Effects of surround articulation on lightness depend on the spatial arrangement of the articulated region

Abrupt darkening under high dynamic range (HDR) luminance invokes facilitation for high-contrast targets and grouping by luminance similarity

When scanning across a scene, luminance can vary by up to 100,000-to-1 (high dynamic range, HDR), requiring multiple normalizing mechanisms spanning from the retina to the cortex to support visual acuity and recognition. Vision models based on standard dynamic range (SDR) luminance contrast ratios below 100-to-1 have limited ability to generalize to real-world scenes with HDR luminance. To characterize how orientation and luminance are linked in brain mechanisms for luminance normalization, we measured orientation discrimination of Gabor targets under HDR luminance dynamics. We report a novel phenomenon, that abrupt 10- to 100-fold darkening engages contextual facilitation, distorting the apparent orientation of a high-contrast central target. Surprisingly, facilitation was influenced by grouping by luminance similarity, as well as by the degree of luminance variability in the surround. These results challenge vision models based solely on activity normalization and raise new questions that will lead to models that perform better in real-world scenes.

Neurocomputational Lightness Model Explains the Appearance of Real Surfaces Viewed Under Gelb Illumination

One of the primary functions of visual perception is to represent, estimate, and evaluate the properties of material surfaces in the visual environment. One such property is surface color, which can convey important information about ecologically relevant object characteristics such as the ripeness of fruit and the emotional reactions of humans in social interactions. This paper further develops and applies a neural model (Rudd, 2013, 2017) of how the human visual system represents the light/dark dimension of color-known as lightness-and computes the colors of achromatic material surfaces in real-world spatial contexts. Quantitative lightness judgments conducted with real surfaces viewed under Gelb (i.e., spotlight) illumination are analyzed and simulated using the model. According to the model, luminance ratios form the inputs to ON- and OFF-cells, which encode local luminance increments and decrements, respectively. The response properties of these cells are here characterized by physiologically motivated equations in which different parameters are assumed for the two cell types. Under non-saturating conditions, ON-cells respond in proportion to a compressive power law of the local incremental luminance in the image that causes them to respond, while OFF-cells respond linearly to local decremental luminance. ON- and OFF-cell responses to edges are log-transformed at a later stage of neural processing and then integrated across space to compute lightness via an edge integration process that can be viewed as a neurally elaborated version of Land's retinex model (Land & McCann, 1971). It follows from the model assumptions that the perceptual weights-interpreted as neural gain factors-that the model observer applies to steps in log luminance at edges in the edge integration process are determined by the product of a polarity-dependent factor 1-by which incremental steps in log luminance (i.e., edges) are weighted by the value <1.0 and decremental steps are weighted by 1.0-and a distance-dependent factor 2, whose edge weightings are estimated to fit perceptual data. The model accounts quantitatively (to within experimental error) for the following: lightness constancy failures observed when the illumination level on a simultaneous contrast display is changed (Zavagno, Daneyko, & Liu, 2018); the degree of dynamic range compression in the staircase-Gelb paradigm (Cataliotti & Gilchrist, 1995; Zavagno, Annan, & Caputo, 2004); partial releases from compression that occur when the staircase-Gelb papers are reordered (Zavagno, Annan, & Caputo, 2004); and the larger compression release that occurs when the display is surrounded by a white border (Gilchrist & Cataliotti, 1994).

Dichoptic perception of brown

Two experiments assessed mechanisms underlying brown induction by presenting a foveal target disk and concentric annular surround stimuli that varied in contrast relative to larger backgrounds. Stimuli were presented under monocular, binocular, and dichoptic viewing conditions. Observers adjusted the luminance of the target disk to a criterion brown level. We found evidence for at least two separate mechanisms for brown induction: one mechanism that is dependent on physically contiguous contrast and operates in monocular pathways and another mechanism that responds to high luminance contrast anywhere in the visual field and can operate after convergence of signals from the two eyes.

No effects of surround complexity on brown induction

A yellow stimulus turns brown when it is made sufficiently darker than its surroundings. Most previous studies have used simple contiguous surround stimuli to induce brown, so we know little about how brown induction may be controlled by more distant and more complex surround features. We begin to address this issue by varying the complexity of two configurations of achromatic surround stimuli. It was shown that the area most immediately contiguous to the test stimulus has strong effects on brown induction. More importantly, we found that neither the number of surround features nor the distribution of light in the surround region had an effect on brown induction, as long as the overall size of the surround region remained constant. Instead, we found that brown induction depended on the total amount of light in the constant-size surround region, regardless of how that light was distributed. This potentially distinguishes the mechanisms of brown induction from those of brightness induction.

A Neurocomputational account of the role of contour facilitation in brightness perception

A new filling-in model is proposed in order to account for challenging brightness illusions, where inducing background elements are spatially separated from the gray target such as dungeon, cube and grating illusions, bullseye display and ring patterns. This model implements the simple idea that neural response to low-contrast contour is enhanced (facilitated) by the presence of collinear or parallel high-contrast contours in its wider neighborhood. Contour facilitation is achieved via dendritic inhibition, which enables the computation of maximum function among inputs to the node. Recurrent application of maximum function leads to the propagation of the neural signal along collinear or parallel contour segments. When a strong global-contour signal is accompanied with a weak local-contour signal at the same location, conditions are met to produce brightness assimilation within the Filling-in Layer. Computer simulations showed that the model correctly predicts brightness appearance in all of the aforementioned illusions as well as in White's effect, Benary's cross, Todorović's illusion, checkerboard contrast, contrast-contrast illusion and various variations of the White's effect. The proposed model offers new insights on how geometric factors (contour colinearity or parallelism), together with contrast magnitude contribute to the brightness perception.

Metacontrast masking and the cortical representation of surface color: dynamical aspects of edge integration and contrast gain control

This paper reviews recent theoretical and experimental work supporting the idea that brightness is computed in a series of neural stages involving edge integration and contrast gain control. It is proposed here that metacontrast and paracontrast masking occur as byproducts of the dynamical properties of these neural mechanisms. The brightness computation model assumes, more specifically, that early visual neurons in the retina, and cortical areas V1 and V2, encode local edge signals whose magnitudes are proportional to the logarithms of the luminance ratios at luminance edges within the retinal image. These local edge signals give rise to secondary neural lightness and darkness spatial induction signals, which are summed at a later stage of cortical processing to produce a neural representation of surface color, or achromatic color, in the case of the chromatically neutral stimuli considered here. Prior to the spatial summation of these edge-based induction signals, the weights assigned to local edge contrast are adjusted by cortical gain mechanisms involving both lateral interactions between neural edge detectors and top-down attentional control. We have previously constructed and computer-simulated a neural model of achromatic color perception based on these principles and have shown that our model gives a good quantitative account of the results of several brightness matching experiments. Adding to this model the realistic dynamical assumptions that 1) the neurons that encode local contrast exhibit transient firing rate enhancement at the onset of an edge, and 2) that the effects of contrast gain control take time to spread between edges, results in a dynamic model of brightness computation that predicts the existence Broca-Sulzer transient brightness enhancement of the target, Type B metacontrast masking, and a form of paracontrast masking in which the target brightness is enhanced when the mask precedes the target in time.