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Outline

Perception of affect in unfamiliar musical chords

2019, PLoS ONE

https://doi.org/10.1371/JOURNAL.PONE.0218570

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/sad-ness. 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.

References (74)

  1. Lahdelma I, Eerola T. Single chords convey distinct emotional qualities to both naïve and expert listen- ers. Psychology of Music. 2016, 44(1):37-54.
  2. Marin MM, Thompson WF, Gingras B, Stewart L. (2015). Affective evaluation of simultaneous tone combinations in congenital amusia. Neuropsychologia. 2015 Nov, 78:207-20. https://doi.org/10.1016/j. neuropsychologia.2015.10.004 PMID: 26455803
  3. Kameoka A, Kuriyagawa M. Consonance theory part I: Consonance of dyads. The Journal of the Acoustical Society of America. 1969, 45:1452-59.
  4. Kameoka A, Kuriyagawa M. Consonance theory part II: Consonance of complex tones and its calcula- tion method. The Journal of the Acoustical Society of America. 1969, 45:1460-69. https://doi.org/10. 1121/1.1911624 PMID: 5803169
  5. Plomp R, Levelt WJM. Tonal consonance and critical bandwidth. The Journal of the Acoustical Society of America. 1965, 38(4):548-560. https://doi.org/10.1121/1.1909741 PMID: 5831012
  6. Crowder RG. Perception of the major/minor distinction: I. Historical and theoretical foundations. Pyschomusicology. 1984 Jan 1, 4(1-2):3-12.
  7. Crowder RG. Perception of the major/minor distinction: II. Experimental investigations. Psychomusicol- ogy, 1985 Jan 1, 5(1-2):3-24.
  8. Heinlein CP. The affective characters of the major and minor modes in music. Journal of Computational Psychology. 1928, 8:101-42.
  9. Balkwill LL, Thompson WF. A cross-cultural investigation of the perception of emotion in music: Psycho- physical and cultural cues. Music Perception. 1999; 17(1):43-64.
  10. Balkwill LL, Thompson WF, Matsanuga R. Recognition of emotion in Japanese, Western, and Hindu- stani music by Japanese listeners. Japanese Psychological Research. 2004 Nov 25, 46(4):337-349.
  11. Juslin PN, Va ¨stfja ¨ll D. Emotional responses to music: The need to consider underlying mechanisms. Behavioural and Brain Sciences. 2008 Oct, 31:559-621.
  12. Cohen AJ. Music as a source of emotion in film: In: Juslin PN, Sloboda JA, editors. Music and emotion: Theory and research. New York: Oxford University Press; 2001. p. 249-72.
  13. Pearce MT, Wiggins GA. Expectation in melody: The influence of context and learning. Music Percep- tion. 2006 Jun, 23(5):377-406.
  14. Milne AJ, Laney R, Sharp DB. A spectral pitch class model of the probe tone data and scalic tonality. Music Perception. 2015 Apr, 32(4):364-93.
  15. Cazden N. Musical consonance and dissonance: A cultural criterion. The Journal of Aesthetics and Art Criticism. 1945 Sep, 4(1):3-11.
  16. Cazden N. The definition of consonance and dissonance. International Review of the Aesthetics and Sociology of Music. 1980 Dec, 11(2):123-68.
  17. Parncutt R, Reisinger D, Fuchs A, Kaiser F. Consonance and prevalence of sonorities in Western polyphony: Roughness, harmonicity, familiarity, evenness, diatonicity. Journal of New Music Research. 2019, 48(1):1-20.
  18. Cousineau M, McDermott JH, Peretz I. The basis of musical consonance as revealed by congenital amusia. PNAS. 2012 Nov 27, 109(40):19858-63.
  19. McDermott JH, Schultz AF, Undurraga EA, Godoy RA. Indifference to dissonance in native Amazonians reveals cultural variation in music perception. Nature. 2016 Jul 28, 535(7613):547-50. https://doi.org/ 10.1038/nature18635 PMID: 27409816
  20. Bailes F, Dean RT, Broughton MC. How different are our perceptions of equal-tempered and microtonal intervals? A behavioural and EEG survey. PLoS ONE. 2015 Aug 18; 10(8):e0135082. https://doi.org/ 10.1371/journal.pone.0135082 PMID: 26285010
  21. Herff SA, Olsen KN, Dean RT, Prince J. Memory for melodies in unfamiliar tuning systems: Investigating effects of recency and number of intervening items. Q J Exp Psychol. 2017 May 26, 71:1367-81.
  22. Leung Y, Dean RT. Learning unfamiliar pitch intervals: A novel paradigm for demonstrating the learning of statistical associations between musical pitches. PLoS ONE. 2018, Aug 30, 13(8):e0203026. https:// doi.org/10.1371/journal.pone.0203026 PMID: 30161174
  23. Leung Y, Dean RT. Learning a well-formed microtonal scale: Pitch intervals and event frequencies. Journal of New Music Research. 2018 Feb 15, 8215:1-20.
  24. Milne AJ. A computational model of the cognition of tonality [dissertation].
  25. The Bohlen-Pierce site [Internet].
  26. Huygens-Fokker; c2013 [cited 2019 Feb 5]. Available from: http:// www.huygens-fokker.org/bpsite/
  27. Mathews MV, Pierce JR, Reeves A, Roberts L. Theoretical and experimental explorations of the Boh- len-Pierce scale. Journal of the Acoustical Society of America. 1988 Jun, 84(4):1214-1222.
  28. Bohlen H. 13 Tonstufen in der Duodezime. Acustica, Jan; 39:67-86.
  29. Friedman RS, Trammell Neill W, Seror GA III, Kleinsmith AL. Average pitch height and perceived emo- tional expression within an unconventional tuning system. Music Perception 2018 Apr, 35(4):518-523.
  30. Huron D. A comparison of average pitch height and interval size in major-and minor-key themes: Evi- dence consistent with affect-related pitch prosody. Empirical Musicology Review. 2008 Apr, 3(2):59-63.
  31. Huron D, Davis M. The harmonic minor scale provides an optimum way of reducing average melodic interval size, consistent with sad affect cues. Empirical Musicology Review. 2012, 7(3):103-117.
  32. Temperley D, Tan D. Emotional connotations of diatonic modes. Music Perception. 2013, 30(3):237- 57.
  33. Loui P, Wessel D L, Hudson Kam C L. Humans rapidly learn grammatical structure in a new musical scale. Music Perception. 2010 June 1, 27(5):377-388. https://doi.org/10.1525/mp.2010.27.5.377 PMID: 20740059
  34. Loui P, Wu E H, Wessel D L, Knight R T. A generalized mechanism for perception of pitch patterns. Journal of Neuroscience. 2009 Jan 14, 29(2): 454-459. https://doi.org/10.1523/JNEUROSCI.4503-08. 2009 PMID: 19144845
  35. Loui P. Learning and liking of melody and harmony: Further studies in artificial grammar learning. Topics in Cognitive Science. 2012 Oct, 4(4):554-567. https://doi.org/10.1111/j.1756-8765.2012.01208.x PMID: 22760940
  36. Helmholtz H. On the sensation of tones as a physiological basis for the theory of music. 2nd ed. Ellis AJ, editor. New York: Dover; 1954/1877.
  37. Sethares WA. Tuning, timbre, spectrum, scale. 2nd ed. London: Springer-Verlag; 2005.
  38. Bowling DL, Purves D. A biological rationale for musical consonance. PNAS. 2015 Sep 8, 112 (36):11155-60. https://doi.org/10.1073/pnas.1505768112 PMID: 26209651
  39. McLachlan N, Marco D, Light M, Wilson S. Consonance and pitch. Journal of Experimental Psychology: General. 2013 Jan 7, 142(4):1142-1158.
  40. Johnson-Laird P, Kang O, Leon Y. On musical dissonance. Music Perception. 2012 Sep, 30(1);19-35.
  41. Dewitt LA, Crowder RG. Tonal fusion of consonant musical intervals: The oomph in Stumpf. Perception & Psychophysics. 1987 Jan, 41(1):73-84.
  42. Parncutt R. The emotional connotations of major versus minor tonality: One or more origins? Musicae Scientiae. 2014 Aug 20, 18(3):324-353.
  43. Milne AJ, Laney R, Sharp DB. Testing a spectral model of tonal affinity with microtonal melodies and inharmonic spectra. Musicae Scientiae. 2016, 20(4):465-494.
  44. McDermott JH, Lehr AJ, Oxenham AJ. Individual differences reveal the basis of consonance. Current Biology. 2010 Jun 8, 20(11):1035-1041. https://doi.org/10.1016/j.cub.2010.04.019 PMID: 20493704
  45. Milne AJ, Sethares WA, Laney R, Sharp DB. Modelling the similarity of pitch collections with expectation tensors. Journal of Mathematics and Music. 2011 May, 5(1):1-20.
  46. Terhardt E, Stoll G, Seewann M. Algorithm for extraction of pitch and pitch salience from complex tonal signals. Journal of the Acoustical Society of America. 1982, 71:679-88.
  47. Dubnov S. Generalization of spectral flatness measure for non-gaussian linear processes. IEEE Signal Processing Letters. 2004 Jul 26, 11(8):698-701.
  48. Johnston J. Transform coding of audio signals using perceptual noise criteria. IEEE Journal on Selected Areas in Communications. 1988 Feb, 6(2):314-332.
  49. Gingras B, Marin MM, Fitch WT. Beyond intensity: Spectral features effectively predict music-induced subjective arousal. Q J Exp Psychol. 2014; 67(7):1428-46.
  50. Milne AJ, Bulger D, Herff SA. Exploring the space of perfectly balanced rhythms and scales. Journal of Mathematics and Music. 2017, 11(2-3):101-33.
  51. Milne AJ, Holland S. Empirically testing Tonnetz, voice-leading, and spectral models of perceived triadic distance. Journal of Mathematics and Music. 2016 Apr 26, 10(1):59-85.
  52. Hong Y, Chau CJ, Horner A. How often and why mode fails to predict mode in low-arousal classical piano music. Journal of New Music Research. 2018 Jun 20, 47(5):462-75.
  53. Scherer KR, Oshinsky JS. Cue utilization in emotion attribution from auditory stimuli. Motivation and Emotion. 1977 Dec, 1(4):331-46.
  54. Maher T. A rigorous test of the proposition that musical intervals have different psychological effects. Am J Psychol. 1980 Jun, 93(2):309-27. PMID: 7406071
  55. Lahdelma I. At the interface between sensation and emotion: perceived qualities of single chords [dis- sertation]. Jyva ¨skyla ¨, Finland: University of Jyva ¨skyla ¨; 2017.
  56. Parncutt R, Hair G. Consonance and dissonance in music theory and psychology: Disentangling disso- nant dichotomies. Journal of Interdisciplinary Music Studies. 2011, 5(2):119-166.
  57. Mu ¨llensiefen D, Gingras B, Musil J, Stewart L. The musicality of non-musicians: An index for assessing musical sophistication in the general population. PLoS ONE. 2014 Feb 26, 9(2):e101091.
  58. Zhang DJ, Susino M, McPherson GE, Schubert E. The definition of a musician in music psychology: A literature review and the six-year rule. Psychology of Music. 2018, Oct 22: https://doi.org/10.1177/ 0305735618804038
  59. De Finetti B. Theory of probability. Vol 1. New York: John Wiley and Sons; 1974.
  60. Van de Schoot R, Depaoli S. Bayesian analyses: where to start and what to report. The European Health Psychologist. 2014, 16(2):75-84.
  61. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria; 2014.
  62. Bu ¨rkner P-C. brms: An R package for Bayesian multilevel models using Stan. Journal of Statistical Soft- ware. 2017 Aug 29, 80(1):1-28.
  63. Bu ¨rkner P-C. Advanced Bayesian multilevel modelling with the R package brms. The R Journal. 2018 Jul, 10(1):395-411.
  64. Barr DJ, Levy R, Scheepers C, Tilyc HJ. Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language. 2013 Apr, 68(3):255-278.
  65. Kruschke JK. Doing Bayesian Data analysis. 2nd ed. Burlington: Academic Press/Elsevier; 2015.
  66. Jeffreys H. The theory of probability. Oxford: Oxford University Press; 1961.
  67. Kruschke JK. Rejecting or accepting parameter values in Bayesian estimation. Advances in Methods and Practices in Psychological Science. 2018, May 8, 1(2):270-280.
  68. Gelman A, Hwang J, Vehtari A. Understanding predictive information criteria for Bayesian models. Sta- tistics and Computing. 2014 Nov, 24(6):997-1016.
  69. Gelman A, Lee D, Guo J. Stan: A probabilistic programming language for Bayesian inference and opti- mization. Journal of Educational and Behavioral Statistics. 2015 Oct 1, 40(5):530-543.
  70. Stolzenburg F. Harmony perception by periodicity detection. Journal of Mathematics and Music. 2015. 9(3):215-38.
  71. Terhardt E. Pitch, consonance, and harmony. The Journal of the Acoustical Society of America. 1974, 55(5):1061-69. https://doi.org/10.1121/1.1914648 PMID: 4833699
  72. Van De Geer J, Levelt W, Plomp R. The connotation of musical consonance. Acta Psychologica. 1962, 20:308-319.
  73. Scherer KR. Expression of emotion in voice and music. Journal of Voice. 1995 Sep, 9(3):235-48. PMID: 8541967
  74. Bowling DL. A vocal basis for the affective character of musical mode in melody. Front Psychol. 2013 Jul 31, 4:464. https://doi.org/10.3389/fpsyg.2013.00464 PMID: 23914179