Computational parametric mapping of functional neuroimaging data

Abstract Elucidating the neural basis of cognition requires theoretical models of cognition to constrain the modelling of neural data. A prevalent strategy in functional neuroimaging is to regress the latent variables of cognitive models onto neural data. Though widely used, this approach restricts the mapping of computational variables to single parameter values. We introduce computational parametric mapping (CPM), which builds on and generalizes the Bayesian population receptive field framework. CPM offers three main advances for cognitive computational modelling. First, it allows the fitting of cognitive models directly to neuroimaging data. Second, it allows for the voxel- or region-wise mapping of parameters of cognitive computational models onto the brain, thus making the topographic mapping methods prevalent in the sensory sciences available to the cognitive computational neuroscientist. Finally, it is efficient enough to make voxel-wise mapping over large regions of interest feasible. Here, we illustrate how CPM can be used to fit reinforcement-learning algorithms to synthetic and real data.