Order, complexity, and aesthetic appreciation.

Geert, Eline Van; Wagemans, Johan

doi:10.1037/aca0000224

Cited by 90 publications

(123 citation statements)

References 119 publications

(300 reference statements)

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“…The numerically largest correlation, though, occurred for the multiple-element pictures, which makes sense, because balancing should play some role if a medium number of elements are present in a picture. These findings are in line with the principle of unity-in-variety (Fechner, 1876) and the related idea that complexity must go along with order for aesthetic appreciation (Van Geert & Wagemans, 2019). Accordingly, balance affects liking only when an image is sufficiently complex, which, in our case, is the case for dynamic-pattern and multiple-element pictures, but not for single-element pictures.…”

Section: Discussionsupporting

confidence: 88%

Perceptual Balance, Stability, and Aesthetic Appreciation: Their Relations Depend on the Picture Type

Hübner

Fillinger

2019

i-Perception

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It is widely assumed that the aesthetic appreciation of a picture depends, among others, on how well the picture’s composition is perceptually balanced, where “perceptual balance” is often defined analogous to mechanics. To what extent this metaphor holds for different picture types, however, is still open. Therefore, in this study, we examined the relationship between balance, liking, and some objective measures with pictures from an aesthetic sensitivity test. These stimuli could be divided into single-element, multiple-element, and dynamic-pattern pictures. The results show that “balance” is interpreted differently, depending on the stimulus type. Whereas “mechanical” balance was applied to assess single-element pictures, the balance of multiple-element and dynamic-pattern pictures was rated more in the sense of gravitational stability. Only for the multiple-element stimuli, there was a positive relation between balance/stability and liking. Together, our findings show that there are different types of balance, and that their relation with liking depends on the picture type.

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Section: Discussionsupporting

confidence: 88%

Perceptual Balance, Stability, and Aesthetic Appreciation: Their Relations Depend on the Picture Type

Hübner

Fillinger

2019

i-Perception

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“…Furthermore, we used the rating dimension complex , which depends primarily on the number and/or density of pictorial elements in an image (Van Geert & Wagemans, 2019). In the present study, we manipulated Line Number as this changes objective complexity.…”

mentioning

confidence: 99%

“…Paradoxically, although curved lines display more orientations that angular (straight) lines, they can be perceived as less complex (Bertamini et al., 2019). The relation between subjective and objective complexity is thus not straightforward and can depend on other stimulus factors, for example, symmetry (Gartus & Leder, 2017) or other factors that reflect order (Van Geert & Wagemans, 2019). By contrast, the effect of objective complexity (as an independent variable) on liking has been studied in more detail, but with heterogeneous results.…”

mentioning

confidence: 99%

Aesthetic Perception of Line Patterns: Effect of Edge-Orientation Entropy and Curvilinear Shape

et al. 2020

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Curvilinearity is a perceptual feature that robustly predicts preference ratings for a variety of visual stimuli. The predictive effect of curved/angular shape overlaps, to a large degree, with regularities in second-order edge-orientation entropy, which captures how independent edge orientations are distributed across an image. For some complex line patterns, edge-orientation entropy is actually a better predictor for what human observers like than curved/angular shape. The present work was designed to disentangle the role of the two features in artificial patterns that consisted of either curved or angular line elements. We systematically varied these patterns across two more dimensions, edge-orientation entropy and the number of lines. Eighty-three participants rated the stimuli along three aesthetic dimensions ( pleasing, harmonious, and complex). Results showed that curved/angular shape was a stronger predictor for ratings of pleasing and harmonious if the stimuli consisted of a few lines that were clearly discernible. By contrast, edge-orientation entropy was a stronger predictor for the ratings if the stimuli showed many lines, which merged into a texture. No such differences were obtained for complexity ratings. Our findings are in line with results from neurophysiological studies that the processing of shape and texture, respectively, is mediated by different cortical mechanisms.

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“…In order to characterize the image complexity, the following four image complexity metrics [ 26 ] were calculated from the RGB images used in the experiments: Self-similarity: by using the histogram intersection kernel and comparing the histogram of oriented gradients (HOG) features of each sub-image at level 3 with those of the entire image at level 0; Complexity: computed as the mean norm of the gradient across all orientations over the gradient image; Birkhoff-like metric: computed as the ratio between the self-similarity and complexity metrics; Anisotropy: calculated as the variance of all the HOG values at level 3. …”

Section: Methodsmentioning

confidence: 99%

Psychophysical Determination of the Relevant Colours That Describe the Colour Palette of Paintings

et al. 2021

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In an early study, the so-called “relevant colour” in a painting was heuristically introduced as a term to describe the number of colours that would stand out for an observer when just glancing at a painting. The purpose of this study is to analyse how observers determine the relevant colours by describing observers’ subjective impressions of the most representative colours in paintings and to provide a psychophysical backing for a related computational model we proposed in a previous work. This subjective impression is elicited by an efficient and optimal processing of the most representative colour instances in painting images. Our results suggest an average number of 21 subjective colours. This number is in close agreement with the computational number of relevant colours previously obtained and allows a reliable segmentation of colour images using a small number of colours without introducing any colour categorization. In addition, our results are in good agreement with the directions of colour preferences derived from an independent component analysis. We show that independent component analysis of the painting images yields directions of colour preference aligned with the relevant colours of these images. Following on from this analysis, the results suggest that hue colour components are efficiently distributed throughout a discrete number of directions and could be relevant instances to a priori describe the most representative colours that make up the colour palette of paintings.

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Order, complexity, and aesthetic appreciation.

Cited by 90 publications

References 119 publications

Perceptual Balance, Stability, and Aesthetic Appreciation: Their Relations Depend on the Picture Type

Perceptual Balance, Stability, and Aesthetic Appreciation: Their Relations Depend on the Picture Type

Aesthetic Perception of Line Patterns: Effect of Edge-Orientation Entropy and Curvilinear Shape

Psychophysical Determination of the Relevant Colours That Describe the Colour Palette of Paintings

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