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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
Published on: March 1, 2022
Inverse MDS: Inferring Dissimilarity Structure from Multiple Item Arrangements.
Nikolaus Kriegeskorte1, Marieke Mur
1Cognition and Brain Sciences Unit, Medical Research Council Cambridge, UK.
Frontiers in Psychology
|August 1, 2012
Summary
We introduce inverse multidimensional scaling (MDS) to infer item dissimilarities from multiple 2D arrangements. This method efficiently estimates continuous dissimilarity structures using adaptive item subset design.
Area of Science:
- Cognitive Science
- Psychometrics
- Data Visualization
Background:
- Pairwise item dissimilarities are traditionally measured via direct judgments.
- Multidimensional scaling (MDS) visualizes these dissimilarities in 2D space.
- Alternative methods like free sorting and single 2D arrangements exist but have limitations.
Purpose of the Study:
- To propose and validate a novel 'inverse MDS' method for inferring pairwise dissimilarities.
- To estimate dissimilarity matrices from multiple 2D arrangements of item subsets.
- To develop an adaptive algorithm for optimal subset design to enhance dissimilarity estimation.
Main Methods:
- Subjects arrange items (icons) in 2D space using drag-and-drop operations.
- Multiple arrangements of item subsets are collected.
- Two estimation procedures are presented: weighted-aligned-average and iterative error minimization.
Main Results:
- The multi-arrangement method generalizes pairwise judgments and free sorting.
- It efficiently captures many dissimilarity judgments in context.
- The method characterizes continuous, high-dimensional dissimilarity structures.
Conclusions:
- Inverse MDS using adaptive multi-arrangement provides an efficient and psychologically valid method for dissimilarity estimation.
- This approach offers advantages over traditional methods for complex dissimilarity structures.
- Software for interactive arrangement and estimation is available.
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