Fuyu Ueno

Background/Objectives: Musical pleasure is considered to be induced by prediction errors (surprise), as suggested in neuroimaging studies. However, the role of temporal changes in musical features in reward processing remains unclear. Utilizing the Information Dynamics of Music (IDyOM) model, a statistical model that calculates musical surprise based on prediction errors in melody and harmony, we investigated whether brain activities associated with musical pleasure, particularly in the θ, β, and γ bands, are induced by prediction errors, similar to those observed during monetary rewards. Methods: We used the IDyOM model to calculate the information content (IC) of surprise for melody and harmony in 70 musical pieces across six genres; eight pieces with varying IC values were selected. Electroencephalographic data were recorded during listening to the pieces, continuously evaluating the participants’ subjective pleasure on a 1–4 scale. Time–frequency analysis of electroencephalographic data was conducted, followed by general linear model analysis to fit the power-value time course in each frequency band to the time courses of subjective pleasure and IC for melody and harmony. Results: Significant positive fits were observed in the β and γ bands in the frontal region with both subjective pleasure and IC for melody and harmony. No significant fit was observed in the θ band. Both subjective pleasure and IC are associated with increased β and γ band power in the frontal regions. Conclusions: β and γ oscillatory activities in the frontal regions are strongly associated with musical rewards induced by prediction errors, similar to brain activity observed during monetary rewards.

Background Research on music-induced emotion and brain activity is constantly expanding. Although studies using inter-subject correlation (ISC), a collectively shared brain activity analysis method, have been conducted, whether ISC during music listening represents the music preferences of a large population remains uncertain; additionally, it remains unclear which factors influence ISC during music listening. Therefore, here, we aimed to investigate whether the ISCs of electroencephalography (EEG) during music listening represent a preference for music reflecting engagement or interest of a large population in music. Methods First, we selected 21 pieces of music from the Billboard Japan Hot 100 chart of 2017, which served as an indicator of preference reflecting the engagement and interest of a large population. To ensure even representation, we chose one piece for every fifth song on the chart, spanning from highly popular music to less popular ones. Next, we recorded EEG signals while the subjects listened to the selected music, and they were asked to evaluate four aspects (preference, enjoyment, frequency of listening, and arousal) for each song. Subsequently, we conducted ISC analysis by utilizing the first three principal components of EEG, which were highly correlated across subjects and extracted through correlated component analysis (CorrCA). We then explored whether music with high preferences that reflected the engagement and interest of large population had high ISC values. Additionally, we employed cluster analysis on all 21 pieces of music, utilizing the first three principal components of EEG, to investigate the impact of emotions and musical characteristics on EEG ISC during music listening. Results A significant distinction was noted between the mean ISC values of the 10 higher-ranked pieces of music compared to the 10 lower-ranked pieces of music [t(542) = −1.97, p = 0.0025]. This finding suggests that ISC values may correspond preferences reflecting engagement or interest of a large population. Furthermore, we found that significant variations were observed in the first three principal component values among the three clusters identified through cluster analysis, along with significant differences in arousal levels. Moreover, the characteristics of the music (tonality and tempo) differed among the three clusters. This indicates that the principal components, which exhibit high correlation among subjects and were employed in calculating ISC values, represent both subjects’ arousal levels and specific characteristics of the music. Conclusion Subjects’ arousal values during music listening and music characteristics (tonality and tempo) affect ISC values, which represent the interest of a large population in music.