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Computational Analysis of Pre-Historic Artifacts Containing Abstract Geometric Patterns in the Context of the Emergence of Human
24: Linguistics and Philosophy
May 30, 2020
Students majoring in materials science, mechanical engineering, or computer science are encouraged to apply for this research project, which is a collaboration with Professors Chrisine Ortiz and Ellan Spero of DMSE. Experience with image analysis software and quantitative computational methods will be beneficial. In this project, combinatorial sequences of images of prehistoric material engravings and cave art containing abstract geometric patterns will be analyzed using a variety of cross-disciplinary computational methods. Such methods will include microstructural image analysis in materials science and engineering, topological methods in mechanical engineering, morphometrics approaches in evolutionary biology, data visualization, analytics, image and pattern recognition in computer science. These data will be used to elucidate aspects of the emergence of human language. There is a broad consensus across a broad array of disciplines that symbolic behavior and language are intimately connected, and the production of symbolic artifacts is consequently viewed as evidence for the availability of linguistic competence. However, the conceptual link between symbolic behavior and language is still nascent. This project addresses this hypothesized connection between symbolic behavior and language by utilizing cross-disciplinary methodologies for comparing these apparently heterogeneous human symbolic expressions and to identify evidence supporting whether the abstract geometric patterns of prehistoric non-figurative engravings and cave art reflect the availability of a formal grammar parallel to that observed in present-day human language. Quantitative computational data will be interpreted in partnership with colleagues bringing expertise in linguistic frameworks for formal syntax, history of human visual narratives, and material culture and archeology.
Experience with image analysis software and quantitative computational methods will be beneficial.