Learning is using past experiences to inform new behaviors and actions. Because all experiences are unique, learning always requires some generalization. An effective way of improving generalization is to expose learners to more variable (and thus often more representative) input. More variability tends to make initial learning more challenging, but eventually leads to more general and robust performance. This core principle has been repeatedly rediscovered and renamed in different domains (e.g., contextual diversity, desirable difficulties, variability of practice). Reviewing this basic result as it has been formulated in different domains allows us to identify key patterns, distinguish between different kinds of variability, discuss the roles of varying task-relevant versus irrelevant dimensions, and examine the effects of introducing variability at different points in training.

学习是利用过去的经验来告知新的行为和行动。因为所有的经历都是独一无二的，所以学习总是需要一些概括。提高泛化能力的一种有效方法是让学习者接触更多可变（因此通常更具代表性）的输入。更多的可变性往往会使初始学习更具挑战性，但最终会导致更普遍和更稳健的表现。这一核心原则在不同的领域（例如，上下文多样性、理想的困难、实践的可变性）被反复重新发现和重新命名。回顾这个在不同领域制定的基本结果使我们能够识别关键模式，区分不同类型的可变性，讨论不同任务相关维度和不相关维度的作用，