Ordinal Characterization of Similarity Judgments.

TitleOrdinal Characterization of Similarity Judgments.
Publication TypeJournal Article
Year of Publication2023
AuthorsVictor JD, Aguilar G, Waraich SA
Date Published2023 Oct 11

Characterizing judgments of similarity within a perceptual or semantic domain, and making inferences about the underlying structure of this domain from these judgments, has an increasingly important role in cognitive and systems neuroscience. We present a new framework for this purpose that makes very limited assumptions about how perceptual distances are converted into similarity judgments. The approach starts from a dataset of empirical judgments of relative similarities: the fraction of times that a subject chooses one of two comparison stimuli to be more similar to a reference stimulus. These empirical judgments provide Bayesian estimates of underling choice probabilities. From these estimates, we derive three indices that characterize the set of judgments, measuring consistency with a symmetric dis-similarity, consistency with an ultrametric space, and consistency with an additive tree. We illustrate this approach with example psychophysical datasets of dis-similarity judgments in several visual domains and provide code that implements the analyses.

Alternate JournalArXiv
PubMed ID37873008
PubMed Central IDPMC10593068
Grant ListR01 EY007977 / EY / NEI NIH HHS / United States