PhD Thesis - Investigation of creativity in learning-driven self-organization

By Natalya WEBER

Creativity is universally recognized as salient, and understanding what fosters creativity is of high interest across all disciplines. Since there are many creative expressions that vary across different domains, and the products of a creative process are evaluated within the standards of that domain, it is challenging to come up with a universal comprehensive understanding of the creativity construct and its determinants. Closely related to creativity is learning. While the products of a learning process in different disciplines may be substantially different, the general principles of learning in any domain are quite similar. They imply an integration of the external process and the internal process. The external process is an interaction between the learner and their environment, and the internal process is an interaction between functions involved in managing the learned content and functions that provide the necessary energy to run the process. From a learning perspective, one can view creativity as the emergence of new connections between diverse areas throughout the learning process. The newly stored information is emergent, since it is represented through the network of new interactions and not reducible to the constituent parts.

A promising direction for studying creativity as an emergent phenomenon is the self-organization process within the complex systems theory. Self-organization is omnipresent. Since the general tendency of a system is to go to equilibrium, it will necessary perform a selection as it will reject some states by leaving them, and retain other states by remaining in them. We can therefore ask what is the relationship between learning and creativity from the perspective of self-organization? A mathematical approach that allows for the investigation of that perspective is the Self-Optimization (SO) model developed by Watson et al. (2011). The advantage of this approach is that it is general enough to address the contextual variation of creativity, it has the potential to clarify the interconnectedness of creativity and learning, and it draws on a wide range of methods from computational, physical and biological domains.