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
Zdroje
1. Juslin PN, Sloboda JA, editors. Music and emotion: Theory and research. Music and emotion: Theory and research. New York, NY, US: Oxford University Press; 2001.
2. Huron D. Is music an evolutionary adaptation? Ann N Y Acad Sci. 2001;930: 43–61. Available: http://www.ncbi.nlm.nih.gov/pubmed/11458859 doi: 10.1111/j.1749-6632.2001.tb05724.x 11458859
3. Scherer KR, Zentner MR. Emotional effects of music: Production rules. Music and emotion: Theory and research. Scherer, Klaus R.: Dept of Psychology, U Geneva, 40 Boulevard du Pont d’Arve, Geneva, Switzerland, CH—1205: Oxford University Press; 2001. pp. 361–392.
4. Juslin PN, Västfjäll D. Emotional responses to music: The need to consider underlying mechanisms. Behav Brain Sci. 2008;31: 559–75; discussion 575–621. doi: 10.1017/S0140525X08005293 18826699
5. Juslin PN, Zentner MR. Current trends in the study of music and emotion: Overture. Music Sci. SAGE PublicationsSage UK: London, England; 2001;5: 3–21. doi: 10.1177/10298649020050S101
6. Fritz T, Jentschke S, Gosselin N, Sammler D, Peretz I, Turner R, et al. Universal Recognition of Three Basic Emotions in Music. Curr Biol. Elsevier; 2009;19: 573–576. doi: 10.1016/j.cub.2009.02.058 19303300
7. Balkwill L-L, Thompson WF. A Cross-Cultural Investigation of the Perception of Emotion in Music: Psychophysical and Cultural Cues. Music Percept An Interdiscip J. 1999;17: 43–64. doi: 10.2307/40285811
8. Savage PE, Brown S, Sakai E, Currie TE. Statistical universals reveal the structures and functions of human music. Proc Natl Acad Sci U S A. National Academy of Sciences; 2015;112: 8987–92. doi: 10.1073/pnas.1414495112 26124105
9. Brown S, Jordania J. Universals in the world’s musics. Psychol Music. SAGE PublicationsSage UK: London, England; 2013;41: 229–248. doi: 10.1177/0305735611425896
10. Maher TF, Berlyne DE. Verbal and Exploratory Responses to Melodic Musical Intervals. Psychol Music. SAGE PublicationsSage CA: Thousand Oaks, CA; 1982;10: 11–27. doi: 10.1177/0305735682101002
11. Chordia P, Rae A. Understanding emotion in raag: an empirical study of listener responses. International symposium on computer music modeling and retrieval. Springer; 2007. pp. 110–124.
12. Wieczorkowska AA, Datta AK, Sengupta R, Dey N, Mukherjee B. On search for emotion in Hindusthani vocal music. Advances in music information retrieval. Springer; 2010. pp. 285–304.
13. Mathur A, Vijayakumar SH, Chakrabarti B, Singh NC. Emotional responses to Hindustani raga music: the role of musical structure. Front Psychol. Frontiers; 2015;6: 513. doi: 10.3389/fpsyg.2015.00513 25983702
14. Laukka P, Gabrielsson A. Emotional Expression in Drumming Performance. Psychol Music. SAGE PublicationsSage CA: Thousand Oaks, CA; 2000;28: 181–189. doi: 10.1177/0305735600282007
15. Cameron DJ, Bentley J, Grahn JA. Cross-cultural influences on rhythm processing: reproduction, discrimination, and beat tapping. Front Psychol. 2015;6: 366. doi: 10.3389/fpsyg.2015.00366 26029122
16. Jairazbhoy NA. The rāgs of North Indian music: their structure and evolution. Popular Prakashan; 1995.
17. Valla JM, Alappatt JA, Mathur A, Singh NC. Music and Emotion-A Case for North Indian Classical Music. Front Psychol. Frontiers Media SA; 2017;8: 2115. doi: 10.3389/fpsyg.2017.02115 29312024
18. BALKWILL L-L, THOMPSON WF, MATSUNAGA R. Recognition of emotion in Japanese, Western, and Hindustani music by Japanese listeners 1. Jpn Psychol Res. John Wiley & Sons, Ltd (10.1111); 2004;46: 337–349. doi: 10.1111/j.1468-5584.2004.00265.x
19. Laukka P, Eerola T, Thingujam NS, Yamasaki T, Beller G. Universal and culture-specific factors in the recognition and performance of musical affect expressions. Emotion. 2013;13: 434–449. doi: 10.1037/a0031388 23398579
20. Scherer KR. Which Emotions Can be Induced by Music? What Are the Underlying Mechanisms? And How Can We Measure Them? J New Music Res. 2004;33: 239–251. doi: 10.1080/0929821042000317822
21. Mesquita B, Frijda NH. Cultural variations in emotions: A review. Psychol Bull. 1992;112: 179–204. doi: 10.1037/0033-2909.112.2.179 1454891
22. Benamou M. Comparing musical affect: Java and the West. world Music. JSTOR; 2003; 57–76.
23. Hevner K. Experimental studies of the elements of expression in music. Am J Psychol. JSTOR; 1936;48: 246–268.
24. Becker J. Exploring the habitus of listening. Handb Music Emot Theory, Res Appl. 2010; 127–158.
25. Juslin PN, Laukka P. Communication of emotions in vocal expression and music performance: Different channels, same code? Psychological Bulletin. Juslin, Patrik N.: Department of Psychology, Uppsala University, Box 1225, Uppsala, Sweden, SE-751 42, patrik.juslin@psyk.uu.se: American Psychological Association; 2003. pp. 770–814. doi: 10.1037/0033-2909.129.5.770 12956543
26. Rao C, Mathur A, Singh NC. ‘Cost in Transliteration’: The neurocognitive processing of Romanized writing. Brain Lang. 2013;124: 205–212. doi: 10.1016/j.bandl.2012.12.004 23400116
27. Lartillot O, Toiviainen P, Eerola T. A matlab toolbox for music information retrieval. Data analysis, machine learning and applications. Springer; 2008. pp. 261–268.
28. Pampalk E, Rauber A, Merkl D. Content-based Organization and Visualization of Music Archives [Internet]. 2002. Available: http://www.ofai.at/~elias.pampalk/publications/pam_mm02.pdf
29. Salamon J, Gomez E. Melody Extraction From Polyphonic Music Signals Using Pitch Contour Characteristics. IEEE Trans Audio Speech Lang Processing. 2012;20: 1759–1770. doi: 10.1109/TASL.2012.2188515
30. Huber PJ, Ronchetti EM. Robust statistics, ser. Wiley Ser Probab Math Stat New York, NY, USA Wiley-IEEE. 1981;52: 54.
31. Venables WN, Ripley BD. Robust statistics. Modern Applied Statistics With S-PLUS. Springer; 1997. pp. 247–266.
32. Trevor H, Robert T, JH F. The elements of statistical learning: data mining, inference, and prediction. New York, NY: Springer; 2009.
33. Chen T, Guestrin C. Xgboost: A scalable tree boosting system. Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. ACM; 2016. pp. 785–794.
34. RColorBrewer S, Liaw MA. Package ‘randomForest.’ 2018;
35. Castellano MA, Bharucha JJ, Krumhansl CL. Tonal hierarchies in the music of north India. J Exp Psychol Gen. 1984;113: 394–412. Available: http://www.ncbi.nlm.nih.gov/pubmed/6237169 6237169
36. EKMAN P. Universals and cultural differences in facial expressions of emotion. Nebraska Symposium on Motivation. University of Nebraska Press; 1972.
37. Ekman P, Friesen W V. Constants across cultures in the face and emotion. J Pers Soc Psychol. American Psychological Association; 1971;17: 124. doi: 10.1037/h0030377 5542557
38. Haidt J, Keltner D. Culture and facial expression: Open-ended methods find more expressions and a gradient of recognition. Cogn Emot. Taylor & Francis; 1999;13: 225–266.
39. Izard CE. Human emotions. New York: Plenum Press; 1977.
40. Schwanenberg E. Izard CE: The Face of Emotion. New York (Appleton-Century-Crofts) 1971, 468 Seiten. Psyche (Stuttg). Berlin: Kemper; 1974;28: 919–920.
41. Hejmadi A, Davidson RJ, Rozin P. Exploring Hindu Indian emotion expressions: Evidence for accurate recognition by Americans and Indians. Psychological Science. United Kingdom: Blackwell Publishing; 2000. pp. 183–187. doi: 10.1111/1467-9280.00239 11273401
42. Ravignani A, Delgado T, Kirby S. Musical evolution in the lab exhibits rhythmic universals. Nat Hum Behav. Nature Publishing Group; 2016;1: 0007. doi: 10.1038/s41562-016-0007
43. Giannantonio S, Polonenko MJ, Papsin BC, Paludetti G, Gordon KA. Experience Changes How Emotion in Music Is Judged: Evidence from Children Listening with Bilateral Cochlear Implants, Bimodal Devices, and Normal Hearing. PLoS One. Public Library of Science; 2015;10: e0136685. Available: doi: 10.1371/journal.pone.0136685 26317976
Článek vyšel v časopise
PLOS One
2019 Číslo 9
- Tisícileté topoly, mokří psi, stárnoucí kočky a ospalé octomilky – „jednohubky“ z výzkumu 2024/41
- Jaké jsou aktuální trendy v léčbě karcinomu slinivky?
- Menstruační krev má značný diagnostický potenciál, mimo jiné u diabetu
- Proč jsou nemocnice nepřítelem spánku? A jak to změnit?
- Metamizol jako analgetikum první volby: kdy, pro koho, jak a proč?