A neural network instantiation of Decision Field Theory (Busemeyer & Townsend, 1993) for multiple choice decision tasks is presented. First it is shown how under certain situations this dynamic model reduces to two well-known static models of choice. Next, model simulations of two well-known findings in multiple choice decision literature are presented. The first is the effect of similarity (Tversky, 1972). Several choice models also predict this effect. However, a more challenging effect, which is not predicted by numerous static choice models is the decoy effect (Huber, Payne, & Puto, 1982). Simulations show that the current model predicts this finding by using the concept of lateral inhibition. Finally, predictions of the model are made about the dynamic nature of the deliberation process in the decoy effect. If empirical results are found to be in agreement with this prediction, it would be a strong test of the model.