Exploring the Feasibility of Across-Trial Variability in Boundary Separation

The Diffusion Decision Model (DDM) is an effective tool for studying human decision-making across various domains (Krajbich, 2019; Ratcliff et al., 2016). In practice, including across-trial variability parameters allows the model to account for a variety of behavioral patterns, including fast errors, slow errors, and crossover effects (Ratcliff & Rouder, 1998; Ratcliff & Tuerlinckx, 2002; Van Zandt & Ratcliff, 1995). In this study, we are interested in using the DDM to fit data from many participants but with few observations per participant. By doing so, the across-trial variability parameters in the original model then become across-trial participant parameters. Though typically, across-trial variability has been estimated for the drift rate (sv), starting point (sz), and non-decision time (st) parameters (Boehm et al., 2018; Ratcliff & Childers, 2015; Ratcliff & Tuerlinckx, 2002). However, we know different participants have different boundary separation parameter values. To account for that, we modify the DDM to include across-trial variability in boundary separation (sa). Through simulation, we demonstrate that across-trial variability in boundary separation can produce distinct patterns, including fast errors, a reduction in the fastest response quantiles, and an increase in the slowest response quantiles. We next demonstrate the parameter's identifiability by successfully recovering across-trial variability in boundary separation for an extensive set of parameters. Ultimately, this study provides initial support for the feasibility of using across-trial variability in boundary separation to examine group-level parameters using a few observations per participant.