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

Abstract

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.

Preserving Sculptural Heritage in the Era of Digital Transformation: Methods and Challenges of 3D Art Assessment

“…The senior group gave higher ratings to physical sculpture abstraction, indicating that they have a more traditional and conservative aesthetic concept. Imagination is at the heart of innovative thinking in digital art [16,94]. According to our findings, digital artworks score higher on this dimension, implying that younger people consider digital artworks with imaginative, fantasy, or surreal content more creative.…”

“…Academic research has predominantly examined objective aspects of form perception, such as the golden ratio and the Fibonacci sequence, comparing them to people's subjective preferences using classical and statistical learning methods [13][14][15]. To the best of our understanding, however, none of these investigations satisfactorily evaluated the full picture of digital transformation, including those that employed statistical learning methods as objective metrics [16]. Conversely, scholars specialising in the visual arts and psychology employ linear statistical techniques to evaluate the degree of artistic ingenuity, relying predominantly on subjective assessments [17,18].…”

Cross-cultural comparison of beauty judgments in visual art using machine learning analysis of art attribute predictors among Japanese and German speakers

“…Our current study is built on prior work and analysis 17 , 18 , assessing the relationship between aesthetic judgements and art attributes to underscore how individuals formulate certain aesthetic judgements based on data-driven approaches. Spee and colleagues 17 investigated the judgment of creativity using a machine-learning approach.…”

“…Our current study is built on prior work and analysis 17 , 18 , assessing the relationship between aesthetic judgements and art attributes to underscore how individuals formulate certain aesthetic judgements based on data-driven approaches. Spee and colleagues 17 investigated the judgment of creativity using a machine-learning approach. They found that art attributes such as symbolism, emotional expressiveness, and imaginativeness of the contents significantly predict the evaluated creativity judgment of artworks in a German-speaking population.…”

“…Following this pioneering work, the present study further investigated, as a next step, how socio-cultural backgrounds might influence this result, focusing on beauty judgments. To extend our research, we used the existing dataset from the German-speaking population of Spee et al 17 , 18 and collected additional data in Japan with a Japanese-speaking sample to enable the cross-cultural comparison of beauty judgments. The cross-cultural component of our study is particularly significant as it addresses how cultural backgrounds influence the perception and evaluation of beauty in visual art.…”