Inductive reasoning model

• Verfasst von: Krueger, Joachim I. [VerfasserIn]
• Verfasst von: Grüning, David [VerfasserIn]
• Verfasst von: Heck, Patrick [VerfasserIn]
• Verfasst von: Freestone, David [VerfasserIn]
• Titel: Inductive reasoning model
• Verf.angabe: Joachim I. Krueger, David J. Grüning, Patrick Heck, and David Freestone
• E-Jahr: 2024
• Jahr: January-March 2024
• Umfang: 15 S.
• Illustrationen: Illustrationen
• Fussnoten: Online veröffentlicht: 31. Juli 2024 ; Gesehen am 05.05.2025
• Titel Quelle: Enthalten in: Psychological inquiry
• Ort Quelle: New York, NY [u.a.] : Psychology Press, Taylor & Francis Group, 1990
• Jahr Quelle: 2024
• Band/Heft Quelle: 35(2024), 1, Seite 11-25
• ISSN Quelle: 1532-7965
• DOI: doi:10.1080/1047840X.2024.2366766
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Inductive reasoning
• Sach-SW: model
• Sach-SW: social perception
• Sach-SW: theoretical
• K10plus-PPN: 1924630613

Abstract

We introduce the Inductive Reasoning Model (IRM) as a comprehensive platform for the study of several phenomena central to self- and social perception. Going beyond the traditional phenomenon-focused research strategy, the model shows how to generate point-specific hypotheses about the size of individual effects and how to predict the interrelations among phenomena of interest. The model points to additional psychological processes at play when outputs cannot be accounted for within the confines of the IRM alone. The IRM is parsimonious in its assumptions and generative in its predictions. Using two empirically-based inputs, namely, the positivity of a person’s self-image and the strength of social projection, the model predicts the direction and extent of four higher-order phenomena: intergroup accentuation, self-enhancement, ingroup favoritism, and differential accuracy. The model affords precise predictions pertaining to the relationships among these phenomena. Critically, alternative conceptions of social perceptions are not rendered irrelevant. Researchers can ask if the IRM over- or underpredicts social-perceptual phenomena in contexts of interest, and if alternative models can explain the differences.