The role of executive function in shaping reinforcement learning

Reinforcement learning (RL) models have advanced our understanding of how animals learn and make decisions, and how the brain supports learning. However, the neural computations that are explained by RL algorithms fall short of explaining many sophisticated aspects of human learning and decision making, including the generalization of behavior to novel contexts, one-shot learning, and the synthesis of task information in complex environments. Instead, these aspects of behavior are assumed to be supported by the brain?s executive functions (EF). We review recent findings that highlight the importance of EF in instrumental learning. Specifically, we advance the theory that EF sets the stage for canonical RL computations in the brain, providing inputs that broaden their flexibility and applicability. Our theory has important implications for how to interpret RL computations in both brain and behavior.

Automated summary

Reinforcement learning models have advanced understanding of animal learning, decision making, and brain support, but fall short in explaining many sophisticated aspects of human learning. Executive functions play an important role in instrumental learning, providing inputs that enhance the flexibility and applicability of canonical RL computations in the brain.