Stimulus information and sequential dependencies in magnitude estimation and cross-modality matching

Abstract

Descriptive models of magnitude estimation and cross-modality matching derived from two different approaches to psychophysical judgment, the response ratio hypothesis and the fuzzy judgment approach, are compared. The two approaches emphasize different bodies of facts but both attempt to account for sequential dependencies in psychophysical judgments. Both models suggest a hierarchical multiple linear regression model for such data. Some of the predictions of the models are explored in the context of two experiments in which the amount of stimulus information available to subjects in magnitude estimation and cross-modality matching tasks is varied. The fuzzy judgment approach generally does better in explaining the form of such data.