Understanding the development of reward learning through the lens of meta-learning

Determining how environments shape how people learn is central to understanding individual differences in goal-directed behaviour. Studies of the effects of early-life adversity on reward learning have revealed that the environments that infants and children experience exert lasting influences on reward-guided behaviour. However, the varied findings from this research are difficult to reconcile under a unified computational account. Studies of adaptive reinforcement learning have demonstrated that learning algorithms and parameters dynamically adapt to support reward-guided behaviour in varied contexts, but this body of research has largely focused on learning that proceeds within the short timeframes of experimental tasks. In this Perspective, we argue that, to understand how the structure of experienced environments shapes reward learning across development, computational accounts of the effects of environmental statistics on reinforcement learning need to be extended to encompass learning across multiple nested timescales of experience. To this end, we consider the development of reward learning through the lens of meta-learning models, in particular meta-reinforcement learning. This computational formalization can inspire new hypotheses and methods for empirical research to understand how features of experienced environments give rise to individual differences in learning and adaptive behaviour across development.