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September 18th, 2018

arXiv:1809.05832 [pdf, other]
Statistical Evolutionary Laws in Music Styles
Eita Nakamura, Kunihiko Kaneko
Comments: 18 pages, 4 figures, with supplemental material
Subjects: Physics and Society (physics.soc-ph)

If a cultural feature is transmitted over generations and exposed to stochastic selection when spreading in a population, its evolution may be governed by statistical laws, as in the case of genetic evolution. Music exhibits steady changes of styles over time, with new characteristics developing from traditions. Recent studies have found trends in the evolution of music styles, but little is known about quantitative laws and theories. Here we analyze Western classical music data and find statistical evolutionary laws. For example, distributions of the frequencies of some rare musical events (e.g. dissonant intervals) exhibit steady increase in the mean and standard deviation as well as constancy of their ratio. We then study an evolutionary model where creators learn their data-generation models from past data and generate new data that will be socially selected by evaluators according to novelty and typicality. The model reproduces the observed statistical laws and its predictions are in good agreement with real data. We conclude that some trends in music culture can be formulated as statistical evolutionary laws and explained by the evolutionary model incorporating statistical learning and the novelty-typicality bias.

https://arxiv.org/pdf/1809.05832.pdf

arXiv:1809.05760 [pdf, other]
History of art paintings through the lens of entropy and complexity
Higor Y. D. Sigaki, Matjaz Perc, Haroldo V. Ribeiro
Comments: 10 two-column pages, 5 figures; accepted for publication in PNAS [supplementary information available at this http URL ]
Journal-ref: Proceedings of the National Academy of Sciences 115, E8585-E8594 (2018)
Subjects: Physics and Society (physics.soc-ph); Applications (stat.AP)

Art is the ultimate expression of human creativity that is deeply influenced by the philosophy and culture of the corresponding historical epoch. The quantitative analysis of art is therefore essential for better understanding human cultural evolution. Here we present a large-scale quantitative analysis of almost 140 thousand paintings, spanning nearly a millennium of art history. Based on the local spatial patterns in the images of these paintings, we estimate the permutation entropy and the statistical complexity of each painting. These measures map the degree of visual order of artworks into a scale of order-disorder and simplicity-complexity that locally reflects qualitative categories proposed by art historians. The dynamical behavior of these measures reveals a clear temporal evolution of art, marked by transitions that agree with the main historical periods of art. Our research shows that different artistic styles have a distinct average degree of entropy and complexity, thus allowing a hierarchical organization and clustering of styles according to these metrics. We have further verified that the identified groups correspond well with the textual content used to qualitatively describe the styles, and that the employed complexity-entropy measures can be used for an effective classification of artworks.

https://arxiv.org/pdf/1809.05760.pdf