ABSTRACT
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.
