Identification model based on the maximum information entropy principle

A new theoretical approach to stimulus identification is proposed through a probabilistic multidimensional model based on the maximum information entr opy principle. The approach enables us to derive the multidimensional scali ng (MDS) choice model, without appealing to Luce's choice rule and without defining a similarity function. It also clarifies the relationship between the MDS choice model and the optimal version of the identification model ba sed on Ashby's general recognition theory; it is shown theoretically that t he identification model derived from the new approach includes these two mo dels as special cases. Finally, as an application of our approach, a model of similarity judgement is proposed and compared with Ashby's extended simi larity model. (C) 2001 Academic press.