Set-size effects for sampled shapes: experiments and model

• Published in: Frontiers in computational neuroscience Vol. 7; p. 67
• Main Authors: Kempgens, Christian, Loffler, Gunter, Orbach, Harry S.
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 2013; Frontiers Media S.A
• Subjects: Experiments; Information systems; Medical research; Neuroscience; Orientation behavior; Orientation Discrimination; sampled shapes; set-size effect; shape model; Shape Perception; visual search; Visual system; set-size effect; shape model; orientation discrimination; visual search; shape perception; heterogeneity detection; radial frequency patterns; sampled shapes
• ISSN: 1662-5188; 1662-5188
• DOI: 10.3389/fncom.2013.00067

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.