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Assessing cognitive processes with diffusion model analyses: a tutorial based on fast-dm-30.

Voss A, Voss J, Lerche V - Front Psychol (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Psychologisches Institut, Ruprecht-Karls-Universität Heidelberg Heidelberg, Germany.

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.

No MeSH data available.


Related in: MedlinePlus

Example of the console output. First, information on the selected control file, the precision and method of estimation, the format of the data files and the estimated and fixed parameters are given. Any parameter depending on a condition is indexed with the name of the condition variable(s). Estimated parameters and numbers of responses at lower and upper threshold are presented separately for each condition. The three “-LL” values result from the three consecutive runs of the simplex algorithm. In the following lines the estimated values for all parameters are displayed. Finally, the number of processed data sets and the required computational time is given.
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Figure 3: Example of the console output. First, information on the selected control file, the precision and method of estimation, the format of the data files and the estimated and fixed parameters are given. Any parameter depending on a condition is indexed with the name of the condition variable(s). Estimated parameters and numbers of responses at lower and upper threshold are presented separately for each condition. The three “-LL” values result from the three consecutive runs of the simplex algorithm. In the following lines the estimated values for all parameters are displayed. Finally, the number of processed data sets and the required computational time is given.

Mentions: The output of the estimation procedure is shown directly in the console (Figure 3). First, the name of the control file and central characteristics of the estimation procedure are presented (precision, method of estimation, format of data files, estimated and fixed parameters). Then, parameters that are estimated within each condition of an experiment are listed (numbers represent fixed parameters). For parameters that depend on conditions the labels of conditions as found in the appropriate columns of the data files are attached to the parameter identifier. At the end of these lines, the number of observed responses at lower and upper threshold (coded with 0 or 1 in the data file, respectively) within each condition are presented.


Assessing cognitive processes with diffusion model analyses: a tutorial based on fast-dm-30.

Voss A, Voss J, Lerche V - Front Psychol (2015)

Example of the console output. First, information on the selected control file, the precision and method of estimation, the format of the data files and the estimated and fixed parameters are given. Any parameter depending on a condition is indexed with the name of the condition variable(s). Estimated parameters and numbers of responses at lower and upper threshold are presented separately for each condition. The three “-LL” values result from the three consecutive runs of the simplex algorithm. In the following lines the estimated values for all parameters are displayed. Finally, the number of processed data sets and the required computational time is given.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4376117&req=5

Figure 3: Example of the console output. First, information on the selected control file, the precision and method of estimation, the format of the data files and the estimated and fixed parameters are given. Any parameter depending on a condition is indexed with the name of the condition variable(s). Estimated parameters and numbers of responses at lower and upper threshold are presented separately for each condition. The three “-LL” values result from the three consecutive runs of the simplex algorithm. In the following lines the estimated values for all parameters are displayed. Finally, the number of processed data sets and the required computational time is given.
Mentions: The output of the estimation procedure is shown directly in the console (Figure 3). First, the name of the control file and central characteristics of the estimation procedure are presented (precision, method of estimation, format of data files, estimated and fixed parameters). Then, parameters that are estimated within each condition of an experiment are listed (numbers represent fixed parameters). For parameters that depend on conditions the labels of conditions as found in the appropriate columns of the data files are attached to the parameter identifier. At the end of these lines, the number of observed responses at lower and upper threshold (coded with 0 or 1 in the data file, respectively) within each condition are presented.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Psychologisches Institut, Ruprecht-Karls-Universität Heidelberg Heidelberg, Germany.

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.

No MeSH data available.


Related in: MedlinePlus