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A Joint Modeling Approach for Reaction Time and Accuracy in Psycholinguistic Experiments

Published online by Cambridge University Press:  01 January 2025

T. Loeys ,
Y. Rosseel  and
K. Baten
T. Loeys*
Affiliation:
Ghent University
Y. Rosseel
Affiliation:
Ghent University
K. Baten
Affiliation:
Ghent University
*
Requests for reprints should be sent to T. Loeys, Ghent University, Ghent, Belgium. E-mail: tom.loeys@ugent.be
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Abstract

In the psycholinguistic literature, reaction times and accuracy can be analyzed separately using mixed (logistic) effects models with crossed random effects for item and subject. Given the potential correlation between these two outcomes, a joint model for the reaction time and accuracy may provide further insight. In this paper, a Bayesian hierarchical framework is proposed that allows estimation of the correlation between time intensity and difficulty at the item level, and between speed and ability at the subject level. The framework is shown to be flexible in that reaction times can follow a (log-) normal or (shifted) Weibull distribution. A simulation study reveals the reduction in bias gains possible when using joint models, and an analysis of an example from a Dutch–English word recognition study illustrates the proposed method.

Keywords

joint modelingBayesian estimationreaction timepsycholinguistic experiment
Type
Original Paper
Information
Psychometrika , Volume 76 , Issue 3 , July 2011 , pp. 487 - 503
Copyright
Copyright © 2011 The Psychometric Society

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References

Baayen, R.H., Davidson, D.J., Bates, D.M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of Memory and Language, 59, 390–412.CrossRefGoogle Scholar
Baten, K., Hofman, F., & Loeys, T. (2011, in press). Cross-lingual activation in bilingual sentence processing: the role of word class meaning. Bilingualism: Language and Cognition.CrossRefGoogle Scholar
Bates, D.M., & Sarkar, D. (2007). lme4: linear mixed-effects models using S4 classes (version 0.9975-12) [R software package]. Retrieved from http://CRAN.R-project.org/.Google Scholar
Bloxom, B. (1985). Considerations in psychometric modeling of response time. Psychometrika, 50, 383–397.CrossRefGoogle Scholar
Carlin, B.P., Louis, T.A. (2000). Bayes and empirical Bayes methods of data analysis, (2nd ed.). Boca Raton: Chapman and Hall.CrossRefGoogle Scholar
Clark, H.H. (1973). The language-as-fixed-effect fallacy: a critique of language statistics in psychological research. Journal of Verbal Learning and Verbal Behavior, 12, 335–359.CrossRefGoogle Scholar
Entink, R.H.K., Hornke, L.F., Kuhn, J.-T., Fox, J.-P. (2009). Evaluating cognitive theory: a joint modeling approach using responses and response times. Psychological Methods, 14, 54–75.CrossRefGoogle Scholar
Farrell, S., Ludwig, C.J.H. (2008). Bayesian and maximum likelihood estimation of hierarchical response time models. Psychonomic Bulletin & Review, 15, 1209–1217.CrossRefGoogle ScholarPubMed
Jaeger, F.T. (2008). Categorical data analysis: away from ANOVAs (transformation or not) and towards logit mixed models. Journal of Memory and Language, 59, 434–446.CrossRefGoogle ScholarPubMed
Lunn, D.J., Thomas, A., Best, N., Spiegelhalter, D. (2000). WinBUGS—a Bayesian modelling framework: concepts, structure and extensibility. Statistics and Computing, 10, 325–337.CrossRefGoogle Scholar
Ntzoufras, I. (2009). Bayesian modeling using WinBUGS, Hoboken: Wiley.CrossRefGoogle Scholar
Raaijmakers, J.G.W., Schrijnemakers, J.M.C., Gremmen, F. (1999). How to deal with “The language-as-fixed-effect fallacy”: common misconceptions and alternative solutions. Journal of Memory and Language, 41, 416–426.CrossRefGoogle Scholar
Raudenbush, S.W. (1993). A crossed random effects model for unbalanced data with applications in cross-sectional and longitudinal research. Journal of Educational Statistics, 18, 321–349.CrossRefGoogle Scholar
R Development Core Team (2009). R: a language and environment for statistical computing. Retrieved from http://www.R-project.org.Google Scholar
Rouder, J., Sun, D., Speckmanm, P.L., Lu, J., Zhou, D. (2003). A hierarchical Bayesian statistical framework for response time distributions. Psychometrika, 68, 589–606.CrossRefGoogle Scholar
Rouder, J., Lu, J. (2005). An introduction to Bayesian hierarchical models with an application in theory of signal detection. Psychonomic Bulletin & Review, 12, 573–604.CrossRefGoogle ScholarPubMed
Rouder, J., Lu, J., Sun, D., Speckman, P., Morey, R., Naveh-Benjamin, M. (2007). Signal detection with random participant and item effects. Psychometrika, 72(4), 621–642.CrossRefGoogle Scholar
Rouder, J., Lu, J., Morey, R., Sun, D., Speckman, P. (2008). A hierarchical process-dissociation model. Journal of Experimental Psychology: General, 137(2), 370–389.CrossRefGoogle ScholarPubMed
Rouder, J., Tuerlinckx, F., Speckman, P., Lu, J., Gomez, P. (2008). A hierarchical approach for fitting curves to response time measurements. Psychonomic Bulletin & Review, 15(6), 1201–1208.CrossRefGoogle ScholarPubMed
Spiegelhalter, D.J., Best, N.G., Carlin, B., van der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society, Ser. B, 64, 586–639.CrossRefGoogle Scholar
Sturtz, S., Ligges, U., Gelman, A. (2005). R2WinBUGS: a package for running WinBUGS from R. Journal of Statistical Software, 12(3), 1–16.CrossRefGoogle Scholar
Van Breukelen, G. (2005). Psychometric modeling of response speed and accuracy with mixed and conditional regression. Psychometrika, 70(2), 359–376.CrossRefGoogle Scholar
Vandekerckhove, J., Verheyen, S., Tuerlinckx, F. (2010). A crossed random effects diffusion model for speeded semantic categorization decisions. Acta Psychologica, 133, 269–282.CrossRefGoogle ScholarPubMed
Van der Linden, W. (2006). A lognormal model for response times on test items. Journal of Educational and Behavioral Statistics, 31(2), 181–204.CrossRefGoogle Scholar
Van der Linden, W. (2007). A hierarchical framework for speed and accuracy on test items. Psychometrika, 72(3), 287–308.CrossRefGoogle Scholar
Van der Linden, W. (2009). Conceptual issues in response-time modeling. Journal of Educational Measurement, 46(3), 247–272.CrossRefGoogle Scholar
Van der Linden, W. (2010). Linking response-time parameters onto a common scale. Journal of Educational Measurement, 47(1), 92–114.CrossRefGoogle Scholar
Wenger, M.J., Gibson, B.S. (2004). Using hazard functions to assess changes in processing capacity in an attentional cuing paradigm. Journal of Experimental Psychology, 30(4), 708–719.Google Scholar