Reaction time (RT) is a window into understanding human decision-making processes. The Evidence Accumulation Model (EAM) is a dominant computational framework for modeling RT. However, EAMs, such as the Drift Diffusion Model (DDM), offer statistical descriptions of decision outcomes without detailed algorithms for stimulus encoding or neural mechanisms, thereby omitting the algorithmic and hardware levels in David Marr’s three-level framework (computation, algorithm, and hardware). We suggest that these limitations can be addressed by combining Artificial Neural Networks (ANNs) and evidence accumulation models to simulate the entire decision-making process—from stimulus encoding to decision output. These new models, termed Cognitive Decision Neural Networks, enable in silico modeling of human decision-making, providing a novel approach to understanding cognitive processes.