Cognitive process modeling of context independence violations in the ABCD Study stop-signal task

The stop-signal paradigm is a cornerstone of research on response inhibition and there is a rich history of formal cognitive models that explain individuals’ behavior on the task. It is therefore not surprising that this task has been included as the primary measure of response inhibition in the Adolescent Brain Cognitive Development (ABCD) Study, a longitudinal neuroimaging study of unprecedented scale that is in the process of following over 11,000 youth from middle childhood though age 20. However, the ABCD Study's unique task design involves a visual stop-signal that replaces the choice stimulus, creating a masking effect that impedes information processing on trials with short stop-signal delays. As this design feature violates the critical "context independence" assumption shared by most current methods for estimating stop-signal reaction time (SSRT), some experts have called for the task to be changed or for previously collected ABCD data to be used with caution. We present a cognitive process modeling framework that provides a parsimonious explanation for the impact of this design feature by combining prior insights on the effects of visual masking on choice evidence accumulation with recent “hybrid” racing-diffusion ex-Gaussian (RDEX) approaches to modeling the stop-signal task. We demonstrate that the resulting model, RDEX-ABCD, successfully accounts for key behavioral trends in ABCD data, including the inhibition function and the impact of context independence violations on choice accuracy rates. Simulation studies using this model suggest that failing to account for context independence violations in the ABCD design can lead to erroneous inferences in several realistic scenarios. However, RDEX-ABCD effectively addresses these violations and can be used to accurately measure the timing of response inhibition processes and additional mechanistic parameters of interest. More broadly, results demonstrate the feasibility of addressing context independence violations by building process-based explanations for them into models of the stop-signal task.