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Psychology of Music 1 –17
© The Author(s) 2015Reprints and permissions:
sagepub.co.uk/journalsPermissions.navDOI: 10.1177/0305735615589214
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Perception of basic emotions in music: Culture-specific or multicultural?
Heike Argstatter
AbstractThe perception of basic emotions such as happy/sad seems to be a human invariant and as such detached from musical experience. On the other hand, there is evidence for cultural specificity: recognition of emotional cues is enhanced if the stimuli and the participants stem from the same culture. A cross-cultural study investigated the following research questions: (1) How are six basic universal emotions (happiness, sadness, fear, disgust, anger, surprise) perceivable in music unknown to listeners with different cultural backgrounds?; and (2) Which particular aspects of musical emotions show similarities and differences across cultural boundaries? In a cross-cultural study, 18 musical segments, representing six basic emotions (happiness, sadness, fear, disgust, anger, surprise) were presented to subjects from Western Europe (Germany and Norway) and Asia (South Korea and Indonesia). Results give evidence for a pan-cultural emotional sentience in music. However, there were distinct cultural, emotion and item-specific differences in emotion recognition. The results are qualified by the outcome measurement procedure since emotional category labels are language-based and reinforce cultural diversity.
Keywordscross-cultural, culture, emotion, perception, representations, sociocultural
Emotion perception is “the ability to detect and decipher emotions in faces, pictures, voices, and cultural artifacts” (such as musical pieces) (Scherer & Scherer, 2011). Human universals, the search for cross-cultural similarities, have been demonstrated for several diverse aspects, such as natural language (Hupka, Lenton, & Hutchinson, 1999), personality (Goldberg, 1980) and introspection (subjective well-being) (Diener & Diener, 1996). In the realm of emotions, a pan-cultural emotional lexicon has been found, meaning that the conceptual organization of
Deutsches Zentrum für Musiktherapieforschung (Viktor Dulger Institut) (German Center for Music Therapy Research) DZM e.V., Germany
Corresponding author:Heike Argstatter, Deutsches Zentrum für Musiktherapieforschung (Viktor Dulger Institut) DZM e.V., Maaßstr. 32/1, 69123 Heidelberg, Germany.Email: [email protected]
589214 POM0010.1177/0305735615589214Psychology of MusicArgstatterresearch-article2015
Article
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2 Psychology of Music
emotion terms seems to be universal rather than specific to culture or language (Russel, 1983). Similar findings have been reported for the appraisal of facial (Ekman, 1972) and vocal (Scherer, Banse, & Wallbott, 2001) emotion expression.
Several investigations have been conducted in order to measure the subjective perception of musically encoded emotions (Scherer, 2004). Depending on the test paradigm, the accuracy of emotion detection in musical pieces is comparable to facial or verbal emotional detection (Fritz et al., 2009; Gabrielsson & Juslin, 1996; Juslin, 1997; Laukka, Eerola, Thingujam, Yamasaki, & Beller, 2013; Sundberg, 1993).
An important moderating variable influencing the ability to detect musically encoded emo-tions seems to be the musical aptitude of the participants; more elaborated musical expertise leads to more sophisticated emotional differentiation (Bigand, Vieillard, Madurell, Marozeau, & Dacquet, 2005).
Nevertheless, there exists a universal sensitivity for basic emotions in music since “conso-nance and permanent sensory dissonance universally influence the perceived pleasantness of music” (Fritz et al., 2009). Investigations with participants from both Western (Canada, Sweden, Germany) and Non-Western (Japan, India, Cameroon) countries suggest a cross- cultural sensitivity to unfamiliar musically expressed emotions (Balkwill & Thompson, 1999; Balkwill, Thompson, & Matsunaga, 2004; Fritz et al., 2009; Laukka et al., 2013). Basic emo-tions such as happy/sad, especially, seem to be human invariants and as such detached from musical experience (Krumhansl, 1997).
The decoding of acoustically encoded information is essential for successful prosody appre-ciation and as such at the base of the ontogeny of communication. The prosodic qualities of speech are musical features, leading the way from pre-verbal interaction to sensible speech. Pre-verbal communication between baby and parents is predominantly aimed at emotional regulation. Voice quality is considered to be the most informative channel for affective expres-sion in early infancy (Trehub, 2001). Cross-cultural investigations confirm that “motherese”, that is, infant directed speech, has universal prosodic features (Saint-Georges et al., 2013). And even in mature speech, vocal cues indicating positive and negative emotional sound character-istics have been found on a phoneme level; that is to say, words with a positive connotation sound different from words with negative connotations (Nastase, Sokolova, & Shirabad, 2007).
On the other hand, there is evidence for cultural specificity of emotion recognition. An experiment on music recognition (Demorest, Morrison, Jungbluth, & Beken, 2008) found empirical evidence for the effect of enculturation on music cognition: when comparing two groups from different cultures, both groups are better at remembering music of their own cul-ture. A cross-cultural study investigated the accuracy of decoding vocal expressions in non-sense sentences made up from Western European syllables presented to participants from Germany, other West-European countries and Indonesia (Scherer et al., 2001). All groups achieved recognition rates above chance level, with a similar overall performance among all Western European countries. The overall recognition rate of the Indonesian sample was lowest compared to all Western European samples.
The cue-redundancy model of emotional communication (Balkwill & Thompson, 1999) explains the perception of emotion in music by an interplay of music-inherent cues and culture specific cues. The music-inherent cues have been explained as psychophysical cues (Balkwill & Thompson, 1999), auditory features (Koelsch, 2011), or “indexical sign qualities” (Fritz et al., 2013) common to all tonal systems (e.g., tempo, rhythm, complexity or timbre). Culture spe-cific cues are culturally determined conventions (e.g., scales, harmonic relationships) that lis-teners have acquired during their enculturation. Some emotions like happiness/joy or sadness seem to be more salient due to distinct musical dimensions such as tempo, rhythm, complexity
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Argstatter 3
or timbre while other emotional states (such as anger or surprise) seem to be more complex and cannot equally well be explained by simple musical patterns (for an overview, see Eerola & Vuoskoski, 2013 and Hunter & Schellenberg, 2010).
According to the dock-in model (Fritz, 2013), there is a distinct connection between a set of underlying musical universals and perceptual universals. Listeners with a certain cultural background will be able to decode a certain amount of information from the “central hub of universals”. However, the further away a person is from another culture’s dock, the less they are able to decode the culture specific cues, possibly due to iconic meaning in music (while being present) being easily overwritten by cultural associations.
Rationale of the present study
In order to explore the possibility of basic emotions being recognized in acoustic stimuli unknown to the participants, the German Centre of Music Therapy Research developed a test of emotion perception in music (Busch et al., 2003). A pilot study demonstrated that 18 musi-cal segments representing six emotions (happiness, anger, disgust, surprise, sadness, and fear) could be reliably allocated to the intended emotional categories. There was no significant differ-ence in classification accuracy between music therapy students and controls without musical training. In a further study, these results were replicated with a larger number of participants consisting of Norwegian students (Mohn, Argstatter, & Wilker, 2011).
The rationale of the current study was to extend the samples to Asian countries in order to explore both the universality and cultural variations of the ability to decode musically encoded basic emotions.
Due to the exploratory nature of this study, no specific hypotheses were formulated. The fol-lowing research questions were asked: (1) are emotions universally perceivable in music unknown to listeners with a different cultural background? Western music is ubiquitous in large parts of the world, hence it is possible that the cross-cultural differences in affect recognition are marginal. If, however, individual enculturation prevails, cultural proximity should lead to simi-lar emotional classification results, resulting in an in-group advantage; that is, participants from Western countries should outperform participants from Asian countries; (2) Which particular aspects of musical emotions show similarities and differences across cultural boundaries? Since some emotions seem to be musically more salient than others, we were particularly interested in the recognition pattern cross-culturally: which musical examples would be reliably identified as the intended emotion, and which examples would be mistaken for a false emotion category.
Methods
Subjects and procedure
The sample consisted of two groups from Western Europe and two groups from Asia. All par-ticipants had to be born and have grown up in the target country. They had to be native-speak-ers and able to understand the spoken and written language of their home country. Immigrants or foreign persons living in the target countries were excluded.
The Western European participants came from Germany and Norway, and the Asian par-ticipants came from Indonesia and South Korea. Participants were recruited through adver-tisements, email lists and personal contact by native-speaking experimenters in the target countries. Demographic characteristics and geographic origins of the participants are depicted in Table 1.
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4 Psychology of Music
Tab
le 1
. D
emog
raph
ic c
hara
cter
istic
s an
d ge
ogra
phic
ori
gins
of p
artic
ipan
ts.
Cou
ntr
yG
ende
rA
ge (y
ears
)M
usi
cal b
ackg
rou
nd
Ori
gin
M
ale
(%)
Fem
ale
(%)
Mu
sici
an (%
)G
ener
al (%
)
Ger
man
yn
= 3
1 (3
7.8
)n
= 5
1 (6
2.2
)2
7.6
(9.5
)n
= 4
6 (4
4.7
)n
= 5
7 (5
5.3
)St
ude
nts
at t
he
Un
iver
sity
of A
pplie
d Sc
ien
ces
Hei
delb
erg,
Ger
man
y.
Par
tici
pan
ts fr
om th
e ge
ner
al p
opu
lati
on.
Nor
way
n =
41
(35
.7)
n =
74
(64
.3)
27
.7 (7
.2)
—n
= 1
15
(10
0)
Stu
den
ts a
t th
e U
niv
ersi
ty o
f Osl
o, N
orw
ay.
Kor
ean
= 9
1 (3
5.7
)n
= 1
51
(62
.4)
29
.8 (1
0.8
)n
= 5
7 (2
3.8
)n
= 1
83
(76
.2)
Stu
den
ts a
t th
e U
niv
ersi
ties
of C
hon
nam
an
d W
onkw
ang,
Sou
th K
orea
.
Par
tici
pan
ts fr
om th
e ge
ner
al p
opu
lati
on.
Indo
nes
ian
= 4
9 (4
4.5
)n
= 6
1 (5
5.5
)2
4.5
(6.5
)n
= 5
5 (5
0.0
)n
= 5
5 (5
0.0
)St
ude
nts
at t
he
Un
iver
sity
of P
elit
a H
arap
an,
Indo
nes
ia.
P
arti
cipa
nts
from
the
gen
eral
pop
ula
tion
.
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Argstatter 5
The participants were tested individually or in groups of up to seven in non-soundproof rooms. The music segments were played on a portable stereo 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. The study was approved by the local ethical committees, and all participants signed consent forms prior to the test.
Test of emotion perception in music
The current trial investigated the same musical items that were used in our previous studies (Busch et al., 2003; Mohn et al., 2011). Professional musicians were instructed to improvise short musical pieces on instruments of their choice in a way that a listener should be able to decode one of the intended basic emotions (happiness, anger, disgust, surprise, sadness, and fear). Duration of the segments was limited to a maximum of 7 seconds following the notion of William Stern’s “mental presence time” (Stern, 1897) which is known to be 4–7 seconds for auditory stimuli. Due to the temporal patterns of music, the emotional content of a musical piece might vary with time. The limitation of the duration to a maximum of 7 seconds ensures that the musical segments will be perceived as an entity representing predominantly one emotion. Eighteen music segments (three segments for each emotional quality, see Table 2; see supplementary material) made up the test (for more details on the test see Mohn et al., 2011).
The musical segments were burnt on a compact disc (CD) in randomized order. 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 classify each segment as one of six emotions and to mark the most appropriate emotion category on a forced-choice answer sheet (the answer schedule is depicted in Figure 1). Before the test, six trial seg-ments (one for each emotional quality) were presented in order to familiarize the subjects with the task. An internet version of the test is available at https://www.soscisurvey.de/emu/, depict-ing the instructions and the examples. In the present trial, these instructions and answers were given in a pen-and-paper version. The entire musical emotions test procedure lasted 10 min-utes (for more details see Appendix 1).
The answer categories for the Norwegian, Korean and Indonesian questionnaire sheets were obtained through translation and re-translation. Since the valence of emotional categories var-ies considerably between cultures, the final expressions of the Asian questionnaires were checked by professionals of Korean studies and Indonesian language respectively.
Statistical analysis
Statistical analysis of the data was performed using parametrical and non-parametrical tests in SPSS 20 (SPSS Inc.). Group differences of sociodemographic data were analyzed with the χ²-test or univariate analysis of variance (ANOVA). Assessment of emotion perception was evalu-ated using multivariate analysis of variance (MANOVA) and t-tests for independent samples. Level of significance was p < .05, adjusted for multiple testing by the Scheffé method if neces-sary. All analyses were two-tailed.
Results
Perception of musical emotions
Percentages of correct and incorrect classification of musical emotions in the 18 segments by the four nations are depicted in Table 3.
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6 Psychology of Music
The classification performance differed considerably between the four nations (ANOVA F(3, 564) = 26.73, p < .001): the German participants achieved about 67% (SD = 13%) correct classi-fications, the Norwegian participants 60% (SD = 38%), the Korean participants 48% (SD = 13%) and the Indonesian participants 45% (SD = 20%).
Accuracy levels for the overall recognition in all groups were well above the levels expected from chance guessing (1/6 = 16.7%). Comparison of the recognition accuracy with the cut-off score of 16.7 by single sample t-test revealed cultural, emotion and item-specific differences. On the cultural level, the Indonesian sample classified the emotion categories “Surprise” (p = .268)
Table 2. Characteristics of the musical segments.
Emotion Duration (seconds)
Instrument Musical characteristics Number on CD
Happiness 1 3 Tuba Vivid expression, staccato, broad timbre, high volume, fast tempo
2
Happiness 2 5 Guitar Major mode, dance-like 3/4 rhythm, large intervals, loud volume, no dissonances
12
Happiness 3 5 Piano Major mode, strong timbre, vivid expression, rapid tempo
16
Sadness 1 5 Electric bass Legato, light, subdued ascending and descending tones, slow tempo
3
Sadness 2 5 French horn Minor mode, a stepwise intervals, weak touch, medium volume, consonant harmony
7
Sadness 3 5 Piano Minor mode, weak touch, low volume, slow tempo with large variations
18
Surprise 1 4 Electric bass Short tones, staccato, jumping ascending dynamics, medium volume
6
Surprise 2 4 Piano Major mode, jumping, ascending melody, broad expression, crescendo
11
Surprise 3 3 French horn Major mode, staccato, jumping ascending melody, medium volume, crescendo
13
Fear 1 4 Cello Short, “shivering” vibrato, low volume, fast tempo
1
Fear 2 4 Guitar Very rapid touch, ascending volume, tempo, and dynamics
9
Fear 3 5 Tuba Rapid, irregular vibrato, low pitch, medium volume, from crescendo to decrescendo
15
Anger 1 3 Piano Hard touch, staccato, loud volume, rapidly ascending tempo, dissonant harmony
5
Anger 2 3 Tuba Staccato, low pitch, short intervals between tones, loud volume
10
Anger 3 5 Cello Minor mode, staccato, low pitch, strong vibrato, rapid tempo
17
Disgust 1 5 Violin “Screeching”, medium volume, several variations with changing expression and emphasis
4
Disgust 2 5 Cello Uncontrolled tones in rapid succession, ascending and descending movements
8
Disgust 3 3 Electric bass Weak touch, subdued timbre, slow tempo, low volume, diminuendo
14
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Argstatter 7
and “Disgust” (p = .022) below chance. This was due to the low accuracy performance for the examples Surprise 2 (p = .097), Surprise 3 (p = .656), Disgust 2 (p = .580) and Disgust 3 (p = .469). Furthermore, Anger 2 (p = .045) was classified below chance. The remaining nations clas-sified the following single items below chance level: German: Disgust 2 (p = .199); Norwegian: Surprise 2 (p = .891), Fear 3 (p = .567), Disgust 3 (p = .046), Anger 2 (p = .046); Korea: Happiness 1 (p = .010). On the item level, the example “Disgust 2” was the most indistinct example which did not achieve accuracy above chance level for all participants irrespective of cultural origin.
Cultural proximity led to similar emotional classification results; that is, neither the two West-European (Germany and Norway) samples (p = .102) nor the two Asian (Korea and Indonesia) samples (p = .063) differed in their overall recognition performance, calculated as the total percentage of correct classifications of all 18 examples.
Confusions
Some musical examples seemed to be very representative for the emotional category regardless of the cultural background of the participants (such as Happiness 3, Sadness 3). For other exam-ples, depending on the cultural background, the emotional classification seemed to be rather confused. Therefore, Chi-square analyses were performed on the nominal data representing the
Figure 1. Forced-choice answer sheet. Answer schedule: 1 = English, 2 = German, 3 = Norwegian, 4 = Indonesian, 5 = Korean.
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8 Psychology of Music
Tab
le 3
. A
nsw
er s
heet
(pe
rcen
tage
of c
lass
ifica
tion)
.
Inte
nd
Per
c
Hap
py 1
Hap
py 2
Hap
py 3
Sad
1Sa
d 2
Sad
3Su
rpr
1Su
rpr
2Su
rpr
3Fe
ar 1
Fear
2Fe
ar 3
Dis
gust
1D
isgu
st 2
Dis
gust
3A
nge
r 1
An
ger
2A
nge
r 3
Ger
man
y
n =
82
Hap
py80.5
93.2
94.7
——
0.8
16
.84
4.4
54
.13
.06
.1—
0.8
3.8
—2
.39
.02
.3
Sad
3.0
——
60.9
99.2
99.2
——
0.8
2.3
—3
.80
.8—
37
.6—
0.8
2.3
Surp
rise
9.0
6.0
3.8
9.8
——
74.0
51.1
43.6
21
.11
2.2
0.8
0.8
2.3
0.8
37
.64
.54
.5
Fear
0.8
——
10
.50
.8—
3.8
3.8
1.5
62.4
62.6
31.6
6.0
30
.11
9.5
9.8
3.0
21
.1
Dis
gust
0.8
——
12
.8—
—3
.8—
—7
.53
.12
7.8
85.0
24.8
31.6
3.0
6.8
—
An
ger
6.0
0.8
1.5
6.0
——
1.5
0.8
—3
.81
6.0
36
.16
.83
9.1
10
.547.4
75.9
69.9
Nor
way
n
= 1
15
Hap
py70.0
83.5
93.3
7.0
—3
.51
6.5
83
.35
2.2
—0
.90
.9—
2.6
1.7
20
.04
.16
.1
Sad
3.5
——
65.2
100
95.7
——
—0
.9—
5.2
6.1
2.6
47
.00
.95
.27
.8
Surp
rise
15
.71
5.7
3.5
11
.3—
0.9
78.3
16.7
46.1
3.5
7.0
1.7
2.6
3.5
5.2
41
.73
.51
.7
Fear
——
—2
.6—
—0
.9—
1.7
80.0
75.7
19.1
11
.43
2.2
13
.98
.73
.53
0.4
Dis
gust
7.8
—1
.71
3.0
——
3.5
——
8.7
1.7
34
.870.2
30.4
25.2
3.5
24
.37
.0
An
ger
2.6
0.9
0.9
0.9
——
0.9
——
7.0
14
.83
8.3
9.6
28
.77
.025.2
59.1
47.0
Kor
ea
n =
40
7H
appy
24.2
69.2
92.1
2.9
2.9
5.8
15
.46
3.8
60
.82
.10
.40
.80
.41
.32
.12
3.8
3.3
7.1
Sad
10
.00
.44
.261.7
78.3
76.3
2.5
1.3
1.7
8.8
2.1
3.3
4.2
2.1
26
.70
.80
.82
.5
Surp
rise
22
.91
6.7
2.9
1.3
1.3
0.4
42.1
25.4
25.8
7.1
20
.85
.03
.84
.65
.83
6.3
12
.92
0.8
Fear
13
.35
.8—
22
.11
1.3
10
.08
.33
.34
.257.1
52.1
32.1
12
.59
.23
9.6
6.7
13
.32
7.1
Dis
gust
9.6
0.8
—5
.42
.92
.91
1.3
2.1
5.0
17
.91
0.0
32
.960.4
48.3
12.9
7.1
19
.67
.1
An
ger
20
.07
.10
.86
.73
.34
.62
0.4
4.2
2.5
7.1
14
.62
5.8
18
.83
4.6
12
.925.4
50.0
35.4
Indo
nes
ia
n =
10
9H
appy
41.8
82.7
96.4
——
—2
6.4
74
.56
0.9
4.5
7.3
——
—0
.92
9.1
6.4
5.5
Sad
10
.92
.7—
60.0
90.9
97.3
0.9
4.5
10
.91
.81
.87
.34
.50
.97
.30
.92
.77
.3
Surp
rise
13
.61
0.0
0.9
4.5
—0
.930.9
11.8
15.5
22
.72
1.8
6.4
20
.01
30
.64
.52
6.4
18
.21
3.6
Fear
5.5
——
21
.86
.41
.85
.53
.66
.439.1
34.5
34.5
29
.11
1.8
63
.6—
16
.42
6.4
Dis
gust
19
.1—
—1
0.9
2.7
—3
3.6
0.9
1.8
4.5
2.7
34
.532.7
19.1
14.5
6.4
30
.91
.8
An
ger
9.1
4.5
2.7
2.7
——
2.7
4.5
4.5
27
.33
1.8
17
.31
3.6
54
.59
.1%
37.3
25.5
45.5
Not
e. D
ark
grey
fiel
ds a
nd b
old
num
bers
rep
rese
nt c
orre
ct h
its (
inte
nded
em
otio
n =
per
ceiv
ed e
mot
ion)
; lig
ht g
rey
field
s an
d ita
lic n
umbe
rs in
dica
te fa
lse
hits
(ab
ove
chan
ce (
> 1
7%)
choi
ce o
f em
otio
n)
and
whi
te fi
elds
and
non
-bol
d nu
mbe
rs r
epre
sent
the
wro
ng c
lass
ifica
tion.
at UNIV FEDERAL DA PARAIBA on July 11, 2015pom.sagepub.comDownloaded from
Argstatter 9
emotional categories recognized by the participants. This procedure revealed three patterns: 1. “Correct hits” were defined as examples which were classified as the intended emotion; 2. “False hits” were defined as examples that were classified as a distinct emotion different from the intended emotion; and 3. “Confusions” were examples where the classifications were distrib-uted among one or more emotional categories (e.g., example Fear 3 was classified as either “fear” or “disgust” or “anger” in equal measure).
“Correct hits” were all three examples representing sadness (Sadness 1, Sadness 2, Sadness 3), two examples representing happiness (Happiness 2, Happiness 3), and one example representing fear (Fear1) and disgust (Disgust 1) respectively.
“False hits” were represented by examples Surprise 2, which was perceived as “happiness” by the Norwegians, Koreans and Indonesians; Disgust 2, which was mistaken for “anger” by the Indonesians, Disgust 3, which was classified as “sadness” by the Norwegians and as “fear” by the Indonesians and Koreans; and Anger 1, which was categorized as “surprise” by the Norwegians and the Koreans.
The remaining examples represent “confusions”. For details see Table 4.
Emotion recognition profile
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 leading to an “emotion recognition profile” (see Figure 2).
Happiness and Sadness were interculturally easier to classify correctly than the other four emotions, though a repeated measures ANOVA revealed significant differences for all six emo-tions (main effect emotion F(5,560) = 187.65, p < .001). The identification performance was cross-culturally very homogenous (main effect nation F(3,564) = 68.64, p < .001), which led to a culturally specific emotion recognition pattern (interaction nation x emotion F(15,1686) = 9.12, p < .001).
Musical background
Detection accuracy of musically encoded emotions was possibly influenced by musical back-ground. The musical examples have a West European background, therefore it might be easier for participants with training in Western European music to decode the intended emotions. The samples from Germany, Indonesia and Korea consisted of both music students (students at a conservatory of music or students of music therapy) and participants from the general popula-tion. Among the Norwegian participants, there were no professional musicians or music stu-dents, therefore the Norwegian group had to be excluded from this analysis.
For the remaining three countries, a multivariate ANOVA with “culture” (three nations) and “musicality” (music students vs. general population) as group variables and the percentage of correct hits of the 18 musical examples was calculated (see Figure 3). This analysis displayed a “musicality” main effect (F(18, 521) = 4.23, p < .001) due to higher correct hit rates of the musicians for the examples Happiness 1 (p < .001), Sadness 3 (p = .009), and Anger 1 (p = .007) and significantly lower correct hit rates of the musicians for the example Surprise 3 (p = .016) (the remaining examples were statistically indistinguishable, all p > .070). A main effect “cul-ture” (F(54, 1569) = 11,43, p < .001) resulted from significant differences in all items (all p < .001) except for Happiness 3 (p = .873), Sadness 1 (p = .460) and Fear 3 (p = .097). There was no “musicality” x “culture” interaction (F(26, 1044) = 1.28, p = .124).
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10 Psychology of Music T
able
4.
Cla
ssifi
catio
n of
em
otio
nal c
ateg
orie
s (c
orre
ct h
its, f
alse
hits
, con
fusi
ons)
by
natio
n.
Cou
ntr
yEm
otio
nG
erm
any
Nor
way
Kor
eaIn
don
esia
Tot
al
Hap
pyH
appi
nes
s 1
——
Hap
pin
ess/
Surp
rise
——
χ(
1) =
.80
, p =
.77
8
Hap
pin
ess
2—
——
——
Hap
pin
ess
3—
——
——
Sad
Sadn
ess
1—
——
——
Sadn
ess
2—
——
——
Sadn
ess
3—
——
——
Surp
rise
Surp
rise
1—
——
Hap
pin
ess/
Surp
rise
/D
isgu
st—
χ(
1) =
.80
, p =
.77
8
Surp
rise
2—
Hap
pin
ess
Hap
pin
ess
Hap
pin
ess
Hap
pin
ess
χ(
1) =
51
.56
, p =
.00
0χ(
1) =
39
.56
, p =
.00
0χ(
1) =
50
.12
, p =
.00
0χ(
1) =
97
.85
, p =
.00
0Su
rpri
se 3
Hap
pin
ess/
Surp
rise
Hap
pin
ess/
Surp
rise
Hap
pin
ess
Hap
pin
ess
Hap
pin
ess
χ(
1) =
1.6
7, p
= .1
96
χ(1
) = .4
3, p
= .5
10
χ(1
) = 3
3.9
2, p
= .0
00
χ(1
) = 2
9.7
6, p
= .0
00
χ(1
) = 4
6.8
7, p
= .0
00
Fear
Fear
1—
——
——
Fear
2—
——
Fear
/An
ger
—
χ(1
) = .1
2, p
= .7
25
Fe
ar 3
Fear
/Dis
gust
/An
ger
Dis
gust
/An
ger
Fear
/Dis
gust
/An
ger
Fear
/Dis
gust
Fear
/Dis
gust
/An
ger
χ(
1) =
.24
, p =
.88
6χ(
1) =
.19
, p =
.66
3χ(
1) =
2.3
8, p
= .3
05
χ(1
) = 0
.00
, p =
1.0
00
χ(1
) = 2
.33
, p =
.31
2D
isgu
stD
isgu
st 1
——
——
—D
isgu
st 2
Fear
/Dis
gust
/An
ger
Fear
/Dis
gust
/An
ger
Dis
gust
/An
ger
An
ger
Dis
gust
/An
ger
χ(
1) =
5.0
3, p
= .0
81
χ(1
) = .2
3, p
= .8
92
χ(1
) = 5
.47
, p =
.01
9χ(
1) =
18
.78
, p =
.00
0χ(
1) =
1.1
6, p
= .2
78
Dis
gust
3Sa
dnes
s/Fe
ar/D
isgu
stSa
dnes
sFe
arFe
arSa
dnes
s/Fe
ar
χ (1
) = 5
.36
, p =
.06
8χ(
1) =
7.5
3, p
= .0
06
χ(1
) = 3
2.5
1, p
= .0
00
χ(1
) = 3
3.9
1, p
= .0
00
χ(1
) = 3
.73
, p =
.05
3A
nge
rA
nge
r 1
Surp
rise
/An
ger
Surp
rise
Surp
rise
Hap
pin
ess/
Surp
rise
/A
nge
rSu
rpri
se/A
nge
r
χ(
1) =
2.5
9, p
= .1
08
χ(1
) = 4
.69
, p =
.03
0χ(
1) =
4.5
7, p
= .0
33
χ(1
) = 2
.29
, p =
.31
8χ(
1) =
.85
, p =
.35
7A
nge
r 2
——
—D
isgu
st/A
nge
r—
χ(
1) =
.58
, p =
.44
6
An
ger
3—
—Fe
ar/A
nge
r
χ(
1) =
2.6
7, p
= .1
02
Not
e. “
corr
ect
hit”
: the
inte
nded
em
otio
n w
as r
ecog
nize
d co
rrec
tly (
Chi
-squ
are
resu
lts n
ot in
clud
ed),
“fal
se h
it” =
If p
< .0
5, t
he e
xam
ple
was
cla
ssifi
ed a
s on
e di
stin
ct e
mot
ion
diffe
rent
from
the
in-
tend
ed e
mot
ion
(e.g
., Su
rpris
e 2
was
mis
take
n fo
r “H
appi
ness
”), “
conf
usio
n” =
if p
> .0
5 m
ore
than
one
em
otio
n w
as in
dica
ted
inst
ead
of t
he in
dent
ed e
mot
ion
(e.g
., Fe
ar 3
was
cla
ssifi
ed a
s ei
ther
“fe
ar”
or “
disg
ust”
or
“ang
er”
likew
ise)
.
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Argstatter 11
Figure 2. “Emotion recognition profile” by nation (identification accuracy in percentage of correct answers).
Figure 3. Correct hits by example and musical background (musicians vs. general population).
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12 Psychology of Music
Discussion
The primary aims of this study were to investigate two main issues (1) are emotions universally perceivable in music unknown to listeners with different cultural backgrounds? and (2) are there music-specific similarities and differences across cultural boundaries?
Our results give evidence for both issues under question: generally, the musically encoded basic emotions were classified as the intended emotion well above chance level, independently of the cultural origin, though a detailed analysis disentangled a more complex pattern and could thus confirm cultural specifics.
In-group advantage
On a cultural level, cultural proximity led to similar emotional classification results, that is, the two West European (Germany and Norway) samples and the two Asian (Korea and Indonesia) samples achieved similar recognition patterns while the European participants outperformed the Asian participants. This phenomenon is known as in-group advantage: emotional cues (e.g., faces or vocal stimuli) are better recognized if the stimuli and the participants stem from the same culture (Elfenbein & Ambady, 2002). For example, monolingual speakers of Argentine Spanish performed significantly better in an emotion detection test in their native language than in foreign languages (Pell, Monetta, Paulmann, & Kotz, 2009). There was evidence for an in-group advantage in our investigation, whereby emotions are recognized more accurately when they are both expressed and perceived by members of the same regional group (Western European items and Western European Participants).
Data from a meta-analysis on emotion recognition by European versus Asian samples revealed that “judgments made by participants from the same cultural group as the posers were an average of 13.4% (SD = 9.5%) more accurate than cross-cultural judgments” (Elfenbein & Ambady, 2002). In our analysis, the European samples outperformed the Asian samples by 16.4% (SD = 14.5%) and thus confirmed the typical in-group advantage.
Emotional quality
The universal ability to detect emotional quality in musical pieces seems to be restricted to cer-tain emotional categories. In addition, accuracy of emotion classification (i.e., percentage of correct hits) varied considerably depending on the emotion category. This finding indicates that some of the musical stimuli used in this study seem to be more prototypical than others. Each emotional category had both significant cross-cultural accuracy as well as a significant in-group advantage.
As expected, happiness and sadness were the most distinct emotions. Two “happy” (Happiness 2, Happiness 3), and two “sad” (Sadness 2, Sadness 3) items were unambiguously classified cross-culturally. This fact has been shown in nearly every investigation concerning musically encoded emotions (Balkwill et al., 2004; Fritz et al., 2009; Juslin, 1997; Laukka et al., 2013; Scherer, 2004; Yong & McBride-Chang, 2007).
“Surprise” was the emotion most difficult to decode cross-culturally. Often it was confused with “happiness”, possibly due to similar musical features. The negative emotions “fear”, “disgust” and “anger” revealed a broad range of confusions. Examples resembling “fear” were mainly categorized as “disgust” or “anger” but also “surprise”, while examples repre-senting “disgust” were classified as “fear” or “anger” but also “sadness”. Examples expressing “anger” achieved a broad range of confusions except for not being assigned as “sadness” interculturally.
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Argstatter 13
For both the West European as well as the Korean participants, for all emotions but “sur-prise,” at least one most distinctive, pancultural item could be identified. The Indonesian par-ticipants could identify “happiness” and “sadness” only; for the remaining emotions no single example was instantly recognizable, and Disgust 2 was significantly cross-classified as “anger”.
Musical training/musical background
Musical training influences the accuracy of emotional denotation (Morrison, Demorest, Aylward, Cramer, & Maravilla, 2003); (Lima & Castro, 2011; Park et al., 2014). In our samples, the trained musicians did achieve a slight though significant predominance independent from the cultural background. These differences are due to differences in single items and do not represent a general pattern.
Korean participants outperformed the Indonesian participants – one reason could be the more profound musical training in the Korean educational system. Nearly two thirds of all preschool age children are enrolled in preschool or child care centres in South Korea involving early musical education (Clarke-Stewart, Lee, Allhusen, Kim, & McDowell, 2006). Most pre-ferred musical genres used in preschool education are (Korean) Pop-music and (Western) clas-sical music (Lee, 2009). Korean music has been heavily affected by Western influences (Byeon Jiyeon, 2004), most strikingly represented by Korean pop (K-Pop). This genre incorporates Western popular genres like rap, rock and techno house (Hartong, 2006) and is the most popu-lar musical genre in Korea. The Korean participants are thus exposed to the underlying musical principles of the Western musical pieces from an early age leading to an enhanced emotion recognition ability compared to the Indonesians (Yong & McBride-Chang, 2007).
Indonesia’s culture is extremely diverse since the archipelago consists of about 17,000 islands with approximately 300 ethnic groups. Main influences are the traditional gamelan music, and – depending on the cultural background of the different geographical regions – Western arts (especially from the Portuguese colonialists and from North America) as well as Hindustani (Indian), Arab (based on Islamic traditions) and Malay musical patterns (Anderson & Campbell, 2010). Since these musical styles differ greatly from Western music, this different enculturation might be the reason for the different emotional recognition patterns.
Language
Some of the emotional category labels might be disparate in the different translations due to diverse cultural connotations (Adiwimarta, 1997; Hǒ, 2003). The term “surprise” is ambigu-ous itself and has many different connotations. The chosen translations (Indonesian: terkejut = “startled, shocked” and kaget = “upset, frightened, startled, staggered” and Korean 놀람 (“nol-lam”) = “alarm, amazement, astonishment, dismay, marvel”) have a negative touch resulting in high confusion rates for “Surprise 1” with disgust (Korea) and anger (Indonesia). A prototype analysis of emotion concepts revealed five emotional categories in the Indonesian language (Shaver, Murdaya, & Fraley, 2001): inta (love), senang (happiness), marah (anger), kawatir/takut (anxiety/fear), and sedih (sadness). “Surprise” does not form a distinct emotion category in the Indonesian language and thus is a very untypical word for Indonesian.
Another very difficult emotion seems to be “disgust”. Disgust is not an inherited instinct but rather shaped in the course of cultural socialization, therefore it might be extremely difficult to find an international musical “code” for this very complex emotional category. In the Indonesian language, the emotional category “disgust” is not among the five top-level emotional catego-ries, but is allocated to the category “anger”. This explains the below chance performance of
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14 Psychology of Music
the Indonesian sample for the emotion category “disgust”. Most striking was the high confu-sion rate for Disgust 2 which was significantly allocated to “anger” in the Indonesian sample.
Comparison to other channels
Stimuli from static channels (such as pictures or photographs) are far easier to decode than stimuli from dynamic channels (such as vocal expression or video tapes of emotions) (Elfenbein & Ambady, 2002; Scherer, 2004). One reason seems to be that dynamic stimuli are much more complex and less prototypical than static stimuli. Therefore it is not surprising that the accu-racy rate of musically decoded emotions is below the expected accuracy level obtained through static (mostly visual) stimuli.
Methodological improvement/shortcomings
We decided to use a forced-choice response format following the notion of emotional categories rather than the dimensional approach (valence/arousal). Due to the cross-cultural language entanglements in particular, a response format which is language independent was highly desirable. Nonverbal stimuli require nonverbal responses to minimize the cultural aspects.
The study has an unbalanced design; that is, West European participants did not judge emo-tions expressed by members of the Asian group. Musical samples from Korea are already recorded and data will be presented in a future paper.
Conclusion
Overall, there seems to be evidence for a pancultural musical sentience, but the universal ability to detect emotional quality in musical pieces in our study was restricted to the categories “happy” and “sad”. A clear in-group advantage has been shown, especially for emotions which are untypical for musical expression (such as “disgust”). The results are qualified by the out-come measurement procedure since emotional category labels are language-based and rein-force the cultural diversity.
Acknowledgements
My special thanks go to Dr. Christine Mohn (Olso, Norway), Mihyun Seo and Sookyeong Park (Chonnam 전남, South Korea), Amelia Delphina Kho (Jakarta, Indonesia) for their contribution to intercultural data collection and to Friedrich-Wilhelm Wilker (University of Applied Sciences Heidelberg) for assistance dur-ing data acquisition.
Funding
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
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Argstatter 17
Appendix 1. Solution sheet.
Example Intended emotion
example 1 fearexample 2 happinessexample 3 sadnessexample 4 disgustexample 5 angerexample 6 surpriseexample 7 sadnessexample 8 disgustexample 9 fearexample 10 angerexample 11 surpriseexample 12 happinessexample 13 surpriseexample 14 disgustexample 15 fearexample 16 happinessexample 17 angerexample 18 sadness
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