Back to the inverted-U for music preference: A review of the literature

Abstract

This study investigated the inverted-U model of preference for music as a function of collative variables (especially familiarity and complexity) over the last 115 years. The results of 57 studies on music preference were categorized according to their patterns of preference. Fifty of the 57 studies (87.7%) were categorized as compatible with an overarching (segmented) inverted-U model, while the results of five studies (8.8%) were interpreted as mixed, showing both compatible and incompatible results. Two studies (3.5%) were categorized as completely incompatible with the model. In contrast to authors who describe the model as defunct, this review has observed that studies producing results compatible with the inverted-U are still prevalent. We propose that while there may be inconsistencies with Berlyne's psychobiological theory from a scientific, arousal-based standpoint, the inverted-U model is able to explain a considerable amount of data. Rather, it seems that research interests have moved elsewhere, but caution is urged in asserting denial or dismissal of the relationship in music preference research.Why do we like certain pieces or types of music, and does our liking change over time? In the 1960s Daniel Berlyne's psychobiological theory provided a general explanation of aesthetic preference that could directly address these questions. He proposed that preference varies systematically as a function of collative variables, such as familiarity/novelty and complexity. Having links to research conducted by Wundt nearly a century earlier, the theory came to dominate all aspects of psychological research on aesthetic preference, including music preference,

Unusualness as a predictor of music preference

“…We therefore recommend further study in this area with additional exposures. Regardless, even if further research finds an eventual climb in preference with additional, distributed exposures (for example), it would still indicate a very slowly rising segment of the inverted-U that is atypical with respect to the current understanding of music preference research (Chmiel & Schubert, 2017a). Further, the inclusion of additional variables might aid in the detailed analysis of the relationship between preference and collative variables.…”

“…Collative variables were introduced by Berlyne (1960Berlyne ( , 1971Berlyne ( , 1974 as perceivable variables that can be analyzed and compared (or simply, collated). The most commonly tested collative variables in studies of music preference are complexity and familiarity/novelty (Chmiel & Schubert, 2017a;Finnäs, 1989), although Berlyne also refers to change, suprisingness, ambiguity, and puzzlingness as other examples (Berlyne, 1971, p. 69). Berlyne proposed that collative variables are the primary determinants of preference, introducing an inverted-U model of preference to explain the relationship between the variables.…”

The Effect of Music Teacher Education on Musical Taste of Music Majors

“…However, there are some studies showing only a positive correlation or only negative correlation. Chmiel and Schubert (2017) stated that those results show presumably only the first or the second part of the inverted-U and therefore those studies do not disprove the theory. In their research Chmiel and Schubert (2017) investigate the studies on collative variables done over 115 years and conclude that the results of 87.7% of the analyzed studies are parallel with the inverted-U model.…”

Imaginative Enrichment Produces Higher Preference for Unusual Music Than Historical Framing: A Literature Review and Two Empirical Studies

“…We also collected additional data (complexity, familiarity, puzzlingness, and interest, henceforth "secondary variables"). While these variables are known to be related to preference (e.g., Berlyne, 1960Berlyne, , 1971Berlyne, , 1974Martindale and Moore, 1989;Martindale et al, 1990;North and Hargreaves, 2000;Silvia, 2005;Hargreaves and North, 2010;Chmiel and Schubert, 2017), our intention for including the secondary variables was to help us to ascertain the unusualness (characterized by high complexity, high puzzlingness, and low familiarity) of the stimuli selected and to help explain any unexpected results.…”