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Nir-Cohen Gal, Arad Boaz, Ben-Shahar Ohad; Multi-inducer grouping for curve completion: Perceptual and computational exploration. Journal of Vision 2017;17(9):8. doi: https://doi.org/10.1167/17.9.8.
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© ARVO (1962-2015); The Authors (2016-present)
The human visual system excels in object recognition and scene interpretation even in scenes in which some (or even all) observed objects are partially occluded or fragmented. This highly efficient capacity is facilitated by constructive processes of contour completion between inducers to yield the perception of whole objects across gaps. A fundamental problem of the process is when and how the visual system groups different inducers in the visual scene between which completion occurs. Previous studies on this grouping problem, inspired mostly by relatability theory (Kellman & Shipley, 1991), focused on one good continuation condition that dictates whether a given pair of inducers would group together or not. Left open, however, was the question of how good continuation interacts with other grouping factors and how the perceptual system groups inducers when more than two of them are present in a scene (as would be in most typical natural stimuli). Here, we address both issues by exploring observers' perceptual response in multi-inducer scenes in which inducers and their relationships are determined by three independent factors: relative proximity, relative orientation, and curvature. Employing the dot localization paradigm, we indirectly inferred the type of inducer grouping constructed by the observer's visual system and analyzed the three-dimensional response function. Furthermore, we propose a simple parametric model that suggests an opponency relationship between two of the grouping factors and predicts subject's likely response with high accuracy. We also analyzed the discrepancy between our results and model the predictions made by the classical relatability theory.
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