Cultural differences in the use of acoustic cues for musical emotion experience
Autoři:
Vishal Midya aff001; Jeffrey Valla aff001; Hymavathy Balasubramanian aff001; Avantika Mathur aff001; Nandini Chatterjee Singh aff001
Působiště autorů:
Language, Literacy, and Music Laboratory, National Brain Research Centre, Manesar, Haryana, India
aff001; Division of Biostatistics and Bioinformatics, Department of Public Health, Penn State College of Medicine, Pennsylvania State University, Hershey, Pennsylvania, United States of America
aff002
Vyšlo v časopise:
PLoS ONE 14(9)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0222380
Souhrn
Does music penetrate cultural differences with its ability to evoke emotion? The ragas of Hindustani music are specific sequences of notes that elicit various emotions: happy, romantic, devotion, calm, angry, longing, tension and sad. They can be presented in two modes, alaap and gat, which differ in rhythm, but match in tonality. Participants from Indian and Non-Indian cultures (N = 144 and 112, respectively) rated twenty-four pieces of Hindustani ragas on eight dimensions of emotion, in a free response task. Of the 192 between-group comparisons, ratings differed in only 9% of the instances, showing universality across multiple musical emotions. Robust regression analyses and machine learning methods revealed tonality best explained emotion ratings for Indian participants whereas rhythm was the primary predictor in Non-Indian listeners. Our results provide compelling evidence for universality in emotions in the auditory domain in the realm of musical emotion, driven by distinct acoustic features that depend on listeners’ cultural backgrounds.
Klíčová slova:
Biology and life sciences – Psychology – Emotions – Music cognition – Music perception – Neuroscience – Cognitive science – Cognitive psychology – Sensory perception – Social sciences – Sociology – Culture – Cross-cultural studies – Physical sciences – Physics – Acoustics – Mathematics – Statistics – Computer and information sciences – Artificial intelligence – Machine learning – Research and analysis methods – Mathematical and statistical techniques – Statistical methods – Regression analysis – Decision analysis – Decision trees – Engineering and technology – Management engineering
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