Modeling Trends, Dependence, and Tail Structure in Sequential Response Time Data

Ohio State University, The, Columbus OH

Investigators

Abstract

Repeated measurements taken on the same person are usually highly correlated and also influenced by changes in that individual's mental or physical state over time. Accurate conclusions about why a person's performance changes over time, as well as accurate hypotheses about how tasks are performed, require non-Gaussian time series models that can separate changes in performance due to changes in task conditions from changes in a person's mental or physical state. At present, techniques available for the analysis of human performance data are limited. In particular, models for repeated response time measurements usually fail to consider that the measurements are correlated and that overall speed may naturally fluctuate over time, even when all other task conditions remain the same. This project will address this problem by developing realistic models for response time series that provide a basis for simultaneously explaining the generating mechanism as well as describing trends due to changes in a person's state over time. An important component of this project will be the development of new computational methods for effectively fitting these statistical models as well as evaluating the model fits. Response time measurements are important because how well people perform tasks is often measured, at least in part, by how quickly they can accomplish those tasks. In many situations, tasks are repetitive, requiring decisions and actions that recur many times within a fixed period of time. Such tasks, while commonly performed in psychological laboratories, are also used in a number of real-world settings such as assembly line work, during standardized testing, and in athletics. This project will result not only in better techniques of analysis for these kinds of measurements, but also in the development of more accurate and realistic models of how people perform repetitive tasks. A diverse cross-section of students (psychological and statistical) will be mentored in methods that both bridge and strengthen their two disciplines. All data collected and general-purpose software developed under this award will be made available (via the World Wide Web) to the research community.