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Abstract:
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 stim- uli) and function learning (learning continuous input-output relation- ships), to domains such as reinforcement learning and latent struc- ture 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 sup- port rapid knowledge transfer, while similarity is computationally sim- ple and flexible. Today, these debates have culminated in the devel- opment of hybrid models grounded in Bayesian principles, effectively marrying the precision of rules with the flexibility of similarity. The on- going 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.
