Multinomial models of the repetition-based truth effect: Shift in response bias or reduced discrimination ability?

The repetition-based truth effect refers to the phenomenon that repeated statements are more likely judged to be true than new statements. So far, it is not fully clear whether the truth effect reflects a reduced ability of discriminating between true and false statements or a shift in response bias towards judging statements to be true. To address this question, we use two multinomial processing tree (MPT) models for truth judgments proposed by Fazio et al. (2015). The knowledge-conditional model assumes that repetition leads to a shift in response bias conditional on a lack of knowledge. In contrast, the fluency-conditional model assumes that knowledge is used only when not relying on a general, fluency-induced response bias which results in a reduced discrimination ability. To compare the models, we extended the classical truth-effect paradigm. In three online experiments, we manipulated prior knowledge and response tendencies by informing participants about the base rate of true statements in the judgment phase. Besides testing the two models empirically, we illustrate differences in the predicted response patterns using receiver operating characteristic (ROC) curves and highlight important auxiliary assumptions as well as identifiability constraints.