The thoughts imputed to the animal can then serve as latent targets for neural analyses. Using these targets, we provide a framework for interpreting the linked processes of encoding, recoding, and decoding of neural data in light of the rational model for behavior. We first demonstrate the merits of this approach on synthetic neural data during a foraging task. We then analyze real neural activity in primate prefrontal cortex (PFC) and posterior parietal cortex (PPC) to discover computations relevant to foraging tasks. In PFC, we find that reward dynamics are represented in a subspace of the high-dimensional population activity, and predict animal’s subsequent choice better than either the true experimental variables or the raw neural responses. In PPC, we find representations of latent navigation-relevant variables, and find that task manipulations alter the coupling between neurons, suggesting that these interactions reflect the mental model used to perform task-relevant computations. Overall, our approach may identify explainable structure in complex neural activity patterns. This framework lays a foundation for discovering how the brain chooses to act using dynamic beliefs about the uncertain world.