The sound of beauty: How complexity determines aesthetic preference

Abstract

Stimulus complexity is an important determinant of aesthetic preference. An influential idea is that increases in stimulus complexity lead to increased preference up to an optimal point after which preference decreases (inverted-U pattern). However, whereas some studies indeed observed this pattern, most studies instead showed an increased preference for more complexity. One complicating issue is that it remains unclear how to define complexity. To address this, we approached complexity and its relation to aesthetic preference from a predictive coding perspective. Here, low-and high-complexity stimuli would correspond to low and high levels of prediction errors, respectively. We expected participants to prefer stimuli which are neither too easy to predict (low prediction error), nor too difficult (high prediction error). To test this, we presented two sequences of tones on each trial that varied in predictability from highly regular (low prediction error) to completely random (high prediction error), and participants had to indicate which of the two sequences they preferred in a two-interval forced-choice task. The complexity of each tone sequence (amount of prediction error) was estimated using entropy. Results showed that participants tended to choose stimuli with intermediate complexity over those of high or low complexity. This confirms the century-old idea that stimulus complexity has an inverted-U relationship to aesthetic preference.

Thinking through other minds: A variational approach to cognition and culture

“…Unfortunately, here again, apart from anecdotal evidence, no experimental data are offered by the authors to corroborate their claim. Crucially, against FEP, numerous studies have shown that in esthetics (Delplanque et al 2019), music perception (Chmiel & Schubert 2017), visual perception (Chetverikov & Kristjánsson 2016), consumer behavior (Kao & Wang 2013), etc., humans have a preference for medium entropy patterns rather than low-entropy patterns. Entropy and liking follow an inverted U curve: expected (low entropy) patterns are judged to be boring, completely unexpected (high entropy) patterns are deemed too difficult/demanding, and medium entropy patterns are liked and looked for (cf.…”

The Relation Between Preference for Predictability and Autistic Traits

“…In fact, to further ensure we approached this endeavor from multiple angles, we used three different paradigms measuring preference for predictability. First, in the Music Preference paradigm (as developed by Delplanque et al, 2019), participants listened to specific tone sequences varying in predictability, followed by a forced-choice preference judgment task. Earlier findings show an inverted U-shape pattern, indicating strongest preferences for stimuli with medium complexity (Delplanque, De Loof, Janssens, & Verguts, 2019).…”

“…First, in the Music Preference paradigm (as developed by Delplanque et al, 2019), participants listened to specific tone sequences varying in predictability, followed by a forced-choice preference judgment task. Earlier findings show an inverted U-shape pattern, indicating strongest preferences for stimuli with medium complexity (Delplanque, De Loof, Janssens, & Verguts, 2019). Second, we used a Perceptual Fluency paradigm in which predictability was manipulated subliminally.…”

Product Design Award Prediction Modeling: Design Visual Aesthetic Quality Assessment via DCNNs

“…The complexity of sound stimulus and sound preference was recognized as inverted-U pattern, which can achieve an optimal point. J. Delplanque et al studied the sound complexity and its relation to aesthetic preference and confirmed this century-old pattern [21]. Apart from multimedia contents, we are also exposed to the influence of aesthetic labor in various services.…”