Developmental Change in Structure Learning Reflects a Shift From Recency‐Based to Relational Prediction

Journal article

Children are adept statistical learners, capable of parsing streams of structured input into meaningful units, but the cognitive processes they engage during learning may differ from those of adults. To date, however, it is unclear how learners of different ages predict upcoming experience when navigating environments with complex structure, as well as how changes in predictive learning mechanisms influence structured knowledge acquisition. To address this question, we tested 106 children, adolescents, and adults, ages 8–22 years, on a predictive learning task, in which they experienced sequences of stimuli with a higher‐order temporal structure. After an initial learning phase, participants’ explicit knowledge of the relations between stimuli was probed via two additional task measures. We used a recently introduced computational model to characterize participants’ response times during learning, and found that all participants relied on simple, recency‐based prediction, anticipating that they would encounter stimuli they recently encountered in the past. With increasing age, however, participants demonstrated greater evidence of additionally relying on a more sophisticated learning mechanism, which captured a predictive representation of the conditional relations between stimuli. Though predictive learning changed with age, we found only weak evidence that these changes related to the acquisition of explicit knowledge of the environment. Our results suggest that the learning mechanisms through which people parse continuous streams of experience change with age, influencing their predictions about upcoming events. Summary: Children, adolescents, and adults all learn to segment continuous streams of structured perceptual input, but they may do so via different learning processes. We examined how the learning mechanisms that enable people to predict upcoming experiences change and relate to structured knowledge acquisition across development. Computational modeling revealed that in a graph‐learning task, younger participants relied on simple, recency‐based prediction, while older participants tracked temporal relations between stimuli. The extent to which participants engaged in this sophisticated form of predictive learning only weakly related to their knowledge of the task's structure.

Authors: Nussenbaum, K; Kahn, AE; Zhang, A; Daw, ND; Hartley, CA

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Wiley
• Journal: Developmental Science
• Volume: 29
• Issue: 4
• Article number: e70227
• Publication date: 2026-06-03
• Acceptance date: 2026-05-08
• DOI: 10.1111/desc.70227
• EISSN: 1467-7687
• ISSN: 1363755X, 1363-755X
• Language: English
• Keywords: knowledge acquisition; cognitive development; temporal abstraction; computational modeling; structure‐learning; successor representation; schema formation