The quality of response time data inference: A blinded, collaborative approach to the validity of cognitive models

G. Dutilh and J. Annis and S. D. Brown and P. Cassey and N. J. Evans and R. P. P. P. Grasman and G. E. Hawkins and A. Heathcote and W. R. Holmes and A.-M. Krypotos and C. N. Kupitz and F. P. Leite and V. Lerche and Y.-S. Lin and G. D. Logan and T. J. Palmeri and J. J. Starns and J. S. Trueblood and L. van Maanen and D. van Ravenzwaaij and J. Vandekerckhove and I. Visser and A. Voss and C. N. White and T. V. Wiecki and J. Rieskamp and C. Donkin Most data analyses rely on models. To complement statistical models, psychologists have developed cognitive models, which translate observed variables into psychologically interesting constructs. Response time models, in particular, assume that response time and accuracy are the observed expression of latent variables including 1) ease of processing, 2) response caution, 3) response bias, and 4) non-decision time. Inferences about these psychological factors hinge upon the validity of the models' parameters. Here, we use a blinded, collaborative approach to assess the validity of such model-based inferences. Seventeen teams of researchers analyzed the same 14 data sets. In each of these two-condition data sets, we manipulated properties of participants' behavior in a two-alternative forced choice task. The contributing teams were blind to the manipulations, and had to infer what aspect of behavior was changed using their method of choice. The contributors chose to employ a variety of models, estimation methods, and inference procedures. Our results show that, although conclusions were similar across different methods, these "modeler's degrees of freedom" did affect their inferences. Interestingly, many of the simpler approaches yielded as robust and accurate inferences as the more complex methods. We recommend that, in general, cognitive models become a typical analysis tool for response time data. In particular, we argue that the simpler models and procedures are sufficient for standard experimental designs. We finish by outlining situations in which more complicated models and methods may be necessary, and discuss potential pitfalls when interpreting the output from response time models. DOI: 10.3758/s13423-017-1417-2


Dutilh, G., Annis, J., Brown, S. D., Cassey, P., Evans, N. J., Grasman, R. P. P. P., Hawkins, G. E., Heathcote, A., Holmes, W. R., Krypotos, A.-M., Kupitz, C. N., Leite, F. P., Lerche, V., Lin, Y.-S., Logan, G. D., Palmeri, T. J., Starns, J. J., Trueblood, J. S., van Maanen, L., van Ravenzwaaij, D., Vandekerckhove, J., Visser, I., Voss, A., White, C. N., Wiecki, T. V., Rieskamp, J., & Donkin, C. (2019). The quality of response time data inference: A blinded, collaborative approach to the validity of cognitive models. Psychonomic Bulletin & Review, 26, 1051–1069.


  title     = {The quality of response time data inference: A blinded, collaborative approach to the validity of cognitive models},
  author    = {Dutilh, G. and Annis, J. and Brown, S. D. and Cassey, P. and Evans, N. J. and Grasman, R. P. P. P. and Hawkins, G. E. and Heathcote, A. and Holmes, W. R. and Krypotos, A.-M. and Kupitz, C. N. and Leite, F. P. and Lerche, V. and Lin, Y.-S. and Logan, G. D. and Palmeri, T. J. and Starns, J. J. and Trueblood, J. S. and van Maanen, L. and van Ravenzwaaij, D. and Vandekerckhove, J. and Visser, I. and Voss, A. and White, C. N. and Wiecki, T. V. and Rieskamp, J. and Donkin, C.},
  journal   = {Psychonomic Bulletin \& Review},
  year      = {2019},
  volume    = {26},
  pages     = {1051--1069},
  doi       = {10.3758/s13423-017-1417-2},