One-Reason Decision Making Unveiled: A Measurement Model of the Recognition Heuristic

The fast-and-frugal recognition heuristic (RH) theory provides a precise process description of comparative judgments. It claims that, in suitable domains, judgments between pairs of objects are based on recognition alone, whereas further knowledge is ignored. However, due to the confound between recognition and further knowledge, previous research lacked an unbiased measure of RH use. Also, model comparisons have not been based on goodness-of-fit and model complexity as criteria. To overcome both limitation, we introduce and test a multinomial processing tree model showing that it fits empirical data and provides an unbiased measure of RH use. Analyses of 8 data sets reveal that the RH alone cannot account for the data, not even when it is implemented in a probabilistic way. That is, information integration beyond recognition plays a vital role and cannot merely account for empirical data better due to model flexibility. Also, we present several validations of the central model parameter and provide demonstrations of how the model can be applied to study the less-is-more effect as well as determinants of (and individual differences in) RH use.