The Diffusion Model Is Not a Deterministic Growth Model: Comment onJones and Dzhafarov (2014)

摘要

claim that several current models of speeded decision making in cognitive tasks, including the diffusion model, can be viewed as special cases of other general models or model classes. The general models can be made to match any set of response time (RT) distribution and accuracy data exactly by a suitable choice of parameters and so are unfalsifiable. The implication of their claim is that models like the diffusion model are empirically testable only by artificially restricting them to exclude unfalsifiable instances of the general model. We show that Jones and Dzhafarov’s argument depends on enlarging the class of “diffusion” models to include models in which there is little or no diffusion. The unfalsifiable models are deterministic or near-deterministic growth models, from which the effects of within-trial variability have been removed or in which they are constrained to be negligible. These models attribute most or all of the variability in RT and accuracy to across-trial variability in the rate of evidence growth, which is permitted to be distributed arbitrarily and to vary freely across experimental conditions. In contrast, in the standard diffusion model, within-trial variability in evidenceis the primary determinant of variability in RT. Across-trial variability, whichdetermines the relative speed of correct responses and errors, is theoreticallyand empirically constrained. Jones and Dzhafarov’s attempt to includethe diffusion model in a class of models that also includes deterministic growthmodels misrepresents and trivializes it and conveys a misleading picture ofcognitive decision-making research.