How balance, contour, symmetry, and complexity predict the perceived emotions in Chinese traditional music

Understanding how musical structure shapes emotion perception remains a central question in music psychology, yet the role of mid-level structural features in Chinese traditional music is underexplored. This study investigated how balance, contour, symmetry, and complexity (BCSC framework) predict perceived emotions in Chinese traditional music using both linear (Lasso-selected multiple regression) and nonlinear (random forest regression) modeling approaches. Analyses included dimensional emotions (valence, arousal) and seven discrete emotions (anger, gentleness, happiness, peacefulness, sadness, solemnness, transcendence). Linear regressions showed that BCSC features explained 35.1% of variance in valence and 37.1% in arousal. For valence, Total Asymmetry (β = 0.266, p < 0.001), Average Local First-Order Entropy (β = 0.862, p < 0.001), Third-Order Duration Entropy (β = 0.511, p = 0.022), and Event Density (β = −0.890, p < 0.001) were among the strongest predictors. For arousal, Duration Abruptness, Total Asymmetry, Average Local First-Order Entropy, and Third-Order Duration Entropy were significant positive predictors (all ps < 0.01), while Rhythmic Abruptness, Event Density, and First-Order Duration Entropy showed significant negative effects (all ps < 0.001). For discrete emotions, predictive power ranged from adjusted R² = 0.157 (peacefulness) to 0.386 (happiness), with emotion-specific patterns: e.g., Bi-Unbalance (β = 0.316, p < 0.001) predicted anger, while First-Order Duration Entropy had opposing effects for anger (β = −0.252, p < 0.001) and peacefulness (β = 0.403, p < 0.001). Nonlinear models confirmed feature importance patterns, highlighting the roles of temporal irregularity, melodic interval size, and structural imbalance. Overall, these findings demonstrate that mid-level structural features provide systematic elements that serve as emotional cues for perceived emotions in Chinese traditional music.

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Publication Details

Journal
Humanities and Social Sciences Communications
Published
2026-10-26
DOI
https://doi.org/10.1057/s41599-026-09611-9
Primary Topic
Neuroscience and Music Perception
Type
article
Field-Weighted Citation Impact
0.00

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article

How balance, contour, symmetry, and complexity predict the perceived emotions in Chinese traditional music

Yishan Liu, Chiju Chao, Liang Xu, Heng Shen et al.
Humanities and Social Sciences Communications
Neuroscience and Music Perception
article

How balance, contour, symmetry, and complexity predict the perceived emotions in Chinese traditional music

Yishan Liu, Chiju Chao, Liang Xu, Heng Shen, Jiahao Li, Yiwen Wu
article en

Abstract

Understanding how musical structure shapes emotion perception remains a central question in music psychology, yet the role of mid-level structural features in Chinese traditional music is underexplored. This study investigated how balance, contour, symmetry, and complexity (BCSC framework) predict perceived emotions in Chinese traditional music using both linear (Lasso-selected multiple regression) and nonlinear (random forest regression) modeling approaches. Analyses included dimensional emotions (valence, arousal) and seven discrete emotions (anger, gentleness, happiness, peacefulness, sadness, solemnness, transcendence). Linear regressions showed that BCSC features explained 35.1% of variance in valence and 37.1% in arousal. For valence, Total Asymmetry (β = 0.266, p < 0.001), Average Local First-Order Entropy (β = 0.862, p < 0.001), Third-Order Duration Entropy (β = 0.511, p = 0.022), and Event Density (β = −0.890, p < 0.001) were among the strongest predictors. For arousal, Duration Abruptness, Total Asymmetry, Average Local First-Order Entropy, and Third-Order Duration Entropy were significant positive predictors (all ps < 0.01), while Rhythmic Abruptness, Event Density, and First-Order Duration Entropy showed significant negative effects (all ps < 0.001). For discrete emotions, predictive power ranged from adjusted R² = 0.157 (peacefulness) to 0.386 (happiness), with emotion-specific patterns: e.g., Bi-Unbalance (β = 0.316, p < 0.001) predicted anger, while First-Order Duration Entropy had opposing effects for anger (β = −0.252, p < 0.001) and peacefulness (β = 0.403, p < 0.001). Nonlinear models confirmed feature importance patterns, highlighting the roles of temporal irregularity, melodic interval size, and structural imbalance. Overall, these findings demonstrate that mid-level structural features provide systematic elements that serve as emotional cues for perceived emotions in Chinese traditional music.

Humanities and Social Sciences Communications
Hangzhou Wanxiang Polytechnic (CN), Zhejiang University of Technology (CN), Zhejiang University (CN)
Humanities and Social Sciences Youth Foundation, Ministry of Education of the People's Republic of China
Openalex Percentile: Top 14%
Neuroscience and Music Perception
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