Diffusion Model Analysis with fast-dm-30
- fast-dm Downloads
- A very short Introduction to Diffusion Modeling
- A very short Introduction to fast-dm
- A very short Introduction to construct-samples
- A very short Introduction to plot-cdf
- Recent Publications
Fast-dm is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License. See the file COPYING in the source code archives for details.
We provide the source code, precompiled binaries (for Windows only), and a MS Visual Studio project. If you just want to apply fast-dm and you are using MS Windows, you will only need the binaries.
Stefan Radev and Veronika Lerche recently developed a Python-based graphical user interface for fast-dm. The GUI has the same functionality as the command-line version of fast-dm. In addition, the GUI allows the creation of plots that are useful to check the model fit. Note that the plots feature is still under development. Dependencies are not yet included and further plot options will be added in the near future. If you would like to use the GUI, please visit the GUI site.
Fast-dm-30.2 implements an guessing parameter p (see Ratcliff & Tuerlincks, 2004). p estimates the amout of guessings in your data with the assumtion of an equal RT distribution of guesses between the fastes and the slowest response. For most applications, you want to set p to 0, especially when using small trial numbers.
Additionally, fast-dm-30.2 comprises some minor corrections of code.
Note: The file win32erf.c, needs to be included only if an elder version of Visual Studio is used for compilation.
Installation on Unix-like systems
A very short Introduction to Diffusion Modeling
Diffusion-model data analyses are based on the assumption that information is accumulated continuously until one of two thresholds is hit. With a diffusion-model data analysis it is possible to analyze data from fast binary decision tasks. The analysis is based on the distributions of both correct and erroneous responses. From these distributions a set of parameters is estimated that allows to draw conclusions about the underlying cognitive processes. For more information, see Voss, Rothermund, & Voss (2004) or Voss, Nagler, & Lerche (2013) (Password is VOSS).
Parameters estimated by fast-dm
|Threshold Separation (a)||0.5 < a < 2||Amount of information that is considered for a decision. Large values indicate a conservative decisional style.|
|Relative Starting Point (zr)||0.3 < zr < 0.7||Indicator of an a priori bias in decision making. When the relative starting point zr deviates from 0.5, the amount of information necessary for a decision differs between response alternatives.||In earlier versions of fast-dm the absolute starting point z was estimated. In recent versions, fast-dm uses zr = z/a, which is scaled from 0 to 1 with zr = 0.5 indicating the absence of a decisional bias.|
|Drift (v)||-5 < v < 5||Average slope of the information accumulation process. The drift gives information about the speed and direction of the accumulation of information. Large (absolute) values of drift indicate a good performance. If received information supports the response linked to the upper threshold the sign will be positive and vice versa.|
|Response Time Constant (t0)||0.1 < t0 < 0.5||Average duration of all non-decisional processes (encoding and response execution).||Duration is given in seconds by fast-dm.|
|Differences in Speed of Response Execution (d)||-0.1 < d < 0.1||Positive values indicate that response execution is faster for responses linked to the upper threshold (coded as 1 in fast-dm) than for responses linked to the lower threshold.||Differences are given in seconds by fast-dm.|
|Inter-Trial-Variability of (Relative) Starting Point (szr)||0 < szr < 0.5||Range of a uniform distribution with mean zr describing the distribution of actual starting points from specific trials.||Minimal impact on the RT distributions. Can be fixed to 0 in most applications.|
|Inter-Trial-Variability of Drift (sv)||0 < sv < 2||Standard deviation of a normal distribution with mean v describing the distribution of actual drift rates from specific trials.||Minimal impact on the RT distributions. Can be fixed to 0 in most applications.|
|Inter-Trial-Variability of Non-Decisional Components (st0)||0 < st0 < 0.2||Range of a uniform distribution with mean t0 describing the distribution of actual t0 values across trials.||Accounts for response times below t0. Reduces skew of predicted RT distributions.|
|Percentage of Contaminants (p)||0 < p < 1||Contaminated RTs are modeled as a uniform distribution form the fastest to the slowest RT (Ratcliff & Tuerlinckx, 2004). p is an estimate for the relative amount of contaminated trials.||Very large trial numbers are needed to estmate p. Should be fixed to 0 in most applications. Implemented in fast-dm-30.2|
Estimation Procedures implemented in fast-dm
|Method||Recommended Trial Number (Minimum)||Robustness||Speed of Estimation|
|Maximum Likelihood||Low (n>40)||Low (strict outlier analysis necessary)||Low (if inter-trial-variability parameters are included)|
|Kolmogorov-Smirnov||Medium (n>100)||High||Medium (dependent on trial numbers)|
|Chi-Square||High (n>500)||High||High (independent of trial numbers)|
A very short Introduction to fast-dm
Fast-dm is free open-source software for the estimation of diffusion model parameters. It is a command-line tool that reads commands from a text file (by default named experiment.ctl). Below, we present an overview of possible commands for the control file. A more thorough description is provided by Voss & Voss (2007).
To run fast-dm you have to save the control file, all your data files and fast-dm.exe in the same directory. Then you can start fast-dm by a double click. However, we recommend starting it from a command window so that you have a chance to inspect possible error messages. You can give the name of the control file as an option (e.g., enter fast-dm exp1.ctl); if no option is entered, fast-dm tries to load the file experiment.ctl.
Commands for the Control File
|method CRITERION||Selection of estimation procedure (Maximum Likelihood; Kolmogorov-Smirnov; Chi-Square).||
|precision VALUE||Precision of calculation. VALUE corresponds roughly to the number of decimals of the predicted CDFs that are calculated accuratly.||
|set PARAMETER VALUE||Fix a parameter to a specific value; this parameter will not be estimated by fast-dm.||
|depends PARAMETER CONDITION||Denotes that this parameter may vary between different trial types or conditions. CONDITION need to be defined later by the format command||
|format CONDITION ...||Defines the variable names for the columns of the data file(s). The format command is always required, and you need to define always the variables RESPONSE and TIME (using capital letters). In the data files, responses have to be coded as 0 (lower threshold) or 1 (upper threshold). Times have to be given in seconds (not: ms!).||
|load FILE_NAME||Defines the names of input files (required command).||
|save FILE_NAME||Defines the names of separate output files (one output file for each data set). Either the save or the log command are required.||
|log FILE_NAME||Defines the names of a common output file (one output file for all data sets). Either the save or the log command are required.||
Example of a complete control file
set zr 0.5
set d 0
set szr 0
set sv 0
set p 0
depends v stimulus
format stimulus RESPONSE TIME
A very short Introduction to construct-samplesConstruct-samples is a tool that comes along with fast-dm to simulate data sets from a specific parameter set. Parameter values are entered as command-line options.
Command-Line options of construct-samples
|-a VALUE||threshold separation|
|-z VALUE||relative starting point|
|-t VALUE||duration of non-decisional processes|
|-d VALUE||difference in speed of response execution|
|-Z VALUE||inter-trial variability of relative starting point|
|-V VALUE||inter-trial variability of drift|
|-T VALUE||inter-trial variability of non-decisional processes|
|-n VALUE||trial number per data set|
|-N VALUE||number of data sets|
|-r||responses and times are randomly determined|
|-o FILE_NAME||file name(s) for output|
Example of the usage of construct-samples
|construct-samples.exe -a 1.2 -z 0.5 -v 1 -t 0.300 -d 0 -Z 0 -V 0 -T 0.1 -r -n 500 -N 1000 -o "%d.lst"|
A very short Introduction to plot-cdfPlot-cdf is a tool that comes along with fast-dm to visualize the predicted cumulative distribution functions for a specific parameter set. Parameter values are entered as command-line options (see above; -r, -n, -N do not apply for plot-cdf). The program gives values for the CDF as plain text that can be plotted using different software like Excel or R.
Example of the usage of plot-cdf
|plot-cdf.exe -a 1.2 -z 0.5 -v 1 -t 0.300 -d 0 -Z 0 -V 0 -T 0.1 -o "cdf.lst"|
Below, you will find three examples that may be helpful for your first steps using fast-dm. Each ZIP archive contains some data files, the experiment.ctl file, and the fast-dm.exe file. Additionally you find the R-code that has been used to generate the data files (calling construct-samples).
Twenty data files with 200 trials each are simulated from the same parameter values (a=1; zr=0.5; v=2; t0=0.5; other parameters are zero)
Simulates an experiment with 4 conditions with different drift rates (a=1; zr=0.5; v1=-3; v2=-1; v3=1; v4=3; t0=0.5; other parameters are zero)
One-hundred data sets with 500 trials each with randomly generated parameter values. The file true-scores.lst contains the values that have been used for the simulation.
Recent diffusion model publications from our group
Please note that some of the texts available here are protected by a password. In this case VOSS will help you!
- Lerche, V., & Voss, A. (2016a). Model Complexity in Diffusion Modeling: Benefits of Making the Model More Parsimonious. Frontiers in Psychology, 7(1324). doi: 10.3389/fpsyg.2016.01324
- Lerche, V., & Voss, A. (2016b). Retest Reliability of the Parameters of the Ratcliff Diffusion Model. Psychological Research, 1-24. doi: 10.1007/s00426-016-0770-5
- Lerche, V., Voss, A., & Nagler, M. (2016). How Many Trials are Required for Robust Parameter Estimation in Diffusion Modeling? A Comparison of Different Estimation Algorithms. Behavior Research Methods, 1-25. doi: 10.3758/s13428-016-0740-2
- Voss, A., & Schwieren, C. (in press). The Dynamics of Motivated Perception: Effects of Control and Status on the Perception of Ambivalent Stimuli. Cognition & Emotion. [pdf]
- Voss, A., Voss, J. and Lerche, V. (2015). Assessing Cognitive Processes with Diffusion Model Analyses: A Tutorial based on fast-dm-30. Frontiers in Psychology, 6:336. [html]
- Voss, A., Nagler, M., & Lerche, V. (2013). Diffusion Models in Experimental Psychology: A Practical Introduction. Experimental Psychology, 60, 385-402. [pdf]
- Voss, A., Rothermund, K., Gast, A., & Wentura, D. (2013). Cognitive Processes in Categorical and Associative Priming: A Diffusion Model Analysis. Journal of Experimental Psychology: General, 142, 536-559. [pdf]
- Schmitz, F., & Voss, A. (2012). Decomposing Task-Switching Costs with the Diffusion Model. Journal of Experimental Psychology: Human Perception and Performance, 38, 222-250. [pdf]
- Spaniol, J., Voss, A., & Bowen, H.J., Grady. C.L. (2011). Motivational incentives modulate age differences in visual perception. Psychology and Aging, 26, 932-939. [pdf]
- Voss, A., Stahl, C., & Klauer, K.C. (2011). Cognitive methods in social psychology: Inferring latent processes. In K. C. Klauer, A. Voss, & C. Stahl (Eds.): Cognitive methods in social psychology (pp. 1-14). New York: Guilford Press.
- Klauer, K.C., Stahl, C., & Voss, A. (2011). Multinomial models and diffusion models. In K. C. Klauer, A. Voss, & C. Stahl (Eds.): Cognitive methods in social psychology (pp. 367-390). New York: Guilford Press.
- Klauer, K.C., Voss, A., & Stahl, C. (2011). Cognitive methods in social psychology. New York: Guilford Press
- Voss, A., Voss, J., & Klauer, K.C. (2010). Separating response tendency and decision biases: Arguments for an additional parameter in Ratcliff?s diffusion model.British Journal of Mathematical and Statistical Psychology, 63, 539-555. [pdf]
- Spaniol, J., Voss, A., & Grady, C.L. (2008). Aging and Emotional Memory: Cognitive Mechanisms Underlying the Positivity Effect. Psychology and Aging, 23, 859-872. [pdf]
- Voss, A., Rothermund, K. & Brandtstädter, J. (2008). Interpreting Ambiguous Stimuli: Separating Perceptual and Judgmental Biases. Journal of Experimental Social Psycholgy, 2008, 44, 1048-1056. [pdf]
- Voss, A., & Voss, J. (2008). A Fast Numerical Algorithm for the Estimation of Diffusion-Model Parameters. Journal of Mathematical Psychology, 52, 1-9. [pdf]
- Klauer, K.C., Voss, A., Schmitz, F., & Teige-Mocigemba, S. (2007). Process Components of the Implicit Association Test: A Diffusion-Model Analysis. Journal of Personality and Social Psychology, 93, 353-368. [pdf]
- Voss, A., & Voss, J. (2007). Fast-dm: A Free Program for Efficient Diffusion Model Analysis. Behavioral Research Methods, 39, 767-775. [pdf]
- Spaniol, J., Madden, D.J., & Voss, A. (2006). A diffusion model analysis of adult age differences in episodic and semantic long-term memory retrieval. Journal of Experimental Psychology: Learning, Memory & Cognition, 32, 101-117. [pdf]
- Voss, A., Rothermund, K., & Voss, J. (2004). Interpreting the parameters of the diffusion model: An empirical validation. Memory and Cognition, 32, 1206-1220. [pdf]