Children leverage predictive representations for flexible, value-guided choice

By building a mental model of how the world works and using it to forecast the outcomes of different actions, a learner can make flexible choices in changing environments. However, while children and adolescents readily acquire structured knowledge about their environments, relative to adults, they tend to demonstrate weaker signatures of leveraging this knowledge to plan actions. One explanation for these developmental differences is that using a mental model to prospectively simulate potential choices and their outcomes is computationally costly, taxing cognitive control and working memory mechanisms that continue to develop into adulthood. Here, we ask whether children might effectively leverage structured knowledge to make flexible choices by relying on two alternative strategies that do not require costly mental simulation at choice time. First, through offline replanning, models can be queried before the time of choice to generate possible scenarios and update the values of potential actions. Second, an abstracted predictive model, known as a Successor Representation, can be built and harnessed to enable simplified computation of long-run reward values of candidate actions, without requiring iterative simulation of multiple time steps. To assess whether children, adolescents, and adults aged 7 - 23 years similarly harness these learning strategies, we ran three experiments. In Experiments 1 and 2, we used a reward revaluation task in which we manipulated the opportunity for offline replanning during rest, and found that children flexibly updated their behavior by leveraging structured knowledge in an adult-like manner. Surprisingly, across age, rest did not mediate flexible replanning, raising the possibility that participants may have behaved adaptively by harnessing predictive representations online. In Experiment 3, we directly tested whether children use predictive representations. Here, we observed early-emerging use of the SR, providing a mechanistic account of how children use structured knowledge to guide choice without detailed model-based simulation.