Kahn, A. E.; Daw, N. D.

When faced with a multi-step decision problem, humans and animals must balance flexible and accurate decision making with computational complexity. One prominent approach, the Successor Representation (SR), takes advantage of temporal abstraction of future states: by learning to predict long-run future trajectory independently of rewards, the brain can avoid the costs of iterative, multi-step model-based mental simulation, while retaining some ability to cheaply replan when goals change. Human behavior shows signatures of such temporal abstraction, but the characterization of the strategy of individuals, as well as whether people dynamically adapt their reliance on such abstractions in the face of environmental statistics, e.g. the predictability of long-run states, remains an open question. We developed a novel task to measure SR usage during dynamic, trial-by-trial learning. Using this approach, we find that participants exhibit a mix of SR and model-based learning strategies that varies across individuals. Further, by dynamically manipulating the task structure within-subject, we observe evidence of resource-rational reliance on the SR, which decreases when the ability to use prior experience to build valid temporal abstractions decreases. Our work adds to a growing body of research showing that the brain arbitrates between approximate decision strategies. The current study extends these ideas from simple habits into usage of more sophisticated approximate predictive models, and demonstrates that individuals dynamically adapt these in response to the predictibility of their environment.