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。强化学习理论规定了哪些类型的预测性表征是有用的，以及如何计算这些表征。本文将上述理论观点与认知科学和神经科学的研究成果相结合。我们特别关注继任表征（SR）及其泛化形式，这些表征已被广泛应用为工程工具和脑功能模型。这种趋同性表明，特定类型的预测性表征可能作为智能的通用构建模块发挥作用。