Effective planning involves knowing where different actions take us. However natural environments are rich and complex, leading to an exponential increase in memory demand as a plan grows in depth. One potential solution is to filter out features of the environment irrelevant to the task at hand. This enables a shared model of transition dynamics to be used for planning over a range of different input features. Here, we asked human participants (13 male, 16, female) to perform a sequential decision-making task, designed so that knowledge should be integrated independently of the input features (visual cues) present in one case but not in another. Participants efficiently switched between using a low (cue independent) and a high (cue specific) dimensional representation of state transitions. fMRI data identified the medial temporal lobe as a locus for learning state transitions. Within this region, multivariate patterns of BOLD responses as state associations changed (via trial-by-trial learning) were less ...