Respects for similarity.
Abstract
This article reviews the status of similarity as an explanatory construct with a focus on similarity judgments. For similarity to be a useful construct, one must be able to specify the ways or respects in which two things are similar. One solution to this problem is to restrict the notion of similarity to hard-wired perceptual processes. It is argued that this view is too narrow and limiting. Instead, it is proposed that an important source of constraints derives from the similarity comparison process itself. Both new experiments and other evidence are described that support the idea that respects are determined by processes internal to comparisons
Cites (1)
- Context theory of classification learningMedin, Douglas L., Schaffer, Marguerite M. · 1978 · Psychological Review
Cited by (43)
- The Unreasonable Effectiveness of Deep Features as a Perceptual MetricZhang, Richard, Isola, Phillip, Efros, Alexei A., Shechtman, Eli, Wang, Oliver · 2018 · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- A solution to Plato's problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge.Landauer, Thomas K., Dumais, Susan T. · 1997 · Psychological Review
- Representativeness revisited: Attribute substitution in intuitive judgment.Kahneman, Daniel, Frederick, Shane · 2002 · Heuristics and Biases
- The Affect HeuristicSlovic, Paul, Finucane, Melissa, Peters, Ellen, MacGregor, Donald G. · 2002 · Heuristics and Biases
- Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural LanguageResnik, P. · 1999 · Journal of Artificial Intelligence Research
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Respects for similarity. https://app.cochat.ai/papers/10.1037//0033-295x.100.2.254
- The paper
- Medin, D. L., Goldstone, R. L., & Gentner, D. (1993). Respects for similarity. Psychological Review. https://doi.org/10.1037//0033-295x.100.2.254
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.