Generalization, defined as applying limited experiences to novel situations, represents a cornerstone of human intelligence. Our review traces the evolution and continuity of psychological theories of generalization, from its origins in concept learning (categorizing stimuli) and function learning (learning continuous input-output relationships) to domains such as reinforcement learning and latent structure learning. Historically, there have been fierce debates between approaches based on rule-based mechanisms, which rely on explicit hypotheses about environmental structure, and approaches based on similarity-based mechanisms, which leverage comparisons to prior instances. Each approach has unique advantages: Rules support rapid knowledge transfer, while similarity is computationally simple and flexible. Today, these debates have culminated in the development of hybrid models grounded in Bayesian principles, effectively marrying the precision of rules with the flexibility of similarity. The ongoing success of hybrid models not only bridges past dichotomies but also underscores the importance of integrating both rules and similarity for a comprehensive understanding of human generalization.

中文翻译：

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统一的泛化原则：过去、现在和未来


泛化，定义为将有限的经验应用于新情况，代表了人类智能的基石。我们的综述追溯了泛化心理学理论的演变和连续性，从概念学习（对刺激进行分类）和功能学习（学习连续的输入-输出关系）的起源到强化学习和潜在结构学习等领域。从历史上看，基于基于规则的机制的方法（依赖于对环境结构的明确假设）和基于基于相似性的机制（利用与先前实例的比较）的方法之间存在激烈的争论。每种方法都有独特的优势：规则支持快速知识转移，而相似性在计算上简单而灵活。今天，这些辩论以基于贝叶斯原理的混合模型的发展而告终，有效地将规则的精确性与相似性的灵活性结合起来。混合模型的持续成功不仅弥合了过去的二分法，还强调了整合规则和相似性对于全面理解人类泛化的重要性。