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Concept
Recognition heuristic
Summary
The recognition heuristic, originally termed the recognition principle, has been used as a model in the psychology of judgment and decision making and as a heuristic in artificial intelligence. The goal is to make inferences about a criterion that is not directly accessible to the decision maker, based on recognition retrieved from memory. This is possible if recognition of alternatives has relevance to the criterion. For two alternatives, the heuristic is defined as: 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.
The recognition heuristic is part of the "adaptive toolbox" of "fast and frugal" heuristics proposed by Gigerenzer and Goldstein. It is one of the most frugal of these, meaning it is simple or economical. 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. Participants received pairs of city names and had to indicate which city has more inhabitants. 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). Surprisingly, American students scored higher on German cities, while German participants scored higher on American cities, despite only recognizing a fraction of the foreign cities. This has been labeled the "less-is-more effect" and mathematically formalized.
The recognition heuristic is posited as a domain-specific strategy for inference. It is ecologically rational to rely on the recognition heuristic in domains where there is a correlation between the criterion and recognition. The higher the recognition validity α for a given criterion, the more ecologically rational it is to rely on this heuristic and the more likely people will rely on it.
Official source
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