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Related Experiment Video

Updated: Feb 18, 2026

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

Computational and Experimental Approaches to Visual Aesthetics.

Anselm Brachmann¹, Christoph Redies¹

¹Experimental Aesthetics Group, Institute of Anatomy, Jena University Hospital, School of Medicine, University of Jena, Jena, Germany.

Frontiers in Computational Neuroscience

|November 30, 2017

Summary

Experimental aesthetics and computational aesthetics are merging. This review explores computational methods for predicting aesthetic appeal and compares them with experimental findings, advocating for integrated research.

Keywords:

art history artist identification computational aesthetics experimental aesthetics image features statistical image properties style identification visual preference

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Area of Science:

Interdisciplinary research at the intersection of psychology, computer vision, and art history.
Focus on understanding visual aesthetics through empirical and computational approaches.

Background:

Aesthetic experience arises from perception, cognition, and emotion.
Experimental aesthetics studies these interactions via human perception and brain processes.
Computational aesthetics applies computer vision techniques to model aesthetic stimuli.

Purpose of the Study:

To review recent advancements in computational aesthetics.
To examine the relationship between computational and experimental aesthetics.
To encourage interdisciplinary collaboration for a unified understanding of visual aesthetics.

Main Methods:

Overview of computational methods: prediction of ratings, style/artist identification, art historical analysis (influence detection, forgery detection).
Description of algorithms: classifiers and deep neural networks.
Summary of experimental aesthetics findings on image properties influencing aesthetic appeal.

Main Results:

Computational aesthetics offers powerful tools for analyzing art and predicting aesthetic appeal.
Experimental aesthetics identifies key visual properties affecting aesthetic judgment.
Current research strategies in both fields are compared, highlighting potential synergies.

Conclusions:

Computational and experimental aesthetics have complementary strengths.
Integrated research efforts can significantly advance the understanding of visual aesthetics.
Closer collaboration between disciplines is crucial for a holistic perspective.