STATISTICAL MIMICKING OF REACTION-TIME DATA - SINGLE-PROCESS MODELS, PARAMETER VARIABILITY, AND MIXTURES

Statistical mimicking issues involving reaction time measures are intr oduced and discussed in this article. Often, discussions of mimicking have concerned the question of the serial versus parallel processing o f inputs to the cognitive system. We will demonstrate that there are s everal alternative structures that mimic various existing models in th e literature. In particular, single-process models have been neglected in this area When parameter variability is incorporated into single-p rocess models, resulting in discrete or continuous mixtures of reactio n time distributions, the observed reaction time distribution alone is no longer as useful in allowing inferences to be made about the archi tecture of the process that produced it. Many of the issues are raised explicitly in examination of four different case studies of mimicking . Rather than casting a shadow over the use of quantitative methods in testing models of cognitive processes, these examples emphasize the i mportance of examining reaction time data armed with the tools of quan titative analysis, the importance of collecting data from the context of specific process models, and the importance of expanding the databa se to include other dependent measures.