Abstract
The location of imperfections or heterogeneities in shapes and contours often correlates with points of interest in a visual scene. Investigating the detection of such heterogeneities provides clues as to the mechanisms processing simple shapes and contours. We determined set-size effects (e.g., sensitivity to single target detection as distractor number increases) for sampled contours to investigate how the visual system combines information across space. Stimuli were shapes sampled by oriented Gabor patches: circles and high-amplitude RF4 and RF8 radial frequency patterns with Gabor orientations tangential to the shape. Subjects had to detect a deviation in orientation of one element (“heterogeneity”). Heterogeneity detection sensitivity was measured for a range (7–40) of equally spaced (2.3–0.4°) elements. In a second condition, performance was measured when elements sampled a part of the shapes. We either varied partial contour length for a fixed (7) set-size, co-varying inter-element spacing, or set-size for a fixed spacing (0.7°), co-varying partial contour length. Surprisingly, set-size effects (poorer performance with more elements) are rarely seen. Set-size effects only occur for shapes containing concavities (RF4 and RF8) and when spacing is fixed. When elements are regularly spaced, detection performance improves with set-size for all shapes. When set-size is fixed and spacing varied, performance improves with decreasing spacing. Thus, when an increase in set-size and a decrease in spacing co-occur, the effect of spacing dominates, suggesting that inter-element spacing, not set-size, is the critical parameter for sampled shapes. We propose a model for the processing of simple shapes based on V4 curvature units with late noise, incorporating spacing, average shape curvature, and the number of segments with constant sign of curvature contained in the shape, which accurately accounts for our experimental results, making testable predictions for a variety of simple shapes.
Highlights
Successful interaction with the environment requires the identification of the location of items of interest (Treisman and Gelade, 1980; Palmer et al, 2000), processing their shapes and textures (Field et al, 1993; Wilson and Wilkinson, 1998; Loffler, 2008) and recognizing objects (Biederman and Gerhardstein, 1993).Points of interest catch the observer’s attention, sometimes by a notch in a contour, a dent in a surface or by occlusion/superposition of items in the background by closer objects
While substantial set-size effects occur for uniform patterns, it has recently been shown (Scott-Brown and Orbach, 1998; Kempgens et al, 2007) that signal detection theories of visual search cannot account for heterogeneous configurations, where set-size performance is much worse than predicted
The aim of the experiments presented here was to determine the effects of set-size, shape, spacing and other factors on the detection of contour heterogeneity, and to put our results into context with existing models of visual search and shape processing
Summary
Points of interest catch the observer’s attention, sometimes by a notch in a contour, a dent in a surface or by occlusion/superposition of items in the background by closer objects. Such points can be generalized as locations in a visual scene with some kind of deviation from the surround: a heterogeneity. While substantial set-size effects occur for uniform patterns (e.g., composed of elements with parallel orientation), it has recently been shown (Scott-Brown and Orbach, 1998; Kempgens et al, 2007) that signal detection theories of visual search cannot account for heterogeneous configurations (e.g., elements with random orientations; Orbach et al, 2005), where set-size performance is much worse than predicted. The difference in results for uniform vs. random configurations indicates that the orientational arrangement of pattern elements has a considerable effect on observer sensitivity
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