Music Communicates Social Emotions: Evidence from 750 music excerpts

Music Communicates Social Emotions: Evidence from 750 music excerpts | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Music Communicates Social Emotions: Evidence from 750 music excerpts Elliot Pring, Kirk N. Olsen, Anthony E. D. Mobbs, William Forde Thompson This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4115109/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Humans perceive a range of basic emotional connotations from music, such as joy, sadness, and fear, which can be decoded from structural characteristics of music, such as rhythm, harmony, and timbre. However, despite theory and evidence that music has multiple social functions, little research has examined whether music conveys emotions specifically associated with social status and social connection. This investigation aimed to determine whether the social emotions of dominance and affiliation are perceived in music and whether structural features of music predict social emotions, just as they predict basic emotions. Participants ( n = 1513) listened to subsets of 750 music excerpts and provided ratings of energy arousal, tension arousal, valence, dominance, and affiliation. Ratings were modelled based on ten structural features of music. Dominance and affiliation were readily perceived in music and predicted by structural features including rhythm, harmony, dynamics, and timbre. In turn, energy arousal, tension arousal and valence were also predicted by musical structure. We discuss the results in view of current models of music and emotion and propose research to illuminate the significance of social emotions in music. Biological sciences/Psychology Biological sciences/Psychology/Human behaviour Basic emotions Music perception Social emotions Dominance Affiliation Figures Figure 1 Figure 2 Introduction Music has the capacity to express and evoke a wide range of emotions ([1-3]Cowen et al., 2020; Juslin, 2019; Juslin & Laukka, 2004). Listeners can identify emotional connotations after only a few seconds of music (“perceived emotion”), whereas it takes longer to induce an emotional experience (“felt emotion”) ([4-6] Day & Thompson, 2019; Gabrielsson & Juslin, 2003; Juslin, 2013). In this investigation, we describe the results of an international crowd-sourcing survey in which 1563 participants provided emotion ratings across 750 music excerpts. Regression and principal-component analyses were conducted to identify optimal models of emotional responses elicited by musical attributes, including models of two social emotions that have not been investigated with respect to music. Emotional responses to music are typically assessed within the frameworks of discrete or dimensional models. Discrete models employ emotion labels such as happiness, sadness, anger, and disgust, which are sometimes grouped into types such as social emotions (e.g., feelings of connection, empowerment; [7] Sznycer et al, 2021), moral emotions (e.g., remorse, righteous indignation; [8] Tangney et al., 2007), aesthetic emotions (e.g., awe, transcendence), achievement-related emotions (e.g., pride, disappointment; [9] Camacho-Morles et al., 2021), and epistemic emotions (e.g., curiosity, doubt; [10] Vogl et al., 2021). Such groupings acknowledge that emotions are tethered to the causal and contextual circumstances associated with the feeling state ([11] Thompson, et al., 2023). Dimensional models depict emotions as points on underlying affective continua, such as the degree of energy experienced in the emotion ([12] Eerola & Vuoskoski, 2011). Dimensional models capture complex emotions that vary in intensity but are difficult to label ([13] Y. H. Yang & Chen, 2012). For example, death metal fans may perceive complex mixtures of tension, empowerment, energy, and joy in their preferred music, making it difficult to label ([14] Olsen et al., 2023; [15] Thompson et al., 2019). The circumplex model maps emotion onto two dimensions of affect: arousal and valence ([16] Cohrdes et al., 2018; [17] Russell, 1980; [18] Russell & Barrett, 1999). In some models, arousal is further divided into two forms of activation: energy arousal (EA), which ranges from tired to energetic, and tension arousal (TA), which ranges from calm to nervous ([19] Ilie & Thompson, 2006; [20] Thayer, 1989). The emotions expressed by music are encoded in musical structure (Juslin & Lindström, 2010), including mode, tempo, dynamics (loudness), pitch register, articulation, and timbre (Carr et al., 2023; Eerola et al., 2013; Panda et al., 2015). For example, fast tempo, consonance, major mode, and bright timbre are associated with happiness, high arousal, and positive valence (Bresin & Friberg, 2011; Juslin & Lindström, 2010). Research on music and emotion has focused on basic emotions such as joy, sadness, fear, and anger (Juslin, 2019). Such emotions reflect abstract feeling states that may be experienced by an individual across a range of contexts. However, music is often experienced with other people as part of cultural rituals and ceremonies, and the social functions of music are emphasised across disciplines (Juslin, 2019). Music can trigger a sense of social inclusion for fans (social affiliation) or social exclusion for non-fans (Juslin, 2019; [14] Olsen, et al., 2023). Music can also convey feelings of social status, ranging from empowerment (dominance) to submissiveness and disempowerment ([15] Thompson et al., 2019; [11] Thompson et al., 2023). Dominance and affiliation are core social emotions (Hareli et al., 2016; Hess et al., 2000; Mobbs, 2020; van Kleef & Côté, 2022), and represent two axes of a social emotion space (Mobbs, 2020). Dominance ranges from feelings of leadership and empowerment over others to feelings of subordination and powerlessness. Affiliation ranges from feelings of social connectedness to feelings of isolation, loneliness, and outsiderness. Functional neuroimaging has revealed distinct neural pathways for dominance and affiliation (Quirin et al., 2013). In addition, dominance has previously been proposed as a dimensional measure of affect in conjunction with the circumplex model (Mehrabian, 1996; Mehrabian & Russell, 1977; Russell, 1978). The current research examined the capacity for music to communicate social emotions in 750 musical samples across multiple genres and surveyed over 1500 listeners. Bipolar scales measuring dominance and affiliation (Mobbs, 2020) were used to assess the degree to which these emotions are perceived. Music analysis software and statistical modelling were used to model how various musical attributes predict the emotional meaning that is perceived by listeners, including the social emotions of dominance and affiliation and other emotions such as joy, sadness, and fear. The study also examined whether musical attributes predict responses to dimensional models of emotion. Musical stimuli were restricted to 5-second excerpts to enable the recruitment of a large sample of participants rating multiple music excerpts in a single testing session. Sixteen structural elements such as rhythm, dynamics, harmony, and timbre were extracted from samples (e.g., Brinker et al., 2012; Gingras et al., 2014; Grekow, 2018). Modeling determined whether structural elements predict perceived EA, TA, valence, dominance, and affiliation in music. Several a priori hypotheses were established. First, we predicted that basic and social emotions (dominance and affiliation) should be perceived in music samples, as reflected in mean ratings (H1). Second, we anticipated that basic and social emotions should be predicted by structural element such as rhythm, timbre, and pitch height (H2)(see [19] Ilie & Thompson, 2006; Thompson, Schellenberg & Husain, 2001). Results Descriptive Statistics Descriptive statistics are shown in Table 1 . Given the large sample sizes, the normality of sampling distributions was assumed for all variables. Table 1 Means, Standard Deviations, Skewness, and Kurtosis of Dependent Variables Variable N M SD Skewness Kurtosis EA 749 5.06 0.69 − .30 2.08 TA 749 4.26 0.70 − .03 2.32 Valence 749 5.05 0.44 − .47 2.94 Dominance 749 4.69 0.56 − .12 2.40 Affiliation 749 4.84 0.44 − .02 2.84 Note . N = music stimuli; EA = energy arousal; TA = tension arousal. ------------------------------------------------------- Table 1 About Here ------------------------------------------------------- Correlations Between Rating Measures Bivariate correlations were calculated. Significant correlations between musical attributes and EA, TA, dominance, and affiliation were moderate in strength. Correlations between musical attributes and valence were weak or non-significant. Associations Between Dependent Variables Correlations between the emotion dimensions (see Table 2 ) showed dominance was highly correlated with RMSE and tension, suggesting that this social emotion might be derived from the arousal levels perceived in music excerpts. Table 2 Correlations Between Dependent Variables Variable EA TA Valence Dominance Affiliation EA - TA .66** - Valence − .08* − .56** - Dominance .80** .82** − .29** - Affiliation .62** .12** .44** .39** - *p < .05 ** p < .01 ------------------------------------------------------- Table 2 About Here ------------------------------------------------------- Music Feature Reduction: Addressing Collinearity with Principal Component Analysis Principal component analysis (PCA) was used to assess multicollinearity and variable significance (see Table 3 ). Variables with a loading below 0.5 were excluded from analysis in respect of each musical attribute, and only the most significant five factors were retained for each musical attribute. These criteria excluded six music features from further analysis and retained ten features. The ten features can be grouped into five categories as follows: (1) timbre (spectral centroid and brightness), (2) harmony (dissonance and mode), (3) dynamics (ZCR and RMSE), (4) rhythm (tempo, pulse clarity, and event density), and (5) Pitch. Table 3 Component Loadings for Musical Attributes Variable 1 2 3 4 Pulse clarity .36 .28 .52 .18 RMSE .17 .86 − .27 − .03 Brightness .92 − .14 .10 − .06 Dissonance .20 .82 − .10 − .08 Mode .10 − .06 − .00 .85 Spectral centroid .97 − .07 .07 − .03 Spectral rolloff .95 − .01 .00 − .01 Mean pitch .31 .05 .26 − .44 Event density .23 .49 .49 .20 Spectral spread .92 .05 − .12 .01 Spectral skewness − .88 .05 .30 − .04 Spectral kurtosis − .59 .04 .50 − .09 Spectral flatness .80 − .06 − .12 .04 Spectral entropy .94 − .08 .04 − .04 ZCR .72 − .26 .37 − .06 Note . The highest loadings for each component have been emboldened. ------------------------------------------------------- Table 3 About Here ------------------------------------------------------- Models of Music Features and Perceived Basic Emotions Regression model: Energy arousal There were 749 observations (music stimuli scored according to their musical attributes and perceived affect). The Shapiro-Wilk test indicated that residuals were normally distributed ( p ’s > .05), and inspection of the plot of residuals against fitted values indicates the assumption of linearity was met. However, there was noticeable clustering towards the centre of the residuals plot. White’s heteroscedasticity test indicated that the homoscedasticity assumption was not violated ( p = .129). Therefore, inference testing was conducted with a multiple least-squares regression model. The model assessing whether musical attributes are associated with perceived expression of energy arousal in music was significant, F (10, 738) = 342.66, p < .001), and explained 82.28% of the variance in the perception of EA in music. There were three statistically significant relationships observed: EA and tempo (η p 2 = .636), EA and pulse clarity (η p 2 = .008), and EA and brightness (η p 2 = .013) (see Table 4 ). These relationships were all positive, indicating that as tempo, pulse clarity, or brightness increase, so does the relative perceived magnitude of EA in music when all other variables in the model are held constant. While pulse clarity and brightness were highly significant predictors of perceptions of EA in music, the effect sizes of both predictors were small. EA is closely linked to rhythmic and timbral elements, with the focal point being pulse tempo. Table 4 Regression Statistics for Energy Arousal Model Variable Coef. SE t p 95% CI Tempo 0.78 0.23 34.04 < .001 [0.74, 0.83] Pulse clarity 0.19 0.06 3.02 .003 [0.07, 0.32] RMSE -0.16 0.57 -0.28 .782 [-1.28, 0.96] Spectral centroid -0.00 0.00 -0.40 .686 [0.00, 0.00] Brightness 0.54 0.17 3.15 .002 [0.20, 0.88] Dissonance 0.00 0.00 0.58 .565 [0.00, 0.00] Mode 0.13 0.08 1.74 .082 [-0.02, 0.01] Event density -0.01 0.01 -1.24 .217 [-0.03, 0.01] Mean pitch 0.00 0.00 0.64 .522 [0.00, 0.00] ZCR 0.00 0.00 -1.29 .197 [0.00, 0.00] ------------------------------------------------------- Table 4 About Here ------------------------------------------------------- Regression model: Tension arousal The Shapiro-Wilk test confirmed the assumption of normality of residuals was met ( p > .05) The assumption of linearity was also met after inspection of the residuals and fitted values plot. Slight clustering towards the centre of the residuals against the plot of the fitted values was observed; however, White’s test indicated the assumption of homoscedasticity was not violated ( p = .381). Therefore, multiple regression was deemed appropriate. The general model determining if musical attributes are associated with the perception of TA in music was significant F (10, 738) = 102.01, p < .001, explaining 58.02% of the variance in perceptions of TA in music. Table 5 illustrates four significant negative relationships in the model: TA and RMSE (η p 2 = .005), TA and pulse clarity (η p 2 = .027), TA and spectral centroid (η p 2 = .022), and TA and mode (η p 2 = .006). These relationships indicate that the perception of TA in music decreases in magnitude if the pulse of the music is more discernible or if there is more overall energy in the excerpt. Moreover, perceived TA in music decreases in magnitude when the spectral centroid of mass frequency increases or when the modality value is more positive, holding all other musical attributes constant. Table 5 Regression Statistics for Tension Arousal Model Variable Coef. SE t p 95% CI Tempo 0.71 0.36 19.67 < .001 [0.64, 0.78] Pulse clarity -0.46 0.10 -4.56 < .001 [-0.65, -0.26] RMSE -1.78 0.89 -2.00 .046 [-3.52, -0.03] Spectral centroid 0.00 0.00 -4.09 < .001 [0.00, 0.00] Brightness 0.78 0.27 2.90 .004 [0.25, 1.31] Dissonance 0.00 0.00 5.00 < .001 [0.00, 0.00] Mode -0.26 0.12 -2.13 . 033 [-0.49, -0.02] Event density 0.01 0.01 1.09 .277 [-0.01, 0.04] Mean pitch 0.00 0.00 -0.13 .896 [0.00, 0.00] ZCR 0.00 0.00 1.66 .098 [0.00, 0.00] ------------------------------------------------------- Table 5 About Here ------------------------------------------------------- There were three significant positive relationships in this model: TA and tempo (η p 2 = .344), TA and brightness (η p 2 = .011), and TA and dissonance (η p 2 = .032) (see Table 5 ). These relationships indicate that the perception of TA in music increases in magnitude when either the speed of the musical beat increases, the amount of high-frequency energy in the music increases, or the amount of dissonance present in the music excerpt increases. These positive and negative relationships indicate that perceptions of TA in music are influenced by rhythmic (tempo and pulse clarity), harmonic (mode and dissonance), timbral (spectral centroid and brightness), and dynamic (RMSE) musicological elements. All significant music predictors had small effect sizes except for tempo. Regression model: Valence This model used all 749 observations derived from the relevant music excerpts included in this study. Residuals for the overall model were normally distributed according to the Shapiro-Wilk test ( p > .05). Furthermore, the assumption of linearity was met after inspection of the plot of the residual/fitted values. Slight clustering towards the centre of the residuals was observed; however, White’s test indicated the assumption of homoscedasticity was not violated ( p = .109). The general model determining if musical attributes are associated with perceptions of valence in music was significant F (10, 738) = 11.42, p < .001, explaining 13.40% of the variance in perceived impressions of valence in music. Table 6 shows three significant positive relationships: valence and pulse clarity (η p 2 = .013), valence and spectral centroid (η p 2 = .028) and valence and mode (η p 2 = .010). Therefore, positive valence was associated with a more discernible pulse in the music, a higher spectral centroid and a more positive value for modality (indicating that the music excerpt was more likely to be in a major key), holding all other variables constant. Table 6 Regression Statistics for Valence Model Variable Coef. SE t p 95% CI Tempo -0.06 0.03 -1.91 .057 [-0.13, 0.00] Pulse clarity 0.29 0.09 3.20 .001 [0.11, 0.47] RMSE -0.13 0.80 -0.16 .869 [-1.71, 1.44] Spectral centroid 0.00 0.00 4.62 < .001 [0.00, 0.00] Brightness -0.94 0.24 -3.88 < .001 [-1.42, -0.47] Dissonance 0.00 0.00 -2.97 .003 [0.00, 0.00] Mode 0.28 0.11 2.59 . 010 [0.07, 0.50] Event density -0.03 0.01 -2.77 . 006 [-0.06, -0.01] Mean pitch 0.00 0.00 -0.17 0.864 [0.00, 0.00] ZCR 0.00 0.00 -1.82 .070 [0.00, 0.00 ------------------------------------------------------- Table 6 About Here ------------------------------------------------------- The model revealed three significant negative relationships: valence and brightness (η p 2 = .020), valence and dissonance (η p 2 = .012) and valence and event density (η p 2 = .010) (see Table 6 ). These relationships indicate that a one-unit increase in either brightness, event density, or dissonance was associated with decreased pleasantness. That is, less pleasant valence ratings were related to increased high-frequency energy, higher levels of dissonance, and a higher number of musical events in a segment. Overall, perceptions of valence in music were influenced by multiple broad musicological concepts such as rhythm (event density and pulse clarity), harmony (mode and dissonance), and timbre (brightness and spectral centroid). Models of Musical Attributes and Perceived Social Emotions Regression model: Dominance The residuals were normally distributed according to the Shapiro-Wilk test ( p s > .05), and the assumption of linearity was met after inspection of the residuals against the plot of the fitted values. There was very slight clustering towards the centre of the residual plot. However, White’s test indicated that the assumption of homoscedasticity was not violated ( p = .142). The model for musical attributes influencing the perception of dominance in music was significant, F (10, 738) = 170.35, p < .001, explaining 69.77% of the variance in perceptions of dominance in music. Three significant positive associations were observed: dominance and tempo (η p 2 = .484), dominance and brightness (η p 2 = .018) and dominance and dissonance (η p 2 = .026) (see Table 7 ). That is, higher ratings of dominance were assigned to music excerpts that were faster in tempo, had greater levels of high-frequency energy, and had higher levels of dissonance. Table 7 Regression Statistics for Dominance Model Variable Coef. SE t p 95% CI Tempo 0.65 0.02 26.31 < .001 [0.60, 0.70] Pulse clarity -0.24 0.07 -3.55 < .001 [-0.38, -0.11] RMSE -1.06 0.61 -1.74 .083 [-2.26, 0.14] Spectral centroid 0.00 0.00 -3.68 < .001 [0.00, 0.00] Brightness 0.67 0.18 3.64 < .001 [0.31, 1.03] Dissonance 0.00 0.00 4.44 < .001 [0.00, 0.00] Mode 0.00 0.08 0.05 .964 [-0.16, 0.17] Event density -0.01 0.01 -0.95 .343 [0.00, 0.00] Mean pitch 0.00 0.00 1.68 .094 [0.00, 0.00] ZCR 0.00 0.00 − .033 .743 [0.00, 0.00 ------------------------------------------------------- Table 7 About Here ------------------------------------------------------- The model also revealed two significant negative relationships between perceptions of dominance and pulse clarity (η p 2 = .017) and perceptions of dominance and spectral centroid (η p 2 = .018) (see Table 7 ). Therefore, when the pulse of the music was more discernible or the spectral centroid value was higher, music excerpts were likely to be perceived as less dominant. Overall, the perception of dominance was influenced by rhythmic (tempo and pulse clarity), harmonic (dissonance) and timbral (brightness and spectral centroid) features of music. While these effects were found to be highly significant, the effect sizes were small, except for tempo. Regression model: Affiliation This model included all 749 music excerpt observations. The assumption of normality of residuals was not met according to the Shapiro-Wilk test ( p < .05). After inspection of a residual against fitted values plot, the assumption of linearity was met; however, the assumption of constant variance may be violated due to moderate clustering towards the middle of the plot. This was confirmed by White’s test, indicating that the assumption of homoscedasticity was violated ( p = .032). Despite this potential caveat, we continued with a multiple regression model due to the large sample size and the robust nature of multiple linear regression. The overall model explaining the relationship between musical attributes and perceived affiliation in music was significant, F (10, 738) = 47.53, p < .001, explaining 39.18% of perceived affiliation in music. As seen in Table 8 , tempo (η p 2 = .163, pulse clarity (η p 2 = .027), spectral centroid (η p 2 = .017), and mode (η p 2 = .013) all had a significant positive relationship with the perception of affiliation. Thus, a one-unit increase in any of these predictors while other variables are held constant results in more social perceptions of affiliation. Music excerpts that were higher in tempo, had a more discernible pulse, had a higher spectral centroid value, or a higher likelihood of being in a major key were more likely to be interpreted as prosocial. Table 8 Regression Statistics for Affiliation Model Variable Coef. SE t p 95% CI Tempo 0.33 0.03 12.01 < .001 [0.27, 0.38] Pulse clarity 0.34 0.08 4.52 < .001 [0.19, 0.49] RMSE 1.24 0.67 1.85 .064 [-0.07, 2.56] Spectral centroid 0.00 0.00 3.54 < .001 [0.00, 0.00] Brightness -0.37 0.20 -1.82 .069 [-0.77, 0.03] Dissonance 0.00 0.00 -3.03 .003 [0.00, 0.00] Mode 0.28 0.09 3.08 .002 [0.10, 0.46] Event density -0.04 0.01 -3.58 < .001 [-0.06, 0.02] Mean pitch 0.00 0.00 -0.11 .910 [0.00, 0.00] ZCR 0.00 0.00 -2.11 .035 [0.00, 0.00] ------------------------------------------------------- Table 8 About Here ------------------------------------------------------- Dissonance (η p 2 = .012) and event density (η p 2 = .017) had a significant negative association with the perception of affiliation (see Table 8 ). When controlling for other variables, a one-unit increase in either of these predictors results in decreased perceptions of social affiliation in music. In turn, music excerpts with greater amounts of dissonance or several musical events per segment were more likely to be perceived as antisocial. However, according to the coefficients, confidence intervals, and effect size, the effect of dissonance and spectral centroid were small. Effect sizes for all other significant predictors, excluding tempo, were small. Overall, the model suggests that the perception of affiliation in music is related to rhythmic (tempo, event density and pulse clarity), harmonic (dissonance and mode), and timbral (spectral centroid) features of the music. Discussion This investigation considered whether social emotions (dominance and affiliation) and basic emotions are communicated by brief music excerpts across a large corpus of genres and whether musical attributes of excerpts (e.g. rhythm, timbre, harmony) predict the emotions perceived. Previous research has confirmed that individuals perceive dominant or affiliative social interactions between musicians (Aucouturier & Canonne, 2017 ) but not whether social emotions are perceived in music across genres. Figure 1 provides a framework for interpreting the findings. The figure displays the music features identified in the corpus of music samples and their association with the perception of basic and social emotions. As depicted in the figure and supported in regression analyses, multiple musical attributes converge to predict specific emotional qualities, with different sets of attributes combining to predict different emotional qualities. These different interconnections, along with evidence for the communication of basic and social emotions, will next be discussed with respect to existing models of music and emotion. ------------------------------------------------------- Figure 1 About Here ------------------------------------------------------- Multiple regression models revealed that ratings of dominance and affiliation were significantly predicted by music variables, with a higher percentage of variance explained by psychoacoustic models of dominance (69.77%) than affiliation (39.18%). Therefore, the hypothesis that musical attributes can be grouped and classified according to their impact on ratings of social emotions was supported. This effect was stronger for dominance: musical attributes explained 30.59% more variance in the dominance model than in the affiliation model. Thus, research is needed to elucidate the sources of variance in social emotions that are not explained by musical attributes, especially in the case of affiliation. Bivariate correlations revealed that perceived dominance significantly correlated to all music predictors (except for modality), before subsequent music feature reduction via PCA. Moreover, perceived affiliation was correlated with all musical attributes except for dissonance before subsequent music feature reduction. Many structural features of music are associated with the social dimensions of affiliation and dominance. The magnitude of perceived dominance in music was related to rhythmic (tempo and pulse clarity), harmonic (dissonance), and timbral (brightness and spectral centroid) music features. The magnitude of perceived affiliation in music was related to somewhat different musical predictors but also included varying rhythmic (tempo, pulse clarity, and event density), harmonic (dissonance and modality), and timbral (spectral centroid) features. Given violations of the assumption of normality and homoscedasticity in our analysis, further research on such models is warranted. Each dimensional model of perceived affect in music was highly significant ( p < .001), and dimensional measures of affect were significantly correlated with each other. This observation is consistent with previous research that ratings of valence and tension are negatively correlated ( r = − .70), whereas ratings of energy and tension are positively correlated ( r = .57) ([12] Eerola & Vuoskoski, 2011; Schimmack & Grob, 2000 ). The explained variance in the EA and TA models was larger than the valence model. This finding is consistent with previous evidence that musical attributes are more reliably associated with arousal rather than valence ratings (Tan et al., 2019 ; X. Yang et al., 2018 ). Quite possibly, perceived pleasantness in music is influenced by factors not easily modelled by musical attributes, such as familiarity with pieces or genres, personal associations, and visual imagery (Juslin & Västfjäll, 2008 ). Figure 1 illustrates significant associations. Tempo was the strongest predictor of perceived EA and TA. The effect size was large for both EA and TA, consistent with previous research on the association between tempo and arousal (Brinker et al., 2012 ; Husain et al., 2002 ). Moreover, music excerpts with a more discernible beat (higher pulse clarity) were associated with increased energy but decreased tension. Event density was not related to either dimension. Musical attributes that constitute harmony (mode and dissonance) were significantly associated with perceptions of TA. Music with high levels of dissonance or in a minor key yielded high tension ratings by listeners (note that the coefficient of dissonance was small and rounded to 0.00. MIRtoolbox extracts values for dissonance that range from 3-1390, so coefficients for predictions of ratings on a 7-point scale are small). Increased RMSE (loudness) was significantly associated with decreased perceptions of tension in music. However, the zero-crossing rate was not a significant predictor of arousal. As predicted, higher magnitudes of perceived energy music were significantly predicted by increased brightness. This supports previous findings of the positive relationship between brightness and arousal (Lartillot, et al., 2008 ). Furthermore, tension was also significantly predicted by spectral centroid, revealing that the higher the spectral distribution centroid of a sample, the less likely it will express tension (note that spectral centroid displays similarly small coefficient values to those for dissonance (rounded to 0.00), as spectral centroid values range from 405–6076). Two of the three musical attributes related to rhythm were significant predictors of perceived pleasantness in music. Tempo did not predict perceived pleasantness in music, which is interesting given that emotions such as happiness and sadness have previously been found to be associated with varying levels of tempo (Bresin & Friberg, 2011 ; Gabrielsson, 2002 ; Vieillard et al., 2008 , but see Husain et al., 2002 ). One explanation for this finding is that the dimensional rating scale used in this study was a less sensitive measure of perceived valence as it pertains to musical tempo than models that categorically describe varying valence levels (e.g. happy, joyful, or sad). Pulse clarity and event density were important for predicting perceived pleasantness in music, albeit to a smaller effect size. The directionality of these associations suggests that music with a more discernible beat (pulse clarity) is likely to increase perceived pleasantness for the listener. In contrast, music that contains more notes per musical moment (event density) is likely to decrease perceived pleasantness for the listener. Both structural features related to harmony (mode and dissonance) predicted perceived pleasantness in music. These relationships reveal that musical excerpts in a major mode and with less harsh sounds are more likely to be perceived as pleasant (Juslin & Lindström, 2010 ; Laurier et al., 2009 ). In contrast to previous findings, the mean pitch was not a significant predictor of perceived pleasantness in music (Juslin & Lindström, 2010 ). One interpretation is that the MIRtoolbox could not extract reliable measures of mean pitch from such brief (5-second) samples, suggesting the need to revisit this predictor based on longer music samples. RMSE (loudness) and zero-crossing rate were not significant predictors of perceived pleasantness in music. This finding can be explained by the standardised sound level of most MP3 files used for audio feature extraction. RMSE values ranged from .03 and .25, with most excerpts returning values between .05 and .15. This difference in loudness between music excerpts is unlikely to affect perceived pleasantness. Musical attributes that constitute timbre (brightness and spectral centroid) were both found to be significant predictors of pleasantness in music. The higher an audio sample’s spectral distribution centroid, the more likely the listener will interpret it as pleasant. Moreover, the lower the high-frequency energy in the audio sample, the more likely it is to be perceived as pleasant. Given the high correlation between dominance and energy , a question arises whether listeners simply equated the two concepts. However, this possibility is unlikely, given that models of dominance differ from those of arousal. Moreover, considerable attention was given to ensuring respondents fully understood that dominance was a social emotion, not merely a feeling of arousal. Models incorporating structural features of music predicted both basic and social emotions. Moreover, musical attributes explain energy, tension, and dominance exceptionally well, whereas such factors predict valence and affiliation less well. This investigation suggests that future research needs to address other mechanisms that may facilitate the perception of valence and affiliation in music. Nevertheless, this investigation contributes new evidence that increases the theoretical scope of emotion perception in music and provides a new approach upon which future research can build. A limitation of this study – and that of Cowan et al., (2020) from which stimuli were drawn – was the brief duration of each stimulus. Presenting brief music samples permitted many excerpts to be presented while avoiding potential participant fatigue (Skowronek et al., 2006 ; Y. H. Yang et al., 2008 ), but it is uncertain how much emotional information is contained within such a short sample. For example, complex emotions such as nostalgia and awe may take longer than 5 seconds to convey in music (Juslin & Västfjäll, 2008 ). The brief duration of stimuli likely restricted the kinds of emotions communicated. A second limitation is that participants were asked to rate all measures of affect and social emotions sequentially. This procedure required that each musical stimulus be continually and retrospectively judged in terms of energy, tension, valence, dominance, and affiliation, which may have been difficult given the small amount of musical information provided. Interestingly, listeners did not appear to have difficulty with the rating scales, and examination of the data indicated that all rating scales were systematically related to musical attributes. These findings suggest that the procedure did not interfere with the quality of the results. Our recruitment strategy also limited the collection of detailed demographic information, except that all participants were older than 18 and from the United States of America. It is uncertain how population parameters such as age, gender, or previous musical experience relate to emotion perception. Such information, though not the purpose of the investigation, could have provided additional insight into the connection between music and emotion and remains an important issue for future research. The finding that social emotions are perceived in music may help to explain the strong motivation to engage with music. The associations identified between social emotions and structural features of music, in turn, have implications for music composition, film scoring and musical theatre. Evidence that dominance and affiliation are perceived in music raises a question of what mechanisms are involved in this effect and how these social emotions are conveyed over extended periods. For example, time-series analysis could be explored to investigate the dynamic properties of dominance and affiliation, perhaps revealing that social connotations of music wax and wane in intensity as music unfolds in time. To conclude, this investigation provides a foundation for future research on social emotions in music, with implications for uses of music for social well-being and intercultural understanding. A full understanding of the relationship between basic and social emotions and how they are conveyed in music represents an exciting goal for future research. Method Ethics statement The Human Research Ethics Committee (HREC) of Macquarie University approved the study and protocol (approval number, #52021954830577). In accordance with guidelines established by the HREC, all participants provided informed consent detailing the purpose of the research, potential risks and benefits, confidentiality measures, and their rights as participants. Consent forms are securely retained in accordance with data management protocols outlined by Macquarie University and relevant Australian regulatory bodies. Data collected throughout the study are stored in Macquarie University’s secure servers in accordance with a data management plan to protect the confidentiality and privacy of participants' information. Any personal information obtained from participants is anonymized to ensure confidentiality. Participants Participants were recruited from the crowdsourcing platform Amazon Mechanical Turk (MTurk), with responses from 1563 participants recorded. Participants were based in the United States, were English-speaking, and were at least 18 years old. All participants provided consent after being informed of the aims of the survey. Sampling Procedure Data collection occurred in July 2021. Participants completed the survey for compensation of $ 0.30 USD. To improve quality, MTurk workers were prequalified as residing in the USA, previously completing a minimum of 50 MTurk tasks and achieving a prior minimum MTurk approval rating of 90%. Materials and Measures Stimulus Set Seven hundred and fifty five-second music instrumental excerpts (no spoken words or lyrics included) were gathered and used as test stimuli from an existing database [1]. The corpus of music comprised 16 genres (music metadata): Alternative ( n = 38), Ambient ( n = 95), Classical ( n = 94), Country ( n = 21), EDM ( n = 82), Electronic ( n = 81), Folk ( n = 24), Heavy Metal ( n = 37), Hip-hop ( n = 15), Jazz ( n = 31), Latin ( n = 7), Pop ( n = 64), Rock ( n = 82), R&B ( n = 24), Reggae ( n = 3), and World Music ( n = 52). Randomisation of Stimulus Subsets Presented to Participants From the 750 music excerpts, 50 survey subsets were generated, each containing 15 music excerpts. Each participant was randomly assigned one of the subsets of 15 music excerpts in Qualtrics and responded to all excerpts in the subset to qualify for compensation. Randomisation was constrained in two ways. First, it was evenly distributed across survey subsets from the beginning of data collection, which was accomplished by requiring at least one participant to be allocated to every survey subset and their responses submitted before allocating a second participant to a particular subset. This process was iteratively repeated until all participants were allocated and responses were received. Second, no genre was represented more than three times in any one survey subset, ensuring that participants heard a range of musical genres in their subset. Measures Acoustic Measures MIRtoolbox. Acoustic analyses were performed using the music information retrieval (MIR) toolbox (Lartillot & Toiviainen, 2007) (see Fig. 2 ). ------------------------------------------------------- Figure 2 About Here ------------------------------------------------------- MIR root mean square energy (RMSE). A measure of amplitude (loudness), taken as the global energy of the audio sample, was estimated by taking the square root of the average squared amplitude over a period of time (Lartillot et al., 2008 ). Scores range from 0 to 1, with higher scores indicative of a louder signal in the audio sample. MIR brightness. Brightness measures the energy in an audio sample above a frequency (Juslin, 2000 ; Lartillot et al., 2008 ). Scores range from 0 to 1, with higher scores signalling greater high-frequency energy in the sample. Roughness. Roughness is a measure of global sensory dissonance, corresponding to the “pulsing” phenomenon experienced when multiple sounds slightly deviate in frequency are heard simultaneously (Lartillot et al., 2008 ). Sensory dissonance is estimated by taking the average of all pairs of peaks in an audio sample frequency spectrum (Sethares, 2005 ). Scores range between 3 and 1390, with higher scores indicating increased sensory dissonance and, as a result, more harsher sounds in the audio sample. Rolloff. This measures how much energy in an audio sample is below an energy threshold (Lartillot et al., 2008 ). MIRtoolbox uses a percentage cut-off of .85, as suggested by Tzanetakis & Cook ( 2002 ). Higher values on the rolloff measure indicate that more energy is confined below the threshold. MIR zerocross. The zero-crossing rate measures the amount of noisiness in an audio sample (Tzanetakis & Cook, 2002 ). It is estimated by counting the times the signal crosses the X-axis (zero amplitude) (Banchhor & Khan, 2012 ; Lartillot et al., 2008 ). Higher values indicate a greater zero-crossing rate and a noisier audio sample. MIR mode. A measure audio sample is either major or minor in modality (Lartillot et al., 2008 ). Values closer to 1 indicate audio samples are more likely to be in a major key − 1 suggests a minor key. A value close to 0 indicates that the modality of the audio sample is ambiguous. MIR spectral centroid. A measure of an audio sample’s spectral distribution centroid (Lartillot et al., 2008 ). Spectral centroid is related to brightness, with higher values indicating lower brightness (e.g., an oboe’s spectral centroid is higher than a French horn’s) (McAdams & Giordano, 2015 ). MIR pitch. A measure of mean pitch using autocorrelation to estimate the average frequency of all pairs of peaks in the audio sample (Eerola et al., 2009 ; Lartillot et al., 2008 ). higher values indicate a greater presence of sounds. MIR pulse clarity. Pulse clarity is the musical measure of how easily the listener can identify the underlying beat or metrical pulsation in a given piece of music (Lartillot et al., 2008 ). Scores range from 0 to 1, with higher scores indicating that the listener easily discerns the underlying beat or metrical pulsation. MIR event density. Event density observes the number of events (peaks) occurring in the audio sample and approximates the mean frequency of these events per second (Eerola et al., 2009 ; Lartillot et al., 2008 ). Higher values indicate an increase of notes in each musical moment. MIR spread; skewness; kurtosis; flatness; entropy (spectral properties). These five measures all relate to the spectral dispersion of an audio sample. MIR spread the standard deviation of the spectral distribution. MIR flatness calculates the flatness of data, indicating if the spectral distribution is smooth or peaky. Finally, MIR entropy represents the respective Shannon ( 1948 ) entropy of the spectrum, with higher values signalling more uncertainty and peakiness. Response Measures The circumplex model of affect classifies emotion according to the dimensions of arousal and valence ([17] Russell, 1980), but arousal was further subdivided into energy arousal (EA) and tension arousal (TA) ([12] Eerola & Vuoskoski, 2011; Schimmack & Reisenzein, 2002 ). Two social emotions, dominance and affiliation, were also included (Mobbs, 2020). Thus, the study examined three basic emotions of EA, TA, valence and two social emotions of dominance and affiliation. Participants rated the emotional quality of the music. Each bipolar rating scale ranged from one to seven, with ratings five and above indicating high levels of emotion and ratings of three and below indicating low levels of emotion. Energy arousal ranged from tired to energetic; tension arousal ranged from relaxed to tense; valence ranged from unpleasant to pleasant; dominance ranged from submissive to dominant; and affiliation ranged from antisocial to highly social. Modified Self-Assessment Manikin (SAM). The SAM measures an individual’s pleasure, arousal, and dominance by displaying visual depictions of the different levels of each measure using schematic manikins comparable to emojis (Bradley & Lang, 1994 ). The current study adopted this strategy to explain and measure the dimensions of dominance and affiliation, emphasizing their social meaning. Dominance and affiliation were conceptualised by two scales containing five graphical figures, each representing a 7-point response scale for each dimension. Dominance ranged from − 3 (illustrated by a small vulnerable figure in a large open space) to 3 (depicted by a large figure dominating the surrounding space). Affiliation ranged from − 3 (illustrated by an isolated figure from a group) to 3 (depicted by the same figure now in the middle of the group). Tempo. Tempo ratings on a 7-point scale (7 = fast) were included to assess participants’ perceptions of how fast the music excerpts were. This measure was included to corroborate the MIRtoolbox extraction of tempo, given the samples were so short. Procedure The survey was published on MTurk redirected to a Qualtrics survey. Participants read a project description and provided informed consent before completing the survey. Participants were notified that financial compensation would only be given to participants who responded to all 15 audio samples in the survey. The Macquarie University approved this research. Participants were instructed to remember that the study investigated the emotions we perceive in music, not emotions that might be induced by music. This distinction is important as music can have emotional connotations (e.g., sadness). Moreover, the nature of each bipolar response scale was explained with responses on the far left and far right indicating an intense perception of the dimension in question (e.g., unpleasant pleasant). Afterwards, instructions for judging the dimensions of affiliation and dominance were provided. Participants were instructed to imagine the music as a film score that accompanied a character in the film, and to judge what that score suggested about the characteristics of the character. For affiliation, listeners judged whether the music suggested a social or antisocial character. For dominance, listeners judged whether the music suggested a dominant or submissive character. The modified SAM was also presented alongside these instructions to aid the participants’ understanding of the concepts of affiliation and dominance in music. Participants were informed they could listen to each stimulus twice. Participants were allowed one hour to complete the 15 items in the survey. After listening to the audio sample, participants were instructed to make six ratings: (1) how tired or energetic the audio sample was; (2) how relaxed or tense the audio sample was; (3) how pleasant or unpleasant the audio sample was (4) how submissive or dominant the audio sample was; (5) how antisocial or social the audio sample was; and (6) how slow or fast the audio sample was. Participants had to answer all six response scales before proceeding to the next page. This process was repeated for each of the 15 music stimuli. Data Screening 750 music excerpts were included in this study, with six response scales associated with each excerpt. One music excerpt was excluded from the study as its mean pitch value could not be calculated. Thus, 749 music excerpts were included in the statistical analysis. 1563 participants had their responses recorded. Participants were excluded if they did not give consent ( n = 3), were judged to have been bots ( n = 15) based on reCAPTCHA score, did not read instructions ( n = 11), did not fully respond to the survey ( n = 7), or responded with only one number ( n = 14). After these 50 participants had been removed from the dataset, 1513 participants remained for subsequent statistical analysis. After participant exclusion, randomization into survey subsets resulted in 27 to 32 participants allocated into each survey subset. Therefore, each response scale associated with a music excerpt was answered a minimum of 27 times and a maximum of 32 times. As responses to the 749 music excerpts were distributed across different sets of participants, music excerpts, rather than participants, were treated as the random variable in statistical analyses. In other words, the participants’ responses generated a mean rating scale value for perceived EA, TA, valence, dominance, affiliation, and tempo for each music excerpt. This allows for each excerpt to contain values for both music features (obtained through MIRtoolbox) and perceived ratings of EA, TA, valence, dominance, and affiliation for subsequent statistical analysis. Declarations Author contributions: EP, KNO, TM and WFT contributed to the conception, design, data analysis and interpretation of research. EP wrote the first draft of the manuscript. KNO, TM and WFT provided suggestions and revisions on the manuscript. WFT funded the research. Additional information Data availability statement: Data are available from either WFT or KNO, and held at Macquarie University and Bond University. Competing interests : The author(s) declare no competing interests. References Aucouturier, J. J., & Canonne, C. (2017). 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Olsen","email":"","orcid":"","institution":"Macquarie University","correspondingAuthor":false,"prefix":"","firstName":"Kirk","middleName":"N.","lastName":"Olsen","suffix":""},{"id":286223492,"identity":"dba21235-bd65-44aa-a4da-cc1d176c13d4","order_by":2,"name":"Anthony E. D. Mobbs","email":"","orcid":"","institution":"Macquarie University","correspondingAuthor":false,"prefix":"","firstName":"Anthony","middleName":"E. D.","lastName":"Mobbs","suffix":""},{"id":286223495,"identity":"c0c04105-b46a-4eb8-b8db-1dd6d4e87a2d","order_by":3,"name":"William Forde Thompson","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYBACAwYeCINfAkgA2YwNBLWwQbVIziBZi8ENYrWYy/cefHRzR52c8e3mYw/eMNjIbjjA/EwCnxbLNr5k49wzh43N7hxLN5zDkGa84QCbGV4tBsd4zKRz2w4kbruRYybNw3A4ccMBBqK01CVungHW8h+ohf0bMVqYEzdIgLUcAGrhwW+LZVuOMdgvEneOpUnOMUg2nnmYp9gCnxZz5jOGj3OBIcY/u/mYxJsKO9m+4+0bb+DTAgaIuDAAYmaC6hmIib5RMApGwSgY0QAAPuRHdE/rnboAAAAASUVORK5CYII=","orcid":"","institution":"Bond University","correspondingAuthor":true,"prefix":"","firstName":"William","middleName":"Forde","lastName":"Thompson","suffix":""}],"badges":[],"createdAt":"2024-03-17 02:14:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4115109/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4115109/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-78156-1","type":"published","date":"2024-11-13T15:57:20+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":53965480,"identity":"d09c231c-b8ec-41f6-a1d0-7462b4d8732d","added_by":"auto","created_at":"2024-04-02 19:22:10","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":478351,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eRelationships Between Music Features and Rating Scales\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote. \u003c/em\u003eAll connections represent significant psychoacoustic predictors in each model. Musical attributes explained the following percentages of variance: Energy (82.28%), Tension (58.02%), Valence (13.40%), Dominance (69.77%), and Affiliation (39.18%). Lines ending with a triangle represent positive relationships; lines ending with a circle represent negative relationships. RMSE = root mean square energy.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4115109/v1/1c7d98d45cd0e98ed21cabd0.jpeg"},{"id":53965481,"identity":"8998f214-510b-4b5c-97fd-d3249cb53571","added_by":"auto","created_at":"2024-04-02 19:22:10","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":304606,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMIRtoolbox Computational Methods for Audio Feature Extraction (Lartillot et al., 2008)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4115109/v1/ce156f71304151d139cce261.jpeg"},{"id":69274943,"identity":"74541dff-a523-4f0b-a703-08c90e954f30","added_by":"auto","created_at":"2024-11-18 16:40:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1910570,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4115109/v1/ee064e26-953c-49c7-9fd7-2d88b80f929f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Music Communicates Social Emotions: Evidence from 750 music excerpts","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMusic has the capacity to express and evoke a wide range of emotions ([1-3]Cowen et al., 2020; Juslin, 2019; Juslin \u0026amp; Laukka, 2004). Listeners can identify emotional connotations after only a few seconds of music (\u0026ldquo;perceived emotion\u0026rdquo;), whereas it takes longer to induce an emotional experience (\u0026ldquo;felt emotion\u0026rdquo;) ([4-6] Day \u0026amp; Thompson, 2019; Gabrielsson \u0026amp; Juslin, 2003; Juslin, 2013). In this investigation, we describe the results of an international crowd-sourcing survey in which 1563 participants provided emotion ratings across 750 music excerpts. Regression and principal-component analyses were conducted to identify optimal models of emotional responses elicited by musical attributes, including models of two \u003cem\u003esocial emotions\u0026nbsp;\u003c/em\u003ethat have not been investigated with respect to music.\u003c/p\u003e\n\u003cp\u003eEmotional responses to music are typically\u0026nbsp;assessed\u0026nbsp;within the frameworks of discrete or dimensional models. Discrete models\u0026nbsp;employ emotion labels such as happiness, sadness, anger, and disgust, which are sometimes\u0026nbsp;grouped\u0026nbsp;into\u0026nbsp;types\u0026nbsp;such as social emotions (e.g., feelings of connection, empowerment; [7] Sznycer et al, 2021), moral emotions (e.g., remorse, righteous indignation; [8]\u0026nbsp;Tangney et al., 2007), aesthetic emotions (e.g., awe, transcendence), achievement-related emotions (e.g., pride, disappointment; [9]\u0026nbsp;Camacho-Morles et al., 2021), and epistemic emotions (e.g., curiosity, doubt; [10] Vogl et al., 2021). Such groupings acknowledge that emotions are tethered to the causal and contextual circumstances associated with the feeling state ([11] Thompson, et al., 2023).\u003c/p\u003e\n\u003cp\u003eDimensional models depict emotions\u0026nbsp;as points on underlying affective continua, such as the degree of energy experienced in the emotion ([12] Eerola \u0026amp; Vuoskoski, 2011).\u0026nbsp;Dimensional models capture complex\u0026nbsp;emotions that vary in intensity but are difficult to\u0026nbsp;label\u0026nbsp;([13] Y. H. Yang \u0026amp; Chen, 2012). For example, death metal fans may perceive complex mixtures of\u0026nbsp;tension, empowerment, energy, and joy in their preferred music, making it difficult to label ([14] Olsen et al., 2023; [15] Thompson et al., 2019).\u0026nbsp;The\u0026nbsp;circumplex model maps emotion onto two dimensions of affect: arousal and valence\u0026nbsp;([16] Cohrdes et al., 2018; [17] Russell, 1980;\u0026nbsp;[18] Russell \u0026amp; Barrett, 1999).\u0026nbsp;In some models, arousal\u0026nbsp;is\u0026nbsp;further divided into two\u0026nbsp;forms\u0026nbsp;of activation:\u0026nbsp;\u003cem\u003eenergy\u003c/em\u003e arousal (EA),\u0026nbsp;which ranges\u0026nbsp;from tired to energetic, and \u003cem\u003etension\u003c/em\u003e arousal (TA),\u0026nbsp;which\u0026nbsp;ranges from calm to nervous\u0026nbsp;([19] Ilie \u0026amp; Thompson, 2006; [20] Thayer, 1989).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe emotions expressed\u0026nbsp;by\u0026nbsp;music are encoded\u0026nbsp;in musical structure\u0026nbsp;(Juslin \u0026amp; Lindstr\u0026ouml;m, 2010), including mode, tempo, dynamics (loudness), pitch register, articulation, and timbre (Carr et al., 2023; Eerola et al., 2013; Panda et al., 2015). For example, fast tempo, consonance, major mode, and bright timbre are associated with happiness, high arousal, and positive valence (Bresin \u0026amp; Friberg, 2011; Juslin \u0026amp; Lindstr\u0026ouml;m, 2010).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResearch on music and emotion has focused on basic emotions such as joy, sadness, fear, and anger (Juslin, 2019). Such emotions reflect abstract feeling states that may be experienced by an individual across a range of contexts. However, music is often experienced with other people as part of cultural rituals and ceremonies, and the social functions of music are emphasised across disciplines (Juslin, 2019). Music can trigger a sense of social inclusion for fans (social affiliation) or social exclusion for non-fans (Juslin, 2019; [14] Olsen, et al., 2023). Music can also convey feelings of social status, ranging from empowerment (dominance) to submissiveness and disempowerment ([15] Thompson et al., 2019; [11] Thompson et al., 2023).\u003c/p\u003e\n\u003cp\u003eDominance and affiliation are\u0026nbsp;core\u0026nbsp;social emotions (Hareli et al., 2016;\u0026nbsp;Hess et al., 2000; Mobbs, 2020; van Kleef \u0026amp; C\u0026ocirc;t\u0026eacute;, 2022), and\u0026nbsp;represent two axes of a social emotion space (Mobbs, 2020).\u0026nbsp;Dominance ranges from feelings of\u0026nbsp;leadership and empowerment over others to\u0026nbsp;feelings of subordination and powerlessness. Affiliation ranges from feelings of social connectedness to feelings of isolation, loneliness, and outsiderness. Functional neuroimaging has revealed distinct neural pathways for dominance and affiliation (Quirin et al., 2013). In addition, dominance has previously been proposed as a dimensional measure of affect in conjunction with the circumplex model (Mehrabian, 1996; Mehrabian \u0026amp; Russell, 1977; Russell, 1978).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe current research\u0026nbsp;examined\u0026nbsp;the\u0026nbsp;capacity for\u0026nbsp;music\u0026nbsp;to communicate\u0026nbsp;social emotions in 750 musical samples across multiple genres and\u0026nbsp;surveyed\u0026nbsp;over 1500 listeners.\u0026nbsp;Bipolar scales measuring dominance and affiliation (Mobbs, 2020) were used to assess the degree to which these emotions are perceived. Music analysis software and statistical modelling were used to model how\u0026nbsp;various musical attributes predict the emotional meaning\u0026nbsp;that is perceived by listeners,\u0026nbsp;including\u0026nbsp;the social emotions of dominance and affiliation and other\u0026nbsp;emotions such as joy, sadness, and fear.\u0026nbsp;The\u0026nbsp;study also examined whether musical attributes predict responses to\u0026nbsp;dimensional models\u003cem\u003e\u0026nbsp;\u003c/em\u003eof emotion. Musical stimuli were restricted to 5-second excerpts to\u0026nbsp;enable the recruitment of a large sample of\u0026nbsp;participants\u0026nbsp;rating\u0026nbsp;multiple music excerpts in a single testing session.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSixteen structural elements such as rhythm, dynamics, harmony, and timbre were extracted from samples\u0026nbsp;(e.g., Brinker et al., 2012; Gingras et al., 2014; Grekow, 2018). Modeling determined whether structural elements predict perceived EA, TA, valence, dominance, and affiliation in music. Several \u003cem\u003ea priori\u003c/em\u003e hypotheses were established. First, we predicted that basic and social emotions (dominance and affiliation) should be perceived in music samples, as reflected in mean ratings (H1). Second, we anticipated that basic and social emotions should be predicted by structural element such as rhythm, timbre, and pitch height (H2)(see [19] Ilie \u0026amp; Thompson, 2006; Thompson, Schellenberg \u0026amp; Husain, 2001).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive Statistics\u003c/h2\u003e \u003cp\u003eDescriptive statistics are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Given the large sample sizes, the normality of sampling distributions was assumed for all variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eMeans, Standard Deviations, Skewness, and Kurtosis of Dependent Variables\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDominance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAffiliation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote\u003c/em\u003e. \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;music stimuli; EA\u0026thinsp;=\u0026thinsp;energy arousal; TA\u0026thinsp;=\u0026thinsp;tension arousal.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e-------------------------------------------------------\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e About Here\u003c/p\u003e \u003cp\u003e-------------------------------------------------------\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCorrelations Between Rating Measures\u003c/h2\u003e \u003cp\u003eBivariate correlations were calculated. Significant correlations between musical attributes and EA, TA, dominance, and affiliation were moderate in strength. Correlations between musical attributes and valence were weak or non-significant.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eAssociations Between Dependent Variables\u003c/h2\u003e \u003cp\u003eCorrelations between the emotion dimensions (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) showed dominance was highly correlated with RMSE and tension, suggesting that this social emotion might be derived from the arousal levels perceived in music excerpts.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eCorrelations Between Dependent Variables\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eValence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDominance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAffiliation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.66**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.08*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.56**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDominance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.80**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.82**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.29**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAffiliation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.62**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.12**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.44**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.39**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003e*p\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05 **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e-------------------------------------------------------\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e About Here\u003c/p\u003e \u003cp\u003e-------------------------------------------------------\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003eMusic Feature Reduction: Addressing Collinearity with Principal Component Analysis\u003c/h2\u003e \u003cp\u003ePrincipal component analysis (PCA) was used to assess multicollinearity and variable significance (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Variables with a loading below 0.5 were excluded from analysis in respect of each musical attribute, and only the most significant five factors were retained for each musical attribute. These criteria excluded six music features from further analysis and retained ten features. The ten features can be grouped into five categories as follows: (1) timbre (spectral centroid and brightness), (2) harmony (dissonance and mode), (3) dynamics (ZCR and RMSE), (4) rhythm (tempo, pulse clarity, and event density), and (5) Pitch.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eComponent Loadings for Musical Attributes\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulse clarity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.52\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e.86\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrightness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e.92\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDissonance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e.82\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e.85\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpectral centroid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e.97\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpectral rolloff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e.95\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean pitch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvent density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.49\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpectral spread\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e.92\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpectral skewness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;\u0026thinsp;.88\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpectral kurtosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;\u0026thinsp;.59\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpectral flatness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e.80\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpectral entropy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e.94\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e.72\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote\u003c/em\u003e. The highest loadings for each component have been emboldened.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e-------------------------------------------------------\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e About Here\u003c/p\u003e \u003cp\u003e-------------------------------------------------------\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eModels of Music Features and Perceived Basic Emotions\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eRegression model: Energy arousal\u003c/h2\u003e \u003cp\u003eThere were 749 observations (music stimuli scored according to their musical attributes and perceived affect). The Shapiro-Wilk test indicated that residuals were normally distributed (\u003cem\u003ep\u003c/em\u003e\u0026rsquo;s\u0026thinsp;\u0026gt;\u0026thinsp;.05), and inspection of the plot of residuals against fitted values indicates the assumption of linearity was met. However, there was noticeable clustering towards the centre of the residuals plot. White\u0026rsquo;s heteroscedasticity test indicated that the homoscedasticity assumption was not violated (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.129). Therefore, inference testing was conducted with a multiple least-squares regression model.\u003c/p\u003e \u003cp\u003eThe model assessing whether musical attributes are associated with perceived expression of energy arousal in music was significant, \u003cem\u003eF\u003c/em\u003e(10, 738)\u0026thinsp;=\u0026thinsp;342.66, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), and explained 82.28% of the variance in the perception of EA in music. There were three statistically significant relationships observed: EA and tempo (η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.636), EA and pulse clarity (η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.008), and EA and brightness (η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.013) (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These relationships were all positive, indicating that as tempo, pulse clarity, or brightness increase, so does the relative perceived magnitude of EA in music when all other variables in the model are held constant. While pulse clarity and brightness were highly significant predictors of perceptions of EA in music, the effect sizes of both predictors were small. EA is closely linked to rhythmic and timbral elements, with the focal point being pulse tempo.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eRegression Statistics for Energy Arousal Model\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTempo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.74, 0.83]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulse clarity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.07, 0.32]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[-1.28, 0.96]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpectral centroid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.00, 0.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrightness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.20, 0.88]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDissonance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.00, 0.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[-0.02, 0.01]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvent density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[-0.03, 0.01]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean pitch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.00, 0.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.00, 0.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e-------------------------------------------------------\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e About Here\u003c/p\u003e \u003cp\u003e-------------------------------------------------------\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eRegression model: Tension arousal\u003c/h2\u003e \u003cp\u003eThe Shapiro-Wilk test confirmed the assumption of normality of residuals was met (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.05) The assumption of linearity was also met after inspection of the residuals and fitted values plot. Slight clustering towards the centre of the residuals against the plot of the fitted values was observed; however, White\u0026rsquo;s test indicated the assumption of homoscedasticity was not violated (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.381). Therefore, multiple regression was deemed appropriate.\u003c/p\u003e \u003cp\u003eThe general model determining if musical attributes are associated with the perception of TA in music was significant \u003cem\u003eF\u003c/em\u003e(10, 738)\u0026thinsp;=\u0026thinsp;102.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, explaining 58.02% of the variance in perceptions of TA in music. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates four significant negative relationships in the model: TA and RMSE (η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.005), TA and pulse clarity (η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.027), TA and spectral centroid (η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.022), and TA and mode (η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.006). These relationships indicate that the perception of TA in music decreases in magnitude if the pulse of the music is more discernible or if there is more overall energy in the excerpt. Moreover, perceived TA in music decreases in magnitude when the spectral centroid of mass frequency increases or when the modality value is more positive, holding all other musical attributes constant.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eRegression Statistics for Tension Arousal Model\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTempo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.64, 0.78]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulse clarity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[-0.65, -0.26]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e.046\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[-3.52, -0.03]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpectral centroid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.00, 0.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrightness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.25, 1.31]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDissonance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.00, 0.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.\u003cb\u003e033\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[-0.49, -0.02]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvent density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[-0.01, 0.04]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean pitch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.00, 0.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.00, 0.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e-------------------------------------------------------\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e About Here\u003c/p\u003e \u003cp\u003e-------------------------------------------------------\u003c/p\u003e \u003cp\u003eThere were three significant positive relationships in this model: TA and tempo (η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.344), TA and brightness (η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.011), and TA and dissonance (η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.032) (see Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). These relationships indicate that the perception of TA in music increases in magnitude when either the speed of the musical beat increases, the amount of high-frequency energy in the music increases, or the amount of dissonance present in the music excerpt increases. These positive and negative relationships indicate that perceptions of TA in music are influenced by rhythmic (tempo and pulse clarity), harmonic (mode and dissonance), timbral (spectral centroid and brightness), and dynamic (RMSE) musicological elements. All significant music predictors had small effect sizes except for tempo.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRegression model: Valence\u003c/h2\u003e \u003cp\u003eThis model used all 749 observations derived from the relevant music excerpts included in this study. Residuals for the overall model were normally distributed according to the Shapiro-Wilk test (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.05). Furthermore, the assumption of linearity was met after inspection of the plot of the residual/fitted values. Slight clustering towards the centre of the residuals was observed; however, White\u0026rsquo;s test indicated the assumption of homoscedasticity was not violated (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.109).\u003c/p\u003e \u003cp\u003eThe general model determining if musical attributes are associated with perceptions of valence in music was significant \u003cem\u003eF\u003c/em\u003e(10, 738)\u0026thinsp;=\u0026thinsp;11.42, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, explaining 13.40% of the variance in perceived impressions of valence in music. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows three significant positive relationships: valence and pulse clarity (η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.013), valence and spectral centroid (η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.028) and valence and mode (η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.010). Therefore, positive valence was associated with a more discernible pulse in the music, a higher spectral centroid and a more positive value for modality (indicating that the music excerpt was more likely to be in a major key), holding all other variables constant.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eRegression Statistics for Valence Model\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTempo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[-0.13, 0.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulse clarity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[0.11, 0.47]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[-1.71, 1.44]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpectral centroid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[0.00, 0.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrightness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[-1.42, -0.47]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDissonance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[0.00, 0.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.\u003cb\u003e010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[0.07, 0.50]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvent density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.\u003cb\u003e006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[-0.06, -0.01]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean pitch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[0.00, 0.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[0.00, 0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e-------------------------------------------------------\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e About Here\u003c/p\u003e \u003cp\u003e-------------------------------------------------------\u003c/p\u003e \u003cp\u003eThe model revealed three significant negative relationships: valence and brightness (η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.020), valence and dissonance (η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.012) and valence and event density (η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.010) (see Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). These relationships indicate that a one-unit increase in either brightness, event density, or dissonance was associated with decreased pleasantness. That is, less pleasant valence ratings were related to increased high-frequency energy, higher levels of dissonance, and a higher number of musical events in a segment. Overall, perceptions of valence in music were influenced by multiple broad musicological concepts such as rhythm (event density and pulse clarity), harmony (mode and dissonance), and timbre (brightness and spectral centroid).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eModels of Musical Attributes and Perceived Social Emotions\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003eRegression model: Dominance\u003c/h2\u003e \u003cp\u003eThe residuals were normally distributed according to the Shapiro-Wilk test (\u003cem\u003ep\u003c/em\u003es\u0026thinsp;\u0026gt;\u0026thinsp;.05), and the assumption of linearity was met after inspection of the residuals against the plot of the fitted values. There was very slight clustering towards the centre of the residual plot. However, White\u0026rsquo;s test indicated that the assumption of homoscedasticity was not violated (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.142).\u003c/p\u003e \u003cp\u003eThe model for musical attributes influencing the perception of dominance in music was significant, \u003cem\u003eF\u003c/em\u003e(10, 738)\u0026thinsp;=\u0026thinsp;170.35, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, explaining 69.77% of the variance in perceptions of dominance in music. Three significant positive associations were observed: dominance and tempo (η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.484), dominance and brightness (η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.018) and dominance and dissonance (η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.026) (see Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). That is, higher ratings of dominance were assigned to music excerpts that were faster in tempo, had greater levels of high-frequency energy, and had higher levels of dissonance.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eRegression Statistics for Dominance Model\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTempo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.60, 0.70]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulse clarity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[-0.38, -0.11]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[-2.26, 0.14]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpectral centroid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.00, 0.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrightness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.31, 1.03]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDissonance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.00, 0.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[-0.16, 0.17]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvent density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.00, 0.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean pitch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.00, 0.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.00, 0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e-------------------------------------------------------\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e About Here\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e-------------------------------------------------------\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe model also revealed two significant negative relationships between perceptions of dominance and pulse clarity (η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.017) and perceptions of dominance and spectral centroid (η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.018) (see Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Therefore, when the pulse of the music was more discernible or the spectral centroid value was higher, music excerpts were likely to be perceived as less dominant. Overall, the perception of dominance was influenced by rhythmic (tempo and pulse clarity), harmonic (dissonance) and timbral (brightness and spectral centroid) features of music. While these effects were found to be highly significant, the effect sizes were small, except for tempo.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRegression model: Affiliation\u003c/h2\u003e \u003cp\u003eThis model included all 749 music excerpt observations. The assumption of normality of residuals was not met according to the Shapiro-Wilk test (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05). After inspection of a residual against fitted values plot, the assumption of linearity was met; however, the assumption of constant variance may be violated due to moderate clustering towards the middle of the plot. This was confirmed by White\u0026rsquo;s test, indicating that the assumption of homoscedasticity was violated (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.032). Despite this potential caveat, we continued with a multiple regression model due to the large sample size and the robust nature of multiple linear regression.\u003c/p\u003e \u003cp\u003eThe overall model explaining the relationship between musical attributes and perceived affiliation in music was significant, \u003cem\u003eF\u003c/em\u003e(10, 738)\u0026thinsp;=\u0026thinsp;47.53, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, explaining 39.18% of perceived affiliation in music. As seen in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, tempo (η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.163, pulse clarity (η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.027), spectral centroid (η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.017), and mode (η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.013) all had a significant positive relationship with the perception of affiliation. Thus, a one-unit increase in any of these predictors while other variables are held constant results in more social perceptions of affiliation. Music excerpts that were higher in tempo, had a more discernible pulse, had a higher spectral centroid value, or a higher likelihood of being in a major key were more likely to be interpreted as prosocial.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eRegression Statistics for Affiliation Model\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTempo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.27, 0.38]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulse clarity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.19, 0.49]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[-0.07, 2.56]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpectral centroid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.00, 0.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrightness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[-0.77, 0.03]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDissonance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.00, 0.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.10, 0.46]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvent density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[-0.06, 0.02]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean pitch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.00, 0.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.00, 0.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e-------------------------------------------------------\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e About Here\u003c/p\u003e \u003cp\u003e-------------------------------------------------------\u003c/p\u003e \u003cp\u003eDissonance (η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.012) and event density (η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.017) had a significant negative association with the perception of affiliation (see Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). When controlling for other variables, a one-unit increase in either of these predictors results in decreased perceptions of social affiliation in music. In turn, music excerpts with greater amounts of dissonance or several musical events per segment were more likely to be perceived as antisocial. However, according to the coefficients, confidence intervals, and effect size, the effect of dissonance and spectral centroid were small. Effect sizes for all other significant predictors, excluding tempo, were small. Overall, the model suggests that the perception of affiliation in music is related to rhythmic (tempo, event density and pulse clarity), harmonic (dissonance and mode), and timbral (spectral centroid) features of the music.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis investigation considered whether social emotions (dominance and affiliation) and basic emotions are communicated by brief music excerpts across a large corpus of genres and whether musical attributes of excerpts (e.g. rhythm, timbre, harmony) predict the emotions perceived. Previous research has confirmed that individuals perceive dominant or affiliative social interactions between musicians (Aucouturier \u0026amp; Canonne, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) but not whether social emotions are perceived in music across genres.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides a framework for interpreting the findings. The figure displays the music features identified in the corpus of music samples and their association with the perception of basic and social emotions. As depicted in the figure and supported in regression analyses, multiple musical attributes converge to predict specific emotional qualities, with different sets of attributes combining to predict different emotional qualities. These different interconnections, along with evidence for the communication of basic and social emotions, will next be discussed with respect to existing models of music and emotion.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e-------------------------------------------------------\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e About Here\u003c/p\u003e \u003cp\u003e-------------------------------------------------------\u003c/p\u003e \u003cp\u003eMultiple regression models revealed that ratings of dominance and affiliation were significantly predicted by music variables, with a higher percentage of variance explained by psychoacoustic models of dominance (69.77%) than affiliation (39.18%). Therefore, the hypothesis that musical attributes can be grouped and classified according to their impact on ratings of social emotions was supported. This effect was stronger for dominance: musical attributes explained 30.59% more variance in the dominance model than in the affiliation model. Thus, research is needed to elucidate the sources of variance in social emotions that are not explained by musical attributes, especially in the case of affiliation. Bivariate correlations revealed that perceived dominance significantly correlated to all music predictors (except for modality), before subsequent music feature reduction via PCA. Moreover, perceived affiliation was correlated with all musical attributes except for dissonance before subsequent music feature reduction.\u003c/p\u003e \u003cp\u003eMany structural features of music are associated with the social dimensions of affiliation and dominance. The magnitude of perceived dominance in music was related to rhythmic (tempo and pulse clarity), harmonic (dissonance), and timbral (brightness and spectral centroid) music features. The magnitude of perceived affiliation in music was related to somewhat different musical predictors but also included varying rhythmic (tempo, pulse clarity, and event density), harmonic (dissonance and modality), and timbral (spectral centroid) features. Given violations of the assumption of normality and homoscedasticity in our analysis, further research on such models is warranted.\u003c/p\u003e \u003cp\u003eEach dimensional model of perceived affect in music was highly significant (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001), and dimensional measures of affect were significantly correlated with each other. This observation is consistent with previous research that ratings of valence and tension are negatively correlated (\u003cem\u003er\u003c/em\u003e = − .70), whereas ratings of energy and tension are positively correlated (\u003cem\u003er\u003c/em\u003e = .57) ([12] Eerola \u0026amp; Vuoskoski, 2011; Schimmack \u0026amp; Grob, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The explained variance in the EA and TA models was larger than the valence model. This finding is consistent with previous evidence that musical attributes are more reliably associated with arousal rather than valence ratings (Tan et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; X. Yang et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Quite possibly, perceived pleasantness in music is influenced by factors not easily modelled by musical attributes, such as familiarity with pieces or genres, personal associations, and visual imagery (Juslin \u0026amp; Västfjäll, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates significant associations. Tempo was the strongest predictor of perceived EA and TA. The effect size was large for both EA and TA, consistent with previous research on the association between tempo and arousal (Brinker et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Husain et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Moreover, music excerpts with a more discernible beat (higher pulse clarity) were associated with increased energy but decreased tension. Event density was not related to either dimension.\u003c/p\u003e \u003cp\u003eMusical attributes that constitute harmony (mode and dissonance) were significantly associated with perceptions of TA. Music with high levels of dissonance or in a minor key yielded high tension ratings by listeners (note that the coefficient of dissonance was small and rounded to 0.00. MIRtoolbox extracts values for dissonance that range from 3-1390, so coefficients for predictions of ratings on a 7-point scale are small). Increased RMSE (loudness) was significantly associated with decreased perceptions of tension in music. However, the zero-crossing rate was not a significant predictor of arousal.\u003c/p\u003e \u003cp\u003eAs predicted, higher magnitudes of perceived energy music were significantly predicted by increased brightness. This supports previous findings of the positive relationship between brightness and arousal (Lartillot, et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Furthermore, tension was also significantly predicted by spectral centroid, revealing that the higher the spectral distribution centroid of a sample, the less likely it will express tension (note that spectral centroid displays similarly small coefficient values to those for dissonance (rounded to 0.00), as spectral centroid values range from 405–6076).\u003c/p\u003e \u003cp\u003eTwo of the three musical attributes related to rhythm were significant predictors of perceived pleasantness in music. Tempo did not predict perceived pleasantness in music, which is interesting given that emotions such as happiness and sadness have previously been found to be associated with varying levels of tempo (Bresin \u0026amp; Friberg, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Gabrielsson, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Vieillard et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, but see Husain et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). One explanation for this finding is that the dimensional rating scale used in this study was a less sensitive measure of perceived valence as it pertains to musical tempo than models that categorically describe varying valence levels (e.g. happy, joyful, or sad). Pulse clarity and event density were important for predicting perceived pleasantness in music, albeit to a smaller effect size. The directionality of these associations suggests that music with a more discernible beat (pulse clarity) is likely to increase perceived pleasantness for the listener. In contrast, music that contains more notes per musical moment (event density) is likely to decrease perceived pleasantness for the listener.\u003c/p\u003e \u003cp\u003eBoth structural features related to harmony (mode and dissonance) predicted perceived pleasantness in music. These relationships reveal that musical excerpts in a major mode and with less harsh sounds are more likely to be perceived as pleasant (Juslin \u0026amp; Lindström, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Laurier et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In contrast to previous findings, the mean pitch was not a significant predictor of perceived pleasantness in music (Juslin \u0026amp; Lindström, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). One interpretation is that the MIRtoolbox could not extract reliable measures of mean pitch from such brief (5-second) samples, suggesting the need to revisit this predictor based on longer music samples.\u003c/p\u003e \u003cp\u003eRMSE (loudness) and zero-crossing rate were not significant predictors of perceived pleasantness in music. This finding can be explained by the standardised sound level of most MP3 files used for audio feature extraction. RMSE values ranged from .03 and .25, with most excerpts returning values between .05 and .15. This difference in loudness between music excerpts is unlikely to affect perceived pleasantness. Musical attributes that constitute timbre (brightness and spectral centroid) were both found to be significant predictors of pleasantness in music. The higher an audio sample’s spectral distribution centroid, the more likely the listener will interpret it as pleasant. Moreover, the lower the high-frequency energy in the audio sample, the more likely it is to be perceived as pleasant.\u003c/p\u003e \u003cp\u003eGiven the high correlation between \u003cem\u003edominance\u003c/em\u003e and \u003cem\u003eenergy\u003c/em\u003e, a question arises whether listeners simply equated the two concepts. However, this possibility is unlikely, given that models of dominance differ from those of arousal. Moreover, considerable attention was given to ensuring respondents fully understood that dominance was a social emotion, not merely a feeling of arousal.\u003c/p\u003e \u003cp\u003eModels incorporating structural features of music predicted both basic and social emotions. Moreover, musical attributes explain energy, tension, and dominance exceptionally well, whereas such factors predict valence and affiliation less well. This investigation suggests that future research needs to address other mechanisms that may facilitate the perception of valence and affiliation in music. Nevertheless, this investigation contributes new evidence that increases the theoretical scope of emotion perception in music and provides a new approach upon which future research can build.\u003c/p\u003e \u003cp\u003eA limitation of this study – and that of Cowan et al., (2020) from which stimuli were drawn – was the brief duration of each stimulus. Presenting brief music samples permitted many excerpts to be presented while avoiding potential participant fatigue (Skowronek et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Y. H. Yang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), but it is uncertain how much emotional information is contained within such a short sample. For example, complex emotions such as nostalgia and awe may take longer than 5 seconds to convey in music (Juslin \u0026amp; Västfjäll, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The brief duration of stimuli likely restricted the kinds of emotions communicated.\u003c/p\u003e \u003cp\u003eA second limitation is that participants were asked to rate all measures of affect and social emotions sequentially. This procedure required that each musical stimulus be continually and retrospectively judged in terms of energy, tension, valence, dominance, and affiliation, which may have been difficult given the small amount of musical information provided. Interestingly, listeners did not appear to have difficulty with the rating scales, and examination of the data indicated that all rating scales were systematically related to musical attributes. These findings suggest that the procedure did not interfere with the quality of the results.\u003c/p\u003e \u003cp\u003eOur recruitment strategy also limited the collection of detailed demographic information, except that all participants were older than 18 and from the United States of America. It is uncertain how population parameters such as age, gender, or previous musical experience relate to emotion perception. Such information, though not the purpose of the investigation, could have provided additional insight into the connection between music and emotion and remains an important issue for future research.\u003c/p\u003e \u003cp\u003eThe finding that social emotions are perceived in music may help to explain the strong motivation to engage with music. The associations identified between social emotions and structural features of music, in turn, have implications for music composition, film scoring and musical theatre. Evidence that dominance and affiliation are perceived in music raises a question of what mechanisms are involved in this effect and how these social emotions are conveyed over extended periods. For example, time-series analysis could be explored to investigate the dynamic properties of dominance and affiliation, perhaps revealing that social connotations of music wax and wane in intensity as music unfolds in time.\u003c/p\u003e \u003cp\u003eTo conclude, this investigation provides a foundation for future research on social emotions in music, with implications for uses of music for social well-being and intercultural understanding. A full understanding of the relationship between basic and social emotions and how they are conveyed in music represents an exciting goal for future research.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003cdiv id=\"Sec24\" class=\"Section4\"\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Method","content":"\u003ch2\u003eEthics statement\u003c/h2\u003e\u003cp\u003e The Human Research Ethics Committee (HREC) of Macquarie University approved the study and protocol (approval number, #52021954830577). In accordance with guidelines established by the HREC, all participants provided informed consent detailing the purpose of the research, potential risks and benefits, confidentiality measures, and their rights as participants. Consent forms are securely retained in accordance with data management protocols outlined by Macquarie University and relevant Australian regulatory bodies. Data collected throughout the study are stored in Macquarie University’s secure servers in accordance with a data management plan to protect the confidentiality and privacy of participants' information. Any personal information obtained from participants is anonymized to ensure confidentiality.\u003c/p\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eParticipants were recruited from the crowdsourcing platform Amazon Mechanical Turk (MTurk), with responses from 1563 participants recorded. Participants were based in the United States, were English-speaking, and were at least 18 years old. All participants provided consent after being informed of the aims of the survey.\u003c/p\u003e\u003ch2\u003eSampling Procedure\u003c/h2\u003e\u003cp\u003eData collection occurred in July 2021. Participants completed the survey for compensation of \u003cspan\u003e$\u003c/span\u003e0.30 USD. To improve quality, MTurk workers were prequalified as residing in the USA, previously completing a minimum of 50 MTurk tasks and achieving a prior minimum MTurk approval rating of 90%.\u003c/p\u003e\u003ch2\u003eMaterials and Measures\u003c/h2\u003e\u003ch2\u003eStimulus Set\u003c/h2\u003e\u003cp\u003eSeven hundred and fifty five-second music instrumental excerpts (no spoken words or lyrics included) were gathered and used as test stimuli from an existing database [1]. The corpus of music comprised 16 genres (music metadata): Alternative (\u003cem\u003en\u003c/em\u003e = 38), Ambient (\u003cem\u003en\u003c/em\u003e = 95), Classical (\u003cem\u003en\u003c/em\u003e = 94), Country (\u003cem\u003en\u003c/em\u003e = 21), EDM (\u003cem\u003en\u003c/em\u003e = 82), Electronic (\u003cem\u003en\u003c/em\u003e = 81), Folk (\u003cem\u003en\u003c/em\u003e = 24), Heavy Metal (\u003cem\u003en\u003c/em\u003e = 37), Hip-hop (\u003cem\u003en\u003c/em\u003e = 15), Jazz (\u003cem\u003en\u003c/em\u003e = 31), Latin (\u003cem\u003en\u003c/em\u003e = 7), Pop (\u003cem\u003en\u003c/em\u003e = 64), Rock (\u003cem\u003en\u003c/em\u003e = 82), R\u0026amp;B (\u003cem\u003en\u003c/em\u003e = 24), Reggae (\u003cem\u003en\u003c/em\u003e = 3), and World Music (\u003cem\u003en\u003c/em\u003e = 52).\u003c/p\u003e\u003ch2\u003eRandomisation of Stimulus Subsets Presented to Participants\u003c/h2\u003e\u003cp\u003eFrom the 750 music excerpts, 50 survey subsets were generated, each containing 15 music excerpts. Each participant was randomly assigned one of the subsets of 15 music excerpts in Qualtrics and responded to all excerpts in the subset to qualify for compensation. Randomisation was constrained in two ways. First, it was evenly distributed across survey subsets from the beginning of data collection, which was accomplished by requiring at least one participant to be allocated to every survey subset and their responses submitted before allocating a second participant to a particular subset. This process was iteratively repeated until all participants were allocated and responses were received. Second, no genre was represented more than three times in any one survey subset, ensuring that participants heard a range of musical genres in their subset.\u003c/p\u003e\u003ch2\u003eMeasures\u003c/h2\u003e\u003ch2\u003eAcoustic Measures\u003c/h2\u003e\u003cp\u003e \u003cb\u003eMIRtoolbox.\u003c/b\u003e Acoustic analyses were performed using the music information retrieval (MIR) toolbox (Lartillot \u0026amp; Toiviainen, 2007) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cp\u003e-------------------------------------------------------\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e About Here\u003c/p\u003e\u003cp\u003e-------------------------------------------------------\u003c/p\u003e\u003cp\u003e \u003cb\u003eMIR root mean square energy (RMSE).\u003c/b\u003e A measure of amplitude (loudness), taken as the global energy of the audio sample, was estimated by taking the square root of the average squared amplitude over a period of time (Lartillot et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Scores range from 0 to 1, with higher scores indicative of a louder signal in the audio sample.\u003c/p\u003e\u003cp\u003e \u003cb\u003eMIR brightness.\u003c/b\u003e Brightness measures the energy in an audio sample above a frequency (Juslin, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Lartillot et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Scores range from 0 to 1, with higher scores signalling greater high-frequency energy in the sample.\u003c/p\u003e\u003cp\u003e \u003cb\u003eRoughness.\u003c/b\u003e Roughness is a measure of global sensory dissonance, corresponding to the “pulsing” phenomenon experienced when multiple sounds slightly deviate in frequency are heard simultaneously (Lartillot et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Sensory dissonance is estimated by taking the average of all pairs of peaks in an audio sample frequency spectrum (Sethares, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Scores range between 3 and 1390, with higher scores indicating increased sensory dissonance and, as a result, more harsher sounds in the audio sample.\u003c/p\u003e\u003cp\u003e \u003cb\u003eRolloff.\u003c/b\u003e This measures how much energy in an audio sample is below an energy threshold (Lartillot et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). MIRtoolbox uses a percentage cut-off of .85, as suggested by Tzanetakis \u0026amp; Cook (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Higher values on the rolloff measure indicate that more energy is confined below the threshold.\u003c/p\u003e\u003cp\u003e \u003cb\u003eMIR zerocross.\u003c/b\u003e The zero-crossing rate measures the amount of noisiness in an audio sample (Tzanetakis \u0026amp; Cook, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). It is estimated by counting the times the signal crosses the X-axis (zero amplitude) (Banchhor \u0026amp; Khan, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Lartillot et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Higher values indicate a greater zero-crossing rate and a noisier audio sample.\u003c/p\u003e\u003cp\u003e \u003cb\u003eMIR mode.\u003c/b\u003e A measure audio sample is either major or minor in modality (Lartillot et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Values closer to 1 indicate audio samples are more likely to be in a major key − 1 suggests a minor key. A value close to 0 indicates that the modality of the audio sample is ambiguous.\u003c/p\u003e\u003cp\u003e \u003cb\u003eMIR spectral centroid.\u003c/b\u003e A measure of an audio sample’s spectral distribution centroid (Lartillot et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Spectral centroid is related to brightness, with higher values indicating lower brightness (e.g., an oboe’s spectral centroid is higher than a French horn’s) (McAdams \u0026amp; Giordano, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e \u003cb\u003eMIR pitch.\u003c/b\u003e A measure of mean pitch using autocorrelation to estimate the average frequency of all pairs of peaks in the audio sample (Eerola et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Lartillot et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). higher values indicate a greater presence of sounds.\u003c/p\u003e\u003cp\u003e \u003cb\u003eMIR pulse clarity.\u003c/b\u003e Pulse clarity is the musical measure of how easily the listener can identify the underlying beat or metrical pulsation in a given piece of music (Lartillot et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Scores range from 0 to 1, with higher scores indicating that the listener easily discerns the underlying beat or metrical pulsation.\u003c/p\u003e\u003cp\u003e \u003cb\u003eMIR event density.\u003c/b\u003e Event density observes the number of events (peaks) occurring in the audio sample and approximates the mean frequency of these events per second (Eerola et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Lartillot et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Higher values indicate an increase of notes in each musical moment.\u003c/p\u003e\u003cp\u003e \u003cb\u003eMIR spread; skewness; kurtosis; flatness; entropy (spectral properties).\u003c/b\u003e These five measures all relate to the spectral dispersion of an audio sample. MIR spread the standard deviation of the spectral distribution. MIR flatness calculates the flatness of data, indicating if the spectral distribution is smooth or peaky. Finally, MIR entropy represents the respective Shannon (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1948\u003c/span\u003e) entropy of the spectrum, with higher values signalling more uncertainty and peakiness.\u003c/p\u003e\u003ch2\u003eResponse Measures\u003c/h2\u003e\u003cp\u003eThe circumplex model of affect classifies emotion according to the dimensions of arousal and valence ([17] Russell, 1980), but arousal was further subdivided into energy arousal (EA) and tension arousal (TA) ([12] Eerola \u0026amp; Vuoskoski, 2011; Schimmack \u0026amp; Reisenzein, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Two \u003cem\u003esocial\u003c/em\u003e emotions, dominance and affiliation, were also included (Mobbs, 2020). Thus, the study examined three basic emotions of EA, TA, valence and two social emotions of dominance and affiliation.\u003c/p\u003e\u003cp\u003eParticipants rated the emotional quality of the music. Each bipolar rating scale ranged from one to seven, with ratings five and above indicating high levels of emotion and ratings of three and below indicating low levels of emotion. \u003cem\u003eEnergy arousal\u003c/em\u003e ranged from tired to energetic; \u003cem\u003etension arousal\u003c/em\u003e ranged from relaxed to tense; \u003cem\u003evalence\u003c/em\u003e ranged from unpleasant to pleasant; \u003cem\u003edominance\u003c/em\u003e ranged from submissive to dominant; and \u003cem\u003eaffiliation\u003c/em\u003e ranged from antisocial to highly social.\u003c/p\u003e\u003cp\u003e \u003cb\u003eModified Self-Assessment Manikin (SAM).\u003c/b\u003e The SAM measures an individual’s pleasure, arousal, and dominance by displaying visual depictions of the different levels of each measure using schematic manikins comparable to emojis (Bradley \u0026amp; Lang, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). The current study adopted this strategy to explain and measure the dimensions of dominance and affiliation, emphasizing their social meaning. Dominance and affiliation were conceptualised by two scales containing five graphical figures, each representing a 7-point response scale for each dimension. Dominance ranged from − 3 (illustrated by a small vulnerable figure in a large open space) to 3 (depicted by a large figure dominating the surrounding space). Affiliation ranged from − 3 (illustrated by an isolated figure from a group) to 3 (depicted by the same figure now in the middle of the group).\u003c/p\u003e\u003cp\u003e \u003cb\u003eTempo.\u003c/b\u003e Tempo ratings on a 7-point scale (7 = fast) were included to assess participants’ perceptions of how fast the music excerpts were. This measure was included to corroborate the MIRtoolbox extraction of tempo, given the samples were so short.\u003c/p\u003e\u003ch2\u003eProcedure\u003c/h2\u003e\u003cp\u003eThe survey was published on MTurk redirected to a Qualtrics survey. Participants read a project description and provided informed consent before completing the survey. Participants were notified that financial compensation would only be given to participants who responded to all 15 audio samples in the survey. The Macquarie University approved this research.\u003c/p\u003e\u003cp\u003eParticipants were instructed to remember that the study investigated the emotions we \u003cem\u003eperceive\u003c/em\u003e in music, not emotions that might be \u003cem\u003einduced\u003c/em\u003e by music. This distinction is important as music can have emotional connotations (e.g., sadness). Moreover, the nature of each bipolar response scale was explained with responses on the far left and far right indicating an intense perception of the dimension in question (e.g., unpleasant pleasant). Afterwards, instructions for judging the dimensions of \u003cem\u003eaffiliation\u003c/em\u003e and \u003cem\u003edominance\u003c/em\u003e were provided. Participants were instructed to imagine the music as a film score that accompanied a character in the film, and to judge what that score suggested about the characteristics of the character. For affiliation, listeners judged whether the music suggested a social or antisocial character. For dominance, listeners judged whether the music suggested a dominant or submissive character. The modified SAM was also presented alongside these instructions to aid the participants’ understanding of the concepts of \u003cem\u003eaffiliation\u003c/em\u003e and \u003cem\u003edominance\u003c/em\u003e in music. Participants were informed they could listen to each stimulus twice. Participants were allowed one hour to complete the 15 items in the survey.\u003c/p\u003e\u003cp\u003e After listening to the audio sample, participants were instructed to make six ratings: (1) how tired or energetic the audio sample was; (2) how relaxed or tense the audio sample was; (3) how pleasant or unpleasant the audio sample was (4) how submissive or dominant the audio sample was; (5) how antisocial or social the audio sample was; and (6) how slow or fast the audio sample was. Participants had to answer all six response scales before proceeding to the next page. This process was repeated for each of the 15 music stimuli.\u003c/p\u003e\u003ch2\u003eData Screening\u003c/h2\u003e\u003cp\u003e750 music excerpts were included in this study, with six response scales associated with each excerpt. One music excerpt was excluded from the study as its mean pitch value could not be calculated. Thus, 749 music excerpts were included in the statistical analysis.\u003c/p\u003e\u003cp\u003e1563 participants had their responses recorded. Participants were excluded if they did not give consent (\u003cem\u003en\u003c/em\u003e = 3), were judged to have been bots (\u003cem\u003en\u003c/em\u003e = 15) based on reCAPTCHA score, did not read instructions (\u003cem\u003en\u003c/em\u003e = 11), did not fully respond to the survey (\u003cem\u003en\u003c/em\u003e = 7), or responded with only one number (\u003cem\u003en\u003c/em\u003e = 14). After these 50 participants had been removed from the dataset, 1513 participants remained for subsequent statistical analysis.\u003c/p\u003e\u003cp\u003eAfter participant exclusion, randomization into survey subsets resulted in 27 to 32 participants allocated into each survey subset. Therefore, each response scale associated with a music excerpt was answered a minimum of 27 times and a maximum of 32 times. As responses to the 749 music excerpts were distributed across different sets of participants, music excerpts, rather than participants, were treated as the random variable in statistical analyses. In other words, the participants’ responses generated a mean rating scale value for perceived EA, TA, valence, dominance, affiliation, and tempo for each music excerpt. This allows for each excerpt to contain values for both music features (obtained through MIRtoolbox) and perceived ratings of EA, TA, valence, dominance, and affiliation for subsequent statistical analysis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003eEP, KNO, TM and WFT contributed to the conception, design, data analysis and interpretation of research. EP wrote the first draft of the manuscript. KNO, TM and WFT provided suggestions and revisions on the manuscript. WFT funded the research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u0026nbsp;\u003c/strong\u003eData are available from either WFT or KNO, and held at Macquarie University and Bond University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e: The author(s) declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAucouturier, J. J., \u0026amp; Canonne, C. (2017). Musical friends and foes: The social cognition of affiliation and control in improvised interactions. \u003cem\u003eCognition\u003c/em\u003e, \u003cem\u003e161\u003c/em\u003e, 94\u0026ndash;108. https://doi.org/10.1016/j.cognition.2017.01.019\u003c/li\u003e\n \u003cli\u003eBanchhor, S. K., \u0026amp; Khan, A. (2012). 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Feature selection and feature learning in arousal dimension of music emotion by using shrinkage methods. \u003cem\u003eMultimedia Systems\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(2), 251\u0026ndash;264. https://doi.org/10.1007/s00530-015-0489-y\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Basic emotions, Music perception, Social emotions, Dominance, Affiliation","lastPublishedDoi":"10.21203/rs.3.rs-4115109/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4115109/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHumans perceive a range of basic emotional connotations from music, such as joy, sadness, and fear, which can be decoded from structural characteristics of music, such as rhythm, harmony, and timbre. However, despite theory and evidence that music has multiple social functions, little research has examined whether music conveys emotions specifically associated with social status and social connection. This investigation aimed to determine whether the social emotions of \u003cem\u003edominance\u003c/em\u003e and \u003cem\u003eaffiliation\u003c/em\u003e are perceived in music and whether structural features of music predict social emotions, just as they predict basic emotions. Participants (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1513) listened to subsets of 750 music excerpts and provided ratings of energy arousal, tension arousal, valence, dominance, and affiliation. Ratings were modelled based on ten structural features of music. Dominance and affiliation were readily perceived in music and predicted by structural features including rhythm, harmony, dynamics, and timbre. In turn, energy arousal, tension arousal and valence were also predicted by musical structure. We discuss the results in view of current models of music and emotion and propose research to illuminate the significance of social emotions in music.\u003c/p\u003e","manuscriptTitle":"Music Communicates Social Emotions: Evidence from 750 music excerpts","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-02 19:22:05","doi":"10.21203/rs.3.rs-4115109/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-01T05:19:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-23T21:00:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"151834180095102020621728783620554048005","date":"2024-06-12T18:00:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-08T16:00:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4d9f4826-cce3-4602-a8a8-bcbd1720d170","date":"2024-04-05T09:43:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5262cd6d-e7b5-49f3-9c60-c2b3944b6dce","date":"2024-04-02T08:57:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-02T08:00:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-02T07:59:28+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-03-28T05:27:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-28T05:17:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-03-17T02:04:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8e1a0f9c-c1c3-452f-b67b-60ac742037af","owner":[],"postedDate":"April 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":30126005,"name":"Biological sciences/Psychology"},{"id":30126006,"name":"Biological sciences/Psychology/Human behaviour"}],"tags":[],"updatedAt":"2024-11-18T16:01:02+00:00","versionOfRecord":{"articleIdentity":"rs-4115109","link":"https://doi.org/10.1038/s41598-024-78156-1","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-11-13 15:57:20","publishedOnDateReadable":"November 13th, 2024"},"versionCreatedAt":"2024-04-02 19:22:05","video":"","vorDoi":"10.1038/s41598-024-78156-1","vorDoiUrl":"https://doi.org/10.1038/s41598-024-78156-1","workflowStages":[]},"version":"v1","identity":"rs-4115109","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4115109","identity":"rs-4115109","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}