Liking of art and the perception of color.

Color has been a defining feature of paintings throughout art history. Despite the great diversity in the use of color between epochs, there are some surprisingly stable and unifying features in chromatic properties across visual artworks. For example, artists' palettes seem to be biased toward the yellow-red range of the spectrum. Here, we assess the impact of a holistic color manipulation (i.e., rotating the color gamut) on aesthetic liking and perceived colorfulness of abstract paintings. We presented 6 versions each of 100 abstract artworks that differed only in the rotational degree of their color gamut within the CIELAB space. Results indicated a very stable preference for the original color compositions-both on a participant level and on an item level. Furthermore, participants perceived original color compositions as more colorful than rotated versions. This effect remained robust even when the exact number of different colors-among other chromatic features-was taken into account in covariate analyses. Thus, it seems that original color compositions are inherently special. Specifically, it seems that the aesthetic appeal of original artworks arises from nontrivial color features, which are characterized by their distribution within the visible spectrum. We assume that the rotation manipulation may change the perception of some colors more strongly than others due to differences in sensitivity of our visual system to these hues. We discuss these findings with respect to category-specific color perception, which may be a potential contender for a neurobiological foundation of the observed effects. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

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

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.

Edge-Orientation Entropy Predicts Preference for Diverse Types of Man-Made Images.

We recently found that luminance edges are more evenly distributed across orientations in large subsets of traditional artworks, i.e., artworks are characterized by a relatively high entropy of edge orientations, when compared to several categories of other (non-art) images. In the present study, we asked whether edge-orientation entropy is associated with aesthetic preference in a wide variety of other man-made visual patterns and scenes. In the first (exploratory) part of the study, participants rated the aesthetic appeal of simple shapes, artificial ornamental patterns, facades of buildings, scenes of interior architecture, and music album covers. Results indicated that edge-orientation entropy predicts aesthetic ratings for these stimuli. However, the magnitude of the effect depended on the type of images analyzed, on the range of entropy values encountered, and on the type of aesthetic rating (pleasing, interesting, or harmonious). For example, edge-orientation entropy predicted about half of the variance when participants rated facade photographs for pleasing and interesting, but only for 3.5% of the variance for harmonious ratings of music album covers. We also asked whether edge-orientation entropy relates to the well-established human preference for curved over angular shapes. Our analysis revealed that edge-orientation entropy was as good or an even better predictor for the aesthetic ratings than curvilinearity. Moreover, entropy could substitute for shape, at least in part, to predict the aesthetic ratings. In the second (experimental) part of this study, we generated complex line stimuli that systematically varied in their edge-orientation entropy and curved/angular shape. Here, edge-orientation entropy was a more powerful predictor for ratings of pleasing and harmonious than curvilinearity, and as good a predictor for interesting. Again, the two image properties shared a large portion of variance between them. In summary, our results indicate that edge-orientation entropy predicts aesthetic ratings in diverse man-made visual stimuli. Moreover, the preference for high edge-orientation entropy shares a large portion of predicted variance with the preference for curved over angular stimuli.

Computational and Experimental Approaches to Visual Aesthetics.

Aesthetics has been the subject of long-standing debates by philosophers and psychologists alike. In psychology, it is generally agreed that aesthetic experience results from an interaction between perception, cognition, and emotion. By experimental means, this triad has been studied in the field of experimental aesthetics, which aims to gain a better understanding of how aesthetic experience relates to fundamental principles of human visual perception and brain processes. Recently, researchers in computer vision have also gained interest in the topic, giving rise to the field of computational aesthetics. With computing hardware and methodology developing at a high pace, the modeling of perceptually relevant aspect of aesthetic stimuli has a huge potential. In this review, we present an overview of recent developments in computational aesthetics and how they relate to experimental studies. In the first part, we cover topics such as the prediction of ratings, style and artist identification as well as computational methods in art history, such as the detection of influences among artists or forgeries. We also describe currently used computational algorithms, such as classifiers and deep neural networks. In the second part, we summarize results from the field of experimental aesthetics and cover several isolated image properties that are believed to have a effect on the aesthetic appeal of visual stimuli. Their relation to each other and to findings from computational aesthetics are discussed. Moreover, we compare the strategies in the two fields of research and suggest that both fields would greatly profit from a joined research effort. We hope to encourage researchers from both disciplines to work more closely together in order to understand visual aesthetics from an integrated point of view.

Statistical Image Properties in Large Subsets of Traditional Art, Bad Art, and Abstract Art.

Several statistical image properties have been associated with large subsets of traditional visual artworks. Here, we investigate some of these properties in three categories of art that differ in artistic claim and prestige: (1) Traditional art of different cultural origin from established museums and art collections (oil paintings and graphic art of Western provenance, Islamic book illustration and Chinese paintings), (2) Bad Art from two museums that collect contemporary artworks of lesser importance (© Museum Of Bad Art [MOBA], Somerville, and Official Bad Art Museum of Art [OBAMA], Seattle), and (3) twentieth century abstract art of Western provenance from two prestigious museums (Tate Gallery and Kunstsammlung Nordrhein-Westfalen). We measured the following four statistical image properties: the fractal dimension (a measure relating to subjective complexity); self-similarity (a measure of how much the sections of an image resemble the image as a whole), 1st-order entropy of edge orientations (a measure of how uniformly different orientations are represented in an image); and 2nd-order entropy of edge orientations (a measure of how independent edge orientations are across an image). As shown previously, traditional artworks of different styles share similar values for these measures. The values for Bad Art and twentieth century abstract art show a considerable overlap with those of traditional art, but we also identified numerous examples of Bad Art and abstract art that deviate from traditional art. By measuring statistical image properties, we quantify such differences in image composition for the first time.

Using CNN Features to Better Understand What Makes Visual Artworks Special.

One of the goal of computational aesthetics is to understand what is special about visual artworks. By analyzing image statistics, contemporary methods in computer vision enable researchers to identify properties that distinguish artworks from other (non-art) types of images. Such knowledge will eventually allow inferences with regard to the possible neural mechanisms that underlie aesthetic perception in the human visual system. In the present study, we define measures that capture variances of features of a well-established Convolutional Neural Network (CNN), which was trained on millions of images to recognize objects. Using an image dataset that represents traditional Western, Islamic and Chinese art, as well as various types of non-art images, we show that we need only two variance measures to distinguish between the artworks and non-art images with a high classification accuracy of 93.0%. Results for the first variance measure imply that, in the artworks, the subregions of an image tend to be filled with pictorial elements, to which many diverse CNN features respond (richness of feature responses). Results for the second measure imply that this diversity is tied to a relatively large variability of the responses of individual CNN feature across the subregions of an image. We hypothesize that this combination of richness and variability of CNN feature responses is one of properties that makes traditional visual artworks special. We discuss the possible neural underpinnings of this perceptual quality of artworks and propose to study the same quality also in other types of aesthetic stimuli, such as music and literature.

High entropy of edge orientations characterizes visual artworks from diverse cultural backgrounds.

We asked whether "good composition" or "visual rightness" of artworks manifest themselves in a particular arrangement of basic image features, such as oriented luminance edges. Specifically, we analysed the layout of edge orientations in images from a collection of >1600 paintings of Western provenance by comparing pairwise the orientation of each edge in an image with the orientations of all other edges in the same image. From the resulting orientation histograms, we calculated Shannon entropy and parallelism (i.e., the degree to which lines are parallel in the image). For comparison, we analysed the same second-order image properties in photographs of diverse natural patterns and man-made objects and scenes. Results showed that Shannon entropy of relative orientations of edge pairs was high and parallelism was low for the paintings and some of the natural patterns, but differed from other sets of photographs, including other man-made stimuli. The differences were also observed when images were matched for image content. Moreover, high entropy of edge orientations was found in traditional artworks produced by different techniques, in artworks that represented different content matter and art genres, as well as in artworks from other cultural backgrounds (East Asian and Islamic). In conclusion, we found that high entropy of edge orientations characterizes diverse sets of traditional artworks from various cultural backgrounds.

Statistical Image Properties in Works from the Prinzhorn Collection of Artists with Schizophrenia.

The Prinzhorn Collection preserves and exhibits thousands of visual artworks by patients who were diagnosed to suffer from mental disease. From this collection, we analyzed 1,256 images by 14 artists who were diagnosed with dementia praecox or schizophrenia. Six objective statistical properties that have been used previously to characterize visually aesthetic images were calculated. These properties reflect features of formal image composition, such as the complexity and distribution of oriented luminance gradients and edges, as well as Fourier spectral properties. Results for the artists with schizophrenia were compared to artworks from three public art collections of paintings and drawings that include highly acclaimed artworks as well as artworks of lesser artistic claim (control artworks). Many of the patients' works did not differ from these control images. However, the artworks of 6 of the 14 artists with schizophrenia possess image properties that deviate from the range of values obtained for the control artworks. For example, the artworks of four of the patients are characterized by a relative dominance of specific edge orientations in their images (low first-order entropy of edge orientations). Three patients created artworks with a relatively high ratio of fine detail to coarse structure (high slope of the Fourier spectrum). In conclusion, the present exploratory study opens novel perspectives for the objective scientific investigation of visual artworks that were created by persons who suffer from schizophrenia.