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A framework for modeling performers' beat-to-beat heart intervals using music features and Interpretation Maps
Mateusz Soliński1,2, Courtney N Reed1,2, Elaine Chew1,2
1School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom.
Frontiers in Psychology
|September 19, 2024
Summary
Musicians
Area of Science:
- Neuroscience
- Music Psychology
- Computational Biology
Background:
- Music significantly influences the autonomic nervous system, observable in heart rate variability (HRV).
- Musicians' beat-to-beat heart (RR) intervals reflect autonomic responses to music features and performance decisions.
Purpose of the Study:
- To develop a novel computational model for predicting musicians' RR interval variations.
- To analyze the impact of specific music features and interpretive choices on autonomic responses.
Main Methods:
- Collected RR interval data from a professional ensemble (violin, cello, piano) performing Schubert's Trio No. 2.
- Utilized linear mixed models to predict RR intervals based on music features (tempo, loudness, note density), interpretive choices, and a starting factor.
Main Results:
- Models explained approximately 50% of RR interval variability across musicians (R-squared: 0.606 violinist, 0.494 cellist, 0.540 pianist).
- Loudness, climax, moment of concern, and starting factor were the strongest predictors of RR interval changes.
Conclusions:
- Established a strong link between musical interpretation (Interpretation Map) and autonomic responses (RR interval changes).
- Demonstrated the potential of music features to predict physiological reactions in performers and listeners.
- Highlighted the importance of modeling music-evoked autonomic responses for potential therapeutic interventions.
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