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Thalmann, M. ; Schulz, E.

How can we characterize human generalization and distinguish it from generalization in machines?

Curr. Dir. Psychol., DOI: 10.1177/09637214251336212 (2025)
Verlagsversion DOI
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
People appear to excel at generalization: They require little experience to generalize their knowledge to new situations. But can we confidently make such a conclusion? To make progress toward a better understanding, we characterize human generalization by introducing three proposed cognitive mechanisms allowing people to generalize: applying simple rules, judging new objects by considering their similarity to previously encountered objects, and applying abstract rules. We highlight the systematicity with which people use these three mechanisms by, perhaps surprisingly, focusing on failures of generalization. These failures show that people prefer simple ways to generalize, even when simple is not ideal. Together, these results can be subsumed under two proposed stages: First, people infer what aspects of an environment are task relevant, and second, while repeatedly carrying out the task, the mental representations required to solve the task change. In this article, we compare humans to contemporary AI systems. This comparison shows that AI systems use the same generalization mechanisms as humans. However, they differ from humans in the way they abstract patterns from observations and apply these patterns to previously unknown objects—often resulting in generalization performance that is superior to, but sometimes inferior to, that of humans.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter generalization; mental representations; cognitive processes; memory; abstraction; Category; Similarity
ISSN (print) / ISBN 0963-7214
e-ISSN 1467-8721
Zeitschrift Current Directions in Psychological Science
Verlag Sage
Verlagsort 2455 Teller Rd, Thousand Oaks, Ca 91320 Usa
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of AI for Health (AIH)