Preferred musical attribute dimensions underlie individual differences in music-induced analgesia

Krzysztof Basiński, Agata Zdun-Ryżewska, David M Greenberg, Mikołaj Majkowicz, Krzysztof Basiński, Agata Zdun-Ryżewska, David M Greenberg, Mikołaj Majkowicz

Abstract

Music-induced analgesia (MIA) is a phenomenon that describes a situation in which listening to music influences pain perception. The heterogeneity of music used in MIA studies leads to a problem of a specific effect for an unspecified stimulus. To address this, we use a previously established model of musical preferences that categorizes the multidimensional sonic space of music into three basic dimensions: arousal, valence and depth. Participants entered an experimental pain stimulation while listening to compilations of short musical excerpts characteristic of each of the three attribute dimensions. The results showed an effect on the part of music attribute preferences on average pain, maximal pain, and pain tolerance after controlling for musical attributes and order effects. This suggests that individual preferences for music attributes play a significant role in MIA and that, in clinical contexts, music should not be chosen arbitrarily but according to individual preferences.

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Pain outcomes as a function of music preference. Each data point represents one trial by one participant. Outcomes are reported as differences between one of three music conditions and the control condition. A value of 0 indicates no change from control. Random jitter was applied to all data points to facilitate the visualization of overlapping points. Presented outcomes are (a) average pain, (b) maximal pain, (c) pain controllability, (d) pain threshold, and (e) pain tolerance.

References

    1. IASP Task Force On Taxonomy. Part III: Pain Terms, A Current List with Definitions and Notes on Usage (with later updates). In Classification of chronic pain (eds. Merskey, H. & Bogduk, N.) 209–214 (IASP Press, 1994).
    1. Kuner R, Flor H. Structural plasticity and reorganisation in chronic pain. Nat. Rev. Neurosci. 2016;18:20–30. doi: 10.1038/nrn.2016.162.
    1. Crombez G, Eccleston C, Van Damme S, Vlaeyen JWS, Karoly P. Fear-avoidance model of chronic pain The next generation. Clin. J. Pain. 2012;28:475–483. doi: 10.1097/AJP.0b013e3182385392.
    1. Bushnell MC, Čeko M, Low LA. Cognitive and emotional control of pain and its disruption in chronic pain. Nat. Rev. Neurosci. 2013;14:502–511. doi: 10.1038/nrn3516.
    1. Roy M, Peretz I, Rainville P. Emotional valence contributes to music-induced analgesia. Pain. 2008;134:140–147. doi: 10.1016/j.pain.2007.04.003.
    1. Mitchell LA, MacDonald RAR, Knussen C. An investigation of the effects of music and art on pain perception. Psychol. Aesthet. Creat. Arts. 2008;2:162–170. doi: 10.1037/1931-3896.2.3.162.
    1. Mitchell LA, MacDonald RAR, Brodie EE. A comparison of the effects of preferred music, arithmetic and humour on cold pressor pain. Eur. J. Pain. 2006;10:343–351. doi: 10.1016/j.ejpain.2005.03.005.
    1. Hole J, Hirsch M, Ball E, Meads C. Music as an aid for postoperative recovery in adults: A systematic review and meta-analysis. The Lancet. 2015;6736:1–13.
    1. Lu X, Yi F, Hu L. Music-induced analgesia: An adjunct to pain management. Psychol. Music. 2020 doi: 10.1177/0305735620928585.
    1. Garza-Villarreal EA, et al. Music reduces pain and increases functional mobility in fibromyalgia. Front. Psychol. 2014;5:1–10. doi: 10.3389/fpsyg.2014.00090.
    1. Pando-Naude V, et al. Functional connectivity of music-induced analgesia in fibromyalgia. Sci. Rep. 2019;9:1–17. doi: 10.1038/s41598-019-51990-4.
    1. Finlay KA, Wilson JA, Gaston P, Al-Dujaili EAS, Power I. Post-operative pain management through audio-analgesia: Investigating musical constructs. Psychol. Music. 2016;44:493–513. doi: 10.1177/0305735615577247.
    1. Lunde SJ, Vuust P, Garza-Villarreal EA, Vase L. Reply to Martin-Saavedra and Saade-Lemus. Pain. 2019;160:1483–1484. doi: 10.1097/j.pain.0000000000001534.
    1. Rentfrow PJ, Greenberg DM. The social psychology of music. In: Rentfrow PJ, Levitin DJ, editors. Foundations in music psychology: Theory and research. Cambridge: MIT Press; 2019.
    1. Greenberg DM. Music and personality. In: Zeigler-Hill V, Shackelford TK, editors. Encyclopedia of personality and individual differences. Berlin: Springer; 2019.
    1. Rentfrow PJ, Gosling SD. The do re mi’s of everyday life: The structure and personality correlates of music preferences. J. Pers. Soc. Psychol. 2003;84:1236–1256. doi: 10.1037/0022-3514.84.6.1236.
    1. Greenberg DM, et al. The song is you: Preferences for musical attribute dimensions reflect personality. Soc. Psychol. Pers. Sci. 2016;7:597–605. doi: 10.1177/1948550616641473.
    1. Fricke KR, Greenberg DM, Rentfrow PJ, Herzberg PY. Measuring musical preferences from listening behavior: Data from one million people and 200,000 songs. Psychol. Music. 2019 doi: 10.1177/0305735619868280.
    1. Fricke KR, Herzberg PY. Personality and self-reported preference for music genres and attributes in a German-speaking sample. J. Res. Pers. 2017;68:114–123. doi: 10.1016/j.jrp.2017.01.001.
    1. Fricke KR, Greenberg DM, Rentfrow PJ, Herzberg PY. Computer-based music feature analysis mirrors human perception and can be used to measure individual music preference. J. Res. Pers. 2018;75:94–102. doi: 10.1016/j.jrp.2018.06.004.
    1. Finlay KA, Rogers J. Maximizing self-care through familiarity: The role of practice effects in enhancing music listening and progressive muscle relaxation for pain management. Psychol. Music. 2014;43:511–529. doi: 10.1177/0305735613513311.
    1. Rentfrow PJ, et al. The song remains the same: A replication and extension of the MUSIC model. Music. Percept. 2012;30:161–185. doi: 10.1525/mp.2012.30.2.161.
    1. ReplayGain 1.0 Specification. .
    1. Mitchell LA, MacDonald RAR, Brodie EE. Temperature and the cold pressor test. J. Pain. 2004;5:233–237. doi: 10.1016/j.jpain.2004.03.004.
    1. Van Der Walt S, Colbert SC, Varoquaux G. The NumPy array: A structure for efficient numerical computation. Comput. Sci. Eng. 2011;13:22. doi: 10.1109/MCSE.2011.37.
    1. Pandas Development Team, T. pandas-dev/pandas: Pandas. (2020).
    1. Seabold, S. & Perktold, J. Statsmodels: Econometric and statistical modeling with Python. In 9th Python in Science Conference (2010).
    1. Waskom M, 2020. Seaborn.
    1. R Core Team. R: A Language and Environment for Statistical Computing. (2018).
    1. Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models using {lme4} J. Stat. Softw. 2015;67:1–48. doi: 10.18637/jss.v067.i01.
    1. Kuznetsova A, Brockhoff PB, Christensen RHB. lmerTest Package: Tests in linear mixed effects models. J. Stat. Softw. 2017;82:1–26. doi: 10.18637/jss.v082.i13.
    1. Green P, Macleod CJ. SIMR: An R package for power analysis of generalized linear mixed models by simulation. Methods Ecol. Evol. 2016;7:493–498. doi: 10.1111/2041-210X.12504.
    1. Lu X, Thompson WF, Zhang L, Hu L. Music reduces pain unpleasantness: Evidence from an EEG study. J. Pain Res. 2019;12:3331–3342. doi: 10.2147/JPR.S212080.
    1. Juslin PN. From everyday emotions to aesthetic emotions: Towards a unified theory of musical emotions. Phys. Life Rev. 2013;10:235–266. doi: 10.1016/j.plrev.2013.05.008.
    1. Salimpoor VN, Zald DH, Zatorre RJ, Dagher A, McIntosh AR. Predictions and the brain: How musical sounds become rewarding. Trends Cogn. Sci. 2015;19:86–91. doi: 10.1016/j.tics.2014.12.001.
    1. Greenberg DM, Rentfrow PJ. Music and big data: A new frontier. Curr. Opin. Behav. Sci. 2017;18:50–56. doi: 10.1016/j.cobeha.2017.07.007.
    1. Buss DM. Selection, evocation, and manipulation. J. Pers. Soc. Psychol. 1987;53:1214. doi: 10.1037/0022-3514.53.6.1214.
    1. Greenberg DM, Baron-Cohen S, Stillwell DJ, Kosinski M, Rentfrow PJ. Musical preferences are linked to cognitive styles. PLoS ONE. 2015;10:1–22.
    1. Nave G, et al. Musical preferences predict personality: Evidence from active listening and facebook likes. Psychol. Sci. 2018;29:1145–1158. doi: 10.1177/0956797618761659.
    1. Anderson I, et al. ‘Just the way you are’: Linking music listening on Spotify and personality. Soc. Psychol. Pers. Sci. 2020 doi: 10.1177/1948550620923228.
    1. Greenberg, D. M., Matz, S. C., Schwartz, H. A. & Fricke, K. R. The self-congruity effect of music. J. Pers. Soc. Psychol. (2020).

Source: PubMed

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