Neural Network Plasticity in Response to Experience: Contributions of Learning and Development

Neural systems do not exist in stasis; rather the brain is composed of an ever-evolving ensemble of neural networks that are responsible for changes in behavior and cognition across the lifespan. However, our understanding of these processes in the field of developmental neuroimaging remains relatively nascent. In this dissertation I review a theoretical framework for understanding and measuring neural change across time, and present two empirical studies of specific questions within this framework. In Study 1, I show that the brain shows distinct neural and behavioral adaptations during prospective rule-learning that reflects the ability to derived behavioral rules using only stimulus cues and reinforcement history. In Study 2, I test a theoretical model of interaction between learning-related experience and development-related maturation. Using a novel modeling approach, the multilevel multiple-growth model, I show that experience-driven behavioral and neural change unfolds differentially across age, suggesting that maturation, in part, determines how learning proceeds. Furthermore, I show that the contribution of neural phenotypes changes across age in ways that suggest that adolescent-typical neural organization advantages learning. Finally, I discuss how findings from Study 1 and 2 contribute to the theoretical framework of neural plasticity, as well as future research directions prompted by the findings herein.Doctor of Philosoph