Combining universal beauty and cultural context in a unifying model of visual aesthetic experience

The effect of expertise on the creation and evaluation of visual compositions in terms of creativity and beauty

The identification of artistically creative individuals is an important matter in the fields of art, design, and psychology. One promising approach involves assessing a person's products rather than his or her personality or cognitive processes. However, the necessity of expert involvement in such evaluations is still debated. To investigate this issue, two experiments were conducted, each consisting of a production phase and an evaluation phase. In Experiment 1, participants were asked to compose a most beautiful picture, which was subsequently assessed in terms of beauty. Experiment 2 was analogous, but participants were asked to compose a most creative picture, which was then assessed in terms of creativity and beauty. The results revealed that expertise did not play a crucial role in the creation or evaluation of beauty. Both experts and non-experts largely agreed on what constitutes beauty. However, when it came to the production and assessment of creative pictures, experts had an advantage. They were the only group that was able to predict a person's creativity based on the evaluation of his or her product.

Integrative processing in artificial and biological vision predicts the perceived beauty of natural images

Previous research shows that the beauty of natural images is already determined during perceptual analysis. However, it is unclear which perceptual computations give rise to the perception of beauty. Here, we tested whether perceived beauty is predicted by spatial integration across an image, a perceptual computation that reduces processing demands by aggregating image parts into more efficient representations of the whole. We quantified integrative processing in an artificial deep neural network model, where the degree of integration was determined by the amount of deviation between activations for the whole image and its constituent parts. This quantification of integration predicted beauty ratings for natural images across four studies with different stimuli and designs. In a complementary functional magnetic resonance imaging study, we show that integrative processing in human visual cortex similarly predicts perceived beauty. Together, our results establish integration as a computational principle that facilitates perceptual analysis and thereby mediates the perception of beauty.

Enjoying art: an evolutionary perspective on the esthetic experience from emotion elicitors

The ubiquity of human art prompted evolutionary psychologists to explore its origins as a potential adaptation to the environment. Here we focus on emotionally charged art and posit that affective affordances embedded into some artworks play a pivotal role in explaining why these artworks are enjoyed from an evolutionary perspective. Such features, recurring in various art forms, are interpreted as cues to the emotional state of others, enabling art consumers to engage in empathetic experiences and vicarious emotions. We explore the adaptive value of deriving pleasure from vicarious emotions, while also addressing the seemingly counterintuitive enjoyment of artworks that evoke negative emotions. We discuss the appreciation of vicarious emotions irrespective of their valence and maintain this appreciation to hold adaptive significance for three key reasons. Firstly, it aids art consumers in refining their interpretational schemes of internal states, potentially enhancing emotional regulation skills. Secondly, it contributes to a deeper understanding of the emotions of others, thereby fostering emotional intelligence and empathy. Lastly, the enjoyment of affectively charged artworks reinforces social cohesion by harmonizing the emotions of group members. This perspective provides a comprehensive framework for understanding the evolutionary underpinnings of the human capacity for art appreciation and emotional engagement.

The mediating effect of the need for cognition between aesthetic experiences and aesthetic competence in art

Although the role of aesthetics and aesthetic education in everyday life was discussed as early as the ancient philosophers, the psychological mechanisms shaping the aesthetic quotient have hardly been investigated by empirical studies. The aim of this study was to examine the direct relationship between experience and aesthetic competence, and the mediating role of need for cognition. The study involved 201 Polish adults, aged 18 to 76 (M = 26.40; SD = 11.89), 65% of whom were women. The respondents completed anonymous questionnaires on an online platform. The surveys included a metric, the Aesthetic Competence Scale (ACS), the Aesthetic Experience Questionnaire (AEQ) and the Need for Cognition Scale (NCS). A positive correlation coefficient was obtained between all three variables studied, with need for cognition acting as a mediator in the relationship between experience and aesthetic competence. The findings indicate that individuals reporting intense aesthetic experiences have a higher aesthetic competence if this relationship is mediated by a high need for cognitive effort.

Aesthetic Experience and the Ability to Integrate Beauty: The Mediating Effect of Spirituality

Background

The ability to integrate beauty (AIB) is the ability to inner transformation including thinking about oneself, perceived phenomena, or the world through exposure to an aesthetic object (or phenomenon). Previous research indicates that the AIB is positively related to aesthetic experience. Still, it is unclear whether spirituality can mediate the relationship between the two variables. Spirituality is understood as an experience of transcendence that relates to the unseen and is "larger than human". The aim of the study was to analyze the relationship between emotional and cognitive experiences related to the reception of art (as the most representative form of beauty) and the ability to connect with spirituality and aesthetic experiences.

Methods

The online survey included a sample of N = 195 adults (74% female) between the ages of 18 and 54. The Spirituality Scale (SD-36), the Aesthetic Experience Questionnaire (AEQ) and the Ability to Integrate Beauty Scale (AIBS) were used to test hypotheses.

Results

The analysis revealed a statistically significant, moderate relationship between the ability to integrate beauty and both the total aesthetic experience score and the spirituality scale score. The results support the hypothesis that there is a relationship between aesthetic experience in art and spirituality. The study also confirmed the mediating effect of spirituality on the relationship between aesthetic experience and aesthetic intelligence.

Conclusion

Individuals with a higher level of spiritual development tend to have a greater ability to integrate beauty and have more intense aesthetic experiences, which in turn may increase their aesthetic intelligence. The results suggest that a deepened spirituality contributes to a greater ability to integrate beauty.

The "Creativity Crisis" as a Mind in Crisis: A Cultural-Historical Activity Theory Position

Creativity is considered a global ability and crucial for ordinary-daily and special (e.g., science, aesthetic) activities. In this paper, from the position of Cultural-Historical Activity Theory (CHAT), we expand the debate about the creativity crisis and hypothesize that the noted crisis is only the tip of the iceberg represented by the crisis of the postmodern' incoherent mind, reflecting the crisis of self-realization as a leading activity in the individualistic epoch. By investigating creativity as an original functionality of the mind, two key titles are stressed. One is the halting of the activity system; two, it is the inconsistency between the objective meanings sphere and the subjective sense-making sphere. Both titles represent the epistemological rupture embedded in the mainstream culture and praxis rooted in the internal contradictions of individualism and post-modernism as worldview and practices, leading the mind to close its eyes on the contradictions which are the crucial source of grasping the internal content (abstraction and generalization) of the given experience, hence, a crucial source of creativity. Thus, it is considered that not only creativity is in crisis, but also the coherence of the mind as well, as an extreme result of the shattered postmodern existence.

Beautification of images by generative adversarial networks

Finding the properties underlying beauty has always been a prominent yet difficult problem. However, new technological developments have often aided scientific progress by expanding the scientists' toolkit. Currently in the spotlight of cognitive neuroscience and vision science are deep neural networks. In this study, we have used a generative adversarial network (GAN) to generate images of increasing aesthetic value. We validated that this network indeed was able to increase the aesthetic value of an image by letting participants decide which of two presented images they considered more beautiful. As our validation was successful, we were justified to use the generated images to extract low- and mid-level features contributing to their aesthetic value. We compared the brightness, contrast, sharpness, saturation, symmetry, colorfulness, and visual complexity levels of "low-aesthetic" images to those of "high-aesthetic" images. We found that all of these features increased for the beautiful images, implying that they may play an important role underlying the aesthetic value of an image. With this study, we have provided further evidence for the potential value GANs may have for research concerning beauty.

Machine learning revealed symbolism, emotionality, and imaginativeness as primary predictors of creativity evaluations of western art paintings

Creativity is a compelling yet elusive phenomenon, especially when manifested in visual art, where its evaluation is often a subjective and complex process. Understanding how individuals judge creativity in visual art is a particularly intriguing question. Conventional linear approaches often fail to capture the intricate nature of human behavior underlying such judgments. Therefore, in this study, we employed interpretable machine learning to probe complex associations between 17 subjective art-attributes and creativity judgments across a diverse range of artworks. A cohort of 78 non-art expert participants assessed 54 artworks varying in styles and motifs. The applied Random Forests regressor models accounted for 30% of the variability in creativity judgments given our set of art-attributes. Our analyses revealed symbolism, emotionality, and imaginativeness as the primary attributes influencing creativity judgments. Abstractness, valence, and complexity also had an impact, albeit to a lesser degree. Notably, we observed non-linearity in the relationship between art-attribute scores and creativity judgments, indicating that changes in art-attributes did not consistently correspond to changes in creativity judgments. Employing statistical learning, this investigation presents the first attribute-integrating quantitative model of factors that contribute to creativity judgments in visual art among novice raters. Our research represents a significant stride forward building the groundwork for first causal models for future investigations in art and creativity research and offering implications for diverse practical applications. Beyond enhancing comprehension of the intricate interplay and specificity of attributes used in evaluating creativity, this work introduces machine learning as an innovative approach in the field of subjective judgment.

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.

Do we enjoy what we sense and perceive? A dissociation between aesthetic appreciation and basic perception of environmental objects or events

This integrative review rearticulates the notion of human aesthetics by critically appraising the conventional definitions, offerring a new, more comprehensive definition, and identifying the fundamental components associated with it. It intends to advance holistic understanding of the notion by differentiating aesthetic perception from basic perceptual recognition, and by characterizing these concepts from the perspective of information processing in both visual and nonvisual modalities. To this end, we analyze the dissociative nature of information processing in the brain, introducing a novel local-global integrative model that differentiates aesthetic processing from basic perceptual processing. This model builds on the current state of the art in visual aesthetics as well as newer propositions about nonvisual aesthetics. This model comprises two analytic channels: aesthetics-only channel and perception-to-aesthetics channel. The aesthetics-only channel primarily involves restricted local processing for quality or richness (e.g., attractiveness, beauty/prettiness, elegance, sublimeness, catchiness, hedonic value) analysis, whereas the perception-to-aesthetics channel involves global/extended local processing for basic feature analysis, followed by restricted local processing for quality or richness analysis. We contend that aesthetic processing operates independently of basic perceptual processing, but not independently of cognitive processing. We further conjecture that there might be a common faculty, labeled as aesthetic cognition faculty, in the human brain for all sensory aesthetics albeit other parts of the brain can also be activated because of basic sensory processing prior to aesthetic processing, particularly during the operation of the second channel. This generalized model can account not only for simple and pure aesthetic experiences but for partial and complex aesthetic experiences as well.

Characterizing Dynamic Neural Representations of Scene Attractiveness

Aesthetic experiences during natural vision are varied: They can arise from viewing scenic landscapes, interesting architecture, or attractive people. Recent research in the field of neuroaesthetics has taught us a lot about where in the brain such aesthetic experiences are represented. Much less is known about when such experiences arise during the cortical processing cascade. Particularly, the dynamic neural representation of perceived attractiveness for rich natural scenes is not well understood. Here, I present data from an EEG experiment, in which participants provided attractiveness judgments for a set of diverse natural scenes. Using multivariate pattern analysis, I demonstrate that scene attractiveness is mirrored in early brain signals that arise within 200 msec of vision, suggesting that the aesthetic appeal of scenes is first resolved during perceptual processing. In more detailed analyses, I show that even such early neural correlates of scene attractiveness are partly related to interindividual variation in aesthetic preferences and that they generalize across scene contents. Together, these results characterize the time-resolved neural dynamics that give rise to aesthetic experiences in complex natural environments.

EXPRESS: Investigating the Role of Working Memory Resources across Aesthetic and Non-Aesthetic Judgments

Aesthetic judgments dominate much of daily life by guiding how we evaluate objects, people, and experiences in our environment. One key question that remains unanswered is the extent to which more specialised or largely general cognitive resources support aesthetic judgments. To investigate this question in the context of working memory, we examined the extent to which a working memory load produces similar or different response time interference on aesthetic compared to non-aesthetic judgments. Across three pre-registered experiments that used Bayesian multi-level modelling approaches (N>100 per experiment), we found clear evidence that a working memory load produces similar response time interference on aesthetic judgments relative to non-aesthetic (motion) judgments. We also showed that this similarity in processing across aesthetic versus non-aesthetic judgments holds across variations in the form of art (people vs landscape; Exps. 1-3), medium type (artwork vs photographs; Exp. 2) and load content (art images vs letters; Exps. 1-3). These findings suggest that across a range of experimental contexts, as well as different processing streams in working memory (e.g., visual vs verbal), aesthetic and motion judgments commonly rely on a domain-general cognitive system, rather than a system that is more specifically tied to aesthetic judgments. In doing so, these findings shine new light on the working memory resources that supports aesthetic judgments, as well as how domain-general cognitive systems operate more generally in cognition.

Does Amount of Information Support Aesthetic Values?

Obtaining information from the world is important for survival. The brain, therefore, has special mechanisms to extract as much information as possible from sensory stimuli. Hence, given its importance, the amount of available information may underlie aesthetic values. Such information-based aesthetic values would be significant because they would compete with others to drive decision-making. In this article, we ask, "What is the evidence that amount of information support aesthetic values?" An important concept in the measurement of informational volume is entropy. Research on aesthetic values has thus used Shannon entropy to evaluate the contribution of quantity of information. We review here the concepts of information and aesthetic values, and research on the visual and auditory systems to probe whether the brain uses entropy or other relevant measures, specially, Fisher information, in aesthetic decisions. We conclude that information measures contribute to these decisions in two ways: first, the absolute quantity of information can modulate aesthetic preferences for certain sensory patterns. However, the preference for volume of information is highly individualized, with information-measures competing with organizing principles, such as rhythm and symmetry. In addition, people tend to be resistant to too much entropy, but not necessarily, high amounts of Fisher information. We show that this resistance may stem in part from the distribution of amount of information in natural sensory stimuli. Second, the measurement of entropic-like quantities over time reveal that they can modulate aesthetic decisions by varying degrees of surprise given temporally integrated expectations. We propose that amount of information underpins complex aesthetic values, possibly informing the brain on the allocation of resources or the situational appropriateness of some cognitive models.

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.

The Role of Art Expertise and Symmetry on Facial Aesthetic Preferences

Humans, like other species, have a preference for symmetrical visual stimuli, a preference that is influenced by factors such as age, sex, and artistic training. In particular, artistic training seems to decrease the rejection of asymmetry in abstract stimuli. However, it is not known whether the same trend would be observed in relation to concrete stimuli such as human faces. In this article, we investigated the role of expertise in visual arts, music, and dance, in the perceived beauty and attractiveness of human faces with different asymmetries. With this objective, the beauty and attractiveness of 100 photographs of faces with different degrees of asymmetry were evaluated by 116 participants with different levels of art expertise. Expertise in visual arts and dance was associated with the extent to which facial asymmetry influenced the beauty ratings assigned to the faces. The greater the art expertise in visual arts and dance, the more indifferent to facial asymmetry the participant was to evaluate beauty. The same effect was not found for music and neither for attractiveness ratings. These findings are important to help understand how face aesthetic evaluation is modified by artistic training and the difference between beauty and attractiveness evaluations.

The role of expertise in the aesthetic evaluation of mathematical equations

There is a notion that mathematical equations can be considered aesthetic objects. However, whereas some aesthetic experiences are triggered primarily by the sensory properties of objects, for mathematical equations aesthetic judgments extend beyond their sensory qualities and are also informed by semantics and knowledge. Therefore, to the extent that expertise in mathematics represents the accumulation of domain knowledge, it should influence aesthetic judgments of equations. In a between-groups study design involving university students who majored in mathematics (i.e., experts) or not (i.e., laypeople), we found support for the hypothesis that mathematics majors exhibit more agreement in their aesthetic judgments of equations—reflecting a greater degree of shared variance driven by formal training in the domain. Furthermore, their judgments were driven more strongly by familiarity and meaning than was the case for laypeople. These results suggest that expertise via advanced training in mathematics alters (and sharpens) aesthetic judgments of mathematical equations.

Fractality and Variability in Canonical and Non-Canonical English Fiction and in Non-Fictional Texts

This study investigates global properties of three categories of English text: canonical fiction, non-canonical fiction, and non-fictional texts. The central hypothesis of the study is that there are systematic differences with respect to structural design features between canonical and non-canonical fiction, and between fictional and non-fictional texts. To investigate these differences, we compiled a corpus containing texts of the three categories of interest, the Jena Corpus of Expository and Fictional Prose (JEFP Corpus). Two aspects of global structure are investigated, variability and self-similar (fractal) patterns, which reflect long-range correlations along texts. We use four types of basic observations, (i) the frequency of POS-tags per sentence, (ii) sentence length, (iii) lexical diversity, and (iv) the distribution of topic probabilities in segments of texts. These basic observations are grouped into two more general categories, (a) the lower-level properties (i) and (ii), which are observed at the level of the sentence (reflecting linguistic decoding), and (b) the higher-level properties (iii) and (iv), which are observed at the textual level (reflecting comprehension/integration). The observations for each property are transformed into series, which are analyzed in terms of variance and subjected to Multi-Fractal Detrended Fluctuation Analysis (MFDFA), giving rise to three statistics: (i) the degree of fractality ( H ), (ii) the degree of multifractality ( D ), i.e., the width of the fractal spectrum, and (iii) the degree of asymmetry ( A ) of the fractal spectrum. The statistics thus obtained are compared individually across text categories and jointly fed into a classification model (Support Vector Machine). Our results show that there are in fact differences between the three text categories of interest. In general, lower-level text properties are better discriminators than higher-level text properties. Canonical fictional texts differ from non-canonical ones primarily in terms of variability in lower-level text properties. Fractality seems to be a universal feature of text, slightly more pronounced in non-fictional than in fictional texts. On the basis of our results obtained on the basis of corpus data we point out some avenues for future research leading toward a more comprehensive analysis of textual aesthetics, e.g., using experimental methodologies.

Habitat selection and human aesthetic responses to flowers

Although the aesthetic appreciation of flowers is a well-known aspect of human behaviour, theories explaining its origin are missing. The only exception is the evolutionary theory of Heerwagen and Orians. Surprisingly, it has not yet been empirically tested. The authors suggest that humans aesthetically respond to flowers because they signal food availability. The logic of the theory implies that fruits are more reliable and direct food availability signals than flowers. Therefore, fruits should elicit stronger aesthetic responses than flowers. To test this assumption, we performed two online studies in the Czech Republic. The participants (n = 2792 and 744, respectively) indicated on a six-point scale their aesthetic response to photographs of 14 edible Czech plant species (study A) and 20 edible plant species from the African savannas (study B), varying in growth stage (flowering, fruiting). We found no difference between the Czech fruiting and flowering plants and a stronger aesthetic response to African flowering plants. A third study (n = 817) confirmed that flowers were preferred to fruits, using a forced-choice paradigm. Our results suggest that the theory cannot fully explain human aesthetic responses to flowers. We discuss alternative explanations. This topic deserves renewed attention from researchers working in related fields.

Review of computational neuroaesthetics: bridging the gap between neuroaesthetics and computer science

The mystery of aesthetics attracts scientists from various research fields. The topic of aesthetics, in combination with other disciplines such as neuroscience and computer science, has brought out the burgeoning fields of neuroaesthetics and computational aesthetics within less than two decades. Despite profound findings are carried out by experimental approaches in neuroaesthetics and by machine learning algorithms in computational neuroaesthetics, these two fields cannot be easily combined to benefit from each other and findings from each field are isolated. Computational neuroaesthetics, which inherits computational approaches from computational aesthetics and experimental approaches from neuroaesthetics, seems to be promising to bridge the gap between neuroaesthetics and computational aesthetics. Here, we review theoretical models and neuroimaging findings about brain activity in neuroaesthetics. Then machine learning algorithms and computational models in computational aesthetics are enumerated. Finally, we introduce studies in computational neuroaesthetics which combine computational models with neuroimaging data to analyze brain connectivity during aesthetic appreciation or give a prediction on aesthetic preference. This paper outlines the rich potential for computational neuroaesthetics to take advantages from both neuroaesthetics and computational aesthetics. We conclude by discussing some of the challenges and potential prospects in computational neuroaesthetics, and highlight issues for future consideration.

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.

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.

The Way I Paint—How Image Composition Emerges During the Creation of Abstract Artworks

In recent years, there has been an increasing number of studies on objective image properties in visual artworks. Little is known, however, about how these image properties emerge while artists create their artworks. In order to study this matter, I produced five colored abstract artworks by myself and recorded state images at all stages of their creation. For each image, I then calculated low-level features from deep neural networks, which served as a model of responses properties in visual cortex. Two-dimensional plots of variances that were derived from these features showed that the drawings differ greatly at early stages of their creation, but then follow a narrow common path to terminate at or close to a position where traditional paintings cluster in the plots. Whether other artists use similar perceptive strategies while they create artworks remains to be studied.

Viewing Art in Different Contexts

While aesthetic experiences are not limited to any particular context, their sensorial, cognitive and behavioral properties can be profoundly affected by the circumstances in which they occur. Given the ubiquitous nature of contextual effects in nearly all aspects of behavior, investigations aimed at delineating the context-dependent and context-independent aspects of aesthetic experience and engagement with aesthetic objects in a diverse range of settings are important in empirical aesthetics. Here, we analyze the viewing behavior of visitors (N = 19) freely viewing 15 paintings in the 20th-century Australian collection room at the Art Gallery of New South Wales. In particular, we focus on how aspects of viewing behavior including viewing distance in the gallery condition and eye gaze measures such as fixation count, total fixation duration and average fixation duration are affected by the artworks’ physical characteristics including size and image statistics properties such as Fourier amplitude spectrum, fractal dimension and entropy. In addition, the same artworks were viewed in the laboratory, either scaled to fit most of the screen (N = 22) or to preserve their relative size as in the museum condition (N = 17) to assess the robustness of these relationships across different presentation contexts. We find that the effects of presentation context are modulated by the artworks’ physical characteristics.

A Combined Method of r-NPS and t-NPS Evaluations for Identification of Negative Triggers of Detractors’ Experience

User experience has garnered increasing interest from researchers in the field of sustainable human–computer interaction (HCI) design. One of the key pillars to building sustainable user experience for digital applications is to be able to identify powerful triggers of detracting behavior and especially those of radical detraction. In the past, traditional methods of user experience analysis have often led to the assumption that pragmatic aspects constitute the priority issues for user experience improvement given the prevalence of these aspects in user feedback surveys. However, our econometric analysis based on net promoter score (NPS) survey results defeats this assumption and reveals that the most powerful degraders of the detractors’ experience are in fact the emotive aspects of experience such as transparency of transactions and customer service interaction. Based on our analysis, we arrive at several insights regarding the building of a sustainable HCI strategy. First, hedonic aspects of user experience are the most significant determinants of the degree of user detraction among the detractors. Second, membership cannot be taken for granted as a token of customer loyalty. Building on the theoretical framework of Hassenzahl, Haines-Gadd, and others, we generate new evidence for the importance of servicing hedonic aspects of user experience for digital applications businesses to form a sustainable customer relation and product strategy.

Creating a Computable Cognitive Model of Visual Aesthetics for Automatic Aesthetics Evaluation of Robotic Dance Poses

Inspired by human dancers who can evaluate the aesthetics of their own dance poses through mirror observation, this paper presents a corresponding mechanism for robots to improve their cognitive and autonomous abilities. Essentially, the proposed mechanism is a brain-like intelligent system that is symmetrical to the visual cognitive nervous system of the human brain. Specifically, a computable cognitive model of visual aesthetics is developed using the two important aesthetic cognitive neural models of the human brain, which is then applied in the automatic aesthetics evaluation of robotic dance poses. Three kinds of features (color, shape and orientation) are extracted in a manner similar to the visual feature elements extracted by human brains. After applying machine learning methods in different feature combinations, machine aesthetics models are built for automatic evaluation of robotic dance poses. The simulation results show that our approach can process visual information effectively by cognitive computation, and achieved a very good evaluation performance of automatic aesthetics.

Art in the Age of Social Media: Interaction Behavior Analysis of Instagram Art Accounts

Instagram is the top preferred social media platform in the art world, however, we know little about the features of the most-liked artworks, and what role does the interaction between artists and followers play in the most-liked artworks? This study used quantitative and qualitative methods to examine the interaction between artists and followers on Instagram and the features of the most-liked artworks. Data from 706 artists’ accounts 497,649 posts on Instagram and 35 questionnaires. The results reveal that likes and comments were greatly influenced by interactions, with confusion and curiosity being a big reason to engage. The artist’s life experience and interaction with the followers had a positive influence on the most-liked artworks. Interaction with followers does not have much impact on their artistic creation, although artists expect more likes. Our study expands the research of mobile social media interaction in the art world, which is of great significance for the research on the interactive psychology of artwork and digital marketing communications on social media. The findings can also support future research on citizen curators and sociology analytics research areas.

Aesthetic Experiences Across Cultures: Neural Correlates When Viewing Traditional Eastern or Western Landscape Paintings

Compared with traditional Western landscape paintings, Chinese traditional landscape paintings usually apply a reversed-geometric perspective and concentrate more on contextual information. Using functional magnetic resonance imaging (fMRI), we discovered an intracultural bias in the aesthetic appreciation of Western and Eastern traditional landscape paintings in European and Chinese participants. When viewing Western and Eastern landscape paintings in an fMRI scanner, participants showed stronger brain activation to artistic expressions from their own culture. Europeans showed greater activation in visual and sensory-motor brain areas, regions in the posterior cingulate cortex (PCC), and hippocampus when viewing Western compared to Eastern landscape paintings. Chinese participants exhibited greater neural activity in the medial and inferior occipital cortex and regions of the superior parietal lobule in response to Eastern compared to Western landscape paintings. On the behavioral level, the aesthetic judgments also differed between Western and Chinese participants when viewing landscape paintings from different cultures; Western participants showed for instance higher valence values when viewing Western landscapes, while Chinese participants did not show this effect when viewing Chinese landscapes. In general, our findings offer differentiated support for a cultural modulation at the behavioral level and in the neural architecture for high-level aesthetic appreciation.

Perception and Preference Analysis of Fashion Colors: Solid Color Shirts

When it comes to pollution, we do not usually think about the clothes we wear, but the clothing industry is really endangering our planet. The market economy has transferred the decision-making power of the garment industry from enterprises to consumers. To make the fashion industry sustainable, in addition to technological innovation, it is also necessary to conduct research on the service objects of the industry. Consumer clothing preference research is an important part of the sustainable development of the clothing industry, and it will also have an impact on environmental and design sustainability. Hence, a psychophysical experiment based on solid color shirts is carried out to analyze people’s perceptions and preferences concerning fashion colors, including the aesthetic differences and similarities between males and females, and establish a hierarchical feed-forward model of color preferences relating to solid color shirts. Firstly, 480 colors of solid shirts from different clothing brands were collected, and the mean shift clustering algorithm was used to classify them into 19 clusters in the CIELAB color space. Secondly, another 22 solid colors, combined with the 19 colors of the cluster centers, formed a solid color scheme. Thirdly, 41 solid male and female shirts and fabrics were simulated as stimuli in three dimensions, and they were presented on a calibrated computer display. The simulations were assessed by 34 observers (consisting of 17 males and 17 females) in terms of 11 semantic scales, including cold/warm, heavy/light, passive/active, dirty/clean, tense/relaxed, plain/gaudy, traditional/modern, masculine/feminine, slim-look/fat-look, hard-to-match/easy-to-match, and dislike/tike. The experimental results demonstrated that the hard-to-match/easy-to-match response was found to be highly correlated with dislike/like. Furthermore, the response of the females concerning hard-to-match/easy-to-match had a strong correlation with two adjective pairs (plain/gaudy and slim-look/fat-look), while that of the males also had a strong correlation with two adjective pairs (plain/ gaudy and masculine/feminine). Finally, a hierarchical feed-forward model of aesthetic perception for solid color shirts was established to predict the shirt preference degree. These findings could be used to develop a more robust and comprehensive theory of fashion color preferences and provide a reference for the design of solid color shirts. A more comprehensive color preference theory is not only an effective tool to solve the problem of pollution in the clothing industry, but also an important theoretical basis for the “sustainable design” of clothing, which is of great significance to the sustainable development of the clothing industry.

The Power of Visual Texture in Aesthetic Perception: An Exploration of the Predictability of Perceived Aesthetic Emotions

How to interpret the relationship between the low-level features, such as some statistical characteristics of color and texture, and the high-level aesthetic properties, such as warm or cold, soft or hard, has been a hot research topic of neuroaesthetics. Contrary to the black-box method widely used in the fields of machine learning and pattern recognition, we build a white-box model with the hierarchical feed-forward structure inspired by neurobiological mechanisms underlying the aesthetic perception of visual art. In the experiment, the aesthetic judgments for 8 pairs of aesthetic antonyms are carried out for a set of 151 visual textures. For each visual texture, 106 low-level features are extracted. Then, ten more useful and effective features are selected through neighborhood component analysis to reduce information redundancy and control the complexity of the model. Finally, model building of the beauty appreciation of visual textures using multiple linear or nonlinear regression methods is detailed. Compared with our previous work, a more robust feature selection algorithm, neighborhood component analysis, is used to reduce information redundancy and control computation complexity of the model. Some nonlinear models are also adopted and achieved higher prediction accuracy when compared with the previous linear models. Additionally, the selection strategy of aesthetic antonyms and the selection standards of the core set of them are also explained. This research also suggests that the aesthetic perception and appreciation of visual textures can be predictable based on the computed low-level features.

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.

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.

Brain Connectivity Networks and the Aesthetic Experience of Music

Listening to music is above all a human experience, which becomes an aesthetic experience when an individual immerses himself/herself in the music, dedicating attention to perceptual-cognitive-affective interpretation and evaluation. The study of these processes where the individual perceives, understands, enjoys and evaluates a set of auditory stimuli has mainly been focused on the effect of music on specific brain structures, as measured with neurophysiology and neuroimaging techniques. The very recent application of network science algorithms to brain research allows an insight into the functional connectivity between brain regions. These studies in network neuroscience have identified distinct circuits that function during goal-directed tasks and resting states. We review recent neuroimaging findings which indicate that music listening is traceable in terms of network connectivity and activations of target regions in the brain, in particular between the auditory cortex, the reward brain system and brain regions active during mind wandering.

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.

Inducing preference reversals in aesthetic choices for paintings: Introducing the contrast paradigm

Understanding what leads people to reverse their choices is important in many domains. We introduce a contrast paradigm for studying reversals in choices—here between pairs of abstract paintings—implemented in both within-subject (Experiment 1; N = 320) and between-subject (Experiment 2; N = 384) designs. On each trial, participants chose between a pair of paintings. A critical pair of average-beauty paintings was presented before and after either a reversal or control block. In the reversal block, we made efforts to bias preference away from the chosen average-beauty painting (by pairing it with more-beautiful paintings) and toward the non-chosen average-beauty painting (by pairing it with less-beautiful paintings). Meta-analysis revealed more reversals after reversal blocks than after control blocks, though only when the biasing manipulations succeeded. A second meta-analysis revealed that reversals were generally more likely for participants who later misidentified their initial choice, demonstrating that memory for initial choices influences later choices. Thus, the contrast paradigm has utility both for inducing choice reversals and identifying their causes.

Face Attractiveness versus Artistic Beauty in Art Portraits: A Behavioral Study

From art portraits, the observer may derive at least two different hedonic values: The attractiveness of the depicted person and the artistic beauty of the image that relates to the way of presentation. We argue that attractiveness is a property that is predominantly driven by perceptual processes, while the perception of artistic beauty is based predominantly on cognitive processing. To test this hypothesis, we conducted two behavioral experiments. In a gist study (Experiment 1), we showed that ratings on attractiveness were higher after short-term presentation (50 ms) than after long-term presentation (3000 ms), while the opposite pattern was found for artistic beauty. In an experiment on perceptual contrast (Experiment 2), we showed that the perceptual contrast effect was stronger for attractiveness than for artistic beauty. These results are compatible with our hypothesis that appreciation of artistic beauty is cognitively modulated at least in part, while processing of attractiveness is predominantly driven perceptually. This dichotomy between cognitive and perceptual processing of different kinds of beauty suggests the participation of different neuronal mechanisms.

Toward Model Building for Visual Aesthetic Perception

Several models of visual aesthetic perception have been proposed in recent years. Such models have drawn on investigations into the neural underpinnings of visual aesthetics, utilizing neurophysiological techniques and brain imaging techniques including functional magnetic resonance imaging, magnetoencephalography, and electroencephalography. The neural mechanisms underlying the aesthetic perception of the visual arts have been explained from the perspectives of neuropsychology, brain and cognitive science, informatics, and statistics. Although corresponding models have been constructed, the majority of these models contain elements that are difficult to be simulated or quantified using simple mathematical functions. In this review, we discuss the hypotheses, conceptions, and structures of six typical models for human aesthetic appreciation in the visual domain: the neuropsychological, information processing, mirror, quartet, and two hierarchical feed-forward layered models. Additionally, the neural foundation of aesthetic perception, appreciation, or judgement for each model is summarized. The development of a unified framework for the neurobiological mechanisms underlying the aesthetic perception of visual art and the validation of this framework via mathematical simulation is an interesting challenge in neuroaesthetics research. This review aims to provide information regarding the most promising proposals for bridging the gap between visual information processing and brain activity involved in aesthetic appreciation.

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.

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.

Beauty in the blink of an eye: The time course of aesthetic experiences

Under normal circumstances, perception runs very fast and seemingly automatic. In just a few ms, we go from sensory features to perceiving objects. This fast time course does not only apply to general perceptual aspects but also to what we call higher-level judgements. Inspired by the study on 'very first impressions' by Bar, Neta, and Linz (2006, Emotion, 6, 269) the current research examined the speed and time course of three aspects of the aesthetic experience, namely beauty, specialness, and impressiveness. Participants were presented with 54 reproductions of paintings that covered a wide variety of artistic styles and contents. Presentation times were 10, 50, 100 and 500 ms in Experiment 1 and 20, 30 and 40 ms in Experiment 2. Our results not only show that consistent aesthetic judgements can be formed based on very brief glances of information, but that this speed of aesthetic impression formation also differs between different aesthetic judgements. Apparently, impressiveness judgements require longer exposure times than impressions of beauty or specialness. The results provide important evidence for our understanding of the time course of aesthetic experiences.

Complementarity As Generative Principle: A Thought Pattern for Aesthetic Appreciations and Cognitive Appraisals in General

In experimental aesthetics the relationship between the arts and cognitive neuroscience has gained particular interest in recent years. But has cognitive neuroscience indeed something to offer when studying the arts? Here we present a theoretical frame within which the concept of complementarity as a generative or creative principle is proposed; neurocognitive processes are characterized by the duality of complementary activities like bottom-up and top-down control, or logistical functions like temporal control and content functions like perceptions in the neural machinery. On that basis a thought pattern is suggested for aesthetic appreciations and cognitive appraisals in general. This thought pattern is deeply rooted in the history of philosophy and art theory since antiquity; and complementarity also characterizes neural operations as basis for cognitive processes. We then discuss some challenges one is confronted with in experimental aesthetics; in our opinion, one serious problem is the lack of a taxonomy of functions in psychology and neuroscience which is generally accepted. This deficit makes it next to impossible to develop acceptable models which are similar to what has to be modeled. Another problem is the severe language bias in this field of research as knowledge gained in many languages over the ages remains inaccessible to most scientists. Thus, an inspection of research results or theoretical concepts is necessarily too narrow. In spite of these limitations we provide a selective summary of some results and viewpoints with a focus on visual art and its appreciation. It is described how questions of art and aesthetic appreciations using behavioral methods and in particular brain-imaging techniques are analyzed and evaluated focusing on such issues like the representation of artwork or affective experiences. Finally, we emphasize complementarity as a generative principle on a practical level when artists and scientists work directly together which can lead to new insights and broader perspectives on both sides.

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.

Arousal Rules: An Empirical Investigation into the Aesthetic Experience of Cross-Modal Perception with Emotional Visual Music

Emotional visual music is a promising tool for the study of aesthetic perception in human psychology; however, the production of such stimuli and the mechanisms of auditory-visual emotion perception remain poorly understood. In Experiment 1, we suggested a literature-based, directive approach to emotional visual music design, and inspected the emotional meanings thereof using the self-rated psychometric and electroencephalographic (EEG) responses of the viewers. A two-dimensional (2D) approach to the assessment of emotion (the valence-arousal plane) with frontal alpha power asymmetry EEG (as a proposed index of valence) validated our visual music as an emotional stimulus. In Experiment 2, we used our synthetic stimuli to investigate possible underlying mechanisms of affective evaluation mechanisms in relation to audio and visual integration conditions between modalities (namely congruent, complementation, or incongruent combinations). In this experiment, we found that, when arousal information between auditory and visual modalities was contradictory [for example, active (+) on the audio channel but passive (−) on the video channel], the perceived emotion of cross-modal perception (visual music) followed the channel conveying the stronger arousal. Moreover, we found that an enhancement effect (heightened and compacted in subjects' emotional responses) in the aesthetic perception of visual music might occur when the two channels contained contradictory arousal information and positive congruency in valence and texture/control. To the best of our knowledge, this work is the first to propose a literature-based directive production of emotional visual music prototypes and the validations thereof for the study of cross-modally evoked aesthetic experiences in human subjects.

Global Sensory Qualities and Aesthetic Experience in Music

A well-known tradition in the study of visual aesthetics holds that the experience of visual beauty is grounded in global computational or statistical properties of the stimulus, for example, scale-invariant Fourier spectrum or self-similarity. Some approaches rely on neural mechanisms, such as efficient computation, processing fluency, or the responsiveness of the cells in the primary visual cortex. These proposals are united by the fact that the contributing factors are hypothesized to be global (i.e., they concern the percept as a whole), formal or non-conceptual (i.e., they concern form instead of content), computational and/or statistical, and based on relatively low-level sensory properties. Here we consider that the study of aesthetic responses to music could benefit from the same approach. Thus, along with local features such as pitch, tuning, consonance/dissonance, harmony, timbre, or beat, also global sonic properties could be viewed as contributing toward creating an aesthetic musical experience. Several such properties are discussed and their neural implementation is reviewed in the light of recent advances in neuroaesthetics.

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.