Accounting for taste: Individual differences in preference for harmony
Although empirical research on aesthetics has had some success in explaining the average preferences of groups of observers, relatively little is known about individual differences in preference, and especially about how such differences might covary across different domains. In this study, we identified a new factor underlying aesthetic response—preference for harmonious stimuli—and examined how it varies over four domains (color, shape, spatial location, and music) across individuals with different levels of training in art and music. We found that individual preferences for harmony are strongly correlated across all four dimensions tested and decrease consistently with training in the relevant aesthetic domains. Confirmatory factor analysis revealed that cross-domain preference for harmony is well-represented as a single, unified factor, with effects separate from those of training and of common personality measures.
KeywordsPerceptual organization Visual perception Music cognition Good gestalt Aesthetic preference
Do individuals differ systematically in their aesthetic preferences, and, if so, how? The well-known adage, “There’s no accounting for taste,” suggests that individual differences (IDs) in aesthetic preference are either completely arbitrary or otherwise inexplicable (e.g., Chandler 1928; Woodworth, 1938). However, modern behavioral research on empirical aesthetics has shown that scientifically meaningful statements can be made about average preferences among colors (see, e.g., Ling & Hurlbert, 2009; Ou, Luo, Woodcock, & Wright, 2004; Palmer & Schloss, 2010), shapes (e.g., Amir, Biederman, & Hayworth, 2011; Bar & Neta, 2006; Silvia & Barona, 2009), spatial compositions (e.g., McManus & Kitson, 1995; Palmer, Gardner, & Wickens, 2008), and music (e.g., Smith & Melara, 1990; Trainor & Heinmiller, 1998). It therefore seems reasonable that similar techniques could be used to characterize (and thereby “account for”) IDs in aesthetic preference.
Some headway has been made in this direction already. For example, Jacobsen (2004) modeled IDs in the preference for simple spatial compositions and isolated specific cues that seemed to drive the preference decisions in different individuals. McManus (1980) showed that preference for the shapes of triangles and rectangles varies widely across individuals and yet is consistent within an individual over a time span of more than 2 years. More recently, McManus, Cook, and Hunt (2010) tried to tie these differences to particular personality scales—including the Big Five personality traits as well as need for cognition, tolerance of ambiguity, schizotypy, vocational types, and aesthetic activities—but found no significant correlations.
An older body of research, by Eysenck (1940) on IDs in aesthetic preference, is still more closely related to the present research. Using widely varying domains (e.g., black-and-white photographs, colors, polygons, and odors), Eysenck had participants order stimuli within a given domain from most to least preferred. He then correlated each individual’s ordering with the average ranking for that domain and found that a single factor could reliably predict the results. Eysenck interpreted this factor (t) as a measure of the degree to which each individual had what he called “good taste.” Interestingly, this t factor was uncorrelated with other ID factors, including general IQ (G).
In the present research, we take research on IDs in aesthetic preference an important step further by identifying a new variable. We call this variable “preference for harmony” because it is an index of the degree to which a person systematically likes (or dislikes) stimuli that are harmonious, in the sense of being “good gestalts.” This construct brings together two strands of research: one on perceptual goodness (or “good gestalt,” or “Prägnanz”; e.g., Garner, 1974; Palmer, 1991), and the other on aesthetic preference (e.g., Palmer et al., 2008; Schloss & Palmer, 2011). In preference experiments, people judge how much they “like” stimuli using some response of relative aesthetic preference (see Palmer et al., 2012). In perceptual goodness experiments, people judge how “good” the same stimuli are in terms of simplicity, regularity, and/or harmony, depending on the type of stimulus being judged. We will generically call this dimension “harmony” but intend it to stand for the appropriate term in each domain: “harmony” for music and color, “good fit” for spatial composition, and “figural goodness” for shape.
The finding that suggested the present research arose from studies of the relation between preference and harmony in color combinations (Schloss & Palmer, 2011). In the art historical literature, most color theorists have taken preference and harmony to be identical (e.g., Chevreul, 1839/1967; Itten, 1970). A few, including artist Josef Albers (1963), have disagreed, suggesting that harmonious combinations need not be liked, nor dissonant combinations disliked. Schloss and Palmer asked 48 participants to rate 992 pairs of 32 colors for both preference (how much they liked the color combination) and harmony (how well the two colors went together, regardless of preference). To convey the possibility that preference and harmony ratings need not be the same, the participants were given a musical analogy: Almost everyone would agree that Mozart’s music is more harmonious than Stravinsky’s, but some people like Stravinsky’s better than Mozart’s, and others the opposite.
Schloss and Palmer (2011) found a strong positive correlation between the average ratings of preference and harmony for the same color combinations (r = +.79), but the corresponding correlations for individuals ranged widely, from –.03 to +.70. Schloss and Palmer also found a systematic relation between preference for harmony and color training: Preference for harmony was highest in individuals with moderate amounts of color training (average r = +.52) and lower in individuals with either the least (average r = +.33) or the most (average r = +.25) color training, consistent with Berlyne’s (1971) theory of aesthetic dynamics.
These findings suggest a number of interesting questions: Do the same kind of IDs in preference for harmony exist in other aesthetic domains, such as music and/or spatial domains? Would preference for harmony across different domains be correlated, as one might expect if Eysenck’s (1940) t factor were due to preference for harmony? Finally, how do other personality factors, such as the Big Five Index (BFI; John, Donahue, & Kentle, 1991) or the Sensation Seeking Scale (SSS; Zuckerman, 1979), and/or levels of training and experience in the relevant domains relate to preference for harmony?
A set of 90 participants from three different educational groups were studied: 30 students each from psychology, art practice, and music (average age, 21.4 years). None had color vision deficiency using the Dvorine Pseudo-Isochromatic Plates. All gave informed consent and were naïve as to the purpose of the study. The Committee for the Protection of Human Subjects at the University of California, Berkeley, approved the experimental protocol.
The participants rated 127 stimuli first for aesthetic preference and later for harmony (using different names in different domains; see below). The order of the two tasks was important, because previous studies on preferences for color pairs have shown preference ratings to be more variable than harmony ratings (Schloss & Palmer, 2011). Our general philosophy was to have participants make the more subjective, variable ratings first, in order to minimize influences of more objective tasks on the less objective ones. The instructions defined “preference” simply as how much the participant “liked” a given stimulus as compared to all others in the set, and no participant requested further clarification of this instruction. The instructions for the harmony ratings differed by domain (see below), in order to make their meaning more obvious, and included the musical analogy mentioned above. Finally, participants completed the 44-item BFI, the SSS, and two questionnaires about art and music training.
Musical compositions sampled for the auditory stimuli
Beethoven, Ludwig van
Bach, C. P. E.
Beethoven, Ludwig van
Bach, J. S.
Beethoven, Ludwig van
Beethoven, Ludwig van
Participants rated their preferences for each stimulus on a 400-pixel continuous rating scale with Not at all below the left endpoint and Very much below the right endpoint. An identical rating scale was used for the second viewing of the stimuli to indicate how “X” it was, with different labels for the endpoints of dimension “X” (i.e., Harmonious/Disharmonious for the color pairs and musical selections, Simple/Complex for the dot patterns, and Good fit/Bad fit for the circle-in-a-frame images). Participants were told that the vertical mark crossing the center of the scale represented a neutral point.
The visual stimuli were presented on a 20-in. iMac (2007) computer monitor (1,680 × 1,050 pixels; 60-Hz refresh rate) in a darkened room from a distance of approximately 70 cm. The background was always neutral gray (CIE x = 0.312, y = 0.318, Y = 19.26). The chromaticity and luminance functions of the red, green, and blue guns were measured using a Minolta CS100 Chroma Meter, which was then used to calculate the appropriate RGB values to ensure accurate presentation of the CIE xyY values for our colors. The auditory stimuli were presented on the iMac computer through Sennheiser HD-270 headphones at a volume set by the participant. All displays were generated and presented using the Presentation software (www.neurobs.com).
The 127 stimuli were blocked by category in the following order: dot patterns, color pairs, circles in frames, and music. The stimuli were randomized within each block. Before participants were allowed to respond, each musical selection was presented in its entirety and each visual display was presented for 2,000 ms. A 500-ms interval occurred between trials. In the first phase, the participants were asked to rate their aesthetic preferences, and in the second phase, to rate the relevant dimension of harmony (see above). The recorded datum on each trial corresponded to the nearest integer x-coordinate (−100 to + 100) at which the participant clicked on the scale.
After rating all stimuli for both preference and harmony, the participants completed computerized versions of the 44-item BFI (John et al., 1991) and the SSS (Zuckerman, 1979), as well as a modified version of the Queens Questionnaire for Musical Background (Cuddy, Balkwill, Peretz, & Holden, 2005, as modified by Bhatara, Quintin, Heaton, Fombonne, & Levitin, 2009) and a questionnaire about their background in visual art and color (Schloss & Palmer, 2011).
Cross-domain correlations of preference-for-harmony (PfH) scores
Big Five Index
Sensation Seeking Scale
Thrill & Adventure
We also found a significant interaction between stimulus domains and educational groups [F(6, 348) = 3.20, p < .01]. The general pattern was that art and music majors tended to have lower PfH values than did psychology majors, but the lowest average PfH scores were always found in the students with the most training in the relevant domain (Fig. 3). Many, but not all, of these differences were statistically significant, as is indicated in Fig. 3.
These group differences are potentially relevant to a possible alternative interpretation of the present results: Perhaps some participants simply have no basis on which to judge harmony, and therefore report their preferences as a proxy for harmony ratings. Participants who show high preferences for harmony might then be the ones for whom harmony is a poorly defined concept. If this were true, however, we should find systematically lower agreement (lower alpha scores) among the harmony ratings for the groups who had less expertise, because they are the individuals for whom harmony would be poorly defined. In fact, we found that agreement on harmony ratings was essentially the same in the different educational groups. To examine this issue more closely, we computed the differences between the average correlations of each participant in each group and (a) each other participant in the same educational group and (b) each participant in each of the other educational groups—that is, measures of within-group agreement versus between-group agreement. This was done for both the preference and harmony ratings in all domains. We found a highly significant difference between the inter- and intragroup agreements for the preference ratings [F(1, 178) = 43.82, p < .001], but no significant difference for the harmony ratings [F(1, 178) = 3.51, p = .06]. This pattern suggests that all groups were judging the same thing when they rated harmony, whereas their ratings of preference differed.
Average training levels of participants by major
Mean Years of Art Training
Mean Years of Music Training
Correlations between variables relevant to our expanded model
The results of this experiment support our initial hypothesis that preference for harmony represents a domain-general individual difference in aesthetic preference. This intercorrelation extends to all four tested domains, including both visual and auditory modalities. Preference for harmony seems to be represented best as a single general factor that is unrelated to the traditional personality measures studied (the five subscales of the BFI and the four subscales of the SSS). We believe that preference for harmony provides a plausible explanation for Eysenck’s t for any aesthetic domain in which the group averages show that people generally prefer more harmonious to less harmonious stimuli, because the preferences of people with a high preference for harmony will necessarily correlate more strongly with the group average than will those of people with lower preferences for harmony.
The results of structural equation modeling showed that expertise is significantly related only to the relevant domains (spatial harmony for art training and musical harmony for music training) after taking into account the effect of the general factor. However, the art and music majors also showed lower general-factor values, in addition to direct effects of expertise. The direction of causality in this relationship is an interesting issue. It is possible that individuals having lower preferences for harmony might be predisposed to enter the fields of art and music, but one could also hypothesize that formal art and music training has the effect of lowering their preference for harmony. Both effects are entirely possible, of course.
Several caveats should be mentioned about the present results. One is that they can only explain IDs in domains for which the concept of “harmony” is relevant, implying a relational aspect. Eysenck (1940) studied individual colors and odors, which are not explicitly relational in the way that our stimuli were. Nevertheless, the concept of harmony can be meaningful in such domains, to the extent that it signifies how well the given color or odor “goes with” other colors or odors in general. For example, Schloss and Palmer (2011) found that light (pastel) and desaturated (muted) colors are rated as being more harmonious across all possible combinations than are saturated (vivid) colors, suggesting that single pastel and muted colors may indeed be perceived as being more harmonious. We collected ratings from 25 participants of how “harmonious” 32 single chromatic colors were and found that the average ratings correlated very positively with the average harmony ratings of that color in combination with the 31 other colors (r = .71, p < .0001), even though the latter ratings were provided by 48 different participants (Schloss & Palmer, 2011). It is not obvious that this would also be true for odors or for other individual stimuli, however (e.g., rectangular shapes, as studied by McManus et al., 2010).
A second caveat is that the concept of harmony investigated here is presumably but one of many features relevant to IDs in aesthetic judgments. Harmony, for example, is not the same as complexity, which has previously been taken to denote the number of elements in different stimuli (e.g., Berlyne, 1971). Whether and how harmony and complexity might be related in terms of IDs is an important topic for further study.
Third, the concept of harmony studied here is a subjective perceptual attribute rather than an objective stimulus property that at present can be calculated from physically measurable features. The fact that the variability is systematically lower for ratings of harmony than for preferences does suggest that harmony may be “more objective” than preference, however. Investigating the underlying stimulus attribute(s) influencing perceived harmony will be an important avenue for further research.
We believe that our results constitute compelling evidence that preference for harmony is an individual difference in aesthetic style that crosses traditional domain boundaries and affects aesthetic judgments that go beyond those of domain-specific training. More broadly, our findings show that empirical research into aesthetic preference is valuable not only for describing the average preferences of a sample of people, but also for increasing our understanding of and ability to predict IDs in aesthetic preference. We further believe that the concept of preference for harmony, as defined here, can serve as a tool for future research in aesthetics and personality that seeks to investigate preferences across multiple domains and at multiple levels of analysis.
We thank Karen Schloss, Rosa Poggesi, Kyle Jennings, and other members of the Palmer Lab for their help on this project. Funding for this research was provided by the National Science Foundation (Grant Nos. BCS-0745820 and BCS-1059088) and by a gift from Google.
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