Recognition models evaluated: A commentary on Keren and Baggen

In a recent paper (1981), Keren and Baggen proposed two new models for alphanumeric confusion data, based on Tversky’s (1977) feature model of similarity, and compared them with Luce’s (1963) biased choice model. On the basis of their data, they concluded that, although the choice model fit slightly better, their models were to be preferred on grounds of parsimony and interpretability. It is shown here that both of these models are special cases of the Luce model, so that the general Luce model will necessarily fit better. This leads to considerable reinterpretation of Keren and Baggen’s conclusions. Finally, better methods of estimating parameters and evaluating goodness-of-fit are suggested, taking advantage of this relation between the models.