Abstract
According to the recognition heuristic, people infer that an object they recognize has a higher value on a criterion of interest than an object they do not recognize. This model has been analyzed mathematically and conditions for the less-is-more effect -- where recognizing fewer objects increases inferential accuracy -- have been derived. We propose an extension of the heuristic that incorporates the empirical finding that people recognize some objects for which they believe they have low criterion value. We call these recognized objects unsatisfying, in contrast to recognized-satisfying objects which the inference-maker believes to have a high criterion value. We analyze the model and provide a number of results: First, we derive closed-form expressions for the parameters of our model, as well as for the parameters of the original model, in terms of the distributions of recognition and other cues over the objects. Second, we use the expressions to analyze the less-is-more effect for both models. Third, we use the expressions to calculate and compare the accuracy of the two models and derive conditions under which the models equal or surpass the accuracy of random inference. Our results are general and can thus be linked to any model of recognition-based inference.
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