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

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.

The role of expertise and culture in visual art appreciation

Is art appreciation universal? Previous evidence suggests a general preference for representational art over abstract art, and a tendency to like art originating from one’s own culture more than another culture (an ingroup bias), modulated by art expertise. However, claims about universality are difficult given that most research has focused on Western populations. Across two pre-registered and statistically powered experiments, we explore the role of culture and art expertise in the aesthetic evaluation of Indian and Western paintings and dance depicting both abstract and representational content, by inviting expert and art-naïve Indian and Western participants to rate stimuli on beauty and liking. Results suggest an ingroup bias (for dance) and a preference for representational art (for paintings) exists, both modulated by art expertise. As predicted, the ingroup bias was present only in art-naïve participants, and the preference for representational art was lower in art experts, but this modulation was present only in Western participants. The current findings have two main implications: (1) they inform and constrain understanding of universality of aesthetic appreciation, cautioning against generalising models of empirical aesthetics to non-western populations and across art forms, (2) they highlight the importance of art experience as a medium to counter prejudices.

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 differential power of extraneous influences to modify aesthetic judgments of biological and artifactual stimuli

We addressed the question of the extent to which external information is capable of modifying aesthetic ratings given to two different categories of stimuli-images of faces (which belong to the biological category) and those of abstract paintings with no recognizable objects (which sit in the artifactual category). A total of 51 participants of different national origins rated the beauty of both sets of stimuli, indicating the certainty of their rating; they then re-rated them after being exposed to the opinions of others on their aesthetic status. Of these 51 participants, 42 who met our criteria were selected to complete the experiment. The results showed that individuals were less prone to modifying their ratings of stimuli belonging to the biological category compared to those falling into the artifactual category. We discuss this finding in light of our theoretical Bayesian-Laplacian model and on the evidence given by previous empirical research.

The Effect of Painting Beauty on Eye Movements

The current study aimed to determine relationships between oculomotor behavior and aesthetical evaluation of paintings. We hypothesized that paintings evaluated as beautiful compared to nonbeautiful would be associated with different oculomotor behavior in terms of fixation duration, viewing time, and temporal and spatial distribution of attention. To verify these hypotheses, we examined forty participants that looked at and evaluated 140 figurative paintings while their eye movements were recorded. To analyze data, we used divergence point analysis (DPA) and recurrence quantification analysis (RQA). The results of the DPA suggested that fixation durations longer than 229 ms are sensitive to the effect of aesthetical evaluation. We also found that the effect of aesthetical evaluation was significant in the time window between 2.3 s and 19.8 s of viewing a painting. The results of the RQA suggested that the participants viewed paintings evaluated as beautiful in a more structured manner compared to those evaluated as nonbeautiful, which suggests higher involvement of top-down processes while facing beautiful artwork. We discuss and refer these results to the literature on cognitive processes related to aesthetical evaluation of paintings.

Cognitive basis for the development of aesthetic preference: Findings from symmetry preference

Where is the visual aesthetic preference rooted from and what's its relationship with the perceptual preference that is emerging early? Do young children naturally prefer some visual stimuli or do they learn to appreciate visual stimuli for aesthetic pleasure? Here, for young preschool children who are on the age that the preferences are developing, we provide findings from a study to show that the interplay between early emerging perceptual sensitivity and perceptual exposure promotes the emergence of preschool children's aesthetic preferences for simple visual patterns. Specifically in the experiments, 4-year-old children were exposed to either symmetric or asymmetric non-figurative forms in a perceptually demanding game; the group of children who received exposure to symmetric patterns showed aesthetic preference to the exposed patterns, while no preference was found in the group that received exposure to asymmetric patterns. The following recognition test then showed that the symmetric objects were differentiated better and remembered more clearly by the children, indicating that the symmetry was perceptually encoded better. These findings suggest that the early emerging perceptual sensitivity to 'good features' such as symmetry provides the prior cognitive prerequisites, allowing visual perceptual exposure to nourish the eventual formation of aesthetic preference. Thus, the preferences for aesthetic appreciation are likely the outcome of the interplay between biological and ecological adaptation.

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.

And the nominees are: Using design-awards datasets to build computational aesthetic evaluation model

Aesthetic perception is a human instinct that is responsive to multimedia stimuli. Giving computers the ability to assess human sensory and perceptual experience of aesthetics is a well-recognized need for the intelligent design industry and multimedia intelligence study. In this work, we constructed a novel database for the aesthetic evaluation of design, using 2,918 images collected from the archives of two major design awards, and we also present a method of aesthetic evaluation that uses machine learning algorithms. Reviewers’ ratings of the design works are set as the ground-truth annotations for the dataset. Furthermore, multiple image features are extracted and fused. The experimental results demonstrate the validity of the proposed approach. Primary screening using aesthetic computing can be an intelligent assistant for various design evaluations and can reduce misjudgment in art and design review due to visual aesthetic fatigue after a long period of viewing. The study of computational aesthetic evaluation can provide positive effect on the efficiency of design review, and it is of great significance to aesthetic recognition exploration and applications development.