlations were run in order to explore the relationship

between performance on passages with musical conditions and the participant’s

perception of these tasks with musical stimuli. Results indicated that there was a

significant positive association between the statements “Did the musical selection

interfere with your reading?” and “I performed better on my tasks when I had music”

r(296) = .49, p = .00), a significant negative association between the statements “I

performed better on my tasks when I had music” and “I find listening to music while

working/studying to be distracting” r(296) = -.35, p = .00), a significant negative

association between the statements “I find listening to music while working/studying to

be distracting” and “Did the musical selection interfere with your reading?” r(296) = -.14,

p = .01), and a significant positive association between the statements “I performed better

on my tasks when I had music” and scores in the reading comprehension task r(296) =

.196, p = .00) (Table 29).

69

Table 29

Correlations between Post-task Questionnaire Responses and Scores in Reading

Comprehension

1 2 3 4

1. Did the musical selection interfere with your reading? --

2. I performed better on my tasks when I had music .49** -- 3. I find listening to music while working/studying to be

distracting -.14* -.35** -- 4. Score in the reading comprehension task 0.11 .17** 0.08 --

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

The second series of correlations were run to understand the relationship between

the participant’s score during slow-condition passages, their preferences for music, and

their perceived performance. Results indicated there was a significant negative

association between the statements “I do not prefer listening to fast music while

working/studying” and “I performed better on tasks when I was listening to slow music”

r(148) = -.20, p = .01) (Table 30).

Table 30

Correlations for Preferences of Condition and Reading Comprehension Scores

1 2 3

1. Score in the reading comprehension task --

2. I do not prefer listening to fast music while working/studying -0.15 --

3. I performed better on tasks when I was listening to slow music 0.11 -.20* --

*. Correlation is significant at the 0.05 level (2-tailed).

4.6 Results Summary

In summary of these findings, results indicated that participants experienced increased

mean expressions of Joy (.02), Fear (.17) and Contempt (-.03) while experiencing

increased Galvanic Skin Responses (.74) of greater intensity (8.08), while they had lower

scores (M = 3.61) in the fast tempo condition of their reading comprehension task (Figure

1).

70

Figure 1. Summary of Findings

Chapter 5 Discussion

5.1 Chapter Overview

This chapter discusses the results and findings from the previous chapter based on

multiple discussion points, connecting findings within the context of a theme or research

question. The results will be discussed as they pertain to drawing conclusions pertaining

to: 1) the effects of performance and tempo in music cognition literature, and 2) a

discussion of how emotion and learning performance varied between conscious and

unconscious appraisal systems. At the end of this chapter, a general discussion will take

place, bringing together all the findings and merging them to discuss the significance of

these findings to the broader field of education research.

5.1.1 Performance and Effects of Tempo in Music Cognition

Results from the GEEs of the effect of condition on scoring indicated that there was a

significant effect in scoring between fast and slow music, as well as fast and no music

conditions. More importantly, the pairwise comparisons indicated that there were

significant differences in scoring (p = .00), 95% CI [.22, .64] between slow and fast

conditions. Between these two conditions, individual estimates indicated that participants

were more likely to score higher (M = 4.04) when given a slow-condition passage,

71

compared to being given a fast-condition passage (M= 3.61). These findings indicate that

fast-music conditions tended to predict lower-mean scoring, and slow-music conditions

tended to predict higher-mean scoring within participants.

These results, and the effect of fast background music, concur with Thompson,

Schellenberg and Letnic (2012) that fast music does indeed negatively impact

performance and decrease success in reading comprehension scores. Suggestions from

Pronin and Jacobs (2008) may indicate that, as the tempo of background music increases,

cognitive load increases, resulting in less working memory resources that the listener can

muster to deal with the cognitive processing demands of the task. As tempo increases,

arousal levels become higher and reach a point where cognitive stimulation reaches a

point where too few resources can be mustered to effectively execute the task to a high

standard (Ünal, de Waard, Epstude, & Steg, 2013). Much in the same way, Feng, Suri

and Bell (2014) discovered that faster tempi resulted in increased arousal and decreased

engagement or sustainment during computational math problems. Within cognitively

demanding tasks that require spatial as well as perceptual awareness, researchers concur

that this fast music can lead to decreased ability for continual engagement with a

learner’s task, resulting in attentional behaviours waning, and causing decreased

performance in the ability to recall information and make decisions while learning.

Contrary to Husain, Thompson and Schellenberg (2002), increasing tempo does not lead

to greater performance. Results suggest that perhaps the arousing effects of higher tempo

hit a ‘peak’ in their ability to modulate arousal, and afterwards become distracting to an

individual. According to Fernández-Sotos, Fernández-Caballero and Latorre’s (2016)

model of the perceptual effects of tempo, these findings align with their prediction that

music above 150bpm (including sixteenth notes) has the tendency to induce a ‘stressful’

state within the listener, leading to decreased cognitive performance. This present

research can corroborate the field’s understanding of the detrimental effects of fast tempo

music on learning performance, with several questions that can be further addressed. The

findings also point to the indicators that slow tempo conditions predict higher-mean

performance within listeners. This concurs with findings from Kuribayashi and Nittono

(2015), as well as McAuley (2010), and McAuley, Henry, and Tkach, (2012), suggesting

that the optimal speed for background music to be played at, to have any form of

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appreciable benefit, would be around 100bpm. Perhaps the ‘calming’ effects noted by

these researchers suggest some form of stimulated states that could encourage cognitive

attention, without the overly salient features that faster tempo music may elicit. In

contrast to the findings of this present study, researchers have also associated fast tempo

conditions with high, positive arousal leading to greater performance in scoring tasks

(Dalla Bella, Peretz, Rousseau, & Gosselin, 2001; Gomez & Danauer, 2007). While these

results do suggest contrasting findings, a caveat to these studies is that tempo, as well as

mode (major versus minor key) were dependent variables. Results of these studies

indicated that high-positive arousal and positive valence (more will be discussed in 5.1.2)

regarding affective outcomes, were rated as a result of these fast tempi in testing

conditions. While the results of this present study do corroborate the notion that high-

tempo music arouses and perhaps has greater salience, these results do speak to the

complex nature and contextual setting of perceptual effects of tempo on music cognition.

These suggestions, from a topographical viewpoint of analyzing performance

outcomes, could support the Yerkes-Dodson Law in this case and the opinion that

perhaps these musical features represent the two peaks of the U-shape arch of stimulation

that precipitate decrease in measurable performance. While these findings corroborate

existing work in the field that suggests that music can have a positive benefit to help

improve the performance of learners/workers completing cognitively demanding tasks

(Lesuik, 2005; Schellenberg & Winner, 2001; Sahebdel & Khodadust, 2014; Su et al.,

2017; Thompson, Schellenberg, & Hussain, 2001), there are still several questions with

regards to ‘how’ music is capable of altering psycho-cognitive states and performance.

Performance provides researchers with confirmation of the results of the process, and as

such, the most significant findings are regarding the mechanisms that permit such

performances.

5.1.2 Emotions and Cause for Performance

Findings from the expressed emotions provide a great amount of information for

discussion. The plurality of theories regarding the generation and byproducts of

emotional states necessitates a comprehensive review of how these theories interact with

findings from the present study. A central component of the exploration of affect and

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emotion are a series of concepts that help form what is known as the appraisal theories.

These theories collectively argue that appraisal, the processes by which humans make

judgements regarding their interpretation of the psychological circumstances that

surround them, not only underlie emotional behavior, but play a pivotal role in

generating cognitive decision-making abilities (Scherer, Schorr, & Johnstone, 2001).

Through these appraisals, individuals make coordinated evaluations that occur on various

levels of consciousness and as a result of differing stimuli inputs (Gratch & Marsella,

2004). Appraisal does not involve the fixing of an event’s value; instead, it is the

interpretation and context of an individual’s beliefs, intentionality and situation that

informs their judgement of that event. According to Gross (2015), the two most common

appraisal strategies are cognitive reappraisal and suppression. Reappraisal strategies

have been consistently identified as being more effective and allowing for greater

adaptation (Chauncey-Strain & D’Mello 2015; Leroy et al. 2012), especially in learning

settings where the learner should be challenged to mold their attitudes towards

assimilating new knowledge instead of inhibiting it. What makes the reappraisal of

emotions significant is that this approach is an antecedent-focused strategy and involves

altering the way one thinks about a situation before an emotional experience has occurred

(Harley, Jarrell, & Lajoie, 2019). If one can alter their affective appraisal of a situation

before the emotion is externalized, the individual may have a greater chance that the

emotion being judged will adequately fit the needs of the situation. Within these stimuli

receptors, some research argues that these appraisals are initiated and framed through a

set of distinct, discrete emotions with neurological correlates to measurable bodily

responses at an unconscious level (LeDoux, 1996), whereas others argue that emotions

are the result of appraisals to reflect the interaction of underlying mechanisms that occur

at a conscious level (Pekrun et al., 2011). Given these contrasting positions, exploration

of conscious and unconscious levels of appraisal will be conducted to interpret the

emotional expression findings of this study. By exploring the results of this study from

defined theories of conscious and unconscious appraisal, it may be possible to draw some

fluid conclusions as to how affect may be modulating cognitive response patterns and

performance.

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This present discussion will take place in order to examine these findings within

the context of unconscious and conscious appraisal theories. This discussion will help to

frame these findings from various angles and dimensions to discover how each is a valid

and capable method to explore the interaction of music, emotion and cognitive affect.

This chapter will conclude in section 5.1.2.3 with the proposition of a working model,

based on these discussions, that merges various components of psychology, music

cognition, and learning science that can best describe the holistic emotional experience of

music’s impact on cognitive performance.

5.1.2.1 Unconscious Appraisal

5.1.2.1.1 Dimensional Model Argument

As the dimensional model (Russell, 1980) describes emotions as the result of appraisals

of the: 1) valence (positive versus negative) and 2) intensity (high stimulation versus low

stimulation) of a situation, we can chart the emotions present within these results as

fitting within this affective framework. For learners to function in complex learning

situations, it may be necessary for them to make complex, affective judgements at an

unconscious level, requiring some varying degree of automated responses that emerge

before we can make a conscious appraisal of the situation. As they respond to these

situations, they may be functioning across a grid of response that actively pushes and

pulls the learners to unconsciously absorb and appraise their situation as they move

through these tasks. Joy can arise from several different outcomes. Joy can be elicited

when something desirable has either happened or is inevitable, increasing the desirability

and arousing state that is elicited (Gratch & Marsella, 2004). Moreover, joy is one of the

first emotions to be observed in infants. Due to this early period and developmental role,

this emotion should be closely related to reward signals that continue to grow with the

individual as they develop and mature (Broekens, Jacobs, & Jonker, 2015). As the

individual develops and accomplishes goals (including learning objectives), they express

joy when they receive success, and distress (the anthesis of joy) when they fail to

accomplish this. However, anticipation and unexpected circumstances, through

modulations in the appraisal process, can also result in expressions of joy (Sprott, 2005).

Fear arises from a belief that something bad may happen or has happened. This emotion

emerges from a goal that is unestablished, or the feeling that the individual is somehow

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being threatened or will be threatened in some future state (Gratch & Marsella, 2004).

Schaffer (1974) describes the development of fear as requiring an active comparison of

multiple events that the individual can gauge to understand the degree of risk or threat

and as that which requires a more complex network than other emotions to assemble. To

further solidify the current and forward-thinking nature of fear, it is described as the

emotion that is about the ‘anticipation of a negative outcome’ (Ortony, Clore, & Collins,

1988). From these descriptions, we see that fear as an affective expression requires a

great series of complex appraisals that help the individual anticipate the negative

valancing of a situation in the present and future states.

Dimensional models of response may be able to describe this interaction.

According to the circumplex model (Russell, 1980), the expressions of fear, seen during

the fast tempo conditions, fit within the negative-activating quadrant, and joy fits within

the positive-activating quadrant of response. To make sense of these results, it may be

argued that the increased expressions of fear may be caused by the appraisal of that

learning condition (induced by the fast music), which stimulates the individual to engage

in a future-response pattern to the settings that they are in. At the same time, there is

something within the circumstances that the individual perceived to be salient and

warranting a negatively-valenced response. Not falling in line with existing literature,

fast tempo music can be associated with varying degrees of negative affective response

that listeners perceived as being distracting, thereby detrimentally affecting performance

(Pronin & Jacobs 2008). Moreover, the dimensional model of tempi affects assembled by

Fernández-Sotos, Fernández-Caballero and Latorre (2016) indicated that fast tempo

music (150 bpm, with rapid sixteenth-note patterns throughout) was situated within a

grid that was low valence, yet high arousal, creating a “stressing” condition in the

listener. Within that same model, expressions of joy were fixed within a quadrant that

was high valence-high arousal. If we view the responses of joy that were simultaneously

observed during that fast tempo condition, we may be able to suggest that there is a

similar activating-dimension that joy fits into, yet according to Russell (1980, 1989),

expressions of joy are associated with positive feelings in their intensity. While this may

appear to be odd, music cognition literature supports the understanding that listening to

fast tempo music is often associated with positively-valenced affects that make listeners

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feel happy and positive about their task (Almeida et al., 2015; Gagnon & Peretz, 2003;

Thompson, Schellenberg & Husain, 2016). This support from the literature can indeed

support the idea that listening to music of this tempo may lead to increased affective

valences that would lead an individual to feel ‘activated’ in this dimension.

Although expressions of joy and fear appear to be disconnected due to their

location within the dimensional grid, music cognition literature is more inclined to

suggest their relationship may be more closely associated with each other than first meets

the eye, due to the high-arousal qualities within their design. The challenge that this

cognition literature poses, is how can these two contrasting musical emotions exist within

a similar field? To examine this, it is necessary to explore the dual-occurrence of musical

emotions (Evans & Schubert, 2008), identified within the testing of Gabrielsson’s (2002)

dimensional model of coordinating emotion that participants noticed the feeling of

multiple emotions as they described how they coordinated the feeling and perception of

music’s implicit emotion. Some of these emotions occurred within similar planes of each

other despite having contrasting qualities. What we can infer from this into the current

argument is that it is not beyond the realm of possibility to suggest that emotions with

coordinating arousal levels may be present within an individual at the same time. While

joy and fear may have opposing valences, they both push the learner to anticipate future

response patterns that have yet to occur. While these patterns of cognitive mechanisms

that permit emotions that encourage prediction are valuable, the presence of these

emotions during lower-performing situations may be suggesting a point at which the

emotions that encourage unconscious appraisal and activation become overwhelming for

the learner, thus decreasing their cognitive performance and decision making abilities,

and leading to a lower number of resources that can be mustered to help remedy poor

performance.

The nature of cognitive performance has as much to do with psychophysiological

response as it does with psychological states of stimulation. Through an analysis of the

sympathetic nervous system, skin conductance functions as a reliable, autonomic

indicator of linear response patterns that can be exhibited in individuals (Benedek &

Kaernbach, 2010). Through the autonomic, self-activating system of response, skin

conductance can provide a fair analysis of the unconscious response of an individual to

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various external stimuli. The results within this present study can indicate that as

participants exhibited a higher mean number of SCRs (indicating that they were more

likely to be stimulated during the fast tempo condition), the greater the stimulation that

can be inferred from the situation (Nishiyama et al., 2001). While these results concur

with previous finding regarding autonomic nervous system response to stimuli, these

results can be viewed in the context of cognitive load theory (CLT; Sweller, 1988) and

the relationship between load and affective response. According to CLT, the human

cognitive complex, which mediates decision making and the allocation of resources to

accomplish tasks, has a finite number of neural resources that it can allocate, until there

comes a point when the strain of the situation causes the individual to decrease their

performance. Within learning situations, this performance-based environment generates

multiple stressors (including the desire to perform well, language and semantic

interpretation of questions, physical barriers, etc.) that the human cognitive complex

interprets as challenges that require neural resources to execute (Chandler & Sweller,

1996). The more resources that are required to help overcome these challenges, the more

unconscious, autonomic responses are released by the eccrine glands that elicit GSR

responses (Boucsein, 2012). Over time, the resources become depleted and the

autonomic system releases more responses that are more intense in nature to indicate a

greater amplitude of changes. What we may have seen in this present study was an

increase of SCRs and a greater amplitude of SCRs across the fast tempo conditions that

participants were given, which indicated a decrease in the cognitive resources that an

individual had available as they were unconsciously, yet actively, interpreting the

challenges of their condition.

Not only can these changes be explained through load, the psychophysiological

response patterns of GSR can accurately support the presence of increased cognitive load

(Nourbakhsh et al., 2017) and predict lower performance. As these differences were

more strongly exhibited within the fast tempo condition where participants had a greater

likelihood of exhibiting lower scores, it could be suggested that decreased cognitive

resources caused by increased cognitive load were generating these GSR responses.

Findings from Nourbakhsh et al., (2017) have challenged the notion that

psychophysiology is incapable of correlating and predicting greater stresses of cognitive

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load (Engstrom, Johansson, & Ostlund. 2005; Haapalainen, Kim, Forlizzi, & Dey, 2010),

and that correlations between psychophysiology and cognitive load are challenging to

uncover. The findings of this present study suggest that psychophysiology, even in larger

sampling periods, can indicate changes within the affective state of the participant. But

more importantly, the psychophysiological responses can be interpreted as physical

changes in the attentional direction of participants. As the participants were exposed to

the fast tempo condition, the attentional focus through the salient features of the stimuli

could be drawing the listener’s attentional resources away from their task. This could be

happening on an unconscious level of response due to the individual exhibiting more

significant response patterns and periods of increased intensity. In comparison to other

conditions, the fast condition may be drawing too many attentional resources away from

the participants, precipitating a more complex series of responses to be exhibited.

Researchers have also began exploring the relationship between these GSR

responses and generalized affective response patterns. Through examination of GSR

patterns, higher and more erratic patterns with less frequent peak responses have been

found to be indictors of more intense, negatively valenced emotions (Goshvarpour,

Abbasi, Goshvarpour, & Daneshvar 2017). Similarly, Bailenson et al. (2008) have

explored the interrelationship between facial emotions and GSR ratings, leading them to

conclude that emotions such as amusement and sadness exhibited patterns that were more

easily correlated to each individual’s emotional and psychophysiological patterns.

Although the researchers did indicate that their model is not yet complete, it offers an

emerging understanding of how it may be possible to form 1-to-1 relationships between

response and affective patterns. While participants in this present study did exhibit

increased expressions of fear and joy that coincided with increased GSR activity in the

forms of greater SCRs and amplitude of responses, it could be suggested that these new

technologies offer great promise in helping to map plausible, causal relationships

between these unconscious appraisal systems. The unconscious and autonomic

relationships that GSR and other forms of EDA offer to researchers are immediate and

precise responses that happen in close proximity to the appraisal of environmental and

situational activities. These relationships, combined with the dimensional appraisal

models of emotion, offer a pathway to understanding the results seen in this present

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study. What we may be seeing is that fast tempo music may contain salient elements that

interact on the unconscious response patterns of listeners, precipitating a complex series

of judgements that leads to the simultaneous generation of cognitive stressors and

subsequent psychophysiological responses that indicate diminishing cognitive

performance.

5.1.2.1.2 Core Affect

The concept of Core Affect (CA; Russell, 2005) can be interpreted as an outgrowth of

dimensional emotion theories. As Russell describes, emotions that are classified by terms

such as fear, anger, joy, sadness, happiness, etc., are constructs that are mired in several

weaknesses, including an origin in folk theories of psychology that are propagated by

dichotomous schisms between mind-body and nature-nurture, as well as empirical

difficulties in describing the processes of externalized response exhibited in humans

(Russell, 2005, 2009). According to Russell, “core affect is that neurophysiological state

consciously accessible as the simplest raw (primary or non-reflective) feelings most

evident in moods and emotions and emotionally charged moments” (2005, p. 28). Where

CA differs from other emotion theories is that it is not so much a theory of emotion, but

an argument for the nature of response patterns. The patterns exhibited by humans are

caught in searches for dialectics that describe states that are either too complex or too

multi-faceted to adequately measure. Instead, the concept of CA works to describe

human response in much more visceral, simplistic terms that come to describe broad, yet

highly salient expressions such as feeling good or bad, or feeling energized or enervated

(Russell, 2017, p. 112). While these descriptors sound categorically similar in a way to

those that are proposed by Russell’s dimensional model (1980), they do differ in some

fundamental ways. While the occurrence of these labels resembles the 2-dimensional

planes of activating vs. deactivating or arousing vs. unrousing, the argument for CA is

that it is a component of a more complex emotional process. Along with changes in

cognitive judgements, multiple psychophysiological changes, and visible changes in

behavioural observations, this process can be described as a traditional ‘emotion’ (Yik,

Russell, & Steiger, 2011). While CA is not a replacement for theories of emotion, it

provides a pivotal shift in the discussion regarding how humans describe externalized,

affective behaviours.

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The theoretical underpinnings of CA revolve around the inherent responses that

act as building blocks to human response. A criticism leveraged against emotion theory is

the degree to which generalizations made about affective descriptors (anger, fear, joy,

curiosity, etc.) apply to universal circumstances. Understanding the complexity that

occurs in the human mind when understanding a display of anger, for example, would

necessitate a study of the environmental, situational, and personal factors at play, as well

as a whole host of other cues that might precipitate that response. The question,

therefore, is whether the response to anger could be universal, and whether we could

describe anger as having the same set of ‘ingredients’ across totally different tasks

(Russell, 2017). In this sense, the most basic ingredients of anger should be able to move

an individual to respond with expressions that would make them not feel good and

energize them in order to produce a response to whatever in their world may be moving

them to express that response. The CA is not making them feel angry; rather, once that

CA is combined with more complex assessments of the individual’s environment, that is

what precipitates that emotional expression. Another criticism that manifests itself in

emotion theories is with regards to the boundaries of emotions. Part of numerous discrete

and dimensional models are their reliance on an ever-expanding series of qualifiers that

are, in part, according to Russell (2009), needed because researchers lack the necessary

nuance to describe what response patterns clearly delineate between these emotions.

Despite these criticisms of emotion theory, CA provides a very relevant backdrop to

describe the ever-evolving circumstances that help describe the complex behavioural and

cognitive moves that surround affective response.

The results of this present study could be explained through a lens of CA. To

begin, the contextual setting of this study permits a multidimensional examination of the

nature of affect and response. Examining this present study as working within the

intersecting realms of educational psychology, music cognition and learning science, the

setting of this study was actively involved in hybridizing the working theories of all three

areas in order to describe human response, performance and affect. Examining the

literature from all three areas seen in Chapter 2, one can realize that all three have very

diverse and ever-expanding definitions of emotion and performance, and theories for

how the two concepts may be linked in some measurable form. More importantly, the

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definitions and impetus for emotion, which work independently yet overlap in some

regards, work as a challenging hurdle to draw equal comparison between the ways each

of these fields categorically generate and define how and under what circumstances

emotional responses are elicited and modulated, under which circumstances, and to what

desired effect. It is important to acknowledge the limitations of this type of research to

describe the observed emotional responses in detail. CA can act as an explanation to

describe these findings. Firstly, CA works as an underlying mechanism to describe

human response under varying circumstances. As mentioned, the inability to accurately

dissect the constituent components of the learning emotions, musical emotions, and

neuropsychological mechanisms within this study makes it almost impossible to draw

causal relationships that can be studied. Instead of focusing on isolating these domains,

perhaps the most effective way is to examine CA to explain the elements of a situation

that may create activating and de-activating qualities. The introduction of fast tempo

music, which precipitated increased expressions of fear, could relate to the marked

contrast in stimulation in comparison to the results from the control condition. The

differences in the introduction of the musical stimulant, along with existing literature

suggesting that fast tempo correlates to increased feelings of activation and stimulation,

may concur with CA in describing the increased activation that was measured on a

neuro-emotional and psychophysiological level. This argument can also be supported

within the realm of learning emotion and music cognition literature. Despite all the subtle

and substantial differences in setting, test design and stimuli, fast tempo music has been

associated with lower cognitive performance and decreased performance in a variety of

tasks. Even with the ability to control for the various factors that are present in these

studies, it may be fair to suggest that CA may be leading to a negative, over-stimulated

state that precipitates negatively valenced emotional responses within learners, resulting

in a combined cognitively induced state. Although research, including the present study,

does its best to control for the settings of empirical studies, the multitude of factors to

control for are too vast to engage in perfect replication. Nevertheless, given concurring

findings of the effects of fast tempo background music, it is reasonable to assume that

there is some effect of CA displayed through these findings.

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In reviewing the findings of this present study, there was a perplexing conundrum

posed by the indicators that participants’ affective responses when given fast tempo

conditions suggested elevated levels of both joy and fear. While these emotions are

expressions of discrete emotions, their likely expression within the same individual, at

the same time, under the same stimulation, posed a set of perplexing questions given

traditional music cognition and psychology literature. As mentioned earlier, these two

emotions presented both positively and negatively valenced emotional qualifiers that

were difficult to categorize within this stimulus condition. Exploring this dilemma, it is

possible to suggest that a degree of co-occurrence (Larsen & McGraw, 2011) may be

seen in these results, wherein an individual is experiencing two seemingly conflicting

affective expressions simultaneously. According to the authors, unique cases of co-

occurrence can become visible in a relatively small number of events that have the

tendency to be linked with salient, personal and emotionally charged experiences.

Referencing the literature of Larsen and McGraw (2011), Russell (2017) goes on to say

that the emotional experiences most likely to be expressed in co-occurrence involve

diametrically opposed emotional valences (joy and sadness in most cases) that typify

‘bittersweet’ emotional responses that have hit a resonating note within the individual.

Understanding this, it could be suggested that based on the findings from this present

study, the presence of joy and fear may optimize the experience of co-occurrence within

the listener, where the emotionally salient qualities of the fast tempo music result in the

simultaneous expression of these two emotions. More importantly, the co-occurrence of

these emotional expressions, particularly within fast tempo music, suggests that there

may be an underlying psycho-acoustic mechanism acting as a global stimulant to elicit

these activating feelings. Understanding where these two emotions situate themselves

within discrete and dimensional models, they sit on the spectrum of activating emotions

that align with CA as experiences that apply the most basic expressive criteria needed to

affect some response from a human. These, combined with the psychophysiological

responses of increased SCRs and amplitude of responses within the sympathetic nervous

system, suggest the visceral CA is necessitating the individual to respond in a direct and

immediate manner.

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Understanding how CA may interact with more complex affective mechanisms in

our mind, there is also space to understand the habituation of musical stimuli that we may

experience. As discussed previously, not all musical experiences are identical, and

different musical experiences have varying affects on the individuals who experience

them. If we infer that CA can work to create activating and dichotomous effects on

human stimulation, it would not be unfair to suggest that varying musical stimuli could

have varying effects, or degrees of effects, on global stimulation. What may be suggested

through the performance and measurable body responses in this present study is that

tempo conditions have varying degrees of stimulation, and there may be some patterns of

habituation within that stimulation. As noted within the results, fast tempo conditions

exhibited the most significant results on scores, emotion ratings, and psychophysiological

results. While these results are interesting to observe as isolated effects, the impact of the

control and slow tempo conditions should not be ignored because they can help to

articulate the possible effects of habituation of stimuli. The work of Pavlov (1927/1960)

indicated that effects of habituation result from the conditioning process that leads to the

desensitization of the participant to further presentations of the stimulus. In this present

study, the multiple presentations of the stimulus had a constant and even effect over time

on the participant’s performance during all 3 conditions that were presented. While these

results were constant throughout the study, the differences experienced, particularly

between the significance of fast and control conditions, could be indicative of an

attraction and conditioned response that was offered in one learned condition but not

another (Hall & Rodriquez, 2017). Whereas the fast condition emerged as a more salient

condition that offered significant differences in mean performance and expressed

multimodal data collected during their task, the lack of difference could be related to

what the authors describe as gradual reduction in salient response to a learned condition

(in this case, the slow music condition), which leads to less marked response patterns in

performance and expression. This learned response pattern could help to describe the

lack of CA that was elicited through the participants’ activation and response patterns.

In summary, Core Affect provides a viable lens to examine the unconscious

appraisal dimension of these results. From a more universalist perspective to response

and performance, CA offers researchers a far-reaching blanket explanation of affective

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conditions and serves as a prerequisite to more cognitively developed emotional

responses. The findings from this study may demonstrate an example of how CA may be

activated through a response to fast tempo musical stimuli, therefore inhibiting response

via emotional and psychophysiological avenues. While these findings are inconclusive

and broader observations are needed from greater study, they do provide a noteworthy

illustration of the functionality and utilitarian effect of musical stimuli on learning

performance.

5.1.2.2 Conscious Appraisal

Conscious appraisal of emotional valence is another lens through which the results of this

present study can be examined to assess how appraisals function across affective

regulation. Conscious appraisal takes into account the active judgement of the

individual’s circumstances, including the climate, environment, affordances of the task,

etc. This alternative lens can allow an alternative dimension to provide inferences into

the psychological mechanisms that may be in play. A model to begin analysis of active

appraisal can come in the form of the Control-Value theory (CVT; Pekrun, 2006; Pekrun

& Perry, 2014). Emotions that learners generate from these appraisals function as a by-

product of the individual’s perceived: 1) control over the learning task/situation, and 2)

subjective value of a learning situation that the learner appraises. This model takes into

account several key issues that were observed in this present study. Firstly, it helps to

describe the active judgement processes that go on while the learner in engrossed in their

learning task. Learners are constantly faced with complex, multidimensional learning

tasks that require active comprehension and understanding of their environment, the time

that is afforded to them to complete the learning task, the cognitive resources and

prerequisite knowledge they possess, and ultimately the value that such learning may

have to their educational future (in the form of grades, achievement scores, diplomas,

etc.). This appraisal of control can consist of one’s perception of their competencies and

abilities to successfully perform in learning tasks, wherein academic self-image and self-

efficacy play a role, and of how we attain our learning objectives or outcomes (Pekrun et

al., 2017). The value that the participant places on the learning task also impacts the

emotional appraisal. The higher personal value the learner places on the task, the more

they will feel activated to engage with it; consequently, placing a lower value on the

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situation will precipitate lower affective engagement. Once the value and control

parameters are established for the learner, they precipitate emotional responses that help

to explain performance (Linnenbrick & Pintrich, 2002) on measurable learning

outcomes.

CVT maintains that there are 4 broad-categories of responses that are possible when a

learner emotionally responds to their task:

1. Positive activation (e.g., enjoyment, hope, pride)

2. Positive deactivation (e.g., relaxation, relief)

3. Negative activation (e.g., anger, anxiety, shame)

4. Negative deactivation (e.g., boredom, hopelessness).

Each of these dimensions and accompanying affects are thought to represent varying

effects on the learner’s performance. Positive activating emotions, such as enjoyment and

joy in learning, are thought to preserve cognitive resources and focus attention on the

learning tasks while supporting intrinsic motivation and facilitating deep learning

(Pekrun et al., 2017; Pekrun & Linnenbink-Garcia, 2012). And accordingly, negative

emotions, such as anxiety about future performance, fear and hopelessness perpetuate

negative achievement qualities and decreased abilities. The results of this present study

can fall in line with this existing literature and help to explain some of these results. If we

examine the presence of increased expressions of fear within the fast conditions that

participants experienced, CVT would suggest that the participant is experiencing

negative-valenced expressions arising from a perceived lack of control that could arise

from the salient qualities that the perception of fast tempo music offers (Ünal, de Waard,

Epstude, & Steg, 2013; Bramley, Dibben, & Rowe, 2016), thus resulting in participants’

having a perceived lower control over success during those conditions. The lower level

of control that they appraise due to the stimulus presented to them activates them to

negatively valence the situation and place lower performance expectations on

themselves. The result of this increased expression of fear can result in lower cognitive

resources being deployed because they may be crippled and unable to help the learner

overcome the inhibitory paralysis associated with the emotion. Through this, the

decreased cognitive resources do not converge and effectively remediate poor

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performance, resulting in decreased learning performance and outcomes in the task

(Choi, Van Merriënboer, & Paas, 2014; Kalyuga, 2011; Sweller, 2010). This similar

situation could also help to explain the modulating effect of joy that was seen

concurrently. As literature has suggested, joy works to move the learner towards

achievement by helping them to equate joy with reward. The positive emotional valence

that is associated with joy activates us to want to remain in this positive, joyful state.

Nevertheless, if we approach joy in the same way that others have positioned confusion

(D’Mello et al., 2014), joy can stand on the knife edge as both an activator of more

happiness, as well as the initiator of distress, to which joy leads if it is not fulfilled

(Broekens, Jacobs, & Jonker, 2015). In this sense, if we see joy as functioning in

conjunction with another activating, negatively valenced emotion like fear, we could be

seeing that negative control explained by the fast tempo music that is guiding

participants’ appraisal of the situation.

The negative appraisal through decreased perception of control is one component

of this appraisal. The value that is placed on a learning situation is equally as important

to the appraisal of the learning situation. The value appraisal describes learners’ ascribed

value of the type of task, as well as their evaluation of the outcome that they wish to

fulfill; this informs them of how important the task is. In this present study, participants

were told to “read each passage and answer each question to the best of your abilities” in

the introductory portion of the task. The value placed on this situation, it could be

surmised, was that each participant should aim to be as successful as possible during the

completion of this task. Then not only is the evaluation and value of the learning

outcome essential in appraising the situation, but so is the value placed on the outcome

by the learner (Pekrun & Perry, 2014; Stark et al., 2018). The results from this present

study indicated that the condition of a passage in the Nelson Denny H test had an equal

effect on the performance outcome, so that no one passage skewed performance results.

Knowing this, it is fair to say that performance outcome was closely tied to the condition

of the passage, and as such it could be suggested that the lower performance outcomes

that were seen on average in the fast tempo condition could possibly suggest that

participants have given lower value ratings to passages in this condition. Given the lower

value ratings that might result from this condition (which exceeds an optimal stimulating

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range) (Kuribayashi & Nittono, 2015; McAuley, 2010; McAuley, Henry, & Tkach,

2012), participants may have been more inclined to decrease their perceived value

because of the cognitive load of the stimulus. This appraisal of the situation may be

supported by results from the Wolf post-task questionnaire. There was a strong, negative

correlation r(148) = -.20, p = .01) between the statements that asked participants to

compare their perceived performance and preferences and the conditions they were

exposed to during their task (“I performed better on tasks when I was listening to slow

music” and “I do not prefer listening to fast music while working/studying”). The

relationship between these statements can suggest that participants performed better

when given a slow tempo condition and when they do not prefer to listen to fast music.

This negative relationship could indicate that individuals would have generally

experienced an unfavorable musical condition, which could therefore explain the

perceived lower value that was placed on passages that were given this condition.

The role of fear and joy also fall within Ekman’s (1992) discrete emotions theory,

as emotions that are universally experienced across cultures. The final question that this

present study posed from a conscious appraisal standpoint was, how could the presence

of contempt be rationalized given this study design? As researchers have noted, strong

emotional responses are often surrounded by similar and contrasting affective responses

(Oatley & Johnson-Laird, 2011). The definition of contempt and how it is assembled

within the literature warrants evaluation within the context of this present study. Since

the introduction and definition of ‘contempt’ by Ekman and Friesen (1986) and its

associated antecedents, the study of this emotion has branched into numerous areas.

Their mapping of ‘contempt’ as a basic emotion has elicited counterarguments from

other researchers (Scherer, 2009), who suggest that this emotion is far more complex and

works at a higher level, with more variables than others that are needed to appraise this

emotion. This complexity has led to Gervais and Fessler (2017) paying special attention

to this emotion, as they argue that contempt is an affect rooted in the situational

sentiment of contempt (p. 3). What makes this emotion unique is its ability to function in

relationship to its gross evaluative measurement. Although most emotions are understood

to function (in part) as appraisals of situations, contempt is viewed as being explicitly

rooted to a particular object (Hutcherson & Gross, 2011), as the individual gradually

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grows to hold great contempt for that object in their environment. More notably, as the

individual experiences greater amounts of contempt towards their object of focus, they

report greater loss of value or respect towards their situation. The presence of elevated

contempt ratings seen within this present study can be framed in a new light through

these ideas. As CVT indicates, a loss of value in the task by the learner precipitates

changes to the affective response during the task. If that can be transferred over to an

appraisal of contempt, we could suggest that contempt is a manifestation of a global level

appraisal of the value that the task has. This loss of value for a given stimulus condition

may explain how contempt is emerging within these findings.

5.1.2.3 Describing the Interrelation between Affect, Physiological Response and Cognitive Performance

The results of the previous section have articulated the findings of this present study

through the examination of unconscious and conscious appraisal systems, as well as

through Core Affect, in order to explain the possible psychological systems that may be

working to mediate response and performance patterns in participants. Each of these

arguments has significant contributions to make with regards to the associated literature,

rationales, and scope from which to view the relationship between performance and

embodied experience of affect to describe the interaction of emotion, psychophysiology,

and music on cognitive response. In reviewing the results of conscious and unconscious

appraisals that generate emotional responses, it is necessary to describe the effects that

these responses have on the psychological mechanisms that link these emotional

constructs to measurable performance.

Perhaps the most important as they pertain to the most explicit outputs of this

affective learning process are executive functions (EFs) and attention, as well as

cognitive performance. EFs are essential for effective academic instruction and learning

processes because they enhance learning capacity and behavioral modification at all

stages of the learner’s life. Superior EF manifests in better learning behaviors such as

improved planning skills (Gathercole et al., 2008) and better problem-solving abilities

(Van de Sande, Segers, & Verhoeven, 2015). The abilities that pertain to EF refer to a

broad range of cognitive functions, including inhibitory skills, working memory, and

cognitive flexibility, which govern behavioral control and cognitive process and inform

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the learner how to regulate them when it becomes necessary to accomplish specific

learning tasks. To accomplish these executive functions, 3 categories of subtasks are

often identified in literature as constituting the mechanisms that permit these functions:

1) inhibition, as it pertains to the ability to ignore distractions and assign cognitive

resources to a task, 2) updating, which is the process of monitoring and making

alterations to the working memory and one’s ability to store short-term memories, and 3)

shifting, the ability to flexibly switch between tasks or concepts that are being examined

by the learner (Gijselaers et al., 2017). The process of updating also interacts with the

short-term storage mechanisms in the brain that form the learner’s working memory

(Smith & Jonides, 1999). These components are the drivers behind the learner’s EF.

Without these 3 mechanisms that play into the learner’s ability to self-regulate while

engaged in learning tasks, they are not able to demonstrate sufficient control to

accomplish cognitively demanding tasks. Because of the need to muster resources and

regulate responses in order to accomplish learning tasks, EF is seen as an excellent

dimension for measuring learning performance to understand how the learner goes about

accomplishing increasingly difficult academic tasks (Knouse, Feldman, & Blevins,

2014). Researchers have noted that there is a particularly strong relationship between EF

and performance in adolescents (Best, Miller, & Naglieri, 2011) across a variety of

subject areas.

The process of EF also relates to the attentional capabilities of the learner. As the

inhibitory functions of EF are controlled with greater strength, the individual learner can

exercise increasingly longer periods of attentional dedication to a task. The role of

attention and EF are an essential part of the learning experience and are connected to the

complete embodied experience that this model discusses. Without sufficient EF, a

learner’s attention capacity and inhibitory abilities can become weakened. They can

become weakened due to the impact of poor and undesirable emotional regulation and

psychophysiological stresses that result from negative appraisals of the learning task.

After all these negative effects have built up in the learner, the detrimental effects of poor

EF and attention result in weaker cognitive performance as a result of weaker working

memory resources (Smith & Jonides, 1999), which inhibit the mind from shifting the

appropriate cognitive resources to a task. When we examine the application of emotions

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in these types of cognitively demanding scenarios, we realize that the learner’s emotions

are the intermediary between these EF systems and our external response to the world

(Canento et al., 2011; Keltner, Oakley, & Jenkins, 2014). If we see these expressions as

the language of the mind, we can take an approach to emotions and their reflexivity to

their meaning in our world (Olson & Oatley, 2014). The emotions seen in this present

study and represented in this model reflect the world of the learner by developing

combined psycho-emotional responses to their perceived efficacy, control and response

to their task. If what Olson is suggesting is true, the ‘language’ of the mind and world

can be manipulated to reflect the external and internal processes that help shape it. In this

present model, the judgements and processes that take appraisals of emotion and

psychophysiology and produce cognitive responses that are visible in the world are

mediated through this process of EF altering the types of behaviours and attention that

can be offered by the learner during a task. Moreover, the manipulation of affect and the

expressive nature of it as a force of meaning can manifest itself in the form of

consciousness and reasoning (Olson, 2013), which can help share our interpretation of a

learning situation. As the learner is exposed to a greater palette of stimuli and develops

the necessary EF and attentional capabilities to adjust and shift their response through

emotional stimuli, those emotions can expand their palette of response in the hope that

EF will mature accordingly.

Within this present study, the results have indicated that lower scores (M = 3.61)

in the fast tempo condition of the reading comprehension task occurred alongside higher

expressions of Joy (.02), Fear (.17) and Contempt (-.03), which could suggest that the

lingering effects of this condition resulted as a byproduct of the attentional shift that

negatively affected EFs and the ability for the participant to muster the necessary

resources to adequately complete this task. As the listener was exposed to this fast tempo

condition, the multitude of emotions, including contrasting affective valences through co-

occurrence (Larsen & McGraw, 2011), led the participant to feel these contradicting

emotional valences that led to their attention shifting away and a state of distraction

setting in on them. While they were distracted through the inability to reconcile these

emotions, they gradually had to use more EF space to attempt to neutralize these

inhibitory emotions. This deviation of EF and attentional space further exacerbated their

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poor performance due to their inability to allocate executive resources to engage in the

recall, working memory and comprehension space necessary to score higher in their task.

What makes these results quite interesting to examine from an attentional and EF

perspective is the fact that tests for equivalency indicated that performance throughout all

conditions, ordering, and passages was consistent (p= 1.00), indicating that if there was

an effect of decreased working memory as a global-level function throughout these tasks,

performance results would have indicated an unequal effect of these three tests on

performance results. Knowing that the randomization of samples was equal throughout

the task, the decreased performance scores seen in fast tempo condition must be isolated

and did not adversely affect future performance in adjoining passages. Therefore, if

working memory was being decreased throughout the task, we would see a gradual

decline in performance across subsequent passages. With this, we see that an argument

can be made that this decreased performance came as a result of attentional alteration that

was set upon the participant via the fast tempo condition, and did not act as a sort of

contagion that would effect future performance and more global-level functions in the

participant. It is therefore necessary to examine the emotions expressed in these fast

conditions to describe this temporary loss of performance capacity. As the expressions of

joy, fear and contempt differ in this condition, it is not unreasonable to suggest that they

may contribute to the temporary attentional shift that is generating this loss of cognitive

capacity. This condition, combined with increased Galvanic Skin Responses (.74) of

greater intensity (8.08), can lead us to suggest that an ‘unstable’ psycho-emotional state

temporarily besets the participant that results in these fast tempo conditions. These

expressions contribute to a general increase in stimulation and what could colloquially be

described as being ‘on edge’ about the future, to suggest that maybe they do not believe

that they have the capability to perform given this state that they may be in.

The contrasting nature of the performance results between the fast (M = 3.61) and

slow (M = 4.04) tempo conditions suggests that condition is indeed playing a role in

participant performance, but the contrasting effect of the emotional expressions suggests

that the slow condition is not eliciting emotional expressions that are significantly

different from other conditions. This could suggest that there is something particularly

salient about the presentation of fast tempo music that is eliciting change on a

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performative and emotional level. The expressions of emotions and physiological

responses may be elicited via the fast tempo to decrease performance, but they may be

absent or function ‘differently’ to encourage performance in the slow tempo condition.

These differences in the appraisal systems speak to the plastic nature of affect and the

role that they can have of measurable output and performance. The end product of these

internal forces are the measurable impact on cognitive processing, decision making, and

ultimately learning results. By breaking down the flow from stimulation through results,

it may be possible to continue developing more elaborate, and no doubt complex, ways to

articulate these processes within learning.

Chapter 6 Conclusions

6.1 Significance of this study

Over the course of data collection, analysis and writing this dissertation, this process has

informed how I think about the relationship between emotions, learning and music. This

study was about moving into the unknown and applying the soundest analytic methods to

explore how music effects humans in complex and ever-changing ways. The findings of

this study contribute a small piece towards our collective understanding of the mind and

how emotions work to modulate our response to the world around us. Throughout the

course of data collection and analysis, may implications to this study became evident, as

well as how I personally saw this study taking shape.

As both a ‘large’, yet ‘small’ study within the broader definition of education

research, this study came into being to add to our continual search to discover truth and

develop understanding within research. By working to measure the psycho-emotional

and psychophysiological dimensions to the learning experience, this study adds a great

degree of depth to helping describe the embodied experience that processes like learning

are meant to be. The shaping of knowledge, skills, and most importantly, attitudes,

defines the human learning experience. The major findings from this study suggest that

changes to the speed of background music do indeed have a measurable effect on reading

comprehension performance, as well as our expressed emotional regulation and

physiological response, that can be detrimental to performance. These results not only

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support existing theories in the field but add to our theoretical use of technological-

supported measurement designs to help identify multimodal effects on human cognitive

performance. While these findings are significant and warrant further discussion, the

most important addition they make to our field is the understanding that through the

measurement of the embodied experience of music, we can begin understanding how to

regulate emotional response in learning.

In this next section, the significance of this study will be revisited to outline the

various implications and effects this study contributes to. This will be done by analyzing

these effect and contributions for varying groups of education and music cognition

researchers, as well as developing considerations for practitioners in classroom settings.

After this, some limitations to the study will be outlined, considerations and areas of

future study will be made, as well as concluding statements regarding this project.

6.2 Implications of Research

6.2.1 Implications for Music Cognition

The application of musical stimuli in this study reinforces what various researchers have

noticed about the perceptual effects of tempo on cognitive performance. This study

provides real-time observations as to the expressions of music and an insight into the

relationship between felt and expressed emotions in music (Evans & Schubert, 2008;

Gabrielsson, 2002; Juslin & Sloboda, 2011). Although not definitive, the application of

real-time emotion expression software provides music cognition researchers into how we

can continue to explore the relationship between the intrinsic desire to feel music, and

how those feeling may be transformed into externalized expressions of musical affect.

These combined with psychophysiological measurements indicate that alterations to

tempo in the music we listen to have a combined emotional and psychophysiological

impact on human response, not just working through a singular modal dimension. This

work can suggest that there are more complex cognitive processes that involve the

appraisal, processing and judgement of musical expressions. These expressions may go

beyond discrete (Eerola & Vuoskoski, 2011) and dimensional (Thayer, 1991; Vieillard et

al., 2008) models of how affect arises and is generated through music. What is still

necessary to explore are the idiosyncrasies of how the interaction of these systems leads

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to affective states and the expression of music. More importantly, what comes first: the

registering of a musical emotion or the psychophysiological changes that typify it?

While these findings suggest that we re-examine the impact of interrelated

systems on the psycho-musical experience, they also suggest that there is continued

exploration into the nature of expression in music. Perception and feeling, as indicated

previously, are two-dimensions that researcher have regarded the musical experience as

having the music significant effect through. The continued exploration into expressions

via musical cognition (Cunningham, Boykin, & Allen, 2017) presents a continued

venture into discovering the ways that music works through expressive systems to

manifest itself in the external world. As the tools to measure expression become more

readily available, a more complex understanding of expression and how valuable it may

be to comprehending music’s value may be. Perhaps more importantly, the music

cognition literature that helped spawn this current study is contributing to the

advancement of the larger and broader world of educational psychology. Moving this

music cognition literature out of the proverbial ‘basement’ where they do not have the

necessary connection to larger and more complex connections to societal needs, and into

a place where music cognition can indeed be an invaluable part of learning instruction is

a major move forward for the field. By helping to hybridize music cognition with

educational psychology through this type of work, researchers on both sides can help

bridge the gap and find increased value of how each corpus can learn off the other.

6.2.2 Implications for Education and Learning Science

The first series of implications that can be drawn from this study pertain to learning

sciences and education research. The findings suggest that education researchers should

continue to explore a broad range of emotions that may be indicative of performance-

states while learning. Current literature trends in literature have been keenly interested on

epistemic emotions (Brun, Doğuoğlu, & Kuenzle, 2008; Chevrier et al., 2019) that relate

to object-focused generation of emotions that relate to understanding how learners

approach complex questions about learning and questioning. In this regard, literature has

focused on how these emotions, such as curiosity and confusion, help facilitate higher-

order thought processes and interconnected knowledge patterns (Muis et al., 2015),

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including the modulating effects of confusion (D’Mello et al., 2014) on performance.

These ratings systems and scales (Pekrun et al., 2017) have largely centered around a

limited series of emotions in order to describe how these emotions impact the regulatory

mechanisms learning to various forms of cognitive dissonance. The findings of this study

can perhaps suggest that the ‘palate’ of epistemic emotions may reach further back to

describe more primitive emotions (Brun, Doğuoğlu, & Kuenzle, 2008) that may also

have an overlooked role in how we perform and regulate our emotional state. The

reassertion that emotions such as joy and fear play an evolutionary, as well as cognitively

engaging emotions, stand to suggest that they too have a reaching effect into the learning

process with some measurable effect on outcomes. With this said, there is still work to be

done to discover the nature of epistemological emotions. Firstly, this study helps to

provide weight to claims from within the field, to the necessity of multimodal data

streams to help carve and define the process by which learners come and enter a state of

affective performance. Although the results of this study are far from definitive, they

provide researchers with suggestions for a path to help explore the interrelationship

between emotional expression and performance states. Secondly, researchers can hope to

redefine the nature of how they see expressed emotions functioning within a learning

setting. Although research has validated the use of emotion recognition software, it is a

reminder that quantifying these qualitative states through labels should be seen as broad

affectively valenced categories, and not concrete descriptors of emotion. The existence of

expressed emotions within real-time measurement technologies should act as markers

along our collective research to understand affect, not to act as definitive benchmarks for

how emotions will work concretely in every learning application.

The second area that these results speak to are the implication for the role of

attentional modification in learning. The findings of this study can suggest that

attentional modification as a result of musical stimulation can temporarily alter emotional

expressions and Executive Function resources, leading to a decreased cognitive

performance capacity. While these results continue to describe the critical role that

attention modifications play in the learning process (Gottlieb, 2012; Le Pelley et al.,

2016; Rusch, Korn, & Gläscher, 2017) these findings open several questions as to how

attention modification interacts with the embodied expressions of learners. Firstly, what

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relationship do expressed emotions and attentional capacity have with each other; and

more importantly, how does musical stimulation interact with this process? Findings

from this study suggest that activating emotional appraisals may responsible for

negatively affecting attentional processes beyond just the valancing (good or bad labeling

of expressions) of emotions. It could be the arousal dimension (if one uses Russell,

1980) that could be responsible this decrease in performance capacity, perhaps in equal

or perhaps greater effect that the valancing of an emotion. Continued work in examining

how researchers interpret these emotional labels, as well as the process by which we are

interpreting the interrelationship between emotional activation and attentional capacity in

achievement settings.

The broader theme that these results speak to is the field of emotion regulation

and learning. Not only can the present study’s findings indicate how emotions, and

performance as a byproduct, modulate through stimulated states, but they can infer a

degree of regulatory capability that music may be capable of. The literature regarding

techniques and theories of emotion regulation (Jarrell & Lajoie, 2017) articulate the

necessity for new innovations and developments in the implementation of new strategies

to enable higher metacognitive function, leading to improved performance. Whether

through EPM (Gross, 2015b) as learners experience Valuation of emotional antecedents,

CVT’s (Pekrun & Perry, 2014) focus on learner appraisal of the circumstances of

learning, PARE (Tyson, Linnenbrick-Garcia, & Hill, 2009) with a focus on ecological

adaptation, emotion regulation theories/models has described the process of regulating

learner emotions through the integration of personal judgements and environmental cues.

These combined with the need within the literature to begin examining emotion

regulation through analyzing multimodal data (Azevedo & Gasevic, 2019; Greco et al.,

2014; Villaneuva et al., 2018), incorporating integrated emotional and

psychophysiological data streams adds to the depth of understanding that is being built to

uncover the embodied experience of emotional responses while learning. The

introduction of emotionally rich media, like what we have seen in music, offers a new

avenue to describe how learners come to both process environmental stimuli, as well as

how they make appraisals of their learning circumstances through a tool like music.

Within the broader focus of educational psychology and emotion regulation studies, it is

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my suggest that we collectively start exploring the nuances of music, as well as other

affective media, as well continue to explore and develop theories for regulation analysis.

The findings of this present study also reinforce the continued need to expand and

explore our collective understanding between the accepted and assumed relationships

between emotional expressions and their psychophysiological components (Harley et al.,

2015; Harley, Jarrell, & Lajoie, 2019) during learning tasks. Although the results of this

present study indicate corroborating findings between emotional and affective systems

dependent on condition, more work is necessary to continue exploring how these two

systems interact and the type of causal (if any) relationships exist within the learner’s

mind via musical stimulation. Although these results are not definitive in describing the

long-term application and value of music in all learning settings, they provide insights

and crucial first steps towards understanding how music modulates emotion and helps

describe performance.

Finally, the issue of what to learn and how to go about doing it must be discussed.

As mentioned within Chapter 2.3, the search for understanding as the highest point of

learning must be kept in mind as we find new ways to optimize the learning environment

and tools that are available to educators. Helping learners advance their understanding

through optimizing the media selections and how to affectively engage the learner at all

levels of their knowledge-journey necessitates the exploration of how those tools

effectively accomplish their task. The results of this present study provide information

regarding the affective states associated with comprehension and the building block

towards understanding. Without those valuable initial steps, more complex knowledge

cannot be built. It is the goal of future study to uncover how music and emotion

regulation can work in tandem to develop more complex states that help learners to

develop these complex affective networks. While these next few steps are in the distance,

the impact that this study offers will help collectively advance teaching towards a

direction that we wish to move in.

6.3 Limitations

The objective of this study was to deconstruct the application of music and analyze its

effect on a combined psycho-emotional and psychophysiological level. Given this desire

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to singularly focus on the effect of tempo and its role on music perception and cognition,

there are several limitations to this study. As examined in Chapter 2.4, the multiple

components of music including melody, mode (key of music), dynamics & articulation,

form, timbre (the quality of sound) all influence the perception of music. In this regard,

more quantitative analysis of how these components of background music are needed in

isolated, lab studies to develop theories for how these affective patterns function. In the

same regard, there should be a limitation to lab studies. Given the nature of this study as

both an exploration of a multimodal method as well as the limitations in scope of a

dissertation, more work is needed in applying background music in naturalistic settings.

In order to isolate the response patterns and to ensure a predictable testing environment

for data collection, a lab setting was selected as the most favorable environment.

Knowing the limitations of this environment, findings a way to study and replicate these

findings within the noise, unpredictable environment of the classroom is a necessary next

step. Perhaps these dynamic and less uniform situations that learners find themselves in

will provide new data towards the response patterns and considerations for application.

A construct that was not explored within this study was learner-selected music.

The musical selection that was used for this study was a piece of Western, Classical

music that falls within tradition canon for style as well as application within existing

literature. Due to the limitations of avoiding the testing of a new stimulus, a choice was

made early on to limit this selection to a single piece. Based on this limitation, it is

necessary to explore how users select background music for personal use. Not only the

selection of genre, but also how that music is being selected, at what state in the learning

process, as well as how learners develop perceptual habits on using music in learning

settings.

Recalling the discussion on the nature of understanding versus comprehension in

Chapter 2.3, a limitation of this study was its reliance on a multiple-choice measure.

Differentiation between ‘understanding’ and ‘comprehension’ was made in order to help

frame this study. Comprehension is the most primitive level in order to build

understanding within the learning. The decision to use a comprehension measure was

done for expediency in order to create a dichotomous system to gauge performance. This

was perfectly acceptable given the use of these measures in existing literature, yet to

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understand more complex nuances of response while learning, a more open-ended

measure may be needed in the future.

A final limitation that is worthy of discussion revolves around the broader impact

of emotion recognition and the language used to define this terminology. As has been

exhibited through literature and results of this study, the exacting definitions applied to

emotion labels are rather imprecise and far reaching in scope. To accurately define the

psycho-emotional and physiological conditions that are definitive of a particular emotion,

let alone their function and context are complex and necessitate greater research. As

Gratch and Marsella indicated, “the specific definition of emotional terms such as ‘‘joy’’

or ‘‘fear’’ are less important than the processes that underlie them (2004, p. 272). The

labels that are applied to emotions should be view with the proverbial “grain of salt” to

avoid reliance of the application of arbitrary titles for these complex phenomena in the

human mind. Given the use of automated facial emotion recognition technology in the

form of iMotions’ FACET, AFFDEX, Noldus’ FaceReader, as well as Microsoft’s

Openface, while accurate, these systems should be viewed correctly with a degree of

skepticism as to how the recognition and extract of these emotional components are

being computed. Not only are developmental challenges faced through the automation of

these processes, but there is still work to be done one the back-end of development to

help improve the contextual cues of these technologies to improve how they can tune

themselves to smaller changes in affect that are more challenging to notice (Yitzhak et al.,

2017). While these technologies are invaluable in helping researchers maintain

naturalistic observations without breaking participant engagement, these technologies are

not infallible and require keen human observation to contextualize the data extracted

form them. The value that they possess for the researcher should be tempered with

correlating these ‘emotional valences’ with other measures to help locate these affects

within more wholistic appraisals of emotion. While this is not a limitation to this study,

the application of facial emotion recognition technology is in a developmental state and

improvement to the software, the recognition and study of how the affects are correlated,

will help to improve the ecological validity of future work.

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6.4 Areas for Future Research

These results of this study provide numerous avenues for future study. An one may

imagine, a dissertation of merely the ‘Opus. 1’ of a career in research and provides the

point of departure for more extensive work and future contributions. As a result, there are

a few focused, yet broad, areas of interest that are worthy of future study. The first of

these is regarding the nature of musical stimuli. Future study is needed to further

understand how the nature of both the expressed and felt ‘music ingredients’ of pitch,

timbre, form, dynamic, and mode play a role in perception and modulation of emotions

within an educational setting. By expanding research and understanding the ways with

these musical components alter affect, researchers, practitioners and composers can have

a more comprehensive understanding of the ways in which these utilitarian features can

be shaped and harnessed to deliver a desired effect to the listener or learner. Not only can

these findings help advance music cognition but understanding how these musical

components interact to offer measurable stimulation will help further our collective value

of music within an educational capacity. Another musical consideration that needs to be

made is to understand how musical preferences function within the application of

background music. While some research have suggested the tendency for background

music to devolve into ‘white noise’ with minimal cognitive benefit to learners (Lehmann

& Seufert, 2017), the element of control over the musical stimulus may hold great impact

when applied to the embodied experience of music while learning. Not only does these

applications of music prove to be challenging for researchers to interpret, but

practitioners must also the initial steps to begin incorporating these findings into a

classroom practice. Future study must make the initiative to begin developing lab-

classroom partnerships to help blend the application of affective media within training

and emotion regulation regimes. Without considerations and practical applicability that

can only be offered though trials, we will inevitably be unable to make definitive

statements regarding how affect can regulate performance in learning settings.

Having control and the nature of the stimulus while learning is a major are of

future study. Exploring the nature of modulating and measuring how and where learners

can exercise active agency in their learning (Pekrun, Götz & Perry, 2005; Pekrun et al.,

2011) can provide further information on how affective states interact with the cognitive

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control mechanisms of the mind. This construct was described in a far less developed

manner during this study but providing more work into developing more accurate and

further articulating how emotions function particularly in real-time settings. Existing

measures (GEMS; Zentner, Grandjean, & Scherer, 2008) have provided to suitable for

helping understand the multiple dimensions for the affective musical experience, yet the

addition of new dimensions of control and facility in these settings requires a new series

of tools to be able to adequately support developing theories of affect.

102

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Appendices

Appendix 1. Informed Consent Letter

Informed Consent and Information Letter Project Title: “The Impact of Tempo on Emotions and Learning” Hello, My name is Matthew Moreno. I am a doctoral student at the University of Toronto, working under the supervision of Dr. Earl Woodruff. I am conducting research as part of my dissertation on the role that music has on learning and performance. Background and Purpose of the Research. This study will look at the emotions experience while engaged in a reading comprehension task and to see how music may alter the emotional profile of the learner during this time. This study will provide the field of affective learning with a more comprehensive understanding of how music may be able to alter emotions of readers and impact the learning process. During this study, you will be asked to fill out 3 short questionnaires. The researcher will present you with a series of short reading passages from the Nelson-Denny Reading Test that will be read through a computer-generated reading system. While these texts are being read, accompanying music will be played. Upon reading the text, you will be asked to complete a short series of questions that accompany each reading task.

While completing this activity, you will be monitored by 3 systems. Firstly, a facial expression monitoring software (FACET) will monitor the emotional expression displayed on your face. The video files will be analyzed within FACET software to code the emotions experienced during the task indicating nine basic emotions (joy, sadness, anger, fear, surprise, disgust, contempt, confusion, and frustration). The second system will be Electrodermal Response (EDR) sensors, also know as Galvanic Skin Response (GSR), and Electroencephalogram (EEG). These sensors will be placed on each ears and the EEG sensor will be placed on the crown of the head in order to monitor brain wave presence during the task. These are both passive sensors and do not offer any feedback. Confidentiality. Your participation in this study is voluntary and consent can be withdrawn at any time without consequence. If you wish to withdraw from the study, any collected data will be destroyed and permanently deleted from computers and other storage devices. All of your information will be kept confidential. Information collected from questionnaires will be anonymous and data collected from in-person sessions will only be viewed by either the principal investigator or the supervisor. All text, audio and video data will be saved in the researcher’s lab computer, which can only be accessed to either the principal investigator or the supervisor. To meet the University of Toronto’s data security and encryption standards, data files will be encrypted using a software that has comprehensive functions to protect and secure the data. All physical data collected will be kept in a locked drawer in our lab and will only be accessible to the researchers listed below. The information collected for this study will be saved for five years before being destroyed and will not be used for any purpose other than informing the current study.

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What are the risks? There are no known risks to participating in this study. The Nelson-Denny Reading Test is a standardized, well tested tool. Music that is present during this trial does not contain any material that may be interpreted as uncomfortable or offensive. If you feel uncomfortable at any time, you are free to withdraw consent to participate, without any penalty.

During the study, you will be connected to two biometric measurement devices. Facial recognition data will track the facio-muscular movements of participants to produce scores for emotions and facial action-units (AUs). Biometric measures including (EDR) and electroencephalogram (EEG) will collect data on dermal response and brain activity. These sensor attachments (on hand and scalp) do not pose any immediate risks and co-we will ensure that sensors are attached in a non-intrusive manner. If you have any questions or concerns regarding your rights as a participant of this study, please contact The University of Toronto, Office of Research Ethics at ethics.review@utoronto.ca or 416-946-3273. The research ethics program may have confidential access to data to help ensure participant protection procedures are followed. What are the benefits? Your participation in this study will help us understand the emotional experience that music has on learners as they work to accomplish a reading task. This work hopes to eventually identify cognitive and emotional profiles of learners and how music engages individuals to become more effective and productive learners. For successful completion of the trial, you will receive a $10 Tim Hortons gift certificate. Should you choose to withdraw prior to the successful completion of the trial, you will not receive the gift certificate.

You are welcome to contact the researchers with any questions that occur to you during the trial. If you have further questions once the interview is completed, you are encouraged to contact the researchers using the contact information given below. Upon completion of this study, I will be offering a summary of the findings to all involved in the study. I, _______________________________________ (name; please print), have read the above information. I freely agree to participate in this study. I understand that I am free to refuse to answer any questions and to withdraw from my participation at any time. I understand that all information collected for the purposes of this study will be kept confidential. ______Consent to participation Participant number (for researcher)______ _________________________ Signature ____________ Date Principal Investigator Matthew Moreno, MEd, OCT, PhD (Candidate) Department of Curriculum, Teaching and Learning OISE, University of Toronto

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647.618.1555 matthew.moreno@mail.utoronto.ca Supervisor Dr. Earl Woodruff Department of Applied Psychology and Human Development (APHD) OISE, University of Toronto 416.978.1068 earl.woodruff@utoronto.ca

Appendix 2. Demographic Survey

Please complete this short demographic questionnaire to the best of your ability. You

may skip questions you do not wish to respond to.

1) How old are you at the time of this study?

i) 16

ii) 17

iii) 18

iv) 19

v) 20 or older

2) What is your Sex?

i) Male

ii) Female

iii) Other

3) How many people currently live in your household?

i) 1

ii) 2-3

iii) 4-5

iv) 6 or more

4) What is the highest level of education that you have attained?

i) High school diploma

ii) College diploma

iii) Undergraduate Degree

iv) Other

5) Have you been diagnosed with a learning disability?

i) No

ii) Yes

6) Have you been diagnosed with a condition across the autism spectrum?

i) No

ii) Yes

7) Have you taken music lesson or received instruction in voice or an instrument?

i) No, I have never taken any instruction

ii) Yes, but I no longer take any instruction

iii) Yes, I am currently receiving instruction

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8) How many days per week do you listen to music (of any genre)?

i) I do not listen to music

ii) 1-2

iii) 3-4

iv) 5-6

v) Everyday

Appendix 3. Gold-MSI

MU¨LLENSIEFEN, D., GINGRAS, B., STEWART, L., & MUSIL, J. (2012).

Goldsmiths Musical Sophistication Index (Gold-MSI): Technical report and

documentation [Technical report]. London, UK: Goldsmiths, University of London.

1. I spend a lot of my free time doing music-related activities.

2. I sometimes choose music that can trigger shivers down my spine.

3. I enjoy writing about music, for example on blogs and forums.

4. If somebody starts singing a song I don’t know, I can usually join in.

5. I am able to judge whether someone is a good singer or not.

6. I usually know when I’m hearing a song for the first time.

7. I can sing or play music from memory.

8. I’m intrigued by musical styles I’m not familiar with and want to find out more.

9. Pieces of music rarely evoke emotions for me.

10. I am able to hit the right notes when I sing along with a recording

11. I find it difficult to spot mistakes in a performance of a song even if I know the tune.

12. I can compare and discuss differences between two performances or versions of the

same piece of music.

13. I have trouble recognizing a familiar song when played in a different way or by a

different performer.

14. I have never been complimented for my talents as a musical performer.

15. I often read or search the internet for things related to music.

16. I often pick certain music to motivate or excite me.

17. I am not able to sing in harmony when somebody is singing a familiar tune.

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18. I can tell when people sing or play out of time with the beat.

19. I am able to identify what is special about a given musical piece.

20. I am able to talk about the emotions that a piece of music evokes for me.

21. I don’t spend much of my disposable income on music.

22. I can tell when people sing or play out of tune.

23. When I sing, I have no idea whether I’m in tune or not.

24. Music is kind of an addiction for me - I couldn’t live without it.

25. I don’t like singing in public because I’m afraid that I would sing wrong notes

26. When I hear a piece of music I can usually identify its genre.

27. I would not consider myself a musician.

28. I keep track of new music that I come across (e.g. new artists or recordings).

29. After hearing a new song two or three times, I can usually sing it by myself.

30. I only need to hear a new tune once and I can sing it back hours later.

31. Music can evoke my memories of past people and places.

Please circle the most appropriate category: 1 Completely Disagree 2 Strongly Disagree

3 Disagree 4 Neither Agree nor Disagree 5 Agree 6 Strongly Agree 7 Completely Agree

Appendix 4. Nelson Denny H

Passage 2

Many insects communicate through sound. Male crickets use sound to attract

females and to warn other males away from their territories. They rub a scraper on one

forewing against a vein on the other forewing to produce chirping sounds. Each cricket

species produces several calls that differ from those of other cricket species. In fact,

because many species look similar, entomologists often use the calls to identify the

species. Mosquitoes depend on sound, too. Males that are ready to mate home in on the

buzzing sounds produced by females. The male senses this buzzing by means of tiny

hairs on their antennae, which vibrate only to the frequency emitted by a female of the

same species.

Insects may also communicate by tapping, rubbing, or signaling. Fireflies use

flashes of light to find mates. Each species of firefly has its own pattern of flashes. Males

emit flashes in flight, and females flash back in response. This behavior allows male

fireflies to locate a mate of the proper species. However, they must beware of female

fireflies from the genus Photuris, which can mimic the flashes of other species. If a male

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of a different species responds to the flashes of the Photuris female and attempts to mate,

the female devours him. This is surely one of the more unusual behavioral adaptations in

the enormously successful world of insects.

When male fireflies emit flashes,

1. Female fireflies ignore them.

2. They become fatigued within one hour.

3. Other insects fly away immediately.

4. Female fireflies flash back to them.

5. They exhaust their food supply.

Male mosquitoes use the buzzing sound produced by females to

1. Locate food.

2. Locate water.

3. Identify a mate.

4. Accompany their “songs.”

5. Drown out their “songs.”

Male crickets use sound to

1. Call other males.

2. Frighten off females.

3. Corral their offspring.

4. Confuse their predators.

5. Attract their mates.

Fireflies of the genus Photuris can

1. Be easily caught.

2. Be impostors.

3. Grow unusually large.

4. Flash brighter than other fireflies.

5. Be found in all climates.

In the phrase “home in on the buzzing sounds,” home means

1. Travel.

2. House.

3. Listen.

4. Focus.

5. Join.

Passage 3

Gwendolyn Brooks was born in Topeka, Kansas, but grew up in Chicago, Illinois,

the setting for much of her writing. Her love of poetry began early. At the age of seven,

she “began to put rhymes together,” and when she was thirteen, one of her poems was

published in a children’s magazine. During her teens she contributed more than seventy-

five poems to a Chicago newspaper. In 1941 she began attending classes in poetry

writing at the South Side Community Arts Center, and several years later her poems

began appearing in Poetry and other magazines. Her first collection of poems, A Street in

Bronzeville, was published in 1945. Four years later, Annie Allen, her second collection,

appeared. Called “essentially a novel,” it is divided into three parts- “Notes from the

Childhood and the Girlhood,” “The Anniad,” and “The Womanhood”- and tells the story

of Annie’s life. Brooks has also published a novel, Maud Martha (1953), about a young

black girl growing up in Chicago.

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In 1950 Brooks was awarded the Pulitzer Prize for Annie Allen. She has received

a number of other awards and honors, including several Poetry Workshop Awards of the

Midwest Writers’ Conference, two Guggenheim fellowships, an award from the

American Academy of Arts and Letters, and the Eunice Tietjens Memorial Award given

by Poetry magazine.

During her teen years, which of the following published Brooks's works?

1. a Chicago newspaper

2. a Topeka newspaper

3. the Southside Community Arts Center

4. The Anniad

5. Guggenheims

One would assume that Brooks

1. published her poetry.

2. taught her children to write poetry.

3. found writing poetry drudgery.

4. enjoyed poetry immensely.

5. wrote poetry primarily for income.

Brooks published Annie Allen in

1. 1941

2. 1945

3. 1949

4. 1950

5. 1953

The passage is primarily

1. humorous.

2. entertaining.

3. evaluative.

4. persuasive.

5. informational.

This selection is best described as

1. historical.

2. literary.

3. scientific.

4. biographical.

5. fictional.

Passage 4

One of Jung’s best-known contributions in his personality typology of two basic

attitudes, or orientations, toward life: extraversion and introversion. Both orientations are

viewed as existing simultaneously in each person, with one usually dominant. The

extravert’s energy is directed toward external objects and events, while the introvert is

more concerned with inner experiences. The extravert is outgoing and makes friends

easily; the introvert frequently prefers solitude and cultivates few friendships. There is a

substantial amount of empirical evidence indicating that extraversion-introversion is

indeed a significant personality dimension. For example, in anxiety-provoking situations,

there is evidence that extraverts are much more likely to choose to be with other people

than to be alone.

133

Although Jung’s distinction between extraversion and introversion has been

confirmed, most investigators now view extraversion-introversion as a single personality

dimension along which people vary, in contrast to Jung’s conception of a pair of

opposing attitudes. For Jung, these attitudes exist simultaneously and in opposition, even

though one may dominate the other. When there is exaggerated activity in the service of

one attitude (say, when an extravert has spent several days and evenings in social

activity), then, Jung believed, psychological activities will occur that are directed toward

achieving balance.

An extravert was said to

1. gravitate toward an executive position.

2. make friends easily.

3. assume leadership roles.

4. live a happier married life.

5. be more intelligent.

The concept of extraversion and introversion was one of Jung’s

1. earliest contributions.

2. most controversial contributions.

3. most widely known contributions.

4. most recent contributions.

5. most widely accepted contributions.

You would infer that extraverts would most likely be

1. speakers.

2. listeners.

3. readers.

4. writers.

5. researchers.

Jung believed that exaggerated activity in socializing would

1. lead to boredom.

2. lead to moves to achieve balance.

3. heighten interest.

4. result in irritation.

5. overstimulate.

You would infer that introverts would most likely be

1. merchants.

2. salespeople.

3. executives.

4. speakers.

5. writers.

Passage 5

A compound is a substance that is made up of two or more elements, chemically

combined in a definite proportion by mass or weight. Unlike mixtures, compounds have

a definite composition. Water, for instance, is made up of hydrogen and oxygen in the

ratio of 11.1% hydrogen to 88.9% oxygen by weight. No matter what the source of the

water, it is always composed of hydrogen and oxygen in this ratio. This idea, that every

compound is composed of elements in a certain fixed proportion, is called the Law of

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Definite Composition (or the Law of Definite Proportions). It was first proposed by

French chemist Joseph Proust in about 1800.

The properties of a compound need not be similar to the properties of the

elements that compose it. For example, water is a liquid, whereas hydrogen and oxygen

are both gases. When two or more elements form a compound, they truly form a new

substance.

Compounds can be broken apart into elements only by chemical means- unlike

mixtures, which can be broken down by physical means. More than 6 million compounds

have been reported to date, and millions more may be discovered. Some compounds you

may be familiar with are sodium chloride (table salt) and sucrose (cane sugar).

The ratio of hydrogen to oxygen in water is

1. 25.8 to 74.2.

2. 11.1 to 88.9.

3. 10.5 to 89.5.

4. 37 to 70.

5. 40 to 60.

A compound is made up of

1. two elements.

2. any number of substances.

3. two of three substances.

4. fewer than six elements.

5. two or more elements.

The author’s purpose is to

1. illustrate.

2. persuade.

3. interpret.

4. discuss.

5. inform.

You would judge this selection is most likely from

1. an introductory text on chemistry.

2. a technical summary.

3. an advanced chemistry text.

4. a dietary report.

5. a popular magazine article.

How many compounds have been reported to date?

1. 3 million

2. 5 million

3. 6 million

4. More than 6 million

5. Number not given

Passage 6

Soil conservationists help farmers and other land owners to develop plans for

conserving soil and water. Their assistance is of great value in planning land use. Soil

conservationists may do detailed mapping of an area to record its soil, water, and

vegetation. They recommend methods of preventing further soil erosion and stabilizing

runoff. They frequently have to evaluate different plans in terms of cost and

135

effectiveness. Once a conservation program has been decided upon, the soil

conservationist gives the land manager continuing assistance in carrying out the program,

and helps in solving any problems that may arise.

Most soil conservationists are employed by government agencies at the federal,

state or provincial, or local level. There is increasing employment of soil conservationists

by private industry as land-use planning grows in importance. A bachelor’s degree in soil

science or a related field is the minimum education required for work as a soil

conservationist.

Social conservationists should enjoy working outdoors, since a large part of their

work is done in the field. The ability to write good reports is useful. In addition, a soil

conservationist should be friendly and tactful and like helping others.

According to the passage, soil conservationists should

1. have excellent math skills.

2. have good library skills.

3. be able to get along well with people.

4. be able to get along well with animals.

5. be able to prevent soil pollution.

Apparently soil conservationists are primarily concerned with

1. soil erosion.

2. stabilizing crop rotation.

3. stabilizing land transfers.

4. financing farm expansion.

5. financing agricultural education.

Conservationists contribute directly to the

1. destruction of the usable land.

2. preservation of our environment.

3. distribution of residential land.

4. distribution of industrial land.

5. management of preservation of residential land.

One could assume that soil conservationists

1. work only with farmers.

2. work only with land owners.

3. work for government agencies exclusively.

4. assist chemical companies.

5. assist land owners and farmers.

Soil conservationists work primarily for

1. farmers.

2. government agencies.

3. agriculture states.

4. chemical companies.

5. universities.

Passage 7

Symbols can be classified as being referential (concrete) or expressive (abstract).

Referential symbols are those which denote or refer to real objects in the external world.

The word table is a referential symbol: it refers to an object or a class of objects whose

existence in the external world can be verified. If someone asks you what a table is, you

136

can simply point to a table. Expressive symbols, on the other hand, refer to objects or

events that cannot be verified in the external world. The meanings they convey are often

emotional and highly personal. The word God is an expressive symbol. To some it may

evoke feelings of love and fellowship; to others it may evoke fear; to still others it may

carry no particular emotional meaning. Some symbols, of course, are both referential and

expressive. The word father, for example, refers to a male parent; however, to any

particular person it may express authority, understanding, love, discipline, or knowledge.

Expressive symbols are particularly important to culture because they contribute

to social cohesion. This is most obvious in the performance of ritual (a series of symbolic

acts that are repeated on ceremonial occasions). Through ritual we affirm our group

membership.

Rituals were said to be used

1. at committee meetings.

2. in educational settings.

3. on ceremonial occasions.

4. at business conventions.

5. during social get-togethers.

Expressive symbols are

1. concrete.

2. experiential.

3. abstract.

4. educational.

5. ritualistic.

The word ‘TV’ should be classified as what kind of symbol?

1. referential

2. expressive

3. universal

4. American

5. common

The word ‘love’ should be classified as what kind of symbol?

1. referential

2. emotional

3. universal

4. expressive

5. common

The word ‘grandmother’ should be classified as what kind of symbol?

1. referential

2. expressive

3. common

4. universal

5. both referential and expressive

137

Appendix 5. Wolfe Post-Task Questions

Question Response Scale 1 Scale 7

1 Did the musical selection interfere with your reading?

It very much did

It very much did not

2 How much did you like the musical selection that was played?

Dislike very much

Like very much

3 How often do you listen to music while working/studying?

Never Regularly

4 I performed better on my tasks when I had music

Strongly disagree

Strongly agree

5 I find listening to music while working/studying to be distracting

Strongly disagree

Strongly agree

6 Do you enjoy listening to music while working/studying?

I very much do not

I very much do

7 I prefer listening to fast music while working/studying

Strongly disagree

Strongly agree

8 I do not prefer listening to fast music while working/studying

Strongly disagree

Strongly agree

9 I performed better on tasks when I was listening to slow music

Strongly disagree

Strongly agree

10 I performed poorly on tasks when I was listening to fast music

Strongly disagree

Strongly agree

138

Appendix 6. Recruitment Ad

139

Appendix 7. Recruitment Email Message

Emotion and Music Study

Hello, My name is Matthew Moreno. I am a doctoral candidate at the Ontario Institute for Studies in Education/University of Toronto, working under the supervision of Dr. Earl Woodruff. I am conducting this research as part of my dissertation to explore the role that music has on how we learn and the emotions that surround that process. Please read the attached recruitment flyer to learn more about this cutting-edge research. This study is open in students who are:

1. 18 years or older 2. Enrolled as a 1st year undergraduate student 3. Speak and read English 4. Willing to attend an in-person trial that takes approximately 30

minutes at a time that is convenient for you.

For your successful participation, you will be given a $10 Tim Hortons gift card.

Thank you and I hope to hear from you,

Matthew Moreno matthew.moreno@mail.utoronto.ca

Appendix 8. Pairwise Comparisons for the Effect of Passage and Condition

(I) Passage*

Condition of the

trial

(J) Passage*

Condition of the

trial

MD

(I-J)

Std.

Error df

Bonferroni

Sig.

95% CI

[LL,

UL]

[Passage=2]* no

music

[Passage=2]*

slow music -0.30 0.37 1 1.00

-1.62,

1.02

[Passage=2]*fast

music 0.27 0.33 1 1.00

-0.93,

1.47

[Passage=3]*no

music 0.33 0.44 1 1.00

-1.25,

1.91

[Passage=3]*slow

music -0.09 0.33 1 1.00

-1.28,

1.09

140

[Passage=3]*fast

music 0.44 0.30 1 1.00

-0.63,

1.51

[Passage=4]*no

music -0.02 0.36 1 1.00

-1.33,

1.28

[Passage=4]*slow

music -0.23 0.31 1 1.00

-1.35,

0.88

[Passage=4]*fast

music -0.07 0.32 1 1.00

-1.20,

1.07

[Passage=5]*no

music -0.16 0.33 1 1.00

-1.33,

1.02

[Passage=5]*slow

music -0.13 0.32 1 1.00

-1.29,

1.04

[Passage=5]*fast

music 0.25 0.39 1 1.00

-1.13,

1.63

[Passage=6]*no

music 0.26 0.35 1 1.00

-1.01,

1.52

[Passage=6]*slow

music -0.08 0.32 1 1.00

-1.22,

1.05

[Passage=6]*fast

music 0.46 0.38 1 1.00

-0.89,

1.81

[Passage=7]*no

music 0.00 0.33 1 1.00

-1.19,

1.19

[Passage=7]*slow

music -0.23 0.33 1 1.00

-1.43,

0.98

[Passage=7]*fast

music 0.05 0.35 1 1.00

-1.19,

1.29

[Passage=2]*

slow music

[Passage=2]*no

music 0.30 0.37 1 1.00

-1.02,

1.62

[Passage=2]*fast

music 0.57 0.28 1 1.00

-0.43,

1.57

[Passage=3]*no

music 0.63 0.41 1 1.00

-0.83,

2.09

[Passage=3]*slow

music 0.21 0.28 1 1.00

-0.78,

1.19

[Passage=3]*fast

music 0.74 0.31 1 1.00

-0.38,

1.86

[Passage=4]*no

music 0.28 0.31 1 1.00

-0.83,

1.39

[Passage=4]*slow

music 0.07 0.27 1 1.00

-0.90,

1.04

[Passage=4]*fast

music 0.24 0.29 1 1.00

-0.79,

1.26

141

[Passage=5]*no

music 0.15 0.28 1 1.00

-0.86,

1.15

[Passage=5]*slow

music 0.18 0.27 1 1.00

-0.80,

1.15

[Passage=5]*fast

music 0.55 0.37 1 1.00

-0.78,

1.88

[Passage=6]*no

music 0.56 0.33 1 1.00

-0.64,

1.76

[Passage=6]*slow

music 0.22 0.28 1 1.00

-0.79,

1.23

[Passage=6]*fast

music 0.76 0.27 1 0.81

-0.22,

1.74

[Passage=7]*no

music 0.31 0.32 1 1.00

-0.86,

1.47

[Passage=7]*slow

music 0.08 0.29 1 1.00

-0.97,

1.12

[Passage=7]*fast

music 0.35 0.32 1 1.00

-0.81,

1.51

[Passage=2]*fast

music

[Passage=2]*no

music -0.27 0.33 1 1.00

-1.47,

0.93

[Passage=2]*slow

music -0.57 0.28 1 1.00

-1.57,

0.43

[Passage=3]*no

music 0.06 0.39 1 1.00

-1.34,

1.46

[Passage=3]*slow

music -0.36 0.21 1 1.00

-1.12,

0.39

[Passage=3]*fast

music 0.17 0.25 1 1.00

-0.71,

1.05

[Passage=4]*no

music -0.29 0.26 1 1.00

-1.21,

0.63

[Passage=4]*slow

music -0.50 0.23 1 1.00

-1.33,

0.33

[Passage=4]*fast

music -0.34 0.24 1 1.00

-1.20,

0.53

[Passage=5]*no

music -0.43 0.20 1 1.00

-1.12,

0.27

[Passage=5]*slow

music -0.39 0.21 1 1.00

-1.16,

0.37

[Passage=5]*fast

music -0.02 0.35 1 1.00

-1.26,

1.23

[Passage=6]*no

music -0.01 0.30 1 1.00

-1.10,

1.08

142

[Passage=6]*slow

music -0.35 0.24 1 1.00

-1.23,

0.52

[Passage=6]*fast

music 0.19 0.29 1 1.00

-0.86,

1.24

[Passage=7]*no

music -0.26 0.27 1 1.00

-1.25,

0.72

[Passage=7]*slow

music -0.49 0.26 1 1.00

-1.44,

0.45

[Passage=7]*fast

music -0.22 0.29 1 1.00

-1.26,

0.82

[Passage=3]*no

music

[Passage=2]*no

music -0.33 0.44 1 1.00

-1.91,

1.25

[Passage=2]*slow

music -0.63 0.41 1 1.00

-2.09,

0.83

[Passage=2]*fast

music -0.06 0.39 1 1.00

-1.46,

1.34

[Passage=3]*slow

music -0.42 0.38 1 1.00

-1.80,

0.95

[Passage=3]*fast

music 0.11 0.39 1 1.00

-1.30,

1.52

[Passage=4]*no

music -0.35 0.41 1 1.00

-1.83,

1.13

[Passage=4]*slow

music -0.56 0.38 1 1.00

-1.93,

0.81

[Passage=4]*fast

music -0.40 0.39 1 1.00

-1.78,

0.99

[Passage=5]*no

music -0.49 0.36 1 1.00

-1.76,

0.79

[Passage=5]*slow

music -0.45 0.39 1 1.00

-1.85,

0.94

[Passage=5]*fast

music -0.08 0.44 1 1.00

-1.65,

1.49

[Passage=6]*no

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1.44

[Passage=6]*slow

music -0.41 0.37 1 1.00

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0.93

[Passage=6]*fast

music 0.13 0.40 1 1.00

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1.57

[Passage=7]*no

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1.22

[Passage=7]*slow

music -0.55 0.34 1 1.00

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0.68

143

[Passage=7]*fast

music -0.28 0.35 1 1.00

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0.97

[Passage=3]*slow

music

[Passage=2]*no

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1.28

[Passage=2]*slow

music -0.21 0.28 1 1.00

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0.78

[Passage=2]*fast

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1.12

[Passage=3]*no

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1.80

[Passage=3]*fast

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1.40

[Passage=4]*no

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0.96

[Passage=4]*slow

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0.73

[Passage=4]*fast

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0.91

[Passage=5]*no

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0.67

[Passage=5]*slow

music -0.03 0.22 1 1.00

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0.74

[Passage=5]*fast

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1.54

[Passage=6]*no

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1.33

[Passage=6]*slow

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0.92

[Passage=6]*fast

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1.49

[Passage=7]*no

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1.09

[Passage=7]*slow

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0.83

[Passage=7]*fast

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1.16

[Passage=3]*fast

music

[Passage=2]*no

music -0.44 0.30 1 1.00

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0.63

[Passage=2]*slow

music -0.74 0.31 1 1.00

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0.38

[Passage=2]*fast

music -0.17 0.25 1 1.00

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0.71

144

[Passage=3]*no

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1.30

[Passage=3]*slow

music -0.54 0.24 1 1.00

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0.32

[Passage=4]*no

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0.57

[Passage=4]*slow

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0.12

[Passage=4]*fast

music -0.51 0.26 1 1.00

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0.42

[Passage=5]*no

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0.16

[Passage=5]*slow

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0.16

[Passage=5]*fast

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1.00

[Passage=6]*no

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0.90

[Passage=6]*slow

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0.31

[Passage=6]*fast

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1.04

[Passage=7]*no

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0.49

[Passage=7]*slow

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0.30

[Passage=7]*fast

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0.58

[Passage=4]*no

music

[Passage=2]*no

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1.33

[Passage=2]*slow

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0.83

[Passage=2]*fast

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1.21

[Passage=3]*no

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1.83

[Passage=3]*slow

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0.81

[Passage=3]*fast

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1.50

[Passage=4]*slow

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0.82

145

[Passage=4]*fast

music -0.04 0.28 1 1.00

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0.97

[Passage=5]*no

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0.82

[Passage=5]*slow

music -0.10 0.26 1 1.00

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0.83

[Passage=5]*fast

music 0.27 0.37 1 1.00

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1.61

[Passage=6]*no

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1.47

[Passage=6]*slow

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0.88

[Passage=6]*fast

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1.54

[Passage=7]*no

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1.03

[Passage=7]*slow

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0.73

[Passage=7]*fast

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1.16

[Passage=4]*slow

music

[Passage=2]*no

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1.35

[Passage=2]*slow

music -0.07 0.27 1 1.00

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0.90

[Passage=2]*fast

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1.33

[Passage=3]*no

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1.93

[Passage=3]*slow

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1.01

[Passage=3]*fast

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1.47

[Passage=4]*no

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1.24

[Passage=4]*fast

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1.08

[Passage=5]*no

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0.81

[Passage=5]*slow

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0.93

[Passage=5]*fast

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1.59

146

[Passage=6]*no

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1.50

[Passage=6]*slow

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0.96

[Passage=6]*fast

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1.75

[Passage=7]*no

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1.29

[Passage=7]*slow

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0.94

[Passage=7]*fast

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[Passage=4]*fast

music

[Passage=2]*no

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1.20

[Passage=2]*slow

music -0.24 0.29 1 1.00

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0.79

[Passage=2]*fast

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1.20

[Passage=3]*no

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1.78

[Passage=3]*slow

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0.85

[Passage=3]*fast

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1.43

[Passage=4]*no

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1.06

[Passage=4]*slow

music -0.17 0.26 1 1.00

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0.75

[Passage=5]*no

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0.71

[Passage=5]*slow

music -0.06 0.23 1 1.00

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0.78

[Passage=5]*fast

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1.58

[Passage=6]*no

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1.43

[Passage=6]*slow

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0.83

[Passage=6]*fast

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-0.57,

1.63

[Passage=7]*no

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-0.97,

1.11

147

[Passage=7]*slow

music -0.16 0.29 1 1.00

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0.90

[Passage=7]*fast

music 0.12 0.28 1 1.00

-0.88,

1.11

[Passage=5]*no

music

[Passage=2]*no

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1.33

[Passage=2]*slow

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0.86

[Passage=2]*fast

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1.12

[Passage=3]*no

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1.76

[Passage=3]*slow

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0.79

[Passage=3]*fast

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1.36

[Passage=4]*no

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1.09

[Passage=4]*slow

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0.66

[Passage=4]*fast

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0.89

[Passage=5]*slow

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0.80

[Passage=5]*fast

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1.63

[Passage=6]*no

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1.32

[Passage=6]*slow

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0.73

[Passage=6]*fast

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1.61

[Passage=7]*no

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1.15

[Passage=7]*slow

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0.87

[Passage=7]*fast

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1.06

[Passage=5]*slow

music

[Passage=2]*no

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-1.04,

1.29

[Passage=2]*slow

music -0.18 0.27 1 1.00

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0.80

148

[Passage=2]*fast

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1.16

[Passage=3]*no

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1.85

[Passage=3]*slow

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0.80

[Passage=3]*fast

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1.29

[Passage=4]*no

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1.04

[Passage=4]*slow

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0.71

[Passage=4]*fast

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0.89

[Passage=5]*no

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0.74

[Passage=5]*fast

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1.60

[Passage=6]*no

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1.46

[Passage=6]*slow

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0.87

[Passage=6]*fast

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1.58

[Passage=7]*no

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1.09

[Passage=7]*slow

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0.87

[Passage=7]*fast

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1.16

[Passage=5]*fast

music

[Passage=2]*no

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1.13

[Passage=2]*slow

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0.78

[Passage=2]*fast

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1.26

[Passage=3]*no

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1.65

[Passage=3]*slow

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0.85

[Passage=3]*fast

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1.38

149

[Passage=4]*no

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1.06

[Passage=4]*slow

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0.63

[Passage=4]*fast

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0.95

[Passage=5]*no

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0.81

[Passage=5]*slow

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0.85

[Passage=6]*no

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1.34

[Passage=6]*slow

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0.83

[Passage=6]*fast

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1.54

[Passage=7]*no

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1.18

[Passage=7]*slow

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0.68

[Passage=7]*fast

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1.18

[Passage=6]*no

music

[Passage=2]*no

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-1.52,

1.01

[Passage=2]*slow

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0.64

[Passage=2]*fast

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1.10

[Passage=3]*no

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-1.44,

1.59

[Passage=3]*slow

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0.63

[Passage=3]*fast

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1.27

[Passage=4]*no

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0.91

[Passage=4]*slow

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0.53

[Passage=4]*fast

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0.79

[Passage=5]*no

music -0.41 0.25 1 1.00

-1.32,

0.50

150

[Passage=5]*slow

music -0.38 0.30 1 1.00

-1.46,

0.70

[Passage=5]*fast

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1.32

[Passage=6]*slow

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0.71

[Passage=6]*fast

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1.42

[Passage=7]*no

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0.99

[Passage=7]*slow

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0.68

[Passage=7]*fast

music -0.21 0.32 1 1.00

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0.95

[Passage=6]*slow

music

[Passage=2]*no

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1.22

[Passage=2]*slow

music -0.22 0.28 1 1.00

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0.79

[Passage=2]*fast

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-0.52,

1.23

[Passage=3]*no

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1.75

[Passage=3]*slow

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0.90

[Passage=3]*fast

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1.36

[Passage=4]*no

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1.00

[Passage=4]*slow

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0.66

[Passage=4]*fast

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0.86

[Passage=5]*no

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0.59

[Passage=5]*slow

music -0.04 0.23 1 1.00

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0.78

[Passage=5]*fast

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-0.83,

1.5

[Passage=6]*no

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-0.71,

1.39

[Passage=6]*fast

music 0.54 0.30 1 1.00

-0.52,

1.61

151

[Passage=7]*no

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-0.95,

1.12

[Passage=7]*slow

music -0.14 0.27 1 1.00

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0.84

[Passage=7]*fast

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0.94

[Passage=6]*fast

music

[Passage=2]*no

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0.89

[Passage=2]*slow

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0.22

[Passage=2]*fast

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0.86

[Passage=3]*no

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-1.57,

1.30

[Passage=3]*slow

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0.38

[Passage=3]*fast

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1.00

[Passage=4]*no

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0.57

[Passage=4]*slow

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0.36

[Passage=4]*fast

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0.57

[Passage=5]*no

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0.37

[Passage=5]*slow

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0.41

[Passage=5]*fast

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1.12

[Passage=6]*no

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1.01

[Passage=6]*slow

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0.52

[Passage=7]*no

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0.75

[Passage=7]*slow

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0.34

[Passage=7]*fast

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-1.65,

0.82

[Passage=7]*no

music

[Passage=2]*no

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-1.19,

1.19

152

[Passage=2]*slow

music -0.31 0.32 1 1.00

-1.47,

0.86

[Passage=2]*fast

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1.25

[Passage=3]*no

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1.87

[Passage=3]*slow

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0.89

[Passage=3]*fast

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1.36

[Passage=4]*no

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0.98

[Passage=4]*slow

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0.82

[Passage=4]*fast

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0.97

[Passage=5]*no

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0.83

[Passage=5]*slow

music -0.13 0.27 1 1.00

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0.83

[Passage=5]*fast

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1.67

[Passage=6]*no

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1.49

[Passage=6]*slow

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0.95

[Passage=6]*fast

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1.67

[Passage=7]*slow

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0.94

[Passage=7]*fast

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1.22

[Passage=7]*slow

music

[Passage=2]*no

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1.43

[Passage=2]*slow

music -0.08 0.29 1 1.00

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0.97

[Passage=2]*fast

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1.44

[Passage=3]*no

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-0.68,

1.79

[Passage=3]*slow

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-0.83,

1.09

153

[Passage=3]*fast

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-0.30,

1.63

[Passage=4]*no

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-0.73,

1.14

[Passage=4]*slow

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0.92

[Passage=4]*fast

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1.22

[Passage=5]*no

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-0.87,

1.01

[Passage=5]*slow

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1.07

[Passage=5]*fast

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1.63

[Passage=6]*no

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-0.68,

1.64

[Passage=6]*slow

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1.12

[Passage=6]*fast

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-0.34,

1.71

[Passage=7]*no

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-0.94,

1.39

[Passage=7]*fast

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-0.87,

1.42

[Passage=7]*fast

music

[Passage=2]*no

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1.19

[Passage=2]*slow

music -0.35 0.32 1 1.00

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0.81

[Passage=2]*fast

music 0.22 0.29 1 1.00

-0.82,

1.26

[Passage=3]*no

music 0.28 0.35 1 1.00

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1.53

[Passage=3]*slow

music -0.14 0.28 1 1.00

-1.16,

0.87

[Passage=3]*fast

music 0.39 0.27 1 1.00

-0.58,

1.37

[Passage=4]*no

music -0.07 0.30 1 1.00

-1.16,

1.02

[Passage=4]*slow

music -0.28 0.25 1 1.00

-1.19,

0.63

154

[Passage=4]*fast

music -0.12 0.28 1 1.00

-1.11,

0.88

[Passage=5]*no

music -0.21 0.24 1 1.00

-1.06,

0.65

[Passage=5]*slow

music -0.17 0.28 1 1.00

-1.16,

0.82

[Passage=5]*fast

music 0.20 0.39 1 1.00

-1.18,

1.59

[Passage=6]*no

music 0.21 0.32 1 1.00

-0.95,

1.37

[Passage=6]*slow

music -0.13 0.23 1 1.00

-0.94,

0.68

[Passage=6]*fast

music 0.41 0.34 1 1.00

-0.82,

1.65

[Passage=7]*no

music -0.04 0.33 1 1.00

-1.22,

1.13

[Passage=7]*slow

music -0.27 0.32 1 1.00

-1.42,

0.87

Pairwise comparisons of estimated marginal means based on the original scale of

dependent variable Score in the reading comprehension task