Changing Song Traditions
The proposed research is aimed at providing a cultural evolutionary account of how song and music traditions live and die. What makes some of them stick, thrive, or go extinct? Finding answers to these questions serves to further our understanding of the fundamental and more general question of how to explain prevalent culture, in which some cultural artifacts are more likely to survive than others (see e.g., Sperber 1996).
With the recent rise of computational approaches to analysing culture, large audiences of younger scholars in the Humanities and Social Sciences are confronted with the need to understand and integrate computational methodologies in their scholarly work. However, they often lack the theoretical insights to evaluate which methodologies are needed for their purposes, or they lack the technical skills to tackle such an undertaking. Together with colleagues like Mike Kestemont and Allen Riddell, I attempt to remedy this situation by developing course materials, workshops, trainings and tutorials on programming and data analysis for the humanities and allied social sciences.
To what extent can neural network text generation systems be of aid to professional writers? In the Asibot project, we collaborated with award-winning author Ronald Giphart to find out.
The hypothesis of this project is that tunes and tales both consist of motif sequences acting as vehicles of oral transmission. First, a computer program will be developed for the automatic recognition of motifs in large amounts of data. Secondly, generative models will be created to simulate oral transmission including the inherent variation. This model will be able to predict the occurrence of variable motif patterns in oral tradition over time.