In this article, some additive models of behavioral measures are defined, and distributional tests of them are proposed. Major theoretical results include (a) conditions for additivity of components to predict the highest level of dominance in a model-free stochastic dominance hierarchy, (b) methods of identifying the shape of the hazard rate function of an added component from certain relationships among the observable density and distribution functions, and (c) effects of stochastic dependence between components on the distributional tests. Feasibility and usefulness of the methods are demonstrated by application to choice RT and ratings experiments.