Developmental Changes in Learning: Computational Mechanisms and Social Influences.

Area of Science:

• Developmental Psychology
• Computational Neuroscience
• Cognitive Science

Background:

• Human learning and decision-making abilities evolve throughout the lifespan.
• Computational models offer a framework for understanding these developmental changes.
• Reinforcement learning models are increasingly used to study age-related learning and socio-emotional influences.

Purpose of the Study:

• To introduce computational reinforcement learning models to developmental scientists.
• To highlight model parameters and variants relevant for developmental research.
• To review applications of these models in studying social influences on learning across development.

Main Methods:

• Review of computational reinforcement learning principles.
• Discussion of model parameters and variants for developmental science.
• Synthesis of recent studies applying reinforcement learning to social learning in development.

Main Results:

• Computational models provide a quantitative approach to understanding developmental changes in learning.
• Reinforcement learning models can elucidate the impact of social information on learning across different age groups.
• These models offer a bridge between psychological and neurobiological theories of development.

Conclusions:

• Computational reinforcement learning models are valuable tools for developmental science.
• They enhance understanding of developmental mechanisms in learning and decision-making.
• These models facilitate interdisciplinary research connecting psychology and neuroscience.