Brain's World Model is Smarter Than We Think

Planning is often described as the use of a world model to evaluate possible futures before choosing an action. In this review, we ask how such models are acquired, utilized, and revised in the brain. We frame the world model as the combination of a transition model, which predicts possible future latent states, and an outcome model, which assigns decision-relevant consequences to states and trajectories. We organize the review according to four complementary perspectives. Intuitive physics contributes to transition-model learning by providing a physics prior over object-centered latent dynamics. Model-free reinforcement learning contributes to outcome-model learning by providing structured teaching signals over reward distributions, task-relevant features or states, and temporal horizons. Successor representation supports flexible outcome-model updating by learning predictive maps that combine with reward functions to generalize value, adapt policy, and refine outcome estimates. Model-based reinforcement learning then deploys the acquired world model during deliberation and, through metacognitive control, evaluates whether it should continue to be queried, trusted, revised, or replaced. Together, these perspectives suggest that biological planning may depend not on a single monolithic representation, but on coordinated predictive resources that constrain possible transitions, shape outcome expectations, support flexible outcome update, and govern model usage. This framework also suggests that brain-inspired artificial intelligence should consider not only whether an agent has a world model, but what kind of predictive organization supports its use.