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dmat toolbox  (MathWorks Inc)


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    MathWorks Inc dmat toolbox
    Dmat 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/product/dmat+toolbox+for/pm38990662-559-19-22
    Average 90 stars, based on 1 article reviews
    dmat toolbox - by Bioz Stars, 2026-09
    90/100 stars

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    Related Articles

    other:

    Article Title: Explicitly versus implicitly driven temporal expectations: No evidence for altered perceptual processing due to top-down modulations.
    Article Snippet: Code availabilityData were analysed with JASP (freely available) and the DMAT toolbox for Matlab (version 0.4).

    Article Title: Sleep Deprivation Is Associated with Attenuated Parametric Valuation and Control Signals in the Midbrain during Value-Based Decision Making
    Article Snippet: Specifically, using the DMAT toolbox for Matlab ( Vandekerckhove and Tuerlinckx, 2007 , 2008 ), we fit a seven-parameter version of the RDM to the data, allowing all parameters to vary freely.

    Diffusion-based Assay:

    Article Title: The effect of noise-induced variance on parameter recovery from reaction times
    Article Snippet: .. In each iteration, we first sampled 100 data points from a diffusion process using the DMAT toolbox for MATLAB [ ]. ..



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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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    diffusion analysis toolbox (dmat  (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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    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