Abstract
This paper presents a nonspatial operationalization of the Krumhansl (1978, 1982) distancedensity model of similarity. This model assumes that the similarity between two objects i and j is a function of both the interpoint distance between i and j and the density of other stimulus points in the regions surrounding i and j. We review this conceptual model and associated empirical evidence for such a specification. A nonspatial, tree-fitting methodology is described which is sufficiently flexible to fit a number of competing hypotheses of similarity formation. A sequential, unconstrained minimization algorithm is technically presented together with various program options. Three applications are provided which demonstrate the flexibility of the methodology. Finally, extensions to spatial models, three-way analyses, and hybrid models are discussed.
Original language | English (US) |
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Pages (from-to) | 229-253 |
Number of pages | 25 |
Journal | Psychometrika |
Volume | 55 |
Issue number | 2 |
DOIs | |
State | Published - Jun 1990 |
All Science Journal Classification (ASJC) codes
- General Psychology
- Applied Mathematics