Structure Inference in Complex Environments Improves From Childhood to Adulthood

Area of Science:

• Cognitive Development
• Developmental Psychology
• Computational Neuroscience

Background:

• Children infer world structure from limited data via structure learning.
• Structure learning and decision-making are reciprocally linked but often studied separately.
• Developmental research lacks integrated studies on structure learning and exploratory decisions.

Purpose of the Study:

• Compare structure learning and exploratory decisions across children, adolescents, and adults.
• Investigate how causal models influence exploration in a dynamic environment.
• Examine the developmental trajectory of uncertainty-sensitive decision-making.

Main Methods:

• Utilized a patch-foraging task to assess behavior in a structured environment.
• Compared decision-making and exploration strategies across age groups.
• Employed computational modeling to analyze participants' causal models of the environment.

Main Results:

• Younger participants explored more broadly by leaving patches sooner than adults.
• Computational models revealed age-related differences in causal model granularity.
• Participants across all ages demonstrated uncertainty-sensitive decision-making.

Conclusions:

• Structure learning develops gradually, with less granular representations in younger individuals.
• Uncertainty-sensitive decision-making emerges early and supports adaptive behavior with imprecise models.
• Developmental differences in exploration stem from distinct causal representations of the environment.