Object perception and masking: Contributions of sides and convexities

Poirier, Frédéric J.A.M.; Wilson, Hugh R.

doi:10.1016/j.visres.2007.08.003

Cited by 22 publications

(23 citation statements)

References 73 publications

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“…This is consistent with experimental results (Loffler et al, 2003) showing that RF discrimination sensitivity is affected most strongly when points of convex curvature maxima are occluded, although other parts of the contour have been shown to play a non-trivial role (Poirier and Wilson, 2007; Hancock and Peirce, 2008; Bell et al, 2010). These observations were, however, for RFs with low amplitude, which do not contain concavities.…”

Section: Model For Shape Processingsupporting

confidence: 89%

Set-size effects for sampled shapes: experiments and model

Kempgens¹,

Löffler²,

Orbach³

2013

Front. Comput. Neurosci.

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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.

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Section: Model For Shape Processingsupporting

confidence: 89%

Set-size effects for sampled shapes: experiments and model

Kempgens¹,

Löffler²,

Orbach³

2013

Front. Comput. Neurosci.

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“…By showing that RF discrimination thresholds increase disproportionately when introducing small gaps at the peaks of RF patterns, Loffler et al (2003) suggested that points of maximum convex curvature play a key role in RF discrimination. Findings from lateral masking experiments are consistent with the hypothesis of maximum curvature dominance (Habak et al, 2004) although other studies have highlighted a non-trivial role of other parts of the contour (Poirier and Wilson, 2007; Hancock and Peirce, 2008; Bell et al, 2010). If local curvature maxima were relied upon, one may expect shape detection sensitivity to increase with increasing shape amplitude and frequency as these manipulations increase the maximum curvature, but this is inconsistent with the data (Figures 3A,B).…”

Section: Discussionsupporting

confidence: 77%

Detecting shapes in noise: tuning characteristics of global shape mechanisms

Schmidtmann¹,

Gordon²,

Bennett³

et al. 2013

Front. Comput. Neurosci.

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The proportion of signal elements embedded in noise needed to detect a signal is a standard tool for investigating motion perception. This paradigm was applied to the shape domain to determine how local information is pooled into a global percept. Stimulus arrays consisted of oriented Gabor elements that sampled the circumference of concentric radial frequency (RF) patterns. Individual Gabors were oriented tangentially to the shape (signal) or randomly (noise). In different conditions, signal elements were located randomly within the entire array or constrained to fall along one of the concentric contours. Coherence thresholds were measured for RF patterns with various frequencies (number of corners) and amplitudes (“sharpness” of corners). Coherence thresholds (about 10% = 15 elements) were lowest for circular shapes. Manipulating shape frequency or amplitude showed a range where thresholds remain unaffected (frequency ≤ RF4; amplitude ≤ 0.05). Increasing either parameter caused thresholds to rise. Compared to circles, thresholds increased by approximately four times for RF13 and five times for amplitudes of 0.3. Confining the signals to individual contours significantly reduced the number of elements needed to reach threshold (between 4 and 6), independent of the total number of elements on the contour or contour shape. Finally, adding external noise to the orientation of the elements had a greater effect on detection thresholds than adding noise to their position. These results provide evidence for a series of highly sensitive, shape-specific analysers which sum information globally but only from within specific annuli. These global mechanisms are tuned to position and orientation of local elements from which they pool information. The overall performance for arrays of elements can be explained by the sensitivity of multiple, independent concentric shape detectors rather than a single detector integrating information widely across space (e.g. Glass pattern detector).

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“…Similarly, symmetry perception in textured patches benefit from outline information (Gurnsey et al, 1998;Labonté et al, 1995). Shape symmetry may be evaluated by point-by-point comparisons of salient features, such as corners or convexities , which have been shown to play a more important role in object perception than edges (Attneave, 1954;Bertamini, 2001Bertamini, , 2004Biederman, 1987;Habak, Wilkinson, Zakher, & Wilson, 2004;Loffler, Wilson, & Wilkinson, 2003;Poirier & Wilson, 2007;Shevelev, Kamenkovich, & Sharaev, 2003; but see Hess, Wang, & Dakin, 1999;Mullen & Beaudot, 2002).…”

Section: Center Vs Outlinementioning

confidence: 99%

A biologically plausible model of human shape symmetry perception

Poirier

Wilson

2011

Journal of Vision

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Symmetry is usually computationally expensive to encode reliably, and yet it is relatively effortless to perceive. Here, we extend F. J. A. M. Poirier and H. R. Wilson's (2006) model for shape perception to account for H. R. Wilson and F. Wilkinson's (2002) data on shape symmetry. Because the model already accounts for shape perception, only minimal neural circuitry is required to enable it to encode shape symmetry as well. The model is composed of three main parts: (1) recovery of object position using large-scale non-Fourier V4-like concentric units that respond at the center of concentric contour segments across orientations, (2) around that recovered object center, curvature mechanisms combine multiplicatively the responses of oriented filters to encode object-centric local shape information, with a preference for convexities, and (3) object-centric symmetry mechanisms. Model and human performances are comparable for symmetry perception of shapes. Moreover, with some improvement of edge recovery, the model can encode symmetry axes in natural images such as faces.

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Object perception and masking: Contributions of sides and convexities

Cited by 22 publications

References 73 publications

Set-size effects for sampled shapes: experiments and model

Set-size effects for sampled shapes: experiments and model

Detecting shapes in noise: tuning characteristics of global shape mechanisms

A biologically plausible model of human shape symmetry perception

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