An Instance-Based Learning Model of Stimulus-Response Compatibility Effects in Mixed Location-Relevant and Location-Irrelevant Tasks

Varun Dutt, Carnegie Mellon University
Motonori Yamaguchi, Purdue University
Cleotilde Gonzalez, Carnegie Mellon University
Robert W. Proctor, Purdue University

In A. Howes, D. Peebles, R. Cooper (Eds.), 9th International Conference on Cognitive Modeling – ICCM2009, Manchester, UK.

Abstract or Description

This paper presents a cognitive model of stimulus-response compatibility (SRC) effects for a situation in which location-relevant and location-irrelevant tasks are intermixed within a single trial block. We provide a computational explanation of the cognitive processing involved in the mixed-task condition. The model is based on the Instance-Based Learning Theory, developed originally to explain decision making in dynamic tasks, and based on the ACT-R theory of cognition. The comparison of the model's outputs to human data demonstrates high similarity, and the model offers an explanation for sequential modulations of the SRC effects observed when compatible and incompatible trials repeat or switch. Several possibilities to apply this model to novel tasks are discussed.