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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+dmat/pmc07794484-427-20-26
Average 90 stars, based on 1 article reviews

diffusion model analysis toolbox (dmat) in - by Bioz Stars, 2026-10

90/100 stars

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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

other:

Article Title: Acquisition of decision making criteria: Reward rate ultimately beats accuracy
Article Snippet: Data Analysis DDM Fits Individual participants’ 2AFC data were first pooled across sessions 10 and greater and then were fit by the DDM using the diffusion model analysis toolbox (DMAT) in Matlab ( Vandekerckhove & Tuerlinckx, 2008 ).

Article Title: The optimality of sensory processing during the speed-accuracy tradeoff
Article Snippet: Nevertheless, in order to demonstrate that our modeling results are not specific to our choice of model and fitting procedures, we also fit our behavioral data using the Diffusion Model Analysis Toolbox (DMAT) implemented in MATLAB ( Vandekerckhove and Tuerlinckx, 2007 ; 2008 ).

Article Title: Acquisition of decision making criteria: Reward rate ultimately beats accuracy
Article Snippet: Individual participants’ 2AFC data were first pooled across sessions 10 and greater and then were fit by the DDM using the diffusion model analysis toolbox (DMAT) in Matlab ( Vandekerckhove & Tuerlinckx, 2008 ).

Article Title: Stimulus probability effects on temporal bisection performance of mice (Mus musculus).
Article Snippet: In the temporal bisection task, participants classify experienced stimulus durations as short or long based on their temporal similarity to previously learned reference durations.. Temporal decision making in this task should be influenced by the experienced probabilities of the reference durations for adaptiveness.. In this study, we tested the temporal bisection performance of mice (Mus musculus) under different short and long reference duration probability conditions implemented across two experimental phases.

Diffusion-based Assay:

Article Title: A systematic exploration of temporal bisection models across sub- and supra-second duration ranges
Article Snippet: An integral component to the validity of timing models is their ability to accurately fit behavioral data from detection and discrimination tasks such as the temporal bisection procedure.. Two of the most prominent timing models are the Sample Known Exactly (SKE), based on scalar timing theory, and the pseudo-logistic model (PLM).. Recently, evidence accumulation models based on drift–diffusion processes (DDM) have been utilized for modeling temporal bisection data.

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.

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

Article Title: Acquisition of decision making criteria: Reward rate ultimately beats accuracy
Article Snippet: .. DDM Fits Individual participants’ 2AFC data were first pooled across sessions 10 and greater and then were fit by the DDM using the diffusion model analysis toolbox (DMAT) in Matlab ( Vandekerckhove & Tuerlinckx, 2008 ). ..