Computational modeling of behavioral data allows for a precise characterization of distinct aspects of decision making related to the neurocognitive process of IC. In the Diffusion Model for Conflict (DMC; Ulrich et al., 2015)—drift rate (the amount of information absorbed per time unit) and boundary separation (the amount of information accumulation required for action)—have been found to be differentiable processes implicated in IC. In the current study, we evaluated these DMC parameters in independent community/student samples (Ns=150, 199) completing a flanker task and electroencephalogram recording during a novelty-oddball task to elicit a P300 brain response shown to index IC (Brennan & Baskin-Sommers, 2018). Our results showed that only boundary separation significantly correlated (r=-.20,-.28) with amplitude of the P300 brain response, and this effect replicated across both samples. These findings suggest that computational modeling of behavior is better able to bind together measurement of IC across different measurement modalities.