Scientists Show How Statistical Physics Can Reveal Art Trends Across Time and Culture

Statistical Physics Reveals Art Trends

Algorithms found a remarkably smooth change in the composition of Western landscape paintings from 1500 to 2000 AD. This may suggest a potential bias in art curation or art history literature.

Drawing the Line to Answer Art’s Big Questions

Algorithms have shown that the compositional structure of Western landscape paintings changed “suspiciously” smoothly between 1500 and 2000 AD, potentially indicating a selection bias by art curators or in art historical literature, physicists from the Korea Advanced Institute of Science and Technology (KAIST) and colleagues report in the Proceedings of the National Academy of Sciences (PNAS).

KAIST statistical physicist Hawoong Jeong worked with statisticians, digital analysts, and art historians in Korea, Estonia, and the US to clarify whether computer algorithms could help resolve long-standing questions about design principles used in landscape paintings, such as the placement of the horizon and other primary features.

Algorithm Progressively Dissects Painting

Image 1. The algorithm progressively dissects the painting based on the amount of information in each subsequent partition. Credit: KAIST

“A foundational question among art historians is whether artwork contains organizing principles that transcend culture and time and, if yes, how these principles evolved over time,” explains Jeong. “We developed an information-theoretic approach that can capture compositional proportion in landscape paintings and found that the preferred compositional proportion systematically evolved over time.”

Digital versions of almost 15,000 canonical landscape paintings from the Western Renaissance in the 1500s to the more recent contemporary art period were run through a computer algorithm. The algorithm progressively divides artwork into horizontal and vertical lines depending on the amount of information in each subsequent partition. It allows scientists to evaluate how artists and various art styles compose landscape artwork, in terms of placement of a piece’s most important components, in addition to how high or low the landscape’s horizon is placed.

Painting Categories Algorithm

Image 2. The paintings were divided into four categories depending on how their compositions were partitioned by the first two lines developed by the algorithm. Credit: KAIST

The scientists started by analyzing the first two partitioning lines identified by the algorithm in the paintings and found they could be categorized into four groups: an initial horizontal line followed by a second horizontal line (H-H); an initial horizontal line followed by a second vertical line (H-V); a vertical followed by horizontal line (V-H); or a vertical followed by a vertical line (V-V) (see image 1 and 2). They then looked at the categorizations over time.

They found that before the mid-nineteenth century, H-V was the dominant composition type, followed by H-H, V-H, and V-V. The mid-nineteenth century then brought change, with the H-V composition style decreasing in popularity with a rise in the H-H composition style. The other two styles remained relatively stable.

The scientists also looked at how the horizon line, which separates sky from land, changed over time. In the 16th century, the dominant horizon line of the painting was above the middle of the canvas, but it gradually descended to the lower middle of the canvas by the 17th century, where it remained until the mid-nineteenth century. After that, the horizon line began gradually rising again.

Interestingly, the algorithm showed that these findings were similar across cultures and artistic periods, even through periods dominated by a diversity in art styles. This similarity may well be a function, then, of a bias in the dataset.

“In recent decades, art historians have prioritized the argument that there is great diversity in the evolution of artistic expression rather than offering a relatively smoother consensus story in Western art,” Jeong says. “This study serves as a reminder that the available large-scale datasets might be perpetuating severe biases.”

The scientists next aim to broaden their analyses to include more diverse artwork, as this particular dataset was ultimately Western and male-biased. Future analyses should also consider diagonal compositions in paintings, they say.

Reference: “Dissecting landscape art history with information theory” by Byunghwee Lee, Min Kyung Seo, Daniel Kim, In-seob Shin, Maximilian Schich, Hawoong Jeong and Seung Kee Han, 27 October 2020, Proceedings of the National Academy of Sciences.
DOI: 10.1073/pnas.2011927117
This work was supported by the National Research Foundation (NRF) of Korea.

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