Using machine learning to predict judgments on Western visual art along content-representational and formal-perceptual attributes.

ABSTRACT: Art research has long aimed to unravel the complex associations between specific attributes, such as color, complexity, and emotional expressiveness, and art judgments, including beauty, creativity, and liking. However, the fundamental distinction between attributes as inherent characteristics or features of the artwork and judgments as subjective evaluations remains an exciting topic. This paper reviews the literature of the last half century, to identify key attributes, and employs machine learning, specifically Gradient Boosted Decision Trees (GBDT), to predict 13 art judgments along 17 attributes. Ratings from 78 art novice participants were collected for 54 Western artworks. Our GBDT models successfully predicted 13 judgments significantly. Notably, judged creativity and disturbing/ir

SUBMITTER: Spee BTM 

PROVIDER: S-EPMC11379394 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature