Sirvi Autor "Kirikal, Johan" järgi

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Modelling Creativity Using Artificial Neural Networks
(Tartu Ülikool, 2025) Kirikal, Johan; Aru, Jaan, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
The mental processes and algorithms that lead to creativity are still largely unknown. Psychological theories suggest that creativity arises from a balance between associative memory structure and the executive processes that retrieve and recombine information. In this thesis, I explore whether artificial neural networks can exhibit creative-like behaviour through controlled entropy modulation. Using the Continuous Generative Flow Network (C-GFN), a biologically inspired architecture, I simulate the impact of increased stochasticity on creative output. By varying the standard deviation in the model’s inner dynamics, I test the hypothesis that higher internal entropy leads to more creative outputs, as the entropy modulation theory of creativity predicted. As a result, models with elevated entropy not only generated more diverse symbolic representations but also did so more quickly and covered a greater distance in their latent space, emulating the faster-and-further phenomenon observed in human creativity research. These findings provide computational support for the associative and executive theories of creativity and highlight the role of entropy modulation in creative behaviour.