Decisions in the natural world are rarely made in isolation. Each action that an organism selects will affect the future situations in which it finds itself, and those situations will in turn affect the future actions that are available. Achieving real-world goals often requires successfully navigating a sequence of many actions. An efficient and flexible way to achieve such goals is to construct an internal model of the environment, and use it to plan behavior multiple steps into the future. This process is known as multi-step planning, and its neural mechanisms are only beginning to be understood. Here, we review recent advances in our understanding of these mechanisms, many of which take advantage of multi-step decision tasks for humans and animals.

自然界中的决策很少孤立地做出。生物体选择的每个动作都会影响其自身发现的未来状况，而这些状况又会影响未来可用的动作。实现现实世界的目标通常需要成功地导航一系列许多动作。实现这些目标的有效而灵活的方法是构建环境的内部模型，并使用它来规划行为的未来多个步骤。此过程称为多步骤计划，其神经机制才刚刚开始被理解。在这里，我们回顾了对这些机制的理解的最新进展，其中许多利用了对人类和动物的多步骤决策任务。