Learning the consequences our choices have as we interact with our world is critical for flexible behavior. Relational knowledge of one’s environment gives structure to otherwise-individual one-to-one stimulus-outcome mappings, providing a substrate to globally update behavioral contingencies in the face of changes in the landscape of reward. In the brain, this relational knowledge is thought to be encoded in the hippocampus (HPC) in the form of a cognitive map, while prefrontal regions, such as orbitofrontal cortex (OFC), are thought to instantiate subjective estimates of location on the map, though direct neurophysiological evidence is lacking. In this talk, I will present recent work demonstrating the causal relationship between HPC and OFC as nonhuman primates perform a reward learning task requiring them to learn and maintain knowledge of changing stimulus-outcome associations. I will then provide direct evidence that single primate hippocampal neurons represent an abstract map of the value space defined by the task. Finally, I use behavioral modeling to highlight one possible strategy by which knowledge of value space is exploited by animals to detect changes in choice-outcome mappings and proactively update their behavior in response.

Human learners acquire not only disconnected bits of information, but complex interconnected networks of relational knowledge. The capacity for such learning naturally depends on the architecture of the knowledge network itself, and also on the architecture of the computational unit – the brain – that encodes and processes the information. Here, I will discuss emerging work assessing network constraints on the learnability of relational knowledge, and the neural correlates of that learning.