Shape discrimination along morph-spaces. (May 2019)

• Record Type: Journal Article
• Authors: Destler, Nathan; Singh, Manish; Feldman, Jacob
• Is Part Of: Vision research. Volume 158(2019)
• Page Start: 189
• Page End: 199
• Publication Date: 2019-05
• Subjects: Shape -- Bayesian -- Model -- Skeleton -- Similarity -- Discrimination -- Categorization -- Medial axis; Vision -- Periodicals; 573.88
• DOI: 10.1016/j.visres.2019.03.002
• Languages: English
• ISSNs: 0042-6989

Abstract

Abstract: We investigated the dimensions defining mental shape space, by measuring shape discrimination thresholds along "morph-spaces" defined by pairs of shapes. Given any two shapes, one can construct a morph-space by taking weighted averages of their boundary vertices (after normalization), creating a continuum of shapes ranging from the first shape to the second. Previous studies of morphs between highly familiar shape categories (e.g. truck and turkey) have shown elevated discrimination at category boundaries, reflecting a kind of "categorical perception" in shape space. Here, we use this technique to explore the underlying representation of unfamiliar shapes. Subjects were shown two shapes at nearby points along a morph-space, and asked to judge whether they were the same or different, with an adaptive procedure used to estimate discrimination thresholds at each point along the morph-space. We targeted several potentially important categorical distinctions, such one- vs. two-part shapes, two- vs. three-part shapes, changes in symmetry structure, and other potentially important distinctions. Observed discrimination thresholds showed substantial and systematic deviations from uniformity at different points along each shape continuum, meaning that subjects were consistently better at discriminating at certain points along each morph-space than at others. We introduce a shape similarity measure, based on Bayesian skeletal shape representations, which gives a good account of the observed variations in shape sensitivity.