The Anterior Cingulate Cortex Predicts Future States to Mediate Model-Based Action Selection

Behavioral control is not unitary. It comprises parallel systems, model based and model free, that respectively generate flexible and habitual behaviors. Model-based decisions use predictions of the specific consequences of actions, but how these are implemented in the brain is poorly understood. We used calcium imaging and optogenetics in a sequential decision task for mice to show that the anterior cingulate cortex (ACC) predicts the state that actions will lead to, not simply whether they are good or bad, and monitors whether outcomes match these predictions. ACC represents the complete state space of the task, with reward signals that depend strongly on the state where reward is obtained but minimally on the preceding choice. Accordingly, ACC is necessary only for updating model-based strategies, not for basic reward-driven action reinforcement. These results reveal that ACC is a critical node in model-based control, with a specific role in predicting future states given chosen actions. •A novel two-step task disambiguates model-based and model-free RL in mice•ACC represents the task state space, and reward is contextualized by state•ACC predicts future states given chosen actions and encodes state prediction surprise•Inhibiting ACC prevents state transitions, but not rewards, from influencing choice Akam et al. investigate mouse anterior cingulate cortex (ACC) in a sequential decision-making task, finding that ACC predicts future states given chosen actions and indicates when these predictions are violated. Transiently inhibiting ACC prevents mice from using observed state transitions to guide subsequent choices, impairing model-based reinforcement learning.