JSM 2004 - Toronto

Abstract #302101

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Activity Number: 299
Type: Topic Contributed
Date/Time: Wednesday, August 11, 2004 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract - #302101
Title: Modeling Dependence in Response Time Data
Author(s): Mario Peruggia*+ and Peter F. Craigmile and Trisha Van Zandt
Companies: Ohio State University and Ohio State University and Ohio State University
Address: Dept. of Statistics, 404 CH, Columbus, OH, 43210-1247,
Keywords: probability inverse transformation ; ARMA models ; Bayesian models ; ex-Gaussian distribution ; long-range dependence
Abstract:

Human response time data are widely used in cognitive psychology to evaluate theories of mental processing. Typically, the data constitute the times taken by a subject to react to a succession of stimuli under varying experimental conditions. The sequential nature of the experiments induces serial dependencies in the data that are often ignored in the analysis. We use data from a variety of experiments to explore in detail the nature of these dependencies and compare several alternative modeling strategies. We employ a Bayesian framework to incorporate known characteristics of RT data into our models. Our approach allows us to investigate explicitly the effects of experimental covariates and of outlying observations on the types of dependencies that might arise.

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