RESEARCH ARTICLE Using Data-Driven Model-Brain Mappings to Constrain Formal Models of Cognition

In this paper we propose a method to create data-driven mappings from components of cog-nitive models to brain regions. Cognitive models are notoriously hard to evaluate, especially based on behavioral measures alone. Neuroimaging data can provide additional con-straints, but this requires a mapping from model components to brain regions. Although such mappings can be based on the experience of the modeler or on a reading of the litera-ture, a formal method is preferred to prevent researcher-based biases. In this paper we used model-based fMRI analysis to create a data-driven model-brain mapping for five mod-ules of the ACT-R cognitive architecture. We then validated this mapping by applying it to two new datasets with associated models. The new mapping was at least as powerful as an existing mapping that was based on the literature, and indicated where the models were supported by the data and where they have to be improved. We conclude that data-driven model-brain mappings can provide strong constraints on cognitive models, and that model-based fMRI is a suitable way to create such mappings