Computational Parametric Mapping: A Method For Mapping Cognitive Models Onto Neuroimaging Data

Elucidating the neural basis of cognition requires incorporating cognitive models into the modelling of neural data. A prevalent strategy in neuroimaging is to fit computational models of cognition to concurrent behavior, and then regress the latent variables of those models onto fMRI data. Though widely used, this approach is restricted to mapping computational variables that are in some way expressed in behavior. Here we introduce a computational parametric mapping (CPM), a framework which builds on and generalizes the Bayesian population receptive field framework. CPM offers three main advances for cognitive computational modelling. First, by embedding cognitive models into generative models that can be fit directly to neuroimaging data, CPM relaxes the constraint of mapping only behaviorally relevant variables. Second, CPM allows model comparison statistics and model parameters of cognitive computational models to be topographically mapped onto anatomical structures. This makes the topographic mapping strategies prevalent in the sensory sciences available to the cognitive computational neuroscientist. Finally, despite the computationally intensive process of estimating generative models independently for each voxel, our procedures are now fast enough to make it feasible to map extensive neural systems and model spaces. We illustrate this approach via synthetic reward learning data. We speculate that this method can play a role in discovering topographic principles underlying the neural coding of cognitive computational processes.