A comparison of the discrete and dimensional models of emotion in music

Abstract

The primary aim of the present study was to systematically compare perceived emotions in music using two different theoretical frameworks: the discrete emotion model, and the dimensional model of affect. A secondary aim was to introduce a new, improved set of stimuli for the study of music-mediated emotions. A large pilot study established a set of 110 film music excerpts, half were moderately and highly representative examples of five discrete emotions (anger, fear, sadness, happiness and tenderness), and the other half moderate and high examples of the six extremes of three bipolar dimensions (valence, energy arousal and tension arousal). These excerpts were rated in a listening experiment by 116 non-musicians. All target emotions of highly representative examples in both conceptual sets were discriminated by self-ratings. Linear mapping techniques between the discrete and dimensional models revealed a high correspondence along two central dimensions that can be labelled as valence and arousal, and the three dimensions could be reduced to two without significantly reducing the goodness of fit. The major difference between the discrete and categorical models concerned the poorer resolution of the discrete model in characterizing emotionally ambiguous examples. The study offers systematically structured and rich stimulus material for exploring emotional processing.

Hidden sources of joy, fear, and sadness: Explicit versus implicit neural processing of musical emotions

“…While a growing body of research has explored the brain substrates of musical emotions, so far the implicit processing of emotions in music has remained neglected despite music often being used as an accompaniment to another activity. Behavioral data confirmed that even when shortened, the music soundtrack clips from the database by Eerola et al (2011) were able to convey sad, fearful, and happy emotions. In implicit compared to explicit processing, we found large cortical areas of activity in the temporal, frontal, and parietal lobes as well as in reward-related areas.…”

“…The stimuli were musical excerpts taken from a movie soundtrack database; the music clips in this database were previously rated on a wide range of affective categories and validated for their target emotion (Eerola et al, 2011). Film music stimuli were chosen for this study because music in movies is composed to represent and induce emotions in listeners.…”

“…Film music stimuli were chosen for this study because music in movies is composed to represent and induce emotions in listeners. We chose musical clips that were ranked highly for the target emotions (happiness, sadness, and fear) in the Eerola et al (2011) study. Eighty-one musical stimuli were edited using Adobe Audition to last four seconds with a 500 ms fade-in and fade-out and normalized to each other to match loudness levels as measured by the average root mean square (RMS) over the whole duration of the stimulus.…”

“…Each of the music clips was analyzed for 22 acoustic features that correspond to perceptual attributes and are well-studied in the psychoacoustic literature (Aucouturier & Bigand, 2013). The features were extracted from the stimuli on a frame-by-frame basis (for more details on computational feature extraction see Brattico et al, 2011;Eerola et al, 2011;Alluri et al, 2012). The values were converted to z-scores and grouped into six sets according to a classification implemented in Eerola et al (2011) in order to minimize Type I errors resulting from multiple comparisons.…”

“…The features were extracted from the stimuli on a frame-by-frame basis (for more details on computational feature extraction see Brattico et al, 2011;Eerola et al, 2011;Alluri et al, 2012). The values were converted to z-scores and grouped into six sets according to a classification implemented in Eerola et al (2011) in order to minimize Type I errors resulting from multiple comparisons. The six sets included dynamic features (root mean square, RMS energy, and low energy), rhythm features (tempo and pulse clarity), timbre features (zero cross, centroid, brightness, spread, skewness, kurtosis, flatness, entropy, roughness, irregularity, and spectral flux), pitch features (chroma peak), tonality features (key clarity, mode, HCDF, and spectral entropy) and articulation features (attack time and attack slope).…”