Humans invest a substantial amount of time in the creation of artworks. For generations, humans around the world have learned and shared their knowledge and skills on artistic traditions. Albeit large experimental settings or online databases
have brought considerable insights on the evolutionary role and trajectory of art,
why humans invest in art, what information artworks carry and how art functions
within the community still remain elusive. To address these unresolved questions,
this present thesis integrates ethnographic accounts with data governance and statistical approaches to systematically investigate a large corpus of art. This thesis specifically focuses on a large corpus of Tamil kolam art from South India to provide an exemplary case study of artistic traditions. The foundation for the projects presented in this thesis was the design and construction of a robust data infrastructure that enabled the synthesis of raw data from various sources into one database for systematic analyses. The data infrastructure on the kolam artistic system enabled the development of complex statistical methods to explore the substantial investments and information complexity in art. In the first chapter, I examine artists’ strategic decisions in the creation of kolam art and how they strive to optimize the complexity of their artworks under constraints using evolutionary signaling theory and theoretically guided statistical methods. Results revealed that artists strive to maintain a stable and invariant complexity measured as Shannon information entropy, regardless of the size of the artwork. In order to achieve an optimal artistic complexity “sweet spot”, artists trade-off two standard measures of biological diversity in ecology: evenness and richness. Additionally, results showed that although kolam art arises in a highly stratified and multi-ethnic society, artistic complexity is strategically optimized across the population and not constrained by group boundaries. Instead, the trade-off can most likely be explained by aesthetic preferences or cognitive limitations. While artistic complexity in kolam art can be strategically optimized across the population, distinct styles and patterns can still be employed by artists. Thus, in the second chapter, I focus on how artistic styles in kolam art covary along cultural boundaries. I employ a novel statistical method to measure the mapping between styles onto group boundaries on a large corpus of kolam art by decomposing the system into sequential drawing decisions. In line with Chapter 1, results demonstrate limited group-level variation. Distinct styles or patterns in kolam art can only be weakly mapped to caste boundaries, neighborhoods or previous migration. Both chapters strongly suggest that kolam art is primarily a sphere where artists differentiate themselves from others by displaying their unique skill set and knowledge. Thus, variability in kolam art is largely dominated by individual-level variation and not reflective of group boundaries or narrow socialization channels. This thesis contributes to an emergent understanding of how artists conceptualize what they are doing and how art functions within the community. Taken together, this thesis serves as an example approach that demonstrates an optimized workflow and novel approaches for the evolutionary study of a large corpus of artistic traditions.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:78381 |
| Date | 09 March 2022 |
| Creators | Tran, Ngoc-Han |
| Contributors | Universität Leipzig |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | https://doi.org/10.3389/fpsyg.2021.742577, https://doi.org/10.1017/ehs.2021.14 |
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