ArticleNature human behaviour2021
Aesthetic preference for art can be predicted from a mixture of low- and high-level visual features.
Article in Nature human behaviour, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
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Who cites it
51 citing papers in PubMed.
- The SPOT Grid: Naturalistic images with predefined settings, patterns, objects, and textures.Behavior research methods · 2026Article
- Visual features explain dynamic aesthetic experiences across distinct movie content.Communications psychology · 2026Article
- A Design-by-Analogy Method for Integrating Chinese Paper-Cutting Art into Product Innovation: Understanding Attractiveness Associations via Expert Evaluations and Eye Tracking.Biomimetics (Basel, Switzerland) · 2026Article
- Information and affective valence influence judgments of complexity, liking and understanding.British journal of psychology (London, England : 1953) · 2026Article
- Explainable generative 3D design driven by social media semantics.Nature communications · 2026Article
- Tracing aesthetic experience from perception and conception to appraisal using deep convolutional neural networks.iScience · 2026Article
- Latent neural architecture organising shared aesthetic evaluations of visual artworks.Nature communications · 2026Article
- Artistic style classification in traditional cultural heritage using convolutional neural networks.Scientific reports · 2026Article
- Unveiling the factors of aesthetic preferences with explainable AI.British journal of psychology (London, England : 1953) · 2026Article
- Traces of Intentionality: Balance, Complexity, and Organization in Artworks by Humans and Apes.Topics in cognitive science · 2026Article
- Aesthetic experience is supported by spontaneous autobiographical memory recollection.Memory & cognition · 2026Article
- Effect evaluation of integrating art education into medical aesthetics postgraduate training: a single-arm pre-post study.BMC medical education · 2026Article
- The Minimum Semantic Content (MSC) Dataset: A Large, Balanced Resource for Computational Aesthetics Research.Scientific data · 2026Article
- Cross domain consistency of aesthetic preference-driven social behavior.Frontiers in neural circuits · 2026Article
- Judging AI-generated paintings: art-education differences in source classification.Frontiers in psychology · 2026Article
- Autistic traits influence aesthetic judgments of abstract color works.BMC psychology · 2025Article
- An EEG Dataset on Aesthetic and Creative Judgments of Brief Structured Poetry.Scientific data · 2025Article
- Less is more: Aesthetic liking is inversely related to metabolic expense by the visual system.PNAS nexus · 2025Article
- Multi-Level Attribute-Guided-Based Adaptive Multi-Dilated Convolutional Network for Image Aesthetic Assessment.Journal of imaging · 2025Article
- Affective abstraction predicts variation in alexithymia, depression, and autism spectrum quotient.Emotion (Washington, D.C.) · 2025Article
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5 authors.
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Abstract
It is an open question whether preferences for visual art can be lawfully predicted from the basic constituent elements of a visual image. Here, we developed and tested a computational framework to investigate how aesthetic values are formed. We show that it is possible to explain human preferences for a visual art piece based on a mixture of low- and high-level features of the image. Subjective value ratings could be predicted not only within but also across individuals, using a regression model with a common set of interpretable features. We also show that the features predicting aesthetic preference can emerge hierarchically within a deep convolutional neural network trained only for object recognition. Our findings suggest that human preferences for art can be explained at least in part as a systematic integration over the underlying visual features of an image.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.