Neural mechanisms of credit assignment for inferred relationships in a structured world

Abstract

Animals have been proposed to abstract compact representations of a tasks structure that could, in principle, support accelerated learning and flexible behavior. Whether and how such abstracted representations may be used to assign credit for inferred, but unobserved, relationships in structured environments are unknown. Here, we develop a novel hierarchical reversal-learning task and Bayesian learning model to assess the computational and neural mechanisms underlying how humans infer specific choice-outcome associations via structured knowledge. We find that the medial prefrontal cortex (mPFC) efficiently represents hierarchically related choice-outcome associations governed by the same latent cause, using a generalized code to assign credit for both experienced and inferred outcomes. Furthermore, mPFC and lateral orbital frontal cortex track the inferred current "position" within a latent association space that generalizes over stimuli. Collectively, these findings demonstrate the importance both of tracking the current position in an abstracted task space and efficient, generalizable representations in prefrontal cortex for supporting flexible learning and inference in structured environments.