This project investigated the application of standard evidence accumulation models (EAMs) to self-control in decision-making. We analyzed data from four cognitive conflict tasks drawn from a larger battery developed by Eisenberg et al. (2019). Although previous studies have applied diffusion models to these tasks, they often did not compare alternative model types or parameterizations. Furthermore, more complex EAMs have also been used, but their lack of tractable likelihoods limits their practical utility. To address these gaps, we designed, estimated, and systematically compared five standard EAMs, including the Diffusion Decision Model and several race models, and different parameterizations using Bayesian estimation and model selection criteria. Our goal was to identify the best-fitting model and parameterization for each task and to achieve an accurate fit to the observed data. We found that race models provided a good fit to both reaction time and accuracy patterns for all tasks, although the particular model parametrizations leading to good fit differed between tasks.. This approach provides a practical methodology for measuring the psychological mechanisms involved in a variety of commonly used decision-conflict tasks.