Beauty and structural complexity

TL;DR

This study investigates how human preference for images relates to their statistical and algorithmic complexity, revealing maximum appreciation at intermediate levels and proposing potential universal criteria for aesthetic judgment.

Contribution

It demonstrates that the algorithmic complexity of coarse-grained images predicts aesthetic preferences, linking structural complexity to human appreciation.

Findings

Maximum preference occurs at intermediate entropic complexity.

Algorithmic complexity of coarse-grained images predicts preferences.

Potential existence of universal criteria for aesthetic judgment.

Abstract

We revisit the long-standing question of the relation between image appreciation and its statistical properties. We generate two different sets of random images well distributed along three measures of entropic complexity. We run a large-scale survey in which people are asked to sort the images by preference, which reveals maximum appreciation at intermediate entropic complexity. We show that the algorithmic complexity of the coarse-grained images, expected to capture structural complexity while abstracting from high frequency noise, is a good predictor of preferences. Our analysis suggests that there might exist some universal quantitative criteria for aesthetic judgement.