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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Main Authors: Andreas eVoss, Jochen eVoss, Veronika eLerche
Format: Article
Language:English
Published: Frontiers Media S.A. 2015-03-01
Series:Frontiers in Psychology
Subjects:
Online Access:http://journal.frontiersin.org/Journal/10.3389/fpsyg.2015.00336/full
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spelling 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
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