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Abstract

The present dissertation focuses on the dynamics inherent in cognitive processes. The central argument of this thesis is that dynamics in cognitive process model parameters do matter. After exploring the dynamics in a specific cognitive process to underscore this importance, the dissertation is dedicated to overcoming significant limitations of stationary cognitive models. I propose a novel, innovative approach called neural superstatistics, which not only addresses dynamics within cognitive parameters but also does it highly efficiently. By providing reproducible open-source code and by discussing important practical aspects, I provide other researchers with a tool to account for dynamics across a broad spectrum of cognitive processes. Through various applications to in silico and in vivo experiments, I demonstrate its feasibility and the inherently dynamic nature of cognitive constructs. This dissertation marks a step in advancing cognitive process models to a new level. Its contributions are invaluable in deepening our comprehension of cognitive processes and in building more realistic models of cognitive processes.