Modeling the link between the plausibility of statements and the truth effect.

People judge repeated statements as more true than new ones. This repetition-based truth effect is a robust phenomenon when statements are ambiguous. However, previous studies provided conflicting evidence on whether repetition similarly affects truth judgments for plausible and implausible statements. Given the lack of a formal theory explaining the interaction between repetition and plausibility on the truth effect, it is important to develop a model specifying the assumptions regarding this phenomenon. In this study, we propose a Bayesian model that formalizes the simulation-based model by Fazio, Rand, and Pennycook (2019; Psychonomic Bulletin & Review). The model specifies how repetition and plausibility jointly influence the truth effect in light of nonlinear transformations of binary truth judgments. We test our model in a reanalysis of experimental data from two previous studies by computing Bayes factors for four competing model variants. Our findings indicate that, while the truth effect is usually larger for ambiguous than for highly implausible or plausible statements on the probability scale, it can simultaneously be constant for all statements on the probit scale. Hence, the interaction between repetition and plausibility may be explained by a constant additive effect of repetition on a latent probit scale.