The influence of contrast and spatial factors in the perceived shape of boundaries

Roncato, Sergio; Casco, Clara

doi:10.3758/bf03194850

Cited by 17 publications

(21 citation statements)

References 32 publications

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“…One important factor that contributes to the perception of transparent layers is edge contrast polarity (Adelson, 2000; Roncato and Casco, 2003). But transparent layering cannot be explained solely by sorting layers based on contrast polarity.…”

Section: Comparison With Gamut Relativity Theory: Transparency and “Dmentioning

confidence: 99%

A cortical edge-integration model of object-based lightness computation that explains effects of spatial context and individual differences

Rudd

2014

Front. Hum. Neurosci.

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Previous work has demonstrated that perceived surface reflectance (lightness) can be modeled in simple contexts in a quantitatively exact way by assuming that the visual system first extracts information about local, directed steps in log luminance, then spatially integrates these steps along paths through the image to compute lightness (Rudd and Zemach, 2004, 2005, 2007). This method of computing lightness is called edge integration. Recent evidence (Rudd, 2013) suggests that human vision employs a default strategy to integrate luminance steps only along paths from a common background region to the targets whose lightness is computed. This implies a role for gestalt grouping in edge-based lightness computation. Rudd (2010) further showed the perceptual weights applied to edges in lightness computation can be influenced by the observer's interpretation of luminance steps as resulting from either spatial variation in surface reflectance or illumination. This implies a role for top-down factors in any edge-based model of lightness (Rudd and Zemach, 2005). Here, I show how the separate influences of grouping and attention on lightness can be modeled in tandem by a cortical mechanism that first employs top-down signals to spatially select regions of interest for lightness computation. An object-based network computation, involving neurons that code for border-ownership, then automatically sets the neural gains applied to edge signals surviving the earlier spatial selection stage. Only the borders that survive both processing stages are spatially integrated to compute lightness. The model assumptions are consistent with those of the cortical lightness model presented earlier by Rudd (2010, 2013), and with neurophysiological data indicating extraction of local edge information in V1, network computations to establish figure-ground relations and border ownership in V2, and edge integration to encode lightness and darkness signals in V4.

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Section: Comparison With Gamut Relativity Theory: Transparency and “Dmentioning

confidence: 99%

A cortical edge-integration model of object-based lightness computation that explains effects of spatial context and individual differences

Rudd

2014

Front. Hum. Neurosci.

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“…This illusion is not novel on the phenomenal level, but instead we have a new explanation to apply, based on the notion of local "association field" illustrated in Figure 1, defined as a set of local orientation signals that radiate by contour extrapolation in all directions (Field, Hayes, & Hess, 1993;Shipley & Kellman, 2003). Inspired by this model we have studied the spatial conditions by which local oriented edges are combined into a unique spatial contour using a yes-no detection paradigm (Roncato & Casco, 2003, 2006, and found the precise values of the spatial parameters (relative separation and/or alignment) that allow neighboring edge segments to bind into a smooth contour, in contrast with the values that degrade the perception and cause the edges to no longer conform to a smooth contour. Our previous results show that binding occurs between the association field projections propagating from the two edges, and that these edges are perceived as a unique contour with illusory tilt, providing that two conditions are met: first, the projections have to have the same contrast polarity and, second, their vertical and horizontal distances have to be short: their vertical misalignment (D2) has to be smaller than 7 arcmin and the horizontal gap (D1) has to be smaller than 13 arcmin.…”

Section: Introductionmentioning

confidence: 99%

A new "tilt" illusion reveals the relation between border ownership and border binding

Roncato

Casco

2009

Journal of Vision

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The "association field" models of contour detection predict specific spatial conditions for linking or grouping neighboring elements into smooth contours. We previously suggested that the "association field" model may account for perceptual binding of near-collinear luminance edges of same contrast polarity and their consequent unification into a unique contrast border with illusory tilt. This approach is now developed into a new version of the tilt illusion, the seesaw illusion, in which the contrast border is perceived as inverting concave-convex illusory curvature when background luminance is inverted, indicating that contrast polarity must be incorporated into the notion of "association field" to account for the seesaw illusion. We found that although tile-edge segmentation into alternating black-white segments produces conflicting local tilts, the illusion remains, up to 16 arcmin edge distance. This occurs at extreme background luminance for long segments (where only congruent edge segments of higher contrast bind, the others being perceptually assimilated into the background surface) and, when segments are too short for their orientation to be detected, at all background luminance values except that equidistant from black and white stripes. Our findings provide further confirmation that these are striking border ownership phenomena, demonstrating that figure/ground organization precedes perceptual binding of edges through association fields.

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“…For these effects—both binding and induction—to arise it not necessary that four surfaces converge at the same point as seen in the image of an X-junction. The edges can be non-collinear and their endings can be separated by a gap; in fact if the distance and the deviation of colinearity are within a short range (Roncato and Casco, 2003 , 2006 ) the effects are clearly visible. This explains why we perceive induction effects even with configurations where the X-junctions are not replicated (Bressan, 2001 ).…”

Section: Discussionmentioning

confidence: 99%

“…Gabor units sharing the same CP are easier to perceptually group when distributed among other Gabor units (McIlhagga and Mullen, 1996 ; Field et al, 2000 ); real or illusory contours are easier to detect when of the same CP (Cavanagh and Leclerc, 1989 ; Spehar, 2000 ). Research on orientation misperception (Roncato and Casco, 2003 , 2006 ; Van Lier and Csathó, 2006 ) demonstrated that this tendency manifests locally as illusory tilts at the edge extremities when they have the same contrast polarity and extend within a spatial range of 7–15°.…”

Section: Introductionmentioning

confidence: 99%

Brightness/darkness induction and the genesis of a contour

Roncato

2014

Front. Hum. Neurosci.

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Visual contours often result from the integration or interpolation of fragmented edges. The strength of the completion increases when the edges share the same contrast polarity (CP). Here we demonstrate that the appearance in the perceptual field of this integrated unit, or contour of invariant CP, is concomitant with a vivid brightness alteration of the surfaces at its opposite sides. To observe this effect requires some stratagems because the formation in the visual field of a contour of invariant CP normally engenders the formation of a second contour and then the rise of two streams of induction signals that interfere in different ways. Particular configurations have been introduced that allow us to observe the induction effects of one contour taken in isolation. I documented these effects by phenomenological observations and psychophysical measurement of the brightness alteration in relation to luminance contrast. When the edges of the same CP complete to form a contour, the background of homogeneous luminance appears to dim at one side and to brighten at the opposite side (in accord with the CP). The strength of the phenomenon is proportional to the local luminance contrast. This effect weakens or nulls when the contour of the invariant CP separates surfaces filled with different gray shades. These conflicting results stimulate a deeper exploration of the induction phenomena and their role in the computation of brightness contrast. An alternative perspective is offered to account for some brightness illusions and their relation to the phenomenal transparency. The main assumption asserts that, when in the same region induction signals of opposite CP overlap, the filling-in is blocked unless the image is stratified into different layers, one for each signal of the same polarity. Phenomenological observations document this “solution” by the visual system.

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The influence of contrast and spatial factors in the perceived shape of boundaries

Cited by 17 publications

References 32 publications

A cortical edge-integration model of object-based lightness computation that explains effects of spatial context and individual differences

A cortical edge-integration model of object-based lightness computation that explains effects of spatial context and individual differences

A new "tilt" illusion reveals the relation between border ownership and border binding

Brightness/darkness induction and the genesis of a contour

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