the role of personality and mood in music-use during a high-cognitive demand task

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The Role of Personality and Mood in Music-UseDuring a High-Cognitive Demand TaskAndrew PanayidesUniversity of Miami, [email protected]

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UNIVERSITY OF MIAMI

THE ROLE OF PERSONALITY AND MOOD IN MUSIC-USE DURING A HIGH-COGNITIVE DEMAND TASK

By

Andrew G. Panayides

A THESIS

Submitted to the Faculty of the University of Miami

in partial fulfillment of the requirements for the degree of Master of Music

Coral Gables, Florida

May 2013

UNIVERSITY OF MIAMI

A thesis submitted in partial fulfillment of the requirements for the degree of

Master of Music


Andrew G. Panayides

Approved: ________________________ ________________________ Teresa Lesiuk, Ph.D. M. Brian Blake, Ph.D. Associate Professor, Music Therapy Dean of the Graduate School ________________________ ________________________ Shannon K. de l’Etoile, Ph.D. Carlos Abril, Ph.D. Associate Professor, Music Therapy Associate Professor, Music Education ________________________ Mitsunori Ogihara, Ph.D. Professor, Department of Computer Science

PANAYIDES, ANDREW G. (M.M., Music Therapy)

The Role of Personality and (May 2013) Mood in Music-Use During a High-Cognitive Demand Task Abstract of a thesis at the University of Miami. Thesis supervised by Professor Teresa L. Lesiuk. No. of pages in text. (145)

The purpose of this thesis was to investigate the ways in which individuals use

music during a high-cognitive demand task – computer programming. This thesis also

examined relationships among music-use, personality, and mood. Thirty-four university

students with varying levels of computer programming experience participated in the

study. Initially, participants completed a demographic questionnaire and personality

inventory during an individual meeting with the researcher. The second portion of the

study was completed using a study webpage, in which participants submitted responses to

a mood scale, task assessment, and music-use questionnaire. The mood scale was

completed immediately prior to a computer programming task accompanied by music

listening, and the music-use questionnaire was completed immediately after the task. The

music-use questionnaire consisted of a music-use scale, two open-ended items, and

questions about the listening experience.

Music-use during a computer programming task appears to be a complex process,

being impacted by individual differences and contextual factors. Bivariate correlations

were used to examine relationships between study variables. Results indicated several

significant relationships. First, the personality factor of Openness was positively

correlated with both Cognitive and Emotional-use of music, and the relationship between

Openness and Cognitive-use was supported in a predictive model. No significant

correlations were found between any of the mood and music-use variables. However,

some of the demographic and contextual factors were significantly correlated with music-

use. Computer programming proficiency was positively correlated with Emotional-use

of music. Next, music activity level, listening duration, and music focus were each

positively correlated Cognitive-use of music, while computer programming background

and task difficulty were each negatively correlated with Cognitive-use. An analysis of

variance revealed a significant effect of computer programming background on

Cognitive-use of music.

The themes that emerged in open-ended responses from this study generally

supported the quantitative results obtained. Participant statements typically related to one

of the music-use categories, and the distribution of responses was similar to the

distribution of scores on the music-use scale. In addition to utilizing words related to the

music-use categories, participants employed specific language to describe the type of

music they chose and its influence on overall productivity.

ACKNOWLEDGEMENTS

I would first like to thank the members of my committee for their guidance and

support: Dr. Teresa Lesiuk, Dr. Shannon de l’Etoile, Dr. Mitsunori Ogihara, and Dr.

Carlos Abril. Thanks also to Corinne Huggins, for her ever-gracious assistance with the

research design and data analysis. I would like to extend special thanks to Dr. Teresa

Lesiuk for her patience and compassion throughout the development and completion of

this thesis, and much appreciation as well to Dr. Shannon de l’Etoile for her thoughtful

advice during my entire graduate school experience.

I would also like to thank Sarah Zaharako for volunteering an extra set of eyes to

the editing process, Bob Ladue for sharing with me one of your many talents, web-

design, and Carolyn Dachinger for your mentorship.

Naturally, my mother and father have a great deal to do with this project. Thank

you and much love to you both for imparting in me the values of kindness and dedication,

without stifling my imagination and creativity. Thank you also to my sisters and my

brothers for being totally different, showing me the values of patience and openness.

And thanks as well to my stepmother and her extended family for always welcoming me

and encouraging me. To those I have lost in this life, I feel your foundation beneath me.

I am also grateful for support from a list of companions: Thanks to Jessie and

Shelva for always letting me in when I show up at your house, to Greg for walking next

to me, to Richard for staying alive, to Rajan for calling me, to Nat for packages in the

mail, to Jeff for bicycles, to Brian for taking breaks to watch sports, to Brent for flying

coast-to-coast, and to my roomies, Lika and Ed, for reminding me to eat.

Finally, thank you to the students who volunteered to participate in this study.

iii

TABLE OF CONTENTS

Page

LIST OF FIGURES ..................................................................................................... vi LIST OF TABLES ....................................................................................................... vii Chapter 1 INTRODUCTION ........................................................................................... 1 Statement of the Problem ................................................................................. 1 Definition of Terms.......................................................................................... 4 Need for the Study ........................................................................................... 6 Purpose Statement ............................................................................................ 9 2 REVIEW OF LITERATURE .......................................................................... 10 Music Perception ............................................................................................. 10 Everyday Music-Use ........................................................................................ 12 Affect and Cognition........................................................................................ 16 Computer Programming and High-Cognitive Demand ................................... 21 Personality and Music-Use .............................................................................. 28 The Effect of Music on Affect ......................................................................... 30 The Effect of Personality and Music on Cognition ......................................... 33 Summary of Literature Review ........................................................................ 39 Research Questions .......................................................................................... 43 3 METHOD ........................................................................................................ 44 Participants ....................................................................................................... 44 Design and Variables ....................................................................................... 44 Measures .......................................................................................................... 45 Procedure ......................................................................................................... 52 Data Collection ................................................................................................ 55 Data Analysis ................................................................................................... 55 4 RESULTS ........................................................................................................ 59 Descriptive Results .......................................................................................... 59 Inferential Results ............................................................................................ 70 Content Analyses ............................................................................................. 77

iv

5 DISCUSSION .................................................................................................. 83 Review of the Research Questions .................................................................. 83 Review of the Content Analyses ...................................................................... 91 Limitations of the Study................................................................................... 93 Theoretical Implications .................................................................................. 95 Clinical Implications ........................................................................................ 96 Recommendations for Future Research ........................................................... 99 Summary and Conclusions .............................................................................. 100 REFERENCES ............................................................................................................ 103 APPENDIX A: Demographic Questionnaire............................................................... 111 APPENDIX B: NEO-FFI Instructions & Sample Items .............................................. 113 APPENDIX C: Job Affect Scale.................................................................................. 114 APPENDIX D: Task Assessment ................................................................................ 115 APPENDIX E: Music-Use Questionnaire ................................................................... 117 APPENDIX F: Study Advertisement .......................................................................... 122 APPENDIX G: Informed Consent Form ..................................................................... 123 APPENDIX H: Other Significant Relationships ......................................................... 126 APPENDIX I: Open-Ended Responses to Music-Use Questionnaire ......................... 133 APPENDIX J: Participant Music Selections Reported ................................................ 140

v

LIST OF FIGURES

Page

FIGURE 1: Diagram Depicting Study Variables .................................................... 46 FIGURE 2: Flow Chart Depicting Sequence of Study Measures ........................... 47 FIGURE 3: Pie Chart Depicting Proportions of Participants’ Computer Programming Proficiency ................................................... 64 FIGURE 4: Scatterplots Depicting Correlations between Openness and Cognitive and Emotional-uses of Music ....................................... 72 FIGURE 5: Pie Charts Depicting Results of Uses of Music Inventory and Directed Content Analysis ............................................................ 78

vi

LIST OF TABLES

Page

TABLE 1: Frequency of Participant Demographics ................................................. 61 TABLE 2: Frequency of Participant Programs of Study .......................................... 62 TABLE 3: Frequency of Participant Computer Programming Experience .............. 63 TABLE 4: Frequency of Participant Computer Programming Task Characteristics ................................................................................ 66 TABLE 5: Frequency of Participant Listening Experience Characteristics ............. 67 TABLE 6: t-tests for Personality Factor Sample Means and Typical Means .......... 68 TABLE 7: Means and Standard Deviations for Mood Variables and Subscales ..... 69 TABLE 8: Means and Standard Deviations for Music-Use Variables ..................... 70 TABLE 9: Correlations for Music-Use Categories with Personality Factors........... 71 TABLE 10: Multiple Regression Analysis for Personality Factors Predicting Cognitive-use of Music ......................................................... 73 TABLE 11: Multiple Regression Analysis for Personality Factors Predicting Emotional-use of Music ........................................................ 73 TABLE 12: Correlations for Music-Use Variables with Mood Variables and Subscales ....................................................... 74 TABLE 13: Correlations for Music-Use Variables with Demographics, Computer Programming Task Characteristics, and Listening Experience Variables ....................................................... 75 TABLE 14: Analysis of Variance for Effect of Computer Programming Background on Cognitive-use of Music ................................................. 76 TABLE 15: Frequency of Responses to First Open-Ended Item on Music-Use Questionnaire................................................................... 77 TABLE 16: Frequency of Responses to Second Open-Ended Item on Music-Use Questionnaire................................................................... 77

vii

TABLE 17: Conventional Content Analysis for First Open-Ended Item on Music-Use Questionnaire .......................................................... 80 TABLE 18: Conventional Content Analysis for Second Open-Ended Item on Music-Use Questionnaire .......................................................... 81 TABLE 19: Summative Content Analysis for Open-Ended Items on Music-Use Questionnaire................................................................... 82

viii

Chapter One

Introduction

Many individuals listen to music in conjunction with work tasks. Personal music

libraries are now highly accessible via mobile devices and the internet, and music-use is

more widely accepted in the workplace than in the past. What influences individuals to

use music in these instances, and how exactly is the music being used? Finite answers to

these daunting questions are not yet possible. A body of research must be conducted in a

number of domains before conclusions may be drawn. This study supports such an aim

by supplying data from computer programmers that listen to music while working.

This thesis specifically explores the relationships between music-use, personality,

and mood within the specific context of a computer programming task. By examining

various uses of music and collecting responses from student computer programmers, an

informed observation will be documented concerning the role of music in the workplace.

Such data will be a valuable contribution to the field of computer programming and

similar professions as it has, until now, been rare in literature.

Statement of the Problem

Individuals choose and utilize music for different reasons at different times.

Listening experiences depend on musical, environmental, psychological, and social

factors. Behavior and affect are influenced by these factors, and corresponding music

choices are developed for each listening experience (Cassidy & MacDonald, 2007;

Rentfrow, Goldberg, & Levitin, 2011). Active listening tends to occur during moments

of leisure, when one has the opportunity to focus entirely on the music. Occurring more

frequently, passive listening takes place when one uses music to accompany a

1

2

nonmusical event (Sloboda, 2010). For example, many students and professionals listen

to music as they complete their work. Their personalities, mood, and the nonmusical task

at hand all play a role in how, when, or if they utilize the music within the context of their

work. Listening to music likely plays a role in the quality of work that ensues, but

productivity is often difficult to quantify. Moreover, measurement of productivity is

particularly challenging when evaluating the work of individuals who complete more

cognitively demanding tasks.

Relationships between personality, mood, and context may influence why and

how a particular person makes use of music. Before applied research can assess the

effect of music-use on productivity in high-cognitive demand tasks, basic research must

explore existing relationships between music-use and a list of factors. Contextual forces,

trait inclinations, and state preferences all play a role in music-use (Sloboda & O’Neill,

2001). Contextual forces include situational goals and constraints, such as task

directives, setting, and deadlines. Trait inclinations are reflective of personality,

knowledge, and experience, while state preferences align with a particular mood or

emotion. Recent studies have begun to explore these individual differences and

contextual limitations, and there is a call for more research (Chamorro-Premuzic &

Furnham, 2007; Chamorro-Premuzic, Swami, Furnham, & Maakip, 2009; Lesiuk, Polak,

Stutz, & Hummer, 2011; Rentfrow et al., 2011).

The present-day information age offers new and unique challenges to the

professionals responsible for creating efficient infrastructure for information. They are

asked to provide instant access to knowledge that would have been difficult to conceive

during the previous industrial age. The tasks of computer programmers require them to

3

learn new languages quickly and devise creative solutions under tight deadlines

(Sonnentag, Niessen, & Volmer, 2006; Woszczynski, Guthrie, & Shade, 2005). These

demands place a high amount of stress on computer programmers. For the companies

who employ these individuals, stress can lead to high turnover and absenteeism, low

morale, decreased productivity, workplace conflict, and poor teamwork (Longenecker,

Schaffer, & Scazzero, 1999). For the computer programmers, stress can hinder energy

levels, attitude, health, cooperation, loyalty, and commitment. They may experience

feelings of frustration, anger, aggression, helplessness, mood swings, sleeplessness,

depression, and lack of motivation. Computer programmers must be able to identify

causes of stress and develop problem-solving skills to suppress it. Such skills may be

part of a proactive personal development plan that is often missing from the job

descriptions of information technology professionals. Management and employees must

work together to conceive new solutions to improve the workplace environment,

including positive changes in morale and productivity (Longenecker et al., 1999).

Music-use during computer programming may be one simple method for

improving quality of life at work. Investigating the role of music within the context and

demands of a computer programming task has the potential to reveal the interactions

between personality, mood, and music-use. Computer programmers may have certain

personality traits that allow them to meet the high demands of their work. Their mood

may be reflective of this personality, and together these trait and state characteristics can

influence their music choices at any given time. Additionally, the high-cognitive demand

4

of computer programming tasks may have an anticipatory influence on the incoming

mood of computer programmers, manipulating the way in which these individuals choose

and utilize music to accompany their work.

Definition of Terms

High-cognitive demand. High-cognitive demand is generally defined as a “need

for focus and selective attention to systematic analysis and creative problem solving”

(Lesiuk, 2010b, p.1). Systematic analysis involves translation of information from

various domains into a working unit. Abstract planning, such as pattern recognition, is a

component of cognitive demand. During a high-cognitive demand task, relations must be

identified between domains, and organized connections must be made using domain-

specific knowledge and metacognitive knowledge. Metacognition may be described as

thinking about thinking (Sonnentag et al., 2006). Next, creative problem-solving requires

generation of ideas beyond information given. Pertinent knowledge from past

experiences is retrieved from memory and combined with the given information to form a

cohesive plan (Forgas, 1998; Forgas & George, 2001). According to Piaget's cognitive

development theory, systematic analysis and creative problem-solving fall into the formal

operations level of cognition, which is the highest level one can reach. Formal operations

involve logic, abstract thinking, hypothesis generation, systematic problem-solving, and

mental manipulation (Nairne, 2009a). Because individual cognitive skills exist on a

continuum, the amount of cognitive demand necessary to be deemed 'high' is relative

(Lesiuk, 2010b).

5

Music-use. Music-use describes why, how, when, and where music is used. This

thesis comprises three music-uses, including 1) background or social, 2) cognitive,

intellectual, or rational, and 3) emotional (Chamorro-Premuzic & Furnham, 2007).

Music-use for background and social purposes may be used while working, studying,

socializing, or performing a task. Music-use for cognition may be used in an intellectual

way, including analysis and evaluation. Emotional-use of music involves regulation of

mood (Chamorro-Premuzic et al., 2009). Music-use also includes the circumstances

under which an individual uses music. Music may be played using a portable device

through headphones or on an external device through speakers. Individuals may listen to

music while completing various tasks in their home, at work, in the library, or on-the-go.

Music may be used before, during, or after a task. People listen to music in specific

sequences for various lengths of time (Lesiuk et al, 2011).

Personality. Personality consists of stable trait characteristics that are both

behavioral and emotional. Personal goals influence behavior, and motivation is believed

to underlie these characteristics (Depue & Collins, 1999). Individuals display varying

degrees of intensity for each personality trait. These individual differences define each

unique personality, and this individual makeup may be summarized using five

dimensions: neuroticism, extraversion, openness to experience, agreeableness, and

conscientiousness. The dimensions comprise the Big Five personality factors, as

theorized by Costa and McCrae (1992). First, neuroticism involves a tendency to

experience negative affect and have irrational ideas. These qualities are linked to poor

impulse control and coping skills. Second, extraversion is associated with active social

tendencies, loquaciousness, outward confidence, and cheerful disposition. Third,

6

openness to experience involves active imagination, curiosity about oneself and the

world, and independence of judgment that is often associated with creativity. Fourth,

agreeableness is concerned with interpersonal tendencies, including sympathy, empathy,

and willingness to help others. Last, conscientiousness includes willpower,

determination, self-discipline, and reliability. These qualities help one to control

impulsiveness (Costa & McCrae, 1992).

Mood. Mood is impacted by context, or state dependent motivating factors.

These factors include a level of activation, intensity, or arousal. Hedonic value, or

pleasure, is another component of mood. Mood may be described as positive or pleasant,

or by contrast, negative or unpleasant (Berlyne, 1971a, 1971b; Gfeller, 2005; Thaut,

2005). Mood is a psychological event with a neurophysiological response and an

expressive reaction. A conscious subjective experience or feeling occurs congruently

with these responses and reactions (Nairne, 2009b). This thesis defines mood as a level

of positive or negative affect intensity. Positive affect is further categorized using

subscales for relaxation and enthusiasm. Negative affect subscales include nervousness

and fatigue (Oldham, Cummings, Mischel, Schmidtke, & Zhou, 1995).

Need for the Study

Theoretical relevance. This thesis has both theoretical and practical relevance.

Theoretically, this research enhances knowledge about why individuals naturally use

music in relation to a cognitive task. The study design allows for “actual, behaviorally-

determined music usage” based on situational restraints (Chamorro-Premuzic, Swami,

Furnham, & Maakip, 2009, p. 26). Results of this thesis further the understanding of the

relationship between music, personality, mood, and cognition. By providing knowledge

7

about their personalities, moods, music-uses, and the contexts within which student

computer programmers work, this thesis informs future studies that attempt to measure

the effects of music on behavior and affect (Cassidy & MacDonald, 2007). Research that

investigates the relationship of personality, mood, and music-use among computer

programmers is limited. A research model has been proposed, however, which accounts

for the influence of personality differences in the interaction between person and

environment, with regard to mood and music-use (Lesiuk et al, 2009). Personality testing

increases knowledge of trends among computer programmers, revealing diverse ways of

thinking and working (Woszczynski et al., 2005). An assessment of mood prior to

music-use and the music listening choices that follow provide a supplement to existing

research on the mood and music interaction (Rentfrow et al., 2011). Furthermore, studies

that collect data on music-use in general are scarce in the literature.

Additionally, through a review of current research literature, this thesis provides

information on the nature of computer programming, including the cognitive demands,

associated stress, and its role in the systems development life cycle. The literature review

also explores the relationships between mood and cognition, music and mood, music and

cognition, and personality and mood.

Practical relevance. The thesis topic informs the areas of music therapy,

cognitive psychology, organizational psychology, information technology, music

marketing, and industrial and organizational design in the workplace. Practically, this

research provides insight into the role of music-use within the context of a high-cognitive

demand work task, including the impact of one’s personality and incoming mood state.

This study explores the music-use and working context of student computer programmers

8

(Chamorro-Premuzic & Furnham, 2007; Chamorro-Premuzic, Swami, Furnham, &

Maakip, 2009). The information provided in this thesis is valuable to individuals

interested in music as a commodity and music as a resource. Studies of individual

differences and music-use have relevance to music recommendation services. Analysis

of past listening choices may allow personal music libraries to be grouped in terms of

structure, energy, emotion, and intended use. Consequently, these categorized music

libraries may become a resource for improving well-being, productivity, and generally,

bettering quality of work and life. Songs may be automatically selected for the age,

gender, education, and income of the listener (Rentfrow et al., 2011).

Music may also be used as a resource for computer programmers, influencing

human factors that affect their work. Music-use has been linked with increases in

positive mood and subsequent improvement in work performance of computer

information systems developers (Lesiuk, 2005, 2010b). Quality of work improved and

task length decreased when participants were able to choose the music and the

circumstances under which they listened. They reported that music was valuable for

mood, perception, and thought enhancement (Lesiuk, 2010a). Through an examination

of personality, mood, and music-use within the context a high-cognitive demand task,

this thesis is taking into consideration the person-environment fit (Lesiuk et al., 2011).

Such a fit occurs when an individual successfully adjusts to their surroundings,

constructing a preferred environment or adapting it to their needs in the moment.

9

Purpose Statement

The purpose of this study was to investigate the ways in which student computer

programmers use music while programming. Personality, mood, and music-use data

were collected in connection with a high-cognitive demand task – computer

programming. Uses of music were related to personality and mood variables.

Additionally, other variables were considered as factors related to music-use. These

variables included demographics, computer programming and musical experience, and

contextual factors.

Chapter Two

Review of Literature

This chapter will review research literature pertinent to understanding the role of

personality and mood in music-use during a high-cognitive demand task – computer

programming. The review will begin by explaining music perception and clarifying

everyday music-use. Personality and affective response models will also be described as

they apply to cognition. Additionally, the cognitive demands of computer programming

will be described in detail. Literature that explores relationships between personality,

affect, music, and cognition will then be reviewed. The final section of the chapter will

summarize research that tests the effect of music on high-cognitive demand task

performance. This literature review will provide rationale for surveying the everyday use

of music during high-cognitive demand tasks, including personality and mood accounts

from the individuals who use the music.

Music Perception

Music is processed bilaterally in several areas of the brain. Researchers identify

these areas by monitoring physiological responses, utilizing brain imaging techniques,

and studying the development of affective responses to music (Trainor & Schmidt, 2003).

Music perception comprises neocortically mediated cognitive processes and subcortically

mediated affective responses.

Neocortically, music activates areas in the temporal and frontal cortices, including

Broca’s area in the left hemisphere (Peretz & Coltheart, 2003; Peretz & Zatorre, 2005).

The frontal cortex is an area associated with high levels of cognition, or executive

functions. Creative problem-solving and abstract thinking are examples of executive

10

11

functions. This neural evidence also suggests that listening to music involves cognitive

processes of attention and working memory (Lesiuk, 2010b). Additionally, the

cerebellum and basal ganglia work together with motor cortical areas to process time

relations in music (Peretz & Coltheart, 2003; Peretz & Zatorre, 2005).

Affective response to music involves subcortical structures and psychological

theory. Music activates areas of the brain associated with pleasure and reward. These

areas include the anterior cingulate gyrus, dorsal midbrain, ventral tegmental, nucleus

accumbens in the ventral striatum, orbitofrontal cortex, amygdala, hippocampus and

insula (Blood & Zatorre, 2001). In addition to music, this brain reward system responds

to other highly rewarding stimuli, such as food, sex, and drugs. When music has high

hedonic value, arousal occurs. With arousal, increased physiological activity is expressed

in the autonomic nervous system, neuroendocrine system, and central nervous system.

Examples of physiological functions include cerebral blood flow and endorphin release

(Goldstein, 1980; Rickard, 2004; Trainor & Schmidt, 2003). The arousal system is

comprised of the brain stem reticular formation, hypothalamus, and the thalamus. The

thalamus provides a relay between subcortical structures and neocortical structures

(Blood & Zatorre, 2001).

Humans seek arousal, and according to Berlyne’s (1971a) optimal arousal theory,

music’s value or emotional meaning is derived from the arousal properties of structures

in the music itself. Three music stimulus properties have been determined. First,

psychophysical properties are the structural aspects of music, including spatial and timing

ranges, which portray levels of energy. Tempo and amplitude are examples of

psychophysical properties. Next, collative properties are patterned descriptors, such as

12

complexity and novelty. These musical patterns allow a listener to build expectations,

providing opportunities for surprise or familiarity, depending on how the music stimuli

progresses. According to Meyer’s (2001) expectation theory, arousal occurs when

expectation is inhibited. An individual’s experience of music is derived from his or her

affective response to the music, which is a function of a relationship within the music

itself. Listeners bring with them a vast body of musical experiences that condition their

response to music as it unfolds. Music’s evocative power derives from its capacity to

generate, suspend, prolongate, or violate these expectations. The final music stimulus

property, ecological, refers to the extramusical associations one makes with the music,

which assist in the perception and interpretation of emotional processing (Berlyne,

1971b).

Everyday Music-Use

Music-use cannot be accurately evaluated without the inclusion of context

(Rentfrow et al., 2011). Sloboda and O'Neill (2001) recommend that studies of music-

use include contextual factors, trait preferences, and state preferences. In this thesis, a

difficult computer programming task is part of the context in which personality, mood,

and music-use interact. These social, psychological, musical, and environmental factors

all have an influence on the effects of music on behavior and affect.

Sloboda (2010) has established 10 dimensions of “everyday” music, each of

which contributes to defining the context of music-use. Everyday music-use tends to be

relatively mundane and trivial, providing accompaniment to some other nonmusical

activity (North, Hargreaves, & Hargreaves, 2004). Each dimension of everyday music is

thought to have influence, nonetheless, on affective response to music.

13

The first two dimensions are related to one’s past use of music in everyday

contexts. First, frequency of occurrence, refers to the regularity of music-use. This

dimension’s influence on affect is dependent on the experience and habits of the listener.

For example, music-use that occurs frequently lacks the element of surprise, and

therefore may elicit a weaker affective response (Meyer, 2001; Sloboda, 2010). Second,

ordinariness versus specialness of the context or experience, refers to the social or

cultural weight of music-use. When music is used in everyday situations, its personal

significance to the listener and the associated affective response are likely to be

attenuated. Consequently, memory for this type of music experience may be diminished

(Bower, 1981; Levene, 1997; Levine & Safer, 2002).

The next three dimensions of everyday music are dependent on factors in the

present that impact music-use. First, location of occurrence, refers to the setting in which

music-use takes place. Everyday music-use occurs at home and in public places, where

significant distractions and changes in experience may occur. Affective responses are not

predictable in situations with such fluctuations (Sloboda, 2010). Next, circumstance of

exposure: the role of choice, refers to the listener’s hierarchical adjustment of musical

choice, as a result of factors outside of the listener’s control. Individuals are not always

given the choice of what to hear in everyday life. Even when music is chosen, the

unpredictable nature of everyday experience may lower the priority of that choice. Such

lack of control over music choice may yield a negative affective response (Sloboda,

2010). Nature of transmission, the fifth dimension, refers to the physical source of the

14

music. Most everyday music is recorded, being transmitted by an audio device.

Therefore, listeners are not pondering the person or instrument producing the music, and

social emotions are not included in their affective response (Sloboda, 2010).

The centrality of music to the experience and the salience of the context comprise

the sixth dimension of everyday music. The nonmusical activity requires more attention

relative to the music in everyday circumstances. Thus, the concurrent affective response

is likely to be less dependent on the music and more reflective of the nonmusical activity.

Additionally, individual variation in affective response is to be expected, because the

interaction between music activity and nonmusical activity differs for each individual

(Sloboda, 2010).

The seventh dimension, the nature of the music, involves the imprecise distinction

between art music and vernacular music. An assumption is that everyday music-use

typically includes vernacular music or art music that has been in some way sliced into

smaller sections. A second assumption is that a function of vernacular music is to

provide recognizable symbols with clear affective meaning. Given these assumptions,

the aesthetic affective response to everyday music may be understated (Sloboda, 2010).

Research considerations and strategies are also addressed when investigating

everyday use of music. First, the method of investigation, Sloboda’s (2010) eighth

dimension, recommends post-hoc interviews or questionnaires to capture affective

response to everyday music. Next, the intellectual stance of writer/research suggests that

researchers take a specific perspective when assessing everyday music-use. Investigators

should take the point of view of the consumer, who is as interested in activities where

music is accompaniment as he is in activities where music is the focal point. Finally, the

15

contextual specificity of judgment obtained distinguishes studies of everyday music-use

from typical studies of musical preference. In everyday music-use studies, preferences

must be linked to context. Furthermore, a final assumption is that everyday music plays a

functional role in an individual’s affective goal achievement. Thus, studies of everyday

music-use must measure affective response relative to mood regulation, instead of

affective response related to stable traits or attitudes (Sloboda, 2010).

To summarize, music does not appear to have a homogenous effect on the

listener. The context determines the value of the musical experience, and reasons for

using music change based on one’s present motivation for listening (Lamont & Greasley,

2009). Each listening experience involves cultural preconceptions about which type of

music is suitable for a particular circumstance, and several reciprocal feedback

relationships exist between stimulus characteristics, the listener, and the situation.

Additionally, affect-optimization may underlie the reasons for using music, in order to

maximize the listening experience (Sloboda, 2010). Consequently, individuals may

decide to use music in various situations with differing levels of engagement. Choice of

music within a context occurs with little thought, nonetheless, providing a cognitively

undemanding accompaniment to a task (North et al., 2004).

Everyday music-use may be categorized in terms of function. Three music-use

categories have been established: background or social use of music; cognitive,

intellectual, or rational use of music; emotional use of music (Chamorro-Premuzic,

Fagan, & Furnham, 2010; Chamorro-Premuzic & Furnham, 2007; Chamorro-Premuzic,

Gomà-i-Freixanet, Furnham, & Muro, 2009; Chamorro-Premuzic, Swami, Furnham, &

Maakip, 2009). These categories describe why music is used within a context, and they

16

were derived from the Uses of Music Inventory (Chamorro-Premuzic & Furnham, 2007).

The items on this self-report questionnaire were created through a review of literature and

qualitative pilot testing. During focus groups and open interview sessions, open-ended

and non-directive questions were used to elicit opinions about music, when it was

listened to, and why. Thematic analysis then took place to derive categories. Thus, the

authors determined that music-use can be divided into three categories, including

background to other activities, cognitive/intellectual use of music, and

manipulation/regulation of emotions. Examples of background-use include listening to

music while working, studying, socializing, or performing a task. Cognitive-use involves

listening to music in an intellectual way to improve focus and increase cognitive

efficiency. Emotional-use refers to the extent to which an individual uses music to

regulate emotions (Chamorro-Premuzic & Furnham, 2007).

Affect and Cognition

Affect, whether positive or negative, is a general term that encompasses both

emotion and mood. Emotions are brief and tied to a stimulus event (Sloboda & Juslin,

2010). Moods are generally longer lasting than emotions, providing a tonic-affective

background that can change the likelihood that a particular emotion will occur. Mood

may occur as a result of its significance, meaning, and reward or aversive nature in

connection with a neural response (Davies, 2010). A circumplex model has been used to

place mood variables along a horizontal axis of activation and vertical axis of valence

(Russell, 1980; Sloboda & Juslin, 2010; Zentner & Eerola, 2010). Additionally, a

connection between the thalamus and the amygdala, which bypasses the thalamocortical

17

pathway, provides evidence that mood may be experienced in a preconscious or

automatic manner. Therefore, one does not have to be conscious of mood to experience

an affective response (Sloboda & Juslin, 2001).

Literature has linked affective response with cognition in a number of domains.

Mild positive affect may be induced in subtle and common ways, using conventional

methods, and having a constructive impact on social behaviors and thought processes.

Examples of affect induction methods include the presentation of a gift or reward,

suggestive thinking, and affect-laden stories, videos, or music (Amabile, Barsade,

Mueller, & Staw, 2005). When individuals experience mild positive affect, they are able

to think more clearly to make choices that are socially responsible and helpful. These

individuals tend to enjoy what they are doing, thus finding more motivation and openness

to accomplish goals (Isen, 2009).

Mild positive affect increases the availability of cognitive material for processing,

which in turn increases the number of cognitive elements available for association (Isen,

2009). Attention is influenced by affect, particularly in terms of breadth. Mild positive

affect helps to defocus attention, expanding the cognitive context, consequently adding to

the scope of available cognitive elements. Mild positive affect may also influence

flexibility focus, a process by which one simultaneously takes a broader focus without

losing focus on task details (Isen, 2009).

Individuals with mild positive affect perceive information more carefully and

fully, and they are able to consider numerous aspects of a situation at once. These

individuals also choose and evaluate behaviors in light of the situation and task demands

(Ashby, Isen, & Turken, 1999; Isen, 2009). Flexible thinking describes the ability to

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identify reasonable connections, consider multiple perspectives and solutions, and

prioritize as needed to address a problem. These cognitive processes are necessary for

problem-solving that involves multiple goal considerations. Mild positive affect

facilitates flexible thinking, which translates into creativity and effective problem-solving

(Estrada, Isen, & Young, 1997; Forgas, 1998; Forgas & George, 2001). Creative

problem-solving requires the generation of ideas beyond information given. In this

process, information is retrieved from memory and systematically combined with the

given information (Isen, 2009).

Mild positive affect also has a relationship with expectation, particularly in terms

of motivation. Expectancy is a cognitive process by which one determines the valence of

an outcome. Cognitive effort is then calibrated, based on the evaluation (Isen, 2009).

Expectancy theory predicts that individuals are motivated by the expectation of obtaining

a positive outcome, or reward. When an individual’s expectation has a negative outcome,

motivation is less likely to occur. Motivation is also tied to cognitive performance. In a

study that explored expectancy and motivation in university students, positive affect was

induced and tested for its effect on cognitive performance (Erez & Isen, 2002). Affect

was measured using a Likert scale, which assessed the degree to which participants

experienced different feelings. Motivation was calculated in three components, including

valence, expectancy, and instrumentality. Expectancy refers to the appraisal of rewards

and involves estimations about the strength of relationships between effort and

performance. Instrumentality refers to similar evaluations and hypotheses about links

between performance and outcome. Participants in this study solved anagrams to

measure cognitive performance. Results showed that positive affect facilitated both

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motivation and performance. Given their propensity to anticipate a positive outcome,

individuals with mild positive affect were more effortful and persistent (Erez & Isen,

2002).

The relationship between negative affect and cognition has also been documented.

Negative affect is associated with pervasive negative emotionality and self-concept.

Individuals with high negative affect tend to focus excessively on negative aspects of

themselves, others, and the world. Compared to individuals with low negative affect,

these individuals are more likely to experience significant levels of distress in any

situation (Brief, Burke, George, Robinson, & Webster, 1988; Isen, 2009; Watson &

Clark, 1984). Negative affect also includes a prevalence of unpleasant arousal.

Unpleasantness and overly high levels of arousal narrow attention and prevent processing

resources from effectively influencing a decision task (Mano, 1992; Watson & Tellegen,

1985). Additionally, negative affect is more narrowly represented in long-term memory

than positive affect. In other words, memories with a positive affective tone are more

prevalent in long-term storage. Therefore, negative affect is not as useful as a memory

retrieval cue, when compared to positive affect (Isen, 2009). Also, in a study that

explored the association between affect and creativity in adult professionals, positive

affect was more commonly associated with creativity than negative affect (Amabile et al.,

2005).

A dopaminergic theory also provides evidence for the relationship between affect

and cognition. Increases of the neurotransmitter, dopamine, in the anterior cingulate

gyrus occur in relation to mild positive affect. The anterior cingulate gyrus is involved in

episodic long-term memory as well as working memory functions. A rush of dopamine

20

to this area also influences frontal brain regions involved in problem-solving, cognitive

flexibility, and other neural areas identified with thinking and working memory (Ashby et

al., 1999; Isen, 2009).

Stressful situations play a role in the relationship between affect and cognition

(Brief, Butcher, & Roberson, 1995). In a study of negative affect and job related stress in

adult professionals, several significant correlations resulted. Negative life stress was

negatively correlated with life satisfaction and positively correlated with symptoms of

depression. Negative affect was positively correlated with negative life stress. Negative

affect was also positively correlated with indices of distress and negatively correlated

with indices of satisfaction on both the life and job satisfaction measures (Brief et al,

1988). A later study also addressed the connection between affect and job satisfaction

among adult professionals. As hypothesized, a significant negative correlation occurred

between negative affect and job satisfaction, and a significant positive correlation

occurred between positive affect and job satisfaction (Fisher, 2000).

Personality, affect, and cognition. Trait personality and state dependent

affective response appear to interact. Dopaminergic theory also accounts for individual

differences in personality, including an association with affect. Individuals who are high

in extraversion are assumed to have relatively stable levels of positive emotionality,

particularly in the form of incentive motivation. These individuals may have increased

dopamine activity in the ventral tegmental area, which is part of the brain reward system,

but this theory is still under review (Depue & Collins, 1999). Therefore, more research is

necessary to explain the relationship between personality and affect.

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Rusting (1999) studied the interaction between personality and affect, including

an examination of their influence on emotion-congruent memory and judgment, in

university students. The study measured personality in terms of extraversion and

neuroticism and affect in terms of positive affectivity and negative affectivity. Findings

revealed that extraversion and positive affectivity were linked to positive memory

retrieval and a tendency to make positive judgments. Neuroticism and negative

affectivity were linked to negative memory retrieval and a tendency to make negative

judgments. Due to several significant personality and mood effects on judgment and

memory, the influence of personality and mood did not appear to be independent of one

another (Rusting, 1999). Personality and affect indices tend to be correlated with one

another, in fact, making it difficult to distinguish the independent effects of stable

personality and transient mood on cognition (Rusting, 2001). Again, the relationship

between these variables must continue to be tested.

Computer Programming and High-Cognitive Demand

A computer program consists of objects, calculations, and procedures. In object-

oriented programming, which is the most prominent programming paradigm, a computer

program is a hierarchy of so-called “objects,” each of which is composed of data fields

and methods. Each of the data fields are either a primitive data value or an instantiation

of an object, and each of the methods is a series of operations on the data fields.

Computer programming is the process of translating calculation or procedure

specifications into such a hierarchy (Dennis & Wixom, 2000).

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To understand a program and the underlying problem, computer programmers

must find relations and create connections between various cognitive domains.

Grammatical rules exist for creating a program, requiring knowledge and efficient

retrieval of syntax guidelines, semantics, and schemata. Computer programming requires

“focus and selective attention to systematic analysis and creative problem-solving”

(Lesiuk, 2010b, p. 137). Computer programmers must be able to organize domain-

specific knowledge and meta-cognitive knowledge. Then, they must also engage in

domain-specific problem-solving focused on abstract concepts and goals, including

abstract planning and evaluation (Sonnentag et al., 2006). Computer programming

strategies are diverse, having been described as top-down versus bottom-up, forward

versus backward development, or breadth-first versus depth-first (Détienne & Bott,

2002).

Based on present-day cognitive theories, computer programming is a high-

cognitive demand task. According to Piaget’s cognitive development theory, the formal

operations level of cognition is necessary to complete computer programming tasks

(White & Sivitanides, 2005). This level of cognitive development is the highest level one

can reach. Formal operations cognition involves logic, abstract thinking, hypothesis

generation, systematic problem-solving, and mental manipulation (Nairne, 2009a).

The cognitive demand of computer programming may also be described using

cognitive load theory, which is built on the limitations of working memory and involves

three types of cognitive load (Garner, 2002). First, intrinsic cognitive load refers to the

mental demands of a task. When separate schemata are able to be processed serially,

rather than simultaneously, low intrinsic cognitive load occurs. Computer programming

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has high intrinsic cognitive load, as it requires interactive processing between multiple

schemata at once. Next, extraneous cognitive load is dependent on the format of

instruction used to teach and learn a process. Computer programming training methods

have been developed to reduce extraneous cognitive load, because a combination of high

extraneous and high intrinsic cognitive load may exceed the capabilities of working

memory. Extraneous cognitive load may be lessened, for example, by using computer

programming textbooks that integrate diagrams and text. Last, germane cognitive load

refers to the conscious processing of unused working memory to construct schemata in a

particular domain. When adequate instructional design has limited extraneous cognitive

load, conscious processing of abstract schemata is made possible, increasing germane

cognitive load. High germane cognitive load is desirable for computer programming, and

training techniques have been developed to promote this type of cognitive load. A

method of encouraging high germane cognitive load is the use of incomplete worked

examples, which require students to use abstract thinking to fill in missing schemata

(Garner, 2002).

The high-cognitive demand tasks of a computer programmer are part of the

systems development life cycle (Valacich, George, & Hoffer, 2006). This ongoing cycle

of activities can be divided into four phases. During the initial systems planning and

selection phase, systems analysts identify, prioritize, and organize system needs into a

written plan for development. Then, during the systems analysis phase, systems analysts

and developers study, dissect, and combine existing systems to produce alternatives that

may be considered within the cost, labor, and technical levels of the project. Next, during

the systems design phase, systems developers and software engineers describe a new

24

system, including a logical design and a physical design. The logical design shows the

system’s function in a business organization, and the physical design shows the technical

specifications to be followed when programming and constructing the system. During

the final systems implementation and operation phase, the system is coded, tested, and

installed by computer programmers. The system is also maintained and repaired as

necessary to keep it running and useful for the end user (Valacich et al., 2006).

Computer programmers do the bulk of their work during the systems

implementation and operation phase, but they may also contribute during the systems

analysis and systems design phases. In essence, computer programmers translate a

software program design that they receive from computer software engineers and systems

analysts into a series of instructions that the computer can comprehend (Valacich et al.,

2006). Computer programmers write (or code) these instructions using a number of

computer programming languages, and they choose a language based on the design and

personal preferences. Commonly used programming languages include C++, JAVA, and

Python (Bureau of Labor Statistics, U.S. Department of Labor, 2009).

Computer programmers must also update, modify, expand, and repair existing

programs. They may use automated computer applications and libraries of basic code to

increase productivity and reliability. Computer programmers may also work in teams to

complete a program. Like software engineers and systems analysts, computer

programmers must interact with the end users. Computer programmers receive feedback

from these individual or commercial users, and they may be asked to design or redesign

part of the program to meet customer needs (Bureau of Labor Statistics, U.S. Department

of Labor, 2009; Norton, 1997; Woszczynski et al., 2005).

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The personality of computer programmers. Some personality traits are typical

among computer programmers. Although music psychologists often use a five-factor

personality inventory to collect personality data, information technology studies that

utilize this structure are scarce in the literature. The Myers-Briggs Type Indicator

(MBTI) is another prevalent personality scale, and this measure has been used in

information technology research (Myers, 1962; Myers, McCaulley, Quenk, & Hammer,

2003). The MBTI consists of four personality dichotomies: extraversion (E)/introversion

(I), sensing (S)/intuition (N), thinking (T)/feeling (F), and judging (J)/perception (P).

Preferences for the E/I dichotomy are called attitudes, S/N and T/F preferences are called

functions, and J/P preferences are called lifestyle. Individuals with a preference for

extraversion are social, action-oriented, and pursue quantity over quality. Individuals

with a preference for introversion are independent, thoughtful, and detail-oriented. The

functions preference pairs are dichotomous, and each pair is also related to a lifestyle

preferences. First, the sensing/intuition functions are related to perception, in that they

define how an individual comprehends new information. Individuals with a preference

for sensing trust what they perceive using the five senses, relying on fact over instinct,

while individuals with a preference for intuition place trust in theory and possibilities.

Next, the thinking/feeling functions are related to judging, in that they define how an

individual makes decisions. Individuals with a preference for thinking base judgments on

reason and logic, while individuals with a preference for feeling make choices using

empathy and seeking harmony. Completion of the MBTI results in one of 16 personality

“types,” which includes four letters, one from each of the dichotomies (e.g. ISTP).

26

Results of the MBTI identify inherent personality tendencies, and trained administrators

of the measure explain how to either accept and foster these tendencies, or compensate

for and modify them as necessary (Myers et al., 2003).

The cognitive ability to problem-solve is related to the sensing/intuition and

thinking/feeling functions (Myers et al., 2003). In a study that related the personality

profiles of novice computer programming students to final average grades in an entry

level computer programming course, the specific preferences of intuitive thinkers were

more abundant than the other preferences, including intuitive feelers, sensor thinkers,

and sensor feelers (Woszczynski et al., 2005). Based on an analysis of variance

(ANOVA), students with different personality profiles scored differently in programming

principles I, which is a typical introductory course in computer programming. The

intuitive thinkers’ final average was higher than averages for intuitive feelers, sensor

thinkers, and sensor feelers preferences respectively. The intuitive thinkers’ final average

was also significantly higher than the sensor feelers’ average at the 0.05 level. Thus, the

intuitive thinker preference may be common among computer programming students who

progress beyond the novice level of proficiency. Since no other significant differences

occurred in this study, however, diverse personality profiles are to be expected in this

population (Woszczynski et al., 2005)

To assess differences between novice and expert computer programmers, a study

used the MBTI to compare the personality profiles of undergraduate computer

programmers to professional computer programmers (Kenner, 1993). Significant

differences emerged between undergraduates and professionals in the sensing-feeling and

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introversion-intuition-thinking-judging preferences. Results showed that the groups were

similar, however, for introversion, thinking, judging, and intuition-feeling preferences.

Again, results of this study increase the expectation for diverse personality profiles

among computer programmers, and diversity may occur at various levels of proficiency

(Kenner, 1993).

Another exploratory pilot study that used the MBTI to examine 32 computer

information systems developers found a prevalence of introversion, thinking, and judging

preferences (Lesiuk, Pons, & Polak, 2009). Also, introversion preferences outnumbered

extraversion preferences two to one in this sample, which is opposite to the trend of the

general population. Individuals with a preference for introversion are reported to select

occupations that demand sustained attention to and interest in concepts and ideas, while

individuals with a preference for extraversion tend to select occupations that have a

primary focus on people (Myers et al., 2003). These natural inclinations may make the

field of information technology more attractive to individuals with a preference for

introversion, with positive ramifications on quality of work. In a later study of systems

analysts, in fact, individuals with a preference for introversion performed better on a

work performance task (Lesiuk et al., 2011). The relationship between personality and

mood in information technology professionals is another recently documented subject of

research. The exploratory pilot study by Lesiuk et al. (2009) examined personality and

mood in computer information systems developers. The negative trait mood was greater

for individuals with preferences for introversion and feeling. Then, Lesiuk et al. (2011)

found a link between personality and mood in the sample of computer systems analysts.

When individuals who scored high on the conscientiousness personality factor listened to

28

music, which was hypothesized to improve mood, they reported a significant reduction in

fatigue over time. Decreased fatigue is an indicator of reduced negative affect (Lesiuk et

al., 2011).

To summarize, some personality preferences may be expected among information

technology professionals, including introversion, intuition, thinking, and judging (Lesiuk

et al., 2009; Woszczynski et al., 2005). Introversion has an association with an

individual’s propensity to choose occupations that require interest in and sustained

attention to concepts and ideas (Myers et al., 2003). A positive relationship between

introversion and work performance has also been shown (Lesiuk et al., 2011). Intuition

and thinking preferences represent the MBTI functions, which are related to one’s ability

to problem-solve (Myers et al., 2003). Additionally, personality has an interaction with

mood. Introversion and feeling preferences have been positively linked with negative

trait mood, and the conscientiousness personality factor has been negatively linked with

fatigue (Lesiuk et al., 2009, 2011).

Personality and Music-Use

A series of studies were conducted across cultures using the Big Five personality

variables and the Uses of Music Inventory music-use categories (Chamorro-Premuzic &

Furnham, 2007; Chamorro-Premuzic, Gomà-i-Freixanet, Furnham, & Muro, 2009;

Chamorro-Premuzic, Swami, Furnham, & Maakip 2009). In these studies, university

students in America, Great Britain, Spain, and Malaysia completed measures of

personality and music-use. General findings revealed that of the Big Five personality

variables, neuroticism, extraversion, and openness to experience were most closely

related to music-use.

29

In the first study with university students in America and Great Britain,

neuroticism was positively correlated with emotional-use of music, extraversion was

negatively correlated with emotional-use of music, and openness to experience was

positively correlated with cognitive-use of music. Additionally, conscientiousness was

negatively correlated with emotional-use of music. All relationships were significant

with p values less than 0.01 (Chamorro-Premuzic & Furnham, 2007).

In a replication study in Spain, neuroticism was positively correlated with

emotional-use, and openness to experience was positively correlated with cognitive-use.

Contrary to the results of the first study, however, extraversion was positively rather than

negatively correlated with emotional-use in the second study. Extraversion was also

positively correlated with background-use of music. All relationships were significant

with p values less than 0.01 (Chamorro-Premuzic, Gomà-i-Freixanet, Furnham, & Muro,

2009). The results of a replication study in Malaysia were the same as the results in

Spain, but only the p value for the correlation between neuroticism and emotional-use of

music was less than 0.01. The other correlations had p values less than 0.05 (Chamorro-

Premuzic, Swami, Furnham, & Maakip 2009).

Personality also plays a role in aspects of music-use outside the three music-use

categories. In typical individuals, Cassidy and MacDonald (2007) explored perceptions

of the effect of music-use using open-ended questions. Individuals with a preference for

introversion reported choosing music to reduce anxiety, including both psychological and

physical symptoms. Extraverted types were less aware of these positive anxiolytic effects

of music, when compared to introverted types. Individuals with a preference for

extraversion also reported being less distracted by background music, when compared to

30

introverted types (Cassidy & MacDonald, 2007). Later, Lesiuk et al. (2009) monitored

listening habits in a pilot study of computer systems designers, and daily average

listening time differed between personality types. Individuals with preferences for

extraversion listened to twice as much music as introverted types, and feeling types

listened to twice as much as thinking types.

The Effect of Music on Affect

To strengthen the neurologic and theoretical association between music and

affective response, researchers have utilized various methods of investigation to measure

the effect of music on state affect. Eich, Ng, Macaulay, Percy, and Grebneva (2007)

explored methods of modifying mood, including endogenous (e.g., naturally occurring)

and exogenous (e.g., induced/experimental) moods. The investigators listed desirable

attributes of mood-induction as criteria for a specific mood-modification technique,

abbreviated MCI, which was tested in a series of experiments. The technique was

assessed by measuring these qualities, including success rate, time to criterion, ratings of

pleasure and arousal, ratings of positive and negative affect, and ratings of mood

genuineness. Ratings were expected to remain stable over time, and moods were

expected to be reliably induced more than once (Eich, et al., 2007).

Using the MCI method for each experiment, participants were induced into very

pleasant or very unpleasant moods by listening to merry versus melancholic music while

contemplating elating or depressing thoughts about real or imaginary people, places, or

events. Data were collected periodically, and each experiment was conducted twice.

Participants logged their pleasure and arousal levels using a visual matrix, and they also

31

provided self-ratings for feeling very pleasant or very unpleasant regardless of arousal.

Additionally, participants rated positive affect, negative affect, and mood genuineness

throughout each experiment (Eich et al., 2007).

The MCI method was 87 percent successful, having a strong effect on mood,

arousal, positive affect, and negative affect. High ratings of mood genuineness suggested

that authentic moods were being experienced during the experiments. Induced moods

were deemed strong, stable, sincere, and reproducible. The authors concluded by making

suggestions for improvement with future mood-modification techniques, including the

selection of more appropriate musical selections or music that is preferential to the

participants (Eich et al., 2007).

Mitterschiffthaler, Fu, Dalton, Andrew, and Williams (2007) used Western

classical music to induce happy, sad, and neutral moods, and they monitored activity in

the brain using functional magnetic resonance imagining (fMRI). Results showed that

responses to happy music activated the ventral and dorsal striatum, anterior cingulate

gyrus, parahippocampal gyrus, and auditory association areas. Response to sad music

was represented in the hippocampus, amygdala, and auditory association areas. Neutral

music activated the insula and auditory association areas. Many of these structures have

been identified with general affective response (Blood & Zatorre, 2001). Specifically,

the medial temporal areas are associated with the appraisal and processing of emotions.

Additionally, the ventral and dorsal striatum are active during reward experience and

movement, and the anterior cingulate gyrus is involved in targeting attention.

(Mitterschiffthaler et al., 2007).

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Knight and Rickard (2001) investigated the effect of relaxing music on subjective

and physiological response to stress and anxiety. Undergraduate students were exposed

to silence or Pachelbel’s Canon in D major while completing a stressor task – preparation

for an oral presentation. Subjective anxiety, heart rate, blood pressure, cortisol level, and

salivary content were measured at rest and after presentation of the stressor. Subjective

anxiety, heart rate, and systolic blood pressure increased significantly with the stressor.

These increases did not occur when music was present. Even in the absence of stressor,

baseline salivary IgA significantly decreased with music, which is an indication of

reduced stress. This study provides evidence for music as an anxiolytic treatment for

physiological symptoms (Knight & Rickard, 2001).

The relationship between music and affect has also been studied by measuring the

effect of mood on music-use. Greenwood and Long (2009) utilized a series of measures

to test whether individual differences in mood were predictive of mood-specific music-

use in typical individuals. Participants rated how often they used media, such as music or

television, when they recalled the experience of various mood states. Individual

differences in emotion regulation were also measured, along with an assessment of

rumination and reflection tendencies. Three music-use categories emerged during factor

analysis, including music-use in a positive mood, music-use in a negative mood, and

music-use when one is bored. The findings suggest that some individuals are generally

inclined to use music for mood regulation, regardless of whether they are in a positive or

negative mood. Also, predictive relationships emerged between both the negative mood

and bored conditions and music-use for mood regulation (Greenwood & Long, 2009).

33

The Effect of Personality and Music on Cognition

A few studies have been conducted to observe the relationship between

personality, music, and cognitive tasks. In a study of personality and the effect of

background music and background noise on task performance, participants completed an

immediate and delayed recall test, and the Stroop Neuropsychological Screening Test

(Cassidy & MacDonald, 2007). Each individual completed the tests with no music,

background music, and background noise. The Stroop test required participants to read

and vocalize a list of color names printed in a non-concurrent ink color. They were given

a mark for each correct answer completed within time, and the task was negatively

marked for incorrect answers. Negative marking is part of an evaluation process in

which marks are deducted from the actual score for every wrong answer. Results of

multivariate analyses of variance (MANOVA) showed a main effect of personality,

indicating significant differences in performance. Introverts performed significantly

better than extraverts on the immediate recall and delayed recall tests in all listening

conditions. Extraverts performed significantly better than introverts on the negatively

marked task of the Stroop test in the background music and background noise conditions

(Cassidy & MacDonald, 2007).

A similar study used three cognitive measures, including a test of abstract

reasoning, general cognitive ability, and verbal reasoning, to investigate the effect of

personality, background music, and background noise on task performance (Dobbs,

Furnham, & McClelland, 2011). Again, participants completed the tests with no music,

background music, and background noise. Significant positive correlations emerged

between extraversion and performance on all three measures. On the abstract reasoning

34

test, multiple regression analyses showed significant main effects of both extraversion

and background sound. Performance on the abstract reasoning test was significantly

better in silence than with music, and performance with music was significantly better

than with noise. A significant interaction between personality and background sound was

also revealed. During the music and noise conditions, extraversion was a significant

predictor of performance on the abstract reasoning test (Dobbs et al., 2011).

The results were similar on the cognitive ability test, with a few exceptions.

Significant main effects of both extraversion and background sound were found, and a

significant interaction emerged between personality and background sound. Performance

on the cognitive ability test was significantly better both in silence and with music, when

compared to performance with noise. Performance in silence was not, however,

significantly better than performance with music. Last, extraversion was a significant

predictor of performance on the cognitive ability test in all three background sound

conditions (Dobbs et al., 2011).

An earlier study explored the effect of personality and recorded vocal and

instrumental music on cognitive task performance (Furnham, Trew, & Sneade, 1999).

Student participants ages 16 to 18 completed a reading comprehension test, logic

problem, and coding task while listening to music. Despite a lack of significant

interactions, the cognitive task performance of individuals with a preference for

introversion was generally impaired by music in the environment, and the performance of

extraverted types was generally enhanced. Additionally, extraversion had a significant

main effect on reading comprehension (Furnham et al., 1999).

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The effect of music on high-cognitive demand task performance. A series of

studies have been conducted to test music’s influence on high-cognitive demand task

performance. The relationship between music and affect is an important element in all of

these studies, and their interaction is supported by such research. Recently, personality

has also been included as a possible factor (Lesiuk, 2008; Lesiuk et al., 2009, 2011).

Most of the applied research is limited by reliance on self-reports to measure

productivity, but a recent study of systems analysts utilized expert evaluation. Thus, the

study employed an objective measure of productivity (Lesiuk et al., 2011).

Lesiuk (2000) examined affective response in university students during computer

programming tasks and found that state anxiety decreased in response to music. Students

who listened to music prior to and during the tasks had significantly lower levels of state

anxiety than students who used no music. Additionally, in a later study with computer

systems designers, three significant correlations emerged. Anxiety was positively

correlated with listening time during the day, and depression was positively correlated

with listening time during the day and listening time at work. Therefore, as negative

affect increased, participants listened to more music (Lesiuk et al., 2009). This

relationship is supported by an earlier finding in which everyday music listening

experiences were found to be mostly positive (Sloboda & O’Neill, 2001).

Lesiuk (2008) explored music listening and anxiety in another high-cognitive

demand occupation, air traffic control, and personality variables were included.

Personality was represented by extraversion and introversion, and anxiety was divided

into low and high-trait anxiety. Participants either listened to preferred music or sat in

silence. Results showed that state anxiety decreased significantly with and without

36

music, but individuals with high-trait anxiety and a preference for introversion had no

decrease in state anxiety (Lesiuk, 2008). A later study with computer information

systems developers included MBTI personality variables and trait mood variables.

Results of this study showed that negative trait mood was high in individuals with

preferences for introversion and feeling (Lesiuk et al., 2009).

Music-use has also been effective at improving state mood at work, with impacts

on productivity, and familiar music appears to be more influential than unfamiliar music.

In particular, a relaxed mood has been linked to a relationship between music-use and

productivity (Oldham et al., 1995). Another study by Lesiuk (2005) tested the effect of

music on positive affect and work performance in computer information systems

developers. State positive affect was measured prior to and following music listening,

and work performance was assessed for quality-of-work and time-on-task. Data were

collected over a five week period, and music listening occurred on the first, second, third,

and fifth week. Results showed that trait positive affect was high in this population. Pre

to post, state positive affect increased significantly during the music weeks, and the

greatest increase occurred during the week following the week without music. Also, as

trait positive affect increased, listening duration increased. State positive affect was

lowest in the week without music and highest in the third week. Analysis of state

positive affect over time revealed a significant difference between pre-test scores and the

third week and between the third and fourth week (no music). Quality-of-work scores

decreased significantly from baseline to the week without music, and returned to higher

levels when music listening returned in the final week. Additionally, time-on-task was

longer than anticipated during the week without listening. Time-on-task differences were

37

significant between baseline and the week without music, between the third week and the

week without music, and between the week without music and the final week. To

summarize, music-use corresponded with increases in state positive affect, which

appeared to have a positive influence on quality-of-work and time-on-task (Lesiuk,

2005).

A recent study by Lesiuk (2010b) measured the effect of preferred music on mood

and work performance in professional computer information systems developers.

Computer information systems design requires generative processing, or creative

problem-solving, which was the specific cognitive process being tested. The study took

place over a three-week period, and the participants listened to at least 30 minutes of

music during each workday in weeks one and three. A diverse music library was

provided, and/or participants could listen to music from their personal collection. A

narrative work stress questionnaire asked participants to identify and classify the stress

inducers present in this setting. Affect was measured using the Job Affect Scale, which

pairs positive affect with enthusiasm and negative affect with unpleasant arousal (Brief et

al., 1988). Cognitive performance was assessed using a self-assessment questionnaire. A

final music listening questionnaire was given after the study to capture each participant’s

most liked and least liked music listening experience (Lesiuk, 2010b).

Participants identified stressors related to “time pressures, unrealistic deadlines,

volume of work, not knowing how to do something, co-worker problems, client

problems, and layoffs of co-workers” (Lesiuk, 2010b, p. 145). Such stressors have a

negative effect on cognition and decision making (Longenecker et al, 1999). Positive

affect was significantly higher during weeks one and three than during week two.

38

Moreover, negative affect was significantly lower during those weeks, when compared to

the week with no music. Furthermore, higher self-ratings of cognitive performance

occurred during the weeks with music. Four themes for why music was used were

revealed in a final music listening questionnaire: mood, nostalgia, relieving stress, and

work efficiency. Lesiuk highlights a participant comment that emphasizes the choice of

music for focusing: “The right type of music allows me to focus on my work and be free

of distractions. The wrong type of music annoys me and distracts me from my work”

(Lesiuk, 2010b, p. 148). Finally, Lesiuk recommends that employers empower their

employees by increasing awareness of affective response. Researchers are encouraged to

seek more evidence to strengthen the relationship between music and affect in the

workplace, and a theoretical model is requested (Lesiuk, 2010b).

Another recent study explored the effect of music and personality on state mood

and work performance in systems analysts (Lesiuk et al., 2011). All of the participants

completed the personality inventory, mood scale, and a work performance task, and half

of the participants also listened to music prior to the task. Personality was assessed prior

to the task, and mood was assessed immediately before music listening, after music

listening before the task, and after the task. Results showed that extraversion had a

significant main effect on work performance, with higher extraversion being associated

with lower scores on the work performance task. Mood was represented by positive and

negative affect. Negative affect decreased after music listening and increased after the

task, while positive affect increased steadily over the three time points with music

listening (Lesiuk et al, 2011).

39

Summary of Literature Review

Music perception occurs bilaterally in the brain, involving both neocortically

mediated cognitive processes and subcortically mediated affective responses (Blood &

Zatorre, 2001; Peretz & Coltheart, 2003; Peretz & Zatorre, 2005). Many of the neural

areas identified in music perception are involved in the cognitive processes of attention,

working memory, and executive function (Lesiuk, 2010b). The brain reward system is

also activated by music, facilitating arousal (Goldstein, 1980; Rickard, 2004; Trainor &

Schmidt, 2003). Additionally, psychological theories link music stimulus properties to

arousal and expectation (Berlyne, 1971a, 1971b; Meyer 2001).

Everyday music-use is influenced by contextual factors and individual trait and

state preferences (Lamont & Greasley, 2009; Rentfrow et al., 2011; Sloboda & O’Neill,

2001). Everyday music is defined by 10 dimensions, each of which relates to the context

of music-use and has an impact on individual affective response. These dimensions

comprise musical, social, psychological, and environmental factors (Sloboda, 2010).

Recent studies have defined music-use in terms of function, and three categories have

been established: background, cognitive, and emotional-use (Chamorro-Premuzic, et al.,

2010; Chamorro-Premuzic & Furnham, 2007; Chamorro-Premuzic, Gomà-i-Freixanet,

Furnham, & Muro, 2009; Chamorro-Premuzic, Swami, Furnham, & Maakip, 2009).

Affect includes both emotion and mood (Sloboda &Juslin, 2010). Mild positive

affect promotes efficient and flexible cognitive processes (Ashby et al., 1999; Estrada et

al., 1997; Forgas, 1998; Forgas & George, 2001; Isen, 2009). Mild positive affect is also

associated with expectation, specifically in terms of motivation, and a link exists between

motivation and cognitive performance (Erez & Isen, 2002; Isen, 2009). Furthermore, a

40

body of research shows that negative affect is detrimental to cognition, impacting

perception, attention, memory, decision-making skills, and creativity (Amabile et al.,

2005; Brief, et al., 1988; Isen, 2009; Mano, 1992; Watson & Clark, 1984; Watson &

Tellegen, 1985). Movement of the neurotransmitter, dopamine, in the anterior cingulate

gyrus also helps to explain the relationship between affect and memory, problem-solving,

and cognitive flexibility (Ashby et al., 1999; Isen, 2009). Affect appears to interact with

personality as well, with influences on cognition, but this theory necessitates further

research (Depue & Collins, 1999; Rusting, 1999, 2001).

Computer programming is part of an ongoing process, called the systems

development life cycle (Valacich et al., 2006). Computer programming is a process

wherein a series of computer design specifications are translated into an organized

working unit, or program (Dennis & Wixom, 2000). Computer programmers must utilize

high level cognitive processes, including focused and selective attention, creative-

problem solving, and abstract planning (Lesiuk, 2010b; Sonnentag et al., 2006). Thus,

according to present-day cognitive theories, computer programming is a high-cognitive

demand task (Garner, 2002; White & Sivitanides, 2005).

Some personality preferences have been identified as typical among computer

programmers, including intuition and thinking (Woszczynski et al., 2005). These

preferences represent the MBTI functions, which are related to one’s ability to problem-

solve (Myers et al., 2003). Other information technology professionals showed a

preference for introversion and judging (Lesiuk et al., 2009). Introversion is associated

with an individual’s propensity to choose occupations that require interest in and

sustained attention to concepts and ideas (Myers et al., 2003). Furthermore, introversion

41

has been shown to have a positive relationship with work performance (Lesiuk et al.,

2011). Other research evidence suggests, nonetheless, that diverse personality profiles

are likely among computer programmers (Kenner, 1993; Woszczynski et al., 2005).

Personality also has an interaction with mood. Information technology professionals with

preferences for introversion and feeling showed higher negative trait mood (Lesiuk et al.,

2009). Additionally, conscientiousness had a negative relationship with fatigue, and

reduced fatigue is an indication of decreased negative affect (Lesiuk et al., 2011).

General results of a series of studies that explored the role of personality in music-

use suggest that preferences for neuroticism, extraversion, and openness are likely

connected to music-use. Specifically, neuroticism had a positive relationship with

emotional-use of music, extraversion had a positive relationship with background-use of

music, and openness had a positive relationship with cognitive-use of music. (Chamorro-

Premuzic et al., 2007, 2009ab). Individual personality preferences are also related to

other music-use characteristics. Introversion was associated with using music to control

anxiety in typical adults (Cassidy & MacDonald, 2007). Additionally, information

technology professionals with preferences for extraversion and feeling listened to music

for longer periods of time, when compared to introverted and thinking types (Lesiuk et

al., 2009).

Music appears to have an effect on affective response, as evidenced by its role in

successful mood-induction techniques (Eich et al., 2007). Brain imaging data also show

the effect of music in mood-induction (Blood & Zatorre, 2001; Mitterschiffthaler et al.,

2007). Furthermore, music has an anxiolytic effect on physiological symptoms of stress

42

and anxiety (Knight & Rickard, 2001). From another perspective, affect appears to

impact music-use, with certain individuals being inclined to use music to for mood

regulation (Greenwood & Long, 2009).

Music also interacts with personality, with impacts on cognitive performance. In

the presence of background music and noise, typical individuals with a preference for

extraversion performed better than introverted types on a Stroop test. Introverts

performed better than extraverts, however, on immediate recall and delayed recall tests in

background music, background noise, and no music conditions (Cassidy & MacDonald,

2007). Extraversion and background sound also had effects on the cognitive performance

of typical individuals on an abstract reasoning test and a cognitive ability test.

Performance with background music was significantly better than with background noise,

and extraversion was a predictor of performance in the background music and

background noise conditions (Dobbs et al., 2011). Additionally, in a study that tested the

effect of personality and music on cognitive performance in typical individuals,

extraversion had a main effect on reading comprehension (Furnham et al., 1999).

Studies that explore the effect of music on high-cognitive demand tasks include

both mood and personality variables. Students that listened to music during computer

programming tasks reported lower levels of anxiety than students that used no music

(Lesiuk, 2000). A study with air traffic controllers included personality and state anxiety

as variables in music use. Individuals with a preference for introversion and high-trait

anxiety had no decrease in state anxiety with and without music (Lesiuk, 2008).

Additionally, negative trait mood was high in computer information systems developers

with preferences for introversion and feeling (Lesiuk et al., 2009). Moreover, in

43

computer systems designers, increases in negative affect were associated with longer

durations of music-use (Lesiuk et al., 2009). The effect of music on mood also has

implications on productivity at work. Computer information systems developers showed

an increase in state positive mood during music-use, with positive ramifications on

quality-of-work and time-on-task (Lesiuk, 2005, 2010b). A similar study with systems

analysts also included personality variables. Negative affect decreased and positive

affect increased with music, and individuals with a preference for extraversion tended to

have lower scores on a work performance task (Lesiuk et al., 2011).

Research Questions

This study is designed to address the following research questions in regard to

music-use during high-cognitive demand computer programming tasks:

1. What is the relationship between personality and music-use?

2. What is the relationship between mood and music-use?

3. What demographic and/or contextual factors are related to music-use?

Chapter Three

Method

This chapter describes the research design, variables, and procedures used to test

the research questions. Participant eligibility is described, and recruitment data are

included. Next, measurement scales are described in detail, and the recruitment and data

collection procedure is presented. Finally, data analysis techniques are explained.

Participants

Thirty-four university students participated in this study, including students at the

undergraduate, masters, and doctoral levels. Participants were recruited from University

of Miami in Coral Gables, Florida, during the Spring 2012 semester. Students from the

Department of Computer Science, Electrical and Computer Engineering Department,

Visual Journalism program in the School of Communication, and Music Engineering

Technology program in the Frost School of Music were included. Students were eligible

to participate in the study if they were enrolled in computer programming courses within

these academic areas. Recruitment was limited to courses with curriculum that required

students to complete regular computer programming tasks. Female and male students 18

years and older were eligible to participate. All racial and ethnic groups were included,

and both musicians and nonmusicians were accepted. Last, only those students who

usually listened to music while computer programming were eligible to participate.

Design and Variables

This study utilized a cross-sectional survey design. This design was chosen to

describe trends and attitudes of student computer programmers who listen to music while

they work (Creswell, 2009; Fraenkel & Wallen, 2009). A cross-sectional survey design,

44

45

including self-administered and Web-based questionnaires, was the preferred type of data

collection, as it allowed for economy of design and a rapid turnaround. Specifically, the

study design was developed to examine relationships between personality, mood, and

music-use (see Figure 1). Personality was comprised of five factors: Neuroticism,

Extraversion, Openness to Experience (Openness), Agreeableness, and Conscientiousness

(Costa & McCrae, 1992). Mood was expressed as Positive Affect and Negative Affect,

and subscales existed for each variable. Positive Affect subscales included Relaxation

and Enthusiasm. Negative Affect subscales included Nervousness and Fatigue (Oldham

et al., 1995). Music-use was comprised of Background, Cognitive, and Emotional-use

variables (Chamorro-Premuzic & Furnham, 2007).

Measures

Measurement tools were administered in two study phases. During the first study

phase, participants met with the researcher to complete a demographic questionnaire and

an inventory for personality. During the second study phase, participants completed a

standardized mood questionnaire, a computer programming task assessment, and music-

use questionnaire on a study webpage. Both phases were completed within a two week

period. The participants completed a computer programming task of their choosing while

listening to preferred music during the second phase. The task and listening occurred

after the mood scale, and it was immediately followed by the computer programming task

assessment and music-use questionnaire. Figure 2 includes the duration of each measure.

Demographic questionnaire. The researcher-generated demographic

questionnaire first asked participants to report their age, gender, ethnicity, and race (see

Appendix A). The descriptors for ethnicity were “Hispanic or Latino” and “Not Hispanic

46

Figure 1. Study Variables.

Personality & Mood Music-Use

Openness

Conscientiousness

Extroversion Personality

Agreeableness Background

Neuroticism Music-Use Cognitive

EmotionalPositive Affect

RelaxationMood

Enthusiasm

Negative Affect

Nervousness

Fatigue

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Figure 2. Measures Flow Chart.

Phase Measure Duration

Demographic questionnaire 5 min↓

NEO Five-Factor Inventory 15 min

Job Affect Scale 3 min↓

Computer programming task 20+ min↓

Computer programming task assessment 2 min↓

Music use questionnaire 20-30 min

Total: 65-75+ min

Phase One

Phase Two

48

or Latino.” The descriptors for race were “American Indian or Alaska Native,” “Asian,”

“Black or African American,” “Native Hawaiian or Other Pacific Islander,” and “White.”

Then, participants indicated their current level of education and degree being pursued.

Additionally, the participants reported years of computer programming background,

average number of hours spent daily on computer programming, and most prevalently

used computer programming language. Last, the participants rated their level of

proficiency in computer programming using a 5-point scale ranging from 1 (absolute

beginner) to 5 (power user). More specifically, an absolute beginner had little or no

knowledge, a novice had created a few simple computer programs, an intermediate

computer programmer was moderately proficient, an advanced computer programmer

had created complex computer programs, and a power user was an expert in computer

programming (Lesiuk et al., 2011).

NEO Five-Factor Inventory. This personality measure, called the NEO Five-

Factory Inventory (NEO-FFI) consisted of 60 items rated on a 5-point Likert scale (Costa

& McCrae, 1992). Each item on the NEO-FFI was a statement with which the

participants agree or disagree (see Appendix B). Participants responded on a scale from

1 (strongly disagree) to 5 (strongly agree). The NEO-FFI measured the Big Five

personality traits, including Neuroticism, Extraversion, Openness, Agreeableness, and

Conscientiousness. For each of these traits or dimensions, participants received a score

on a continuum from very low to very high. Individuals with a high Neuroticism score

have a tendency to experience negative affect and have irrational ideas. They have little

control of their impulses with poor coping skills. Extraverted personalities tend to be

very social with a cheerful disposition. They are described as active, talkative, and

49

assertive. Individuals with a high Openness score tend to have an active imagination,

curiosity about themselves and the world around them, and independence of judgment

that is often associated with creativity. The trait of Agreeableness is concerned with

interpersonal relationship abilities. Individuals with a high degree of Agreeableness tend

to be sympathetic toward others, and they are willing to help those around them. High

Conscientiousness scorers tend to be strong-willed, determined, powerful, and reliable.

They are usually able to control impulses. Reported reliability coefficients are 0.79 for

Neuroticism, 0.79 for Extraversion, 0.80 for Openness, 0.75 for Agreeableness, and 0.83

for Conscientiousness.

Job Affect Scale. The Job Affect Scale is a 12-item measurement of state mood

in the work setting (see Appendix C). Each item has a single word to describe a feeling

one might have while working. Word examples include “calm,” “excited,” “scornful,”

and “drowsy.” The original scale included 20 items, but an adapted scale that consists of

12 items was later created for the purpose of reducing time spent on questionnaires

during work (Oldham et al., 1995). Participants respond in conjunction with a work task

utilizing a 7-point Likert scale ranging from 1 (extremely slightly) to 7 (extremely

strongly). Responses may occur before, during, or after a work task (Brief et al., 1988).

Six items have words relating to Positive Affect, and six items have words relating to

Negative Affect. Scores range from 6 to 42 for each state affect, with higher scores

representing higher affect intensity. In the Oldham et al. (1995) adaptation, subscales

have been created for each state affect. High Positive Affect is represented by

Enthusiasm, and low Positive Affect is represented by Relaxation. High Negative Affect

is represented by Nervousness, and low Negative Affect is represented by Fatigue. These

50

subscales add a valence dimension to the results. The adapted JAS has reliability

coefficients ranging from 0.66 to 0.91. For the purpose of this study, participants

completed the JAS immediately prior to the computer programming task. They were

instructed to rate each word to describe how they feel at the current moment.

Task assessment. The researcher-generated task assessment collected details

about the computer programming task (see Appendix D). This assessment was included

to explore relationships between computer programming task characteristics and music-

use. First, a 5-point scale ranging from 1 (extremely easy) to 5 (extremely difficult)

measured the complexity of the computer programming task. Participants were then

asked to indicate the length of time in minutes spent on the task. Next, they specified

where they completed the task, choosing from “home, work, library,” or “other.” Last,

participants identified the day of the week and when they completed the task, choosing

from “morning, afternoon,” or “night.”

Music-use questionnaire. The music-use questionnaire gathered information

about the type and role of music that participants choose to accompany their computer

programming task (see Appendix E). The multifaceted questionnaire was primarily

designed to measure music-use, and it also allowed the researcher to collect a playlist and

explore relationships between the listening experience and music-use. Additionally, this

questionnaire included questions about the participants’ musical experience. To conclude

the questionnaire and complete participation in the study, all participants were given an

opportunity to make general comments.

51

Music choices. The participants listed up to 10 songs that they listened to during

the computer programming task. They also reported the artist/band and musical style for

each of the songs. Additionally, participants reported generally how active the music was

that they chose using a 5-point scale ranging from 1 (extremely inactive [very low

energy]) to 5 (extremely active [very high energy]).

Listening experience. The participants reported in minutes the length of time

spent listening to music. They also indicated how focused they were on the music during

the task using a 5-point scale ranging from 1 (extremely unfocused) to 5 (extremely

focused). Additionally, participants reported the device they used to play and listen to the

music and whether or not they used headphones. These data were collected as possible

environmental factors to consider as part of the listening experience.

Music-use. Participants were given an opportunity to generate their own

descriptions of the reasons for listening while programming. They answered two open-

ended questions. The first question asked them to explain why they chose their particular

music to accompany the task. The second question asked them how they thought music

listening influenced them and their work. Participants then completed the Uses of Music

Inventory, which is described in detail next.

Uses of Music Inventory. This measure of music-use, called the Uses of Music

Inventory consists of 15 items rated on a 5-point Likert scale (Chamorro-Premuzic &

Furnham, 2007). Each item consists of a statement which a person may use to describe

their feelings about listening to music (see Appendix E). Participants respond on a scale

from 1 (strongly disagree) to 5 (strongly agree). Examples of statements include “I

enjoy listening to music while I work, Listening to music is an intellectual experience for

52

me,” and “Listening to music really affects my mood.” The Uses of Music Inventory

categories include Background, Cognitive, and Emotional-use. Five items have

statements relating to Background-use, five items have statements relating to Cognitive-

use, and five items have statements relating to Emotional-use. Scores range from 5 to 25

for each category, with higher scores representing a higher likelihood of using music in

this way. Background-use assesses the extent to which an individual uses music while

working, studying, socializing, or performing a task. Cognitive-use is an indication of

the degree to which an individual listens to music in an intellectual way. Emotional-use

refers to the extent to which an individual uses music for emotional regulation.

Cronbach’s α ranges from 0.61 to 0.64, and 0.6 is an acceptable value for 5-item scales

(Chamorro-Premuzic & Furnham, 2007; Chamorro-Premuzic, Swami, Furnham, &

Maakip, 2009).

Musical experience. Two questions collected the musical experience of each

participant. They reported the number of years they have played a musical instrument or

sung in a choir. Participants also choose from six spans of time that they typically spend

listening to music each day, ranging from “0-1 hours” to “10+ hours.”

Procedure

University students were recruited from class meetings with permission from

course instructors. Professors were contacted via email or in person during office hours.

A study advertisement was attached to email correspondences, delivered by hand, and

posted on approved department bulletin boards (see Appendix F). For each course, the

researcher made a study announcement and distributed study advertisements during the

first five minutes of an initial class meeting. Immediately following this class, the

53

researcher met with students to field questions and schedule individual appointments for

the first phase of the study. One week later, the researcher returned to each class prior to

its start time to follow-up with students, address any questions or concerns, and schedule

new appointments.

Study Phase 1. Participants met individually with the researcher at various

campus locations to complete the first phase of the study. Locations included library

study rooms, computer labs, and student common areas. Initially, each participant signed

an informed consent form (see Appendix G) and was given a participant number.

Participant email addresses and phone numbers were included on the informed consent

form for the purpose of communicating an assigned study website login. Only participant

numbers were included on each subsequent measure. Names and contact information

were omitted to protect the identity of each student. Then, participants completed the

researcher-generated demographic questionnaire and the NEO-FFI measure of

personality to conclude the first phase of the study. This meeting occurred in person to

help establish a connection with each participant and ensure that he or she understood the

study requirements. Furthermore, the NEO-FFI requires permission and a fee to

administer via a study website. Each meeting lasted approximately 20 to 30 minutes.

Study Phase 2. To initiate the second phase of the study, the researcher sent an

email to each participant containing a unique link to the study website. By providing

each individual participant with a dedicated link to the study website, participants were

only granted access to their own online measures and responses. This security measure

54

ensured the privacy of each participant. On the study website, each individual measure

began by requesting the participant number, allowing the researcher to link website data

to data collected during the first phase of the study.

The second phase of the study was completed by each participant at a time of

their choosing. The researcher asked participants to finish the second phase within two

weeks of the first phase to reduce the threat of mortality. On the study website,

participants began by completing the Job Affect Scale of state mood. Then, they were

instructed to complete a computer programming task of their choice, which lasted a

minimum of 20 minutes without interruption. This length of time has been reported to be

typical for a continuous computer programming task (M. Ogihara, personal

communication, October 5, 2011). The researcher requested that each participant be

prepared with a “difficult coding task” prior to the study, as research suggests that the

likelihood of music having an effect on task performance is higher with more complex

mental tasks (Cassidy & MacDonald, 2007; M. Ogihara, personal communication,

November 4, 2011). A “difficult” computer programming task was relative for each

participant, based on their level of expertise. During this task, participants were also

expected to listen to at least 10 minutes of music. Research studies have indicated that

mood can be affected within this amount of time (Barnes-Holmes, Barnes-Holmes,

Smeets, & Luciano, 2004; Eich et al., 2007; Smith & Noon, 1998; Standage, Ashwin, &

Fox, 2010).

55

Upon completion of the computer programming task, participants returned to the

study website. Next, they completed the computer programming task assessment to

gather data about each individual task. Finally, they finished the second phase and

concluded the study by completing the music-use questionnaire. The second phase of the

study lasted 45 minutes or more, depending on the length of the chosen computer

programming task. Total participation in the study lasted a minimum of 65 minutes (see

Figure 2).

Data Collection

The researcher collected demographic information and NEO-FFI data during the

first phase of the study, utilizing hard copies of the demographic questionnaire and the

NEO-FFI Test Booklet-Form S (Adult). The Job Affect Scale, computer programming

task assessment, and music-use questionnaire data were collected via the study website.

Access to the study website was username/password protected and given only to the

researchers. The study website, www.UMmusicwhileyouwork.info, was hosted on

www.hostpapa.com. All data were stored in a locked file cabinet in the researcher’s

home office. Electronic data was stored on a password-protected computer. Data was

entered by participant number with no identifying information.

Data Analysis

Data collected were analyzed using several methods. Frequencies, means, and

standard deviations were calculated for the demographic, personality, mood, and music-

use data. Frequencies were calculated for computer programming task data and listening

experience data. The NEO-FFI table of mean age-matched scores was used for

comparison to the study sample (Costa & McCrae, 1992). Thus, t-tests for independent

56

means were conducted to compare the study sample means for each of the personality

factors to means of age-matched adults (Creswell, 2009; Gravetter & Wallnau, 2011).

The test was appropriate, because the researcher wished to identify significant differences

between mean scores from two dissimilar groups on the same measure (Fraenkel &

Wallen, 2009).

To address research questions 1 and 2, bivariate Pearson product-moment

correlation coefficients were calculated for the purpose of ascertaining the degree of

relatedness amongst the continuous variables, including each of the personality factors,

mood variables, mood subscales, and music-use variables. Pearson correlations

measured the degree and direction of the linear relationship between two of the variables

at a time (Creswell, 2009; Gravetter & Wallnau, 2011). Scatterplots were constructed to

illustrate the data visually. The analyses were appropriate, because the researcher was

exploring relationships between quantitative variables within one group (Fraenkel &

Wallen, 2009). Further, multiple regression analyses were used to test significant

correlations in a predictive model. The analysis was appropriate to test the correlation

between a criterion music-use variable and two or more predictor variables (Fraenkel &

Wallen, 2009). For research question 3, Pearson product-moment correlation coefficients

were calculated for the music-use variables and other continuous variables, such as age,

musical background, task duration, and listening duration.

Next, Spearman rank correlation coefficients were calculated between the

continuous and ordinal variables. Ordinal variables included school level, computer

programming background, computer programming proficiency, computer programming

hours per day, computer programming task difficulty, music activity level, listening hours

57

per day, and music focus. The analysis was applicable, because correlated data from an

ordinal scale may yield results that show a consistent direction, but not necessarily a

linear relationship (Creswell, 2009; Gravetter & Wallnau, 2011).

To conclude the statistical analysis, a one-way analysis of variance (ANOVA)

was used to determine whether a significant effect of computer programming background

on music-use existed (Creswell, 2009; Gravetter & Wallnau, 2011). The analysis was

possible because two similarly sized groups could be formed from the study sample

based on computer programming background. The test allowed the researcher to analyze

variation both within and between each group. Since this ANOVA only compared two

groups, an F test was sufficient to determine significance (Fraenkel & Wallen, 2009).

In addition to quantitative methods, content analysis was utilized to code

responses to open-ended items on the music-use questionnaire (Creswell, 2009; Fraenkel

& Wallen, 2009). To reduce the threat of researcher bias, four random adult volunteers

rated the responses. Raters were given the definitions of background, cognitive, and

emotional-use of music, and they were instructed to choose the best placement of the

each participant comment into each of the music use categories. Raters were also given

the option of choosing “other” when none of the provided descriptions were applicable.

These directed content analyses were conducted to compare open-ended responses on the

music-use questionnaire to the Uses of Music Inventory data (Hsieh & Shannon, 2005).

The researcher also employed conventional and summative content analyses.

These qualitative methods were utilized to identify themes in responses to open-ended

items. The conventional content analysis used categories that were derived directly from

58

the content for coding. In summative content analysis, key terms or phrases were

counted and compared for interpretation (Fraenkel & Wallen, 2009; Hsieh & Shannon,

2005).

Chapter Four

Results

The recruitment and data collection phase lasted approximately three months,

from February 2012 through May 2012. A total of 34 participants completed all tasks

and measures of the study, and the results are presented in group aggregate below. This

chapter begins by presenting descriptive results for the total sample on each variable.

Inferential results are reported next, based on the research questions. Finally, results of

content analyses are reported at the end of the chapter.

Statistical analyses were completed using the software Statistical Package for

Social Sciences (SPSS) v. 16.0. Certain measurements collected ordinal data. For

example, some items contained spans of time (e.g., 0-1 year, 2-3 years, 4-5 years, etc.),

and other items had levels (e.g., novice, intermediate, advanced, etc.). These data are

displayed in frequency tables, and they were also coded into whole numbers (e.g., 1, 2, 3,

etc.) in SPSS in order to determine group measures of central tendency.

Descriptive Results

The descriptive results report frequencies, means, and standard deviations for the

demographics, including musical and computer programming background data.

Frequencies are also shown for the computer programming task data and listening

experience data. Additionally, personality, mood, and music-use data are summarized.

Demographics. Demographic characteristics of this sample are summarized in

Table 1. The final sample included 34 participants, comprised of 31 males and 3 females.

This distribution of sexes is representative of the student computer programming

population (M. Ogihara, personal communication, November 1, 2012). The mean age of

59

60

the total sample was 23.03 years (SD = 6.28), with ages ranging from 18 to 50 years.

Ethnicity and race were recorded as separate variables. Four of the participants were

Hispanic or Latino, and all four were White. Thirty of the participants were not Hispanic

or Latino. Of the participants who were not Hispanic or Latino, 23 were White, 3 were

Asian, 2 were Black or African American, 1 was American Indian or Alaska Native, and

1 was Native Hawaiian or Other Pacific Islander. The participants were university

students pursuing a variety of degrees. Programs of study among these students included

computer science, electrical engineering, music engineering, computer engineering, math,

and others (see Table 2). Twenty-seven participants were undergraduates, four were

master’s degree candidates, and three were doctoral students. Participants’ musical

background, as shown in the number of years playing a musical instrument or singing,

had a mean of 9.12 years (SD = 5.87), with a range from 2 to 25 years. Four participants

had no musical background. Last, participants most frequently reported listening to 2-3

hours of music daily, and the group average was also 2-3 hours daily.

Computer programming experience. The computer programming experience

of this sample is summarized in Table 3. The participants most frequently reported 1-2

years of computer programming background, and the group averaged 3-4 years of

background. Participant self-ratings for level of computer programming proficiency were

most frequently at the level of intermediate, and the group also averaged an intermediate

proficiency (see Figure 3). Participants most frequently reported completing 0-1 or 2-3

hours of computer programming each day, and the group average was 2-3 hours. Finally,

Java, C++, and MATLAB were the most frequently reported computer programming

languages preferred by this sample.

61

Table 1

Demographic Characteristics of Sample (n = 34)

Frequency Distribution f % Gender

Female

3 8.8

Male 31 91.2 Ethnicity

Hispanic or Latino

4 11.8

Not Hispanic or Latino 30 88.2 Race

American Indian or Alaska Native

1 2.9

Asian

3 8.8

Black or African American

2 5.9

Native Hawaiian or Other Pacific Islander

1 2.9

White 27 79.4 School Level

Undergraduate

27 79.4

Masters

4 11.8

Doctoral 3 8.8 Daily Music Listening (hours)

0-1

4 11.8

2-3

21 61.8

4-5

8 23.5

6-7

0 0.0

8-9

1 2.9

10+ 0 0.0 Note. The sum of distribution percentage values may not equal 100%, due to rounding.

62

Table 2

Programs of Study Reported (n = 34)

Frequency Distribution f % Computer Science

10 29.4

Electrical Engineering

5 14.7

Music Engineering

5 14.7

Computer Engineering

3 8.8

Math

3 8.8

Applied Physics

2 5.9

Music Composition

2 5.9

Communications

1 2.9

Economics

1 2.9

Environmental Engineering

1 2.9

German 1 2.9 Note. The sum of distribution percentage values may not equal 100%, due to rounding.

63

Table 3

Computer Programming Experience of Sample (n = 34)

Frequency Distribution f % Background (years) Less than 1

4 11.8

1-2

12 35.3

3-4

9 26.5

5-6

2 5.9

7-8

1 2.9

More than 8 6 17.6 Proficiency

Absolute Beginner

0 0.0

Novice

7 20.6

Intermediate

13 38.2

Advanced

12 35.3

Power User 2 5.9 Programming Hours/Day

0-1

14 41.2

2-3

14 41.2

4-5

2 5.9

6-7

1 2.9

8-9 3 8.8

64

Figure 3. Levels of computer programming proficiency reported. (x) = number of times level was reported.

(7) Novice

(13) Intermediate

(12) Advanced

(2) Power User

65

Computer programming task characteristics. Participants completed a

computer programming task of their choosing. They were instructed to complete a

difficult coding task lasting at least 20 minutes without interruption. They could

complete the task at any location, during any time of day, and on any day of the week.

The computer programming task characteristics of the group are summarized in Table 4.

The participants most frequently reported completing tasks of moderate difficulty, and

the group average was also moderate. Computer programming task durations had a mean

of 70.79 minutes (SD = 64.38), with a range from 15 to 240 minutes. Participants most

frequently completed their task at home. Other locations included computer labs, the

library, work, and study rooms. Most participants did their work in the evening, as

opposed to the morning. They worked every day of the week, but less than 15 percent of

the participants completed their tasks on Fridays and Saturdays.

Listening experience characteristics. Participants listened to music of their

choosing while they completed the computer programming task. They were instructed to

listen to at least 10 minutes of music. Music-use durations had a mean of 56.12 minutes

(SD = 43.84), with a range from 15 to 200 minutes. Participants could listen using an

audio device of their choosing, with headphones or without. Thirty-two participants

listened to music on their personal computer. One participant used a portable mp3 audio

player, and one participant used his or her phone. Twenty of the participants used

headphones. Other music-use characteristics of the group are summarized in Table 5.

The participants most frequently reported listening to music that was active (high

energy), and the group average was also active. They were asked to report how focused

they were on the music using a 5-point scale ranging from 1 (extremely unfocused) to 5

66

(extremely focused). Participants most frequently reported being focused on the music,

but the group average was neutral.

Table 4

Computer Programming Task Characteristics (n = 34)

Frequency Distribution f % Task Difficulty

Extremely Easy

1 2.9

Easy

7 20.6

Moderate

14 41.2

Difficult

9 26.5

Extremely Difficult 3 8.8 Task Location

Home

19 55.9

Work

2 5.9

Library

4 11.8

Other 9 26.5 Task Time of Day

Morning

5 14.7

Evening 29 85.3 Task Day of Week

Sunday

6 17.6

Monday

6 17.6

Tuesday

4 11.8

Wednesday

6 17.6

Thursday

7 20.6

Friday

2 5.9

Saturday 3 8.8 Note. The sum of distribution percentage values may not equal 100%, due to rounding.

67

Table 5

Listening Experience Characteristics (n = 34)

Frequency

Distribution f % Music Activity Level

Extremely Inactive (very low energy)

0 0.0

Inactive (low energy)

1 2.9

Moderate

13 38.2

Active (high energy)

17 50.0

Extremely Active (very high energy) 3 8.8 Music Focus

Extremely Unfocused

1 2.9

Unfocused

6 17.6

Neutral

13 38.2

Focused

14 41.2

Extremely Focused 0 0.0 Note. The sum of distribution percentage values may not equal 100%, due to rounding.

Personality factors. The results of the NEO-FFI were distributed among five

personality factors, and scores indicated the intensity of each factor. Scores between 0

and 48 were possible for Neuroticism, Extraversion, Openness, Agreeableness, and

Conscientiousness. The sample means for each of these factors are summarized in Table

6. Additionally, an inferential statistical test was used to compare how the personality of

this sample differs from the personality of typical adults. The sample means were

compared with age-matched adult means, as reported in the NEO PI-R professional

manual (Costa & McCrae, 1992). Results of one-sample t-tests showed one significant

difference (see Table 6). A significant difference between groups emerged in the

68

Openness factor, with the study group having a higher mean score for Openness (M =

34.71, SD = 6.02) than the typical mean score for Openness (M = 27.03, SD = 5.84); t(33)

= 7.43, p = 0.001. This result shows that Openness scores in this sample are significantly

different from Openness scores among typical adults. Specifically, Openness scores in

this sample are higher than in typical adults.

Table 6

NEO-FFI Factor Means for Sample and Typical Adults

Mean Score Factor Sample Typical t df Neuroticism

17.91 (6.75)

19.07 (7.68)

-1.00

33

Extraversion

28.03 (6.57)

27.69 (5.85)

0.30 33

Openness

34.71 (6.02)

27.03 (5.84)

7.43*** 33

Agreeableness

31.47 (6.55)

32.84 (4.97)

-1.22 33

Conscientiousness

33.00 (6.14)

34.57 (5.88)

-1.49 33

Note. *** p <.001, two-tailed. Standard Deviations appear in parentheses below means. Mood variables and subscales. The results of the Job Affect Scale are provided

for Positive Affect and Negative Affect. Two mood subscales exist for each of the

variables. Relaxation and Enthusiasm are subscales of Positive Affect, and Nervousness

and Fatigue are subscales of Negative Affect. Scores between 6 and 42 were possible for

each variable, and scores between 3 and 21 were possible for each subscale. The means

and standard deviations for each of the variables and subscales are summarized in Table

7. Differences between mean scores for Positive Affect and Negative Affect were

69

negligible, and both scores were near the value of 21. Positive Affect was nearly evenly

divided between Relaxation and Enthusiasm, with both scores falling near the value of

10.5. Negative Affect was not evenly divided between its subscales. In fact, Fatigue

accounted for 77 percent of the total Negative Affect score. Mean scores for Fatigue

were near the value of 17. Mean scores for Nervousness were near the value of 5.

Table 7

Mood Results

Score Variable Subscale M SD Positive Affect

20.94

3.92

Relaxation

10.38 3.77

Enthusiasm

10.56 3.04

Negative Affect

21.68

3.09

Nervousness

5.03 2.70

Fatigue

16.65 2.90

Note. Variable scores were possible between 6 and 42. Subscale scores were possible between 3 and 21. Music-use variables. The results of the Uses of Music Inventory were

distributed between three music-use variables, Background, Cognitive, and Emotional.

Scores between 5 and 25 were possible for each variable. The group means and standard

deviations for each of these variables are summarized in Table 8. The scores were nearly

evenly represented among the music-use variables, with mean scores for Cognitive-use

70

being slightly less than scores for Background and Emotional-use. Mean scores for

Background and Emotional-use were both near the value of 16. Mean scores for

Cognitive-use were near the value of 15.

Table 8

Music-Use Results

Score Variable M SD Background

16.38

2.88

Cognitive

15.06 3.90

Emotional

16.24 2.80

Note. Scores were possible between 5 and 25.

Inferential Results

This section presents results of inferential analyses, based on the research

questions. For each question, correlation coefficients were calculated. Multiple

regression analyses of significant relationships were utilized whenever possible, and

scatterplots are provided to support these relationships. Other significant relationships

not pertaining to the research questions are presented in Appendix H.

Research Question 1: What is the relationship between personality and

music-use? Bivariate Pearson product-moment correlation coefficients were calculated

for the continuous variables, including each of the personality factors and the music-use

variables (see Table 9). Two significant correlations were found. Openness was

positively correlated with both Cognitive (p < 0.01) and Emotional-use of music (p <

0.05). Figure 4 shows graphical representations of these relationships. Multiple

regression analyses were used to test these correlations in a predictive model, testing for

71

the combined and unique relationships of the personality factors on each of the music-use

categories separately. The results of these analyses, which include Cognitive or

Emotional-use of music as a dependent variable and the five personality factors as

simultaneous independent variables, are found in Table 10 and Table 11. Openness

significantly predicted Cognitive-use of music scores, β = 0.53, t(34) = 2.84, p < 0.01.

Openness also explained a significant proportion of variance in Cognitive-use of music

scores, R2 = 0.35, F(1, 34) = 3.03, p < 0.05. As can be seen in Table 11, however, none

of the Personality factors significantly predicted or explained a significant proportion of

variance in Emotional-use of music. Additionally, two other significant relationships

emerged during correlational analyses. Musical background was positively correlated

with both Neuroticism (p < 0.05) and Openness (p < 0.01) (see Table A.4, Appendix H).

Table 9

Pearson’s Product Moment Correlations (r) for Music-Use Categories with Personality Factors Music-Use Categories Background Cognitive Emotional Neuroticism

-0.04

0.20

0.20

Extraversion

0.11 -0.04 0.01

Openness

-0.04 0.49** 0.35*

Agreeableness

-0.22 -0.26 -0.23

Conscientiousness

0.14 -0.27 -0.02

Note. * p < .05, two-tailed; ** p < .01, two-tailed. n = 34 for all analyses.

72

Figure 4. Scatterplots for Openness factor with Cognitive and Emotional-uses of music.

0

5

10

15

20

25

30

0 10 20 30 40 50

Cog

nitiv

e-U

se o

f Mus

ic

Openness

0

5

10

15

20

25

0 10 20 30 40 50

Em

otio

nal-U

se o

f Mus

ic

Openness

73

Table 10

Summary of Multiple Regression Analysis for Personality Factors Predicting Cognitive-use Cognitive-use of Music Factor B SE B β Neuroticism

0.00

0.11

0.01

Extraversion

-0.10 0.12 -0.16

Openness

0.34 0.12 0.53**

Agreeableness

-0.06 0.10 -0.10

Conscientiousness

-0.14 0.10 -0.22

R2

0.35

F

3.03*

Note. * p < .05. ** p < .01.

Table 11

Summary of Multiple Regression Analysis for Personality Factors Predicting Emotional-use Emotional-use of Music Factor B SE B β Neuroticism

0.05

0.09

0.12

Extraversion

0.00 0.10 0.01

Openness

0.14 0.10 0.30

Agreeableness

-0.08 0.08 -0.19

Conscientiousness

0.01 0.08 0.02

R2

0.18

F

1.19

74

Research Question 2: What is the relationship between mood and music-use?

Bivariate Pearson product-moment correlation coefficients were calculated for the

continuous variables, including each of the mood variables, mood subscales, and music-

use variables (see Table 12). Significant correlations were not found between any of the

mood variables or subscales and any of the music-use variables.

Table 12

Pearson’s Product Moment Correlations (r) for Music-Use Variables with Mood Variables and Subscales Music-Use Variable Subscales Background Cognitive Emotional Positive Affect

0.10

-0.30

0.13

Relaxation

-0.06 0.06 0.10

Enthusiasm 0.20 -0.11 0.10 Negative Affect

-0.23

-0.14

-0.14

Nervousness

-0.20 -0.01 -0.07

Fatigue

-0.06 -0.14 -0.08

Research Question 3: What demographic and/or contextual factors are

related to music-use? Correlation coefficients were also calculated between music-use

variables and other continuous and ordinal study variables. Initially, bivariate Pearson

product-moment correlation coefficients were calculated between the music-use variables

and the other continuous variables, including age, musical background, task duration, and

listening duration. No significant correlations were found among these variables. Next,

Spearman rank correlation coefficients were calculated to determine relationships among

75

the music-use variables and the ordinal variables, including school level, computer

programming background, computer programming proficiency, computer programming

hours per day, computer programming task difficulty, music activity level, listening hours

per day, and music focus. Several significant correlations are reported (see Table 13).

First, computer programming proficiency was positively correlated with Emotional-use

(p < 0.05). Cognitive-use was included in five other significant correlations. Computer

programming background (p < 0.01) and task difficulty (p < 0.05) were both negatively

correlated with Cognitive-use. Music activity level (p < 0.01), listening hours per day (p

< 0.05), and music focus (p < 0.05) were all positively correlated with Cognitive-use.

Table 13

Spearman’s Rank Correlations (rs) for Music-Use Variables with Ordinal Variables

Music-Use Variables Background Cognitive Emotional School Level

-0.06 -0.01 0.05

Computer Programming Proficiency

0.12 -0.33 0.34*

Computer Programming Background

-0.09 -0.44** 0.23

Computer Programming Hours/Day

-0.00 -0.13 0.04

Computer Programming Task Difficulty

0.04 -0.40* -0.22

Music Activity Level

0.01 0.54** 0.18

Listening Hours/Day

0.24 0.39* 0.34

Music Focus 0.09 0.41* -0.03 Note. * p < .05, two-tailed; ** p < .01, two-tailed.

Other relationships. Several other significant correlations were found during the

data analysis process. Relationships that did not involve the music-use variables are

shown in Tables A.1 through A.9 in Appendix H. In addition to correlational analyses, a

76

one-way analysis of variance (ANOVA) was used to determine whether a significant

effect of computer programming background on music-use existed for two groups. Of

the 34 participants, 16 students had two years or less of computer programming

background, and 18 students had three years or more of computer programming

background. Therefore, two similarly sized groups were analyzed for between group

differences using an ANOVA procedure. The mean Cognitive-use score for participants

with two years or less of computer programming background was 16.81 (SD = 3.80), and

the mean Cognitive-use score for participants with three or more years of computer

programming background was 13.50 (SD = 3.37). ANOVA showed a significant effect

of computer programming background on Cognitive-use of music at the p < 0.05 level for

two groups [F(1, 32) = 7.27, p = 0.011] (see Table 14). As a determination of practical

significance, Cohen's d was calculated to evaluate the effect size between means. The

Cohen’s d value was 0.92, and when the magnitude of d is 0.80 or above, a large effect

size is present (Gravetter & Wallnau, 2011).

Table 14

Summary of ANOVA Results for Effect of Computer Programming Background on Cognitive-use of Music Sum of Squares df Mean Square F Between Groups

92.95

1

92.95

7.27*

Within Groups

408.94 32 12.78

Total 501.88 33 Note. * p < .05.

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Content Analyses

Prior to completing the Uses of Music Inventory, participants answered two open-

ended questions about their music-use. Four volunteer raters coded responses to these

items for comparison to the Uses of Music Inventory data. Results of these directed

content analyses are summarized in Table 15 and Table 16. Figure 5 shows how these

results compare to the music-use variable means obtained from the Uses of Music

Inventory. Complete responses to the open-ended items are also included in Appendix I.

Table 15

Responses to “Please explain why you chose the music you listened to.”

Frequency Distribution f % Background

40 29.63

Cognitive

38 28.15

Emotional

46 34.07

Other 11 8.15

Table 16

Responses to “How do you think music listening influenced you and your work?”

Frequency Distribution f % Background

20 14.71

Cognitive

52 38.24

Emotional

56 41.18

Other 8 5.88 Note. The sum of distribution percentage values may not equal 100%, due to rounding.

78

Figure 5. Directed content analysis and Uses of Music Inventory results

comparison.

Background 30%

Cognitive 28%

Emotional 34%

Other 8%

Responses to“Please explain why you chose the music you listened to.”

Background 15%

Cognitive 38%

Emotional 41%

Other 6%

Responses to “How do you think music listening influenced you and your work?”

Background 34%

Cognitive 32%

Emotional 34%

Uses of Music Inventory Mean Scores

79

The researcher also reviewed responses to open-ended items on the music-use

questionnaire using conventional and summative content analyses. In conventional

analysis, the researcher identified unanimity in the volunteer raters’ responses. Only

open-ended responses that received the same rating from all four volunteers were

included in the analysis, and the statements were organized by type of music-use (see

Table 17 and Table 18). For responses to “Please explain why you chose the music you

listened to,” raters unanimously agreed that two statements were representative of

Background-use, one statement was representative of Cognitive-use, and five statements

were representative of Emotional-use. For responses to “How do you think music

listening influenced you and your work?,” raters did not unanimously agree that any

statements were representative of Background-use. Raters did unanimously agree,

however, that seven statements were representative of Cognitive-use, and 10 statements

were representative of Emotional-use.

In summative content analysis, the researcher categorized and counted key words

and phrases in the open-ended responses. Five categories were determined, and they

included background, cognitive, emotional, productivity, and music. Emotional words

were used most often, followed in descending order by cognitive, music, productivity,

and background (see Table 19). Ten words or phrases were each utilized eight or more

times. In the cognition category, “focus” was used 24 times, and “distract” was used 23

times. In the productivity category, “help” was used 18 times and “productive” was used

eight times. In the music category, “lyrics,” “vocals,” and “words” were together used 14

times. In the emotional category, “relax,” “energy,” and “mood” were each used 13

times. Additionally, “calm” and “enjoy” were each used eight times.

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Table 17

Conventional Content Analysis Results for Open-Ended Item #6 on the Music-Use Questionnaire

Responses to “Please explain why you chose the music you listened to.”

Background I chose this music from a pre-existing playlist that I had knowing that it would be music I liked but at the same time was not so busy that it would be distracting from my assignment. It's very ambient. This band has a very specific style, where every song has a clear driving beat, but it's not a quick tempo and the lyrics are hidden underneath textures rather than the focus of the song. It's easy to not pay attention to what they're saying, but still have something to groove to. Cognitive I listened to recordings I made of myself improvising at the piano. I like the music very much and I like how my brain lights up as I remember making the music, it seems to organize and calm my mind. Emotional So Lonely by the Police is a great feel-good song and always gets me in a good mind set. It calms me and does not distract me. BT is one of my foremost musical idols. I haven't listened to his "Dreaming" remix compilation EP, and the song usually puts me into a mellow, yet energized mood. The song itself has a deep existentialist-like meaning to me as well. It seemed like the right choice for my current state. I chose the jazz tracks (Mehldau) because those are my favorite to actively listen to. The rock track (Gabe Dixon) I chose because I believe that it generally puts me in a more upbeat mood. The alternative tracks (Iron and Wine) were chosen because I enjoy the relaxed vibe that they give me. I listened to the Nicolas Jaar BBC Radio 1 Essential Mix DJ set 05-19-12. It had these songs in it (besides the last two). There were other songs in the set but those were the ones that stood out most to me. I listened to that set because I knew it would be a mix of calming yet interesting works. I chose the music based more on coming home from a long day of work and sitting down with a glass of wine to work with, rather than specifically being related to the task at hand. Yet it helped me get through the task with a more relaxed demeanor.

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Table 18

Conventional Content Analysis Results for Open-Ended Item #7 on the Music-Use Questionnaire

Responses to “How do you think music listening influenced you and your work?” Cognitive

I think that it helps me to code for longer periods of time due to the focus I can maintain while I work. I can be easily distracted at times, and as a result my coding time is very fragmented. Having a solid block of time to sit down and work helps me get an entire task complete rather than try to get bits and pieces done over a couple of days wherein I loose time trying to remember why I did a particular thing, or how a function I wrote was intended to work.

It excites my mind, almost as if it’s pushing any mental block I have in the way.

I don’t know. I prefer to do homework and other activities with music mostly because it helps me lose focus on my surroundings and focus on the task I'm trying to complete.

I think it made me better able to focus on my work for a long time without feeling bored or distracted. It kept me energized and motivated.

It helped me to focus on my task more. In quiet rooms, I tend to get distracted by every sound that crops up. When I play music, I zone out while listening to that, and ignore all the other "unexpected" sounds a lot more. It also helps to keep my energy level moderately high.

Mostly I find that listening to music helps me block out distractions around me especially if I'm in a public place like the library. I also feel like I get "in the zone" when I've got the right kind of music playing. It's hard to explain but having a steady, driving beat can help me stay productive, and focus on problems more easily.

I like to think that it kept my mind stimulated at times when repeating menial, repetitive tasks. It probably also distracted me a bit, but I was okay with that.

Emotional

I believe that the music helped me remain relaxed and allowed me to enjoy the programming assignment.

It made me not freak out when I was working on this project. I can get pretty frustrated when I don't understand exactly why a program is not working. I think the music just makes me say to myself: "Okay, it's all good. What is not working here?" I would say it helps me keep my cool.

The music added a mood to my programming task, which is often bland when unaccompanied by music. This helped at certain points but hindered at others and I had to pause the song for a few minutes to focus on troubleshooting errors.

I think it helped me to stay in a relaxed mode so I wouldn't get frustrated when I encountered problems with my code.

The music relaxed me and let me gather my focus and direct it towards the task at hand. I find working in silence gives me anxiety and that I need music at least in the background to work well.

It alleviates some tedium that might have set in, and also improves my mood. It's difficult to tell if it improved my thinking or quality of work in any way.

It definitely calmed me down; I was a bit jittery before the programming. I don't think it necessarily helped me to focus, though.

relaxed me, also covered some outside noise

It certainly puts me in a better mood, which I can describe as cheerful or up-lifted, plus keeps me awake. Because of its high beat, I tend to act more focused and fast and get things done quicker. If I really need to think on things and plan the flow of the code, I choose to pause the music for a while.

I think the music calmed down a little. I don't usually listen to music and program, but I feel like its occupying a part of my brain that might be stressing out normally.

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Table 19

Summative Content Analysis Results for Open-Ended Items #6 and #7 on the Music-Use Questionnaire

Words Utilized in the Discussion of Music-Use During Computer Programming Tasks f f

Background 15 Productivity 52 background 6 help 18 noise 5 productive 8 quiet/silent 4 easy 6

Cognitive 68 flow 5

focus 24 allow (me to) 5 distract 23 (get in the) zone 3 attention 5 stay 3 block out 3 maintain 2 concentrate 3 awake 2 cognitive 2 Music 57 aware 2 lyrics/vocals/words 14 conscious 2 tempo (fast) 6 mental 2 upbeat 5 stimulate 2 beat 5

Emotional 82 rhythm 5

relax 13 pace 4 energy 13 loop 4 mood 13 instrumental 3 calm 8 mellow 3 enjoy 8 shuffle 3 favorite 5 steady 3 motivate 4 unique 2 interesting 4 frustrated 4 excites 2 inspiration 2 state 2 emotion 2 bored 2

Note. f = number of times the word was used.

Chapter Five

Discussion

The purpose of this study was to investigate the ways in which individuals use

music while working. Personality, mood, and music-use data were collected in

connection with a high-cognitive demand computer programming task. Data were

analyzed to identify relationships between music-use, personality, and mood variables.

Statistical relationships between music-use variables, demographics, experience, and

contextual factors were also explored. Additionally, participants’ years of computer

programming background were categorized into either a less experienced group or more

experienced group. Inferential analyses revealed a significant effect of computer

programming background on type of music-use.

An interpretation of the results of this study will be presented in this chapter,

reviewing each research question. Explanations for other emerging relationships in this

study will follow the research questions. Later, the theoretical and clinical implications of

this study will be discussed. Finally, study limitations and recommendations for future

research studies will be identified.

Review of the Research Questions

The relationship between personality and music-use. The results of this study

revealed significant positive correlations between the personality factor of Openness and

both Cognitive and Emotional-use of music. Openness also emerged as a significant

predictor of Cognitive-use of music. Therefore, the findings provide evidence that a

tendency for Openness in student computer programmers is not only related to a trend to

use music for cognitive reasons, Openness also predicts Cognitive-use of music.

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Although Openness did not predict the probability of Emotional-use of music, the

evidence suggests that a strong preference for Openness in computer programmers is also

related to a propensity to use music for emotional reasons.

The results of this study are similar to past research with other individuals, which

found that Openness has a positive relationship with Cognitive-use of music (Chamorro-

Premuzic et al., 2010; Chamorro-Premuzic, Gomà-i-Freixanet, Furnham, & Muro, 2009;

Chamorro-Premuzic & Furnham, 2007; Chamorro-Premuzic, Swami, & Cermakova,

2012; Chamorro-Premuzic, Swami, Furnham, & Maakip 2009; Isaacson, 2007). The link

between Openness and Cognitive-use of music has been associated with a link between

Openness and intelligence (Chamorro-Premuzic, Gomà-i-Freixanet, Furnham, & Muro,

2009). Individuals with a preference for Openness may use music to help generate

experiences that enhance cognition (Chamorro-Premuzic & Furnham, 2005). Similarly,

open and intelligent individuals were inclined to listen to music described as ‘complex’

and ‘reflective’ in explorations of personality and music preference (Chamorro-Premuzic

et al., 2010; Rentfrow & Gosling, 2003). Presently, studies that examine the relationship

between personality and music-use in computer programmers or other information

technology professionals are scarce in research literature.

Previous research with typical individuals provides evidence that additional

relationships may exist between personality and music-use. According to past research,

Emotional-use of music has a positive relationship with Neuroticism and a negative

relationship with Conscientiousness. (Chamorro-Premuzic et al., 2010; Chamorro-

Premuzic & Furnham, 2007; Chamorro-Premuzic, Gomà-i-Freixanet, Furnham, & Muro,

2009; Chamorro-Premuzic, Swami, Furnham, & Maakip 2009). A positive association

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between Extraversion and Background-use of music has also been shown (Chamorro-

Premuzic et al., 2012; Chamorro-Premuzic, Gomà-i-Freixanet, Furnham, & Muro, 2009;

Chamorro-Premuzic, Swami, Furnham, & Maakip 2009). In one study, Extraversion was

positively linked with Emotional-use of music and negatively linked with Cognitive-use

of music (Chamorro-Premuzic et al., 2012). Although none of these relationships

between personality and music-use were statistically significant in the present study with

student computer programmers, the positive versus negative directions of the correlations

that emerged were alike.

Incidentally, the student computer programmers in this study scored significantly

higher on the Openness factor, when compared to typical adults. The high degree of

Openness in this sample may be related to a number of factors. The task of computer

programming may be attractive to individuals with a preference for Openness. Perhaps

abstract thinking and creative problem-solving demands Openness. The average age and

musical experience of this sample may also be connected with Openness. University

students tend to be explorative in their search for identity and independence (Nairne,

2009a). The participants in this study had an average of over nine years of musical

background and reported listening to two to three hours of music daily. With music

playing such a strong role in these individuals’ lives, it is likely that musical background

and daily listening duration are associated with Openness. In fact, a significant positive

relationship was found between musical background and Openness in this study. A high

degree of Openness is also unsurprising, given that the individuals who agreed to

participate in this study did so without incentive. Therefore, Openness to Experience is

to be expected in this sample.

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The results are consistent with previous research in terms of computer

programmer personality. Past research with university students provided evidence that

the personality preference for intuitive-thinking is more typical in computer

programmers, when compared to the other MBTI personality preferences (Woszczynski

et al., 2005). The NEO-FFI Openness dimension corresponds to the MBTI preference of

intuition (Costa & McCrae, 1992). Additionally, an earlier study showed a MBTI

preference for intuition in students majoring in computer science (Pocius, 1991).

Past research has also shown that introversion, thinking, and judging preferences

were also typical among information technologists. Introversion and thinking

preferences were apparent in an early study of students majoring in computer science

(Pocius, 1991). Similarly, introversion, thinking, and judging preferences were prevalent

in a study with computer information systems developers (Lesiuk et al., 2009). The

MBTI introversion-extraversion dichotomy corresponds to the NEO-FFI Extraversion

dimension, the thinking-feeling dichotomy corresponds with Agreeableness, and the

judging-perceiving dichotomy corresponds with Conscientiousness (Costa & McCrae,

1992). Although the scores for Extraversion, Agreeableness, and Conscientiousness were

not significantly different from age-matched adults in the current study, diverse

personality profiles are to be expected in this population.

The relationship between mood and music-use. Significant correlations did not

emerge between any of the mood variables or subscales and any of the music-use

categories in the current study. The strongest non-significant correlation appeared

between Positive Affect and Cognitive-use of music. A negative correlation value was

calculated between these variables, so using music for cognitive reasons may become less

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likely as Positive Affect increases. Given the assumption that affect is related to arousal,

it is possible that when music becomes too arousing, or incites too much pleasure, it gets

rejected as an aid to cognition.

Studies that explore the relationship between mood and music-use in computer

programmers are scarce in current research literature. However, a predictive relationship

between music listening and mood has been examined, and a few studies have measured

this association in information technology professionals. Greenwood and Long (2009)

found that some individuals were inclined to use music for mood regulation in general,

regardless of whether they were in a positive or negative mood. Furthermore, when

individuals were bored or experiencing a negative mood, the probability of using music

for mood regulation increased. More anecdotal evidence to support the relationship

between mood and music-use was presented by Lonsdale and North (2011), who found

that mood management was a prominent theme in participant responses for reasons why

they listen to music. Subthemes included mood control, arousal management, positive

mood creation, mood enhancement, emotional expression, and emotional exploration.

Although similar results were not shown in the current study, mood is still likely to

interact with music-use during high-cognitive demand tasks.

A series of studies were conducted with information technology professionals to

explore the effect of music-use on mood and subsequent work productivity (Lesiuk,

2010a, 2010b; Lesiuk et al., 2009, 2011). Several effects of music-use on positive and

negative mood states emerged during these studies, and open-ended responses revealed

that these individuals used music specifically to control, enhance, or regulate their

moods. These results suggest that these individuals used music for emotional reasons,

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based on their current mood, and findings were similar in the current study, based on

content analyses of open-ended responses. When participants were asked to explain why

they chose the music they did, 34 percent of the responses were emotional in content.

Likewise, 41 percent of the responses were emotional in content when participants

expressed how they thought music listening influenced them and their work.

One mood subscale emerged as prevalent in the current study. Fatigue accounted

for 77 percent of the total Negative Affect score. Therefore, Negative Affect scores can

be mostly attributed to Fatigue, rather than Nervousness. Fatigue is to be expected in

university students. A low Nervousness score was no surprise, either, since participants

completed the mood scale and computer programming task on their own and at a time

and place of their choosing. Furthermore, participants completed a computer

programming task of their choosing. In other words, the study was specifically designed

to involve a natural unrestricted work situation.

The relationship between demographics, experience, contextual factors and

music-use. Correlations between demographic data and music-use variables revealed no

significant relationships. Significant correlations were found, however, between

computer programming experience, computer programming-related contextual factors

and music-use variables. First, participants’ level of computer programming expertise

was positively correlated with Emotional-use of music. This finding provides evidence

that advanced student computer programmers are more drawn to use music for emotional

reasons, when compared to less advanced student computer programmers. Additionally,

programming background and level of computer programming task difficulty were

negatively correlated with Cognitive-use of music. These findings provide evidence that

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students with more years of computer programming background are less inclined to use

music for cognitive reasons. Likewise, during more difficult computer programming

tasks, Cognitive-use of music is less preferred. Perhaps more complex computer

programming tasks require greater attention and focus, inhibiting Cognitive music-use.

Such a theory is supported by Sloboda’s (2010) sixth dimension of everyday music use,

referred to as centrality of music to the experience and the salience of the context, in

which the nonmusical activity requires more attention relative to the music.

Significant correlations also emerged between listening-related contextual factors

and music-use variables. The level of music activity, duration of daily listening, and

participants’ level of focus on the music were all positively correlated with Cognitive-use

of music. It appears that when student computer programmers listen to music for

cognitive reasons, they choose music that is highly active, rather than music with a low

activity level. This interpretation is supported by Berlyne’s (1971a) optimal arousal

theory, in which arousal and subsequent attention are attributed to psychophysical

properties in the music. Furthermore, student computer programmers who use music in a

cognitive way during computer programming tasks are inclined to listen to longer

durations of music in their daily lives. Last, the evidence suggests that student computer

programmers who use music for cognitive reasons are more highly focused on the music

during computer programming tasks, when compared to peers who use music for

background or emotional reasons. Therefore, Cognitive-use of music appears to demand

a high degree of focus on the music stimulus.

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Studies linking computer programming experience and computer programming-

related contextual factors to music-use are scarce in current research literature.

Furthermore, music-use has not been specifically explored in terms of listening-related

contextual factors. Relationships between demographic variables and music-use have

been measured in past studies, though. Past researchers found that age and school level

were both negatively correlated with Background-use of music in typical individuals.

Specifically, as age and school level increased, the likelihood of using music for

background reasons decreased (Chamorro-Premuzic et al., 2012). Although similar

results were not revealed in the current study, age and school level may still correlate

with music-use during high-cognitive demand tasks.

Another recent study used several methods to explore the way music-use changes

with age in typical individuals (Lonsdale & North, 2011). Although music-use was not

divided into the same quantifiable categories as this study, age had notable relationships

with various aspects of music-use. The past findings provided evidence that music’s

importance in daily life decreases significantly after the age of 30. Individuals over 50

years of age spent significantly less money per month on music, when compared to

individuals between the ages of 18 and 49. Additionally, individuals under the age of 30

spent significantly more time listening to music (Lonsdale & North, 2011). Similarly, a

significant negative correlation emerged between age and music listening duration in a

recent study with computer information systems analysts (Lesiuk, 2005).

The effect of computer programming background on Cognitive-use of music.

By placing participants into two groups, based on years of computer programming

background, the relationship between computer programming background and music-use

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was further tested for effect. In this study, less experienced student computer

programmers had a stronger preference for Cognitive-use of music, when compared to

more experienced programmers. The results showed a significant effect of computer

programming background on Cognitive-use of music for the two groups, providing

evidence that using music for cognitive reasons is directly influenced by a student

computer programmer’s background experience. Perhaps less experienced programmers

are drawn to use music for cognitive reasons because they have yet not acquired enough

computer programming skills to work efficiently. With less computer programming

resources at their disposal, music-use may be an external method to improve focus. Over

time, enhanced attention may aid in the acquisition of a desired computer skill. Then as

these individuals gain computer programming experience, cognitive music-use may lose

importance in light of new knowledge. Related findings are not yet present in research

literature, due to the unique nature of this research question. Future research is requested

to test the interaction between computer programming background and Cognitive-use of

music.

Review of the Content Analyses

Content analyses of open-ended responses regarding music-use were conducted to

compare qualitative and quantitative data and identify trends. The results of directed

content analysis were mostly compatible with results from the Uses of Music Inventory.

In particular, the raters’ distribution of responses to, “Please explain why you chose the

music you listened to,” were quite similar to average scores on each of the Uses of Music

Inventory categories. First, 30 percent of the responses to this item related to

Background-use, and mean scores for Background-use represented 34 percent of total

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scores on the Uses of Music Inventory. Next, 28 percent of the responses to this open-

ended item related to Cognitive-use, and mean scores for Cognitive-use represented 32

percent of scores on the Uses of Music Inventory. Last, 34 percent of responses to this

item related to Emotional-use, and mean scores for Emotional-use represented 34 percent

of scores on the Uses of Music Inventory. The responses to, “How do you think music

listening influenced you and your work?” were more heavily distributed between

Cognitive (38%) and Emotional-use (41%).

The results of conventional content analysis revealed characteristic responses for

each of the music-use categories. A response that was clearly representative of

Background-use was, “It's very ambient. This band has a very specific style, where every

song has a clear driving beat, but it's not a quick tempo and the lyrics are hidden

underneath textures rather than the focus of the song. It's easy to not pay attention to

what they're saying, but still have something to groove to.” It seems that this individual

uses music with specific psychophysical characteristics, meant to accompany, but not

alter his or her work. For Cognitive-use, one participant stated, “Mostly I find that

listening to music helps me block out distractions around me especially if I'm in a public

place like the library. I also feel like I get ‘in the zone’ when I've got the right kind of

music playing. It's hard to explain but having a steady, driving beat can help me stay

productive, and focus on problems more easily.” For this individual, the right type of

music facilitates focus. Last, a response that was representative of Emotional-use was,

“It made me not freak out when I was working on this project. I can get pretty frustrated

when I don't understand exactly why a program is not working. I think the music just

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makes me say to myself: ‘Okay, it's all good. What is not working here?’ I would say it

helps me keep my cool.” It appears that this individual uses music to remain calm during

moments of frustration.

Finally, summative content analysis of open-ended responses resulted in five

subject categories and several commonly used words. In addition to background,

cognitive, and emotional categories, words were sorted into productivity and music

groups. Emotional words were used most often, followed in descending order by

cognitive, music, productivity, and background words. Emotional words were more

diverse as well, with the words “relax,” “energy,” “mood,” “calm,” and “enjoy” each

occurring at least eight times. The words “focus” and “distract” dominated the cognitive

category, each occurring at least 23 times. Music words had two primary themes, one

regarding the presence of lyrics in the music, and another regarding musical tempo. The

most widely used word relating to productivity was “help,” and an argument could be

made for this word being too general to be categorized. More definitive in this category

was the word “productive,” which was used eight times.

Limitations of the Study

Limitations of this study included small sample size and an uneven number of

participants in the computer programming background groups. A larger sample size

would have increased the statistical power of the correlations between personality, mood,

and music-use. Furthermore, a larger sample size may have revealed other relationships

between demographics, experience, contextual factors, and music-use. A larger sample

size would have also increased the statistical power needed to identify other effects of

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computer programming background on music-use. Moreover, a larger sample size may

have enabled participants to be divided into other types of groups to investigate the effect

of different variables on music-use.

Another possible limitation of this study was the use of the Job Affect Scale,

which may not have adequately captured mood. The Job Affect Scale was chosen for its

quick administration, its application to the workplace, and its use in similar research

studies. Since this scale included a list of 12 words that a person may use to describe his

or her feelings while working, it may not have been the most appropriate scale to use in

an educational setting. This scale was also not as long as some other prevalent mood

scales. For instance, the Profile of Mood States (POMS) Brief assessment is an

alternative method of assessing active mood states, consisting of 30 words that describe

feelings people may have in any context (McNair, Lorr, & Droppleman, 1971).

Additionally, location threats were possible, given that participants completed

both phases of the study in various locations. During the first phase, participants met

with the researcher at several locations, over a number of days, and at different times of

day. However, only the demographic questionnaire and personality inventory were

completed during the first phase. These measures gathered static data, so participant

responses should not be variable due to location. During the second study phase,

participants completed online questionnaires in a variety of locations. Since these

measures surveyed state-dependent attitudes, location characteristics may have affected

participant responses. For example, a participant working in a shared computer lab may

have been prone to use music for cognitive reasons, in order to block out distractions in

the room. Therefore, alternate explanations for the results are possible. Yet, the study

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was specifically designed to capture participant responses in a natural setting, and the

location, day of week, and time of day were accounted for on the computer programming

task assessment.

Theoretical Implications

This study indicates that a relationship exists between personality and music-use

during a high-cognitive demand task. A significant positive correlation emerged between

Openness and both Cognitive and Emotional-use of music, and a predictive relationship

was found between Openness and Cognitive-use of music. These results suggest that

individuals with preference for Openness tend to use music for cognitive reasons during

high-cognitive demand tasks.

This study indicates that a relationship may not exist, however, between initial

mood and music-use. The mood variables in this study were not significantly correlated

with any of the music-use variables. Furthermore, a significant relationship was not

found between mood and personality in this study. The absence of these relationships in

this study does not suggest, though, that interactions among these variables are

implausible in other individuals and in different contexts. Mood change in relation to

music-use is another possible interaction that was not explored in the current study

The findings also provide evidence of several other relationships with music-use.

First, a significant positive correlation emerged between computer programming

proficiency and Emotional-use of music. This result suggests that as computer

programming proficiency increases, so does the likelihood of using music for emotional

reasons. Next, Cognitive-use of music was involved in several significant correlations.

Music activity level, listening hours per day, and music focus were each positively

96

correlated with Cognitive-use of music. These results indicate that Cognitive-use of

music increases the likelihood of using music that is highly active and demands more

intense focus on the music. Likewise, the results show that individuals who use music

for cognitive reasons listen to longer durations of music in their daily life. Additionally,

a negative correlation emerged between computer programming task difficulty and

Cognitive-use of music, suggesting that the likelihood of using music for cognitive

reasons decreases as computer programming tasks become more difficult. Last,

computer programming background

was also negatively correlated with Cognitive-use of music. This result suggests that as

years of computer programming background increase, the likelihood of using music for

cognitive reasons decreases.

This study also indicates that computer programming background has an effect on

Cognitive-use of music. Less experienced student computer programmers were more

drawn to use music for cognitive reasons, when compared to more experienced student

computer programmers. This result suggests that the use of music for cognitive reasons

changes with experience. Music-use may lose priority as a cognitive aid when computer

programmers gain other skills more specific to the task. Acquisition of a new coding

language, for example, may be more effective than using music to improve cognition.

Clinical Implications

The results of this study provide implications for the use of music during

computer programming tasks. Regardless of music’s effect on productivity in this

domain, certain individuals find it necessary to listen to music while they work. All of

the participants in this study listen to music while completing computer programming

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tasks. A higher degree of Openness was found among these individuals, when compared

to the typical population. Therefore, the specific personality trait of Openness may be

associated with music-use in general among computer programmers. Furthermore,

individuals in the typical population who have a preference for Openness may be drawn

to use music when they complete work tasks. Music therapists, as well as employers,

may suggest music-use in the workplace for individuals who possess this personality

preference.

Cegielski (2006) found that personality is predictive of computer programming

performance in an undergraduate object-oriented programming course. Compared to past

research, which found that cognitive ability and personality are equally predictive of

computer programming ability, Cegielski found that personality is the stronger predictor.

In particular, performance may be dependent on personality factors related to self-esteem

and self-efficacy (Cegielski, 2006). Self-esteem is determined by one’s evaluation of

worth, and self-efficacy involves the independent assessment of one's ability to complete

tasks and achieve goals. Similarly, the NEO-FFI Openness dimension includes attention

to inner feelings and independence of judgment (Costa & McCrae, 1992). These findings

suggest that employers of computer programmers should pay particular attention to

aspects of personality when assessing a potential employee.

The results of this study also have implications outside the workplace. Music

therapists may use music to facilitate cognitive goals in educational settings, where

abstract thinking and creative problem-solving are often in demand. The use of music in

school may be beneficial to a variety of populations, but individuals with attention-deficit

disorders and other developmental disorders may find music-use to be particularly

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advantageous. Participants in this study reported using music to maintain focus and

block out distractions. Individuals who struggle with selective and sustained attention

may listen to preferred music to improve and increase focus on written work and other

visual stimuli.

Other participants in this study reported using music for emotional reasons during

a high-cognitive demand task. Again, various people in various contexts may use music

as a means of regulating emotions, but certain individuals may benefit from music

listening as a means of functional emotional goal development. Individuals with mood

disorders, for instance, may experience a pervasive undesirable mood or extreme

fluctuations in mood. A disorder implies that these experiences impair one’s

occupational functioning and social skills, and such impairments affect multiple aspects

of daily life (American Psychiatric Association., 2000). Music therapists may use music

listening to help individuals with mood disorders vector and stabilize their mood, thus,

avoiding disturbances in daily life.

Finally, the results of this study may have implications in the commercial domain.

One general theme emerged from this study: Certain individuals use music listening as a

resource, and they appear to understand how and why. For developers of music

recommendation software, this study provides a new music therapy perspective. Instead

of focusing on what is similar or different about the music people choose and trying to

guess what they want to hear next, these developers may turn their attention to the

reasons why listeners choose certain music in relation to a type of activity or task at hand.

A listener may use the software initially to report his or her mood and the impending

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activity. As the program receives more input from the listener and tracks the music that

is chosen, it can begin to identify trends in the listener’s music-use. Later, the listener

can ask the program to recommend music, based on a given mood and upcoming event.

Rather than recommending new music to these listeners, the software may be designed to

hand-pick music from their preferred music library, based on past input.

Recommendations for Future Research

To further explore the role of personality and mood in music-use during high-

cognitive demand tasks, future research should be conducted with a larger sample size.

Additional research that examines the effect of computer programming background on

music-use is also requested, including a larger sample size and an even number of

participants placed in each level of computer programming background. Additionally,

future research could explore all of these interactions in professional computer

programmers, also accounting for workplace demands, such as deadlines and

performance evaluations.

A similar study could measure mood before and after a computer programming

task, analyzing for relationships between, personality, mood change, and music-use.

Such a study may reveal an interaction between mood change and Emotional-use of

music, for instance. One would expect that individuals who report using music for

emotional reasons would also report a change in mood. An exploratory study was

conducted to examine mood change and its association with individual descriptions of the

functions of music in specific contexts (Sloboda, O'Neill, & Ivaldi, 2001). Data were

collected from participants in two-hour increments using a paging device, so mood

change was not measured concurrent to a specific task. Results showed, however, that

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individuals in various contexts used music to control, enhance, or regulate their present

mood (Sloboda et al., 2001). Later, Lesiuk (2005) collected narrative data regarding

mood change from computer information systems analysts, working under the

assumption that an increase in state positive affect has positive effects on work

performance. Participant responses indicated music-use for positive mood change and

improved perception on work tasks (Lesiuk, 2005).

A future study could also test mood and music-use variables on more than one

occasion, accounting for differences in computer programming task difficulty. Anecdotal

interpretation of participant responses in this study suggests that music-use may vary

within one individual, based on state mood and level of task difficulty. Several studies

have taken this more longitudinal view of mood and music-use, although research does

not yet exist to explore this interaction in parallel with computer programming tasks

(Greasley & Lamont, 2011; Greenwood & Long, 2009; Isaacson, 2007; Lonsdale &

North, 2011). A few studies have investigated mood and music-use over time in

computer systems information analysts, but variable difficulty in work tasks was not

accounted for in these studies (Lesiuk, 2005; Lesiuk et al., 2009).

Summary and Conclusions

The purpose of this study was to investigate the ways in which individuals use

music while working. Personality, mood, and music-use data were collected in

connection with a high-cognitive demand computer programming task. Participants were

involved in a computer programming task of their choosing, and they each listened to

music from their personal collection during the task. Personality and demographic data

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were collected before and separate from the computer programming task. Mood data

were collected immediately prior to the task, and music-use data were collected

immediately following the task, both via a study webpage. Based on years of computer

programming background, each participant was placed into a less experienced group or a

more experienced group for analysis purposes.

The findings indicated several significant relationships. In particular reference to

the research questions, positive correlations emerged between the personality factor of

Openness and both Cognitive and Emotional-use of music, and the relationship between

Openness and Cognitive-use was supported in a predictive model. Additionally,

computer programmers in this study scored significantly higher than typical adults on

Openness. No significant correlations were found between any of the mood and music-

use variables. However, some of the demographic, experience, and contextual factor

variables were significantly correlated with music-use. Computer programming

proficiency was positively correlated with Emotional-use of music. Next, music activity

level, listening duration, and music focus were each positively correlated with Cognitive-

use of music. Contrastingly, computer programming background and task difficulty were

each negatively correlated with Cognitive-use of music. Last, the findings also indicated

a significant effect of computer programming background on Cognitive-use of music.

As a result, individuals with a preference for Openness appear to use music for

both cognitive and emotional reasons, but the bond between Openness and Cognitive-use

of music may be stronger. Also, as an individual becomes more proficient at computer

programming, he or she may be more likely to use music for emotional reasons.

Additionally, Cognitive-use of music appears to demand increased focus on the music

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stimulus as well as music with a high activity level. Individuals who use music in a

cognitive way also tend to listen to longer durations of music in their daily lives. The

likelihood of using music for cognitive reasons appears to decrease, however, as the

concurrent computer programming task increases in complexity. Similarly, Cognitive-

use of music appears less likely as years of computer programming background increase.

Furthermore, computer programming background appears to have a differential effect on

Cognitive-use of music. Less experienced student computer programmers showed a

significantly stronger preference for the Cognitive-use of music, when compared to more

experienced computer programmers.

The themes that emerged in open-ended responses from this study generally

supported the quantitative results obtained. Participant statements typically related to one

of the music-use categories, and the distribution of responses was similar to the

distribution of scores on the Uses of Music Inventory. In addition to utilizing words

related to the music-use categories, participants employed specific language to describe

the type of music they chose and its influence on overall productivity.

To conclude, everyday music-use is a complex process, being impacted by

contextual factors and individual differences. Contextual factors differ for each listening

experience, as do mood states. These variables should not be overlooked in future

explorations of music-use. Personality, although more stable than context and mood,

appears to play an important role as well. Thus, similar research considerations should

include all three of these elements as significant contributors to everyday music-use.

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Appendix A

Demographic Questionnaire

Participant Number: ________

Please respond to each item accurately and honestly. Also, please do not skip any items.

Thank you for your participation.

1. Age: ________ 2. Gender: ________________ 3. What is your ethnicity? (select one) [ ] Hispanic or Latino [ ] Not Hispanic or Latino 4. What is your race? Mark one or more races to indicate what you consider yourself to be. [ ] American Indian or Alaska Native [ ] Asian [ ] Black or African American [ ] Native Hawaiian or Other Pacific Islander [ ] White 5. School Level (circle one): Undergraduate Masters Doctoral

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6. Current degree being pursued: ________________________________________________ 7. Years of computer programming experience (circle one): 0 1-2 3-4 5-6 7-8 9+ 8. Average number of hours spent daily on computer programming (choose one): [ ] 0 – 1 hours [ ] 6 – 7 hours [ ] 2 – 3 hours [ ] 8 – 9 hours [ ] 4 – 5 hours [ ] 10+ hours 9. Most prevalent computer programming language used: ____________________________ 10. Level of proficiency in computer programming (check one): ________ 0 = Absolute Beginner (I have little or no knowledge.) ________ 1 = Novice (I have created a few simple computer programs.) ________ 2 = Intermediate (I am moderately proficient.) ________ 3 = Advanced (I have created complex computer programs.) ________ 4 = Power User (I consider myself an expert in computer programming.)

Appendix B

NEO-FFI Instructions

Write only where indicated in the booklet. Carefully read all of the instructions

before beginning. This questionnaire contains 60 statements. Read each statement

carefully. For each statement, fill in the circle with the response that best represents your

opinion. Make sure that your answer is in the correct box.

Fill in (SD) if you strongly disagree or the state is definitely false.

Fill in (D) if you disagree or the statement is mostly false.

Fill in (N) if you are neutral on the statement, if you cannot decide, or if the statement is about equally true and false

Fill in (A) if you agree or the statement is mostly true.

Fill in (SA) if you strongly agree or the statement is definitely true.

Fill in only one response for each statement. Respond to all of the statements,

making sure that you fill in the correct response. If you need to change an answer, make

an “X” through the incorrect response, and then fill in the correct response. Note that the

responses are numbered in rows.

NEO-FFI Sample Items

8. Once I find the right way to do something, I stick to it.

20. I try to perform all the tasks assigned to me conscientiously.

32. I often feel as if I’m bursting with energy.

44. I’m hard-headed and tough-minded in my attitudes.

51. I often feel helpless and want someone else to solve my problems.

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Appendix C

Job Affect Scale Below is a list of words that a person may use to describe their feelings while working.

Please use the scale provided, and indicate how you feel at this time. Please be open

and honest with your responses. Also, please do not skip any items.

1 2 3 4 5 6 7 Extremely Fairly Slightly Moderately Fairly Strongly Extremely Slightly Slightly Strongly Strongly

________ 1. Calm ________ 2. Sleepy ________ 3. Strong ________ 4. Excited ________ 5. Scornful ________ 6. Hostile ________ 7. Relaxed ________ 8. At rest ________ 9. Nervous ________ 10. Drowsy ________ 11. Elated ________ 12. Sluggish

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Appendix D

Task Assessment 1. Please rate the complexity of the computer programming task that you just

completed (choose one answer only). [ ] Extremely Easy [ ] Easy [ ] Moderate [ ] Difficult [ ] Extremely Difficult 2. What was the length of time in minutes that you spent on the computer programming

task? ________________ minutes 3. Where did you complete the computer programming task? (select one) [ ] Home [ ] Work [ ] Library [ ] Other: ________________________ 4. During what time of day did you complete the computer programming task?

(select one) [ ] Morning [ ] Afternoon [ ] Night

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5. During what day of the week did you complete the computer programming task? (select one) [ ] Sunday [ ] Monday [ ] Tuesday [ ] Wednesday [ ] Thursday [ ] Friday [ ] Saturday

Appendix E

Music-Use Questionnaire Please respond to each item accurately and honestly. This questionnaire completes the

research study. When you have finished the questionnaire, please logoff the study

website. Thank you for your participation.

1. List the music that you listened to during the task: Song/Piece Artist/Band Style _____________________________ _____________________________ _____________ _____________________________ _____________________________ _____________ _____________________________ _____________________________ _____________ _____________________________ _____________________________ _____________ _____________________________ _____________________________ _____________ _____________________________ _____________________________ _____________ _____________________________ _____________________________ _____________ _____________________________ _____________________________ _____________ _____________________________ _____________________________ _____________ _____________________________ _____________________________ _____________ 2. Generally, how active was the music that you chose? (choose one) [ ] Extremely Inactive (very low energy) [ ] Inactive (low energy) [ ] Moderate [ ] Active (high energy) [ ] Extremely Active (very high energy)

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3. What was the length of time in minutes that you spent listening to music while working on the computer programming task?

________________ minutes 4. How focused were you on the music during the computer programming task? (choose one) [ ] Extremely Focused [ ] Focused [ ] Neutral [ ] Unfocused [ ] Extremely Unfocused 5. What type of device did you use to play and listen to the music? For example, “MP3 Player” _________________________ Did you use headphones? ________ 6. Please explain why you chose the music you listened to. You may want to refer

to a specific song, artist, or style in your answer. ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________

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7. How do you think music listening influenced you and your work? Please provide a description.

______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________

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8. Uses of Music Inventory Below there is a list of statements which a person may use to describe one’s

feelings about listening to music. Use the scale provided, and answer each item.

Please be open and honest with your responses. Also, please do not skip any

items.

___________________________________________________________________________ ________ a). I often feel very lonely if I don’t listen to music. ________ b). I often enjoy analyzing complex musical compositions. ________ c). Whenever I want to feel happy, I listen to a happy song. ________ d). Listening to music is an intellectual experience for me. ________ e). I don’t enjoy listening to pop music because it’s very primitive. ________ f). Music is very distracting so whenever I study I need to have silence. ________ g). I enjoy listening to music in social events. ________ h). When I listen to sad songs I feel very emotional. ________ i). Listening to music really affects my mood. ________ j). Rather than relaxing, when I listen to music I like to concentrate on it. ________ k). I enjoy listening to music while I work. ________ l). I am not very nostalgic when I listen to old songs I used to listen to. ________ m). I seldom like a song unless I admire the technique of the musicians. ________ n). If I don’t listen to music while I’m doing something, I often get bored. ________ o). Almost every memory I have is associated with a particular song.

1 2 3 4 5 Strongly Disagree Neutral Agree Strongly Disagree Agree

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9. How many years have you played a musical instrument or sung in a choir? _____________ years 10. How much time do you typically spend listening to music each day (choose one)? [ ] 0 – 1 hours [ ] 6 – 7 hours [ ] 2 – 3 hours [ ] 8 – 9 hours [ ] 4 – 5 hours [ ] 10+ hours 11. Any additional comments about the music you listened to while coding? ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________ ______________________________________________________________________________

Appendix F

Study Advertisement

Looking for student computer programmers!

Do you listen to music while you code?

If so, a project is taking place on campus that

needs participants just like you.

The music therapy program is exploring the role of personality and

mood in music-use during a computer programming task. Participants

will initially complete brief personality and demographic

questionnaires under the supervision of the researcher. Later at the

convenience of the participant, Web-based mood and music

questionnaires will be completed in conjunction with a coding task of

their choice. Total participation should last about one hour and no

more than two hours, including the computer programming task.

Interested in participating?

Please contact the investigator, Andy Panayides, at 813-992-0988 or [email protected].

You may also contact Dr. Mitsunori Ogihara at 305-284-2308 or [email protected].

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Appendix G

University of Miami CONSENT TO PARTICIPATE IN A RESEARCH STUDY


The following information describes the research study in which you are being asked to participate. Please read the information carefully. At the end, you will be asked to sign if you agree to participate. You will also be asked to provide your email address and phone number, so that you may be contacted regarding the second portion of this research study. PURPOSE OF STUDY: You are being asked to participate in a research study. This study will investigate the ways in which individuals use music while working. Personality, mood, and music-use data will be collected in connection with a high-cognitive demand task - computer programming. Uses of music will be related to personality and mood variables. You are being asked to be in the study because you usually listen to music while completing computer programming tasks. PROCEDURES: This study requires participation at two separate times, and the second portion must be completed within two weeks of the first. During the first portion and in sequence:

1. You will complete a personality inventory, which takes approximately 15 minutes. 2. You will complete a demographic questionnaire, which takes approximately 5

minutes. During the second portion and in sequence:

1. You will receive an email containing a website address and a participant number. Once you are logged in, you will complete the Job Affect Scale, which will ask you to rate your current mood on a given numerical scale. This assessment takes approximately 3 minutes.

2. You will complete a computer programming task. Specifically, you should complete a difficult coding task that lasts a minimum of 20 minutes without interruption. During this task, you are expected to listen to at least 10 minutes of music. Whenever possible, you will use music software that allows you to track and refer back to a playlist. If you do not typically listen to preferred music while programming, you should not participate in this study.

3. Upon completion of the computer programming task, you will return to the research study website. You will rate the complexity of the computer programming task that you just completed and indicate the length of time that you spent on the task. You will also specify where and when you completed the task. This task assessment will take no longer than 2 minutes.

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4. On the website, you will also complete a music-use questionnaire, which will ask you open-ended and guided questions about the music that you listened to during the computer programming task. It takes approximately 20-30 minutes. It will be helpful if you are able to refer back to the music that you listened to during the computer programming task to accurately and thoroughly complete this final questionnaire. You will also be given the opportunity to upload your playlist to the website.

Participation in the study will last a minimum of 75 minutes, including the computer programming task. The maximum length of time that you are expected to participate is 2 hours. RISKS AND/OR DISCOMFORTS: We do not anticipate that you will experience any personal risk or discomfort from taking part in this study. There may be uncommon or unknown risks. You should report any problems to the researcher. BENEFITS: No direct benefit can be promised to you from your participation in this study. CONFIDENTIALITY: The investigators and their assistants will consider your records confidential to the extent permitted by law. All data will be stored in a secure location within the music therapy department at the University of Miami. Names and individual demographic information will not be reported in the study. The U.S. Department of Health and Human Services (DHHS) may request to review and obtain copies of your records. Your records may also be reviewed for audit purposes by authorized University or other agents who will be bound by the same provisions of confidentiality. COMPENSATION: There will not be compensation for participation in this study. RIGHT TO DECLINE OR WITHDRAW: Your participation in this study is voluntary. You are free to refuse to participate in the study or withdraw your consent at any time during the study. The investigator reserves the right to remove you without your consent at such time that they feel it is in the best interest for you. If you are an employee or student at the University of Miami, your desire not to participate in this study or request to withdraw will not adversely affect your status as an employee or grades at the University of Miami. CONTACT INFORMATION: Teresa Lesiuk, Ph.D., Associate Professor of Music Therapy, at 305-284-3650, will gladly answer any questions you may have concerning the purpose, procedures, and outcome of this project. If you have questions about your rights as a research subject you may contact Human Subjects Research Office at the University of Miami, at (305) 243-3195.

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PARTICIPANT AGREEMENT: I have read the information in this consent form and agree to participate in this study. I have had the chance to ask any questions I have about this study, and they have been answered for me. I am entitled to a copy of this form after it has been read and signed. ____________________________ __________________ Signature of Participant Date ____________________________ Participant Email Address ____________________________ Participant Phone Number ____________________________ __________________ Signature of person obtaining consent Date Participant Number: ________

Appendix H

Other Significant Relationships

Table A.1

Pearson’s Product Moment Correlations (r) Between Personality Factors Personality Factors Extraversion Openness Agreeableness Conscientiousness Neuroticism

-0.40*

0.21

-0.11

-0.03

Extraversion

0.36* 0.31 0.17

Openness

-0.12 -0.00

Agreeableness

0.21

Note. * p < .05, two-tailed.

Table A.2

Pearson’s Product Moment Correlations (r) for Mood Variables with Mood Subscales Variables Subscales Positive Affect Negative Affect Relaxation

0.69**

0.16

Enthusiasm

0.43* 0.05

Nervousness

0.41* 0.51**

Fatigue

-0.18 0.60**

Note. * p < .05, two-tailed; ** p < .01, two-tailed.

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Table A.3

Pearson’s Product Moment Correlations (r) between Mood Subscales Subscales Enthusiasm Nervousness Fatigue Relaxation

-0.36*

0.51**

-0.31

Enthusiasm

-0.11 0.15

Nervousness

-0.39*


Table A.4

Pearson’s Product Moment Correlations (r) for Personality Factors with Other Continuous Variables

Personality Factors Neuroticism Extraversion Openness Agreeableness Conscientiousness

Age

-0.08

0.13

-0.11

0.15

0.10

Musical Background

0.38*

0.20

0.63**

-0.01

-0.16

Task Duration

-0.00 -0.28 -0.15 -0.14 -0.12

Listening Duration

-0.18 -0.12 -0.08 -0.15 -0.11


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Table A.5

Pearson’s Product Moment Correlations (r) Between Other Continuous Variables

Other Continuous Variables Musical Background Task Duration Listening Duration

Age

0.09

-0.04

0.10

Musical Background

-0.34* -0.13

Task Duration

0.75**


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Table A.6

Spearman’s Rank Correlations (rs) for Personality Factors with Ordinal Variables Personality Factors Neuroticism Extraversion Openness Agreeableness Conscientiousness School Level

-0.08

0.18

0.18

0.07

-0.13


0.13

-0.11

-0.13

-0.31

0.06


-0.26

-0.11

-0.22

-0.25

-0.08


-0.09

-0.04

0.03

-0.17

0.12

Computer Program Task Difficulty

0.04

-0.02

-0.21

0.04

0.06


0.15

0.35*

0.35*

-0.10

-0.23

Listening Hours/Day

-0.05

0.18

0.41*

-0.23

-0.02

Music Focus

-0.39* 0.22 0.09 -0.23 -0.08

Note. * p < .05, two-tailed.

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Table A.7

Spearman’s Rank Correlations (rs) for Mood Variables and Subscales with Ordinal Variables Mood Variables Mood Subscales Positive Negative Relaxation Enthusiasm Nervousness Fatigue School Level

-0.02

0.09

-0.24

0.22

0.07

0.14


0.03

0.12

-0.15

0.16

0.02

0.12


-0.03

0.15

-0.20

0.14

0.06

0.18


0.54**

0.07

0.19

0.40*

0.12

0.10

Computer Program Task Difficulty

0.19

0.26

-0.06

0.23

0.29

0.09


-0.05 0.26 0.04 -0.17 -0.01 0.23

Listening Hours/Day

0.20 -0.26 0.12 0.14 0.03 -0.20

Music Focus

0.05 0.04 -0.13 0.21 0.16 0.03

Note. * p < .05, two-tailed; ** p < 0.01, two-tailed.

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Table A.8

Spearman’s Rank Correlations (rs) for Other Continuous Variables with Ordinal Variables Other Continuous Variables

Age Musical Background

Computer Program Task Duration

Listening Duration

School Level

0.68**

0.14

0.20

0.28


0.27 -0.13 0.32 0.27


0.61** -0.18 0.34* 0.34*


0.18 -0.02 0.23 0.23

Computer Programming Task Difficulty

0.12 -0.12 0.18 0.08


-0.17 0.17 0.10 0.02

Listening Hours/Day

-0.26 0.09 0.21 0.22

Music Focus

0.10 -0.17 0.27 0.27

Note. * p < .05, two-tailed; ** p < 0.01, two-tailed.

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Table A.9

Spearman’s Rank Correlations (rs) Between Ordinal Variables

Ordinal Values



Computer Programming

Hours/Day

Music Focus

School Level

0.22

0.47**

0.17

0.29


0.76** 0.51** 0.20


0.38* 0.16


0.35*


Appendix I

Open-Ended Responses to Music-Use Questionnaire

Item #6: Please explain why you chose the music you listened to. You may want to

refer to a specific song, artist, or style in your answer.

1. So Lonely by the Police is a great feel-good song and always gets me in a good mind set.

2. Techno music in general has repetitive sounds that allow me to focus on what I'm doing instead of the music I listen to. I find I get more work done listening to techno than I do with other types of music such as pop, rock, etc.

3. I am listening to Pandora a long time. I believe they have turned my favorite radio stations to my music liking. I wouldn't surprise on this as, they use machine learning to identify the users attitude towards music, using feedback.

4. I listened to recordings I made of myself improvising at the piano. I like the music very much and I like how my brain lights up as I remember making the music, it seems to organize and calm my mind.

5. It calms me and does not distract me. 6. I chose this music from a pre-existing playlist that I had knowing that it would be

music I liked but at the same time was not so busy that it would be distracting from my assignment

7. I prefer the jam or free flowing music that it easy to get lost in. While I'm coding, I tend to alternate getting caught up in the code, and then caught up in the music. The music itself is easy to space out to on its own, so I find it doesn't distract much. I would say it’s there when I need it, but easily forgotten when I don't. Phish, Grateful Dead, and Government Mule are my ideal programming artists. Generally I'll just leave a live concert running.

8. I choose it because I was feeling upbeat as the programming was going well or at least the music made it feel like I was making progress, because I was moving to the music. The time of day all so meant I want to play more uptempo music.

9. I felt like listening to some jazz to set an easy, smooth feel while working on this difficult assignment.

10. I chose to listen to this to block out the noise around me. I find television, my neighbors, and music with words to be very distracting, so in order to concentrate I need to block all of these sounds out. Listening to this type of white noise is perfect for this. I chose this particular thunderstorm soundscape because I really like the sound of rain and stormy weather.

11. I just like this music. It makes me feel pretty chilled out and confident. It's got some soul to it. I like the first song the most: "O Que Sobrou do Céu." These are Brazilian songs, so I get to exercise a little bit of my Portuguese while I am working. It may be helpful to specify that I was working on a program to quiz me on Portuguese words while I listened to this music. I thought it was appropriate.

12. I started out with Kyng because they are a new band I'm listening to. I switched to Toxic Holocaust because I recently started loving them and Lord of the Wasteland is a real headbanger. It's almost like the next level from Kyng. Then I picked a song I

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never listened to by a band that I have listened to in the past. This was pure thrash. Then I went back to a favorite of mine from Toxic Holocaust which is also fast. The vocals are real interesting also. Then I finished with something epic and glorious which was Blood Brothers by Iron maiden. It's full of melody and great vocals/lyrics.

13. Songs like Universal Mind, and Why I Am, Firewall, Chromazone, Inertiatic ESP and Sir Duke have a lot of energy. Their color in their songs and the live upbeat rhythms invigorates me to push forward. Songs like Cissy Strut, #41, Kid A, Paranoid Android are a lot mellower than the other songs. While still having very catchy rhythms and or a lot of musical activity going on, they allow me to relax and retain a sense of motivation at the same time. Progressive music always excites me the most because of my background as a musician I know what's going on and can take feeling from those elements in those songs. It also clarifies my mind- as if understanding time changes and polyrhythms always me to figure out a solution to a program.

14. I like listening to instrumental music when working. Songs with words distract me from what I’m working on and I like explosions in the sky specifically because it’s almost inspirational to finish my task

15. I chose the Black Seeds to relax and read the instructions/get started. The Bibio song gets me energized but not distracted because it has no words and a simple beat. The other songs put me in a similar mood.

16. I had heard of Childish Gambino and wanted to check out his music. I am a huge fan of U2 and Johnny Cash. I'm also a huge fan of Kings of Leon and I've found that they really help me to relax when I am writing code.

17. Most of the music I chose had rhythmic, energetic instrumentals at a medium pace, with sparse lyrics or reduced lyrical emphasis (Interpol's "C'mere" being the exception). I believe that great instrumentals can subconsciously boost mood and productivity, without the cognitive distractions of lyrical recognition.

18. I chose high energy high tempo music without lyrics so that I could listen to the music and focus on my work at the same time. Music with lyrics tends to distract me from the programming task, and low-impact music tends to make me feel unproductive.

19. It's very ambient. This band has a very specific style, where every song has a clear driving beat, but it's not a quick tempo and the lyrics are hidden underneath textures rather than the focus of the song. It's easy to not pay attention to what they're saying, but still have something to groove to.

20. I wanted to listen to this album (Viva la Vida) in particular. I usually listen to rock while programming and today I was in a Coldplay mood. The following songs I put on loop for a bit because I enjoy them more than the rest of the album: Life in Technicolor, Viva la Vida and Death and All His Friends. Other music I listen to occasionally while programming includes OneRepublic, Queen, Billy Joel, and sometimes classical (preferably Beethoven). Mostly rock though. I like straight eighths. Also, when it's a piece/song I really like and put it on loop I find that I focus more on the music-as if I'm more aware of the sounds, but I still maintain focus on the program.

21. I enjoy listening to Bach, and since I already knew these pieces I thought they would not distract me as much as a new piece of music might.

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22. This was just a playlist of about 500 of my favourite songs on shuffle. I did not necessarily pick the order of the music, but it is all music that I enjoy.

23. I chose the music that I did because it was what I wanted to listen to at that moment. The changes in style were just me sifting through my library and picking what sounded good, so there wasn't a whole lot of conscious thought that went into the process.

24. I set Itunes on shuffle, so I didn't specifically choose any of the songs. In general, I choose music because it has interesting harmonies, multiple layers to focus on, and unique timbres.

25. BT is one of my foremost musical idols. I haven't listened to his "Dreaming" remix compilation EP, and the song usually puts me into a mellow, yet energized mood. The song itself has a deep existentialist-like meaning to me as well. It seemed like the right choice for my current state.

26. I "always" listen to music when I do work. I like country music when I'm relaxing or driving. I like rap/heavier dubstep when I'm working out. I like music by or resembling Explosions in the Sky when I'm reading or learning new material. I like mashups, especially new ones when I'm writing a paper. The change of pace really helps get the ideas flowing. If I'm programming, or just doing homework (things I already know how to do, I just have to sit down and crank it out), there is no better music than Electronic/Progressive House. I prefer a majority of uptempo artists like Swedish House Mafia and Kaskade. I save the more demonic sounding music (i.e. Skrillex, ect.) for when I'm working out or getting ready for world war 3.

27. Relaxed listening, low volume, no heavy beats 28. I picked a few songs that were a bit up tempo to get me going. I also find work

inspiration in songs that present a concept or situation that has one emotion but expresses it in a different way. American Pie is about three famous and influential musicians dying in a plane crash. McLean presents some very sad concepts but does so in somewhat of an upbeat manner. I like that. It's sad but it's happy. I'm a big classical fan, and being a brass player at heart, I love the stuff all the eastern Europeans have composed during the various classical eras. I find 1812 very majestic and triumphant (hopefully making my study session the same). I've always enjoyed playing Firebird and I like the composition. I went back to American Pie because I hadn't heard it in awhile, and I was in the mood for another iteration.

29. I usually choose high energy music while coding because it keeps me awake and also because it is sort of an isolation mechanism. (I think that is why I choose to use headphones and high volume.) I also think that it sort of puts me in the mood of fighting and achieving things, sort of a workout mood where you have to do things, and prove that you can do it.

30. It came up on shuffle, and when i hear one song from a musical i usually want to hear the next one.

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31. When I chose music to listen too while working, I look for songs that have a fairly mellow and light sound, but also have a steady, moderate - upbeat tempo, and some energy. I try to stay away from stuff like rock with harsh sounds, and prefer instead things with a lot of airy, ethereal components like synthesized sounds or strings. Even the alternative stuff I choose like Phoenix or the Flaming Lips tend to have some almost electronic sounds to them. I will also occasionally listen to jazz, especially jazz piano, as I find it has a unique sound that has a lot of energy and movement to it, even in ballads or slower pieces.

32. I chose the jazz tracks (Mehldau) because those are my favorite to actively listen to. The rock track (Gabe Dixon) I chose because I believe that it generally puts me in a more upbeat mood. The alternative tracks (Iron and Wine) were chosen because I enjoy the relaxed vibe that they give me.

33. I did not choose this music in relationship to the task I was performing, but rather, I chose music that I wanted to listen to because they were on the tip of my brain. I would say that I chose the music that put the juice in my boots. I'm really into this new album by Kyarypamyupamyu. It's pretty rad. I think it is super cool. I didn't listen to all of it. About the first 5 or 6 tracks or something. Nine inch nails is always a good choice. Especially the fragile. It was nice to revisit this album this morning that I have listened to sooo many times. Rumba is always nice. I went looking for this song "Chano", which I did not find but I found several named "chano pozo", which I think are different. Anyway. I let this collection play through several tracks before moving on. I've listened to the new Meshuggah album Koloss a whole lot. It's nice to compare to their slightly older stuff. Catch 33. They have really come a long way since that album. It's very surprising how much better their song writing has become. After writing this I am listening now listening to Koloss!!!

34. I listened to the Nicolas Jaar BBC Radio 1 Essential Mix DJ set 05-19-12. It had these songs in it (besides the last two). There were other songs in the set but those were the ones that stood out most to me. I listened to that set because I knew it would be a mix of calming yet interesting works. I chose the music based more on coming home from a long day of work and sitting down with a glass of wine to work with, rather than specifically being related to the task at hand. Yet it helped me get through the task with a more relaxed demeanor.

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Item #7: How do you think music listening influenced you and your work? Please

provide a description.

1. It motivated me to go with the flow. 2. I think that it helps me to code for longer periods of time due to the focus I can

maintain while I work. I can be easily distracted at times, and as a result my coding time is very fragmented. Having a solid block of time to sit down and work helps me get an entire task complete rather than try to get bits and pieces done over a couple of days wherein I loose time trying to remember why I did a particular thing, or how a function I wrote was intended to work.

3. Sometimes music is relaxing, sometimes it’s not. It all depends on the mood i am in. 4. It relaxed and energized me, made me more aware of my posture and mental state,

gave me some stimulation to distract my sexual desires...provided a right-brained counterpart to the left-brained work I was doing...

5. It calms me, and allows me to concentrate better. 6. I feel that the music gave me background noise I could control so I could drown out

other noises such as my roommate talking or the traffic outside. 7. The music is there when I need it. If I'm stumped on a problem or frustrated I can sit

back and listen to a few minutes of calming, easy flowing music. On the other hand when I really get into it, the music is easy to tune out. Of course, when a favorite song comes on my mind pretty much drops everything to pay attention to that, but generally the music is just there.

8. I got less frustrated with the programming when errors occurred as I was enjoying the music. It also made the time seem to pass quicker when programming. I don't think I was as productive however as I would have been without the music.

9. I believe that the music helped me remain relaxed and allowed me to enjoy the programming assignment.

10. I think that it helped me to focus more on what I was doing. I felt like I was alone in my own little world since I couldn't hear anything besides the soundtrack. If I hadn't been listening to it I would definitely been distracted by what was going on around me.

11. It made me not freak out when I was working on this project. I can get pretty frustrated when I don't understand exactly why a program is not working. I think the music just makes me say to myself: "Okay, it's all good. What is not working here." I would say it helps me keep my cool.

12. I don't think it influenced it much. Although it did slow me down because I had to go and choose the next song. Also, sometimes I would find myself focusing too much on the music and that loses time.

13. It excites my mind, almost as if its pushing any mental block I have in the way. 14. I don’t know. I prefer to do homework and other activities with music mostly because

it helps me lose focus on my surroundings and focus on the task I’m trying to complete.

15. The music added a mood to my programming task, which is often bland when unaccompanied by music. This helped at certain points but hindered at others and I had to pause the song for a few minutes to focus on troubleshooting errors.

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16. I think it helped me to stay in a relaxed mode so I wouldn't get frustrated when I encountered problems with my code.

17. The music prevents me from becoming terribly bored of my current task and thus staves off the need to seek distractions. The steady rhythmic pace also grants me a certain energy level where, in my opinion, I become more productive.

18. I think it made me better able to focus on my work for a long time without feeling bored or distracted. It kept me energized and motivated.

19. It helped me to focus on my task more. In quiet rooms, I tend to get distracted by every sound that crops up. When I play music, I zone out while listening to that, and ignore all the other "unexpected" sounds a lot more. It also helps to keep my energy level moderately high.

20. When I listen to something I enjoy, I will often put it on loop. Somehow this focuses my attention. If that background "sound" changes, I notice, even if that sound is an entire composition of music. Today I was debugging a particularly annoying program which meant a lot of analysis and looking for that one line that's messing everything up. The added focus from an album allows me to focus on just these two things, the music and the program. It keeps me on track so to speak. Although I've never tested it, my mind probably wanders more without music. I also notice that I'm a tiny bit calmer while listening to a good piece in general. Coincidently I found the bad lines of code while listening to my favorite song on loop. In truth it was kind of awesome for them to coincide. Finding a bug in my code always brings a rush of elation but good music in background makes it better.

21. I found listening to music kept me going at a constant pace (rather than going on and off as I sometimes do while programming) but the speed at which I coded seemed slightly slower than usual. I would notice the music more prominently when I was trying to figure out something about the program that I was unsure of, and it was slightly distracting.

22. The music relaxed me and let me gather my focus and direct it towards the task at hand. I find working in silence gives me anxiety and that I need music at least in the background to work well.

23. I think the music mostly distracted me from my work, because I wanted to sit back and listen to it. I found that while programming I was either really concentrating on what I was doing or really listening to the music, but I couldn't do both at the same time. As a result, I think I ended up with a more fragmented train of thought than normal.

24. It alleviates some tedium that might have set in, and also improves my mood. It's difficult to tell if it improved my thinking or quality of work in any way.

25. It definitely calmed me down; I was a bit jittery before the programming. I don't think it necessarily helped me to focus, though.

26. When I'm working hard or at least "*trying* to, I prefer to work by myself, in a quiet place, with my headphones blaring. I don't like silence. It bothers me, especially when I'm doing work. I honestly can't remember a time I wrote a paper or sat down to do homework without music (I'm still listening to music right now). It could just be a placebo effect, but whatever...that shit still works. Sometimes, when I'm in the zone, I'll feel like the music has my full attention and my fingers are just typing on autopilot. I feel like listening to music keeps me interested in my current task and

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increases my overall productivity. If I get to library and realize I forgot my headphones, I'll leave. I'd rather go home and comeback with headphones than waste time feeling unproductive.

27. relaxed me, also covered some outside noise 28. Twisted Sisters was a bit distracting and loud. It got my energy up but didn't really

help me stay focused on my coding. I felt American Pie put me in the mood to work and I focused better with it on. I was most productive during 1812 and Firebird. This is partially due to the absence of words to distract. Both are also very emotional and motivational pieces so I felt obligated to get work done when they were playing. American Pie the second time was also good for focuses, but not as much as the first listening session was.

29. It certainly puts me in a better mood, which I can describe as cheerful or up-lifted, plus keeps me awake. Because of its high beat, I tend to act more focused and fast and get things done quicker. If I really need to think on things and plan the flow of the code, I choose to pause the music for a while.

30. I think the music calmed down a little. I don't usually listen to music and program, but i feel like its occupying a part of my brain that might be stressing out normally.

31. Mostly I find that listening to music helps me block out distractions around me especially if I'm in a public place like the library. I also feel like I get "in the zone" when I've got the right kind of music playing. It's hard to explain but having a steady, driving beat can help me stay productive, and focus on problems more easily.

32. As an aspiring jazz musician, I feel as though the jazz trio tracks somewhat distracted me from my programming. This makes sense to me, given that I consider playing jazz piano as an cognitively-demanding task alongside programming. I feel as though, during the rock and alternative tracks, I got the most work done - pushing the music into the background, more or less.

33. I'm not really sure. Probably distracted me more towards the music. I am fairly active and analytical listener and I get all juiced up about different stuff. Definitely not applicable to the task at hand. I like to think about the music. I find that if I am not paying attention to the music I usually turn it off.

34. I like to think that it kept my mind stimulated at times when repeating menial, repetitive tasks. It probably also distracted me a bit, but I was okay with that.

Appendix J

Participant Music Selections Reported

Artist Song Style/Genre Adele Set Fire To The Rain Pop Adele Someone Like You Pop Adventure Club Daisy(Adventure Club Remix) Electronic Adventure Club Youth(Adventure Club Remix) Electronic Aerosmith Dream On alternative/rock Alexisonfire Hey, it's your funeral post-hardcore Alexisonfire No Transitory post-hardcore Aphex Twin Jynwythek Ylow Electronic Aphex Twin Nannou Electronic Aphex Twin Bbydhyonchord calm IDM Bach Contrapunctus I Classical Bach Contrapunctus II Classical Bach Contrapunctus III Classical Bach Contrapunctus IV Classical Baywood I Can Breath Again Folk Becca Stevens Band My Girls Folk Bibio Lovers' Carvings Electronic Bill Evans I Love You Porgy Jazz Blue Sky Black Death Our Hearts of Ruin Progressive Rock Blue Sky Black Death And Stars, Ringed Progressive Rock Blue Sky Black Death To The Ends Of The Earth Progressive Rock Blue Sky Black Death Farewell To The Former World Progressive Rock Blue Sky Black Death Falling Short Progressive Rock Blue Sky Black Death Gold In Gold Out Progressive Rock Blue Sky Black Death Where Do We Go Progressive Rock Blue Sky Black Death In The Quiet Absence of God Progressive Rock Blue Sky Black Death Where The Sun Beats Progressive Rock Blue Sky Black Death Starry Progressive Rock Boards of Canada Dayvan Cowboy Electronic Bob Brookmeyer Small Band You'd Be So Nice to Come Home to Jazz Bon Iver Bon Iver folk Brad Mehldau When it Rain Jazz Brad Mehldau Ruckblick Jazz Brad Mehldau Trio Nobody Else But Me Jazz Brad Mehldau Trio Exit Music (For A Film) Jazz Brad Mehldau Trio River Man Jazz Brahms Violin concerto in D Mayor, Op 77 Academic BT Dreaming Electronic Dance

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Artist Song Style/Genre BT Dreaming (Eric Kupper Mix) Electronic Dance BT Dreaming (Libra Mix) Electronic Dance Cake Wheels Rock/alt/country Cake No Phone Rock/alt/country Caribou Odessa Electronic Chick Corea Waltz for Debby Jazz Chiddy Bang Opposite of Adults Rap Childish Gambino Heartbeat Rap Christopher Smith Endless Earth Piano Improvisation Christopher Smith For Every Thing That Lives is Holy Piano Improvisation Christopher Smith Paranoi Piano Improvisation Christopher Smith Death Worship Piano Improvisation Christopher Smith Summer Darkness Piano Improvisation Cobra Starship You Make Me Feel dance Cobra Starship Good Girls Go Bad dance Coldplay Life In Technicolor Indie Rock Coldplay Cemeteries of London Indie Rock Coldplay Lost! Indie Rock Coldplay 42 Indie Rock Coldplay Lovers in Japan Indie Rock Coldplay Yes Indie Rock Coldplay Viva la Vida Indie Rock Coldplay Violet Hill Indie Rock Coldplay Strawberry Swing Indie Rock Coldplay Death and All His Friends Indie Rock Damien Jurado Sheets folk Dark Dark Dark Daydreaming Folk Rock Dave Brubeck Tritonis Jazz Dave Brubeck Benjamin Christopher Davis Brubeck Jazz Dave Brubeck Loverman Jazz Dave Brubeck Tokyo Traffic Jazz Dave Matthews Band #41 Jazz/World Dave Matthews Band Why I Am Rock/World David Guetta Where Them Girls At dance Deadmau5 Ghosts n' Stuff House Death Cab for Cutie Your New Twin Sized Bed Indie Rock Disasterpiece Club Wolf Electronic Don McLean American Pie Folk/Soul Don McLean American Pie Folk/Soul Dubba Johnny All In Dubstep explosions in the sky Your hand in mine instrumental Explosions in the Sky Your Hand In Mine Rock/Electronic

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Artist Song Style/Genre Family of the Year St. Croix Indie Rock Feist Caught A Long Wind indie singer/songwriter Flaming Lips One More Robot Alternative Flo Rida Good Feeling pOP Flux Pavillion Bass Cannon Electronic Foals Miami Indie Galactic Go Go Instrumental Gorillaz Stylo Electronic Rock Igor Stravinsky Firebird Classical Interpol C'mere Alternative Rock Interpol Length of Love Alternative Rock Iron & Wine Love and Some Verses Alternative Iron & Wine Jezebel Alternative Iron Maiden Blood Brothers Metal Jason DeRulo Whatcha Say Pop Jesus Christ Super Star Overture Musical Jesus Christ Super Star Heaven on their minds Musical Jesus Christ Super Star What's the Buzz/Strange thing mystifying Musical Jesus Christ Super Star Everything's Alright Musical Jesus Christ Super Star Jesus must die Musical Jet Cold Hard Bitch Dance/garage rock John Mayer (by Radiohead) Kid A John Mayer Trio (The Meters) Cissy Strut Funk Johnny Cash When The Man Comes Around Country/Rock Johnny Cash Help Me Country/Rock Johnny Greenwood There Will Be Blood Dissonant contemporary Joshua Radin Today folk Justice D.A.N.C.E. Electronic Katy Perry Last Friday Night (T.G.I.F.) Pop Keith Jarrett Toyko Encore, 1974 solo piano jazz Kenny G The Moment Jazz Kenny G The Champion's Theme Jazz Kenny G Passages Jazz Kenny G Northern Lights Jazz Kenny G Moonlight Jazz Kenny G Innocence Jazz Kenny G Havana Jazz Kenny G Gettin' On The Step Jazz Kenny G Eastside Jam Jazz Kenny G Always Jazz Kesha Blow dance Kings of Leon The End Rock

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Artist Song Style/Genre Kings of Leon Pyro Rock Kings of Leon Back Down South Rock Klaypex Dubstep Guns Dubstep Klaypex Lights Dubstep Kyarypamyupamyu pamyupamyurevolution (album) JPOP Kyng Trampled Sun Heavy Rock/Metal Kyng Trails in Veins Heavy Rock/Metal Lauryn Hill Ex-Factor Neo Soul Layz Replay Pop Linkin Park Numb alternative Liquid Tension Experiemnt Universal Mind Progressive Metal luminary Youth Jets To Bangalore Electronic Maceo Plex Gravy Train (Nicolas Jaar Remix) spacey electronic dance Maroon5 & Christina Aguilera Moves Like Jagger dance Meiko Reasons To Love You folk Meshuggah Catch 33 (album) Metal MGMT Time to Pretend Electronic/Indie Rock Michael Dulin Clair De Lune folk Miike Snow The Devil's Work Electronic Rock Mike Stern/Bob Berg Band Chromazone Fusion Modeselektor Berlin Electronic Modest Mouse Dark Center of the Universe Indie Rock Municipal Waste Wolves of Chernobyl Thrash Metal Nick Drake River Man Alternative Nine Inch Nails The Fragile (left) (album) Rock / Metal O Rappa O Que Sobrou Do Céu Samba/Funk O Rappa Se Não Avisar o Bicho Pega Samba/Funk O Rappa Minha Alma [A Paz Que Eu Não Quero] Samba/Funk O Rappa Lado B Lado A Samba/Funk Outkast So Fresh So clean hip hop OutKast Crumblin' Erb Hip-Hop Pearson Sound Footloose leftfield breakbeat Phantogram When I'm Small Phish Backwards down the number line Jam Phish Stealing time from the faulty plan Jam Phish Joy Jam Phish Sugar Shack Jam Phish Ocelot Jam Phish Kill Devil Falls Jam Phish Light Jam Phoenix Countdown Alternative Phoenix Lasso Alternative

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Artist Song Style/Genre Pitbull International Love Pop Pixies Bone Machine Alternative rock Pixies Alec Eiffel Alternative rock Plaid Crumax Rins synth IDM Plaid Upona synth IDM Port O'Brien I Woke Up Today Rock Radiohead Paranoid Android Progressive/Grunge Rock Radiohead Paranoid Android Rock Radiohead No Surprises Rock Radiohead/De la Soul Itsowezee hip hop/Alternative rock Ricardo Villalobos What You Say Is More Than I Can Say weird house Rihanna We found love dance simplynoise.com Thunderstorm Stephen Swartz Ft. Joni Fatora Bullet Train Electronic Steve Aoki WARP Electronic Steve Vai Firewall Funk/Progressive Rock Stevie Wonder Sir Duke Funk Strike911 Trance Insurgency Part 1 Techno Strike911 Trance Insurgency Part 2 Techno Strike911 Death of a Hero Part 1 Techno Strike911 Death of a Hero Part 2 Techno Strike911 The Entrance Techno Strike911 Ayane's Winter (DoA2) Techno Strike911 Helena's Trance (DoA2) Techno Strike911 Sixth Gear Techno Strike911 Chaotic Dreamer (The Attack) Techno Strike911 Victory Techno Swedish House Mafia Save The World Electronic Tchaikovsky 1812 Overture Classical The Black Keys I Got Mine Alternative Rock The Black Seeds Don’t Turn Around Reggae The Black Seeds Dust and Dirt Reggae The Concept D-D-Dance Alternative Pop The Dead Weather Die By The Drop Alternative rock The Field The Little Heart Beats so Fast minimal tech house The Gabe Dixon Band One To The World Rock The Killers Shadowplay Alternative Rock The Lonely Island Threw It On The Ground Comedy The Mars Volta Inertiatic ESP Progressive/Grunge Rock The Middle East Blood Indie Rock The Morning Benders Excuses Alternative The Police So Lonely Classic Rock/Ska

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Artist Song Style/Genre The Police Can't Stand Losing You Classic Rock The Raconteurs Level alternative The White Stripes The Denial Twist Garage Rock Revival Toxic Holocaust Lord of the Wasteland Heavy Rock/Metal/Thrash Toxic Holocaust Burn Thrash Metal Twisted Sisters We're not gonna take it Heavy Metal U2 Beautiful Day Rock U2 Stuck in a Moment You Can't Get Out Of Rock U2 Elevation Rock U2 Stay (So Close, So Far Away) Rock Usher OMG Pop Washed Out Feel It All Around Rhythmic/Electronic Weepies Nobody Knows Me at ALl Wilco Pot Kettle Black Alternative Wildlife Control Analog or Digital Indie Rock various Repertório Rumba (album) Rumba

the role of personality and mood in music-use during a high-cognitive demand task

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