A hierarchical approach for fitting curves to response time measurements

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.

[1]  R. R. Bush,et al.  A mathematical model for simple learning. , 1951, Psychological review.

[2]  Jeffrey N. Rouder,et al.  Are unshifted distributional models appropriate for response time? , 2005 .

[3]  Jeffrey N. Rouder,et al.  A hierarchical model for estimating response time distributions , 2005, Psychonomic bulletin & review.

[4]  S. Andrews,et al.  Distinguishing common and task-specific processes in word identification: a matter of some moment? , 2001, Journal of experimental psychology. Learning, memory, and cognition.

[5]  Jeffrey N. Rouder,et al.  An evaluation of the Vincentizing method of forming group-level response time distributions , 2004, Psychonomic bulletin & review.

[6]  Stan Lipovetsky,et al.  Generalized Latent Variable Modeling: Multilevel,Longitudinal, and Structural Equation Models , 2005, Technometrics.

[7]  W S Murray,et al.  Serial mechanisms in lexical access: the rank hypothesis. , 2004, Psychological review.

[8]  H. H. Clark The language-as-fixed-effect fallacy: A critique of language statistics in psychological research. , 1973 .

[9]  Roger Ratcliff,et al.  A Theory of Memory Retrieval. , 1978 .

[10]  Scott D. Brown,et al.  The power law repealed: The case for an exponential law of practice , 2000, Psychonomic bulletin & review.

[11]  W. Estes The problem of inference from curves based on group data. , 1956, Psychological bulletin.

[12]  W. Press,et al.  Numerical Recipes in C++: The Art of Scientific Computing (2nd edn)1 Numerical Recipes Example Book (C++) (2nd edn)2 Numerical Recipes Multi-Language Code CD ROM with LINUX or UNIX Single-Screen License Revised Version3 , 2003 .

[13]  M. Abramowitz,et al.  Handbook of Mathematical Functions With Formulas, Graphs and Mathematical Tables (National Bureau of Standards Applied Mathematics Series No. 55) , 1965 .

[14]  H. Jeffreys,et al.  Theory of probability , 1896 .

[15]  Robert Schreuder,et al.  The Subjects as a Simple Random Effect Fallacy: Subject Variability and Morphological Family Effects in the Mental Lexicon , 2002, Brain and Language.

[16]  Hilde Haider,et al.  Why aggregated learning follows the power law of practice when individual learning does not: comment on Rickard (1997, 1999), Delaney et al. (1998), and Palmeri (1999). , 2002, Journal of experimental psychology. Learning, memory, and cognition.

[17]  Anthony S. Bryk,et al.  Hierarchical Linear Models: Applications and Data Analysis Methods , 1992 .

[18]  J. Townsend,et al.  Decision field theory: a dynamic-cognitive approach to decision making in an uncertain environment. , 1993, Psychological review.

[19]  R. Ratcliff,et al.  A model of the go/no-go task. , 2007, Journal of experimental psychology. General.

[20]  Jeffrey N. Rouder,et al.  Modeling Response Times for Two-Choice Decisions , 1998 .

[21]  Barbara Anne Dosher,et al.  The Functional Form of Performance Improvements in Perceptual Learning , 2007, Psychological science.

[22]  Jeffrey N. Rouder,et al.  A hierarchical bayesian statistical framework for response time distributions , 2003 .

[23]  Jeffrey N. Rouder,et al.  Assessing the roles of change discrimination and luminance integration: evidence for a hybrid race model of perceptual decision making in luminance discrimination. , 2000, Journal of experimental psychology. Human perception and performance.

[24]  Jun Lu,et al.  An introduction to Bayesian hierarchical models with an application in the theory of signal detection , 2005, Psychonomic bulletin & review.

[25]  G. Logan Toward an instance theory of automatization. , 1988 .

[26]  John R. Anderson Acquisition of cognitive skill. , 1982 .

[27]  Jerry Nedelman,et al.  Book review: “Bayesian Data Analysis,” Second Edition by A. Gelman, J.B. Carlin, H.S. Stern, and D.B. Rubin Chapman & Hall/CRC, 2004 , 2005, Comput. Stat..

[28]  M. Tanner Tools for statistical inference: methods for the exploration of posterior distributions and likeliho , 1994 .

[29]  H. Kucera,et al.  Computational analysis of present-day American English , 1967 .

[30]  M. Meulders,et al.  Cross-Classification Multilevel Logistic Models in Psychometrics , 2003 .

[31]  W. Cleveland LOWESS: A Program for Smoothing Scatterplots by Robust Locally Weighted Regression , 1981 .

[32]  P. McCullagh,et al.  Generalized Linear Models, 2nd Edn. , 1990 .

[33]  Robert V. Hogg,et al.  Introduction to Mathematical Statistics. , 1966 .

[34]  Gerard J. P. Van Breukelen,et al.  Psychometric Modeling of response speed and accuracy with mixed and conditional regression , 2005 .

[35]  Milton Abramowitz,et al.  Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables , 1964 .

[36]  Jun S. Liu,et al.  Generalised Gibbs sampler and multigrid Monte Carlo for Bayesian computation , 2000 .

[37]  John J. L. Morton,et al.  Interaction of information in word recognition. , 1969 .

[38]  Timothy C Rickard,et al.  Strategy execution in cognitive skill learning: an item-level test of candidate models. , 2004, Journal of experimental psychology. Learning, memory, and cognition.

[39]  R. Shepard,et al.  Mental Rotation of Three-Dimensional Objects , 1971, Science.

[40]  Bradley P. Carlin,et al.  Bayesian measures of model complexity and fit , 2002 .

[41]  J. H. Schuenemeyer,et al.  Generalized Linear Models (2nd ed.) , 1992 .

[42]  Gordon D. A. Brown,et al.  Contextual Diversity, Not Word Frequency, Determines Word-Naming and Lexical Decision Times , 2006, Psychological science.

[43]  G. Logan Shapes of reaction-time distributions and shapes of learning curves: a test of the instance theory of automaticity. , 1992, Journal of experimental psychology. Learning, memory, and cognition.

[44]  William H. Press,et al.  The Art of Scientific Computing Second Edition , 1998 .

[45]  Roel Bosker,et al.  Multilevel analysis : an introduction to basic and advanced multilevel modeling , 1999 .

[46]  David B. Dunson,et al.  Bayesian Data Analysis , 2010 .

[47]  P. McCullagh,et al.  Generalized Linear Models , 1992 .

[48]  C. D. Litton,et al.  Theory of Probability (3rd Edition) , 1984 .

[49]  Adrian F. M. Smith,et al.  Sampling-Based Approaches to Calculating Marginal Densities , 1990 .

[50]  Scott D. Brown,et al.  On the linear relation between the mean and the standard deviation of a response time distribution. , 2007, Psychological review.

[51]  William H. Press,et al.  Numerical recipes in C (2nd ed.): the art of scientific computing , 1992 .