Measuring the mixture of rule-based and exemplar-based processes in judgment: A hierarchical Bayesian approach


Izydorczyk, David ; Bröder, Arndt



DOI: https://doi.org/10.1037/dec0000195
URL: https://psycnet.apa.org/record/2023-01897-001
Document Type: Article
Year of publication: 2023
The title of a journal, publication series: Decision
Volume: 10
Issue number: 4
Page range: 347-371
Place of publication: Washington, DC
Publishing house: American Psychological Association
ISSN: 2325-9965 , 2325-9973
Publication language: English
Institution: School of Social Sciences > Allgemeine Psychologie (Bröder 2010-)
Subject: 150 Psychology
Abstract: Based on theoretical and empirical considerations, Bröder et al. (2017) proposed the RulEx-J model to quantify the relative contribution of rule- and exemplar-based processes in numerical judgments. In their original paper, a least-squares optimization procedure was used to estimate the model parameters. Despite general evidence for the validity of the model, the authors suggested that a strong bias in favoring the rule module could arise when there is noise in the data. In this article, we present a hierarchical Bayesian implementation of the RulEx-J model with the goal to rectify this problem. In a series of simulation studies, we demonstrate the ability of the hierarchical Bayesian RulEx-J model to recover parameters accurately and to be more robust against noise in the data, compared to a least-squares estimation routine. One further advantage of the hierarchical Bayesian approach is the direct implementation of hypotheses about group differences in the model structure. A validation experiment as well as reanalyses of two experiments from different labs demonstrate the usefulness of the approach for testing hypotheses about processing differences. Further applications for judgment research are discussed.




Dieser Eintrag ist Teil der Universitätsbibliographie.




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