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Meder, B.* ; Schulz, E.

Unifying principles of generalization: Past, present, and future.

Annu. Rev. Psychol. 76, 275-302 (2025)
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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.
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Publication type Article: Journal article
Document type Review
Corresponding Author
Keywords generalization; concept learning; function learning; value approximation; reinforcement learning; structure induction; Cognitive Maps; Universal Law; Similarity; Knowledge; Humans; Model; Rules; Representations; Categorization; Extrapolation
ISSN (print) / ISBN 0066-4308
e-ISSN 1545-2085
Journal Annual Review of Psychology
Quellenangaben Volume: 76, Issue: 1, Pages: 275-302 Article Number: , Supplement: ,
Publisher Annual Reviews
Publishing Place 4139 El Camino Way, Po Box 10139, Palo Alto, Ca 94303-0139 Usa
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of AI for Health (AIH)