Abstract
Understanding how response time (RT) changes with manipulations has been critical in distinguishing among theories in cognition. It is well known that aggregating data distorts functional relationships (e.g., Estes, 1956). Less well appreciated is a second pitfall: Minimizing squared errors (i.e., OLS regression) also distorts estimated functional forms with RT data. We discuss three properties of RT that should be modeled for accurate analysis and, on the basis of these three properties, provide a hierarchical Weibull regression model for regressing RT onto covariates. Hierarchical regression model analysis of lexical decision task data reveals that RT decreases as a power function of word frequency with the scale of RT decreasing 11% for every doubling of word frequency. A detailed discussion of the model and analysis techniques are presented as archived materials and may be downloaded from www.psychonomic.org/archive.
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This research is supported by Grants SES-0351523 and SES-0095919 from the National Science Foundation, R01-MH071418 from the National Institute of Mental Health, and Fellowship F-04-008 from the University of Leuven, Belgium.
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Rouder, J.N., Tuerlinckx, F., Speckman, P. et al. A hierarchical approach for fitting curves to response time measurements. Psychonomic Bulletin & Review 15, 1201–1208 (2008). https://doi.org/10.3758/PBR.15.6.1201
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DOI: https://doi.org/10.3758/PBR.15.6.1201