Density versus feature weights as predictors of visual identifications: comment on Appelman and Mayzner

Appleman and Mayzner's application of the distance-density model to confusion data is compared with Keren and Baggen's application of the feature-matching model. In both applications, the distinctive features of two stimuli are predictors of the number of confusion errors. However, the models differ in that the feature-matching model assigns weights to the features and assumes that the shared feature weights also affect the probability of confusion. In contrast, the distance-density model assumes that the number of confusions between two stimuli is affected by the number of stimuli in the entire stimulus set that are similar to the two stimuli (density). The two models are compared in the context of a set of digit identification data.