The goal is to make inferences about a criterion that is not directly accessible to the decision maker, based on recognition retrieved from memory.
[3][4][5] In their original experiment, Daniel Goldstein and Gerd Gigerenzer quizzed students in Germany and the United States on the populations of both German and American cities.
In this and similar experiments, the recognition heuristic typically describes about 80–90% of participants' choices, in cases where they recognize one but not the other object (see criticism of this measure below).
Domains in which the recognition heuristic was successfully applied include the prediction of geographical properties (such as the size of cities, mountains, etc.
A number of studies have shown that people do not automatically use the recognition heuristic when it can be applied, but evaluate its ecological validity.
A functional magnetic resonance imaging study tested whether the two processes, recognition and evaluation, can be separated on a neural basis.
They used ERP and analyzed familiarity-based recognition occurring 300-450 milliseconds after stimulus onset in order to predict the participants’ decisions.
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).
[22] Newell & Fernandez[4] performed two experiments to try to test the claims that the recognition heuristic is distinguished from availability and fluency through binary treatment of information and inconsequentiality of further knowledge.
"[23] A reanalysis of these studies at an individual level, however, showed that typically about half of the participants consistently followed the recognition heuristic in every single trial, even in the presence of up to three contradicting cues.
The multinomial processing tree model was shown to be effective and Hilbig et al. claimed that it provided an unbiased measure of the recognition heuristic.
He believes that precise tests have a limited value basically because certain aspects of the recognition heuristic are often ignored and so the results could be inconsequential or misleading.
[19] Goldstein and Gigerenzer[31] state that due to its simplicity, the recognition heuristic shows to what degree and in what situations behavior can be predicted.
Some researchers suggest that the idea of the recognition heuristic should be retired but Pachur believes that a different approach should be taken in testing it.
Using an adversarial collaboration approach, three special issues of the open access journal Judgment and Decision Making have been devoted to unravel the support for and problems with the recognition heuristic, providing the most recent and comprehensive synopsis of the epistemic status quo.