MUSICAL PREFERENCES, PERSONALITY TRAITS AND SPOTIFY FEATURES

Sem Kaylee Dekkers 10555943 University of Amsterdam Master of Arts: Thesis 0 th 15 August J.A. Burgoyne

Introduction

I like to think I present an innocuous, well-socialized face to the world – nothing for anyone to worry about. But if you know that I like [alternative ] then you know a little something else about me. You've gotten a new data point. If you have all of my , the points coalesce to form a picture, an intimate one that doesn't quite match the public persona (Schwarz, 2004).1

Music is everywhere around us. If we walk into our local grocers, shops, gym, when we turn on our radio during a car ride, even when we pay a visit to our dentist. In short, music is a ubiquitous phenomenon. One common use of music in today's society is enjoyment and aesthetic appreciation (Kohut & Levarie, 1950). Music is also the center of many social activities, , dancing, such as choirs, and even in most social gatherings. Music may not always be the primary focus, but it is certainly an essential component – for example, try to imagine Christmas Eve without Christmas carols. Another common use is music's ability to inspire dance and physical movement (Dwyer, 1995; Large, 2000; Ronström, 1999). Music also satisfys a number of needs beyond social context; a personal music selection can serve as a tool to shape their physical and social environments to reinforce their dispositions and self-views (Buss, 1987; Gosling, Ko, Mannarelli, & Morris, 2002; Snyder & Ickers, 1985; Swann, Rentfrow, & Guinn, 2002).2 For some, music is also used for mood regulation and enhancement (North & Hargreaves, 1996; Rentfrow & Gosling, 2003; Roe, 1985).3 In studies involving the uses of music, adolescents have reported that they use music for a distraction from troubles, as a means of mood regulation, for reducing loneliness, and as a badge of identity for inter- and intra-group self-definition (Bleich, Zillmann, & Weaver, 1991; Rentfrow & Gosling, 2006, 2007; Rentfrow, McDonald, & Oldmeadow, 2009; Zillmann & Gan, 1997). Additionally, music is also used to enhance concentration and cognitive function, to maintain alertness and vigilance (Emery, Hsiao, Hill, & Frid, 2003; Penn & Bootzin, 1990; Schellenberg, 2004), and increase worker productivity (Newman, Hunt, & Rhodes, 1966). The final use of music worth mentioning is its role in social and protest movements, where music is used for motivation, group cohesion, and focusing on common goals (Eyerman & Jamison, 1998), whereas therapists encourage patients to choose music to meet various therapeutic goals

1 Peter J. Rentfrow and Samual D. Gosling. 'Message in a : The Role of Music Preferences in Interpersonal Perception.' Psychological Science, Vol. 17, No. 3 (2006): 236. 2 Peter J. Rentfrow and Samual D. Gosling. 'The Do Re Mi's of Life: The Structure and Personality Correlates of Music Preferences'. Journal of Personality and Social Psychology, Vol. 84, No. 6 (2003): 1237. 3 Peter J. Rentfrow, Lewis R. Goldberg and Daniel J. Levitin. 'The Structure of Musical Preferences: A Five-Factor Model'. Journal of Personality and Social Psychology, Vol. 100, No. 6 (2011): 1139. 1

(Davis, Gfeller, & Thaut, 1999; Särkamö et al., 2008).4 Even though music can serve a wide variety of functions, this research aims to shed light on individual musical preferences. Individuals have preferences for different types of music, which undoubtedly will be influenced by psychological and social processes. Obviously, one person might have stronger feelings towards music than someone else, but what lies beneath these preferences? What determines one's music preferences? Is there a pattern that connects certain types of music to certain kind of people? Research suggests there are links between music preferences and personality (Arnett, 1992; Cattell & Anderson, 1953; Cattell & Saunders, 1954; Little & Zuckerman, 1986; McCown, Keiser, Mulhearn, & Williamson, 1997), physiological arousal (Gowensmith & Bloom, 1997; McCamara & Ballard, 1999; Oyama et al., 1983; Rider et al., 1985), and social identity (Crozier, 1998; North & Hargreaves, 1999; North, Hargreaves, & O'Neill, 2000; Tarrant, North, & Hargreaves, 2000).5 Previous research has provided the assumption that individuals seek musical environments that reinforce and reflect aspects of their personalities, attitudes, and emotions (Colley, 2008; Delsing, ter Bogt, Engels, & Meeus, 2008; George et al., 2007; Rentfrow & Gosling, 2003; Rentfrow & McDonald, 2009; Schäfer & Sedlmeier, 2009). Also, than preferences for books, clothing, food, movies, and television shows; individuals consider their preferences for music more revealing of their personality than any of the previously mentioned (Rentfrow & Gosling, 2003). Young adults report significantly stronger preference ratings for music than older people, which might be an explanation for the emphasis on music. According to Rentfrow et al. (2012), much of the research in this area had examined the structure of musical preferences with the aim of developing a foundation on which to develop and test hypotheses about the role of music in everyday life.6

To dig a little deeper into this area, the aim of the present study is to broaden our understanding of the nature of musical preferences and attempt to gain insight into this matter. Nonetheless, we have to keep in mind that this research is an exploratory study. Toward that end, the current study is set out to investigate which aspects of music underlie individual differences in musical preferences. This work offers yet another insight into those aspects, specifically, musical features provided by the streaming service Spotify (https://www.spotify.com/). As Nave et al. (2018) mention: “With the proliferation of Internet-based services for sharing and streaming music

4 Peter J. Rentfrow, Lewis R. Goldberg and Daniel J. Levitin. 'The Structure of Musical Preferences: A Five-Factor Model'. Journal of Personality and Social Psychology, Vol. 100, No. 6 (2011): 1139. 5 Peter J. Rentfrow and Samual D. Gosling. 'The Do Re Mi's of Everyday Life: The Structure and Personality Correlates of Music Preferences'. Journal of Personality and Social Psychology, Vol. 84, No. 6 (2003): 1237. 6 Peter J. Rentfrow et al.. 'The Remains The Same: A Replication and Extension of the MUSIC Model'. : An Interdisciplinary Journal, Vol. 30, No. 2 (2012): 162. 2 on demand, personalized music is becoming a more central and prominent fixture in many people’s lives. This increase coincides with a growing interest in understanding the psychological basis of musical preferences.”7 Incorporating Spotify's musical features in this study, resulting from individuals personal playlists, gives us an insight into a person’s musical preferences and listening behaviour of their everyday life.

The Structure of Musical Preferences

One of the first researchers who investigated individual differences in musical preferences where psychologists Cattell and Anderson (1953). They believed that preferences for certain types of music reveal information about unconscious aspects of personality that is overlooked by most personality inventories. Even though their beliefs in music preferences could open a window into one's unconscious, nowadays researchers view music preferences as a manifestation of more explicit personality traits. For instance, research has shown that sensation seeking appears to be positively related to preferences for rock, heavy metal, and punk music and negatively related to preferences for sound tracks and . Also, personality traits as Extraversion and Psychoticism have been shown to predict preferences for music with exaggerated bass, such as rap and .8 Research from North and Hargreaves (1999) provided evidence linking music preferences and personality. They found that people use music as a “badge” to communicate their values, attitudes, and self-views. However, this matter was moderated by participant's self-esteem. Participants with higher self-esteem perceived more similarity between themselves and the prototype music fan than participants with low self-esteem. Even in different populations, age groups, and cultures, similar results have been found for the notion that one's self-views and self- esteem influence music preferences.9 When it comes to the structure of music preferences, different researchers have begun to map this area with the aim of identifying this structure. For instance, Rentfrow and Gosling (2003) have created a four-factor model that was labelled reflective & complex (compromising classical, , folk, and genres), intense & rebellious (rock, alternative, heavy metal), upbeat & conventional (country, pop, soundtracks, religious), and energetic & (rap, soul,

7 Gideon Nave et al.. 'Musical Preferences Predict Personality: Evidence From Active Listening and Likes.' Psychological Science (2018): 1. 8 Peter J. Rentfrow and Samual D. Gosling. 'The Do Re Mi's of Everyday Life: The Structure and Personality Correlates of Music Preferences'. Journal of Personality and Social Psychology, Vol. 84, No. 6 (2003): 1237. 9 Cf. Idem. 3 electronica). Similar results from research from Delsing and others (2008), who also found four preferences factors, labelled:  rock (compromising rock, heavy metal/, punk/hardcore/, gothic)  elite (classical, jazz, gospel)  urban (hip-hop/rap, soul/R&B)  pop (trance/techno, top40/charts) Colley (2008) also found four factors for female participants, and five factors for male participants. Four factors emerged for both genders; sophisticated,(compromising classical, blues, jazz, opera), rebellious (rap, reggae), heavy (rock, heavy metal), and mainstream (country, folk, chart, pop). For male participants, the mainstream factor could be subdivided into two factors; traditional (country, folk) and pop (chart pop). As you can see, in each of these studies there emerged three very similar factors. One factor was determined mainly by classical and jazz music, another was mainly defined by rock and , and the third factor was defined by rap and hip-hop music. However, not all studies have provided similar music preference-structure results. Studying individual preferences for 30 music genres in a sample of Canadian adults, George, Stcikle, Rachid, and Wopnford (2007) revealed nine music-preference factors. These factors labelled as:  rebellious (grunge, heavy metal, punk, alternative, )  classical (piano, choral, classical , opera/ballet, Disney/Broadway)  rhythmic & intense (hip-hop & rap, pop, R&B, reggae)  (country, popular, , folk/ethnic, swing)  fringe (new age, electronic, ambient, techno)  contemporary Christian (soft contemporary Christian, hard contemporary Christian)  jazz & blues (blues, jazz), and traditional Christian (hymns & southern Gospel, gospel)

Another study, by Schäfer and Seldmeier (2009), assessed individual differences in self- reported preferences for 25 music genres. Their analyses revealed six music-preference factors, labelled as:  sophisticated (compromising classical, jazz, blues, swing)  electronic (techno, trance, house, dance)  rock (rock, punk, metal, alternative, gothic, ska)  rap (rap, hip-hop, reggae), pop (pop, soul, R&B, gospel)  beat, folk, & country (beat, folk, country, rock 'n' roll) In a study mainly involving Dutch participants, Dunn et al. (2011) examined individual differences

4 in preferences for 14 music genres. Their results revealed six music-preference factors, labelled as rhythm 'n' blues (comprising jazz, blues, soul), hard rock (rock, heavy metal, alternative), heavy bass (rap, dance), country (country, folk), soft rock (pop, soundtracks), and classical (classical, religious). Although these results may not be identical, there does appear to be a considerable degree of similarity across these studies. Three factors arose in all studies; one mainly defined by classical and jazz music, one that is mainly defined by rock and heavy metal music, and one mainly defined by rap and hip-hop music. There also appears to be a factor that is comprised mainly of , which occurred in nearly every sample that included singer- or storytelling music. In a few studies, there also appeared a factor that was mainly composed of new age and styles. In summary, looking at all these studies, there appears to be four to five robust music- preference factors: rock, classical, urban, pop, and perhaps country/folk.

Limitations of Past Research

Researchers have attempted to gain insight into musical preferences, and have taken crucial steps to develop a theory of music preferences – a theory that ultimately will explain when, where, how, and why people listen to music.10 Past research has presented a somewhat incomplete picture; most studies examined only a limited selection of music genres, other studies examined only a few personality dimensions.11 The problem with the selection of music genres is that every participant should have knowledge of lots of different music genres, which might not always be the case. Additionally, participants should also be able to express their preferences for each of these genres. When one has no knowledge of a certain genre, it will be impossible to express their preference for this genre. Furthermore, genre-based measures also assume that participants share a similar understanding of all the genres, which is also problematic. Although genres represent a level of analysis that most individuals will be familiar with (Rentfrow & Gosling, 2003), retail stores have been classifying music this way for over 50 years, keeping in mind that music can change a lot over the years.12 Genre categories can change over time – for example, a such as AC/DC was once considered

10 Peter J. Rentfrow and Samual D. Gosling. 'The Do Re Mi's of Everyday Life: The Structure and Personality Correlates of Music Preferences'. Journal of Personality and Social Psychology, Vol. 84, No. 6 (2003): 1236. 11 Cf. Ibid. 1237-1238. 12 Peter J. Rentfrow et al.. 'The Song Remains the Same: A Replication and Extension of the MUSIC Model'. Music Perception: An Interdisciplinary Journal, Vol 30, No. 2 (2012): 162. 5 heavy metal, but is now considered as classic rock.13 Research has shown that participants did not always agree on genre and subgenre labels under which their own favourite music should be placed (Rentfrow et al., 2012). Another problem that can occur with genre-based measures, is the influence of stereotype fans of particular genres. Some genres can activate stereotypes that are associated with a suite of traits, which could influence one's stated musical preferences. Additionally, it is hard to find out which aspects of music influence preferences with genre-based measures. Listeners could be attracted to auditory or psychological facets, such as , pitch, or intensity. These facets are not distinctive for one particular genre, for example, high intensity can be found in heavy metal music, but also in certain electronic music. Especially when these facets are combined with , specific emotional reactions can be triggered to the music that is genre-independent. In short, it could be that similar emotional reactions might occur to musical pieces from different genres, and different reactions might occur to musical pieces from within the same genre.14 Available research shows individual differences in preferences for vocal as opposed to instrumental music, fast versus slow music, and loud versus soft music (Kopacz, 2005; McCown, Keiser, Mulhearn, & Williamson, 1997; McNamara & Ballard, 1999; Rentfrow & Gosling, 2006). Evidence showed that such preferences are related to personality traits such as extraversion, neuroticism, psychoticism, and sensation seeking.15 In addition, personality traits extraversion and psychoticism have been shown to predict preferences for music with exaggerated bass, such as rap and dance music.16 There is also emerging evidence of individual preferences for pieces of music that evoke or signify emotions such as happiness, joy, sadness, and anger (Rickard, 2004; Schellenberg et al., 2008; Zentner et al., 2008). Evidence that individuals are drawn to musical styles with particular social connotations such as toughness, rebellion, distinctiveness, and sophistication also emerged in present studies (Abrams, 2009; Schwartz & Fouts, 2003; Tekman & Hortaçsu, 2002).

13 Cf. Idem. 14 Peter J. Rentfrow et al.. 'The Song Remains the Same: A Replication and Extension of the MUSIC Model'. Music Perception: An Interdisciplinary Journal, Vol 30, No. 2 (2012): 163. 15 Peter J. Rentfrow and Samual D. Gosling. 'The Do Re Mi's of Everyday Life: The Structure and Personality Correlates of Music Preferences'. Journal of Personality and Social Psychology, Vol. 84, No. 6 (2003): 1241. 16 Cf. Ibid. 1237. 6

Overview of Present Research by Peter J. Rentfrow and Colleagues

This section is dedicated to an overview of past research by social and personality psychologist Peter J. Rentfrow and his colleagues, which served as a major inspiration for this study. Therefore, I will summarize some of his research to highlight its importance, and why it is relevant for this work. According to Rentfrow et al. (2003), there has been criticism about the lack of attention to real-world behaviour within social, and personality psychology. One way researchers could address this issue, Funder (2001) says, is to extend their research on the structural components of personality to include behaviour that occurs in everyday life.17 Rentfrow and his colleagues picked this up and implemented this matter in their studies as much as possible, which also accounts for the present study.

As mentioned above, previous studies shows a somewhat incomplete picture; researchers examined only a limited selection of music genres, or they examined only a few personality dimensions. In order to examine the importance of music in everyday life, Peter J. Rentfrow and Samuel D. Gosling (2003) investigated how much importance individuals place on music compared with other leisure activities. In this research collection, six independent studies were conducted. In Study 1, participants were asked to complete a set of questionnaires that were designed to assess their attitudes and beliefs about various lifestyle and leisure activities, which consisted of 8 different domains. Participants were asked to indicate how personally important each domain was to them, to what extent this importance revealed their personality, and the frequency in which they engaged with that particular activity. Results showed that except for hobbies, participants considered music the most important item of the 8 domains (music, movies, books and magazines, TV programs, food preferences, bedrooms, hobbies and activities, and clothes). Participants indicated that along with hobbies and bedrooms, they believed that their music preferences revealed as much, if not more, information about themselves than the other domains. With the exception of hobbies, participants also believed that music preferences reveal at least as much about the personalities of others as the other lifestyle and leisure domains. The second study was an exploratory analysis of music preferences. The primary objective was to identify the basic dimensions of music preferences. These preferences can be measured at different levels of abstraction, ranging from a highly descriptive subordinate level to a very broad subordinate level. Due to the fact that Rentfrow and Gosling's focus lies on everyday music preferences, their goal was to assess music preferences at the level that naturally arises when people

17 Cf. Ibid. 1236. 7 think about and express their music preferences. People tend to describe their preferences first at the level of genres, then expand this to a level of subgenres and only later they step up to broader terms – for instance, loud, percussive – or down to specific artists or songs. In summary, Rentfrow and Gosling decided that the genre and subgenres were the optimal levels at which to start their investigation. These genres were selected in a multistep process, where five different judges were asked to list all the music genres and subgenres that came to mind. Additionally, five music stores were consulted to identify additional genres and subgenres which should also be taken into account. Eventually, this procedure generated a total of 80 music genres and subgenres that varied in specificity. This list was then presented to a group of 30 participants who were asked to indicate their preference for each of the categories. If a participant was not familiar with a certain genre, they were allowed to skip that category. Results showed that very few participants were familiar with all of the specific subgenres, but nearly all of them were familiar with the broader music genres, which indicated that this level was the appropriate level for this research. The final version of their findings resulted in a questionnaire to test a person's music preferences, called the Short Test Of Music Preferences (STOMP). The final list was made up of 14 music genres: alternative, blues, classical, country, electronica/dance, folk, heavy metal, rap/hip- hop, jazz, pop, religious, rock, soul/, and sound tracks. Findings of Rentfrow and Gosling revealed a structure. Seven psychologists were asked to examine the factor structure and to come up with conforming labels that capture the main themes underlying these factors. All this resulted in a five-factor structure, where Factor 1 was named Reflective and Complex. The genres that had the strongest loading for this factor were blues, jazz, classical, and – genres that seem to facilitate introspection and are structurally complex. Factor 2 was defined by the rock, alternative, and heavy metal – genres that all are full of energy and emphasize themes of rebellion – which was labelled as Intense and Rebellious. Factor 3 was defined by country, sound track, religious, and – genres that emphasize positive emotions and are structurally simple – and was named Upbeat and Conventional. Factor 4 was defined by rap/hip-hop, soul/funk, and electronica/dance music – genres that are lively and often emphasize rhythm – and was labelled Energetic and Rhythmic. The results from this exploratory investigation suggest that there is a clear underlying structure to music preferences. These four factors capture a broad range of music preferences. In order to see if this factor structure of the music preferences is generalizable, Rentfrow and Gosling conducted one more study to test this theory. Results showed, again, that there is compelling evidence for the existence of four music-preference dimensions. However, there were some limitations to this research. First, participants' music preferences were derived from self-reports. This measure assumes that participants are able to accurately report their music preferences, but fail 8 to control for the potential biases produced by impression-management motivations. Second, the participants from this study mainly college students who were attending a public university in central Texas, US, which is a hot spot for country music. In order to move away from these limitations, a fourth study was conducted. This study consisted of 500 participants, 10 randomly selected from each state in the US via an online music platform (www.audiogalaxy.com). Music – 20 randomly selected songs – from their personal library was downloaded to represent their music preferences. A user's preference for a particular genre was determined by the number of songs that appeared in each . Again, results indicated a factor structure for four independent dimensions as described above. Rentfrow and Gosling conducted a fifth study to identify the qualities that defined these four dimensions. Music attributes vary across a wide range of moods, energy levels, complexities, and lyrical content. Hence the objective of this study was to systematically examine the attributes of preference dimensions. A total of 20 lyrical attributes were supplemented with an additional 5 musical attributes (fast, slow, acoustic, electric, and voice) which formed a list of 25 music attributes. Again, seven judges were asked to independently rate the songs on the attributes. Results showed that dimension 1, Reflective and Complex, was slower in than the other dimensions, used mostly acoustical instruments, and had very little singing. Dimension 2, Intense and Rebellious, was faster in tempo, used mostly electric instruments, and had a moderate amount of singing. Dimension 3, Upbeat and Conventional, was moderate in tempo, used both acoustic and electric instruments, and had a moderate amount of singing. Dimension 4, Energetic and Rhythmic, also moderate in tempo, used electric instruments, and had a moderate amount of singing. The lyrical attributes were also divided into four general categories: complexity (e.g., simple or clever), positive affect (e.g., happy or romantic), negative affect (e.g., sad or angry), and energy level (e.g., relaxed or energetic). Results showed that the lyrics in the Reflective and Complex dimension were perceived as complex, expressing both positive and negative emotions, and having a low level of energy. The Intense and Rebellious dimension lyrics were perceived as moderately complex, low in positive affect, but high in negative affect and energy level. The lyrics of the Upbeat and Conventional dimension were perceived as simple and direct, low in negative affect, and energy level. Lyrics of the Energetic and Rhythmic dimension were perceived as being somewhat complex, unemotional, and moderate in energy level. A sixth and final study was conducted to investigate how music preferences are related to existing personality characteristics. Participants were asked to fill in questionnaires about their personality (Big Five Inventory) and self-esteem (Rosenberg Self-Esteem Scale), self-views, as well as tested on their cognitive abilities (Wonderlic IQ Test). Results revealed that the Reflective and Complex dimension was positively related to Openness to Experience, self-perceived intelligence, 9 verbal (but not analytic) ability, and political liberalism. This dimension was negatively related to social dominance orientation and athleticism. According to these findings, individuals who enjoy listening to reflective and complex music tend to be inventive, have active imaginations, value aesthetic experiences, consider themselves to be intelligent, tolerant of others, and reject conservative ideals. The Intense and Rebellious dimension showed positive relations to Openness to Experience, athleticism, self-perceived intelligence, and verbal ability. According to these results, individuals who prefer music in this dimension do not appear to display signs of neuroticism or disagreeableness. In general, individuals who prefer this type of music tend to be curious about different things, enjoy taking risks, are physically active, and consider themselves intelligent. The Upbeat and Conventional dimension revealed positive correlations with Extraversion, Agreeableness, Conscientiousness, conservatism, self-perceived physical attractiveness, and athleticism. Negative correlations were found with Openness to Experience, social dominance orientation, liberalism, and verbal ability. These findings suggest that individuals who enjoy listening to music from this dimension are cheerful, socially outgoing, reliable, enjoy helping others, see themselves as physically attractive, and tend to be relatively conventional. The Energetic and Rhythmic dimension showed positive relations with Extraversion, Agreeableness, blirtatiousness, liberalism, self-perceived attractiveness, and athleticism. Negative relations were found with social dominance orientation and conservatism. These results suggest that individuals who enjoy music from this dimension tend to be talkative, full of energy, are forgiving, see themselves as physically attractive, and tend to eschew conservative ideals. In Figure 1, you can see how the musical attributes are lined with each of the music-preference dimensions.

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Fig. 1: Music attributes of each of the music-preference dimensions as described above.18

In summary, Rentfrow's and Goslings research revealed that individuals believe music preferences reveal information about their personalities and that music preferences are used to convey information about one's self. They also showed that music preferences and personality are related to their previous work. In this study, Rentfrow and Gosling (2006) investigated what message a person's musical preferences convey about their personality. Additionally, they also examined what features of these preferences convey interpersonal information. In this research collection, two independent studies were conducted. The first study in this collection investigated conversation topics that naturally seem to arise among young adults during conversations. The content of these conversations between strangers as they were getting acquainted, was measured over a period of 6 weeks and took place on the Internet. Participants were instructed to interact with one another for 6 weeks and were asked to talk about anything that they thought would be best to get to know one another better. The topics that arose from these conversations were coded in terms of seven topics: books, clothing, movies, music, television shows, football, and sports other than football. Participants were students from the University of Texas at Austin, which explains the choice to label football as an individual topic. Results showed that music was the most commonly discussed topic overall and among the most

18 Peter J. Rentfrow and Samual D. Gosling. 'The Do Re Mi's of Everyday Life: The Structure and Personality Correlates of Music Preferences'. Journal of Personality and Social Psychology, Vol. 84, No. 6 (2003): 1249. 11 commonly talked about topics for every 1 of the 6 weeks tested. The second study involved the impact of this information on the impressions participants got from one another's personalities and values and how these are related to one's music preferences. Rentfrow and Gosling assumed that two different sources of music-based information seemed potentially relevant for forming these impressions. First, there can be relied on specific features of one's musical preferences (e.g., fast tempo) that could provide information about that person's behaviours and personality. Second, judgments about one another could be influenced by a stereotype (e.g., heavy metal fan) that is associated with a whole suite of traits (rebellious and disagreeable). These two types of information were examined where one's observations were linked to someone's judgments. This link can work both ways, only if it did – one's music preference links with that person's actual level of the underlying construct (e.g., agreeableness) – one's judgment should converge with the underlying construct that was being observed, which results in an accurate impression of that construct. Participants were asked to complete several personality measures and had to create a list of their top-10 favourite songs. Two types of information on participants' music preferences were then collected: information about the specific features of the songs (e.g., tempo) and information about the genre of each song (e.g., rock). The musical attributes that were created in Rentfrow and Gosling's previous research have been used to rate every song by the participants. Observers were asked to listen to each top-10 list and rate each song on several personality measures and also had to fill in a self-report on their personality. A list of 12 terminal values was selected from 74 targets, which resulted in the next values: a comfortable life, a world at peace, a world of beauty, an exciting life, family security, inner , national security, salvation, self-respect, social recognition, true friendship, and wisdom. The instrumental values resulted in a list of 6 values: ambition, courage, forgiveness, imagination, intellect, and love. The ratings by the observer's on self-reports on self-esteem, positive affect, and negative affect were also collected. Results revealed that the strongest consensus was found for Openness to Experience, followed by Agreeableness, Conscientiousness, the value of social recognition, Extraversion, and the value of imagination. Furthermore, results from the observers show that music preferences convey information very different from that conveyed through the stimuli used in past research, such as photographs or short videos. Music preferences provide more information about participants' Agreeableness, Emotional Stability, and Openness to Experience. Also, the findings suggest that observers' judgments of participants were associated with a number of music attributes and genres. For example, observers' ratings of participants' Extraversion were positively related to such music attributes as energy, enthusiasm, and amount of singing, together with the genres country and hip-hop. Additionally, the top-10 lists of extraverted participants contained music with 12 high energy levels, enthusiasm, and singing, together with the genres country and hip-hop. In sum, specific attributes of individuals' music preferences and music-genre stereotypes differentially influenced observers' impressions of participants' personality traits, values, and effect.

In order to expand the research that had already been carried out, Rentfrow, Goldberg, and Levitin (2011) investigated affective reactions to excerpts of musical excerpts. The goal of this research is to broaden the understanding of the factors that shape the music preferences of ordinary music listeners, as opposed to trained . In this research collection, four independent studies were conducted. According to Rentfrow et al., the advantages of using authentic music as opposed to manufactured music designed for an experiment, is that it is much more likely to represent the music people encounter in their daily lives. This means that those musical excerpts from authentic music will more likely present a more accurate picture of one's music preferences. Also, each piece of music can be coded on a range of musical qualities. Each piece can be coded in both music- specific attributes (e.g., tempo, instrumentation, duration) and psychological attributes (e.g., joy, anger, sadness). Some of the problems that arise with genre-based measures are also tackled using musical excerpts, because these excerpts are far more specific than genres. Participants do not need to have knowledge of a wide variety of genres in order to indicate their liking for a musical excerpt. The same might go for the problem with stereotypes of fans that are associated with particular genres. One could have a different initial impression of a genre but yet another impression by a musical excerpt from that particular genre, which could result in a different indication of liking a musical excerpt. The liking of musical excerpts, thus, serve as a representation of musical preferences that capture both external and intrinsic musical properties. However, it is important that the musical excerpts that are presented to participants are extracted from musical pieces that are not familiar to participants. Past research has provided evidence that well-known pieces of music can serve as powerful cues to autobiographical memories (Janata, Tomic, & Rakowski, 2007) and that familiar music tends to be liked more than unfamiliar music (Dunn, 2011; North & Hargreaves, 1995). The objective of this study was to assess individual differences in preferences for the many different styles of music that people are likely to encounter in their daily lives. Music that served as music-preference stimuli was gathered through an online advertisement on the Internet, where participants were asked to identify broad music styles that would appeal to most people. Another set of participants, consisting of university students, were asked to fill out an open-ended questionnaire to name their favourite music genres (e.g., rock) and subgenres (e.g., classic rock) along with an example of music for each one. All this resulted in 23 identified genres and subgenres that occurred most often on lists. Then, 3 subgenres were added which were mentioned only a small number of times in a previous pilot study, due to the fact that 13 the aim was to cover as wide a range of musical styles as possible. Next, pieces of music were selected that were not hailed from obscure artists, but pieces that were of a similar quality to hits yet were unknown. A group of 10 professionals (musicologists and recording industry veterans) were asked to identify representative pieces for each of the 26 subgenres. They selected pieces from artists from major record labels and had been commercially released, but did not achieve high sale figures. This means that it was unlikely that participants have heard the pieces of music before. Finally, the lists the participants provided were reduced and presented to 500 listeners. They were asked to name the genre or subgenre to each piece and to indicate how well they thought the piece represented that particular (sub)genre. Thus, music preferences were measured by asking participants to indicate their degree of liking for each of the 52 musical excerpts. Rentfrow, Goldberg, and Levitin conducted five-factor solutions in total in order to obtain their results. Firstly, findings showed a two-factor solution that resembles the well-documented highbrow (or sophisticated) and lowbrow music-preference dimensions. The excerpts with high loadings on the Sophisticated/Aesthetic factor were mainly drawn from classical, jazz, and . The excerpts with high loadings on the second factor were predominantly country, heavy metal, and rap. Secondly, their three-factor solution revealed a split in the lowbrow music- preference dimensions. This factor split into subfactors that appeared to differentiate music based on its forcefulness or intensity. One subfactor was labelled as Intense/Aggressive and was comprised of heavy metal, punk, and rock excerpts. The other subfactor was less intense and comprised excerpts from the country, rock 'n' roll (early rock, ), and pop genres, and was named. Unpretentious/Sincere. Third, this factor remained in the five-factor solution, which was named Contemporary/Danceable and comprised mainly rap and electronica music. In Figure 2, you can see how these factors emerged from the factor solutions.

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Fig. 2: Varimax-rotated principal components derived from preferences ratings for 52 commercially released musical excerpts in Study 1. The figure should be read from top to bottom.19

A second study was conducted to test the generalizability of the music-preference factor structure across samples as well as musical stimuli. A new set of music-preference stimuli was created that included only previously unreleased music from unknown, aspiring artists. The musical pieces were hailed from the database of Getty Images (http://www.Getty.com) and should represent the 26 subgenres. Eventually, four pieces of music were compiled for each subgenre. Again, participants were gathered through advertisements on the Internet and were asked to indicate their liking for each of the 94 musical excerpts. This time, a six-factor solution was conducted. Findings revealed a virtually identical final five-factor solutions for both studies. Apparently, the first factor in their two-factor solution was difficult to interpret because it comprised a wide array of musical styles – from classical and soul to electronica and country. The second factor, Intense/Aggressive, remained virtually unchanged through all solutions. The three-factor solution, however, a factor resembling the Sophisticated dimension emerged. This factor comprised classical, jazz, and world music excerpts. A factor resembling the Unpretentious/Sincere factor also emerged in the three- factor solution, and was mainly comprised of country and rock 'n' roll music excerpts. The four- factor solution provided a factor that was mainly composed of rap, electronic, and soul/R&B music excerpts. This factor split into two factors in the five-factor solution, resembling the

19 Peter J. Rentfrow, Lewis R. Goldberg and Daniel J. Levitin. 'The Structure of Musical Preferences: A Five-Factor Model'. Journal of Personality and Social Psychology, Vol. 100, No. 6 (2011): 1144. 15

Contemporary/Danceable and a factor labelled as Mellow. The Contemporary/Danceable factor mainly included rap and electronica music, whereas the Mellow factor predominantly included pop, soft rock, and soul/R&B music excerpts. In Figure 3, you can see how these factors emerged from the factor solutions.

Fig. 3: Varimax-rotated principal components derived from preference ratings for 94 unknown musical excerpts in Study 2.20

A third study in this collection was designed to investigate the generalizability of the music- preference factors across samples and methods. A subset of 25 of the musical excerpts from Study 2 was presented to the participants who were then asked to rate their liking for each excerpt. The results revealed factors that clearly resembled those observed in the previous studies. The first factor emerged from primarily classical, jazz, and world music, and clearly resembled the Sophisticated preference dimension. The second factor clearly resembled the Intense preference dimension, and was mainly composed of heavy metal, rock, and punk music excerpts. The third factor reflected the Contemporary preference dimension and mainly included rap and electronica music excerpts. The fourth factor was mainly comprised of soft rock and excerpts, which resulted in the resemblance of the Mellow dimension. The fifth factor was mainly composed of country and rock 'n' roll excerpts, and thus resembled the Unpretentious dimension. In

20 Peter J. Rentfrow, Lewis R. Goldberg and Daniel J. Levitin. 'The Structure of Musical Preferences: A Five-Factor Model'. Journal of Personality and Social Psychology, Vol. 100, No. 6 (2011): 1147. 16

Figure 4, you can see how these factors emerged from the factor solutions.

Fig. 4: Varimax-rotated principal components derived from preference ratings for 25 musical excerpts in Study 3.21

All three studies together provide compelling evidence for a five-factor music preferences structure; the same five factors emerged from three independent studies that used different methods, stimuli, and participants. Taken together, the five factors form the MUSIC model. These findings, however, are mainly characterized in terms of (sub)genres. Nonetheless, some genres load on more than one music-preference dimension. For example, the genre jazz is represented on both the Sophisticated and the Contemporary factors. Genres are party defined by an emphasis on certain musical attributes, but it could be that individuals have preferences for particular attributes that can be found in more than one genre or factor. Thus, a fourth study was conducted to examine those variables that contribute to the structure of musical preferences. A list of attributes was created, based on sound-related and psychological attributes on which pieces of music could be judged. The set 25 music-descriptive adjectives by Rentfrow and Gosling (2003) was used. Next, to increase the range of music attributes, two expert judges were asked to supplement the initial list with a new set of music-descriptive adjectives. Then, two other

21 Peter J. Rentfrow, Lewis R. Goldberg and Daniel J. Levitin. 'The Structure of Musical Preferences: A Five-Factor Model'. Journal of Personality and Social Psychology, Vol. 100, No. 6 (2011): 1150. 17 judges independently evaluated the extent to which each music descriptor could be used to characterize various aspects of music. All this resulted in seven sound-related attributes – dense, distorted, electric, fast, instrumental, and percussive – and seven psychological oriented attributes – aggressive, complex, inspiring, intelligent, romantic, and sad. A total of 40 judges with no formal music training independently rated the 146 excerpts that were used in Studies 1 and 2 on each of these 14 attributes. Correlations were conducted with factor loadings of each excerpt on each MUSIC factor with the mean sound-related attributes, psychological attributes, and genres of the excerpts. Findings revealed the following:  first, the excerpts with high loadings on the Mellow factor, musically speaking, were perceived as slow, quiet, and not distorted. In terms of psychological attributes, the excerpts were perceived as romantic, relaxing, not aggressive, sad, somewhat simple, but intelligent. The genres that were associated with this factor are soft rock, R&B, , and adult contemporary.  Second, the Unpretentious factor was musically perceived as not distorted, instrumental, loud, electric, nor fast. The psychological attributes were perceived as somewhat romantic, relaxing, sad, and not aggressive, complicated, nor intelligent. The musical styles that are most strongly associated with this factor were subgenres of country music.  Third, the Sophisticated factor revealed that the musical excerpts were, in musical terms, perceived as instrumental and not electric, percussive, distorted, or loud. In terms of psychological attributes, the excerpts were perceived as intelligent, inspiring, complex, relaxing, romantic, and not aggressive. The genres that were highly associated with this factor were classical, marching band, avant-garde classical, , world beat, traditional jazz, and Celtic.  Fourth, the Intense factor was musically perceived as distorted, loud, electric, percussive, and dense. In psychological terms, the excerpts were perceived as aggressive, not relaxing, romantic, intelligent, or inspiring. Classic rock, punk, heavy metal, and music styles were associated most with this factor.  Fifth, the excerpts with high loadings on the Contemporary factor were perceived as percussive, electric, and not sad. The genres that were primarily related to this factor are rap, electronica, Latin, acid jazz, and Euro pop music. Taken together, the results indicate that the MUSIC factors are not the result of preferences only based on genres, but are driven by preferences for certain musical characteristics. The preferences for each dimension are independent of preferences for the other dimensions.

Other research has been carried out in order to further expand knowledge on music preferences, 18

Rentfrow, Goldberg, Stillwell, Kosinski, Gosling, and Levitin (2012) developed new measurement approaches – and abandon the genre-based selection of entirely – that would allow them to access the diversity of music that exists within a genre, and those that latent cross- genre consistencies may exist. In this research, a broader set of attributes and their relation to music preferences was examined. As an extension on their work from 2011, the goal was to develop a deeper and more nuanced understanding of the factors underlying musical preferences. In this research collection, three independent studies were conducted. Study 1 was designed to test whether the MUSIC preference model, as described above, would replicate in a large and representative sample of Internet users, as opposed to music fans. The study was also designed to expand the set of auditory and psychological attributes that were also previously examined. Participants were recruited over the Internet via an online social media platform, Facebook (http://www.facebook.com), where an application called “My Personality” was used. When users agreed to use the application, they were asked for their consent to use their responses to the surveys for research purposes. The participants were presented multiple excerpts and were asked to indicate their degree of liking for each excerpt. A subset of the musical pieces that were used in Rentfrow, Goldberg, and Levitin's (2011) Study 2 were also used in this study. Excerpts of 50 musical pieces from 21 music genres and subgenres were selected. We should keep in mind that these excerpts were hailed from real musical pieces, because people will probably encounter real music most in their daily lives. Two experts were asked to independently generate a list of adjectives that could be used to describe psychological characteristics of music. Both lists then were compared and both experts agreed on a preliminary set of 100 psychological attributes. Afterward, 10 people were presented these 100 adjectives and were then asked to indicate the extent to which each adjective could be useful in describing a musical piece. This resulted in 29 new psychological attributes that were added to the set of seven psychological attributes that were provided by Rentfrow, Goldberg, and Levitin (2011). The attributes were grouped into four general categories: positive affect (e.g., amusing, animated, dreamy, enthusiastic, fun, happy), negative affect (e.g., abrasive, angry, depressing, intense, tense), energy level (e.g., calming, danceable, forceful, gentle, lively, manic, mellow, party music, thrilling), and perceived complexity (e.g., deep, reflective, sophisticated, thoughtful). The sound-related attributes were also broadened. One expert independently generated a list of descriptors in terms of general instrumental . That list was then reviewed and evaluated by a second expert, who was able to add and delete items from that list. This eventually resulted in a set of seven new auditory attributes that were added to the list of seven auditory attributes provided by Rentfrow, Goldberg, and Levitin (2011). The new attributes were: brass, heavy bass, piano, raspy voice, , woodwind, and yelling voice. Following this, 40 judges were divided into four 19 groups and asked to rate 25 musical excerpts on 25 attributes. Again, a five-factor solution was conducted and was consistent with results from Rentfrow, Goldberg, and Levitin (2011) and, thus, clearly resembled the MUSIC model. First, the Mellow factor comprised music as easy listening, R&B/soul, soft rock, adult contemporary, and electronica. Excerpts were perceived as slow, quiet, not distorted, and acoustic. In terms of psychological characteristics, mixed patterns of positive relations were found with the positive affect attributes; the excerpts were perceived as dreamy, romantic, warm, sensual, and inspiring, but not animated, enthusiastic, amusing, or fun. There were also negative relations found for abrasive, tense, intense, angry, and aggressive. Positive relations were found with sad, also, a pattern of correlations appeared with the energy attributes. This indicated that the excerpts from this dimension were perceived as low, gentle, calming, and relaxing, but not lively, forceful, manic, thrilling, party music, or danceable. Furthermore, the excerpts were generally considered cerebral, with positive relations with reflective, thoughtful, deep, sophisticated, and intelligent. Second, the Unpretentious factor comprised excerpts from the avant-garde classical, classical, Latin, traditional jazz, world beat, electronica, and adult contemporary genres. Excerpts were perceived as lacking heavy bass and , and having primarily acoustic instruments and vocals. In psychological terms, the pieces in this factor were perceived as possessing some degree of positive effect, revealed by positive relations with amusing, fun, warm, but a negative relation with strong. Excerpts were also perceived as being low in negative affect, revealed by negative relations with tense, intense, angry, abrasive and aggressive. Additionally, excerpts were perceived as being low in energy, with negative relations to thrilling, manic, and forceful. Pieces in this dimension were generally not perceived as cerebral, with negative correlations with complex and sophisticated. Third, the Sophisticated factor comprised of excerpts from genres as country, bluegrass, country-rock, and rock 'n' roll. Pieces with high loadings on this factor were perceived as quiet, clear sounding, slow, and lacking heavy bass or percussion. They were also perceived as having sounds produced by acoustic instruments, pianos, brass instruments, and not having vocals. Psychologically speaking, the pieces were associated with multiple positive affect attributes, including joyful, inspiring, merry, romantic, warm, dreamy, sensual, and amusing. Excerpts were also perceived as lacking in negative effect, indicated by its negative relations with abrasive, angry, aggressive, and depressing. Furthermore, pieces in this dimension were also perceived as having a somewhat low energy level, as indicated by its positive relations with relaxing, calming, gentle, and mellow, but negative relations associations with party music, forceful, danceable, and manic. Pieces in this factor were generally perceived as being cerebral, with strong positive correlations with sophisticated, intelligent, and thoughtful. 20

Fourth, the Intense factor comprised of excerpts from the punk, classic rock, and heavy metal genres. Sound-related attributes that showed high loadings with this factor were perceived as loud, distorted, fast, percussive, dense, and having heavy bass. The vocalists in these excerpts were perceived as yelling and having raspy voices, whereas the instruments were predominantly electric. In psychological terms, the excerpts were perceived as lacking many aspects of positive affect, with strong negative relations with warm, romantic, sensual, dreamy, joyful, merry, inspiring, happy, and had positive relations with strong, enthusiastic, and animated. The pieces were also perceived as possessing considerable negative effect, revealed by strong positive correlations with angry, abrasive, intense, and tense. Moreover, the excerpts were perceived as being high in energy, as indicated by the strong positive relations with forceful, manic, thrilling, party music, and lively, whereas there were negative relations found with gentle, mellow, calming, and relaxing. These pieces were not perceived as being cerebral, indicated by negative correlations with reflective, thoughtful, sophisticated, and intelligent. Fifth, the Contemporary factor comprised of excerpts from rap, R&B/soul, , and electronica genres. The excerpts in this dimension were associated with a few attributes. In terms of sound-related attributes, the pieces with high loadings in this factor had heavy bass sounds, synthetic sounds, and electric instruments. In terms of psychological attributes, excerpts were perceived as sensual but not inspiring, as well as being low in negative affect, revealed by negative relations with depressing and sad. The excerpts were also perceived as energetic, indicated by positive relations with danceable and party music, whereas the pieces were not perceived as cerebral, as indicated by a negative relation with thoughtful. Overall, these findings indicate that the MUSIC model is robust and raises the possibility that attributes (rather than genre) could be the driving force behind the music-preference dimensions.

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Attributes Mellow Unpretentious Sophisticated Intense Contemporary Sound-related Auditory features slow lacking heavy bass clear sounding loud heavy bass quiet lacking distortion slow distorted synthetic sound not distorted lacking heavy bass fast lacking percussion percussive dense heavy bass yelling voice raspy voice

Instruments acoustic acoustic acoustic electric electric vocals pianos brass instruments not vocal Psychological Positive affect dreamy amusing joyful strong sensual romantic fun inspiring enthusiastic not inspiring warm warm mercy animated sensual not strong romantic not warm inspiring warm not romantic not animated dreamy not sensual not enthusiastic sensual not dreamy not amusing amusing not joyful not fun not merry not inspiring not happy

Negative affect sad not tense not abrasive angry not depressing not abrasive not intense not angry abrasive not sad not tense not angry not aggressive Intense not intense not abrasive not depressing tense not angry not aggressive not aggressive

Energy low not thrilling relaxing forceful danceable gentle not manic calming manic party music calming not forceful gentle thrilling relaxing mellow party music not lively no party music lively not forceful not forceful not gentle not manic not danceable not mellow not thrilling not manic not calming no party music not relaxing not danceable

Cerebral reflective not complex sophisticated reflective not thoughtful thoughtful not sophisticated intelligent thoughtful deep thoughtful sophisticated sophisticated intelligent intelligent

Genre R&B/soul avant-garde classical country soft rock classical bluegrass classic rock R&B/soul adult contemporary Latin country-rock heavy metal Europop electronica traditional jazz rock 'n' roll electronica world beat electronica

Fig. 5: An overview of the MUSIC preferences dimensions with attributes.

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The aim of the Study 2 and 3 in this research collection was to investigate the hypothesis that the MUSIC model reflects individual differences in preferences for particular combinations of musical characteristics, even within a certain genre. Their previous research showed that attributes are not distributed evenly across genres, so in order to broaden their knowledge on the role of musical attributes in music preferences, Rentfrow et al. decided to run their MUSIC model on two different genres that have multiple subgenres that differ greatly from one another. In this study, the individual differences in the structure of preferences for pieces of music from within the same genre. To test this hypothesis, a genre has to be selected that spans a wide variety of musical styles and also has strong connotations. On the basis of these criteria, two genres were selected. First, jazz which is a broad and diverse genre that comprises several subgenres, from , swing, and bebop, to modal, free, and fusion. It also contains certain social connotations or stereotypes with jazz music fans; jazz listeners are believed to be creative, laidback, and introspective (Rentfrow & Gosling, 2007; Rentfrow et al., 2009). Second, rock is also a broad genre that comprises several subgenres, from hard rock, classic rock, and alternative, to soft rock and rock 'n' roll. In this genre, there are also clearly defined stereotypes with fans; rock listeners are believed to be aggressive and hedonistic (Rentfrow & Gosling, 2007; Rentfrow et al., 2009). In Study 2, the genre jazz was being examined. Musical preference data were collected from a large and diverse sample of Internet users. In addition, Rentfrow et al. also collected data from an undergraduate student sample, where there was more control possible over the assessment conditions. Again, the Internet sample was recruited using the same methods as in Study 1; via the online application “My Personality” on online platform Facebook. The participants from the Internet sample were asked to listen to a total of 50 musical excerpts and indicate their degree of liking for each excerpt, whereas the undergraduate student sample participants were asked to indicate their degree of liking for 25 excerpts. The musical pieces that were used, were selected by one judge with an extensive jazz music library, and selected songs with a wide variety of styles. Then, a second judge with extensive knowledge of jazz music examined the selected pieces and removed, or added pieces that thought were more suitable. This resulted in a list that was subsequently judged by a group of three experts – including the aforementioned judges – and contained 50 jazz music excerpts. Rentfrow et al. also compared the musical attributes that defined jazz-preference factors with the musical attributes that were used in Study 1. Results revealed the emergence of a five-factor solution from the Internet sample that was highly consistent with previous work and resembled the MUSIC model. The first factor comprised excerpts from by artists as and , which were interpreted as corresponding to the Mellow factor. Excerpts with high loadings in 23 this factor were perceived as quiet, slow, clear sounding, and to use synthesized sounds. In terms of psychological attributes, the excerpts were perceived as having a positive effect which was indicated by low potency. They were perceived as dreamy and romantic, but not strong or enthusiastic. Also, excerpts were perceived as low in negative effect, as indicated by the negative correlations with many of the negative variables, and were perceived as low in energy. Positive associations were found with the reflective, thoughtful, and deep attributes. The second factor comprised excerpts of early jazz, blues, and jazz vocals music by artists such as Bessie Smith and Louis Armstrong, and resembled the Unpretentious factor. Excerpts with high loadings in this factor were perceived as slow, quiet, clear sounding, and lacking heavy bass and density, and as having primarily vocals and fewer electric instruments. They were also perceived as low in negative affect, with negative correlations with aggressive and abrasive. Furthermore, excerpts in this factor were perceived as being low in energy, with negative associations to forceful and party music, and were perceived as thoughtful but not complex. The third factor comprised excerpts of bebop, modal, and fusion by artists such as Charlie Parker, Pharaoh Sanders, and Bud Powell, and resembled the Sophisticated factor. Excerpts with high loadings on this factor were perceived as fast, loud, and lacking heavy bass; they were also perceived as having sounds produced by non-electric instruments, pianos, woodwinds, and not having vocals. In psychological terms, the excerpts were perceived as having a positive effect, high in potency, as indicated by the strong relations with enthusiastic and strong. Excerpts were also positively related to many of the negative affect variables, suggesting that they were perceived as being high in energy, as they were perceived as lively, manic, and forceful. Excerpts were also perceived as complex and intelligent, but not thoughtful or deep. The fourth factor resembled the Intense factor and was mainly comprised of jazz-fusion pieces from artists such as and Stanley Clarke. Excerpts with high loadings on this factor were perceived as very loud, fast, distorted, dense, and percussive, and the instruments were predominantly electric. They were also high in positive affect with high potency, as indicated by positive associations with animated and strong, but negative relations with sensual, warm, and romantic. Moreover, pieces were perceived as high in negative affect, as reflected by positive links with most of the negative attributes. They were also perceived as very high in energy, as indicated by the strong positive relations with thrilling and forceful. Excerpts in this factor were perceived as complex, but low in all the other cerebral attributes. The fifth factor resembled the Contemporary factor, with excerpts of acid jazz and jazz rap pieces by artists like St. Germain and Us3. Excerpts with high loadings on this factor were perceived as having heavy bass, percussion, and dense synthetic sounds, with electric instruments. They were also perceived as amusing, fun, but not inspiring; they were thought to be low in 24 sadness, and to have some energy, as indicated by the positive correlations with the party music and danceable attributes. The pieces were not perceived as especially cerebral, as indicated by a negative relation with intelligent and reflective. Furthermore, the five factors and attributes that emerged in the Internet sample were very similar to the undergraduate student's sample. Most of the jazz-preference factors were very similar to the cross-genre MUSIC model. However, there was one exception for the Sophisticated jazz factor. The psychological attributes differed in this factor with the cross-genre Sophisticated factor. In the jazz factor, correlations of the psychological attributes suggested that they expressed stronger forms of positive and negative effect and more energy, compared to the cross-genre factor. The pieces of the jazz factor were also perceived as less thoughtful and deep, yet intelligent and more complex than the cross-genre pieces. This means that, in general, four of the five jazz-preference factors resemble the MUSIC factors. In Study 3, Rentfrow et al. conducted a similar experiment as Study 2, however, they assessed individual differences in preferences for a variety of rock music pieces. Again, researchers collected data from an Internet sample and an undergraduate student sample and were recruited via the same methods as before. The selection of the musical preferences stimuli followed the same steps as Study 2, which resulted in a list of 50 rock music excerpts. Participants from the Internet sample were asked to indicate their degree of liking for each of the 50 excerpts, whereas the undergraduate student sample participants were asked to indicate their degree of liking for 25 excerpts. Again, a five-factor solution emerged from the findings of this study. The first factor resembled the Mellow factor, with excerpts of alternative and soft rock by artists as Radiohead, , and . Excerpts with high loadings on this factor were perceived as slow, quiet, clear sounding, and airy, and to use acoustic instruments and synthesized sounds. In terms of psychological attributes, excerpts were perceived as having positive affect low in potency, with positive relations with dreamy and romantic, but nog animated or enthusiastic. They were also negatively related with most of the negative affect descriptors, except for sad and depressing. Furthermore, excerpts were perceived as being low in energy and were considered cerebral with positive relations thoughtful, deep, and reflective. The second factor resembled the Unpretentious factor, with excerpts from classic rock and , by artists such as , , and Simon and Garfunkel. Excerpts with high loadings on this factor were perceived as airy and clear sounding with little percussion, and as having primarily acoustic instruments. They were also perceived as having some degree of positive effect, indicated by the positive relations with amusing, joyful, merry, and happy. Negative correlations were found with negative affect descriptors, suggesting that excerpts were perceived as lacking negative effect. Also, negative relations with forceful and manic emerged, and excerpts 25 were perceived as not complex. The third factor that emerged resembled the Sophisticated factor, and comprised pieces of jazz-rock, fusion, and avant-garde rock, by artists including The RH Factor, Frank Zappa, and Phish. Excerpts with high loadings on this factor were perceived as clear sounding, quiet, and slow, but also as having sounds produced by acoustic instruments, pianos, woodwinds, and as not having vocals. In psychological terms, they were perceived as having positive affect low in potency, as reflected by the positive links with warm and joyful, but negative links to almost all negative affect descriptors. Excerpts were also perceived as being low in energy, as indicated by positive relations with relaxing and calming, and negative relations with forceful and party music. Furthermore, excerpts were positively associated with some of the cerebral descriptors, such as sophisticated, intelligent, and complex. The fourth factor resembled the Intense factor and comprised excerpts of heavy, industrial, and punk music by artists like , Ministry, and the . Excerpts with high loadings on this factor were perceived as dense, loud, fast, and percussive, and the instruments were predominantly electric along with yelling and raspy voices. The pieces were strongly and negatively correlated with most of the positive effect descriptors except strong, which indicated that this factor is low in positive effect. The pieces were also strongly positively related to nearly all the negative affect descriptor except sad, suggesting that the excerpts were high in negative affect. Pieces were also perceived as being very high in energy, with strong relations with forceful and manic, and negative relations with most of the cerebral descriptors. The fifth factor that emerged was similar to the Contemporary factor and comprised excerpts of , synthpop, and new wave pieces. Excerpts with high loadings in this factor were perceived as clear sounding and quiet with singing. Pieces were positively related to almost all the positive affect descriptors. Also, excerpts were perceived as low in negative affect, indicated by the negative links with nearly all the descriptors. Pieces were also perceived as danceable and yet relaxing and had positive links with almost all the cerebral descriptors. In summary, the five factors that emerged in this study were similar in both samples. Results from all three independent studies support the hypothesis that individual differences in musical preferences are based on affective reactions to particular combinations of musical attributes in addition to genre classifications or social connotations.

To overcome limitations that emerged in previous studies, Nave, Minxha, Greenberg, Kosinski, Stillwell, and Rentfrow (2018) conducted two studies where links between musical preferences and personality were investigated, and whether the results from these studies can be generalized across different assessment methods and across age-diversified samples. The primary objective was to 26 determine whether individual differences in the Big Five personality domains can be predicted from musical preferences. In Study 1, Nave et al. used a machine-learning “predictive” approach to examine whether participants' preferences ratings following active listening to novel musical stimuli can be used for out-of-sample predictions of their personalities. Study 2 was a replication and extension of Study 1, but used an ecologically valid behavioural measure of musical preferences, namely: Facebook likes of musical artists. In Study 1, researchers used data from a sample of the online application MyPersonality users. Only data of users who a) completed a Big Five personality questionnaire and b) completed at least one music-preference survey were included in the sample. Musical preferences were estimated using surveys designed according to the five-factor MUSIC model. In two surveys, musical preferences stimuli were purchased from Getty Images, which means that it was unlikely that participants were familiar with the stimuli. Four other surveys included commercially released music by known artists. Two surveys consisted of only rock music excerpts, whereas the other two surveys consisted of only jazz music excerpts. All of these excerpts were used as stimuli that represent the five-factor MUSIC model as presented in previous work. Each participant was assigned to one of three conditions (mixed, rock, or jazz) and was asked to complete the survey. Next, they could choose if they wanted to fill out a second, similar survey. The analyses to obtain results was conducted in one of the six surveys. Findings from Study 1 revealed correlations between music-based personality predictions and the actual traits. The highest correlation was found for Openness to Experience, followed by Extraversion, Agreeableness, Neuroticism, and Conscientiousness. The music-based predictors of Openness to Experience, Extraversion, and Agreeableness were significantly better than a baseline model that predicted solely using gender and age. When musical preferences were added to this baseline model (gender and age), the model significantly increased the predictive accuracy for all the Big Five Traits. These results indicate that preferences for short musical excerpts contain predictive information about personality traits. After adding extra information on participants' preferences ratings, results showed that both specific and general musical preferences underlie the capacity to predict personality from musical preferences. In order to test the generalizability of these findings, the same analyses were carried out for the other five musical preferences surveys. Results from the additional analyses revealed similar findings. The next step in this study was to examine possible associations between the Big Five and preferences for specific MUSIC dimensions. Results showed the following: Openness to Experience was associated with greater liking of sophisticated music, and with less liking of mellow and contemporary music; Extraversion was associated with preference for unpretentious music; Openness to Experience and Extraversion were also associated with a general liking of music. None of the specific correlations exceeded for 27 the remaining three traits. However, Agreeableness was the only trait associated with a general liking of music from those three. Additionally, several patterns emerged from a correlation map. Openness to Experience correlated positively with the liking the sophisticated excerpts, and Extraversion was most strongly correlated with evaluating the unpretentious excerpts more positively.

Fig. 6: The average loadings of the Excerpts' Liking Ratings on the General Factor and the Big Five

personality traits from Study 1.22

Fig. 7: Partial correlations between the Big Five personality traits and the five-factor musical preference structure from Study 1.23

22 Gideon Nave et al.. 'Musical Preferences Predict Personality: Evidence From Active Listening and Facebook Likes.' Psychological Science (2018): 7. 23 Cf. Ibid. 8. 28

Results from Study 1 indicated that preferences for unfamiliar musical stimuli contain some valid information about personality. The objective of Study 2 was to replicate and extend these results to real-world behaviour by investigating whether naturally occurring statements of musical preferences, as represented by Facebook Likes of musical artists, and also to predict personality traits. A Facebook Like represents one person's positive associations with many different types of content, from messages, photo's, and videos, to pages of products, sports, and artists. There already is evidence that Facebook Likes contain information about certain personal attributes, from religiosity and political views to sexual orientation and personality (Kosinski, Stillwell, & Greapel, 2013). However, this study examined Facebook Likes in general, irrespective of content. Thus, Study 2 was conducted in order to examine whether the Likes of musical artists alone can provide information about the personalities of Facebook users. Again, data was used from a sample of the application MyPersonality and faced the same provisions as Study 1. The Facebook Likes were selected by being music-related and were then searched in EchoNest (http://www.theechonest.com), a major online musical database containing over 3 million artists. All Likes that did not appear in this database were excluded from the sample. Also, users that had fewer than 20 Likes were excluded as well. Findings showed that reliable correlations were found between music Likes-based personality predictors and all of the Big Five personality traits. The highest predictive accuracy was for Openness, followed by Extraversion, Conscientiousness, Neuroticism, and Agreeableness. For all the traits, the musical Likes-based predictors were more accurate than the baseline model. Personality inferences based on Facebook Likes were more accurate, compared to inferences based on active listening. In summary, these results indicated that Facebook Likes of musical artists carry personality-relevant information. The majority of predictive personality information can be attributed to individual Likes rather than general tendencies, with the exception of Neuroticism.

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Rentfrow and his colleagues have already done quite some research on music preferences and its relations to personality traits and musical attributes. Results from all these studies show links between musical preferences and personality. Analyses of the psychological, social, and auditory characteristics have led to dimensions that can be defined as Mellow, Unpretentious, Sophisticated, Intense, and Contemporary (MUSIC). This model captures individual differences in preferences for Western music in a large and diverse population. Their findings also showed links with other psychological constructs. However, one limitation of all these studies is that both the musical excerpts that were used and the Facebook Likes, do not automatically reflect what music people actually listen to in their everyday lives. Liking an artist on Facebook does not directly mean that one also listens to that particular music. The Like could also be based on other factors, for instance, peer influence of self-image. As Nave et al. describe: “The increasing use of music-streaming services (e.g., Last FM, Spotify) is expected to allow further investigations of the links between personality and active ecologically valid music listening behaviour.”24 This is where this present research comes in. As an exploratory study, it offers a new insight in this research area. Rentfrow and his colleagues already took several musical attributes into account in their studies but they were trying to predict personalities. Instead of creating a model that could predict personality, musical features provided by the streaming service Spotify offer another point of view. How are Big Five personality traits linked with these features? And what musicians and non-musicians, might there be a difference? In the next section, the present research is extensively described.

24 Gideon Nave et al.. 'Musical Preferences Predict Personality: Evidence From Active Listening and Facebook Likes.' Psychological Science (2018): 11. 30

Method

Participants Initially 81 people opened the survey and created an anonymous ID, subsequently some decided not to follow through the study. Thus, the choice was made to only utilise the data from participants who filled in their Spotify playlist hyperlink into account. One participant filled in all the questionnaires but did not fill in a Spotify hyperlink, thus this data was excluded. This resulted in a total of 40 participants (25 female; 15 male). Participants were asked to indicate their age category (18-24, 25-34, 35-44, 45-54, 55-64, 65-74, 75-84, 85 or older). Participants' age ranged from 18 to 54. Most were between the age of 18 and 24 (50%), followed by the category of 25-34 (37,4%), 35- 44 (7,5%), and 45-54 (5%). Furthermore, most participants were Dutch (92,5%), the other participants were Greek (2,5%), French (2,5%), and Iranian (2,5%). All the participants currently reside in the Netherlands. The majority of the participants were born in the Netherlands and had spent their childhood there (90%), other countries of origin were Greece (2,5%), France (2,5%), (2,5%), and Indonesia (2,5%). The sample size consists of both musicians and non- musicians. All participants participated out of free will and were able to stop at any given time. As the study was conducted as an online survey, participants were able to fill in the questionnaire at a time and place that suited them.

Online Survey For this study, I created an online survey in Qualtrics which consisted of four parts. In the first part, participants were asked to create an anonymous ID. All questionnaires were designed as a self- report inventory. This survey was distributed through e-mail and social media, mainly Facebook (http://www.facebook.com). The entire survey took 10-20 minutes to complete.

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Musical preferences. I estimated musical preferences using a personal Spotify playlist obtained from participants. In order to gain insight into these preferences, participants were asked to share the hyperlink to a personal Spotify playlist, called “Soundtrack 2017.” This playlist contains their 100 most played songs in the year 2017. Participants were shown a tutorial on how they could obtain the specific hyperlink via Spotify. This hyperlink was available at a person's personal Spotify, where they could share this playlist with others. In this case, participants were asked to share their “Spotify URI”, and paste this into the survey. The hyperlink was also available via a December 2017 e-mail from Spotify. 40 participants completed the input of information via the Spotify hyperlink to their Spotify “2017 Soundtrack” playlist. However, one participant's playlist only contained 71 tracks. A second participant filled in the link to the playlist of “Soundtrack 2016”, which contained only 46 tracks.

Demographics. The second part of the survey contained five questions concerning information on participant's demographics (gender, age, nationality, current residency, and country of origin where most of one's childhood was spent). Every participant responded to this part of the survey.

Personality. The third part of the survey pertained to a short personality test. Participants were asked to fill a questionnaire composed of 10 questions on the Big-Five of personality traits. Due to time limitations, the choice was made to use a shortened measure of the Big-Five personality dimensions instead of a test containing a more extensive list. Participants were showed ten personality traits and had to fill in to what extent these traits applied to them or not. They could rate these items on a Likert scale from 1 to 7, whereas 1 stands for “completely disagree”, and 7 for “completely agree”. 39 people filled in the ten-item personality test part of the survey. The list with the Big-Five personality traits and how they can be interpreted is as follows25:

 Openness (to Experience) People who like to learn new things and enjoy new experiences usually score high on this item. Openness includes traits like being insightful and imaginative and having a wide variety of interests.

25 “Big Five personality test traits explained | 123test BV,” accessed on 27th June, 2018, https://www.123test.com/big- five-personality-theory/. 32

 Conscientiousness People that have a high degree of conscientiousness are reliable and prompt. Traits include being organized, methodic, and thorough.

 Extraversion Extraverts get their energy from interacting with others, while introverts get their energy from within themselves. Extraversion includes the traits of being energetic, talkative, and assertive.

 Agreeableness These individuals are friendly, cooperative, and compassionate. People with low agreeableness may be more distant. Traits include being kind, affectionate, and sympathetic.

 Neuroticism (Emotional Stability) Neuroticism is also sometimes called Emotional Stability. This dimension relates to one's emotional stability and degree of negative emotions. People that score high on neuroticism often experience emotional instability and negative emotions. Traits include being moody and tense.

Goldsmith's Music Sophistication Index. The fourth and final part of the study was a questionnaire to indicate a person's Music Sophistication Index by Goldsmith (2011). In short, the Gold-MSI is a self-report inventory and test battery for individual differences in musical sophistication. It measures the ability to engage with music in a flexible, effective and nuanced way.26 These questions served to obtain information about the participant's musical background and as an indication of their engagement with music. There were 31 with statements on music, where participants could rate on a Likert scale from 1 to 7 to what extent those statements applied to them or not, with 1 representing “completely disagree” and 7 “completely agree.” Next, there were 12 questions on their musical background. At the end of the survey, participants were able to leave their e-mail address to be informed of their scores from the full survey. Final data showed that 35 participants filled in the Goldsmith's Music Sophistication Index questionnaire. Hereby a list of the five-factor structure reduced from self-report inventory a study by Müllensiefen et. al. is presented27:

26 “The Goldsmiths Musical Sophistication Index (Gold-MSI), Goldsmiths, University of London | Gold ac,” accessed on 27th June, 2018, https://www.gold.ac.uk/music-mind-brain/gold-msi/. 27 Daniel Müllensiefen, Bruno Gingras, Jason Music, and Lauren Stewart. 'The Musicality of Non-Musicians: An Index for Assessing Musical Sophistication in the General Population.' PLOS ONE, Vol. 9, No. 2 (2014): 7. 33

 Active Engagement This dimension describes to what extent people are (actively) engaged with music. Questions that belong to this dimension are how many music events one attends per year, how much money one spends on music in general, how much time one spends on listening to music attentively, how open one is to unfamiliar music, and so on.

 Perceptual Abilities This dimension describes how well a person's abilities are when it comes to identifying and judge musical pieces. Examples of questions in this category are how well someone can judge another's singing ability, beat performance, along with a person's own and other's tonal performance. As well as how well someone can recognize a familiar tune or identify a musical genre, and so on.

 Musical Training This dimension revolves around a person's musical background when it comes to musical training. Questions here are if someone considers him/herself a , and if so, what instrument do they play, how many years of training did they have, how many hours per day have they trained to obtain this skill, and if someone is able to play more than one instrument. Furthermore, this dimension also contains questions about an individual's background in training.

 Singing Abilities This dimension describes someone's singing ability. Here, applicable questions can be if someone is able to sing along to a song correctly, can remember a song and sing it from memory, and so on.

 Emotions This dimension describes to what extent someone has an emotional engagement with music. Here, applicable questions can be if someone uses music to evoke certain emotions, and if one can communicate these emotions to others. Also, to what extent can they identify what is special about a song, to what extent music evokes memories, and if someone listens to music for shivers down their spine.

In order to gain some more insight on how these personality traits and Goldsmith's Music Sophistication Index, a correlation matrix shows how these are correlated, arranged by R². Yellow represents a p < .001, which accounts for all the Music Sophistication Index items as you can see in table 1.

34

ophistic

FG FG (General S ation) x

X

F5 (signing Abilities) 0,878

X

F4 (Emot ions) 0,583 0,755

X

F3 (Musical Training) 0,616 0,699 0,904

X

F2 (perceptual Abilities) 0,672 0,881 0,678 0,828

X

F1 (Active Engagem ent) 0,711 0,684 0,783 0,702 0,855

X

0,029

Opennes to Opennes Experience 0,202 - 0,188 0,083 0,159 0,195

X

0,200

Agreeable ness - 0,084 0,047 0,134 0,061 0,063 0,087

X

0,146 0,129 0,285 0,249 0,016 0,209 0,217

Emotio nal Stabilit y 0,055 ------

X

0,077

Conscience tiousn 0,305 - 0,169 0,280 0,031 0,148 0,300 0,165 0,171

X

xtrave

0,005 0,101 0,069 0,001

E rsion 0,273 0,039 - 0,288 - 0,091 - - 0,115 0,015

lities)

xtraversion

E conscienctiousn Stability Emotional Agreeableness to Opennes Experience F1 (Active Engagement) F2 (perceptual Abi F3 (Musical Training) F4 (Emotions) F5 (signing Abilities) (General FG Sophistication)

Table 1: Correlation matrix of the Big Five personality traits and the Goldsmith's Music Sophistication Index 35

Prediction algorithm. In order to get insight on participant's musical preferences, their Spotify playlists were analysed and reduced to 12 musical features. A list of these Spotify features is presented below.28 Note that all these features are computed over the entire track.

 Acousticness A confidence measure from 0.0 to 1.0 of whether the track is acoustic. This means that only the acoustic (not amplified) instruments are being measured. Here, 0.0 represents low confidence that the track is acoustic, whereas 1.0 represents high confidence that the track is acoustic.

 Danceability This feature describes how suitable a track is for dancing, based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable.

 Duration_ms This feature indicates the duration of a track in milliseconds (ms).

 Energy Energy is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. For example, high energetic tracks feel fast, loud, and noisy. A genre like has a high energy level, opposed to a prelude from Bach, which scores low on the scale. Perceptual features contributing to this attribute include dynamic range, perceived loudness, timbre, onset rate, and general entropy.

 Instrumentalness This feature predicts whether a track contains only instruments (no vocals). “Ooh” and “aah” sounds are treated as instrumental in this context. Rap or spoken word tracks are clearly “vocal”. The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content. Values above 0.5 are intended to represent instrumental tracks, but confidence is higher als the value approaches 1.0.

28 “Get Audio Features for a Track | Beta Developer Spotify,” accessed on 26th June, 2018, https://developer.spotify.com/documentation/web-api/reference/tracks/get-several-audio-features/. 36

 Key Indicates in what key a specific track is in. In order to map pitches in tracks, the standard Pitch Class Notation is used, which means: 0 = C, 1 = C ♯/D♭ , 2 = D, and so on.

 Liveness This feature detects the presence of an audience in the recording. Higher liveness values represent an increased probability that the track was performed live. A value above 0.8 provides strong likelihood that there is a live audience present on the track, and, thus, is live.

 Loudness The overall loudness of a track in decibels (dB) is indicated. Loudness values are averaged across the entire track and are useful for comparing relative loudness of tracks. Loudness is the quality of a sound that is the primary psychological correlate of physical strength (amplitude). Values typically range between -60 and 0 dB.

 Mode Mode indicates the modality (major or minor) of a track, the type of scale from which its melodic content is derived. Major is represented by 1 and minor by 0.

 Speechiness This feature detects the presence of spoken words in a track. The more exclusively is used in the recording (e.g. Talk show, audiobook, poetry), the closer to 1.0 the attribute value. Values above 0.66 describe tracks that are probably made entirely of spoken words. Values between 0.33 and 0.66 describe tracks that may contain both music and speech, either in sections or layered, including such cases as rap music. Values below 0.33 most likely represent music and other non-speech-like tracks.

 Tempo Tempo represents the overall estimated beats per minute (BPM) in a track. In musical terminology, tempo is the speed or pace of a given piece and derives directly from the average beat duration.

An estimated overall time signature of a track. The time signature (meter) is a notational convention to specify how many beats are in each bar (or measure).

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 Valence In psychology, this term describes whether a person is attracted to something, or repelled by that thing. An entity which draws a person nearer has a positive valence, while one which repels the target has a negative valence.29 In musical terms, this feature is a measure from 0.0 to 1.0, describing the musical positiveness conveyed by a track. Tracks with high valence tend to sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence tend to sound more negative (e.g. sad, depressed, angry).

Working with Bram Kooiman, a neural network was created in order to obtain the values of above mentioned Spotify features in a fast way.30 This data was pre-processed in Python 3.6. In order to help manage the data, the package “pandas” was used. The Machine Learning library PyTorch was used to define and train this neural network. We made the choice to omit the feature “time signature”, which is why this feature is not part of the data of this study. In order to gain insight on how these features are mapped, I also looked at how the Spotify features correlate among themselves. In table 2, the results of a correlation matrix of all the Spotify features are visible, and are arranged by R². Blue represents p < .05, followed by green which represents p < .01, and yellow represents p < .001. It can be seen, for example, that energy correlates positively high with loudness, negatively high with acousticness, and relatively positively high with tempo, and so on. This gives a general representation of how these Spotify features are mapped.

29 “Definition of Valence | Psychology Dictionary,” accessed on 26th June, 2018, https://psychologydictionary.org/valence/. 30 I followed a class called “Computational ”, which served as my tutorial earlier this year. For the final assignment, I worked together with MSc Artificial Intelligence student, Bram Kooiman, and created a neural network that expresses a track in an interpretable space. We then designed this network to indicate one's music preference, based on their music library on Spotify. We investigated if we could predict, alongside Rentfrow et. al. (2012) MUSIC model, one's MUSIC values based on their music in one's Spotify library. For this study, I used this neural network to obtain the values of the Spotify features. We looked at all the Spotify features, and, in addition, we also looked at timbre and delta. All the 150 songs by Rentfrow et. al. (2012) served as our dataset, which would predict one's MUSIC values. Because the available training data was exclusively in 4/4, which would render it no predictive power, we chose to omit this feature. 38

X

Duration_ ms

X

0,140

Tempo -

X

0,129 0,452

Valence - -

-

X

0,128

Live ness - 0,171 0,221

-

X

0,590 0,100

Instru mentalness 0,250 - - 0,690

-

X

0,232 0,179 0,472

Acoustic ness 0,385 - - - 0,306

-

X

29

0,1 0,139 0,187

Speechi ness 0,124 0,188 - 0,011 - -

X

0,370 0,140 0,024

Mode - 0,181 - 0,158 - 0,370 0,028

X

0,351 0,087 0,499 0,014 0,076

Key - - - 0,009 0,098 - 0,051 -

-

X

0,084 0,153 0,807 0,668 0,642

Loud ness 0,290 - - - - 0,112 0,362 0,399 -

X

0,090 0,104 0,906 0,350 0,363

Energy 0,828 0,274 - - - - 0,343 0,218 0,579 -

-

X

0,533

0,296 0,229 0,597 0,265 0,312

Dance ability 0,088 0,415 0,128 - 0,076 - - - 0,627 - --

-

Danceability Energy Loudness Key Mode Speechiness Acousticness Instrumental ness Liveness Valence Tempo Duration_ms

Table 2: Correlation matrix of all the Spotify features.

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Results

The personality traits and Music Sophistication Index items that are related to which Spotify features, based on the responses of the online survey (n = 40) are reported in this section. The results are summarized in table 3, arranged by the size of the adjusted R². The study showed some reliable correlations between Spotify features, personality traits, and the Music Sophistication Index. First, the highest accuracy was for danceability with musical training (F(1, 33) = 10.030, p < .001) and singing abilities (F(2, 32) = 8.757, p = .02), with an R² of 0.313, followed by valence with emotional stability (F(1, 33) = 6.509, p = .011) and extraversion (F(3, 32) = 6.662, p = .021), with an R² of 0.250; duration_ms with emotional stability (F(1, 33) = 4.145, p = .011) and openness to experience (F(2, 32) = 5.645, p = .016), with an R² of 0.215; acousticness with openness to experience (F(1, 33) = 8.464, p = .006), with an R² of 0.184; tempo with musical training (F(1, 33) = 7.150, p = .012), with an R² of 0.153; liveness with extraversion (F(1, 33) = 6.384, p = .016), with an R² of 0.137; energy with openness to experience (F(1, 33) = 5.129, p = .030), with an R² of 0.108; mode with emotional stability (F(1, 33) = 5.003, p = .032), with an R² of 0.105; and speechiness with emotions (F(1, 33) = 4.998, p = .032), with an R² of 0.105. For the other Spotify features, there has no significant predictor been found. These results indicate that, apparently, some Spotify features are somehow related to certain personality traits of the Big Five, and some of the traits from Goldsmith's Music Sophistication Index. In the following section, these results are put into perspective and elaborated on their possible meaning.

40

Predictor b SE β t p Adj. R²

Danceability 0,313

Musical Training -0,006 0,001 -0,817 -4,413 < .001

Singing Abilities 0,005 0,002 0,482 2,444 0,020

Valence 0,250

Emotional Stability -0,025 0,009 -0,403 -2,712 0,011

Extraverion 0,016 0,007 0,359 2,420 0,021

Duration ms 0,215

Emotional Stability 11111 4058 0,428 2,738 0,010

Openness to Experience 13217 5200 0,398 2,542 0,016

Acousticness 0,184

Openness to Experience 0,061 0,021 0,456 2,940 0,006

Tempo 0,153

Musical Training 0,193 0,072 0,422 2,674 0,012

Liveness 0,137

Extraversion -0,007 0,003 -0,403 -2,527 0,016

Energy 0,108

Openness to Experience -0,040 0,018 -0,367 2,237 0,032

Mode 0,105

Emotional Stability -0,025 -0,011 -0,363 -2,237 0,032

Speechiness 0,105

Emotions -0,004 0,002 -0,363 -2,236 0,032

Table 3: Correlation matrix Spotify features, personality traits, and Goldsmith's Music Sophistication Index.

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Fig. 7: The correlations between Danceability, Musical Training, and Singing Abilities.

Fig. 8: The correlations between Valence, Emotional Stability, and Extraversion.

Fig. 9: The correlations between Duration (in ms), Emotional Stability, and Openness to Experience. 42

Results Discussion

Danceability Results revealed a negative correlation between Danceability and Musical Training, but a positive relation with Singing Abilities. This would suggest that one's degree of musical training would indicate one's liking for danceable music. If someone had years of musical training, he or she would have a preference for less danceable music. Unfortunately, I was not able to find any suitable literature that could back up these results. One could speculate that people who have had more musical training, are likely to have greater familiarity with . Currently the available literature does not support this conclusion, because this data has not yet been studied in relations to each other before. For instance, people who received musical training on conservatories are most certainly more familiar with classical music. Perhaps, due to their years of experience in this musical style, they prefer more Sophisticated music instead of the Contemporary dimension which is characterized by being very danceable; comprised by rap/hip-hop, dance/electronica, and pop music genres. However, the positive link between Danceability and Singing Abilities suggests that people who have preferences for danceable music have better singing abilities. This is not surprising since more musical training often also includes training in solfège, a method where a person learns to sing pitch and sight singing of Western music. When gaining more insight on how to correctly sing pitches and sights, it can be assumed that extensive rehearsal will improve one's singing abilities.

Energy Results revealed a negative link with Energy and Openness to Experience. This finding suggests that someone who is more open to new experiences prefers music with higher energy levels. Similar results were found by Rentfrow and Gosling (2003). Their research revealed a four-factor music-preference structure. The Intense and Rebellious dimension from this structure was positively correlated with Openness to Experience. Music in this factor – comprised by rock, alternative, heavy metal – were generally perceived as being high in energy. According to their results, individuals who prefer music from this dimension tend to be curious about different things and enjoy taking risks, which correlates well into a high score on the Openness to Experience trait. This supports the findings from Rentfrow and Gosling (2006). The fourth dimension, Energetic and Rhythmic, was also perceived as being high in energy and was comprised of rap/hip-hop, soul/funk/, and electronica/dance music genres. However, there was no correlation with Openness to Experience trait found in the current study, but with Extraversion.

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Mode Results revealed a negative relation for Mode and Emotional Stability. This would suggest that people who are emotionally less stable have preferences for music in minor keys. Mode manipulations are strongly associated with expressions of happiness and sadness, which implies that mode is a reliable indicator of mood (Peretz, Gagnon, & Bouchard, 1998; Wedin, 1972). In studies among children as young as eight years old, the minor mode is associated with expressions of sadness (Dalla Bella et al., 2001; Gerardi & Gerken, 1995; Gregory, Worral, & Sarge, 1996). In a study on the effect of musical tempo and mode on arousal, mood, and spatial abilities, results showed that the mode of the piece of music was associated with listeners' moods (Husain, Thompson, & Schellenberg, 2002). Those who heard the major mode became more positive in mood, whereas the minor mode caused negative shifts in mood. Another research involving mode and tempo in relation with personality traits found that emotional stability and optimism were significant predictors of preferences for music in fast tempo and major key (Dobrota and Ercegovac, 2014). Their results also showed that emotional stability was a significant predictor of preference for major key music and fast tempo. These results are similar to the findings of the current study. Could we simply say that people who tend to be sad more often than others prefer to listen to sad music in minor keys? Research shows that music can be used to regulate our mood and emotions. According to Vella and Mills (2016), emotional uses of music may represent a common coping modality for acute shifts in negative affective states. Likewise, personality traits characterized by high levels of negative affect are also anticipated to predict emotional uses of music. Results from their study revealed that recent symptoms of depression and stress correlated positively with emotional uses of music, in addition to trait Neuroticism. This suggests that people who are emotionally less stable use music to regulate their emotions. However, it is not clear what kind of music can regulate moods and in wich way. Not all research shows the same results as described above. Vuoskoski, Thompson, McIlwain, and Eerola (2012) investigated what kind of people listen to sad music, and why. Their research revealed that Openness to Experience and Empathy were associated with the liking for sad music and with the intensity of emotional responses induced by sad music, suggesting that aesthetic appreciation and empathetic engagement play a role in the enjoyment of sad music. Their results also showed that responses to sad music are not experienced as negative or unpleasant, unlike most experiences of sad real-life events. Besides sadness, other emotions such as nostalgia, peacefulness, and wonder were also evident. They propose that the enjoyment of sad music may stem from an intense emotional response combined with the aesthetic appeal of sad music. Three findings support their interpretation. First, sad music evoked – in addition to sadness – a range of positive toned, 44 aesthetic emotions. Second, the most ablest to appreciate aesthetic experiences and beauty – those highest in the trait Openness to Experience – most liked sad music. Third, those who experienced the most intense emotions in response to sad music – including participants scoring high in the trait Empathy – also enjoyed sad music the most. This means that one cannot simply conclude that people with less stable emotions prefer “sad” music in minor keys. It could be that people who are less emotionally stable listen to “sad” music in order to experience feelings of nostalgia, peacefulness, or wonder. It could be that they have certain memories tied to particular songs that happen to be in minor keys that induce these feelings. However, these feelings are not experienced as negative, but positive. Then, in order to regulate their mood and emotions, positive feelings evoked by those songs will likely have a positive effect on their mood and emotions.

Speechiness Results revealed a negative relation for Speechiness and Emotions. This link suggests that people who prefer music with higher amounts of speech (e.g., rap/hip-hop) feel less emotionally engaged with music in general. Perhaps people feel more emotionally engaged with music by singing voices instead of speech like-voices. In a study involving chill responses to music, Grewe, Nagel, Kopiez, and Altenmüller (2007) found significant differences between participants' statements for most of the single categories of musical events: entry of a voice and increase in, or change in volume showed the most pronounced differences in experiencing pleasure in music. Especially if the entry of a voice was perceived as pleasurable and was independent of whether it was a human voice or instrument. Their results also showed that responders (chills experienced) and nonresponders (no chills experienced) can be characterized by personality factors, and by their experience with musical styles. Their participants differed in statements concerning how important music is in their lives, how much they identify with their preferred musical style, and in which situations they enjoy music. Responders indicated to engage more with music, and that music plays a larger role in their daily lives. Grewe et al. found that chill responders react more to distinct musical structures such as the entrance of a voice, whereas nonresponders seemed to listen to music in a less discriminating way. Furthermore, their study showed differences between individual participants, but also different chill responses for the same participant. Inter-individually, chills did not occur at a distinct point in time in response to a certain acoustical pattern, but in response to musical structural parts. Intra-individually, some chill- events are relatively stable responses to musical structures, such as the entrance of a voice, and show habituation after a few days. The musical structures that were associated with chills are mainly perceived consciously and are felt as being highly pleasant. 45

The findings from Grewe et al. are corresponding to Goldstein (1980), they found that there were chill responders and nonresponders, and that is was possible to characterize chill responders according to their personality, musical experience, and preferences. Chill responders showed a preference for less intensive stimuli, they are not “thrill and adventure seekers”, and they are more reward dependent, i.e., they especially like approval and positive emotional input from their environment. Also, they found that chill responders are more familiar with classical music. Most chills occurred in classical music, in the standard pieces as well as the personal pieces from participants. According to Grewe et al., this does not mean that it is necessarily classical music that arouses chills, but it is a hint that familiarity with certain musical styles is of importance in order to respond to it with strong emotions. Translated to the current study, could it be that participants who have preferences for genres with high Speechiness loadings – for instance, rap/hip-hop music – have less familiarity to other musical styles, such as classical music? The results from Grewe, Nagel, Kopiez, and Altenmüller (2007) revealed that the entry of a voice induced the most chill responses in participants. Rap and hip-hop music generally do not contain a wide variety of instruments, and voices are more speech like than a singing voice. Could it be that listeners of these kinds of musical styles experience less arousal, in this case, chills, while listening to music which eventually leads to feeling less emotionally engaged with music in general? Particularly not feeling engaged with music styles they are not familiar with? Additionally, chill responders reported listening to music in everyday life in a situation similar to the experiment; listeners were alone and separated from their surroundings via headphones. However, many listening situations in everyday life are much more social, e.g., clubs or concerts. Future research on this matter could include questions on participants' familiarity with musical styles in general, and maybe also even in what occasions people listen to music.

Acousticness Results revealed a positive link with Acousticness and Openness to Experience. This finding suggests that people who prefer music with acoustic instruments are more open to new experiences. Several studies found similar results. In a study involving the role of personality traits and music preferences for mood regulation, Yomaboot and Cooper (2016) found that Openness to Experience predicted a preference for Sophisticated music in both their samples from the UK and Thailand. They also found that preferences for Contemporary music could be predicted by Extraversion in their UK sample, but Agreeableness in their Thai sample. Furthermore, Openness to Experience was strongly correlated with the mood regulation factor “Strong Sensation Seeking” in both UK and Thai samples. These results are similar to those from Nave, Minxha, Greenberg, Kosinski, Stillwell, and Rentfrow (2018), their results showed that Openness to Experience was associated with a 46 greater liking of Sophisticated music. Results of both studies mentioned above fit with the findings from the current study. Sophisticated music is comprised of (avant-garde) classical, world beat, traditional jazz and genres. These genres are often characterized by the use of acoustical instruments. Additional research showed that Openness to Experience is linked to being sensitive to beauty and art, intellectually curious, and to have an emotionally complex life (Costa & McCrea, 1992). Openness to Experience as a trait would predict preferences for music stimuli outside the mainstream pop culture, which is why researchers expect it to be positively correlated with Reflective and Complex musical styles.

Liveness Results revealed a negative association with Liveness and Extraversion. This would suggest that people who are more extraverted prefer music with a live audience present. Unfortunately, I was unable to find any literature that could explain this finding. This result is surprising and I cannot conclude a logical explanation for this link based on the current data. People with high loadings on the Extraversion trait tend to be social, active, and have tendencies to experience joy and pleasure. Perhaps, when hearing a live audience on a track, they are feeling connected to that audience due to their social and active background. Songs with a live audience could evoke more, or greater emotions to them when combined with the presence of a live audience, and thus, may feel more joy or pleasure with those types of songs.

Valence Results revealed that Valence correlated with Emotional Stability negatively, but positively with Extraversion. This would suggest that people who are less emotional stable prefer to listen to happier music. This result fits with the hypothesis by Vella and Mills (2016). As mentioned earlier, the emotional uses of music may represent a common coping modality for acute shifts in negative affective states, which emotionally less stable people will encompass more often than others. Personality traits characterized by high levels of negative affect are also anticipated to predict emotional uses of music. Recent symptoms of depression and stress were linked positively with emotional uses of music and to the trait Neuroticism (i.e., Emotional Stability). This finding also suggests that people who are less emotionally stable use music to regulate their emotions or mood. It may possibly be that when less emotionally stable people encompass negative shifts in their mood, they try to regulate it with music that affects their mood in a positive way. Music that has high valence loadings, indicates that the music is generally perceived as happy and joyful. This may explain why emotionally less stable people would prefer high valence songs. The positive relation with Extraversion suggests that people who prefer happier music, are 47 more extroverted. Costa and McCrae (1992) have described the extraversion construct as a personality dimension comprising a host of traits, including sociability, activity, and tendencies to experience joy and pleasure. Vella and Mills (2016) conceptualized this trait as an approach- motivated construct tending towards high arousal/positive valence on the circumplex model. The circumplex model of emotion was supported in regard to approaching motivation for extraversion: this personality dimension is characterized by sociability, activity, and tendencies to experience positive emotions was predictive of preference for music associated with high arousal/positive valence (ER and UC). Their results confirmed their hypotheses. These results, thus, corroborate partially with the results from the current study.

Tempo Results revealed a positive link between Tempo and Musical Training. This finding suggests that the more musical trained someone is, they would prefer music with faster tempi. A large body of music preference research has found that when given a choice, subjects frequently prefer faster tempi to slower tempi (LeBlanc, 1981; LeBlanc, Colman, McCrary, Sherrill, & Malin, 1988; LeBlanc & Cote, 1983, Sims, 1987; Wapnick, 1980). Researchers have also shown that elementary school students prefer fast tempo in the classical music of various stylistic periods (Prince, 1972) and in jazz music (LeBlanc and McCrary, 1983). In a literature review, Teo (2003) suggested that participants of different ages prefer fast tempo in the music which belongs to different genres (classical, jazz, popular, and folk music). When it comes to classical music, is seems assumable that people with more musical training are more familiar with classical music. Whether this aspect make them prefer faster tempi in classical musical styles, or music in general, remains unknown. Another study involving both tempo and pitch by Geringer (2010), results did not corroborate with previous results. This study revealed that listeners did prefer increased levels of tempo, pitch, and tempo/pitch combined for slow tempo examples. Nonetheless, listeners preferred no change in pitch level and decrease in tempo and pitch/tempo combined in response to the examples classified as fast tempo examples. Taken together, it is still questionable if musicians really have preferences for faster tempi. Furthermore, research showed that a fast tempo version of a piece of music was accompanied by the increase in participants' level of arousal (Husain, Thompson, & Schellenberg, 2002). It could be that faster tempi evoke greater emotions than music with slow tempi. Fast tempi are associated with other terms such as happy, fear, and anger (Balkwill & Thompson, 1999; Dalla, Bella, Peretz, Rousseau, & Gosselin, 2001; Gabrielson & Lindström, 2001; Wedin, 1972), which are strong emotions. Research has also shown that participants' – who were musicians – accuracy in identifying tempo changes increases when the tempo becomes faster (Geringer & Madsen, 1984; 48

Wang, 1983). Perhaps, musicians who, thus, had more musical training than people who are not able to play any instruments at all, prefer faster tempi in music because they can identify these changes better than people with no musical training. Additionally, research also showed that emotional states and physiological arousal can be linked. Results revealed that anger tends to be associated with a high heart rate, happiness with a moderate heart rate, and depression with a low heart rate (Averill, 1969; Cacioppo, Klein, Bernston, & Hatfield, 1993; Ekman, Levenson, & Frieson, 1983). Results from Rentfrow and Gosling (2003) were similar; angry music was perceived as highly energetic, happy music as moderately energetic, and depressing music at least energetic. According to them, one possibility could be that people choose a tempo of music that is consistent with the heart rate that characterizes their current or desired mood. Translated to the findings of the current study, it could be speculated that people with more musical training prefer music with higher tempi, which might be due to a higher heart rate. Future research could also include measuring participants' heart rate to investigate this matter.

Duration Results revealed both positives relations for Duration and Emotional Stability, and Openness to Experience. This would suggest that the more emotionally stable a person is, he or she has a preference for longer songs. The positive relation between Duration and Openness to Experience suggests that people who prefer longer songs are more open to new experiences. Unfortunately, I was not able to find any literature on this matter that would support these results. These findings are surprising and have no logical explanation. Future suggested research could conduct an experiment with different durations of songs and ask participants' to indicate their degree of liking for each song.

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Limitations

This study has faced several limitations which leaves questions open for future research. First, it should be recognized that there are unmeasured person-level variables that could capture information about musical preferences and personality. For instance, age, gender, geolocation, socio-economic status, culture, and preferences for different leisure activities. Second, the data for this study consisted of a very small sample size (N = 40). Larger participant numbers would offer stronger evidence for links between music preference and personality. Additionally, these results could change drastically with greater available data. Third, another limitation in this research was the reliance on a sample containing mainly Dutch college students. As Nave et al. mentioned, past research on musical preferences showed that music is particularly important to young people (Bonneville-Roussy, Rentfrow, Xu, & Potter, 2013) and their peer-group relations (Delsing, ter Bogt, Engels, & Meeus, 2008), which could mean that students may report a stronger preference for musical genres that are popular among their peers, due to social desirability. This would mean that participants' could be listening to certain songs because their peers do too, and would not be a good indication of a person's musical preferences, making it difficult to generalize the findings from this study. Questions on this matter should also be included in questionnaires for future research. Fourth, we should bear in mind that the extraction of these features by Spotify is trade secret and may be incrementally changed from time to time. Additionally, Spotify provides only a handful of auditory features. Future research should investigate a broader array of these features (e.g., timbre, pitch, melodic range, melodic motion, and harmonic attributes). Another factor we should not forget is the fact that the Spotify features are computed over the instrumental counterparts of tracks. This means that the potential influence of the lyrics and their meaning, or emotions they evoke in the listener, are not taken into account. It could be that listeners have preferences for certain types of music or musical attributes in combination with certain lyrics. This could also broaden the knowledge on this area and would be interesting for future research. Fifth, the results from the current study are correlational, and therefore cannot address questions of causality. As seen with some Spotify features, there are correlations with more than one personality trait, which means that there is a triangular relation. It is difficult to say what trait it has more of an influence or effect in those relations. Furthermore, the averages of all the Spotify features were measured, which paints a somewhat unilateral picture. For each feature, an average was measured which indicated a specific person's value on that particular feature. This would indicate that there is only one type of music they listen to, which is probably not the case. Different types of music have different averages for different Spotify features. It is also quite likely that 50 people listen to more than one musical style, which means that there are multiple mappings of styles in one playlist. In future research, it would be better to measure more than one average per feature to give a broader view of a person's preferences when it comes to these features. Sixth, another theoretical model for identifying mechanism at work is based on activity congruence, or the idea that people prefer auditory content that complements the activities they regularly pursue. You could listen to high energy music while exercising or physical labour, or listen to simple, acoustic music when working, or studying. For example, if someone has created a playlist with several songs that he or she likes to listen to while exercising, and this person exercises multiple times a week, these songs will be played a lot. This will probably result in the fact that more of these songs will appear in their “Soundtrack 2017” playlist, as opposed to other songs that they like. The same goes for the fact if someone has to listen to certain songs, artists or for their work, or a study project. If these songs are listened to a lot, regardless of that person's degree of liking for that music, they will most likely appear in their playlist with their most played songs as well. Also, people could have certain associations or memories of particular songs and/or artists, which could be the reason for them to listen to those songs and/or artists. This would mean that those songs are not good indicators of one's musical preference. Seventh, the sample of the current study is composed of Spotify users who owned a Spotify account for at least one year. Thus, it is questionable if these results are generalizable for a) all Spotify users and for b) other populations in general which were not represented in this work. The advantage of this method is that the “Soundtrack 2017” playlists represent what music participants listen to in their everyday lives. If researchers would ask participants' to fill out their favourite songs that represent their musical preferences best, they could provide data that might not be fully representable. Due to peer pressure, or being somewhat insecure, could influence the songs the participants would write down. However, the disadvantage of this method is that it is quite hard to gather enough participants to create a large data sample.

51

General Discussion

The primary purpose of this study was to examine and broaden the landscape of music preferences. This study has tried to offer another sight on the links between personality and music preferences, by investigating Spotify music features and their relations to Big Five personality traits and Goldsmith's Music Sophistication Index. The Spotify features – all sound-related, auditory features – could serve as yet another structure model for musical preferences. Nonetheless, there are several factors that could be added to, or done differently in future research. First, as mentioned before, there is evidence linking music preferences and personality. For example, North and Hargreaves' (1999) findings showed that people use music as a “badge” to communicate their values, attitudes, and self-views. However, this relation was moderated by participants' self-esteem, which revealed that individuals with higher self-esteem perceived more similarity between themselves and the prototype music fan of certain genres than individuals with low self-esteem did. Similar findings were found in studies among different populations, age groups, and cultures which all point to the notion that people's self-views and self-esteem influence music preferences. In this study, participants were asked to fill out several questionnaires, including a Big Five personality questionnaire and the Gold-MSI. Some of the questions in the survey asked participants' to rate some of their personal abilities, for example, their singing abilities. It could be assumed that participants' with low self-esteem would rate their singing ability lower than a participant with high self-esteem. However, in this current study, there were no measurements on this matter. This measurement should be included in future research, for example, the Rosenberg self-esteem scale. Another possible determinant for musical preferences is one's cultural background. Rentfrow and Gosling (2003) state that it seems likely that cultural and environmental will influence the music someone likes. Each culture, country, or even city has its own that could influence its inhabitants. Rentfrow and Gosling point out the example of individuals who grew up in small rural towns in Texas, US. Those individuals will probably be exposed to different types of music than someone who lives in a busy, metropolitan city such as New York. Individuals growing up in Texas will be exposed to lots of county music and its subgenres, which would probably not be the case for a born and bred New Yorker. If there is a generalizable structure for musical preferences, research amongst different cultural groups should reveal similar results. However, little research has been done on music other than Western music, which would also be an interesting direction for future research. Besides someone's current, or past geolocation, their musical preferences can also be influenced by other factors. For instance, people may also be influenced by the musical taste from their parents, peers, and will, later on, develop more autonomy, which means 52 that their personality will then play a larger role. It would be interesting to test older individuals to assess this matter, as they would expect to have developed more independent personal preferences for music. Finally, this study was conducted via the Internet. There is evidence that results are obtained via online surveys are similar to those that were based on paper-and-pencil surveys (Gosling, Vazire, Srivas-tava, & John, 2004), but their circumstances in which participants complete the survey is questionable. With paper-and-pencil surveys, there is a lot more control possible for the researchers who conduct the study. This also means that every participant completes the survey under the same conditions, which is not the case with Internet-based surveys. It will always be difficult to know anything about the conditions in which participants completed these kinds of surveys. Also, paper-and-pencil surveys offer the opportunity for researchers to know exactly who their participants are. When participants are gathered via the Internet, researchers never fully know if participants are being honest and truthful.

53

Conclusion

Music plays an important role in people's everyday lives and serves as a number of uses. You can think of music as mood regulation, social bonding, an identity marker, or to enhance your concentration and cognitive function. With the existence of countless musical styles and (sub)genres, it seems logical to assume that different people have their own individual preferences for music. This suggests that a person's musical preferences could offer information about their personalities. This research has offered more insight on the links between musical preferences, personality traits, and musical features. We found multiple links between musical Spotify features and Big Five personality traits, along with links to information about someone's musical background, stemmed from information retrieved through the Gold-MSI. We also identified some potential landmarks for future research, which could further open up the scope of musical preferences.

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