The recognition heuristic is a model that relies on recognition only. This leads to the testable prediction that people who rely on it will ignore strong, contradicting cues (i.e., do not make trade-offs; so-called noncompensatory inferences). In an experiment by Daniel M. Oppenheimer participants were presented with pairs of cities, which included actual cities and fictional cities. Although the recognition heuristic predicts that participants would judge the actual (recognizable) cities to be larger, participants judged the fictional (unrecognizable) cities to be larger, showing that more than recognition can play a role in such inferences.
