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Relationships between musical structure and psychophysiological measures of emotion.

Gomez, Patrick, Danuser, Brigitta·2007·Emotion

Abstract

Psychophysiological studies with music have not examined what exactly in the music might be responsible for the observed physiological phenomena. The authors explored the relationships between 11 structural features of 16 musical excerpts and both self-reports of felt pleasantness and arousal and different physiological measures (respiration, skin conductance, heart rate). Overall, the relationships between musical features and experienced emotions corresponded well with those known between musical structure and perceived emotions. This suggests that the internal structure of the music played a primary role in the induction of the emotions in comparison to extramusical factors. Mode, harmonic complexity, and rhythmic articulation best differentiated between negative and positive valence, whereas tempo, accentuation, and rhythmic articulation best discriminated high arousal from low arousal. Tempo, accentuation, and rhythmic articulation were the features that most strongly correlated with physiological measures. Music that induced faster breathing and higher minute ventilation, skin conductance, and heart rate was fast, accentuated, and staccato. This finding corroborates the contention that rhythmic aspects are the major determinants of physiological responses to music.

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Metadata

Authors
Gomez, P., & Danuser, B.
Venue
Emotion
Published
2007-05
Volume
7
Issue
2
Pages
377-387
ISSN
  • 1528-3542 (ISSN-L)
  • 1931-1516
OpenAlex
W2008133390
Cited by
470
References
4
FWCI
4.02
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Relationships between musical structure and psychophysiological measures of emotion. https://app.cochat.ai/papers/10.1037/1528-3542.7.2.377
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Gomez, P., & Danuser, B. (2007). Relationships between musical structure and psychophysiological measures of emotion. Emotion. https://doi.org/10.1037/1528-3542.7.2.377

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.