Recognition heuristic

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. 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.

The recognition heuristic is one of the “fast and frugal” heuristics proposed by Gigerenzer et al. 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. 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.

Hilbig et al. state that heuristics are meant to reduce effort and that the recognition heuristic reduces effort in making judgments by relying on one single cue and ignoring other information. In their study they found that the recognition heuristic is more useful in deliberate thought than in intuitive thought. This means it is more useful when thoughts are intentional and not impulsive as opposed to intuitive thought, which is based more on impulse rather than conscious reasoning.

Research shows that the recognition heuristic is relevant to marketing science. Recognition based heuristics help consumers choose which brands to buy in frequently purchased categories.

Ways to Describe and Measure the Recognition Heuristic
Hilbig et al. devised a multinomial processing tree model for the recognition heuristic. A multinomial processing tree model is a simple statistical model often used in cognitive psychology for categorical data. Hilbig et al. claimed that a new model of recognition heuristic use was needed due to the confound between recognition and further knowledge. Goldstein and Gigerenzer claimed that further knowledge about the recognized object is ignored and is therefore insignificant. 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.

The recognition heuristic can also be depicted using neuroimaging techniques. Some researchers have used event-related potentials (ERP) to test psychological mechanisms behind the recognition heuristic. Rosburg, Mecklinger, and Frings used a standard procedure with a city-size comparison task, similar to that used by Goldstein and Gigerenzer. They used ERP and analyzed familiarity-based recognition occurring 300-450 milliseconds after stimulus onset in order to predict the participants’ decisions. Familiarity-based recognition processes are relatively automatic and fast so these results provide evidence that simple heuristics like the recognition heuristic utilize basic cognitive processes.

Smithson explored the "less is more effect" (LIME) with the recognition heuristic. The LIME occurs when a “recognition-dependent agent has a greater probability of choosing the better item than a more knowledgeable agent who recognizes more items.” A mathematical model is used in describing the LIME and Smithson’s study used it and attempted to modify it. The study was meant to mathematically provide an understanding of when the LIME occurs and explain the implications of the results. Goldstein and Gigerenzer describe this model as α and β, where “α is the probability that a correct choice is made on the basis of recognition alone and β is the probability that a correct choice is made when both items are recognized (via additional cues).” LIME occurs if α > β (α > 1/2). The α and β values remain constant and n, the number of recognized items varies.

Support for the Recognition Heuristic
Goldstein and Gigerenzer state that due to its simplicity, the recognition heuristic shows to what degree and in what situations behavior can be predicted. Pachur stated that it is an imperfect model but currently it is still the best model to predict people’s recognition-based inferences. 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. There are some researchers who believe that the recognition heuristic should be investigated using precise tests of the exclusive use of recognition. Pachur 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.

Another study by Pachur suggested that the recognition heuristic is more likely a tool for exploring natural rather than induced recognition (i.e. not provoked in a laboratory setting) when inferences have to be made from memory. In one of his experiments, the results showed that there was a difference between participants in an experimental setting vs. a non-experimental setting.

Tests for the recognition heuristic are mainly focused on noncompensatory processing of recognition. Some researchers have claimed that judgments inconsistent with the use of the recognition heuristic are due to compensatory processing but Gigerenzer and Goldstein state that there is little research on such compensatory models.

Problems with the Recognition Heuristic
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.

Newell & Fernandez 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. The results of their experiments did not support these claims. Newell & Fernandez and Richter & Späth tested the non-compensatory prediction of the recognition heuristic and stated 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."