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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
15
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
16
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
18
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,
19
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
22
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
39
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
40
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 [email protected] 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 [email protected] Supervisor Dr. Earl Woodruff Department of Applied Psychology and Human Development (APHD) OISE, University of Toronto 416.978.1068 [email protected]
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.
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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 [email protected]
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
music -0.07 0.42 1 1.00
-1.59,
1.44
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150
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151
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music
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152
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153
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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