Reinforcement learning across development: What insights can we draw from a decade of research?

The past decade has seen the emergence of the use of reinforcement learning models to study developmental change in value-based learning. It is unclear, however, whether these computational modeling studies, which have employed a wide variety of tasks and model variants, have reached convergent conclusions. In this review, we examine whether the tuning of model parameters that govern different aspects of learning and decision-making processes vary consistently as a function of age, and what neurocognitive developmental changes may account for differences in these parameter estimates across development. We explore whether patterns of developmental change in these estimates are better described by differences in the extent to which individuals adapt their learning processes to the statistics of different environments, or by more static learning biases that emerge across varied contexts. We focus specifically on learning rates and inverse temperature parameter estimates, and find evidence that from childhood to adulthood, individuals become better at optimally weighting recent outcomes during learning across diverse contexts and less exploratory in their value-based decision-making. We provide recommendations for how these two possibilities — and potential alternative accounts — can be tested more directly to build a cohesive body of research that yields greater insight into the development of core learning processes.