Literature DB >> 24671826

A MATLAB toolbox for the efficient estimation of the psychometric function using the updated maximum-likelihood adaptive procedure.

Yi Shen1, Wei Dai, Virginia M Richards.   

Abstract

A MATLAB toolbox for the efficient estimation of the threshold, slope, and lapse rate of the psychometric function is described. The toolbox enables the efficient implementation of the updated maximum-likelihood (UML) procedure. The toolbox uses an object-oriented architecture for organizing the experimental variables and computational algorithms, which provides experimenters with flexibility in experimental design and data management. Descriptions of the UML procedure and the UML Toolbox are provided, followed by toolbox use examples. Finally, guidelines and recommendations of parameter configurations are given.

Entities:  

Mesh：

Year:  2015        PMID： 24671826      PMCID： PMC5516528          DOI： 10.3758/s13428-014-0450-6

Source DB:  PubMed          Journal:  Behav Res Methods        ISSN： 1554-351X


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  14 in total

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Journal:  J Acoust Soc Am       Date:  2019-04       Impact factor: 1.840

Review 3.  Statistical approaches to identifying lapses in psychometric response data.

Authors:  Torin K Clark; Daniel M Merfeld
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4.  Feasibility of interleaved Bayesian adaptive procedures in estimating the equal-loudness contour.

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Journal:  Exp Brain Res       Date:  2015-12-08       Impact factor: 1.972

8.  Bayesian Inference of Two-Dimensional Contrast Sensitivity Function from Data Obtained with Classical One-Dimensional Algorithms Is Efficient.

Authors:  Xiaoxiao Wang; Huan Wang; Jinfeng Huang; Yifeng Zhou; Tzvetomir Tzvetanov
Journal:  Front Neurosci       Date:  2017-01-10       Impact factor: 4.677

9.  Saccade Adaptation and Visual Uncertainty.

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Journal:  Front Hum Neurosci       Date:  2016-05-24       Impact factor: 3.169

10.  Temporal causal inference with stochastic audiovisual sequences.

Authors:  Shannon M Locke; Michael S Landy
Journal:  PLoS One       Date:  2017-09-08       Impact factor: 3.240
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