Measuring the Mixture of Rule-Based and Exemplar-Based Processes in Judgment: A Hierarchical Bayesian Approach

Based on theoretical and empirical considerations, Broder 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 article, a least-squares (LS) 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 an LS 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.

Automated summary

The study presents a hierarchical Bayesian implementation of the RulEx-J model for quantifying the relative contribution of rule- and exemplar-based processes in numerical judgments. Simulation studies and a validation experiment demonstrate the accuracy, robustness, and practicality of the model.