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

Abstract

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.

Citation

Dutilh, G., Annis, J., Brown, S., Cassey, P., Evans, N. J., Grasman, R., Hawkins, G., Heathcote, A., Holmes, W., Krypotos, A., Kupitz, C., Leite, F., Lerche, V., Lin, Y., Logan, G. D., Palmeri, T., Starns, J., Trueblood, J., van Maanen, L., van Ravenzwaaij, D., Vandekerckhove, J., Visser, I., Voss, A., White, C., Wiecki, T., 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.

Bibtex

@article{dutilh_etal:2019:collaborative,
    title   = {{T}he quality of response time data inference: {A} blinded, collaborative approach to the validity of cognitive models},
    author  = {Dutilh, Gilles and Annis, Jeff and Brown, Scott and Cassey, Pete and Evans, Nathan J. and Grasman, Raoul and Hawkins, Guy and Heathcote, Andrew and Holmes, William and Krypotos, Angelos-Miltiadis and Kupitz, Colin and Leite, Fabio and Lerche, Veronika and Lin, Yi-Shin and Logan, Gordon D. and Palmeri, Thomas and Starns, Jeffrey and Trueblood, Jennifer and van Maanen, Leendert and van Ravenzwaaij, Don and Vandekerckhove, Joachim and Visser, Ingmar and Voss, Andreas and White, Corey and Wiecki, Thomas and Rieskamp, Joerg and Donkin, Christopher},
    year    = {2019},
    journal = {Psychonomic Bulletin \& Review},
    volume  = {26},
    pages   = {1051--1069}
}