The Quality of Response Time Data Inference: A Blinded, Collaborative Assessment of the Validity of Cognitive Models

Most data analyses rely on models. To complement statistical models, psychologistshave developed cognitive models, which translate observed variables into psychologicallyinteresting constructs. Response time models, in particular, assumethat response time and accuracy are the observed expression of latent variablesincluding 1) ease of processing, 2) response caution, 3) response bias, and 4) non–decision time. Inferences about these psychological factors, hinge upon the validityof the models’ parameters. Here, we use a blinded, collaborative approach to assessthe validity of such model-based inferences. Seventeen teams of researchersanalyzed the same 14 data sets. In each of these two–condition data sets, we manipulatedproperties of participants’ behavior in a two–alternative forced choice task.The contributing teams were blind to the manipulations, and had to infer what aspectof behavior was changed using their method of choice. The contributors choseto employ a variety of models, estimation methods, and inference procedures. Ourresults show that, although conclusions were similar across different methods, these“modeler’s degrees of freedom” did affect their inferences. Interestingly, many ofthe simpler approaches yielded as robust and accurate inferences as the more complexmethods. We recommend that, in general, cognitive models become a typicalanalysis tool for response time data. In particular, we argue that the simpler modelsand procedures are sufficient for standard experimental designs. We finish byoutlining situations in which more complicated models and methods may be necessary,and discuss potential pitfalls when interpreting the output from response timemodels