How a Brain Sees: Neural Mechanisms

Multiple paradoxical visual percepts are explained using boundary completion and surface filling-in properties, including discounting the illuminant; brightness constancy, contrast, and assimilation; the Craik-O’Brien-Cornsweet Effect; and Glass patterns. Boundaries act as both generators and barriers to filling-in using specific cooperative and competitive interactions. Oriented local contrast detectors, like cortical simple cells, create uncertainties that are resolved using networks of simple, complex, and hypercomplex cells, leading to unexpected insights such as why Roman typeface letter fonts use serifs. Further uncertainties are resolved by interactions with bipole grouping cells. These simple-complex-hypercomplex-bipole networks form a double filter and grouping network that provides unified explanations of texture segregation, hyperacuity, and illusory contour strength. Discounting the illuminant suppresses illumination contaminants so that feature contours can hierarchically induce surface filling-in. These three hierarchical resolutions of uncertainty explain neon color spreading. Why groupings do not penetrate occluding objects is explained, as are percepts of DaVinci stereopsis, the Koffka-Benussi and Kanizsa-Minguzzi rings, and pictures of graffiti artists and Mooney faces. The property of analog coherence is achieved by laminar neocortical circuits. Variations of a shared canonical laminar circuit have explained data about vision, speech, and cognition. The FACADE theory of 3D vision and figure-ground separation explains much more data than a Bayesian model can. The same cortical process that assures consistency of boundary and surface percepts, despite their complementary laws, also explains how figure-ground separation is triggered. It is also explained how cortical areas V2 and V4 regulate seeing and recognition without forcing all occluders to look transparent.

The Perceived Strength of Motion-Defined Edges

Performance on visual tasks involving the use of motion-defined contours is likely to depend on stimulus strength, but presently there are no empirical or experimental assessments of motion-defined contour strength. Therefore, a matching method was used to estimate the strength of suprathreshold motion-defined edges on a luminance-contrast scale. The perceived strength of a motion-defined contour was expressed as an equivalent luminance contrast; this allowed the use of a single scale which accommodates diverse motion-defined stimuli. Motion-defined edge strength estimated in this manner was an inverted U-shaped function of dot density and dot velocity, and spanned at least a fivefold range of edge strengths. For one observer, maximum motion-defined edge strength was equivalent to 79% luminance contrast, at least thirteen times the contrast detection threshold. The results are interpreted via a simple two-stage model for perceiving motion-defined edges.

Parallelism and the Perception of Illusory Contours

The role of symmetry in the perception of illusory contours has been a subject of controversy ever since Kanizsa proposed his theory of illusory contours based on Gestalt principles. Today it is widely agreed that illusory contours do not necessarily occur more readily with inducers that can be ‘amodally’ completed to symmetrical objects than with inducers that cannot. But the question of whether symmetrical inducers produce weaker illusory contours than do unsymmetrical ones is still controversial. A novel determinant of illusory contour strength, parallelism, is proposed. Experiments are reported which indicate that illusory contours induced by ‘blobs’ which have boundaries that are nearby and parallel to the illusory contour are weaker than illusory contours induced by blobs that do not have this property. It is suggested that the display that has been most widely used by researchers to support their claims for a weakening of illusory contours with symmetrical inducers is weak primarily because of parallelism.

The Multiple Determination of Illusory Contours: 2. An Empirical Investigation

Judgments of contour strength or saliency for twenty-four illusory-contour configurations were subjected to a confirmatory factor analysis. A four-factor model that posited the involvement of simultaneous contrast, linear effects (assimilation and dissimilation), depth/completion cues, and feature analyzers accounted for a substantial proportion of the variance in judgments of illusory-contour strength. The hierarchical addition of a fifth factor, diffuse illusory contours, significantly improved the overall fit of the model, but added little to the proportion of explained variance. The taxonomic approach adopted provides support for a multiprocess model of illusory-contour perception.