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.
Keywords: Aesthetic preference; Complexity; Prediction error.
Copyright © 2018 Elsevier B.V. All rights reserved.