A multidimensional stochastic theory of similarity
Journal of Mathematical PsychologyPublished 1 December 1988Open access
Daniel M. Ennis, Joseph Palen, Kenneth Mullen
Citations127
SJR quartileQ2
SJR score0.74
SNIP1.10
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TL;DR
The theory of similarity presented is used as a basis for a Thurstonian extension of Shepard’s model of identification performance and a stochastic approach to multidimensional scaling based on samedifferent judgments is demonstrated.
Abstract
A multidimensional theory of similarity in which the mental representations of stimulus objects are assumed to be drawn from multivariate normal distributions is described. A distance-based similarity function is defined and the expected value of similarity is derived. This theory is the basis for a possible explanation of paradoxical results with highly similar stimuli regarding the form of the similarity function and the distance metric. A stochastic approach to multidimensional scaling based on same-different judgments is demonstrated using artificial and real data sets. The theory of similarity presented is used as a basis for a Thurstonian extension of Shepard's model of identification performance.
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