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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/result/diffusion model analysis toolbox (dmat) in/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    diffusion model analysis toolbox (dmat) in - by Bioz Stars, 2026-03
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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



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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/result/diffusion model analysis toolbox (dmat) in/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    diffusion model analysis toolbox (dmat) in - by Bioz Stars, 2026-03
    90/100 stars
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    diffusion model analysis toolbox  (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. .
    Diffusion Model Analysis Toolbox, 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/result/diffusion model analysis toolbox/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    diffusion model analysis toolbox - by Bioz Stars, 2026-03
    90/100 stars
    		  Buy from Supplier
    diffusion model analysis toolbox for  (MathWorks Inc)
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    MathWorks Inc diffusion model analysis toolbox for
    <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 For, 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/result/diffusion model analysis toolbox for/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    diffusion model analysis toolbox for - by Bioz Stars, 2026-03
    90/100 stars
    		  Buy from Supplier
    diffusion model analysis toolbox package for  (MathWorks Inc)
    90
    MathWorks Inc diffusion model analysis toolbox package for
    <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 Package For, 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/result/diffusion model analysis toolbox package for/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    diffusion model analysis toolbox package for - by Bioz Stars, 2026-03
    90/100 stars
    		  Buy from Supplier

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    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