Distinguishing Between Short-Term Dependence and Trend in Bayesian Hierarchical Models of Response Time Data

• Activity Number: 326
• Type: Topic Contributed
• Date/Time: Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Trisha Van Zandt*+ and Peter F Craigmile and Mario Peruggia
• Companies: The Ohio State University and The Ohio State University and The Ohio State University
• Keywords: Response time data ; Cognitive modeling ; Time series analysis

Abstract

Response time (RT) measurements are the basis for theory construction and modeling in much of cognitive psychology and human performance research. Analyses of these data usually follow traditional, frequentist inferential approaches, in which the data are assumed to be composed of independent and identically-distributed observations from a theoretically interesting population. Craigmile, Peruggia and Van Zandt (2011) recently presented a general theoretical framework that accommodates the trial-by-trial dependencies observed in RT data. In this talk, we will apply this framework to some new data collected with the goal of separating short-term dependence from mean fluctuations across trials. We also consider the stochastic process underlying the RT-generating mechanism and discuss how our analyses can elucidate the components of this process.