Adaptive behavior often requires predicting future events. The theory of reinforcement learning prescribes what kinds of predictive representations are useful and how to compute them. This paper integrates these theoretical ideas with work on cognition and neuroscience. We pay special attention to the successor representation (SR) and its generalizations, which have been widely applied both as engineering tools and models of brain function. This convergence suggests that particular kinds of predictive representations may function as versatile building blocks of intelligence.

翻译：适应性行为通常需要预测未来事件。强化学习理论阐明了哪些类型的预测表征是有用的，以及如何计算它们。本文将这些理论观点与认知科学及神经科学的研究成果相结合。我们特别关注后继表征及其泛化形式，这些方法已作为工程工具和大脑功能模型得到广泛应用。这种趋同性表明，特定类型的预测表征可能作为智能的多功能构建模块发挥作用。