The recognition heuristic has been used as a model in the psychology of judgment and decision making and as a heuristic in artificial intelligence. It states:
“ If one of two objects is recognized and the other is not, then infer that the recognized object has the higher value with respect to the criterion. ”
Daniel Goldstein and Gerd Gigerenzer quizzed students in Germany and the United States on the populations of both German and American cities. Each group scored slightly higher on the foreign cities despite only recognizing a fraction of them. The experimenters theorized that the students would be able to attain such high accuracy on foreign cities if they relied on the heuristic and particular conditions, concerning cue validity for example, were met. They posited the heuristic as a domain specific strategy for inference.
In later research, Daniel M. Oppenheimer presented participants pairs of cities made from 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.
Research by Newell & Fernandez and Richter & Späth tests the non-compensatory prediction of the recognition heuristic and states that “recognition information is not used in an all-or-none fashion but is integrated with other types of knowledge in judgment and decision making.”