Bayesian Analysis of Chronometric Data

Ohio State University Research Foundation -Do Not Use, Columbus OH

Investigators

Abstract

This research will develop accurate and powerful Bayesian modeling and computational methods for the problem of response time (RT) analysis. Although Bayesian techniques are well established in other fields, social scientists very rarely use them because they require a considerable investment in computational resources as well as additional statistical training. The project will develop a number of strategies that will improve the analysis of RT data, including analyses that consider theories about how RTs are produced and new procedures that can help untrained practitioners use Bayesian methods without too much inconvenience. The study also undertakes a program of education and dissemination to improve the overall quality of statistical analyses of RT data. Thus, this research will result in new and better statistical procedures specific for RT (and similar chronometric) data. The importance of this project is considerable. How well a person performs a task is often evaluated by way of how quickly he or she can respond during the task. Measurements of RTs are important for both theoretical and pragmatic reasons. Theoretically, RTs are used to test hypotheses about cognitive structure, the ways in which people use and process information, and how changes in the environment influence human behavior. Pragmatically, RTs are important for evaluating human performance in many areas. They assist machine interface design decisions, such as the optimal way to present information to a pilot or the best place where to put a turn signal lever. They are also used in medicine; diagnoses of some organic brain disorders such as Alzheimer's disease or Attention Deficit Hyperactivity Disorder can be informed by a patient's RTs on certain kinds of tests. Many of the statistical procedures used to test hypotheses based on RTs are suboptimal. They depend on oversimplifying assumptions about RT data that are usually incorrect, and consequently the inferences that are made about RTs collected in different environments can be faulty. This project will result in more accurate characterization of RT data and therefore improved decision making about human capabilities and disease.