艺术研究长期以来一直旨在解开特定属性之间的复杂关联,比如颜色,复杂性,和情感表达,和艺术判断,包括美,创造力,和喜欢。然而,作为艺术品的固有特征或特征的属性与作为主观评价的判断之间的基本区别仍然是一个令人兴奋的话题。本文回顾了近半个世纪的文献,为了识别关键属性,并采用机器学习,特别是梯度增强决策树(GBDT),沿着17个属性预测13个艺术判断。收集了来自78位艺术新手参与者的54件西方艺术品的评分。我们的GBDT模型成功预测了13项显著的判断。值得注意的是,判断的创造力和令人不安/恼人的判断显示出最高的可预测性,模型解释了31%和32%的方差,分别。属性的情感表现力,价,象征主义,以及复杂性成为模型性能的一致和重要贡献者。内容表征属性比形式感知属性发挥了更突出的作用。此外,在某些情况下,我们发现属性和判断之间存在非线性关系,在评级量表的中等水平附近突然倾斜或下降。通过揭示艺术判断行为中的这些潜在模式和动态,我们的研究提供了宝贵的见解,以促进对视觉艺术的审美体验的理解,告知文化习俗,启发未来艺术欣赏领域的研究。
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/irritating judgments showed the highest predictability, with the models explaining 31% and 32% of the variance, respectively. The attributes emotional expressiveness, valence, symbolism, as well as complexity emerged as consistent and significant contributors to the models\' performance. Content-representational attributes played a more prominent role than formal-perceptual attributes. Moreover, we found in some cases non-linear relationships between attributes and judgments with sudden inclines or declines around medium levels of the rating scales. By uncovering these underlying patterns and dynamics in art
judgment behavior, our research provides valuable insights to advance the understanding of aesthetic experiences considering visual art, inform cultural practices, and inspire future research in the field of art appreciation.