MATHEMATICAL PROPERTIES OF THE BIASED CHOICE AND RELATED MODELS

Abstract

The Biased Choice Model is often used in psychology to analyze asymmetric confusion matrices arising from stimulus identification experiments. In this paper, several mathematical properties of the Biased Choice Model as well as the conditions under which the Biased Choice Model completely fits a data matrix are investigated. Then a new parameter estimation procedure is proposed and an interpretation of the Biased Choice Model in terms of maximum entropy principle is presented. There are many sub-models that impose further structure on the Biased Choice Model. This paper clarifies the relationships among such models and the conditions under which these models fit the data.