Few situations in life are completely novel. We effortlessly generalise prior knowledge to solve novel problems, abstracting common structure and mapping it onto new sensorimotor specifics. Here we trained mice on a series of reversal learning tasks that shared the same structure but had different physical implementations. Performance improved across tasks, demonstrating transfer of knowledge. Neurons in medial prefrontal cortex (mPFC) maintained similar representations across multiple tasks, despite their different sensorimotor correlates, whereas hippocampal (dCA1) representations were more strongly influenced by the specifics of each task. Critically, this was true both for representations of the events that comprised each trial, and those that integrated choices and outcomes over multiple trials to guide subjects' decisions. These data suggest that PFC and hippocampus play complementary roles in generalisation of knowledge, with the former abstracting the common structure among related tasks, and the latter mapping this structure onto the specifics of the current situation.

Prefrontal cortex is crucial for learning and decision-making. Classic reinforcement learning (RL) theories centre on learning the expectation of potential rewarding outcomes and explain a wealth of neural data in prefrontal cortex. Distributional RL, on the other hand, learns the full distribution of rewarding outcomes and better explains dopamine responses. Here we show distributional RL also better explains prefrontal cortical responses, suggesting it is a ubiquitous mechanism for reward-guided learning.