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 origins in concept learning (categorizing stimuli) and function learning (learning continuous input-output relation- ships), to domains such as reinforcement learning and latent structure learning. Historically, there have been fierce debates between rule-based mechanisms–using explicit hypotheses about environmental structure–and similarity-based mechanisms–leveraging 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.