Choice Rules Can Affect the Informativeness of Model Comparisons

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