The authors tested a model of category effects on stimulus judgment. The model holds that the goal of stimulus judgment is to achieve high accuracy. For this reason, people place inexactly represented stimuli in the context of prior information, captured in categories, combining inexact fine-grain stimulus values with prior (category) information. This process can be likened to a Bayesian statistical procedure designed to maximize the average accuracy of estimation. If people follow the proposed procedure to maximize accuracy, their estimates should be affected by the distribution of instances in a category. In the present experiments, participants reproduced one-dimensional stimuli. Different prior distributions were presented. The experiments verified that people's stimulus estimates are affected by variations in a prior distribution in such a manner as to increase the accuracy of their stimulus reproductions.
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