Social learning across adolescence: A Bayesian neurocognitive perspective.

Area of Science:

• Neuroscience
• Developmental Psychology
• Computational Psychiatry

Background:

• Adolescence is a critical developmental stage characterized by heightened susceptibility to peer influence and social learning.
• Understanding the developmental trajectory of social learning and the factors influencing it is crucial for adolescent well-being.

Approach:

• This review synthesizes behavioral, neural, and computational findings on adolescent social learning.
• A novel Bayesian reinforcement learning framework is proposed to model social learning processes.
• The framework integrates value-based decision-making, executive functions, and social cognition.

Key Points:

• The framework models how adolescents learn the value of behaviors and manage environmental uncertainty.
• It explains developmental changes in assessing uncertainty and discriminating social information sources.
• It accounts for both positive and negative peer influences through informational and normative social learning.

Conclusions:

• The proposed Bayesian framework offers a unified approach to understanding adolescent social learning.
• It provides insights into the adaptive development of social information processing during adolescence.
• This model can inform interventions aimed at mitigating negative peer influences and promoting positive social development.