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Inverse MDS: Inferring Dissimilarity Structure from Multiple Item Arrangements

The pairwise dissimilarities of a set of items can be intuitively visualized by a 2D arrangement of the items, in which the distances reflect the dissimilarities. Such an arrangement can be obtained by multidimensional scaling (MDS). We propose a method for the inverse process: inferring the pairwis...

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Autores principales: Kriegeskorte, Nikolaus, Mur, Marieke
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Research Foundation 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3404552/
https://www.ncbi.nlm.nih.gov/pubmed/22848204
http://dx.doi.org/10.3389/fpsyg.2012.00245
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author Kriegeskorte, Nikolaus
Mur, Marieke
author_facet Kriegeskorte, Nikolaus
Mur, Marieke
author_sort Kriegeskorte, Nikolaus
collection PubMed
description The pairwise dissimilarities of a set of items can be intuitively visualized by a 2D arrangement of the items, in which the distances reflect the dissimilarities. Such an arrangement can be obtained by multidimensional scaling (MDS). We propose a method for the inverse process: inferring the pairwise dissimilarities from multiple 2D arrangements of items. Perceptual dissimilarities are classically measured using pairwise dissimilarity judgments. However, alternative methods including free sorting and 2D arrangements have previously been proposed. The present proposal is novel (a) in that the dissimilarity matrix is estimated by “inverse MDS” based on multiple arrangements of item subsets, and (b) in that the subsets are designed by an adaptive algorithm that aims to provide optimal evidence for the dissimilarity estimates. The subject arranges the items (represented as icons on a computer screen) by means of mouse drag-and-drop operations. The multi-arrangement method can be construed as a generalization of simpler methods: It reduces to pairwise dissimilarity judgments if each arrangement contains only two items, and to free sorting if the items are categorically arranged into discrete piles. Multi-arrangement combines the advantages of these methods. It is efficient (because the subject communicates many dissimilarity judgments with each mouse drag), psychologically attractive (because dissimilarities are judged in context), and can characterize continuous high-dimensional dissimilarity structures. We present two procedures for estimating the dissimilarity matrix: a simple weighted-aligned-average of the partial dissimilarity matrices and a computationally intensive algorithm, which estimates the dissimilarity matrix by iteratively minimizing the error of MDS-predictions of the subject’s arrangements. The Matlab code for interactive arrangement and dissimilarity estimation is available from the authors upon request.
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spelling pubmed-34045522012-07-30 Inverse MDS: Inferring Dissimilarity Structure from Multiple Item Arrangements Kriegeskorte, Nikolaus Mur, Marieke Front Psychol Psychology The pairwise dissimilarities of a set of items can be intuitively visualized by a 2D arrangement of the items, in which the distances reflect the dissimilarities. Such an arrangement can be obtained by multidimensional scaling (MDS). We propose a method for the inverse process: inferring the pairwise dissimilarities from multiple 2D arrangements of items. Perceptual dissimilarities are classically measured using pairwise dissimilarity judgments. However, alternative methods including free sorting and 2D arrangements have previously been proposed. The present proposal is novel (a) in that the dissimilarity matrix is estimated by “inverse MDS” based on multiple arrangements of item subsets, and (b) in that the subsets are designed by an adaptive algorithm that aims to provide optimal evidence for the dissimilarity estimates. The subject arranges the items (represented as icons on a computer screen) by means of mouse drag-and-drop operations. The multi-arrangement method can be construed as a generalization of simpler methods: It reduces to pairwise dissimilarity judgments if each arrangement contains only two items, and to free sorting if the items are categorically arranged into discrete piles. Multi-arrangement combines the advantages of these methods. It is efficient (because the subject communicates many dissimilarity judgments with each mouse drag), psychologically attractive (because dissimilarities are judged in context), and can characterize continuous high-dimensional dissimilarity structures. We present two procedures for estimating the dissimilarity matrix: a simple weighted-aligned-average of the partial dissimilarity matrices and a computationally intensive algorithm, which estimates the dissimilarity matrix by iteratively minimizing the error of MDS-predictions of the subject’s arrangements. The Matlab code for interactive arrangement and dissimilarity estimation is available from the authors upon request. Frontiers Research Foundation 2012-07-25 /pmc/articles/PMC3404552/ /pubmed/22848204 http://dx.doi.org/10.3389/fpsyg.2012.00245 Text en Copyright © 2012 Kriegeskorte and Mur. http://www.frontiersin.org/licenseagreement This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.
spellingShingle Psychology
Kriegeskorte, Nikolaus
Mur, Marieke
Inverse MDS: Inferring Dissimilarity Structure from Multiple Item Arrangements
title Inverse MDS: Inferring Dissimilarity Structure from Multiple Item Arrangements
title_full Inverse MDS: Inferring Dissimilarity Structure from Multiple Item Arrangements
title_fullStr Inverse MDS: Inferring Dissimilarity Structure from Multiple Item Arrangements
title_full_unstemmed Inverse MDS: Inferring Dissimilarity Structure from Multiple Item Arrangements
title_short Inverse MDS: Inferring Dissimilarity Structure from Multiple Item Arrangements
title_sort inverse mds: inferring dissimilarity structure from multiple item arrangements
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3404552/
https://www.ncbi.nlm.nih.gov/pubmed/22848204
http://dx.doi.org/10.3389/fpsyg.2012.00245
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