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Multidimensional scaling (MDS) models of mental representation assume stimuli are represented by points in a low-dimensional space, such that more similar stimuli are represented by points closer to each other. We consider possible individual differences in MDS representations, using the recently proposed K-INDSCAL model, which allows for both sub-groups of people with different representations, and...
We propose a Bayesian approach to asymmetric multidimensional scaling (MDS), which incorporates an asymmetric data structure. The asymmetry is represented by the hill-climbing model, which introduces a slope vector that measures the extent of the difficulty in going from one point to another instead of vice versa, in the MDS space. By using Bayesian estimation with Markov chain Monte Carlo algorithm,...
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