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diffusion model analysis toolbox (dmat) in  (MathWorks Inc)


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    MathWorks Inc diffusion model analysis toolbox (dmat) in
    <t>DMAT</t> model fits. We fit <t>the</t> <t>diffusion</t> model to the data using the software package DMAT by allowing only the boundary parameter to vary with SAT levels and the drift parameter to vary with contrast. The fits were very similar to the ones with HDDM (Fig. ). We observed good fits for the d′-RT curves except for the “extremely fast” condition (upper left panel), monotonically increasing functions for the RT difference between error and correct trials (upper right panel), monotonically increasing \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{{SD\left( {RT} \right)}}{{mean\left( {RT} \right)}}$$\end{document} S D RT m e a n RT curves (lower left panel), and inverted-U RT skewness curves (lower right panel). Again, none of the empirically observed U-shaped curves were reproduced even qualitatively. All notation is identical to Fig. .
    Diffusion Model Analysis Toolbox (Dmat) In, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/diffusion+model+analysis+toolbox+for/pmc07794484-427-20-26
    Average 90 stars, based on 1 article reviews
    diffusion model analysis toolbox (dmat) in - by Bioz Stars, 2026-11
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    1) Product Images from "Qualitative speed-accuracy tradeoff effects that cannot be explained by the diffusion model under the selective influence assumption"

    Article Title: Qualitative speed-accuracy tradeoff effects that cannot be explained by the diffusion model under the selective influence assumption

    Journal: Scientific Reports

    doi: 10.1038/s41598-020-79765-2

    DMAT model fits. We fit the diffusion model to the data using the software package DMAT by allowing only the boundary parameter to vary with SAT levels and the drift parameter to vary with contrast. The fits were very similar to the ones with HDDM (Fig. ). We observed good fits for the d′-RT curves except for the “extremely fast” condition (upper left panel), monotonically increasing functions for the RT difference between error and correct trials (upper right panel), monotonically increasing \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{{SD\left( {RT} \right)}}{{mean\left( {RT} \right)}}$$\end{document} S D RT m e a n RT curves (lower left panel), and inverted-U RT skewness curves (lower right panel). Again, none of the empirically observed U-shaped curves were reproduced even qualitatively. All notation is identical to Fig. .
    Figure Legend Snippet: DMAT model fits. We fit the diffusion model to the data using the software package DMAT by allowing only the boundary parameter to vary with SAT levels and the drift parameter to vary with contrast. The fits were very similar to the ones with HDDM (Fig. ). We observed good fits for the d′-RT curves except for the “extremely fast” condition (upper left panel), monotonically increasing functions for the RT difference between error and correct trials (upper right panel), monotonically increasing \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{{SD\left( {RT} \right)}}{{mean\left( {RT} \right)}}$$\end{document} S D RT m e a n RT curves (lower left panel), and inverted-U RT skewness curves (lower right panel). Again, none of the empirically observed U-shaped curves were reproduced even qualitatively. All notation is identical to Fig. .

    Techniques Used: Diffusion-based Assay, Software

    Related Articles

    Diffusion-based Assay:

    Article Title: Processing of face identity in the affective flanker task: a diffusion model analysis.
    Article Snippet: Affective flanker tasks often present affective facial expressions as stimuli.. However, it is not clear whether the identity of the person on the target picture needs to be the same for the flanker stimuli or whether it is better to use pictures of different persons as flankers.. While Grose-Fifer, Rodrigues, Hoover & Zottoli (Advances in Cognitive Psychology 9(2):81–91, 2013) state that attentional focus might be captured by processing the differences between faces, i.e. the identity, and therefore use pictures of the same individual as target and flanker stimuli, Munro, Dywan, Harris, McKee, Unsal & Segalowitz (Biological Psychology, 76:31–42, 2007) propose an advantage in presenting pictures of a different individual as flankers.



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    diffusion model analysis toolbox (dmat) in  (MathWorks Inc)
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    <t>DMAT</t> model fits. We fit <t>the</t> <t>diffusion</t> model to the data using the software package DMAT by allowing only the boundary parameter to vary with SAT levels and the drift parameter to vary with contrast. The fits were very similar to the ones with HDDM (Fig. ). We observed good fits for the d′-RT curves except for the “extremely fast” condition (upper left panel), monotonically increasing functions for the RT difference between error and correct trials (upper right panel), monotonically increasing \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{{SD\left( {RT} \right)}}{{mean\left( {RT} \right)}}$$\end{document} S D RT m e a n RT curves (lower left panel), and inverted-U RT skewness curves (lower right panel). Again, none of the empirically observed U-shaped curves were reproduced even qualitatively. All notation is identical to Fig. .
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    <t>DMAT</t> model fits. We fit <t>the</t> <t>diffusion</t> model to the data using the software package DMAT by allowing only the boundary parameter to vary with SAT levels and the drift parameter to vary with contrast. The fits were very similar to the ones with HDDM (Fig. ). We observed good fits for the d′-RT curves except for the “extremely fast” condition (upper left panel), monotonically increasing functions for the RT difference between error and correct trials (upper right panel), monotonically increasing \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{{SD\left( {RT} \right)}}{{mean\left( {RT} \right)}}$$\end{document} S D RT m e a n RT curves (lower left panel), and inverted-U RT skewness curves (lower right panel). Again, none of the empirically observed U-shaped curves were reproduced even qualitatively. All notation is identical to Fig. .
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    <t>DMAT</t> model fits. We fit <t>the</t> <t>diffusion</t> model to the data using the software package DMAT by allowing only the boundary parameter to vary with SAT levels and the drift parameter to vary with contrast. The fits were very similar to the ones with HDDM (Fig. ). We observed good fits for the d′-RT curves except for the “extremely fast” condition (upper left panel), monotonically increasing functions for the RT difference between error and correct trials (upper right panel), monotonically increasing \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{{SD\left( {RT} \right)}}{{mean\left( {RT} \right)}}$$\end{document} S D RT m e a n RT curves (lower left panel), and inverted-U RT skewness curves (lower right panel). Again, none of the empirically observed U-shaped curves were reproduced even qualitatively. All notation is identical to Fig. .
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    <t>DMAT</t> model fits. We fit <t>the</t> <t>diffusion</t> model to the data using the software package DMAT by allowing only the boundary parameter to vary with SAT levels and the drift parameter to vary with contrast. The fits were very similar to the ones with HDDM (Fig. ). We observed good fits for the d′-RT curves except for the “extremely fast” condition (upper left panel), monotonically increasing functions for the RT difference between error and correct trials (upper right panel), monotonically increasing \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{{SD\left( {RT} \right)}}{{mean\left( {RT} \right)}}$$\end{document} S D RT m e a n RT curves (lower left panel), and inverted-U RT skewness curves (lower right panel). Again, none of the empirically observed U-shaped curves were reproduced even qualitatively. All notation is identical to Fig. .
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    Image Search Results


    DMAT model fits. We fit the diffusion model to the data using the software package DMAT by allowing only the boundary parameter to vary with SAT levels and the drift parameter to vary with contrast. The fits were very similar to the ones with HDDM (Fig. ). We observed good fits for the d′-RT curves except for the “extremely fast” condition (upper left panel), monotonically increasing functions for the RT difference between error and correct trials (upper right panel), monotonically increasing \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{{SD\left( {RT} \right)}}{{mean\left( {RT} \right)}}$$\end{document} S D RT m e a n RT curves (lower left panel), and inverted-U RT skewness curves (lower right panel). Again, none of the empirically observed U-shaped curves were reproduced even qualitatively. All notation is identical to Fig. .

    Journal: Scientific Reports

    Article Title: Qualitative speed-accuracy tradeoff effects that cannot be explained by the diffusion model under the selective influence assumption

    doi: 10.1038/s41598-020-79765-2

    Figure Lengend Snippet: DMAT model fits. We fit the diffusion model to the data using the software package DMAT by allowing only the boundary parameter to vary with SAT levels and the drift parameter to vary with contrast. The fits were very similar to the ones with HDDM (Fig. ). We observed good fits for the d′-RT curves except for the “extremely fast” condition (upper left panel), monotonically increasing functions for the RT difference between error and correct trials (upper right panel), monotonically increasing \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{{SD\left( {RT} \right)}}{{mean\left( {RT} \right)}}$$\end{document} S D RT m e a n RT curves (lower left panel), and inverted-U RT skewness curves (lower right panel). Again, none of the empirically observed U-shaped curves were reproduced even qualitatively. All notation is identical to Fig. .

    Article Snippet: We fit the diffusion model to the data using both the hierarchical drift diffusion model (HDDM) python package and the diffusion model analysis toolbox (DMAT) in MATLAB .

    Techniques: Diffusion-based Assay, Software