The Quality of Response Time Data Inference: A Blinded, Collaborative Assessment of the Validity of Cognitive Models
Year: 2018
Type: article
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.
Source: Psychonomic Bulletin & Review
Authors: Gilles Dutilh, Jeffrey Annis, Scott D. Brown, Peter Cassey, Nathan J. Evans, Raoul P. P. P. Grasman, Guy E. Hawkins, Andrew Heathcote, William R. Holmes, Angelos-Miltiadis Krypotos, Colin N. Kupitz, Fábio P. Leite, Veronika Lerche, Yi-Shin Lin, Gordon D. Logan, Thomas J. Palmeri, Jeffrey J. Starns, Jennifer S. Trueblood, Leendert van Maanen, Don van Ravenzwaaij, Joachim Vandekerckhove, Ingmar Visser, Andreas Voss, Corey N. White, Thomas V. Wiecki, Jörg Rieskamp, Chris Donkin
Institutions: University of Basel, Vanderbilt University, University of Newcastle Australia, University of Amsterdam, University of Tasmania, Utrecht University, University of California, Irvine, The Ohio State University, Heidelberg University, University of Massachusetts Amherst, University of Groningen, Missouri Western State University, Brown University, UNSW Sydney
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,790
Awards: The Impact of Dynamically Changing Information on Decision Processes, Cognitive Structural Equation Models, G1344704808, G2224877613, Cognitive models of mental architectures in consumer preference, Perceptual Categorization in Real-World Expertise, Stochastic Models of Visual Decision Making and Visual Search, Estimation of Unidentified Cognitive Models with Physiological Data, Bayesian Methods for Meta-Analysis in the Presence of Publication Bias, Training Grant in Vision Research, Core Grant in Vision Research
OpenAlex ID: W2776250252