Unveiling the factors of aesthetic preferences with explainable AI.
The allure of aesthetic appeal in images captivates our senses, yet the underlying intricacies of aesthetic preferences remain elusive. In this study, we pioneer a novel perspective by utilizing several different machine learning (ML) models that focus on aesthetic attributes known to influence pref...
| Published in: | British Journal of Psychology Vol. 117; no. 2; pp. 444 - 479 |
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| Main Authors: | , |
| Format: | Article |
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Wiley-Blackwell
May2026
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=192785875&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 192785875 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00071269 BJP jtl: British Journal of Psychology issn: 00071269 maglogo: Y pubinfo: dt: May2026 vid: 117 iid: 2 pid: 480 pub: Wiley-Blackwell artinfo: ui: 192785875 10.1111/bjop.12707 ppf: 444 ppct: 35 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P size: 9.4MB tig: atl: Unveiling the factors of aesthetic preferences with explainable AI. aug: au: Soydaner, Derya Wagemans, Johan affil: Department of Brain and Cognition, University of Leuven (KU Leuven), Leuven, Belgium su: Aesthetics Artificial intelligence Patients' attitudes Data mining Research funding Prediction models Clinical decision support systems Artificial neural networks Mathematical models Machine learning Comparative studies Theory Algorithms sug: subj: Aesthetics Artificial intelligence Patients' attitudes Data mining Research funding Prediction models Clinical decision support systems Artificial neural networks Mathematical models Machine learning Comparative studies Theory Algorithms keyword: explainable AI image aesthetics machine learning regression explainable AI image aesthetics machine learning regression ab: The allure of aesthetic appeal in images captivates our senses, yet the underlying intricacies of aesthetic preferences remain elusive. In this study, we pioneer a novel perspective by utilizing several different machine learning (ML) models that focus on aesthetic attributes known to influence preferences. Our models process these attributes as inputs to predict the aesthetic scores of images. Moreover, to delve deeper and obtain interpretable explanations regarding the factors driving aesthetic preferences, we utilize the popular Explainable AI (XAI) technique known as SHapley Additive exPlanations (SHAP). Our methodology compares the performance of various ML models, including Random Forest, XGBoost, Support Vector Regression, and Multilayer Perceptron, in accurately predicting aesthetic scores, and consistently observing results in conjunction with SHAP. We conduct experiments on three image aesthetic benchmarks, namely Aesthetics with Attributes Database (AADB), Explainable Visual Aesthetics (EVA), and Personalized image Aesthetics database with Rich Attributes (PARA), providing insights into the roles of attributes and their interactions. Finally, our study presents ML models for aesthetics research, alongside the introduction of XAI. Our aim is to shed light on the complex nature of aesthetic preferences in images through ML and to provide a deeper understanding of the attributes that influence aesthetic judgements. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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