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

In recent years, a growing body of research uses Evidence Accumulation Models (EAMs) to study individual differences. 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 may be found in parameter estimates despite non-existing true differences. To overcome this limitation, in this study, we introduce a new method that employs Bayesian hierarchical estimation. In this new method, we set the scale by de facto fixing a population-level hyper-parameter through its priors. As proof of concept, we ran a successful parameter-recovery study using the Linear Ballistic Accumulation model. This result suggests that the new method can be reliably used to study individual differences using EAM’s.