Fitting the ANCHOR model to individual data: A case study in Bayesian methodology

This paper presents a memory-based model of direct psychophysical scaling. The model is based on an extension of the cognitive architecture ACT-R and uses anchors that serve as prototypes for the stimuli classified within each response category. Using the ANCHOR model as a specific example, a general Bayesian framework is introduced. It provides principled methods for making data-based inferences about models of this kind. The internal representations in the model are analyzed as hidden variables that are constructed from the stimuli according to probabilistic representation rules. In turn, the hidden representations produce overt responses via probabilistic performance rules. Incremental learning rules transform the model into a dynamic system. A parameter-fitting algorithm is formulated and tested on experimental data.