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Jean Piaget's theory of cognitive development emphasizes the role of thinking in a child's learning process, suggesting that children are naturally curious about their environment. His approach to development is discontinuous, proposing that cognitive abilities progress through distinct stages, each with unique characteristics. Central to Piaget's theory is schemata—mental structures that allow individuals to understand and interpret the world.
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Related Experiment Video

Updated: Jun 4, 2026

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

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

Kate Nussenbaum^1,2, Ari E Kahn^1,3, Alice Zhang^4,5

¹Princeton University, Princeton, New Jersey, USA.

Developmental Science

|June 3, 2026

Summary

This summary is machine-generated.

As children and adults age, their predictive learning strategies evolve. While younger individuals rely on recent experiences, older individuals better track temporal relationships, impacting how they learn structured information.

Keywords:

cognitive development computational modeling knowledge acquisition schema formation structure‐learning successor representation temporal abstraction

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RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

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Published on: July 17, 2021

Area of Science:

Cognitive Development
Developmental Psychology
Computational Neuroscience

Background:

Children are skilled statistical learners, but their cognitive processes during learning may differ from adults.
Understanding how predictive learning mechanisms change with age is crucial for explaining structured knowledge acquisition.
Previous research has not fully clarified age-related differences in predicting upcoming experiences within complex environments.

Purpose of the Study:

To investigate how predictive learning mechanisms change across development (childhood, adolescence, adulthood).
To examine the relationship between age-related changes in predictive learning and the acquisition of explicit knowledge.
To determine how learners of different ages predict upcoming experiences in complex, structured environments.

Main Methods:

Tested 106 participants aged 8-22 years on a predictive learning task with higher-order temporal structure.
Utilized a computational model to analyze response times and characterize learning mechanisms.
Assessed explicit knowledge of stimulus relations through post-learning task measures.

Main Results:

All participants, regardless of age, employed recency-based prediction.
With increasing age, participants showed increased reliance on a sophisticated mechanism tracking conditional relations between stimuli.
Changes in predictive learning with age demonstrated only a weak association with the acquisition of explicit knowledge.

Conclusions:

Learning mechanisms for parsing continuous experience evolve throughout development, influencing predictions about future events.
Age-related shifts in predictive learning involve a transition from simpler recency-based strategies to more complex temporal relationship tracking.
While predictive learning strategies mature with age, their direct impact on explicit structured knowledge acquisition appears limited.