Conference on Cognitive Computational Neuroscience

Animals are proposed to form abstract representations of a task’s structure which 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. We develop a hierarchical reversal-learning task to assess 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 choice-outcome associations governed by the same latent cause and assigns credit for both experienced and inferred outcomes. Furthermore, mPFC and lateral orbital frontal cortex (lOFC) track the current “position” within a latent association space that generalizes over stimuli. Collectively, these findings demonstrate the importance generalizable representations in prefrontal cortex for supporting flexible learning and inference in structured environments.