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Description: In cognitive modeling it is often necessary to complement a core model by a choice rule to derive testable predictions about choice behavior. Researchers can typically choose from a variety of choice rules for a single core model. This article demonstrates that seemingly subtle differences in choice rules' assumptions about how choice consistency relates to underlying preferences can affect the distinguishability of competing models' predictions and, as a consequence, the informativeness of model comparisons. This is demonstrated in a series of simulations and model comparisons between two prominent core models of decision making under risk: expected utility theory (EUT) and cumulative prospect theory (CPT). The results show that, all else being equal, and relative to choice rules which assume a constant level of consistency (trembling hand or deterministic), using choice rules which assume that choice consistency depends on strength of preference (logit or probit) to derive predictions can substantially increase the informativeness of model comparisons (measured using Bayes factors). This is because the latter make it possible to derive predictions that are more readily distinguishable. Overall, the findings reveal that choice rules, although often regarded as auxiliary assumptions, can play a crucial role in model comparisons. More generally, the analyses highlight the importance of testing the robustness of inferences in cognitive modeling with respect to seemingly secondary assumptions, and they exemplify how this can be achieved.