Cross-Cultural Comparison of Beauty Judgments in Visual Art Using Machine Learning Analysis of Art Attribute Predictors Among Japanese and German Speakers

Jan Mikuni (Corresponding author), Blanca Thea Maria Spee (Corresponding author), Gaia Forlani, Helmut Leder, Frank Scharnowski, Koyo Nakamura, Katsumi Watanabe, Hideaki Kawabata, Matthew Pelowski, David Steyrl

Publications: Contribution to journalArticlePeer Reviewed

Abstract

In empirical art research, understanding how viewers judge visual artworks as beautiful is often explored through the study of attributes—specific inherent characteristics or artwork features such as color, complexity, and emotional expressiveness. These attributes form the basis for subjective evaluations, including the judgment of beauty. Building on this conceptual framework, our study examines the beauty judgments of 54 Western artworks made by native Japanese and German speakers, utilizing an extreme randomized trees model—a data-driven machine learning approach—to investigate cross-cultural differences in evaluation behavior. Our analysis of 17 attributes revealed that visual harmony, color variety, valence, and complexity significantly influenced beauty judgments across both cultural cohorts. Notably, preferences for complexity diverged significantly: while the native Japanese speakers found simpler artworks as more beautiful, the native German speakers evaluated more complex artworks as more beautiful. Further cultural distinctions were observed: for the native German speakers, emotional expressiveness was a significant factor, whereas for the native Japanese speakers, attributes such as brushwork, color world, and saturation were more impactful. Our findings illuminate the nuanced role that cultural context plays in shaping aesthetic judgments and demonstrate the utility of machine learning in unravelling these complex dynamics. This research not only advances our understanding of how beauty is judged in visual art—considering self-evaluated attributes—across different cultures but also underscores the potential of machine learning to enhance our comprehension of the aesthetic evaluation of visual artworks.
Original languageEnglish
Article number15948
Number of pages14
JournalScientific Reports
Volume14
Issue number1
DOIs
Publication statusPublished - 10 Jul 2024

Austrian Fields of Science 2012

  • 501001 General psychology
  • 102019 Machine learning

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