Structure Inference in Complex Environments Improves From Childhood to Adulthood

Early in development, children can infer latent structure in the world from sparse and ambiguous evidence. Through a process known as structure learning, they extract statistical regularities, construct causal models from those regularities, and use those models to arbitrate between exploiting known options and exploring novel alternatives. In turn, each decision and its outcomes refine the model that produced them. Despite the clear reciprocal relationship between structure learning and decision-making in the real world, developmental research has largely examined these processes separately. To address this gap, we compared how children, adolescents, and adults behaved in a patch-foraging task designed to reveal how structure learning shapes exploratory decisions in a richly structured, dynamic environment. We found that younger participants left patches sooner than adults, enabling them to explore the environment more broadly within the fixed time window of the study. Computational modeling demonstrated that this difference in exploration arose from differences in participants' causal models of the environments. Younger participants grouped all patches into a single category despite large differences in richness, whereas older participants separated them into distinct categories. Despite differences in representation, participants of all ages used their uncertainty about the environment to guide their decisions. Together, our findings suggest that structure learning undergoes protracted development, but uncertainty-sensitive decision-making emerges earlier and can support adaptive behavior even when representations remain imprecise. SUMMARY: Children and adolescents formed less granular representations of environmental structure than adults. Despite these imprecise representations, younger participants showed adult-like sensitivity to uncertainty, planning further ahead when more confident in their internal models of the environment.