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Children leverage predictive representations for flexible, value-guided choice
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DOI：10.1016/j.cognition.2025.106340.png)
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Abstract
En 中文
By harnessing a mental model of how the world works, learners can make flexible choices in changing environments. However, while children and adolescents readily acquire structured knowledge of their environments, relative to adults, they often 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 mechanisms that develop into adulthood. Here, we ask whether children 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 update the values of potential actions. Second, an abstracted predictive model, known as a successor representation (SR), can enable simplified computation of long-run reward values of candidate actions without requiring iterative simulation of multiple time steps. Here, across three experiments, we assessed whether children, adolescents, and adults aged 7–23 years similarly harness these learning strategies. In a reward revaluation task, we found that children flexibly updated their behavior by leveraging structured knowledge, but that across age, the opportunity for offline replanning during rest did not influence behavior. While participants may have leveraged a detailed mental model of the task structure, they may have also relied on simplified, predictive representations to guide their choices. We then directly tested whether children use predictive representations and 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.
Keywords:
Cognitive development
Value-guided choice
Successor representation
Model-based reinforcement learning
Reward revaluation
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