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 correlated between trials with differing input features in the high compared to the low dimensional case, suggesting that these patterns switched between separable (specific to input features) and shared (invariant to input features) transition models. Finally, we show that transition models are updated more strongly following the receipt of positive compared to negative outcomes, a finding that challenges conventional theories of planning. Together, these findings propose a computational and neural account of how information relevant for planning can be shared and segmented in response to the vast array of contextual features we encounter in our world.<b>SIGNIFICANCE STATEMENT:</b>Effective planning involves maintaining an accurate model of which actions take us to which locations. But in a world awash with information, mapping actions to states with the right level of complexity is critical. Using a new decision-making "heist task" in conjunction with computational modelling and fMRI we show that patterns of BOLD responses in the medial temporal lobe - a brain region key for prospective planning - become less sensitive to the presence of visual features when these are irrelevant to the task at hand. By flexibly adapting the complexity of task state representations in this way, state-action mappings learned under one set of features can be used to plan in the presence of others
