Abstract
Diffusion models can be used to infer cognitive processes involved in fast binary decision tasks. The model assumes that information is accumulated continuously until one of two thresholds is hit. In the analysis, response time distributions from numerous trials of the decision task are used to estimate a set of parameters mapping distinct cognitive processes. In recent years, diffusion model analyses have become more and more popular in different fields of psychology. This increased popularity is based on the recent development of several software solutions for the parameter estimation. Although these programs make the application of the model relatively easy, there is a shortage of knowledge about different steps of a state-of-the-art diffusion model study. In this paper, we give a concise tutorial on diffusion modeling, and we present fast-dm-30, a thoroughly revised and extended version of the fast-dm software (Voss and Voss, 2007) for diffusion model data analysis. The most important improvement of the fast-dm version is the possibility to choose between different optimization criteria (i.e., Maximum Likelihood, Chi-Square, and Kolmogorov-Smirnov), which differ in applicability for different data sets.
Highlights
We give a concise tutorial on diffusion modeling, and we present fast-dm-30, a thoroughly revised and extended version of the fast-dm software (Voss and Voss, 2007) for diffusion model data analysis
The Calculation of Cumulative Density Functions (CDF) The optimization routines based on the KS or CS statistics require the calculation of predicted cumulative density functions (CDF)
The most important extension is the inclusion of different optimization criteria (Maximum Likelihood, KolmogorovSmirnov, and Chi-Square)
Summary
The computed CS value is based on the comparison of the number of observed and predicted responses in so-called bins of the RT-distributions The borders of these bins are defined by convention by the 0.1, 0.3, 0.5, 0.7, and 0.9 quantiles of the empirical response time distributions, separately for the upper and lower threshold. With K conditions of an experiment, N bins per condition (N = 2 · 6 = 12), and P free diffusion model parameters (White et al., 2Strictly speaking, this is not an exact implementation of a chi-square criterion because bins are defined by the data (and not by predicted distributions). CS based parameter estimations are only feasible for medium to large trial numbers (minimum 200 trials) It is especially problematic if empirical response distributions are small at one of the thresholds (e.g., less than 12 trials).
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