Proceedings of the Annual Meeting of the Cognitive Science Society

Decision diffusion models are commonly used to explain the processes underlying decision-making. Many software options exist for cognitive scientists to fit diffusion models to data; however, they tend to lack customizability beyond existing model formulations that are already built into them, stymying new theoretical contributions. We introduce FlexDDM, a new Python package that requires minimal coding to develop new diffusion models. The package is equipped with four standard models of cognitive conflict tasks and a suite of fitting techniques. Our development of FlexDDM aims to broaden the accessibility and applicability of computational methods in cognitive science, thereby accelerating theoretical innovation and contributing to advancements in the field of behavioral sciences.