Context is widely regarded as a major determinant of learning and memory across numerous domains, including classical and instrumental conditioning, episodic memory, economic decision-making, and motor learning. However, studies across these domains remain disconnected due to the lack of a unifying framework formalizing the concept of context and its role in learning. Here, we develop a unified vernacular allowing direct comparisons between different domains of contextual learning. This leads to a Bayesian model positing that context is unobserved and needs to be inferred. Contextual inference then controls the creation, expression, and updating of memories. This theoretical approach reveals two distinct components that underlie adaptation, proper and apparent learning, respectively referring to the creation and updating of memories versus time-varying adjustments in their expression. We review a number of extensions of the basic Bayesian model that allow it to account for increasingly complex forms of contextual learning.

背景被广泛认为是许多领域学习和记忆的主要决定因素，包括古典和工具条件反射、情景记忆、经济决策和运动学习。然而，由于缺乏将情境概念及其在学习中的作用形式化的统一框架，跨这些领域的研究仍然相互脱节。在这里，我们开发了一种统一的白话，允许在不同的情境学习领域之间进行直接比较。这导致贝叶斯模型假设上下文是不可观察的并且需要推断。然后，上下文推理控制记忆的创建、表达和更新。这种理论方法揭示了适应背后的两个不同组成部分，即适当的学习和表观学习，分别指记忆的创建和更新及其表达的时变调整。我们回顾了基本贝叶斯模型的一些扩展，使其能够解释日益复杂的情境学习形式。