Abstract
Adolescence is a dynamic period of brain development, marked by profound changes in learning, decision-making, and higher-order cognition. This review explores how research on the adolescent brain can inform the development of biologically-based computational models of learning and behavior. We highlight how computational frameworks such as reinforcement learning (RL) and artificial neural networks (ANNs) capture key features of adolescent behavior, including shifts in exploration and decision-making strategies. By integrating principles of brain development, such as synaptic pruning and hierarchical development of neural circuits, computational models can offer insights into how the brain adapts to new experiences and challenges. We argue that studying adolescent brain development not only enhances our understanding of cognition but also provides a valuable framework for refining computational models of brain function. We propose future directions for how adolescent research can inform innovations in computational research to better capture dynamic brain states, individual variability, and risk for psychopathology.