Reasoning Goals and Representational Decisions in Computational Cognitive Neuroscience: Lessons From the Drift Diffusion Model

Abstract

Computational cognitive models are powerful tools for enhancing the quantitative and theoretical rigor of cognitive neuroscience. It is thus imperative that model users—researchers who develop models, use existing models, or integrate model-based findings into their own research—understand how these tools work and what factors need to be considered when engaging with them. To this end, we developed a philosophical toolkit that addresses core questions about computational cognitive models in the brain and behavioral sciences. Drawing on recent advances in the philosophy of modeling, we highlight the central role of model users' reasoning goals in the application and interpretation of formal models. We demonstrate the utility of this perspective by first offering a philosophical introduction to the highly popular drift diffusion model (DDM) and then providing a novel conceptual analysis of a long-standing debate about decision thresholds in the DDM. Contrary to most existing work, we suggest that the two model structures implicated in the debate offer complementary—rather than competing—explanations of speeded choice behavior. Further, we show how the type of explanation provided by each form of the model (parsimonious and normative) reflects the reasoning goals of the communities of users who developed them (cognitive psychometricians and theoretical decision scientists, respectively). We conclude our analysis by offering readers a principled heuristic for deciding which of the models to use, thus concretely demonstrating the conceptual and practical utility of philosophy for resolving meta-scientific challenges in the brain and behavioral sciences.