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PreprintCited by 10Aesthetic Perception and AnalysisArt, Technology, and CultureArt, Aesthetics, and Perception

A computational approach to studying aesthetic judgments of ambiguous artworks

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Wang, Xi, Bylinskii, Zoya, Hertzmann, Aaron, Pepperell, Robert C·2023·Psychology of Aesthetics Creativity and the Arts
DOI 10.31234/osf.io/nd653_v1

Preprint — published as 10.1037/aca0000579

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Abstract

Visual ambiguity plays a key role in the perceptual experience of art and has been much exploited by modernist and contemporary artists for aesthetic effects. But it remains unclear how aesthetic judgments are affected by visual ambiguity, and the subjective nature of aesthetic experience makes it difficult to measure. Wang et al. (2020) piloted a methodology in which a large collection of free-form textual descriptions of artworks were gathered from participants. The variability of these descriptions was then quantified computationally with Shannon entropy; ambiguous images tended to generate a greater number and wider diversity of descriptive terms. In the present study, we evaluated how well these measures can predict aesthetic preference for ambiguous images. We designed three crowdsourcing tasks in order to measure aesthetic preferences. The first was a simple rating task, and the other two required the participants to engage more actively with the images. We hypothesized that the number of associations evoked by ambiguous works of art, when computationally measured by entropy and description lengths, is a factor in judgments of their aesthetic value. Following this hypothesis, we made and tested a number of predictions. Our results provide broad support for the hypothesis, but with some interesting caveats and exceptions. We find that the form of the task significantly affects preference ratings and that participants’ responses can be clustered into two categories: those that prefer simple, recognizable imagery, and those that prefer more complex, ambiguous imagery. When taking this clustering into account, we find that our measures of entropy are correlated with aesthetic ratings. We conclude that these computational methods are useful for investigating the variable subjective responses to ambiguous artworks.

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  • Existing benchmark-centered studies on creativity have focused on quantitative metrics for outputs or on automated methods and datasets for artistic image aesthetic assessment.

    “existing benchmark-centered studies on creativity have focused on quantitative metrics for outputs”

    From Dialogue with the Machine and Dialogue with the Art World: Evaluating Generative AI for Culturally-Situated Creativity (2024) · cites this paper as [6]

    The Methods (§3.1; page not provided) describe quantitative ratings of artistic images, and the Results (§5.2; page not provided) compare their average ranks. The paper therefore supports the quantitative-assessment aspect, but it studies aesthetic judgments of ambiguous artworks rather than characterizing benchmark-centered creativity studies or metrics for creativity outputs. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

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Authors
Wang, X., Bylinskii, Z., Hertzmann, A., & Pepperell, R. C.
ORCID
Venue
Psychology of Aesthetics Creativity and the Arts
Publisher
Center for Open Science
Published
2023-02-22
Volume
19
Issue
3
Pages
396-410
ISSN
1931-3896 (ISSN-L)
DOI
10.31234/osf.io/nd653_v1
OpenAlex
W4379015377
Cited by
10
References
62
Citations by year
2026: 2 · 2025: 1 · 2024: 4 · 2023: 3
FWCI
1.50
Type
journal article
Language
en
Open access
Licence
Locations
  • Psychology of Aesthetics Creativity and the Arts (publishedVersion)
  • PsyArXiv (OSF Preprints) (submittedVersion, cc-by)
  • PsyArXiv (OSF Preprints) (submittedVersion)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Aesthetic Perception and Analysis
  • Social Sciences › Arts and Humanities › Visual Arts and Performing Arts › Art, Technology, and Culture
  • Social Sciences › Arts and Humanities › Visual Arts and Performing Arts › Art, Aesthetics, and Perception
Keywords
aesthetic judgment, computational modeling, art perception, aesthetic preference

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