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A Set of 200 Musical Stimuli Varying in Balance, Contour, Symmetry, and Complexity: Behavioral and Computational Assessments


We present a novel set of 200 Western tonal musical stimuli (MUST) to be used in research on perception and appreciation of music. It consists of four subsets of 50 stimuli varying in balance, contour, symmetry, or complexity. All are 4 s long and designed to be musically appealing and experimentally controlled. We assessed them behaviorally and computationally. The behavioral assessment (Study 1) aimed to determine whether musically untrained participants could identify variations in each attribute. Forty-three participants rated the stimuli in each subset on the corresponding attribute. We found that inter-rater reliability was high and that the ratings mirrored the design features well. Participants’ ratings also served to create an abridged set of 24 stimuli per subset. The computational assessment (Study 2) required the development of a specific battery of computational measures describing the structural properties of each stimulus. We distilled nonredundant composite measures for each attribute and examined whether they predicted participants’ ratings. Our results show that the composite measures indeed predicted participants’ ratings. Moreover, the composite complexity measure predicted complexity ratings as well as existing models of musical complexity. We conclude that the four subsets are suitable for use in studies that require presenting participants with short musical motifs varying in balance, contour, symmetry, or complexity, and that the stimuli and the computational measures are valuable resources for research in music psychology, empirical aesthetics, music information retrieval, and musicology. The MUST set and MATLAB toolbox codifying the computational measures are freely available at osf.io/bfxz7.

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  1. Agres, K., Abdallah, S., & Pearce, M. (2018). Information-Theoretic Properties of Auditory Sequences Dynamically Influence Expectation and Memory. Cognitive science, 42(1), 43–76. https://doi.org/10.1111/cogs.12477

  2. Aguinis, H., Gottfredson, R. K., & Joo, H. (2013). Best-Practice Recommendations for Defining, Identifying, and Handling Outliers. Organizational Research Methods, 16(2), 270–301. https://doi.org/10.1177/1094428112470848

  3. Albrecht, J. (2016). Modeling Musical Complexity: Commentary on Eerola (2016). Empirical Musicology Review, 11(1), 20. https://doi.org/10.18061/emr.v11i1.5197

  4. Albrecht, J. D. (2018). Expressive Meaning and the Empirical Analysis of Musical Gesture. Music Theory Online, 24(3). https://doi.org/10.30535/mto.24.3.1

  5. Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of memory and language, 59(4), 390–412. https://doi.org/10.1016/j.jml.2007.12.005

  6. Balch, W. R. (1981). The role of symmetry in the good continuation ratings of two-part tonal melodies. Perception & Psychophysics, 29(1), 47–55. https://doi.org/10.3758/bf03198839

  7. Barr, D. J., Levy, R., Scheepers, C., & Tily, H. J. (2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of memory and language, 68(3), 255–278. https://doi.org/10.1016/j.jml.2012.11.001

  8. Bates, D., Maechler, M., Bolker, B., & Walker, S. (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01.

  9. Begleiter, R., El-Yaniv, R., & Yona, G. (2004). On prediction using variable order Markov models. Journal of Artificial Intelligence Research, 22, 385–421. https://doi.org/10.1613/jair.1491

  10. Berridge, K. C., & Kringelbach, M. L. (2013). Neuroscience of affect: brain mechanisms of pleasure and displeasure. Current Opinion in Neurobiology, 23(3), 294–303. https://doi.org/10.1016/j.conb.2013.01.017

  11. Bertamini, M., Palumbo, L., Gheorghes, T. N., & Galatsidas, M. (2016). Do observers like curvature or do they dislike angularity?. British Journal of Psychology, 107(1), 154–178. https://doi.org/10.1111/bjop.12132

  12. Bianchi, I., Burro, R., Pezzola, R., & Savardi, U. (2017). Matching Visual and Acoustic Mirror Forms. Symmetry, 9(3), 39. https://doi.org/10.3390/sym9030039

  13. Brattico, E., & Pearce, M. T. (2013). The neuroaesthetics of music. Psychology of Aesthetics, Creativity, and the Arts, 7, 48–61. https://doi.org/10.1037/a0031624

  14. Brieber, D., Nadal, M., Leder, H., & Rosenberg, R. (2014). Art in time and space: context modulates the relation between art experience and viewing time. PloS ONE, 9(6), e99019. https://doi.org/10.1371/journal.pone.0099019

  15. Bunton, S. (1997). Semantically motivated improvements for PPM variants. The Computer Journal, 40(2/3), 76–93. https://doi.org/10.1093/comjnl/40.2_and_3.76

  16. Caplin, W. E., Hepokoski, J., & Webster, J. (2010). Musical Form, Forms & Formenlehre, Leuven University Press. https://doi.org/10.2307/j.ctt9qf01v

  17. Cattaneo, Z., Lega, C., Ferrari, C., Vecchi, T., Cela-Conde, C. J., Silvanto, J., & Nadal, M. (2015). The role of the lateral occipital cortex in aesthetic appreciation of representational and abstract paintings: A TMS study. Brain and Cognition, 95, 44–53. https://doi.org/10.1016/j.bandc.2015.01.008

  18. Che, J., Sun, X., Gallardo, V., & Nadal, M. (2018). Cross-cultural empirical aesthetics. The Arts and The Brain - Psychology and Physiology Beyond Pleasure, Progress in Brain Research, 237, 77–103. https://doi.org/10.1016/bs.pbr.2018.03.002

  19. Coalson, J. (2008). Flac-free lossless audio codec. Retrieved from http://flac.sourceforge.Net (1/11/2018)

  20. Conklin, D., & Witten, I. H. (1995). Multiple viewpoint systems for music prediction. Journal of New Music Research, 24(1), 51–73. https://doi.org/10.1080/09298219508570672

  21. Cook, N. (1987). Musical form and the listener. The Journal of aesthetics and art criticism, 46(1), 23-29. https://doi.org/10.2307/431305

  22. Cook, R. D. (1979). Influential observations in linear regression. Journal of the American Statistical Association, 74(365), 169–174.

  23. Corradi, G., Chuquichambi, E. G., Barrada, J. R., Clemente, A., & Nadal, M. (2019). A new conception of visual aesthetic sensitivity. British Journal of Psychology. https://doi.org/10.1111/bjop.12427

  24. Cross, I. (2006). Music, Cognition, Culture, and Evolution. Annals of the New York Academy of Sciences, 930(1), 28–42. https://doi.org/10.1111/j.1749-6632.2001.tb05723.x

  25. De Lange, F. P., Heilbron, M., & Kok, P. (2018). How Do Expectations Shape Perception? Trends in Cognitive Sciences, 22(9), 764–779. https://doi.org/10.1016/j.tics.2018.06.002

  26. Dissanayake, E. (2008). If music is the food of love, what about survival and reproductive success? Musicae Scientiae, 12(1_suppl), 169–195. https://doi.org/10.1177/1029864908012001081

  27. Edmonston, W. E. Jr. (1969). Familiarity and Musical Training in the Esthetic Evaluation of Music. The Journal of Social Psychology, 79(1), 109–111. https://doi.org/10.1080/00224545.1969.9922393

  28. Eerola, T. (2016). Expectancy-violation and information-theoretic models of melodic complexity. Empirical Musicology Review, 11(1), 2–17. https://doi.org/10.18061/emr.v11i1.4836

  29. Eerola, T., Himberg, T., Toiviainen, P., & Louhivuori, J. (2006). Perceived complexity of Western and African folk melodies by Western and African listeners. Psychology of Music, 34(3), 337–371. https://doi.org/10.1177/0305735606064842

  30. Eerola, T., & North, A. C. (2000, August). Expectancy-based model of melodic complexity. In Proceedings of the Sixth International Conference on Music Perception and Cognition. Keele, Staffordshire, UK: Department of Psychology. CD-ROM.

  31. 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), 533–553. https://doi.org/10.3758/s13415-013-0161-y

  32. Fiveash, A., McArthur, G., & Thompson, W. F. (2018). Syntactic and non-syntactic sources of interference by music on language processing. Scientific Reports, 8(1). https://doi.org/10.1038/s41598-018-36076-x

  33. Forsythe, A., Mulhern, G., & Sawey, M. (2008). Confounds in pictorial sets: The role of complexity and familiarity in basic-level picture processing. Behavior Research Methods, 40(1), 116–129. https://doi.org/10.3758/brm.40.1.116

  34. Forsythe, A., Nadal, M., Sheehy, N., Cela-Conde, C. J., & Sawey, M. (2011). Predicting beauty: Fractal dimension and visual complexity in art. British Journal of Psychology, 102, 49–70. https://doi.org/10.1348/000712610x498958

  35. Gartus, A., & Leder, H. (2013). The Small Step toward Asymmetry: Aesthetic Judgment of Broken Symmetries. I-Perception, 4(5), 361–364. https://doi.org/10.1068/i0588sas

  36. Gartus, A., & Leder, H. (2017). Predicting perceived visual complexity of abstract patterns using computational measures: The influence of mirror symmetry on complexity perception. PloS ONE, 12(11), e0185276. https://doi.org/10.1371/journal.pone.0185276

  37. Gerardi, G. M., & Gerken, L. (1995). The Development of Affective Responses to Modality and Melodic Contour. Music Perception: An Interdisciplinary Journal, 12(3), 279–290. https://doi.org/10.2307/40286184

  38. Gingras, B., Pearce, M. T., Goodchild, M., Dean, R. T., Wiggins, G., & McAdams, S. (2016). Linking melodic expectation to expressive performance timing and perceived musical tension. Journal of Experimental Psychology: Human Perception and Performance, 42(4), 594–609.

  39. Gómez-Puerto, G., Munar, E., & Nadal, M. (2015). Preference for curvature: A historical and conceptual framework. Frontiers in Human Neuroscience, 9, 712. https://doi.org/10.3389/fnhum.2015.00712

  40. Grey, T. S. (1988). Wagner, the Overture, and the Aesthetics of Musical Form. 19th-Century Music, 12(1), 3–22. https://doi.org/10.1525/ncm.1988.12.1.02a00010

  41. Hansen, N. C., & Pearce, M. T. (2014). Predictive uncertainty in auditory sequence processing. Frontiers in Psychology, 5, 1052. https://doi.org/10.3389/fpsyg.2014.01052

  42. Harrison, P., & Pearce, M. T. (2018). An energy-based generative sequence model for testing sensory theories of Western harmony. arXiv preprint arXiv:1807.00790.

  43. Heyduk, R. G. (1975). Rated preference for musical compositions as it relates to complexity and exposure frequency. Perception & Psychophysics, 17(1), 84–90.

  44. Hox, J. J., Moerbeek, M., & van de Schoot, R. (2010). Multilevel analysis: Techniques and applications. Routledge.

  45. Huron, D (2003). Is Music an Evolutionary Adaptation? The Cognitive Neuroscience of Music, 57–75. https://doi.org/10.1093/acprof:oso/9780198525202.003.0005

  46. Jacobsen, T., & Höfel, L. E. A. (2002). Aesthetic judgments of novel graphic patterns: analyses of individual judgments. Perceptual and Motor Skills, 95(3), 755–766. https://doi.org/10.2466/pms.2002.95.3.755

  47. Jakesch, M., & Leder, H. (2015). The qualitative side of complexity: Testing effects of ambiguity on complexity judgments. Psychology of Aesthetics, Creativity, and the Arts, 9, 200–205. https://doi.org/10.1037/a0039350

  48. Jolliffe, I. T. (1972).Discarding Variables in a Principal Component Analysis. I: Artificial Data. Applied Statistics, 21(2), 160. https://doi.org/10.2307/2346488

  49. Judd, C. M., Westfall, J., & Kenny, D. A. (2017). Experiments with More Than One Random Factor: Designs, Analytic Models, and Statistical Power. Annual Review of Psychology, 68(1), 601–625. https://doi.org/10.1146/annurev-psych-122414-033702

  50. Juslin, P. N. (2013). From everyday emotions to aesthetic emotions: Towards a unified theory of musical emotions. Physics of Life Reviews, 10(3), 235–266. https://doi.org/10.1016/j.plrev.2013.05.008

  51. Koelsch, S., Vuust, P., & Friston, K. (2018). Predictive Processes and the Peculiar Case of Music. Trends in Cognitive Sciences, 23(1), 63–77. https://doi.org/10.1016/j.tics.2018.10.006

  52. Kringelbach, M. L., & Berridge, K. C. (2009). Towards a functional neuroanatomy of pleasure and happiness. Trends in Cognitive Sciences, 13(11), 479–487. https://doi.org/10.1016/j.tics.2009.08.006

  53. Krumhansl, C. L., Sandell, G. J., & Sergeant, D. C. (1987). The Perception of Tone Hierarchies and Mirror Forms in Twelve-Tone Serial Music. Music Perception: An Interdisciplinary Journal, 5(1), 31–77. https://doi.org/10.2307/40285385

  54. Kuznetsova, A., Brockho, P. B., & Christensen, R. H. B. (2012). lmerTest: Tests for random and fixed effects for linear mixed effect models (lmer objects of lme4 package). Retrieved from http://www.cran.r-project.org/package=lmerTest/ (1/11/2018)

  55. Leichtentritt, H. (1911). Musikalische Formenlehre (Vol. 8). Breitkopf & Härtel.

  56. Levy, D. J., & Glimcher, P. W. (2012). The root of all value: a neural common currency for choice. Current Opinion in Neurobiology, 22(6), 1027–1038. https://doi.org/10.1016/j.conb.2012.06.001

  57. Locher, P., Gray, S., & Nodine, C. (1996). The structural framework of pictorial balance. Perception, 25, 1419–1436. https://doi.org/10.1068/p251419

  58. Machado, P., Romero, J., Nadal, M., Santos, A., Correia, J., & Carballal, A. (2015). Computerized measures of visual complexity. Acta Psychologica, 160, 43–57. https://doi.org/10.1016/j.actpsy.2015.06.005

  59. Madison, G., & Schiölde, G. (2017). Repeated Listening Increases the Liking for Music Regardless of Its Complexity: Implications for the Appreciation and Aesthetics of Music. Frontiers in Human Neuroscience, 11, 147. https://doi.org/10.3389/fnins.2017.00147

  60. Mallik, A., Chandra, M. L., & Levitin, D. J. (2017). Anhedonia to music and mu-opioids: Evidence from the administration of naltrexone. Scientific Reports, 7, 41952. https://doi.org/10.1038/srep41952

  61. Margulis, E. H. (2016). Toward A Better Understanding of Perceived Complexity in Music: A Commentary on Eerola (2016). Empirical Musicology Review, 11(1), 18. https://doi.org/10.18061/emr.v11i1.5275

  62. Marin, M. M., Lampatz, A., Wandl, M., & Leder, H. (2016). Berlyne revisited: evidence for the multifaceted nature of hedonic tone in the appreciation of paintings and music. Frontiers in Human Neuroscience, 10, 536. https://doi.org/10.3389/fnhum.2016.00536

  63. Marin, M. M., & Leder, H. (2013). Examining complexity across domains: relating subjective and objective measures of affective environmental scenes, paintings and music. PLoS ONE, 8(8), e72412. https://doi.org/10.1371/journal.pone.0072412

  64. Mathôt, S., Schreij, D., & Theeuwes, J. (2012). OpenSesame: An open-source, graphical experiment builder for the social sciences. Behavior Research Methods, 44(2), 314–324. https://doi.org/10.3758/s13428-011-0168-7

  65. Mongoven, C., & Carbon, C. C. (2017). Acoustic Gestalt: On the perceptibility of melodic symmetry. Musicae Scientiae, 21(1), 41–59. https://doi.org/10.1177/1029864916637116

  66. Müllensiefen, D., Gingras, B., Musil, J., & Stewart, L. (2014). The Musicality of Non-Musicians: An Index for Assessing Musical Sophistication in the General Population. PLoS ONE, 9(2), e89642. https://doi.org/10.1371/journal.pone.0089642

  67. Munar, E., Gómez-Puerto, G., Call, J., & Nadal, M. (2015). Common Visual Preference for Curved Contours in Humans and Great Apes. PLoS One, 10(11), e0141106. https://doi.org/10.1371/journal.pone.0141106

  68. Nadal, M., Munar, E., Marty, G., & Cela-Conde, C. J. (2010). Visual complexity and beauty appreciation: Explaining the divergence of results. Empirical Studies of the Arts, 28(2), 173–191. https://doi.org/10.2190/em.28.2.d

  69. Narmour, E. (1991). The top-down and bottom-up systems of musical implication: Building on Meyer's theory of emotional syntax. Music Perception: An Interdisciplinary Journal, 9(1), 1–26. https://doi.org/10.2307/40286156

  70. Nieminen, S., Istók, E., Brattico, E., Tervaniemi, M., & Huotilainen, M. (2011). The development of aesthetic responses to music and their underlying neural and psychological mechanisms. Cortex, 47(9), 1138–1146. https://doi.org/10.1016/j.cortex.2011.05.008

  71. Nieuwenhuis, R., te Grotenhuis, H. F., & Pelzer, B. J. (2012). influence.ME: Tools for Detecting Influential Data in Mixed Effects Models. https://doi.org/10.31235/osf.io/a5w4u

  72. Omigie, D., Pearce, M. T., & Stewart, L. (2012). Tracking of pitch probabilities in congenital amusia. Neuropsychologia, 50(7), 1483–1493. https://doi.org/10.1016/j.neuropsychologia.2012.02.034

  73. Omigie, D., Pearce, M. T., Williamson, V. J., & Stewart, L. (2013). Electrophysiological correlates of melodic processing in congenital amusia. Neuropsychologia, 51(9), 1749–1762. https://doi.org/10.1016/j.neuropsychologia.2013.05.010

  74. Palumbo, L., & Bertamini, M. (2016). The curvature effect: A comparison between preference tasks. Empirical Studies of the Arts, 34, 35–52. https://doi.org/10.1177/0276237415621185

  75. Payne, E. (1980). Towards an Understanding of Music Appreciation. Psychology of Music, 8(2), 31–41. https://doi.org/10.1177/030573568082004

  76. Pearce, M., & Müllensiefen, D. (2017). Compression-based modelling of musical similarity perception. Journal of New Music Research, 46(2), 135–155. https://doi.org/10.1080/09298215.2017.1305419

  77. Pearce, M. T. (2005). The construction and evaluation of statistical models of melodic structure in music perception and composition. Doctoral dissertation, City University London.

  78. Pearce, M. T. (2018). Statistical learning and probabilistic prediction in music cognition: mechanisms of stylistic enculturation. Annals of the New York Academy of Sciences, 1423(1), 378–395. https://doi.org/10.1111/nyas.13654

  79. Pearce, M. T., Müllensiefen, D., & Wiggins, G. A. (2010). The role of expectation and probabilistic learning in auditory boundary perception: A model comparison. Perception, 39(10), 1367–1391. https://doi.org/10.1068/p6507

  80. Pearce, M. T., Ruiz, M. H., Kapasi, S., Wiggins, G. A., & Bhattacharya, J. (2010). Unsupervised statistical learning underpins computational, behavioural, and neural manifestations of musical expectation. NeuroImage, 50(1), 302–313. https://doi.org/10.1016/j.neuroimage.2009.12.019

  81. Pereira, C. S., Teixeira, J., Figueiredo, P., Xavier, J., Castro, S. L., & Brattico, E. (2011). Music and Emotions in the Brain: Familiarity Matters. PLoS ONE, 6(11), e27241. https://doi.org/10.1371/journal.pone.0027241

  82. Petrović, M., Ačić, G., & Milanković, V. (2017). Sound of picture vs. picture of sound: musical palindrome. New Sound: International Magazine for Music, 50(2), 217–228.

  83. Pressing, J. (1999). Cognitive complexity and the structure of musical patterns. In Proceedings of the 4th Conference of the Australasian Cognitive Science Society.

  84. Prince, J. B. (2011). The integration of stimulus dimensions in the perception of music. Quarterly Journal of Experimental Psychology, 64, 2125–2152. https://doi.org/10.1080/17470218.2011.573080

  85. Prince, J. B., Thompson, W. F., & Schmuckler, M. A. (2009). Pitch and time, tonality and meter: How do musical dimensions combine? Journal of Experimental Psychology: Human Perception and Performance, 35, 1598–1617. https://doi.org/10.1037/a0016456

  86. Purwins, H., Grachten, M., Herrera, P., Hazan, A., Marxer, R., & Serra, X. (2008). Computational models of music perception and cognition II: Domain-specific music processing. Physics of Life Reviews, 5(3), 169–182. https://doi.org/10.1016/j.plrev.2008.03.005

  87. R Core Team (2018). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from http://www.R-project.org (1/11/2018)

  88. Revelle, W. (2018) psych: Procedures for Personality and Psychological Research, Northwestern University, Evanston, Illinois, USA, https://CRAN.R-project.org/package=psych Version = 1.8.12.

  89. Robinson, T. (1994). SHORTEN: Simple lossless and near-lossless waveform compression.

  90. Rohrmeier, M., Zuidema, W., Wiggins, G. A., & Scharff, C. (2015). Principles of structure building in music, language and animal song. Philosophical Transactions of the Royal Society B: Biological Sciences, 370(1664), 20140097–20140097. https://doi.org/10.1098/rstb.2014.0097

  91. Salimpoor, V. N., & Zatorre, R. J. (2013). Neural interactions that give rise to musical pleasure. Psychology of Aesthetics, Creativity, and the Arts, 7, 62–75. https://doi.org/10.1037/a0031819

  92. 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), 17–39. https://doi.org/10.1037/pmu0000203

  93. Savage, P. E., Brown, S., Sakai, E., & Currie, T. E. (2015). Statistical universals reveal the structures and functions of human music. Proceedings of the National Academy of Sciences, USA, 112, 8987–8992. https://doi.org/10.1073/pnas.1414495112

  94. Schaal, N. K., Banissy, M. J., & Lange, K. (2015). The rhythm span task: comparing memory capacity for musical rhythms in musicians and non-musicians. Journal of New Music Research, 44(1), 3–10. https://doi.org/10.1080/09298215.2014.937724

  95. Schellenberg, E. G. (1997). Simplifying the implication-realization model of melodic expectancy. Music Perception: An Interdisciplinary Journal, 14(3), 295–318. https://doi.org/10.2307/40285723

  96. Schmuckler, M. A. (2015). Tonality and Contour in Melodic Processing. Oxford Handbooks Online. https://doi.org/10.1093/oxfordhb/9780198722946.013.14

  97. Schoenberg, A. (1967). Fundamentals of musical composition. Stein, L., & Strang, G., eds. London: Faber & Faber.

  98. Sears, D. R., Pearce, M. T., Spitzer, J., Caplin, W. E., & McAdams, S. (2018). Expectations for tonal cadences: Sensory and cognitive priming effects. Quarterly Journal of Experimental Psychology, 174702181881447. https://doi.org/10.1177/1747021818814472

  99. Shepard, R. N. (1982). Structural Representations of Musical Pitch. Psychology of Music, 343–390. https://doi.org/10.1016/b978-0-12-213562-0.50015-2

  100. Shmulevich, I., & Povel, D. J. (2000). Measures of temporal pattern complexity. Journal of New Music Research, 29(1), 61–69. https://doi.org/10.1076/0929-8215(200003)29:01;1-p;ft061

  101. Shrout, P. E., & Fleiss, J. L. (1979). Intraclass correlations: uses in assessing rater reliability. Psychological Bulletin, 86(2), 420–428. https://doi.org/10.1037/0033-2909.86.2.420

  102. Silvia, P. J. (2007). An introduction to multilevel modeling for research on the psychology of art and creativity. Empirical Studies of the Arts, 25(1), 1–20. https://doi.org/10.2190/6780-361t-3j83-04l1

  103. Snijders, T. A. B., and Bosker, R. J. (2012). Multilevel analysis. An introduction to basic and advanced multilevel modeling (2nd ed.). London: SAGE Publications.

  104. Snyder, B., & Snyder, R. (2000). Music and memory: An introduction. MIT press.

  105. Steck, L., & Machotka, P. (1975). Preference for musical complexity: Effects of context. Journal of Experimental Psychology: Human Perception and Performance, 1(2), 170–174. https://doi.org/10.1037/0096-1523.1.2.170

  106. Streich, S. (2007). Music complexity: A multi-faceted description of audio content. Doctoral dissertation, University of Pompeu Fabra, Barcelona.

  107. Thoma, M. V., Ryf, S., Mohiyeddini, C., Ehlert, U., & Nater, U. M. (2012). Emotion regulation through listening to music in everyday situations. Cognition and Emotion, 26, 550–560. https://doi.org/10.1080/02699931.2011.595390

  108. Thorpe, L. A. (1986). Perceptual constancy for melodic contour. Infant Behavior and Development, 9, 379. https://doi.org/10.1016/s0163-6383(86)80385-x

  109. Tinio, P. P. L., & Leder, H. (2009). Just how stable are stable aesthetic features? Symmetry, complexity, and the jaws of massive familiarization. Acta Psychologica, 130, 241–250. https://doi.org/10.1016/j.actpsy.2009.01.001

  110. Trainor, L. J., & Unrau, A. (2011). Development of Pitch and Music Perception. Springer Handbook of Auditory Research, 223–254. https://doi.org/10.1007/978-1-4614-1421-6_8

  111. Trehub, S. E. (1985). Auditory Pattern Perception in Infancy. Auditory Development in Infancy, 183–195. https://doi.org/10.1007/978-1-4757-9340-6_10

  112. Trehub, S. E., Bull, D., & Thorpe, L. A. (1984). Infants’ Perception of Melodies: The Role of Melodic Contour. Child Development, 55(3), 821. https://doi.org/10.2307/1130133

  113. Trehub, S. E., & Hannon, E. E. (2006). Infant music perception: Domain-general or domain-specific mechanisms? Cognition, 100(1), 73–99. https://doi.org/10.1016/j.cognition.2005.11.006

  114. Van den Bosch, I., Salimpoor, V. N., & Zatorre, R. J. (2013). Familiarity mediates the relationship between emotional arousal and pleasure during music listening. Frontiers in Human Neuroscience, 7. https://doi.org/10.3389/fnhum.2013.00534

  115. Van Geert, E., & Wagemans, J. (2019). Order, complexity, and aesthetic appreciation. Psychology of Aesthetics, Creativity, and the Arts. https://doi.org/10.1037/aca0000224

  116. Vartanian, O., Navarrete, G., Chatterjee, A., Fich, L. B., Leder, H., Modroño, C., … Nadal, M. (2019). Preference for curvilinear contour in interior architectural spaces: Evidence from experts and nonexperts. Psychology of Aesthetics, Creativity, and the Arts, 13(1), 110–116. https://doi.org/10.1037/aca0000150

  117. Wilson, A., & Chatterjee, A. (2005). The assessment of preference for balance: Introducing a new test. Empirical Studies of the Arts, 23(2), 165–180. https://doi.org/10.2190/b1lr-mvf3-f36x-xr64

  118. Winner, E., Rosenblatt, E., Windmueller, G., Davidson, L., & Gardner, H. (1986). Children's perception of ‘aesthetic’properties of the arts: Domain-specific or pan-artistic?. British Journal of Developmental Psychology, 4(2), 149-160. https://doi.org/10.1111/j.2044-835x.1986.tb01006.x

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The project leading to these results has received funding from “La Caixa” Foundation (ID 100010434) under agreements LCF/BQ/ES17/11600021 and LCF/BQ/DE17/11600022, and from the Spanish Ministerio de Economía, Industria y Competitividad with grant PSI2016-77327-P.

Author information

AC created the stimuli and wrote the manuscript; AC and MV designed the computational measures; MV formalized, implemented, and wrote the measures; AC and MN designed the research, discussed the stimuli, and analyzed the data; AC, GC, GA, and MN contributed to the behavioral assessment; AC, MV, MP, and MN compared and discussed the measures, and revised the manuscript. All authors reported no conflicts of interest and approved the manuscript.

Correspondence to Ana Clemente.

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Clemente, A., Vila-Vidal, M., Pearce, M.T. et al. A Set of 200 Musical Stimuli Varying in Balance, Contour, Symmetry, and Complexity: Behavioral and Computational Assessments. Behav Res (2020). https://doi.org/10.3758/s13428-019-01329-8

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  • music
  • aesthetics
  • MIR
  • balance
  • contour
  • symmetry
  • complexity