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.

强化学习 (RL) 模型加深了我们对动物如何学习和做出决定以及大脑如何支持学习的理解。然而，RL 算法解释的神经计算无法解释人类学习和决策的许多复杂方面，包括将行为推广到新的上下文、一次性学习以及复杂环境中任务信息的合成。相反，假设行为的这些方面是由大脑的执行功能 (EF) 支持的。我们回顾了最近的发现，这些发现强调了 EF 在工具学习中的重要性。具体来说，我们提出了 EF 为大脑中的规范 RL 计算奠定了基础的理论，提供了扩大其灵活性和适用性的输入。