Journal of Mathematical Psychology 45, 603634 (2001) Extending General Processing Tree Models to Analyze Reaction Time Experiments Xiangen Hu The University of Memphis General processing tree (GPT) models are usually used to analyze categorical data collected in psychological experiments. Such models assume functional relations between probabilities of the observed behavior categories and the unobservable choice probabilities involved in a cognitive task. This paper extends GPT models for categorical data to the analysis of continuous data in a class of response time (RT) experiments in cognitive psychology. Suppose that a cognitive task involves several discrete processing stages and both accuracy (categorical) and latency (continuous) measures are obtained for each of the response categories. Furthermore, suppose that the task can be modeled by a GPT model that assumes serialization among the stages. The observed latencies of the response categories are functions of the choice probabilities and processing times (PT) at each of the processing stages. The functional relations are determined by the processing structure of the task. A general framework is presented and it is applied to a set of data obtained from a source monitoring experiment.  2001 Academic Press INTRODUCTION Dependent measures for a cognitive task can be classified into two categories, discrete measures that record the types of behavior observed (e.g., accuracy data) and continuous measures that record the degree or latency of the observed behavior (e.g., RT (response time) data). Cognitive psychologists have a long tradition of analyzing RT data to infer mechanisms of the unobservable mental processes. For certain paradigms, such as the simple detection paradigm and the discrimination paradigm, (see Luce, 1986, for details), mathematical models have been developed doi:10.1006jmps.2000.1340, available online at http:www.idealibrary.com on 603 0022-249601 35.00 Copyright  2001 by Academic Press All rights of reproduction in any form reserved. This research was supported by Grant BNS-8910552 from the National Science Foundation to William H. Batchelder (University of California, Irvine) and David M. Riefer (California State Univer- sity, San Bernardino). The original idea of this paper was evolved from a personal communication between the author and John Kounios. The author thanks Dr. William H. Batchelder, who provided instructional comments to the author about this project. The author also thanks Drs. Barbara Dosher, William Dwyer, John Kounios, David LaBerge, R. Duncan Luce, William Marks, Louis Narens, David M. Riefer, and William Shadish and several anonymous reviewers for their helpful comments. Address correspondence and requests for reprints and computer programs to Dr. Xiangen Hu, Department of Psychology, The University of Memphis, Memphis, TN 38152. E-mail: xhu memphis.edu.