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In the present article, a flexible and fast computer program, called fast-dm, for diffusion model data analysis is introduced. Fast-dm is free software that can be downloaded from the authors' websites. The program allows estimating all parameters of Ratcliff's (1978) diffusion model from the empirical response time distributions of any binary classification task. Fast-dm is easy to use: it reads input data from simple text files, while program settings are specified by command0s in a control file. With fast-dm, complex models can be fitted, where some parameters may vary between experimental conditions, while other parameters are constrained to be equal across conditions. Detailed directions for use of fast-dm are presented, as well as results from three short simulation studies exemplifying the utility of fast-dm.
Diffusion models (Ratcliff, 1978) provide a framework to analyze data from binary decisions. In comparison with simpler approaches (e.g., ANOVAs of response time [RT] means), there are several advantages of this kind of data analysis: First, both RTs and error rates are entered simultaneously in one analysis. This is a great improvement over traditional techniques that are only based on either response times or error rates and thus might lead to misleading results (e.g., about task difficulty) in case of a speed-accuracy trade-off. For example, it might be that the participant's responses slow down not because a task is more difficult but because they set themselves a more conservative response criterion. In this case an analysis based on mean response times would come to wrong conclusions while the effect (adaptation of response strategy) could still be mapped by a diffusion model analysis. second, the diffusion model is well suited to reflect the structure of the given information. Results are not one-dimensional (i.e., good performance vs. bad performance) but consist of several parameters, which makes it possible to draw detailed conclusions about how a task is performed. In other words, the diffusion model helps to explain why responses are fast or slow and why few or many errors are made. Often, psychological theories allow deriving predictions for individual parameters of the diffusion model (Voss, Rothermund, & Voss, 2004). Therefore, the diffusion model is a powerful tool to test psychological theories. These structural results are possible because of a third advantage...