Article
Perception of six basic emotions in music
Psychology of Music 1–15 © The Author(s) 2010 Reprints and permission: sagepub. co.uk/journalsPermission.nav DOI: 10.1177/0305735610378183 http://pom.sagepub.com
Christine Mohn
Department of Psychology, University of Oslo, Norway and Vestre Viken Hospital Trust, Norway
Heike Argstatter
German Center for Music Therapy Research (Viktor Dulger Institute), Heidelberg, Germany
Friedrich-Wilhelm Wilker
Department of Music Therapy, University of Applied Sciences, Heidelberg, Germany
Abstract A test of the ability to perceive six basic emotions (happiness, anger, disgust, surprise, sadness, and fear) in music was presented to 115 participants. Eighteen musical segments, lasting 3–5 seconds, were designed for this test using a variety of solo instruments. The results show that six basic emotions are perceivable in musical segments previously unknown to the listeners, although there was large variability in the percentage of correct classification of each of the segments comprising each emotion, and happiness and sadness were easier to classify than the other emotions. Moreover, the ability to classify musical emotions was not related to childhood or youth musical instruction or personality traits (assessed by NEO-PI-R).
Keywords emotions, music, personality
Introduction According to the neuro-cultural theory of Paul Ekman (Ekman and Friesen, 1971; Ekman, Levenson, & Friesen, 1983), the six emotions – happiness, anger, disgust, surprise, sadness, and fear – are associated with separate autonomic activation patterns and facial expressions. Moreover, the ability to identify these emotions through facial expression seems to be universal among humans (Ekman, 1992; Elfenbein and Ambady, 2002), suggestive of evolutionary theory strongly contributing to explaining the origins of emotion. Specifically, facial emotions are assumed to have evolved in order to allow rapid communication of danger or safety (Ekman, 1993). Corresponding author: Christine Mohn, Department of Pscyhology, University of Oslo, PO Box 1094, Blindem, 0317, Oslo, Norway. [email:
[email protected]]
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Ekman suggests, in accordance with Tomkins (1963), that each of the emotions that may be expressed facially also has a vocal expression (Ekman, 1993). This notion is important regarding individuals whose visual recognition of emotions is impaired, for example because of permanent blindness or situationally restricted vision, who may still be able to judge the emotional content of their communication partners through characteristics of speech, such as loudness, rapidity, and prosody. Moreover, where vision is unobstructed, the recipient may be unsure of what to believe if the verbal content of the message diverges from the emotional content being transmitted, for example if the message ‘Everything is OK’ is spoken in a shrill, rapid manner. There is some evidence that we tend to focus on the emotional tone of the voice rather than the verbal content of the message in cases where these modes of communication are incongruent (Powers and Trevarthen, 2009). Emotion regulation seems to be one of the primary reasons for the use of music in everyday life (Juslin and Laukka, 2004). Several studies have reported that humans are very adept at identifying the emotional content of music (Sloboda and Juslin, 2001). This ability may be so pivotal for human communication that it is manifested early in childhood (Nawrot, 2003). For example, Dalla Bella, Peretz, Rousseau and Gosselin (2001) reported that 5-year-old children are able to discriminate between happiness and sadness using information about tempo, and that a year later they start taking mode into consideration when making the same classification. However, most of the studies on emotion recognition in music have concentrated on a very limited number of emotions, usually the happiness–sadness dichotomy. It seems that several other emotions may be identified acoustically. A survey of the emotion ratings made by music professionals demonstrated that all of the six basic emotions seem to be represented in Western art music, although happiness and sadness are probably much more common as musical themes than – in decreasing order of importance – anger, fear, surprise, and disgust (Kallinen, 2005). In addition, Vieillard et al. (2008) reported that happiness, sadness, fear, and peacefulness may be readily recognized in relatively short stimuli (9–16 seconds). The present study aims to extend the work of Vielliard et al. (2008) by assessing the ability to detect all six basic universal emotions (Ekman, 1992) in unfamiliar musical stimuli.
Personality traits and emotion perception in music Individual differences may influence the perception of emotions in music. A study of participants with unknown musical experience demonstrated a significant relationship between emotional intelligence and the ability to identify the correct emotions of tempo- and loudness-manipulated excerpts of Western art music (Resnicow, Salovey, & Repp, 2004). Moreover, several studies have been performed on the relationship between personality traits and artistic interests (McAdams, 2006). Most seem to conclude that openness (O) is the most relevant trait, in that individuals high in O tend to value fantasies, dreams, artistic leisure activities, and creative and intellectual types of work (McAdams, 2006). In addition to a positive relationship between O and self-reported creativity, Furnham, Zhang, & Chamorro-Premuzic (2006) found a significant negative association between conscientiousness (C) and creativity. In a study of the relationship between personality traits and music preferences, Rawlings and Ciancarelli (1997) reported that high levels of extraversion (E) corresponds to a preference for popular music (such as pop, rock, and ‘easy listening’ music), while high levels of O were related to a wide range of preferences (rock, jazz, folk, classical, and electronic music). A series of six studies (Rentfrow and Gosling, 2003) revealed a robust underlying personality structure of preferences. A preference for cheerful, upbeat (often vocal) music was positively correlated
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with E, and a preference for reflective and complex (often instrumental) music was related to O. Not only preferences for broad genres of music but also for musical elements and structure may be influenced by personality traits. Kopacz (2005) found that, regardless of genre, both extraverted and open-minded and creative individuals tended to prefer a high number of melodic themes. In addition, extraverted individuals preferred fast tempos. North and Hargreaves (2008) noted that different types of music may compensate for certain aspects of an individual’s personality. Extravert individuals may prefer upbeat music with a fast tempo in order to provide strong stimulation of a brain characterized by relativelylow baseline arousal, while introvert people, assumed to display enhanced cortical activity at baseline, may prefer calmer music in order to enjoy themselves. Moreover, music preference may reflect aspects of personality (North and Hargreaves, 2008). Individuals high in O may prefer complex music simply due to their being creative and intellectual. To the best of our knowledge, only one study exists on the influence of personality traits on musical emotions classification accuracy. Chamorro-Premuzic and Furnham (2007) reported that, while high neuroticism (N) was correlated with the self-reported tendency to use music for mood regulation rather than intellectual stimulation, no trait correlated with the self-reported ability to classify musical styles and composers correctly. However, this study did not present the participants with sound stimuli to be classified, and no listening procedure took place. Hence, the investigation of the relationship between personality traits and emotion recognition in musical segments previously unknown to the participants seems warranted. Moreover, high levels of E seem to correlate with social competence and the ability to predict and detect emotional reactions in others (Costa and McCrae, 1992). In this respect, one should expect individuals high in E to perform well on tests of emotion perception. However, there seems to be a general tendency for individuals high in E to emphasize positive affective experiences (Costa and McCrae, 1992). It is therefore possible that E may bias individuals into overestimating the presence of positive emotional stimuli. The present study will therefore investigate the effect of personality traits on the ability to classify emotional stimuli.
Rationale of the present study In order to explore the possibility of six basic emotions being recognized in acoustic stimulation unknown to the participants, the German Center of Music Therapy Research developed a test of emotion perception in music (Busch et al., 2003). A pilot study was used to validate the use of 18 musical segments (described later) for studies of perception of emotions in music. This study demonstrated that these six emotions were identifiable in musical stimuli, and that there was no significant difference in detection accuracy between music therapy students and controls without musical or psychological training (Busch et al., 2003). The sample size of the pilot study (Busch et al., 2003) was relatively small (eight patients with mental illness, 18 music therapy students, and 20 control subjects). Therefore, the present study aims to put these preliminary results on a more secure footing by testing a larger number of participants as well as investigating the effects of personality traits on the identification of emotions. Due to the exploratory nature of this study, no specific hypotheses were formulated. The following research questions were asked: (1) Are the six basic universal emotions perceivable in music unknown to the listeners?; and (2) Are personality traits related to the perception of six basic universal emotions in music?
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Methods Subjects and procedure The participants were 115 undergraduate and graduate students (N = 41, 35.7% males, N = 74, 64.3% females) recruited through advertisements and email lists at the University of Oslo. Exclusion criteria were (self-reported) hearing loss and inability to understand spoken and written Norwegian. Demographic characteristic and data on music education and listening habits are presented in Table 1. The participants were tested individually or in groups of up to four in a non-soundproof classroom at the Department of Psychology, University of Oslo. The music segments were played on a ghetto blaster placed on a table that was 3 metres from the participants. The sound volume at the position of the participants was kept constant at 60 dB. After the musical emotion test, lasting 10 minutes (see later), the subjects filled in questionnaires on demography and personality traits. The entire session, consisting of the music emotion test and the questionnaire completion, lasted one hour. This study was approved by the Regional Committee for Research Ethics (REK-Sør), and all participants signed consent forms prior to the test.
Test of emotion perception in music The current test of perception of emotions in music was the same as the one used in our pilot study (Busch et al., 2003). Twenty-four music segments (six trial segments and 18 test segments) were composed by members of the academic staff at the German Center for Music Therapy Research in Heidelberg, Germany. The segments, all tonal, were intended to represent the six basic emotions, happiness, anger, disgust, surprise, sadness, and fear, and were recorded in a studio. The segments broadly represent classical or jazz music. The musical stimuli were provided by three musicians (pianist, percussionist, and cellist) and four music therapists. The performers played their favourite instruments out of a free choice of instruments. They made no account for their choice. Every performer was instructed to improvise short musical pieces on the six basic emotions in a way that a listener should be able to decode the intended emotion. This procedure led to a great variety of possible improvisations. Stimuli not fulfilling the formal criteria (for example lasted more than 10 seconds or were of pure acoustic quality with no melodic line) were eliminated. For the remaining musical excerpts, distinctiveness and typicality
Table 1. Demography and music habits of the participants Agea child/youtha
Years of music instruction as Listening to music 2–6 times / week Daily Preference for music type Classical Popular Both classical and popular Number of concerts attended last 12 monthsa a Numbers
hop music.
24.7 (SD 7.2) years 6.1 (SD 5.1) years 15.7% 80.9% 8.7% 30.4% 60.9% 11.3 (SD 16.4)
in mean. Classical music: classical and opera music. Popular music: rock, pop, blues, jazz, techno, and hip
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were rated by the improvising performers and additionally by five trained music therapists. This led to the final set of segments. Apart from the musical instruments, the equipment consisted of two large-diaphragm microphones, one hard disc recorder, and a mixing board. The recorded material was transferred from the hard disc recorder to two computers, and edited in a digital sound studio. The segments were transferred to a compact disc (CD) with the order of presentation randomized (Table 2). There was a 10 second pause between each segment. All participants listened to the same CD and were thus exposed to the segments in the same order. The participants were instructed to try to classify each segment as one of six emotions Table 2. Characteristics of the musical segments (N = 115) Number
Emotion
Duration
Instrument
Musical characteristics
1
Fear 1
4 seconds
Cello
2
Happiness 1
3 seconds
Tuba
3
Sadness 1
5 seconds
Electric bass
4
Disgust 1
5 seconds
Violin
5
Anger 1
3 seconds
Piano
6
Surprise 1
4 seconds
Electric bass
7
Sadness 2
5 seconds
French horn
8
Disgust 2
5 seconds
Cello
9
Fear 2
4 seconds
Guitar
10
Anger 2
3 seconds
Tuba
11
Surprise 2
4 seconds
Piano
12
Happiness 2
5 seconds
Guitar
13
Surprise 3
3 seconds
French horn
14
Disgust 3
3 seconds
Electric bass
15
Fear 3
5 seconds
Tuba
16
Happiness 3
5 seconds
Piano
17
Anger 3
5 seconds
Cello
18
Sadness 3
5 seconds
Piano
Short, “shivering” vibrato, low volume, fast tempo Vivid expression, staccato, broad timbre, high volume, fast tempo Legato, light, subdued ascending and descending tones, slow tempo “Schreeching”, medium volume, several variations with changing expression and emphasis Hard touch, staccato, loud volume, rapidly ascending tempo, dissonant harmony Short tones, staccato, jumping ascending dynamics, medium volume Minor modea, stepwise intervals, weak touch, medium volume, consonant harmony Uncontrolled tones in rapid succession, ascending and descending movements Very rapid touch, ascending volume, tempo, and dynamics Staccato, low pitch, short intervals between tones, loud volume Major mode, jumping, ascending melody, broad expression, crescendo Major mode, dance-like 3/4 rhythm, large intervals, loud volume, no dissonances Major mode, staccato, jumping ascending melody, medium volume, crescendo Weak touch, subdued timbre, slow tempo, low volume, diminuendo Rapid, unregular vibrato, low pitch, medium volume, from crescendo to decrescendo Major mode, strong timbre, vivid expression, rapid tempo Minor mode, staccato, low pitch, strong vibrato, rapid tempo Minor mode, weak touch, low volume, slow tempo with large variations
aMode
is described only for those segments that a clear major or minor melody is identifiable
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and to mark the most appropriate emotion category on a questionnaire. They were informed that they had to make a decision and that they had to choose only one emotion for each segment. Before the test, the subjects familiarized themselves with the task by trying to identify one segment each of the six emotions. These trial segments were different from the segments used in the test. The entire musical emotions test procedure lasted 10 minutes.
Demographic characteristics and music preferences After the musical emotion test, the participants filled in a form with questions regarding gender, age, general education, and music experiences and preferences. They were asked to state: (1) whether they had received music instruction on childhood or youth and whether they were still singing or playing an instrument (if so, which type of instrument and number of years of instruction); (2) how often they listened to music at home; (3) number of concerts they had attended during the last 12 months; and (4) which type of music they preferred. Preference for music could be classical instrumental music and opera (referred to as the category ‘classical music’), or rock, pop, blues, jazz, techno, and hip hop music (referred to as the category ‘popular music’). The participants were allowed to state several preferences, and in 60.9% of the cases, both classical and popular music were preferred (Table 1).
Personality traits The revised version of the NEO Personality Inventory (NEO-PI-R, Costa and McCrae, 1992; Norwegian version by Martinsen, Nordvik, & Østbø, 2003) was administered to assess the personality traits neuroticism, extraversion, openness, agreeableness, and conscientiousness. The NEO-PI-R is one of the most widely-used personality inventories. It is reported to have high reliability and validity, and has been validated both cross-culturally, by self-ratings, and by ratings by peers and spouses (Wiggins, 1996). The items of the questionnaire are presented as statements, for example ‘I am not a person that worries’, or ‘I like being surrounded by people’. Responses are made on a five-point Likert scale ranging from ‘strongly disagree’ to ‘strongly agree’. The full version of the NEO-PI-R, consisting of 240 items, was used in this study.
Results All statistical tests were conducted using SPSS for Windows, release 14. Chi-square tests, t-tests, and correlation analyses were two-tailed.
Perception of musical emotions Percentages of correct and incorrect classification of musical emotions in the 18 segments are given in Table 3. In order to test whether there were statistically-significant differences in classification rate across the three segments of each emotion, repeated measures analysis of variances (ANOVAs) with post-hoc comparisons were performed with scores calculated as percentage correct hits. These analyses revealed that anger 1 was significantly more difficult to classify than anger 2 and anger 3 (F = [2] 14.92, p < .001). Disgust 1 was significantly easier to classify correctly than the other two examples of this emotion (F = [2] 32.87, p < . 001). All three segments of surprise were significantly different from each other in terms of classification accuracy (F = [2] 56.27, p < .001). Fear 3 was significantly more difficult to classify as the other to fear segments (F [2] = 76.59, p < .001).
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Mohn et al. Table 3. Recognition and confusion of emotion in musical segments (N = 115) Perceived emotion Happiness
Anger
Disgust
Surprise
Sadness
Fear
Expressed emotion Happiness 1 Happiness 2 Happiness 3
70.0% 83.5% 93.3%
2.6% 0.9% 0.9%
7.8% 0.0% 1.7%
15.7% 15.7% 3.5%
3.5% 0.0% 0.0%
0.0% 0.0% 0.0%
Anger 1 Anger 2 Anger 3
20.0% 4.1% 6.1%
25.2% 59.1% 47.0%
3.5% 24.3% 7.0%
41.7% 3.5% 1.7%
0.9% 5.2% 7.8%
8.7% 3.5% 30.4%
Disgust 1 Disgust 2 Disgust 3
0.0% 2.6% 1.7%
9.6% 28.7% 7.0%
70.2% 30.4% 25.2%
2.6% 3.5% 5.2%
6.1% 2.6% 47.0 %
11.4% 32.2% 13.9%
Surprise 1 Surprise 2 Surprise 3
16.5% 83.3% 52.2%
0.9% 0.0% 0.0%
3.5% 0.0% 0.0%
78.3% 16.7% 46.1%
0.0% 0.0% 0.0%
0.9% 0.0% 1.7%
Sadness 1 Sadness 2 Sadness 3
7.0% 0.0% 3.5%
0.9% 0.0% 0.0%
13.0% 0.0% 0.0%
11.3% 0.0% 0.9%
65.2% 100% 95.7%
2.6% 0.0% 0.0%
Fear 1 Fear 2 Fear 3
0.0% 0.9% 0.9%
7.0% 14.8% 38.3%
8.7% 1.7% 34.8%
3.5% 7.0% 1.7%
0.9% 0.0% 5.2%
80.0% 75.7% 19.1%
Note: Bold numbers represent the accuracy of perceived emotion. Non-bold numbers represent false hits. The numbers may not add to 100% because of rounding.
Visual inspection of Table 3 suggests that confusions of classification, such as instances where anger is incorrectly perceived as fear, may be statistically-significant. Therefore, Chisquare analyses were performed on the nominal data representing the scores of the test, comparing correct hits with false hits. This procedure revealed that anger 1 was significantly misclassified as surprise (c2 [1] = 4.69, p < .05), disgust 3 was significantly misclassified as sadness (c2 [1] = 7.53, p < .01), and that fear 3 was significantly misclassified as both anger (c2 [1] = 8.14, p < .01) and disgust (c2 [1] = 5.92, p < .05). Next the three musical segments representing each emotion were combined into six musical emotion indices by simple aggregation and the results of the identification accuracy given in percentage correct answers (Table 4). A repeated measures ANOVA revealed that happiness Table 4. Perception of musical emotions: indices (N = 115) Musical emotion index
Mean (SD) percent
Happiness Anger Disgust Surprise Sadness Fear
82.6 (22.2) % 43.8 (27.7) % 41.9 (25.0) % 47.0 (24.6) % 87.0 (18.0) % 58.3 (22.9) %
Note: Each index represents the mean accuracy detection score of three musical segments.
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and sadness were significantly easier to classify correctly than the other four emotions, happiness was as easy to classify correctly than sadness was (F = [5] 84.14, p < .001). The number of females was significantly higher than the number of males (c2 [1] = 9.47, p < .01). Therefore, a series of independent-samples t-tests with gender as the grouping variable and the indices as dependent variables were run. The results yielded no effects of gender in the ability to classify musical emotions. Although our previous study did not find significantly different detection accuracy between music therapy students and controls without musical training (Busch et al., 2003), others (Bigand, Vieillard, Madurell, Marozeau, & Dacquet, 2005) have reported a small effect due to musical experience. Therefore, Pearson’s correlation analyses of the association between musical instruction in childhood/youth and the emotion indices revealed a statistically-significant relationship between years of music instruction and the classification of happiness (r = .28, p < .01). When running logistic regression tests on the three items of the happiness index, it was revealed that only happiness 1 (played by a tuba) was related to years of music instruction (β = .14, p < .01). Finally, a series of correlation analyses were run aiming to test the relationship between the number of concerts attended in the last 12 months and the musical emotion indices. These tests generated no statistically-significant results, and this relationship was not studied further in regression analyses.
Personality traits and perception of musical emotions Hierarchical regression analyses were used to test the association between personality traits and the classification of musical emotions. In each regression model, one of the six musical emotion indices was the dependent variable, and the five personality traits of NEO-PI-R (Costa and McCrae, 1992) were the independent variables. This procedure revealed a significant contribution of O to the perception of happiness when O was entered in combination with N and E (β = .28, p < .01) and N, E, and A (β = .28, p < .01), respectively.
Discussion The main finding was that the participants were able to identify six basic universal emotions in completely unknown musical stimuli, with happiness and sadness easier to classify correctly than the other emotions. In addition, there was great variability in the means of percentage recognition, indicating that some of the musical stimuli should be altered or substituted in order to convey the intended emotion in a better way. Moreover, the ability to perceive emotions in music was not more than marginally related to music instruction in childhood or youth and the personality trait openness (O).
Perception of musical emotions The participants of the present study correctly classified the emotional content of unknown musical stimuli, in most cases well above chance level. Thus, we have replicated the results of a pilot study (Busch et al., 2003) with a larger number of participants, putting the previous findings on a more secure footing. In the present study as well as in the pilot study (Busch et al., 2003), sadness and happiness were more readily identifiable than the other emotions, and disgust, surprise, and fear the least
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identifiable. These results are consonant with those of Kallinen (2005), who argues that sadness and happiness are the emotions most often expressed in Western art music. Sadness and happiness are relatively easy to express musically, due to their fairly consistent different characteristics in terms of mode and tempo. Moreover, most Westerners learn at an early age to associate slow music in the minor mode with sorrow and faster music in the major mode with joy due to the large amount of exposure such pieces of music get relative to pieces expressing other emotions or pieces with less clear-cut emotional content (Kallinen, 2005). In this respect, it must be kept in mind that the musical segments of the present study were based on Western tonality; even though they were unknown to the participants, their emotional content may have been recognized because the participants were all Westerners familiar with Western tonality. However, this possibility does not preclude an interpretation of the present study in terms of the neuro-cultural theory of emotion (Ekman and Friesen, 1971). This theory is not completely universalistic, but postulates that cultural factors contribute to the recognition and display of emotions, as evidenced by recognition rates of facial emotions rarely reaching 100 % (Ekman, 1994). If each facial emotion may be expressed by sound (Ekman, 1993), one may assume that the ability to classify emotions through sound is a characteristic of all humans, but that the recognition rate will be highest when the relevant sound stimuli contain certain aspects characteristic of one’s particular cultural auditory environment. In contrast to sadness and happiness, which are either negative or positive emotions, surprise may be difficult to identify musically because this emotion may be regarded as both positive and negative (Kallinen, 2005). In addition, the classification of disgust may be difficult because this emotion is unexpected within the framework of Western tonality, as Western music traditionally has been used for individual enjoyment or to foster social cohesiveness (Kallinen, 2005). In cases where the recognition rate was less than 50% for the negative emotions anger, disgust, and fear, they were most often mistaken for another negative emotion, for example anger being misclassified as fear. Possibly, this may be the result of the fear and anger segments sharing several musical elements, such as loudness, rapid tempo, and changes in dynamic. Alternatively, the evolutionary purpose of emotions may offer an explanation. Both fear and anger are elicited in dangerous situations requiring rapid action. Perhaps rapid action, such as fight or flight, in such situations is so important to our survival that it is difficult for us to ponder the finer points separating the strong, negative emotions. Anger 1 was often mistaken for surprise, an emotion that is not necessarily negative. According to Juslin (2001), anger may be expressed musically through a high sound level, staccato articulation, and a fast mean tempo. These characteristics could easily be perceived in anger 1. However, anger 1 did not contain the relatively-large variations in tone duration that may be an additional prerequisite for anger to be expressed in music (Gabrielsson and Juslin, 1996). Possibly, variation in tone duration is not necessary for anger to be perceived providing the other characteristics are present. A different explanation may come from those claiming that music is incapable of inducing or conveying all the discrete, universal, strong emotions similar to those exhibited by the human face. Scherer (2004) argued that the tendency for cognitive appraisal of aesthetics renders the musical experience an inherently subjective and private one, and that more subtle, changing feelings are associated with music to a much larger degree than is a fixed set of categories of emotions with strong action tendencies. This line of thought may account for the present difficulty in classifying fear and anger correctly. These emotions may simply not be detected in a musical context because they are not expected, as listening to music is a pleasurable activity
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for most people. On the other hand, others (Juslin and Laukka, 2004) have demonstrated that, relative to more complex affective states, the basic emotions, including anger and fear, were more frequently chosen as examples of what music may express. However, giving examples of emotions that may be expressed in music in general is not the same thing as having to judge the emotional content of a specific piece of music one is hearing for the first time, as our participants were instructed to do. In this respect, it is not possible to conclude that the present study provides definitive support for the theory that the perception of emotions in music is best viewed within a universalistic, categorical framework. Participants who had received music instruction in childhood or youth were significantly better at classifying one of the happiness segments. This segment was played by a tuba, and it is possible that music played by this instrument is somewhat difficult to interpret emotionally for musical novices. Compared to other instruments, there are very few pieces written for tuba solo, and the tuba usually enters orchestral pieces during forte segments or when the composer wishes to emphasize the bass line. Thus, most musical novices may have learned to associate the timbre of the tuba with darker or perhaps scary musical segments, while experienced musicians may have learned to pay more attention to the mode and tempo of the segment instead of the instrument on which it is performed. On the other hand, the tuba features prominently in brass band music, a popular genre in Norway, where many children and teenagers receive extra-curricular music instruction in school bands. In this respect, the tuba could be associated with joyful experiences. In contrast to tempo, loudness, and mode, the emotion-expressing aspects of timbre is not well explored (Gabrielsson and Lindström, 2001). Therefore, in the absence of studies of the relationship between the timbre of a large number of instruments and the emotions perceived in the listeners, this explanation remains speculative. Moreover, the music preferences of the present participants seemed broad, in that most of them stated a preference for both classical and popular music. Hence, they seemed to have been exposed to a large variety of music genres and were presumably able to determine the emotional effects of different musical cues. The accuracy of recognition of the emotion indices in our study was less than typically found in studies of facial emotions, which normally range from 97% (happiness) to 67% (anger) (Ekman, 1992; Elfenbein and Ambady, 2003). The accuracy of sadness recognition, however, was in line with studies of facial emotions (87%; Ekman, 1992; Elfenbein and Ambady, 2003). One explanation may be that emotion is more difficult to identify in acoustic than in visual stimuli, as the visual system is the most advanced of the human senses. A second possible explanation concerns the different characteristics of facial emotion stimuli (Ekman, 1992) and musical emotional stimuli. The facial emotions are spatial, and we are immediately able to perceive the totality of the face and the characteristics of the different facial structures producing the emotional expression. In contrast, music has a prominent temporal characteristic and is perceived as a sequence of sounds relating to each other. In our study, the maximum length of a musical segment was 5 seconds. While there is evidence that happiness, sadness, and fear are recognizable in musical segments of less than 4 seconds in duration (Vieillard et al., 2008), emotions with less strong action tendencies, for example surprise, may require stimuli of longer duration to be identified. A third explanation may be that emotions are as easily recognizable in music as in facial expressions, but that this first version of the test contains items that are not sufficiently representative of the emotions they intend to express. According to Ekman (1993), each emotion correlates with unique physiological responses. Happiness, for example, is conveyed facially by increased muscular activity around the lips and eyes. According to Juslin (2000), it is possible
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to isolate musical parameters that symbolize happiness, sadness, fear, and anger. A future task for our laboratory is to identify the parameters, such as mode, tempo, timbre, volume, and dynamics, of the stimuli that uniquely relate to disgust and surprise. Fourth, although Ekman (1993) postulates that each emotion expressed by the face may be expressed through sound, his focus is the human voice. The expression of emotion though art music may, in this perspective, be less related to the need for rapid communication. Therefore, it may be harder to identify the emotional content in instrumental music than in facial expressions. A way to test this hypothesis would be to develop a vocal version of the musical emotions test, in which the emotional segments are sung rather than played. There is evidence that happiness, sadness, anger, and fear are readily detected in spoken messages (Laukka, 2005). Studies of the emotional content of non-verbal vocal stimuli have, to the best of our knowledge, not been undertaken.
The relationship between personality traits and perception of musical emotions There was only a weak association between personality traits and the recognition of musical emotions, in that O contributed significantly to the prediction of happiness when entered as an independent variable together with other personality traits. This is not unexpected, in that O denotes a tendency to creativity and intellectual curiosity. Individuals with high levels of O may be better able to identify emotions simply because they tend to possess prominent intellectual skills (McAdams, 2006). However, in the absence of other studies in this field, this interpretation remains tentative. Possibly, personality traits are much more relevant for music preferences than for emotion detection accuracy in musical stimuli (Rentfrow and Gosling, 2003).
Strengths, limitations, and future studies One strength of the present study is its connection to the neuro-cultural theory of emotion (Ekman and Friesen, 1971), providing a well-established framework for the interpretation of the data and contributing to the theoretical economy by systematizing future research efforts in this field. Second, we employed custom-made musical stimuli, thus ruling out the effects of previouslyformed associative emotional responses to the items of the test. It may be argued that, due to our choice of mostly acoustic instruments and improvisations within the tonal system, the sound clips would automatically create associations to classical music and thus evoke emotional memories of previous classical listening experiences in the participants. In this respect, our stimuli may not be regarded as truly unknown. In order to control for this possibility, future studies of this test should ask the participants to report whether the sound clips reminded them of something they had heard before. However, the ultimate consequence of this line of reasoning would be that one could never avoid associative emotional responses to test stimuli, rendering the study of emotional responses virtually impossible. In this light, our test is a clear improvement in the field of musical emotions research in that it attempts to reduce the influence of the associations the participants carry with them. Despite the strengths of the study, certain limitations must be kept in mind when interpreting our results. First, the musical segments were based on the Western tonal system. This is a potential problem for the cross-cultural study of musical emotions. Due to the large cultural variations in musical expressions, this test may not be suited for cross-cultural research on the
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acoustic recognition of emotions. However, there is evidence that humans are able to recognize the intended emotions in music from unfamiliar cultures. Balkwill and Thompson (1999) reported that Western listeners successfully identified joy, sadness, and anger in Hindustani ragas, and that the musical elements most relevant for this process were tempo, melodic complexity, and timbre. Similar data were obtained from a study of Japanese listeners detecting emotions in Japanese, Western and Hindustani music (Balkwill, Thompson, & Matsunaga, 1999). A study of individuals from the African Mafa population, who had never been exposed to Western culture, showed that this group readily recognized happiness, sadness, and fear in Western art music as well as in their own traditional music (Fritz et al., 2009). Nevertheless, our test should be employed in non-Western cultures in order for us to conclude that it assesses universal emotions. Second, although we instructed our participants to report the emotions they recognized in the musical stimuli and not the emotions those stimuli evoked in them, it is possible that, in some cases, the participants may have identified the emotion they felt rather than the one the music expressed. Whether such possible confusions has a tendency to occur when taking this test, and whether felt emotions influence the classification process, are not topics of the present study. Nevertheless, it should be mentioned that individuals, although they tend to feel positive emotions stronger than negative emotions induced by music, are capable of classifying the different emotions correctly (Gabrielsson, 2002; Kallinen and Revaja, 2006). Moreover, a recent investigation suggests that confusion of expressed and felt emotions in music may be modest and not pose a grave challenge for research in this field (Vieillard et al., 2008). Third, the forced-choice method employed in the current study is a natural choice with a categorical emotions approach as a theoretical point of departure, but may have led to higher recognition rates for emotions that may be relatively hard to classify, such as disgust or anger. In order to provide a clearer picture of classification difficulties as a part of further development of the musical emotions test, Likert scale approaches will be considered. Fourth, the order of presentation of the 18 musical segments was identical for all participants. It is not inconceivable that this may have generated biased results regarding the classification of the last segments. The participants knew that they had to choose between six emotions for each of the 18 stimuli, and if they remembered that they had classified sadness, for example, only twice, it is possible that they felt compelled to perceive the 18th segment as sadness in order to create numerical balance. Whether the order of presentation influences the perception of emotions using this test should be subject to further study. Fifth, there were several statistical tests performed with a relatively small sample. This increases the risk of Type I error, where a true null hypothesis is rejected. This point seems particularly relevant regarding the somewhat scattered results of the analyses of associations between musical education experience, personality traits, and emotion recognition indices. This suggests that our findings of statistically-significant associations between theoretical background, personality, and emotion perception should be interpreted with caution.
Conclusion In conclusion, the results from this first major study of this musical emotions test suggest that the six basic universal emotions are detectable in musical stimuli, and that the ability to do so does not seem to be influenced by musical experience or personality traits.
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Acknowledgements This study was supported by the Department of Psychology, University of Oslo and the German Center for Music Therapy Research. Mr Dag Erik Eilertsen provided valuable statistical advice. Comments and suggestions from Dr Anne Leins, Prof Dr Hans Volker Bolay, Prof Dr Thomas Hillecke, Ms Nicole Meissner, and Mr Tanjef Gross have been greatly appreciated. Financial support for the development of the musical emotions test was provided by Mr Reinhard Walter, FOM Future Office Management GmbH, Heidelberg, Germany. We are grateful for the comments and recommendations of three anonymous reviewers on an earlier version of this manuscript.
References Balkwill, L.L., & Thompson, W.F. (1999). A cross-cultural investigation of the perception of emotion in music: Psychophysical and cultural cues. Music Perception, 17(1), 43–64. Balkwill, L.L., Thompson, W.F., & Matsunaga, R. (2004). Recognition of emotion in Japanese, Western, and Hindustani music by Japanese listeners. Japanese Psychological Research, 46(4), 337–349. Bigand, E., Vieillard, S., Madurell, F., Marozeau, J., & Dacquet, A. (2005). Multidimensional scaling of emotional responses to music: The effect of musical expertise and of the duration of the excerpts. Cognition & Emotion, 19(8), 1113–1139. Busch, V., Nickel, A.K., Hillecke, T.K., Gross, T., Meissner, N., & Bolay, H.V. (2003). Musikalische und mimische Emotionserkennung: Eine Pilotstudie mit psychiatrischen Patienten [Musical and facial emotion recognition: A pilot study with psychiatric patients]. Zeitschrift für Muzik-, Tanz- und Kunsttherapie, 14(1), 1–8. Chamorro-Premuzic, T., & Furnham, A. (2007). Personality and music: Can traits explain how people use music in everyday life? British Journal of Psychology, 98(2), 175–185. Costa, Jr., P.T., & McCrae, R.R. (1992). NEO-PI-R, professional manual. Odessa, FL: Psychological Assessment Resources. Dalla Bella, S., Peretz, I., Rousseau, L., & Gosselin, N. (2001) A developmental study of the affective value of tempo and mode in music. Cognition, 80(3), B1–B10. Ekman, P. (1992). Are there basic emotions? Psychological Review, 99(3), 550–553. Ekman, P. (1993). Facial expression and emotion. American Psychologist, 48(4), 384–392. Ekman, P. (1994). Strong evidence for universals in facial expressions: A reply to Russel’s mistaken critique. Psychological Bulletin, 115(2), 268–287. Ekman, P., & Friesen, W.V. (1971). Constants across cultures in the face and emotions. Journal of Personality and Social Psychology, 17(2), 124–129. Ekman, P., Levenson, R.W., & Friesen, W.V. (1983). Autonomic nervous system activity distinguishes among emotions. Science, 221(4616), 1208–1210. Elfenbein, H.A., & Ambady, N. (2002). On the universality and cultural specificity of emotion regocnition: A meta-analysis. Psychological Bulletin, 128(2), 203–235. Elfenbein, H.A., & Ambady, N. (2003). When familiarity breeds accuracy: Cultural exposure and facial emotion recognition. Journal of Personality and Social Psychology, 85(2), 276–290. Fritz, T., Jentsche, S., Gosselin, N., Sammler, D., Peretz, I., Turner, R. et al. (2009). Universal recognition of three basic emotions in music. Current Biology, 19(7), 573–576. Furnham, A., Zhang, J., & Chamorro-Premuzic, T. (2006). The relationship between psychometric and self-estimated intelligence, creativity, personality and academic achievement. Imagination, Cognition and Personality, 25(2), 119–145. Gabrielsson, A. (2002). Emotion perceived and emotion felt: Same or different? Musicae Scientiae, Special Issue, 123–147.
14
Psychology of Music
Gabrielsson, A., & Juslin, P.N. (1996). Emotional expression in music performance: Between the performer’s intention and the listener’s experience. Psychology of Music, 24(1), 68–91. Gabrielsson, A., & Lindström, E. (2001). The influence of musical structure on emotional expression. In P.N. Juslin & J.A. Sloboda (Eds.), Music and emotion. Theory and research (pp. 223–248). Oxford: Oxford University Press. Juslin, P.N. (2001). Communicating emotion in music performance. In P.N. Juslin & J.A. Sloboda (Eds.), Music and emotion: Theory and research (pp. 301–337). Oxford: Oxford University Press. Juslin, P.N. (2000). Cue utilization in communication of emotion in music performance: Relating performance to perception. Joural of Experimental Psychology: Human Perception and Performance, 26(6), 1797–1813. Juslin, N., & Laukka, P. (2004). Expression, perception, and induction of musical emotions: A review and a questionnaire study of everyday listening. Journal of New Music Research, 33(3), 217–238. Kallinen, K. (2005). Emotional ratings of music excerpts in the Western art music repertoire and their self-organization in the Kohonen Neural Network. Psychology of Music, 33(4), 373–393. Kallinen, K., & Ravaja, N. (2006). Emotion perceived and emotion felt: Same and different. Musicae Scientiae, 10(2), 191–213. Kopacz, M. (2005). Personality and music preferences: The influence of personality traits on preferences regarding musical elements. Journal of Music Therapy, 42(3), 216–239. Laukka, P. (2005). Categorical perception of vocal emotion expressions. Emotion, 5(3), 277–295. Martinsen, Ø., Nordvik, H., & Østbø, L.E. (2003). NEO-PI-R. Oslo: Gyldendal. McAdams, D.P. (2006). The person. Hoboken, NJ: John Wiley. Nawrot, E.S. (2003). The perception of emotional expression in music: Evidence from infants, children, and adults. Psychology of Music, 31(1), 75–92. North, A., & Hargreaves, D. (2008). The social and applied psychology of music. Oxford: Oxford University Press. Powers, N., & Trevarthen, C. (2009). Voices of shared emotion and meaning: Young infants and their mothers in Scotland and Japan. In S. Malloch and C. Trevarthen (Eds.), Communicative musicality. Exploring the basis of human companionship (pp. 309–340). Oxford: Oxford University Press. Rawlings, D., & Ciancarelli, V. (1997). Music preference and the five-factor model of the NEO personality inventory. Psychology of Music, 25(2), 120–132. Rentfrow, P.J., & Gosling, S.D. (2003). The do re mi’s of everyday life: The structure and personality correlates of music preferences. Journal of Personality and Social Psychology, 84(6), 1236–1256. Resnicow, J.E., Salovey, P., & Repp, B.H. (2004). Is recognition of emotion in music performance an aspect of emotional intelligence? Music Perception, 22(1), 145–158. Scherer, K.R. (2004). Which emotions can be induced by music? What are the underlying mechanisms? And how can we measure them? Journal of New Music Research, 33(3), 239–251. Sloboda, J.A., & Juslin, P.N. (2001). Psychological perspectives on music and emotion. In P.N. Juslin & J.A. Sloboda (Eds.), Music and emotion: Theory and research (pp. 71–104). Oxford: Oxford University Press. Tomkins, S.S. (1963). Affect, imagery, consciousness: Vol. 2. The negative affects. New York: Springer. Vieillard, S., Peretz, I., Gosselin, N., Khalfa, S., Gagnon, L., & Bouchard, B. (2008). Happy, sad, scary and peaceful musical excerpts for research on emotions. Cognition & Emotion, 22(4), 720–752. Wiggins, J.S. (1996). The five-factor model of personality. Theoretical perspectives. New York: Guilford Press.
Christine Mohn is a clinical psychologist and a senior research fellow at the Department of Psychology, University of Oslo, and Vestre Viken Hospital Trust. Her research interests include general psychology of music, cognitive neuropsychology, and the psychology of chronic pain.
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Heike Argstatter is a clinical psychologist as well as a music historian. Currently, she is a postdoctoral research fellow at the German Center of Music Therapy Research in Heidelberg. Her main research interests are music therapy in tinnitus and other neurological disorders. Friedrich-Wilhelm Wilker is a clinical psychologist and currently professor of medical psychology at the Faculty for Music Therapy, SRH University of Applied Sciences in Heidelberg. His other areas of interest are general psychotherapy and music therapy.
