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Perception of affect in unfamiliar musical chords

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  • Eline Adrianne Smit
  • Andrew J Milne
  • Roger T Dean
  • Gabrielle Weidemann

Abstract

This study investigates the role of extrinsic and intrinsic predictors in the perception of affect in mostly unfamiliar musical chords from the Bohlen-Pierce microtonal tuning system. Extrinsic predictors are derived, in part, from long-term statistical regularities in music; for example, the prevalence of a chord in a corpus of music that is relevant to a participant. Conversely, intrinsic predictors make no use of long-term statistical regularities in music; for example, psychoacoustic features inherent in the music, such as roughness. Two types of affect were measured for each chord: pleasantness/unpleasantness and happiness/sadness. We modelled the data with a number of novel and well-established intrinsic predictors, namely roughness, harmonicity, spectral entropy and average pitch height; and a single extrinsic predictor, 12-TET Dissimilarity, which was estimated by the chord’s smallest distance to any 12-tone equally tempered chord. Musical sophistication was modelled as a potential moderator of the above predictors. Two experiments were conducted, each using slightly different tunings of the Bohlen-Pierce musical system: a just intonation version and an equal-tempered version. It was found that, across both tunings and across both affective responses, all the tested intrinsic features and 12-TET Dissimilarity have consistent influences in the expected direction. These results contrast with much current music perception research, which tends to assume the dominance of extrinsic over intrinsic predictors. This study highlights the importance of both intrinsic characteristics of the acoustic signal itself, as well as extrinsic factors, such as 12-TET Dissimilarity, on perception of affect in music.

Suggested Citation

  • Eline Adrianne Smit & Andrew J Milne & Roger T Dean & Gabrielle Weidemann, 2019. "Perception of affect in unfamiliar musical chords," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-28, June.
  • Handle: RePEc:plo:pone00:0218570
    DOI: 10.1371/journal.pone.0218570
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    References listed on IDEAS

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    1. Bürkner, Paul-Christian, 2017. "brms: An R Package for Bayesian Multilevel Models Using Stan," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 80(i01).
    2. Josh H. McDermott & Alan F. Schultz & Eduardo A. Undurraga & Ricardo A. Godoy, 2016. "Indifference to dissonance in native Amazonians reveals cultural variation in music perception," Nature, Nature, vol. 535(7613), pages 547-550, July.
    3. Yvonne Leung & Roger Thornton Dean, 2018. "Learning unfamiliar pitch intervals: A novel paradigm for demonstrating the learning of statistical associations between musical pitches," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-21, August.
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    1. Raja Marjieh & Peter M. C. Harrison & Harin Lee & Fotini Deligiannaki & Nori Jacoby, 2024. "Timbral effects on consonance disentangle psychoacoustic mechanisms and suggest perceptual origins for musical scales," Nature Communications, Nature, vol. 15(1), pages 1-22, December.

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