Previous work demonstrates that people arbitrate between control algorithms – e.g. model-based or model-free – according to their relative certainty at the given moment. Here, we examined whether a similar uncertainty-based arbitration could explain the relative pattern of reliance on distinct representations. We employ a novel variant of a standard, two-stage decision task. This task allows us to behaviorally capture the within- and across-trial dynamics of model-based planning. We jointly fit choices and response times with a new computational model that revealed how people select among multiple task representations during planning in environments of differing state-space complexity. In particular, we examined how the reliance on task representations changed both as a function of experience, within-subject, and task complexity, across-subjects (total n = 426). We show that both the complexity of the environment and experience with a given contingency structure inform the kinds of representations we use to make decisions: at the early stages of the task, people start with “conjunctive” representations (combining co-occurring first-stage states) in simpler environments, but a “separated” representation (splitting states according to their second-step outcomes) is preferred in more complex environments. With experience, this pattern is reversed. We show that this shift is likely to be governed by a change in objectives: initially, people focus on minimizing uncertainty, and once this is achieved, they transition to prioritizing efficiency. Taken together, we show that people not only arbitrate between different modes of control, but also between types of representations for efficient planning.