PuSH - Publikationsserver des Helmholtz Zentrums München

Export: TXT Text RIS Endnote (RIS) BIB BibTeX
Meder, B.* ; Schulz, E.

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

Annu. Rev. Psychol. 76, 275-302 (2025)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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.
Impact Factor
Scopus SNIP
Altmetric
29.400
10.246
Tags
Anmerkungen
Besondere Publikation
Auf Hompepage verbergern

Zusatzinfos bearbeiten
Eigene Tags bearbeiten
Privat
Eigene Anmerkung bearbeiten
Privat
Auf Publikationslisten für
Homepage nicht anzeigen
Als besondere Publikation
markieren
Publikationstyp Artikel: Journalartikel
Dokumenttyp Review
Schlagwörter generalization; concept learning; function learning; value approximation; reinforcement learning; structure induction; Cognitive Maps; Universal Law; Similarity; Knowledge; Humans; Model; Rules; Representations; Categorization; Extrapolation
Sprache englisch
Veröffentlichungsjahr 2025
Prepublished im Jahr 2024
HGF-Berichtsjahr 2024
ISSN (print) / ISBN 0066-4308
e-ISSN 1545-2085
Zeitschrift Annual Review of Psychology
Quellenangaben Band: 76, Heft: 1, Seiten: 275-302 Artikelnummer: , Supplement: ,
Verlag Annual Reviews
Verlagsort 4139 El Camino Way, Po Box 10139, Palo Alto, Ca 94303-0139 Usa
Begutachtungsstatus Peer reviewed
Institut(e) Human-Centered AI (HCA)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-540011-001
DOI 10.1146/annurev-psych-021524-110810
WOS ID 001401767200012
Scopus ID 85216135882
PubMed ID 39413252
Erfassungsdatum 2025-01-30
[➜Korrektur melden]