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

What kind of impacts can artwork have on viewers? Establishing a taxonomy for aesthetic impacts

What kinds of impacts can visual art have on a viewer? To identify potential art impacts, we recruited five aesthetics experts from different academic disciplines: art history, neuroscience, philosophy, psychology and theology. Together, the group curated a set of terms that corresponded to descriptive features (124 terms) and cognitive-affective impacts (69 terms) of artworks. Using these terms as prompts, participants (n = 899) were given one minute to generate words for each term related to how an artwork looked (descriptive features) or made them think or feel (cognitive-affective impacts). Using network psychometric approaches, we identified terms that were semantically similar based on participants' responses and applied hierarchical exploratory graph analysis to map the relationships between the terms. Our analyses identified 17 descriptive dimensions, which could be further reduced to 5, and 11 impact dimensions, which could be further reduced to 4. The resulting taxonomy demonstrated overlap between the descriptive and impact networks as well as consistency with empirical evidence. This taxonomy could serve as the foundation to empirically evaluate art's impacts on viewers.

Statistical image properties predict aesthetic ratings in abstract paintings created by neural style transfer

Artificial intelligence has emerged as a powerful computational tool to create artworks. One application is Neural Style Transfer, which allows to transfer the style of one image, such as a painting, onto the content of another image, such as a photograph. In the present study, we ask how Neural Style Transfer affects objective image properties and how beholders perceive the novel (style-transferred) stimuli. In order to focus on the subjective perception of artistic style, we minimized the confounding effect of cognitive processing by eliminating all representational content from the input images. To this aim, we transferred the styles of 25 diverse abstract paintings onto 150 colored random-phase patterns with six different Fourier spectral slopes. This procedure resulted in 150 style-transferred stimuli. We then computed eight statistical image properties (complexity, self-similarity, edge-orientation entropy, variances of neural network features, and color statistics) for each image. In a rating study, we asked participants to evaluate the images along three aesthetic dimensions (Pleasing, Harmonious, and Interesting). Results demonstrate that not only objective image properties, but also subjective aesthetic preferences transferred from the original artworks onto the style-transferred images. The image properties of the style-transferred images explain 50 – 69% of the variance in the ratings. In the multidimensional space of statistical image properties, participants considered style-transferred images to be more Pleasing and Interesting if they were closer to a “sweet spot” where traditional Western paintings (JenAesthetics dataset) are represented. We conclude that NST is a useful tool to create novel artistic stimuli that preserve the image properties of the input style images. In the novel stimuli, we found a strong relationship between statistical image properties and subjective ratings, suggesting a prominent role of perceptual processing in the aesthetic evaluation of abstract images.

Swipes and Saves: A Taxonomy of Factors Influencing Aesthetic Assessments and Perceived Beauty of Mobile Phone Photographs

Digital images taken by mobile phones are the most frequent class of images created today. Due to their omnipresence and the many ways they are encountered, they require a specific focus in research. However, to date, there is no systematic compilation of the various factors that may determine our evaluations of such images, and thus no explanation of how users select and identify relatively “better” or “worse” photos. Here, we propose a theoretical taxonomy of factors influencing the aesthetic appeal of mobile phone photographs. Beyond addressing relatively basic/universal image characteristics, perhaps more related to fast (bottom-up) perceptual processing of an image, we also consider factors involved in the slower (top-down) re-appraisal or deepened aesthetic appreciation of an image. We span this taxonomy across specific types of picture genres commonly taken—portraits of other people, selfies, scenes and food. We also discuss the variety of goals, uses, and contextual aspects of users of mobile phone photography. As a working hypothesis, we propose that two main decisions are often made with mobile phone photographs: (1) Users assess images at a first glance—by swiping through a stack of images—focusing on visual aspects that might be decisive to classify them from “low quality” (too dark, out of focus) to “acceptable” to, in rare cases, “an exceptionally beautiful picture.” (2) Users make more deliberate decisions regarding one’s “favorite” picture or the desire to preserve or share a picture with others, which are presumably tied to aspects such as content, framing, but also culture or personality, which have largely been overlooked in empirical research on perception of photographs. In sum, the present review provides an overview of current focal areas and gaps in research and offers a working foundation for upcoming research on the perception of mobile phone photographs as well as future developments in the fields of image recording and sharing technology.

Visual affects: Linking curiosity, Aha-Erlebnis, and memory through information gain

Current theories propose that our sense of curiosity is determined by the learning progress or information gain that our cognitive system expects to make. However, few studies have explicitly tried to quantify subjective information gain and link it to measures of curiosity. Here, we asked people to report their curiosity about the intrinsically engaging perceptual 'puzzles' known as Mooney images, and to report on the strength of their aha experience upon revealing the solution image (curiosity relief). We also asked our participants (279) to make a guess concerning the solution of the image, and used the distribution of these guesses to compute the crowdsourced semantic entropy (or ambiguity) of the images, as a measure of the potential for information gain. Our results confirm that curiosity and, even more so, aha experience is substantially associated with this semantic information gain measure. These findings support the expected information gain theory of curiosity and suggest that the aha experience or intrinsic reward is driven by the actual information gain. In an unannounced memory part, we also established that the often reported influence of curiosity on memory is fully mediated by the aha experience or curiosity relief. We discuss the implications of our results for the burgeoning fields of curiosity and psychoaesthetics.

The Aesthetic Self. The Importance of Aesthetic Taste in Music and Art for Our Perceived Identity

To what extent do aesthetic taste and our interest in the arts constitute who we are? In this paper, we present a series of empirical findings that suggest an Aesthetic Self Effect supporting the claim that our aesthetic engagements are a central component of our identity. Counterfactual changes in aesthetic preferences, for example, moving from liking classical music to liking pop, are perceived as altering us as a person. The Aesthetic Self Effect is as strong as the impact of moral changes, such as altering political partisanship or religious orientation, and significantly stronger than for other categories of taste, such as food preferences (Study 1). Using a multidimensional scaling technique to map perceived aesthetic similarities among musical genres, we determined that aesthetic distances between genres correlate highly with the perceived difference in identity (Study 2). Further studies generalize the Aesthetic Self Effect beyond the musical domain: general changes in visual art preferences, for example from more traditional to abstract art, also elicited a strong Self Effect (Study 3). Exploring the breadth of this effect we also found an Anaesthetic Self Effect. That is, hypothetical changes from aesthetic indifference to caring about music, art, or beauty are judged to have a significant impact on identity. This effect on identity is stronger for aesthetic fields compared to leisure activities, such as hiking or playing video games (Study 4). Across our studies, the Anaesthetic Self Effect turns out to be stronger than the Aesthetic Self Effect. Taken together, we found evidence for a link between aesthetics and identity: we are aesthetic selves. When our tastes in music and the arts or our aesthetic interests change we take these to be transformative changes.

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.

Psychological and Physiological Responses in Patients with Generalized Anxiety Disorder: The Use of Acute Exercise and Virtual Reality Environment

Virtual exercise therapy is considered a useful method by which to encourage patients with generalized anxiety disorder (GAD) to engage in aerobic exercise in order to reduce stress. This study was intended to explore the psychological and physiological responses of patients with GAD after cycling in a virtual environment containing natural images. Seventy-seven participants with GAD were recruited in the present study and randomly assigned to a virtual nature (VN) or a virtual abstract painting (VAP) group. Their electroencephalogram alpha activity, perceived stress, and levels of restorative quality and satisfaction were assessed at baseline and after an acute bout of 20 min of moderate-intensity aerobic exercise. The results showed that both the VN and VAP groups showed significantly higher alpha activity post-exercise as compared to pre-exercise. The VN group relative to the VAP group exhibited higher levels of stress-relief, restorative quality, and personal satisfaction. These findings imply that a virtual exercise environment is an effective way to induce a relaxing effect in patients with GAD. However, they exhibited more positive psychological responses when exercising in such an environment with natural landscapes.

Global Image Properties Predict Ratings of Affective Pictures

Affective pictures are widely used in studies of human emotions. The objects or scenes shown in affective pictures play a pivotal role in eliciting particular emotions. However, affective processing can also be mediated by low-level perceptual features, such as local brightness contrast, color or the spatial frequency profile. In the present study, we asked whether image properties that reflect global image structure and image composition affect the rating of affective pictures. We focused on 13 global image properties that were previously associated with the esthetic evaluation of visual stimuli, and determined their predictive power for the ratings of five affective picture datasets (IAPS, GAPED, NAPS, DIRTI, and OASIS). First, we used an SVM-RBF classifier to predict high and low ratings for valence and arousal, respectively, and achieved a classification accuracy of 58–76% in this binary decision task. Second, a multiple linear regression analysis revealed that the individual image properties account for between 6 and 20% of the variance in the subjective ratings for valence and arousal. The predictive power of the image properties varies for the different datasets and type of ratings. Ratings tend to share similar sets of predictors if they correlate positively with each other. In conclusion, we obtained evidence from non-linear and linear analyses that affective pictures evoke emotions not only by what they show, but they also differ by how they show it. Whether the human visual system actually uses these perceptive cues for emotional processing remains to be investigated.

Comparison of Outlier-Tolerant Models for Measuring Visual Complexity

Providing the visual complexity of an image in terms of impact or aesthetic preference can be of great applicability in areas such as psychology or marketing. To this end, certain areas such as Computer Vision have focused on identifying features and computational models that allow for satisfactory results. This paper studies the application of recent ML models using input images evaluated by humans and characterized by features related to visual complexity. According to the experiments carried out, it was confirmed that one of these methods, Correlation by Genetic Search (CGS), based on the search for minimum sets of features that maximize the correlation of the model with respect to the input data, predicted human ratings of image visual complexity better than any other model referenced to date in terms of correlation, RMSE or minimum number of features required by the model. In addition, the variability of these terms were studied eliminating images considered as outliers in previous studies, observing the robustness of the method when selecting the most important variables to make the prediction.

Abstract art paintings, global image properties, and verbal descriptions: An empirical and computational investigation

While global image properties (GIPs) relate to preference ratings in many categories of visual stimuli, this relationship is typically not seen for abstract art paintings. Using computational network science and empirical methods, we further investigated GIPs and subjective preferences. First, we replicated the earlier observation that GIPs do not relate to preferences for abstract art. Next, we estimated the network structure of abstract art paintings using two approaches: the first was based on verbal descriptions and the second on GIPs. We examined the extent to which network measures computed from these two networks (1) related to preference for abstract art paintings and (2) determined affiliation of images to specific art styles. Only semantic-based network predicted the subjective preference ratings and art style. Finally, preference and GIPs differed for sub-groups of abstract art paintings. Our results demonstrate the importance of verbal descriptors in evaluating abstract art, and that it is not useful in empirical aesthetics to treat abstract art paintings as a single category.

Visual complexity modelling based on image features fusion of multiple kernels

Humans' perception of visual complexity is often regarded as one of the key principles of aesthetic order, and is intimately related to the physiological, neurological and, possibly, psychological characteristics of the human mind. For these reasons, creating accurate computational models of visual complexity is a demanding task. Building upon on previous work in the field (Forsythe et al., 2011; Machado et al., 2015) we explore the use of Machine Learning techniques to create computational models of visual complexity. For that purpose, we use a dataset composed of 800 visual stimuli divided into five categories, describing each stimulus by 329 features based on edge detection, compression error and Zipf's law. In an initial stage, a comparative analysis of representative state-of-the-art Machine Learning approaches is performed. Subsequently, we conduct an exhaustive outlier analysis. We analyze the impact of removing the extreme outliers, concluding that Feature Selection Multiple Kernel Learning obtains the best results, yielding an average correlation to humans' perception of complexity of 0.71 with only twenty-two features. These results outperform the current state-of-the-art, showing the potential of this technique for regression.

Prediction of beauty and liking ratings for abstract and representational paintings using subjective and objective measures

Recent research on aesthetics has challenged the adage that "beauty is in the eye of the beholder" by identifying several factors that predict ratings of beauty. However, this research has emerged in a piecemeal fashion. Most studies have examined only a few predictors of beauty, and measured either subjective or objective predictors, but not both. Whether the predictors of ratings of beauty versus liking differ has not been tested, nor has whether predictors differ for major distinctions in art, such as abstract vs. representational paintings. Finally, past studies have either relied on experimenter-generated stimuli-which likely yield pallid aesthetic experiences-or on a curation of high-quality art-thereby restricting the range of predictor scores. We report a study (N = 598) that measured 4 subjective and 11 objective predictors of both beauty ratings and liking ratings, for 240 abstract and 240 representational paintings that varied widely in beauty. A crossover pattern occurred in the ratings, such that for abstract paintings liking ratings were higher than beauty ratings, whereas for representational paintings beauty ratings were higher than liking ratings. Prediction was much better for our subjective than objective predictors, and much better for our representational than abstract paintings. For abstract paintings, liking ratings were much more predictable than beauty ratings. Implications and directions for future research are discussed.

Visual mismatch negativity indicates automatic, task-independent detection of artistic image composition in abstract artworks

In complex abstract art, image composition (i.e., the artist's deliberate arrangement of pictorial elements) is an important aesthetic feature. We investigated whether the human brain detects image composition in abstract artworks automatically (i.e., independently of the experimental task). To this aim, we studied whether a group of 20 original artworks elicited a visual mismatch negativity when contrasted with a group of 20 images that were composed of the same pictorial elements as the originals, but in shuffled arrangements, which destroy artistic composition. We used a passive oddball paradigm with parallel electroencephalogram recordings to investigate the detection of image type-specific properties. We observed significant deviant-standard differences for the shuffled and original images, respectively. Furthermore, for both types of images, differences in amplitudes correlated with the behavioral ratings of the images. In conclusion, we show that the human brain can detect composition-related image properties in visual artworks in an automatic fashion.

Wonder, appreciation, and the value of art

There has been much work on what people appreciate in art, but comparatively little on what feelings of appreciation consist in. What do people feel when they encounter artworks that they value? We propose that the value of art is registered by the emotion of wonder. Departing from some standard approaches in empirical aesthetics, we focus on the appreciation of art as art rather than mere aesthetic preference. Aesthetic preferences can have many different correlates outside the domain of art (as when we select graphically appealing consumer items or judge the attractiveness of people), and preference judgments with respect to art can reflect nonaesthetic considerations and tell us rather little about art appreciation. We argue that when it comes to the appreciation of art as such, wonder plays a special role. We introduce wonder and compare it to other candidates that are discussed in the recent empirical literature, such as beauty, interest, and being moved. We analyze wonder and emphasize three subemotional components: cognitive perplexity, perceptual engagement, and a sense of reverence.

Gist Perception of Image Composition in Abstract Artworks

Most recent studies in experimental aesthetics have focused on the cognitive processing of visual artworks. In contrast, the perception of formal compositional features of artworks has been studied less extensively. Here, we investigated whether fast and automatic processing of artistic image composition can lead to a stable and consistent aesthetic evaluation when cognitive processing is minimized or absent. To this aim, we compared aesthetic ratings on abstract artworks and their shuffled counterparts in a gist experiment. Results show that exposure times as short as 50 ms suffice for the participants to reach a stable and consistent rating on how ordered and harmonious the abstract stimuli were. Moreover, the rating scores for the 50 ms exposure time exhibited similar dependencies on image type and self-similarity and a similar pattern of correlations between different rating terms, as the rating scores for the long exposure time (3,000 ms). Ratings were less consistent for the term interesting and inconsistent for the term pleasing. Our results are compatible with a model of aesthetic experience, in which the early perceptual processing of the formal aspects of visual artworks can lead to a consistent aesthetic judgment, even if there is no cognitive contribution to this judgment.

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.

Predicting perceived visual complexity of abstract patterns using computational measures: The influence of mirror symmetry on complexity perception

Visual complexity is relevant for many areas ranging from improving usability of technical displays or websites up to understanding aesthetic experiences. Therefore, many attempts have been made to relate objective properties of images to perceived complexity in artworks and other images. It has been argued that visual complexity is a multidimensional construct mainly consisting of two dimensions: A quantitative dimension that increases complexity through number of elements, and a structural dimension representing order negatively related to complexity. The objective of this work is to study human perception of visual complexity utilizing two large independent sets of abstract patterns. A wide range of computational measures of complexity was calculated, further combined using linear models as well as machine learning (random forests), and compared with data from human evaluations. Our results confirm the adequacy of existing two-factor models of perceived visual complexity consisting of a quantitative and a structural factor (in our case mirror symmetry) for both of our stimulus sets. In addition, a non-linear transformation of mirror symmetry giving more influence to small deviations from symmetry greatly increased explained variance. Thus, we again demonstrate the multidimensional nature of human complexity perception and present comprehensive quantitative models of the visual complexity of abstract patterns, which might be useful for future experiments and applications.

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.

The Ambiguity of Artworks -A Guideline for Empirical Aesthetics Research with Artworks as Stimuli

The aim of this work is to provide researchers from the field of aesthetics with a guideline on working with artworks as stimuli. Empirical aesthetics research is complicated by the uncertainty of the object of research. There is no way to unquestionably tell whether an object is an artwork or not. However, although the extension of the term artwork (i.e., the range of objects to which this concept applies) remains vague, the different intensions of the term artwork (i.e., the internal concept that constitutes a formal definition) are well defined. Here, I review the various concepts of artworks (i.e., intensions) that scientists from different fields use in current research in empirical aesthetics. The selection of stimuli is often not explained and/or does not match the focus of the study. An application of two or more intensions within one study leads to an indeterminacy of the stimuli and, thus, to systematic problems concerning the interpretation and comparability of the experimental results. Based on these intensions and the Pleasure-Interest Model of Aesthetic Liking (Graf and Landwehr, 2015), I compiled a decision tree in order to provide researchers with an instrument that allows a better control over their stimuli.

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.