Multinomial Models of the Repetition-Based Truth Effect: Disentangling Processing Fluency and Knowledge

The repetition-based truth effect refers to the phenomenon that repeated statements are more likely judged as ‘true’ than new statements. Fazio et al. (2015) developed two multinomial processing tree (MPT) models to account for truth judgments. 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 processing fluency, which results in a reduced discrimination ability. We study the formal properties of the competing models using receiver operating characteristic (ROC) curves and highlight important auxiliary assumptions and identifiability constraints. In three experiments, we extended the classic truth-effect paradigm to validate and test different model versions by manipulating the base rate of true statements in the judgment phase. The results supported the reflection of the repetition-based truth effect as a reduced discrimination ability. However, assuming different knowledge parameters for true and false statements improved also allows conceptualization of the repetition-based truth effect as a response bias.