Computational parametric mapping of functional neuroimaging data

Elucidating the neural basis of cognition requires theoretical models of cognition to constrain the modeling 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 modeling. First, it allows the fitting of cognitive models directly to neuroimaging data. Second, it allows for voxelwise or regionwise 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 voxelwise 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.