Gregor U. Hayn-Leichsenring

Beauty in abstract paintings: perceptual contrast and statistical properties.

In this study, we combined the behavioral and objective approach in the field of empirical aesthetics. First, we studied the perception of beauty by investigating shifts in evaluation on perceived beauty of abstract artworks (Experiment 1). Because the participants showed heterogeneous individual preferences for the paintings, we divided them into seven clusters for the test. The experiment revealed a clear pattern of perceptual contrast. The perceived beauty of abstract paintings increased after exposure to paintings that were rated as less beautiful, and it decreased after exposure to paintings that were rated as more beautiful. Next, we searched for correlations of beauty ratings and perceptual contrast with statistical properties of abstract artworks (Experiment 2). The participants showed significant preferences for particular image properties. These preferences differed between the clusters of participants. Strikingly, next to color measures like hue, saturation, value and lightness, the recently described Pyramid of Histograms of Orientation Gradients (PHOG) self-similarity value seems to be a predictor for aesthetic appreciation of abstract artworks. We speculate that the shift in evaluation in Experiment 1 was, at least in part, based on low-level adaptation to some of the statistical image properties analyzed in Experiment 2. In conclusion, our findings demonstrate that the perception of beauty in abstract artworks is altered after exposure to beautiful or non-beautiful images and correlates with particular image properties, especially color measures and self-similarity.

Adaptation Effects to Attractiveness of Face Photographs and Art Portraits are Domain-Specific

We studied the neural coding of facial attractiveness by investigating effects of adaptation to attractive and unattractive human faces on the perceived attractiveness of veridical human face pictures (Experiment 1) and art portraits (Experiment 2). Experiment 1 revealed a clear pattern of contrastive aftereffects. Relative to a pre-adaptation baseline, the perceived attractiveness of faces was increased after adaptation to unattractive faces, and was decreased after adaptation to attractive faces. Experiment 2 revealed similar aftereffects when art portraits rather than face photographs were used as adaptors and test stimuli, suggesting that effects of adaptation to attractiveness are not restricted to facial photographs. Additionally, we found similar aftereffects in art portraits for beauty, another aesthetic feature that, unlike attractiveness, relates to the properties of the image (rather than to the face displayed). Importantly, Experiment 3 showed that aftereffects were abolished when adaptors were art portraits and face photographs were test stimuli. These results suggest that adaptation to facial attractiveness elicits aftereffects in the perception of subsequently presented faces, for both face photographs and art portraits, and that these effects do not cross image domains.

Fourier Power Spectrum Characteristics of Face Photographs: Attractiveness Perception Depends on Low-Level Image Properties

We investigated whether low-level processed image properties that are shared by natural scenes and artworks – but not veridical face photographs – affect the perception of facial attractiveness and age. Specifically, we considered the slope of the radially averaged Fourier power spectrum in a log-log plot. This slope is a measure of the distribution of special frequency power in an image. Images of natural scenes and artworks possess – compared to face images – a relatively shallow slope (i.e., increased high spatial frequency power). Since aesthetic perception might be based on the efficient processing of images with natural scene statistics, we assumed that the perception of facial attractiveness might also be affected by these properties. We calculated Fourier slope and other beauty-associated measurements in face images and correlated them with ratings of attractiveness and age of the depicted persons (Study 1). We found that Fourier slope – in contrast to the other tested image properties – did not predict attractiveness ratings when we controlled for age. In Study 2A, we overlaid face images with random-phase patterns with different statistics. Patterns with a slope similar to those in natural scenes and artworks resulted in lower attractiveness and higher age ratings. In Studies 2B and 2C, we directly manipulated the Fourier slope of face images and found that images with shallower slopes were rated as more attractive. Additionally, attractiveness of unaltered faces was affected by the Fourier slope of a random-phase background (Study 3). Faces in front of backgrounds with statistics similar to natural scenes and faces were rated as more attractive. We conclude that facial attractiveness ratings are affected by specific image properties. An explanation might be the efficient coding hypothesis.

Evaluating Abstract Art: Relation between Term Usage, Subjective Ratings, Image Properties and Personality Traits.

One of the major challenges in experimental aesthetics is the uncertainty of the terminology used in experiments. In this study, we recorded terms that are spontaneously used by participants to describe abstract artworks and studied their relation to the second-order statistical image properties of the same artworks (Experiment 1). We found that the usage frequency of some structure-describing terms correlates with statistical image properties, such as PHOG Self-Similarity, Anisotropy and Complexity. Additionally, emotion-associated terms correlate with measured color values. Next, based on the most frequently used terms, we created five different rating scales (Experiment 2) and obtained ratings of participants for the abstract paintings on these scales. We found significant correlations between descriptive score ratings (e.g., between structure and subjective complexity), between evaluative and descriptive score ratings (e.g., between preference and subjective complexity/interest) and between descriptive score ratings and statistical image properties (e.g., between interest and PHOG Self-Similarity, Complexity and Anisotropy). Additionally, we determined the participants’ personality traits as described in the ‘Big Five Inventory’ (Goldberg, 1990; Rammstedt and John, 2005) and correlated them with the ratings and preferences of individual participants. Participants with higher scores for Neuroticism showed preferences for objectively more complex images, as well as a different notion of the term complex when compared with participants with lower scores for Neuroticism. In conclusion, this study demonstrates an association between objectively measured image properties and the subjective terms that participants use to describe or evaluate abstract artworks. Moreover, our results suggest that the description of abstract artworks, their evaluation and the preference of participants for their low-level statistical properties are linked to personality traits.

JenAesthetics Subjective Dataset: Analyzing Paintings by Subjective Scores

Over the last few years, researchers from the computer vision and image processing community have joined other research groups in searching for the bases of aesthetic judgment of paintings and photographs. One of the most important issues, which has hampered research in the case of paintings compared to photographs, is the lack of subjective datasets available for public use. This issue has not only been mentioned in different publications, but was also widely discussed at different conferences and workshops. In the current work, we perform a subjective test on a recently released dataset of aesthetic paintings. The subjective test not only collects scores based on the subjective aesthetic quality, but also on other properties that have been linked to aesthetic judgment.

Changes of Statistical Properties During the Creation of Graphic Artworks

During the creation of graphic artworks, we studied the evolution of higher-order statistical image properties (complexity, self-similarity, anisotropy of oriented luminance gradients, the slope of log–log plots of radially averaged Fourier power, and the fractal dimension). First, we analyzed two series of lithographs by Pablo Picasso, which represent transformations of highly aesthetic artworks. Second, one of the authors generated a dataset of 20 grayscale drawings using the computer as a drawing tool. The dataset comprised also the unfinished state images that were saved throughout the production process. The final states of the drawings were compared to versions of the same drawings, in which the constituent pictorial elements were shuffled, thereby diminishing the overall compositional intent of the artist. Results show that self-similarity was a property closely associated with artistic merit in the different types of images analyzed. In a psychological experiment, 20 non-expert participants evaluated the original abstract drawings as more harmonious and ordered but less interesting than the shuffled versions. Our study demonstrates that statistical image properties can be studied during the creation of artworks, if artistic and analytical processes are closely coordinated in a computer-based approach, which offers the possibility to produce appropriate control stimuli.

Evaluating the Rule of Thirds in Photographs and Paintings

The rule of thirds (ROT) is one of the best-known composition rules used in painting and photography. According to this rule, the focus point of an image should be placed along one of the third lines or on one of the four intersections of the third lines, to give aesthetically pleasing results. Recently, calculated saliency maps have been used in an attempt to predict whether or not images obey the rule of thirds. In the present study, we challenged this computer-based approach by comparing calculated ROT values with behavioral (subjective) ROT scores obtained from 30 participants in a psychological experiment. For photographs that did not follow the rule of thirds, subjective ROT scores matched calculated ROT values reasonably well. For photographs that followed the rule of thirds, we found a moderate correlation between subjective scores and calculated values. However, aesthetic rating scores correlated only weakly with subjective ROT scores and not at all with calculated ROT values. Moreover, for photographs that were rated as highly aesthetic and for a large set of paintings, calculated ROT values were about as low as in photographs that did not follow the rule of thirds. In conclusion, the computer-based ROT metrics can predict the behavioral data, but not completely. Despite its proclaimed importance in artistic composition, the rule of thirds seems to play only a minor, if any, role in large sets of high-quality photographs and paintings.

Subjective Ratings of Beauty and Aesthetics: Correlations With Statistical Image Properties in Western Oil Paintings

For centuries, oil paintings have been a major segment of the visual arts. The JenAesthetics data set consists of a large number of high-quality images of oil paintings of Western provenance from different art periods. With this database, we studied the relationship between objective image measures and subjective evaluations of the images, especially evaluations on aesthetics (defined as artistic value) and beauty (defined as individual liking). The objective measures represented low-level statistical image properties that have been associated with aesthetic value in previous research. Subjective rating scores on aesthetics and beauty correlated not only with each other but also with different combinations of the objective measures. Furthermore, we found that paintings from different art periods vary with regard to the objective measures, that is, they exhibit specific patterns of statistical image properties. In addition, clusters of participants preferred different combinations of these properties. In conclusion, the results of the present study provide evidence that statistical image properties vary between art periods and subject matters and, in addition, they correlate with the subjective evaluation of paintings by the participants.

There is beauty in gist: An investigation of aesthetic perception in rapidly presented scenes

While an artfully crafted painting can evoke profound aesthetic experience, the same applies to a grand ballroom or sunset. Like fine art, everyday scenes contain aesthetic qualities, with some scenes preferred over others. The meaning or semantic label of a scene, known as scene gist, is extracted rapidly and automatically, with just a brief glance, computed mainly using low spatial frequencies (LSF) in the image. Although we can easily identify a scene, the question remains, if an accurate aesthetic impression can be formed from such rapid and coarse overall representation. We investigated the characteristics of scene gist to determine if aesthetic preference can be extracted with such short display durations. Furthermore, given that scene gist is based on an initial coarse representation, we asked whether LSF renderings of these scenes would elicit similar aesthetic judgments. Using a between-groups design, we found a significant positive correlation between aesthetic judgments on real-world scenes for images viewed for an unlimited amount of time and those viewed for only 45ms, but no significant correlation with the LSF set. This demonstrates that aesthetic judgments can be extracted rapidly and are relatively stable across display durations but do not survive image degradation, suggesting that image content outweighs structure. We performed the Implicit Associations Test by using aesthetically pleasing and non-pleasing images from the previous experiment paired with aesthetically pleasing and non-pleasing words, to examine whether these aesthetic judgments are also made automatically when they are irrelevant to the task. Participants made significantly more classification errors and were slower when pleasing scenes were paired with non-pleasing words. This suggests that participants could not help but make aesthetic judgments on real-world scenes. Additionally, we found that the most pleasing and non-pleasing scenes differed significantly on self-similarity and anisotropy, measures of image statistics relating to computational aesthetics. Meeting abstract presented at VSS 2015.

When noise is beneficial for sensory encoding: Noise adaptation can improve face processing

HighlightsWe studied how noise patterns affect face perception.Face processing is facilitated after adaptation to noise.The adaptation effect is enhanced by the presence of surrounding noise.The spectral slope of the noise patterns affects the size of the effect. Abstract The presence of noise usually impairs the processing of a stimulus. Here, we studied the effects of noise on face processing and show, for the first time, that adaptation to noise patterns has beneficial effects on face perception. We used noiseless faces that were either surrounded by random noise or presented on a uniform background as stimuli. In addition, the faces were either preceded by noise adaptors or not. Moreover, we varied the statistics of the noise so that its spectral slope either matched that of the faces or it was steeper or shallower. Results of parallel ERP recordings showed that the background noise reduces the amplitude of the face‐evoked N170, indicating less intensive face processing. Adaptation to a noise pattern, however, led to reduced P1 and enhanced N170 amplitudes as well as to a better behavioral performance in two of the three noise conditions. This effect was also augmented by the presence of background noise around the target stimuli. Additionally, the spectral slope of the noise pattern affected the size of the P1, N170 and P2 amplitudes. We reason that the observed effects are due to the selective adaptation of noise‐sensitive neurons present in the face‐processing cortical areas, which may enhance the signal‐to‐noise‐ratio.