Geometric Aesthetics: Beauty as Preference Optimization on Perceptual Manifolds

July 19, 2026Open Access

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Key Points

This research aims to formalize aesthetic experience as preference optimization using mathematical concepts from geometry and utility theory.
Developed a mathematical model based on Riemannian metrics and covariance structures to define aesthetic experience.
Conducted a double-blind, pre-registered protocol with Bonferroni correction to test predictions empirically.
Analyzed correlations between aesthetic scores and various metrics, including book ratings and text similarity across cultural traditions.
The aesthetic score positively correlates with Goodreads book ratings (r = 0.048, n = 50,000).
Confirmed the inverted-U relationship between complexity and aesthetic value (F = 11,264, n = 50,000).
Predicted aesthetic richness using eigenspace dimensionality across nine cultural traditions (r = 0.959).

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Examines aesthetic experience, connecting beauty to preference optimization, indicating deeper insights into human perception.

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Andrew Bond (2026) studied this question.

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