Reinforcement learning (RL) is a framework of particular importance to psychology, neuroscience and machine learning. Interactions between these fields, as promoted through the common hub of RL, has facilitated paradigm shifts that relate multiple levels of analysis in a singular framework (for example, relating dopamine function to a computationally defined RL signal). Recently, more sophisticated RL algorithms have been proposed to better account for human learning, and in particular its oft-documented reliance on two separable systems: a model-based (MB) system and a model-free (MF) system. However, along with many benefits, this dichotomous lens can distort questions, and may contribute to an unnecessarily narrow perspective on learning and decision-making. Here, we outline some of the consequences that come from overconfidently mapping algorithms, such as MB versus MF RL, with putative cognitive processes. We argue that the field is well positioned to move beyond simplistic dichotomies, and we propose a means of refocusing research questions towards the rich and complex components that comprise learning and decision-making.

强化学习 (RL) 是一个对心理学、神经科学和机器学习特别重要的框架。这些领域之间的相互作用，通过 RL 的公共枢纽促进，促进了范式转变，将单一框架中的多个分析层次相关联（例如，将多巴胺功能与计算定义的 RL 信号相关联）。最近，已经提出了更复杂的 RL 算法来更好地解释人类学习，特别是它经常记录的对两个可分离系统的依赖：基于模型 (MB) 系统和无模型 (MF) 系统。然而，除了许多好处之外，这种二分法会扭曲问题，并可能导致对学习和决策的看法不必要地狭隘。这里，我们概述了一些来自过度自信映射算法的后果，例如 MB 与 MF RL，以及假定的认知过程。我们认为，该领域处于有利地位，可以超越简单的二分法，我们提出了一种将研究问题重新聚焦于包含学习和决策的丰富而复杂的组成部分的方法。