One Standard for All: Uniform Scale for Comparing Individuals and Groups in Hierarchical Bayesian Evidence Accumulation Modeling

In recent years, a growing body of research uses Evidence Accumulation Models (EAMs) to study individual differences and group effects. This endeavor is challenging because fitting EAMs requires constraining one of the EAM parameters to be equal for all participants, which makes a strong and possibly unlikely assumption. Moreover, if this assumption is violated, differences or lack thereof may be wrongly found. To overcome this limitation, in this study, we introduce a new method that was originally suggested by van Maanen Miletić (2021), which employs Bayesian hierarchical estimation. In this new method, we set the scale at the population level, thereby allowing for individual and group differences, which is realized by de facto fixing a population-level hyper-parameter through its priors. As proof of concept, we ran two successful parameter recovery studies using the Linear Ballistic Accumulation model. The results suggest that the new method can be reliably used to study individual and group differences using EAMs. We further show a case in which the new method reveals the true group differences whereas the classic method wrongly detects differences that are truly absent.