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Hierarchical Bayes Models for Response Time Data

Published online by Cambridge University Press:  01 January 2025

Peter F. Craigmile ,
Mario Peruggia  and
Trisha Van Zandt
Peter F. Craigmile*
Affiliation:
Department of Statistics, The Ohio State University
Mario Peruggia
Affiliation:
Department of Statistics, The Ohio State University
Trisha Van Zandt
Affiliation:
Department of Psychology, The Ohio State University
*
Requests for reprints should be sent to Peter F. Craigmile, Department of Statistics, The Ohio State University, 404 Cockins Hall, 1958 Neil Avenue, Columbus, OH 43210, USA. E-mail: pfc@stat.osu.edu
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Abstract

Human response time (RT) data are widely used in experimental 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. Because of the sequential nature of the experiments there are trends (due to learning, fatigue, fluctuations in attentional state, etc.) and serial dependencies in the data. The data also exhibit extreme observations that can be attributed to lapses, intrusions from outside the experiment, and errors occurring during the experiment. Any adequate analysis should account for these features and quantify them accurately. Recognizing that Bayesian hierarchical models are an excellent modeling tool, we focus on the elaboration of a realistic likelihood for the data and on a careful assessment of the quality of fit that it provides. We judge quality of fit in terms of the predictive performance of the model. We demonstrate how simple Bayesian hierarchical models can be built for several RT sequences, differentiating between subject-specific and condition-specific effects.

Keywords

extreme observationslong tailsmixture modelsreaction timessequential dependenciestime series modelingwavelet-based trend
Type
Original Paper
Information
Psychometrika , Volume 75 , Issue 4 , December 2010 , pp. 613 - 632
Copyright
Copyright © 2010 The Psychometric Society

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Footnotes

This work is supported by the National Science Foundation under award numbers BCS-0738059, DMS-0604963, DMS-0605052, SES-0214574 and SES-0437251.

The authors would like to thank and acknowledge assistance in the early stages of this project from Emily Johnson, Dartmouth College (REU student sponsored by the National Science Foundation under award No. DMS-9988006) and Maria Salotti, University of Wisconsin at Stevens Point (REU student sponsored by the Department of Statistics and the College of Mathematical and Physical Sciences of The Ohio State University).

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