As we navigate the world, we use learned representations of relational structures to explore and reach goals. Studies of how relational knowledge enables inference and planning are typically conducted in controlled small-scale settings. It remains unclear, however, how people use stored knowledge in continuously unfolding navigation, e.g., walking long distances in a city. We hypothesized that predictive representations, organized at multiple scales along posterior-anterior prefrontal and hippocampal hierarchies, guide naturalistic navigation. We conducted model-based representational similarity analyses of neuroimaging data measured during navigation of realistically long paths in virtual reality. We tested the pattern similarity of each point–along each path–to a weighted sum of its successor points within different predictive horizons. We found that anterior PFC showed the largest predictive horizons, posterior hippocampus the smallest, with the anterior hippocampus and orbitofrontal regions in between. Our findings offer novel insights into how cognitive maps support hierarchical planning at multiple scales. ### Competing Interest Statement The authors have declared no competing interest.
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