| Abstract: | 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. Copyright 2001 Academic Press. |
