Have a personal or library account? Click to login
Feeling the Beat: Temporal Predictability is Associated with Ongoing Changes in Music-Induced Pleasantness Cover

Feeling the Beat: Temporal Predictability is Associated with Ongoing Changes in Music-Induced Pleasantness

Open Access
|Jul 2023

References

  1. Aljanaki, A., Yang, Y.-H., & Soleymani, M. (2017). Developing a benchmark for emotional analysis of music. PloS one, 12(3), e0173392. DOI: 10.1371/journal.pone.0173392
  2. Alluri, V., Toiviainen, P., Burunat, I., Kliuchko, M., Vuust, P., & Brattico, E. (2017). Connectivity patterns during music listening: Evidence for action-based processing in musicians. Human brain mapping, 38(6), 29552970. DOI: 10.1002/hbm.23565
  3. Bachorik, J. P., Bangert, M., Loui, P., Larke, K., Berger, J., Rowe, R., & Schlaug, G. (2009). Emotion in motion: Investigating the time-course of emotional judgments of musical stimuli. DOI: 10.1525/mp.2009.26.4.355
  4. Bar, M. (2009). The proactive brain: memory for predictions. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1521), 12351243. DOI: 10.1098/rstb.2008.0310
  5. Berlyne, D. E. (1971). Aesthetics and psychology.
  6. Bianco, R., Gold, B. P., Johnson, A. P., & Penhune, V. B. (2019). Music predictability and liking enhance pupil dilation and promote motor learning in non-musicians. Scientific reports, 9(1), 112. DOI: 10.1038/s41598-019-53510-w
  7. Böck, S., Krebs, F., & Widmer, G. (2014). A Multi-model Approach to Beat Tracking Considering Heterogeneous Music Styles. Paper presented at the ISMIR.
  8. Bonin, T. L., Trainor, L. J., Belyk, M., & Andrews, P. W. (2016). The source dilemma hypothesis: Perceptual uncertainty contributes to musical emotion. Cognition, 154, 174181. DOI: 10.1016/j.cognition.2016.05.021
  9. Brattico, E., Jacobsen, T., De Baene, W., Glerean, E.,, & Tervaniemi, M. (2010). Cognitive vs. affective listening modes and judgments of music–An ERP study. Biological psychology, 85(3), 393409. DOI: 10.1016/j.biopsycho.2010.08.014
  10. Cabrera, D., Ferguson, S., & Schubert, E. (2007). Psysound3: Software for acoustical and psychoacoustical analysis of sound recordings. Paper presented at the Proc. 13th International Conference on Auditory Display.
  11. Cannam, C., Landone, C., & Sandler, M. (2010). Sonic visualiser: An open source application for viewing, analysing, and annotating music audio files. Paper presented at the Proceedings of the international conference on Multimedia. DOI: 10.1145/1873951.1874248
  12. Cannon, J. J., & Patel, A. D. (2021). How beat perception co-opts motor neurophysiology. Trends in Cognitive Sciences, 25(2), 137150. DOI: 10.1016/j.tics.2020.11.002
  13. Chapin, H., Jantzen, K., Kelso, J. A. S., Steinberg, F., & Large, E. W. (2010). Dynamic emotional and neural responses to music depend on performance expression and listener experience. PLoS ONE, 5(12), e13812. DOI: 10.1371/journal.pone.0013812
  14. Cheung, V. K. M., Harrison, Peter M. C., Meyer, L., Pearce, M. T., Haynes, J.-D., & Koelsch, S. (2019). Uncertainty and surprise jointly predict musical pleasure and amygdala, hippocampus, and auditory cortex activity. Current Biology, 29(23), 40844092. e4084. DOI: 10.1016/j.cub.2019.09.067
  15. Chmiel, A., & Schubert, E. (2017). Back to the inverted-U for music preference: A review of the literature. Psychology of Music, 45(6), 886909. DOI: 10.1177/0305735617697507
  16. Coutinho, E., & Cangelosi, A. (2011). Musical emotions: predicting second-by-second subjective feelings of emotion from low-level psychoacoustic features and physiological measurements. Emotion, 11(4), 921. DOI: 10.1037/a0024700
  17. Cross, I. (2014). Music and communication in music psychology. Psychology of Music, 42(6), 809819. DOI: 10.1177/0305735614543968
  18. Daldry, S. (2002). The hours [motion picture]. United States. Paramount Pictures.
  19. Davis, F. C., Neta, M., Kim, M. J., Moran, J. M., & Whalen, P. J. (2016). Interpreting ambiguous social cues in unpredictable contexts. Social cognitive and affective neuroscience, nsw003. DOI: 10.1093/scan/nsw003
  20. Disney, W. (1940). Fantasia [animated picture]. United States. Walt Disney Productions.
  21. Dunbar, R. (2012). On the evolutionary function of song and dance. Music, Language, and Human Evolution, 201. DOI: 10.1093/acprof:osobl/9780199227341.003.0008
  22. Eerola, T. (2011). Are the Emotions Expressed in Music Genre-specific? An Audio-based Evaluation of Datasets Spanning Classical, Film, Pop and Mixed Genres. Journal of New Music Research, 40(4), 349366. DOI: 10.1080/09298215.2011.602195
  23. Eerola, T., Lartillot, O., & Toiviainen, P. (2009). Prediction of Multidimensional Emotional Ratings in Music from Audio Using Multivariate Regression Models. Paper presented at the ISMIR.
  24. Egermann, H, Pearce, M. T, Wiggins, G. A., & McAdams, S. (2013). Probabilistic models of expectation violation predict psychophysiological emotional responses to live concert music. Cognitive, Affective, & Behavioral Neuroscience, 13(3), 533553. DOI: 10.3758/s13415-013-0161-y
  25. Ferreri, L., Mas-Herrero, E., Zatorre, R. J., Ripollés, P., Gomez-Andres, A., Alicart, H., … Valle, M. (2019). Dopamine modulates the reward experiences elicited by music. Proceedings of the National Academy of Sciences, 116(9), 37933798. DOI: 10.1073/pnas.1811878116
  26. Fredrickson, W. E. (1999). Effect of musical performance on perception of tension in Gustav Hoist’s First Suite in E-flat. Journal of Research in Music Education, 47(1), 4452. DOI: 10.2307/3345827
  27. Friston, K. (2005). A theory of cortical responses. Philosophical transactions of the Royal Society B: Biological sciences, 360(1456), 815836. DOI: 10.1098/rstb.2005.1622
  28. Friston, K., FitzGerald, T., Rigoli, F., Schwartenbeck, P., & Pezzulo, G. (2017). Active inference: a process theory. Neural computation, 29(1), 149. DOI: 10.1162/NECO_a_00912
  29. Fujii, S., & Schlaug, G. (2013). The Harvard Beat Assessment Test (H-BAT): a battery for assessing beat perception and production and their dissociation. Frontiers in human neuroscience, 7, 771. DOI: 10.3389/fnhum.2013.00771
  30. Gabrielsson, A. (2014). The relationship between musical structure and perceived expression. DOI: 10.1093/oxfordhb/9780198722946.013.18
  31. Gabrielsson, A., & Lindström, E. (2010). The role of structure in the musical expression of emotions. Handbook of music and emotion: Theory, research, applications, 367400. DOI: 10.1093/acprof:oso/9780199230143.003.0014
  32. Gold, B. P., Mas-Herrero, E., Zeighami, Y., Benovoy, M., Dagher, A., & Zatorre, R. J. (2019). Musical reward prediction errors engage the nucleus accumbens and motivate learning. Proceedings of the National Academy of Sciences, 116(8), 33103315. DOI: 10.1073/pnas.1809855116
  33. Gold, B. P., Pearce, M. T., Mas-Herrero, E., Dagher, A., & Zatorre, R. J. (2019). Predictability and uncertainty in the pleasure of music: a reward for learning? Journal of Neuroscience, 39(47), 93979409. DOI: 10.1523/JNEUROSCI.0428-19.2019
  34. Goupil, L., & Aucouturier, J.-J. (2019). Musical pleasure and musical emotions. Proceedings of the National Academy of Sciences, 116(9), 33643366. DOI: 10.1073/pnas.1900369116
  35. Granot, R. Y., & Eitan, Z. (2011). Musical tension and the interaction of dynamic auditory parameters. Music Perception, 28(3), 219246. DOI: 10.1525/mp.2011.28.3.219
  36. Grewe, O., Nagel, F., Kopiez, R., & Altenmüller, E. (2007). Emotions over time: Synchronicity and development of subjective, physiological, and facial affective reactions to music. Emotion, 7(4), 774. DOI: 10.1037/1528-3542.7.4.774
  37. Grillon, C., Baas, J. P., Lissek, S., Smith, K., & Milstein, J. (2004). Anxious responses to predictable and unpredictable aversive events. Behavioral neuroscience, 118(5), 916. DOI: 10.1037/0735-7044.118.5.916
  38. Herry, C., Bach, D. R., Esposito, F., Di Salle, F., Perrig, W. J., Scheffler, K., … Seifritz, E. (2007). Processing of temporal unpredictability in human and animal amygdala. The Journal of Neuroscience, 27(22), 59585966. DOI: 10.1523/JNEUROSCI.5218-06.2007
  39. Heyduk, R. G. (1975). Rated preference for musical compositions as it relates to complexity and exposure frequency. Perception & Psychophysics, 17(1), 8490. DOI: 10.3758/BF03204003
  40. Honey, C. J., Thompson, C. R., Lerner, Y., & Hasson, U. (2012). Not lost in translation: neural responses shared across languages. The Journal of Neuroscience, 32(44), 1527715283. DOI: 10.1523/JNEUROSCI.1800-12.2012
  41. Huron, D. (2008). Sweet anticipation: Music and the psychology of expectation: MIT press.
  42. Jackson, F., Nelson, B. D., & Proudfit, G. H. (2015). In an uncertain world, errors are more aversive: Evidence from the error-related negativity. Emotion, 15(1), 12. DOI: 10.1037/emo0000020
  43. James, L. R., Demaree, R. G., & Wolf, G. (1984). Estimating within-group interrater reliability with and without response bias. Journal of Applied Psychology, 69, 8598. DOI: 10.1037/0021-9010.69.1.85
  44. Janata, P, Tomic, S. T., & Haberman, J. M. (2012). Sensorimotor coupling in music and the psychology of the groove. Journal of experimental psychology: general, 141(1), 54. DOI: 10.1037/a0024208
  45. Juslin, P. N. (2013). From everyday emotions to aesthetic emotions: towards a unified theory of musical emotions. Physics of life reviews, 10(3), 235266. DOI: 10.1016/j.plrev.2013.05.008
  46. Juslin, P. N., Liljeström, S., Västfjäll, D., & Lundqvist, L.-O. (2010). How does music evoke emotions? Exploring the underlying mechanisms. In P. N. Juslin & J. A. Sloboda (Eds.), Handbook of Music and Emotion: Theory, Research, Applications (pp. 605642): Oxford University Press. DOI: 10.1093/acprof:oso/9780199230143.003.0022
  47. Keller, P. E., & Schubert, E. (2011). Cognitive and affective judgements of syncopated musical themes. Advances in Cognitive Psychology, 7, 142156. DOI: 10.2478/v10053-008-0094-0
  48. Koelsch, S., Vuust, P., & Friston, K. (2018). Predictive processes and the peculiar case of music. Trends in cognitive sciences. DOI: 10.1016/j.tics.2018.10.006
  49. Koelsch, S., Vuust, P., & Friston, K. (2019). Predictive processes and the peculiar case of music. Trends in cognitive sciences, 23(1), 6377. DOI: 10.1016/j.tics.2018.10.006
  50. Koppe, G., Gruppe, H., Sammer, G., Gallhofer, B., Kirsch, P., & Lis, S. (2014). Temporal unpredictability of a stimulus sequence affects brain activation differently depending on cognitive task demands. NeuroImage, 101, 236244. DOI: 10.1016/j.neuroimage.2014.07.008
  51. Krumhansl, C. L. (1997). An exploratory study of musical emotions and psychophysiology. Can J Exp Psychol, 51(4), 336353. DOI: 10.1037/1196-1961.51.4.336
  52. Kubrick, S. (Writer). (1999). Eyes Wide Shut.
  53. Large, E. W., Fink, P., & Kelso, S. J. (2002). Tracking simple and complex sequences. Psychological research, 66(1), 317. DOI: 10.1007/s004260100069
  54. Lartillot, O, Eerola, T., Toiviainen, P., & Fornari, J. (2008). Multi-feature modeling of pulse clarity: Design, validation, and optimization. Paper presented at the ISMIR 2008 International Conference on Music Information Retrieval, Philadelphia, PA. DOI: 10.1007/978-3-540-78246-9_31
  55. Lartillot, O., Toiviainen, P., & Eerola, T. (2008). A matlab toolbox for music information retrieval Data analysis, machine learning and applications (pp. 261268): Springer. DOI: 10.1007/978-3-540-78246-9_31
  56. Madsen, C. K. (1998). Emotion versus tension in Haydn’s Symphony no. 104 as measured by the two-dimensional continuous response digital interface. Journal of Research in Music Education, 46(4), 546554. DOI: 10.2307/3345350
  57. Martens, P. A. (2011). The ambiguous tactus: Tempo, subdivision benefit, and three listener strategies. Music Perception: An Interdisciplinary Journal, 28(5), 433448. DOI: 10.1525/mp.2011.28.5.433
  58. Matthews, T. E., Witek, M. A. G., Heggli, O. A., Penhune, V. B., & Vuust, P. (2019). The sensation of groove is affected by the interaction of rhythmic and harmonic complexity. PLoS One, 14(1), e0204539. DOI: 10.1371/journal.pone.0204539
  59. McKinney, M. F., Moelants, D., Davies, M. E. P., & Klapuri, A. (2007). Evaluation of audio beat tracking and music tempo extraction algorithms. Journal of New Music Research, 36(1), 116. DOI: 10.1080/09298210701653252
  60. McNeill, W. H. (1997). Keeping together in time: Harvard University Press. DOI: 10.4159/9780674040878
  61. Meyer, L. B. (2008). Emotion and meaning in music: University of chicago Press.
  62. Miguel, M. A., Sigman, M., & Fernandez Slezak, D. (2020). From beat tracking to beat expectation: Cognitive-based beat tracking for capturing pulse clarity through time. PloS one, 15(11), e0242207. DOI: 10.1371/journal.pone.0242207
  63. Møller, C., Stupacher, J., Celma-Miralles, A., & Vuust, P. (2021). Beat perception in polyrhythms: Time is structured in binary units. Plos one, 16(8), e0252174. DOI: 10.1371/journal.pone.0252174
  64. Nagel, F., Kopiez, R., Grewe, O., & Altenmuller, E. (2007). EMuJoy: software for continuous measurement of perceived emotions in music. Behavior Research Methods, 39(2), 283290. DOI: 10.3758/BF03193159
  65. Overy, K, & Molnar-Szakacs, I. (2009). Being Together in Time: Musical Experience and the Mirror Neuron System. Music Perception: An Interdisciplinary Journal, 26(5), 489504. DOI: 10.1525/mp.2009.26.5.489
  66. Parisi, E. A., Hajcak, G., Aneziris, E., & Nelson, B. D. (2017). Effects of anticipated emotional category and temporal predictability on the startle reflex. International Journal of Psychophysiology. DOI: 10.1016/j.ijpsycho.2017.03.003
  67. Raz, G., Winetraub, Y., Jacob, Y., Kinreich, S., Maron-Katz, A., Shaham, G., … Hendler, T. (2012). Portraying emotions at their unfolding: a multilayered approach for probing dynamics of neural networks. Neuroimage, 60(2), 14481461. DOI: 10.1016/j.neuroimage.2011.12.084
  68. Saarikallio, S. H., Maksimainen, J. P., & Randall, W. M. (2019). Relaxed and connected: Insights into the emotional–motivational constituents of musical pleasure. Psychology of Music, 47(5), 644662. DOI: 10.1177/0305735618778768
  69. Sachs, M. E., Damasio, A., & Habibi, A. (2015). The pleasures of sad music: a systematic review. Frontiers in human neuroscience, 9, 404. DOI: 10.3389/fnhum.2015.00404
  70. Salimpoor, V. N., Zald, D. H., Zatorre, R. J., Dagher, A., & McIntosh, A. R. (2015). Predictions and the brain: how musical sounds become rewarding. Trends in Cognitive Sciences, 19(2), 8691. DOI: 10.1016/j.tics.2014.12.001
  71. Sauvé, S. A., Sayed, A., Dean, R. T., & Pearce, M. T. (2018). Effects of pitch and timing expectancy on musical emotion. Psychomusicology: Music, Mind, and Brain, 28(1), 17. DOI: 10.1037/pmu0000203
  72. Scherer, K. R., & Zentner, M. R. (2001). Emotional effects of music: Production rules. In P. N. Juslin & J. A. Sloboda (Eds.), Music and emotion: Theory and research (pp. 361392).
  73. Schubert, E. (2004). Modeling Perceived Emotion With Continuous Musical Features. Music Perception: An Interdisciplinary Journal, 21(4), 561585. DOI: 10.1525/mp.2004.21.4.561
  74. Schubert, E. (2013). Reliability issues regarding the beginning, middle and end of continuous emotion ratings to music. Psychology of music, 41(3), 350371. DOI: 10.1177/0305735611430079
  75. Schubert, E., & Dunsmuir, W. (1999). Regression modelling continuous data in music psychology. In S. W. Yi (Ed.), Music, Mind, and Science (pp. 298352). Seoul, Korea: Seoul National University Press.
  76. Shany, O., Singer, N., Gold, B. P., Jacoby, N., Tarrasch, R., Hendler, T., & Granot, R. (2019). Surprise-related activation in the nucleus accumbens interacts with music-induced pleasantness. Social Cognitive and Affective Neuroscience, 14(4), 459470. DOI: 10.1093/scan/nsz019
  77. Singer, N., Jacobi, N., Lin, T., Raz, G., Shpigelman, L., Gilam, G., … Hendler, T. (2016). Common modulation of limbic network activation underlies the unfolding of musical emotions and its temporal attributes. NeuroImage. DOI: 10.1016/j.neuroimage.2016.07.002
  78. Steinbeis, N., Koelsch, S., & Sloboda, J. A. (2006). The role of harmonic expectancy violations in musical emotions: Evidence from subjective, physiological, and neural responses. Journal of cognitive neuroscience, 18(8), 13801393. DOI: 10.1162/jocn.2006.18.8.1380
  79. Stupacher, J., Hove, M. J., & Janata, P. (2016). Audio features underlying perceived groove and sensorimotor synchronization in music. Music Perception: An Interdisciplinary Journal, 33(5), 571589. DOI: 10.1525/mp.2016.33.5.571
  80. Stupacher, J., Wrede, M., & Vuust, P. (2022). A brief and efficient stimulus set to create the inverted U-shaped relationship between rhythmic complexity and the sensation of groove. Plos one, 17(5), e0266902. DOI: 10.1371/journal.pone.0266902
  81. Tarr, B., Launay, J., & Dunbar, R. I. M. (2014). Music and social bonding:“self-other” merging and neurohormonal mechanisms. Frontiers in psychology, 5. DOI: 10.3389/fpsyg.2014.01096
  82. Timmers, R., Marolt, M., Camurri, A., & Volpe, G. (2006). Listeners’ emotional engagement with performances of a Scriabin étude: an explorative case study. Psychology of Music, 34(4), 481510. DOI: 10.1177/0305735606067165
  83. Trapp, S., Shenhav, A., Bitzer, S., & Bar, M. (2015). Human preferences are biased towards associative information. Cognition and Emotion, 29(6), 10541068. DOI: 10.1080/02699931.2014.966064
  84. Trost, W., Frühholz, S., Cochrane, T., Cojan, Y., & Vuilleumier, P. (2015). Temporal dynamics of musical emotions examined through intersubject synchrony of brain activity. Social cognitive and affective neuroscience, 10(12), 17051721. DOI: 10.1093/scan/nsv060
  85. Trost, W., & Vuilleumier, P. (2013). Rhythmic entrainment as a mechanism for emotion induction by music: a neurophysiological perspective. The Emotional Power of Music: Multidisciplinary perspectives on musical arousal, expression, and social control, 213225. DOI: 10.1093/acprof:oso/9780199654888.003.0016
  86. Vuust, P., & Kringelbach, M. L. (2010). The pleasure of making sense of music. Interdisciplinary science reviews, 35(2), 166182. DOI: 10.1179/030801810X12723585301192
  87. Vuust, P., & Witek, M. A. G. (2014). Rhythmic complexity and predictive coding: a novel approach to modeling rhythm and meter perception in music. Frontiers in psychology, 5, 1111. DOI: 10.3389/fpsyg.2014.01111
  88. Walker, E. L. (1972). Psychological complexity and preference: A hedgehog theory of behavior. Pleasure, reward, preference: Their nature, determinants, and role in behavior, 6597. DOI: 10.1016/B978-0-12-092550-6.50008-9
  89. Witek, M. A. G., Clarke, E. F., Wallentin, M., Kringelbach, M. L., & Vuust, P. (2014). Syncopation, Body-Movement and Pleasure in Groove Music. PloS one, 9(4), e94446. DOI: 10.1371/journal.pone.0094446
  90. Yeshurun, Y., Swanson, S., Simony, E., Chen, J., Lazaridi, C., Honey, C. J., & Hasson, U. (2017). Same story, different story: the neural representation of interpretive frameworks. Psychological science, 28(3), 307319. DOI: 10.1177/0956797616682029
  91. Zentner, M., Grandjean, D., & Scherer, K. R. (2008). Emotions evoked by the sound of music: characterization, classification, and measurement. Emotion, 8(4), 494. DOI: 10.1037/1528-3542.8.4.494
DOI: https://doi.org/10.5334/joc.286 | Journal eISSN: 2514-4820
Language: English
Submitted on: Dec 5, 2022
|
Accepted on: Jun 12, 2023
|
Published on: Jul 4, 2023
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2023 Neomi Singer, Nori Jacoby, Talma Hendler, Roni Granot, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.