Assessing Cognitive Processes with Diffusion Model Analyses: A Tutorial based on fast-dm-30
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 esti...
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doaj-ddd1924514c442c786fb2b78a8ce51fb2020-11-24T22:10:29ZengFrontiers Media S.A.Frontiers in Psychology1664-10782015-03-01610.3389/fpsyg.2015.00336124917Assessing Cognitive Processes with Diffusion Model Analyses: A Tutorial based on fast-dm-30Andreas eVoss0Jochen eVoss1Veronika eLerche2Universität HeidelbergUniversity of LeedUniversität HeidelbergDiffusion 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 modelling, and we present fast-dm-30, a thoroughly revised and extended version of the fast-dm software (Voss & 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.http://journal.frontiersin.org/Journal/10.3389/fpsyg.2015.00336/fullCognitive Processesparameter estimationdiffusion modelresponse time distributionFast-dm |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Andreas eVoss Jochen eVoss Veronika eLerche |
spellingShingle |
Andreas eVoss Jochen eVoss Veronika eLerche Assessing Cognitive Processes with Diffusion Model Analyses: A Tutorial based on fast-dm-30 Frontiers in Psychology Cognitive Processes parameter estimation diffusion model response time distribution Fast-dm |
author_facet |
Andreas eVoss Jochen eVoss Veronika eLerche |
author_sort |
Andreas eVoss |
title |
Assessing Cognitive Processes with Diffusion Model Analyses: A Tutorial based on fast-dm-30 |
title_short |
Assessing Cognitive Processes with Diffusion Model Analyses: A Tutorial based on fast-dm-30 |
title_full |
Assessing Cognitive Processes with Diffusion Model Analyses: A Tutorial based on fast-dm-30 |
title_fullStr |
Assessing Cognitive Processes with Diffusion Model Analyses: A Tutorial based on fast-dm-30 |
title_full_unstemmed |
Assessing Cognitive Processes with Diffusion Model Analyses: A Tutorial based on fast-dm-30 |
title_sort |
assessing cognitive processes with diffusion model analyses: a tutorial based on fast-dm-30 |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Psychology |
issn |
1664-1078 |
publishDate |
2015-03-01 |
description |
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 modelling, and we present fast-dm-30, a thoroughly revised and extended version of the fast-dm software (Voss & 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. |
topic |
Cognitive Processes parameter estimation diffusion model response time distribution Fast-dm |
url |
http://journal.frontiersin.org/Journal/10.3389/fpsyg.2015.00336/full |
work_keys_str_mv |
AT andreasevoss assessingcognitiveprocesseswithdiffusionmodelanalysesatutorialbasedonfastdm30 AT jochenevoss assessingcognitiveprocesseswithdiffusionmodelanalysesatutorialbasedonfastdm30 AT veronikaelerche assessingcognitiveprocesseswithdiffusionmodelanalysesatutorialbasedonfastdm30 |
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