A multiscale spatial filtering account of the White effect, simultaneous brightness contrast and grating induction

What Fechner could not do: Separating perceptual encoding and decoding with difference scaling

A key question in perception research is how stimulus variations translate into perceptual magnitudes, that is, the perceptual encoding process. As experimenters, we cannot probe perceptual magnitudes directly, but infer the encoding process from responses obtained in a psychophysical experiment. The most prominent experimental technique to measure perceptual appearance is matching, where observers adjust a probe stimulus to match a target in its appearance along the dimension of interest. The resulting data quantify the perceived magnitude of the target in physical units of the probe, and are thus an indirect expression of the underlying encoding process. In this paper, we show analytically and in simulation that data from matching tasks do not sufficiently constrain perceptual encoding functions, because there exist an infinite number of pairs of encoding functions that generate the same matching data. We use simulation to demonstrate that maximum likelihood conjoint measurement (Ho, Landy, & Maloney, 2008; Knoblauch & Maloney, 2012) does an excellent job of recovering the shape of ground truth encoding functions from data that were generated with these very functions. Finally, we measure perceptual scales and matching data for White's effect (White, 1979) and show that the matching data can be predicted from the estimated encoding functions, down to individual differences.

Isolation of brightness induction effects on target patches from adjacent surrounds and remote backgrounds

The brightness (perceived intensity) of a region of visual space depends on its luminance and on the luminance of nearby regions. This phenomenon is called brightness induction and includes both brightness contrast and assimilation. Historically, and on a purely descriptive level, brightness contrast refers to a directional shift in target brightness away from the brightness of an adjacent region while assimilation refers to a brightness shift toward that of an adjacent region. In order to understand mechanisms, it is important to differentiate the descriptive terms contrast and assimilation from the optical and/or neural processes, often similarly named, which cause the effects. Experiment 1 isolated the effect on target patch (64 cd/m2) matching luminance (brightness) of six surround-ring widths (0.1°–24.5°) varied over 11 surround-ring luminances (32–96 cd/m2). Using the same observers, Experiment 2 examined the effect of the identical surround-ring parameters on target patch matching luminance in the presence of a dark (0.0 cd/m2) and a bright (96 cd/m2) remote background. By differencing the results of Experiment 1 (the isolated effect of the surround-ring) from those of Experiment 2 (the combined effect of the surround-ring with the dark and bright remote background) we further isolated the effect of the remote background. The results reveal that surround-rings and remote backgrounds produce brightness contrast effects in the target patch that are of the same or opposite polarity depending on the luminance polarity of these regions relative to target patch luminance. The strength of brightness contrast from the surround-ring varied with surround-ring luminance and width. Brightness contrast (darkening) in the target from the bright remote background was relatively constant in magnitude across all surround-ring luminances and increased in magnitude with decreasing surround-ring width. Brightness contrast (brightening) from the isolated dark remote background also increased in magnitude with decreasing surround-ring width: however, despite some regional flattening of the functions due to the fixed luminance of the dark remote background, induction magnitude was much reduced in the presence of a surround-ring of greater luminance than the target patch indicating a non-linear interaction between the dark remote background and surround-ring luminance.

Vertical anisotropy in lightness perception not caused by lighting assumption

Our recent study found an illusory effect whereby an image of an upward-facing gray panel appears darker than its 180-degree rotated image. We attributed this inversion effect to the observer's implicit assumption that light from above is more intense than light from below. This paper aims to explore the possibility that low-level visual anisotropy also contributes to the effect. In Experiment 1, we investigated whether the effect could be observed even when the position, the contrast polarity, and the existence of the edge were manipulated. In Experiments 2 and 3, the effect was further examined using stimuli that contained no depth cues. Experiment 4 confirmed the effect using stimuli of even simpler configuration. The results of all the experiments demonstrated that brighter edges on the upper side of the target make it appear lighter, indicating that low-level anisotropy contributes to the inversion effect, even without depth orientation information. However, darker edges on the upper side of the target produced ambiguous results. We speculate that the perceived lightness of the target might be affected by two kinds of vertical anisotropy, one of which is dependent on contrast polarity while the other is independent of it. Moreover, the results also replicated the previous finding that the lighting assumption contributes to perceived lightness. Overall, the present study demonstrates that both low-level vertical anisotropy and mid-level lighting assumption influence lightness.

Empirical evaluation of computational models of lightness perception

Lightness of a surface depends not only on its physical characteristics, but also on the properties of the surrounding context. As a result, varying the context can significantly alter surface lightness, an effect exploited in many lightness illusions. Computational models can produce outcomes similar to human illusory percepts, allowing for demonstrable assessment of the applied mechanisms and principles. We tested 8 computational models on 13 typical displays used in lightness research (11 Illusions and 2 Mondrians), and compared them with results from human participants (N = 85). Results show that HighPass and MIR models predict empirical results for simultaneous lightness contrast (SLC) and its close variations. ODOG and its newer variants (ODOG-2 and L-ODOG) in addition to SLC displays were able to predict effect of White's illusion. RETINEX was able to predict effects of both SLC displays and Dungeon illusion. Dynamic decorrelation model was able to predict obtained effects for all tested stimuli except two SLC variations. Finally, FL-ODOG model was best at simulating human data, as it was able to predict empirical results for all displays, bar the Reversed contrast illusion. Finally, most models underperform on the Mondrian displays that represent most natural stimuli for the human visual system.

Fixational eye movements enable robust edge detection

Human vision relies on mechanisms that respond to luminance edges in space and time. Most edge models use orientation-selective mechanisms on multiple spatial scales and operate on static inputs assuming that edge processing occurs within a single fixational instance. Recent studies, however, demonstrate functionally relevant temporal modulations of the sensory input due to fixational eye movements. Here we propose a spatiotemporal model of human edge detection that combines elements of spatial and active vision. The model augments a spatial vision model by temporal filtering and shifts the input images over time, mimicking an active sampling scheme via fixational eye movements. The first model test was White's illusion, a lightness effect that has been shown to depend on edges. The model reproduced the spatial-frequency-specific interference with the edges by superimposing narrowband noise (1–5 cpd), similar to the psychophysical interference observed in White's effect. Second, we compare the model's edge detection performance in natural images in the presence and absence of Gaussian white noise with human-labeled contours for the same (noise-free) images. Notably, the model detects edges robustly against noise in both test cases without relying on orientation-selective processes. Eliminating model components, we demonstrate the relevance of multiscale spatiotemporal filtering and scale-specific normalization for edge detection. The proposed model facilitates efficient edge detection in (artificial) vision systems and challenges the notion that orientation-selective mechanisms are required for edge detection.

On the synthesis of visual illusions using deep generative models

Visual illusions expand our understanding of the visual system by imposing constraints in the models in two different ways: i) visual illusions for humans should induce equivalent illusions in the model, and ii) illusions synthesized from the model should be compelling for human viewers too. These constraints are alternative strategies to find good vision models. Following the first research strategy, recent studies have shown that artificial neural network architectures also have human-like illusory percepts when stimulated with classical hand-crafted stimuli designed to fool humans. In this work we focus on the second (less explored) strategy: we propose a framework to synthesize new visual illusions using the optimization abilities of current automatic differentiation techniques. The proposed framework can be used with classical vision models as well as with more recent artificial neural network architectures. This framework, validated by psychophysical experiments, can be used to study the difference between a vision model and the actual human perception and to optimize the vision model to decrease this difference.

Simple Assumptions to Improve Markov Illuminance and Reflectance

Murray recently introduced a novel computational lightness model, Markov illuminance and reflectance (MIR). MIR is a promising new approach that simulates human lightness processing using a conditional random field (CRF) where natural-scene statistics of reflectance and illumination are implemented. Although MIR can account for various lightness illusions and phenomena, it has limitations, such as the inability to predict reverse-contrast phenomena. In this study, we improved MIR performance by modifying its inference process, the prior on X-junctions, and that on general illumination changes. Our modified model improved predictions for Checkerboard assimilation, the simplified Checkershadow and its control figure, the influence of luminance noise, and White's effect and its several variants. In particular, White's effect is a partial reverse contrast that is challenging for computational models, so this improvement is a significant advance for the MIR framework. This study showed the high extensibility and potential of MIR, which shows the promise for further sophistication.

A discrete magno-parvo additive model in early vision for explaining brightness perception in varying contrastive contexts

A varying contrastive context filter (VCCF)-based model of brightness perception has been proposed. It is motivated first by a recently proposed difference of difference-of-Gaussian (DDOG) filter. Alongside, it is also inspired from the fact that the nature evolves various discrete systems and mechanisms to carry out many of its complex tasks. A weight factor, used for the linear combination of two filters representing the magnocellular and parvocellular channels in the central visual pathway, has been defined and termed as the factor of contrastive context (FOCC) in the present model. This is a binary variable that lends a property of discretization to the DDOG filter. By analyzing important brightness contrast as well as brightness assimilation illusions, we arrive at the minimal set of values (only two) for FOCC, using which one is able to successfully predict the direction of brightness shift in both situations of brightness contrast, claimed and categorized here as low contrastive context, and those of brightness assimilation, claimed and categorized here as high contrastive context perception, depending upon whether the initial M-channel-filtered stimulus is above or below a threshold of the contrastive context. As distinct from Michelson/Weber/RMS contrast, high or low, the contrastive context claimed is dependent on the edge information in the stimulus determined by the Laplacian operator, also used in the DDOG model. We compared the proposed model against the already well-established oriented difference-of-Gaussian (ODOG) model of brightness perception. Extensive simulations suggest that though for most illusions both ODOG and VCCF produce correct output, for certain intricate cases in which the ODOG filter fails to correctly predict the illusory effect, our proposed VCCF model continues to remain effective.

Dynamic decorrelation as a unifying principle for explaining a broad range of brightness phenomena

The visual system is highly sensitive to spatial context for encoding luminance patterns. Context sensitivity inspired the proposal of many neural mechanisms for explaining the perception of luminance (brightness). Here we propose a novel computational model for estimating the brightness of many visual illusions. We hypothesize that many aspects of brightness can be explained by a dynamic filtering process that reduces the redundancy in edge representations on the one hand, while non-redundant activity is enhanced on the other. The dynamic filter is learned for each input image and implements context sensitivity. Dynamic filtering is applied to the responses of (model) complex cells in order to build a gain control map. The gain control map then acts on simple cell responses before they are used to create a brightness map via activity propagation. Our approach is successful in predicting many challenging visual illusions, including contrast effects, assimilation, and reverse contrast with the same set of model parameters.

Depth Plane Separation Affects Both Lightness Contrast and Assimilation

Lightness contrast and assimilation are two opposite phenomena: contrast occurs when a gray target perceptually acquires a complementary color than the bordering, inducing, surfaces; assimilation is when a gray target perceptually acquires the same color component as the inducers. Previous research has shown that both phenomena are affected by the manipulation of depth between the inducers and target. However, different results have been reported; it is not clear whether contrast persists when inducers are non-coplanar with the target. Previous studies differ for the spatial configuration of the stimuli and the technique adopted to manipulate depth. The aim of this research was to measure the effects of manipulating the depth between inducers and target in comparable conditions. Results show that contrast persists, but largely reduces, after depth manipulation while assimilation reverses to contrast. Furthermore, interesting asymmetries between white and black inducers emerged with white inducers favoring contrast and black inducers favoring assimilation. These results provide further evidence that high-level processes of visual processing are involved in both phenomena, with important consequences for lightness theories.

The perception threshold of the panda illusion, a particular form of 2D pulse-width-modulated halftone, correlates with visual acuity

To call attention to the danger of extinction of the panda bear, the Lithuanian artist Ilja Klemencov created the artwork “They can disappear”. The illustration is composed of black-and-white zigzagged lines, which form the famous panda logo of the World Wild Fund For Nature (WWF) when seen from a distance. If one is too close to the artwork, it is difficult to spot the bear, however, if one steps back or takes off one’s glasses the panda suddenly appears. This led us to ask if the ability to see the panda is related to the visual acuity of the observer and if therefore, the panda illusion can be used to assess the spatial resolution of the eye. Here we present the results of the comparison between visual acuity determined using the Landolt C and that predicted from the panda illusion in 23 healthy volunteers with artificially reduced visual acuity. Furthermore, we demonstrate that the panda illusion is based on a 2D pulse-width modulation, explain its technical history, and provide the equations required to create the illusion. Finally, we explain why the illusion indeed can be used to predict visual acuity and discuss the neural causes of its perception with best-corrected visual acuity.

A model of lightness perception guided by probabilistic assumptions about lighting and reflectance

Lightness perception is the ability to perceive black, white, and gray surface colors in a wide range of lighting conditions and contexts. This ability is fundamental for any biological or artificial visual system, but it poses a difficult computational problem, and how the human visual system computes lightness is not well understood. Here I show that several key phenomena in lightness perception can be explained by a probabilistic graphical model that makes a few simple assumptions about local patterns of lighting and reflectance, and infers globally optimal interpretations of stimulus images. Like human observers, the model exhibits partial lightness constancy, codetermination, contrast, glow, and articulation effects. It also arrives at human-like interpretations of strong lightness illusions that have challenged previous models. The model's assumptions are reasonable and generic, including, for example, that lighting intensity spans a much wider range than surface reflectance and that shadow boundaries tend to be straighter than reflectance edges. Thus, a probabilistic model based on simple assumptions about lighting and reflectance gives a good computational account of lightness perception over a wide range of conditions. This work also shows how graphical models can be extended to develop more powerful models of constancy that incorporate features such color and depth.

Mechanisms underlying simultaneous brightness contrast: Early and innate

In the phenomenon of simultaneous brightness contrast, two patches, one on a dark background and the other on a light one, appear to have different brightness despite being physically equi-luminant. Elucidating the phenomenon's underlying mechanisms is relevant for the larger question of how the visual system makes photometric judgments in images. Accounts over the past century have spanned low-, mid- and high-level visual processes, but a definitive resolution has not emerged. We present three studies that collectively demonstrate that the computations underlying this phenomenon are low-level, instantiated prior to binocular fusion, and available innately, without need for inferential learning via an individual's visual experience. In our first two studies, we find that strong brightness induction is obtained even when observers are unaware of any luminance differences in the neighborhoods of the probe patches. Results with dichoptic displays reveal that eye of origin, although not evident consciously, has a marked influence on the eventual brightness percept of the probe patches, thereby localizing brightness estimation to a site preceding binocular fusion. The third study uses conventional simultaneous brightness contrast displays, but an unusual group of participants: Congenitally blind children whom we were able to treat surgically. The results demonstrate an immediate susceptibility to the simultaneous brightness illusion after sight onset. Together, these data strongly constrain the search for mechanisms underlying a fundamental brightness phenomenon.

Lightness induction enhancements and limitations at low frequency modulations across a variety of stimulus contexts

Lightness illusions are often studied under static viewing conditions with figures varying in geometric design, containing different types of perceptual grouping and figure-ground cues. A few studies have explored the perception of lightness induction while modulating lightness illusions continuously in time, where changes in perceived lightness are often linked to the temporal modulation frequency, up to around 2–4 Hz. These findings support the concept of a cut-off frequency for lightness induction. However, another critical change (enhancement) in the magnitude of perceived lightness during slower temporal modulation conditions has not been addressed in previous temporal modulation studies. Moreover, it remains unclear whether this critical change applies to a variety of lightness illusion stimuli, and the degree to which different stimulus configurations can demonstrate enhanced lightness induction in low modulation frequencies. Therefore, we measured lightness induction strength by having participants cancel out any perceived modulation in lightness detected over time within a central target region, while the surrounding context, which ultimately drives the lightness illusion, was viewed in a static state or modulated continuously in time over a low frequency range (0.25–2 Hz). In general, lightness induction decreased as temporal modulation frequency was increased, with the strongest perceived lightness induction occurring at lower modulation frequencies for visual illusions with strong grouping and figure-ground cues. When compared to static viewing conditions, we found that slow continuous surround modulation induces a strong and significant increase in perceived lightness for multiple types of lightness induction stimuli. Stimuli with perceptually ambiguous grouping and figure-ground cues showed weaker effects of slow modulation lightness enhancement. Our results demonstrate that, in addition to the existence of a cut-off frequency, an additional critical temporal modulation frequency of lightness induction exists (0.25–0.5 Hz), which instead maximally enhances lightness induction and seems to be contingent upon the prevalence of figure-ground and grouping organization.

Lateral effects in pattern vision

There is a large literature on lateral effects in pattern vision but no consensus about them or comprehensive model of them. This paper reviews the literature with a focus on the effects of parallel context in the central fovea. It describes seven experiments that measure detection and discrimination thresholds in annular and Gabor-pattern contexts at different separations. It presents a model of these effects, which is an elaboration of Foley's (1994) model. The model describes the results well, and it shows that lateral context affects the response to the target by both multiplicative excitation and additive inhibition. Both lateral effects extend for several wavelengths beyond the target. They vary in relative strength, producing near suppression and far enhancement of the response to the target. The model describes the detection and discrimination results well, and it also describes the results of experiments on lateral effects on perceived contrast. The model is consistent with the physiology of V1 cells.

An Upward-Facing Surface Appears Darker: The Role Played by the Light-From-Above Assumption in Lightness Perception

The human visual system can extract information on surface reflectance (lightness) from light intensity; this, however, confounds information on reflectance and illumination. We hypothesized that the visual system, to solve this lightness problem, utilizes the internally held prior assumption that illumination falls from above. Experiment 1 showed that an upward-facing surface is perceived to be darker than a downward-facing surface, proving our hypothesis. Experiment 2 showed the same results in the absence of explicit illumination cues. The effect of the light-from-left prior assumption was not observed in Experiment 3. The upward- and downward-facing surface stimuli in Experiments 1 and 2 showed no difference in a two-dimensional configuration or three-dimensional structure, and the participants' perceived lightness appeared to be affected by the observers' prior assumption that illumination is always from above. Other studies have not accounted for this illusory effect, and this study's finding provides additional insights into the study of lightness perception.

Slow Temporal Dynamics of Motion-Induced Brightness Shift Reveals Impact of Adaptation

Brightness of an object is determined by various factors including ambient illumination, surface reflectance of the object, and spatial and temporal relation between the object and its surrounding context. Recently, it has been demonstrated that the motion of an object alters its own and nearby object’s appearance such as brightness and color. This study aims to unveil mechanisms of the motion-induced brightness shift by measuring its temporal dynamics. We found that the motion-induced brightness shift occurred instantaneously with the motion onset when the motion was introduced abruptly. However, the brightness of a stationary object was altered gradually by a nearby moving object in about 2 s time window when the stationary dot was introduced abruptly. Two distinct temporal dynamics (slow vs. fast) of the motion-induced brightness shift demonstrate that both slow neural adaptation and fast neural normalization processes determine the brightness shift induced by the object’s motion.

A Compound Computational Model for Filling-In Processes Triggered by Edges: Watercolor Illusions

The goal of our research was to develop a compound computational model with the ability to predict different variations of the “watercolor effects” and additional filling-in effects that are triggered by edges. The model is based on a filling-in mechanism solved by a Poisson equation, which considers the different gradients as “heat sources” after the gradients modification. The biased (modified) contours (edges) are ranked and determined according to their dominancy across the different chromatic and achromatic channels. The color and intensity of the perceived surface are calculated through a diffusive filling-in process of color triggered by the enhanced and biased edges of stimulus formed as a result of oriented double-opponent receptive fields. The model can successfully predict both the assimilative and non-assimilative watercolor effects, as well as a number of “conflicting” visual effects. Furthermore, the model can also predict the classic Craik–O'Brien–Cornsweet (COC) effect. In summary, our proposed computational model is able to predict most of the “conflicting” filling-in effects that derive from edges that have been recently described in the literature, and thus supports the theory that a shared visual mechanism is responsible for the vast variety of the “conflicting” filling-in effects that derive from edges.

Color induction in equiluminant flashed stimuli

Color induction is the influence of the surrounding color (inducer) on the perceived color of a central region. There are two different types of color induction: color contrast (the color of the central region shifts away from that of the inducer) and color assimilation (the color shifts towards the color of the inducer). Several studies on these effects have used uniform and striped surrounds, reporting color contrast and color assimilation, respectively. Other authors [J. Vis.12(1), 22 (2012)1534-736210.1167/12.12.1] have studied color induction using flashed uniform surrounds, reporting that the contrast is higher for shorter flash duration. Extending their study, we present new psychophysical results using both flashed and static (i.e., non-flashed) equiluminant stimuli for both striped and uniform surrounds. Similarly to them, for uniform surround stimuli we observed color contrast, but we did not obtain the maximum contrast for the shortest (10 ms) flashed stimuli, but for 40 ms. We only observed this maximum contrast for red, green, and lime inducers, while for a purple inducer we obtained an asymptotic profile along the flash duration. For striped stimuli, we observed color assimilation only for the static (infinite flash duration) red-green surround inducers (red first inducer, green second inducer). For the other inducers' configurations, we observed color contrast or no induction. Since other studies showed that non-equiluminant striped static stimuli induce color assimilation, our results also suggest that luminance differences could be a key factor to induce it.

What image features guide lightness perception?

Lightness constancy is the ability to perceive black and white surface colors under a wide range of lighting conditions. This fundamental visual ability is not well understood, and current theories differ greatly on what image features are important for lightness perception. Here we measured classification images for human observers and four models of lightness perception to determine which image regions influenced lightness judgments. The models were a high-pass-filter model, an oriented difference-of-Gaussians model, an anchoring model, and an atmospheric-link-function model. Human and model observers viewed three variants of the argyle illusion (Adelson, 1993) and judged which of two test patches appeared lighter. Classification images showed that human lightness judgments were based on local, anisotropic stimulus regions that were bounded by regions of uniform lighting. The atmospheric-link-function and anchoring models predicted the lightness illusion perceived by human observers, but the high-pass-filter and oriented-difference-of-Gaussians models did not. Furthermore, all four models produced classification images that were qualitatively different from those of human observers, meaning that the model lightness judgments were guided by different image regions than human lightness judgments. These experiments provide a new test of models of lightness perception, and show that human observers' lightness computations can be highly local, as in low-level models, and nevertheless depend strongly on lighting boundaries, as suggested by midlevel models.

Luminance gradients and non-gradients as a cue for distinguishing reflectance and illumination in achromatic images: A computational approach

The brain analyses the visual world through the luminance patterns that reach the retina. Formally, luminance (as measured by the retina) is the product of illumination and reflectance. Whereas illumination is highly variable, reflectance is a physical property that characterizes each object surface. Due to memory constraints, it seems plausible that the visual system suppresses illumination patterns before object recognition takes place. Since many combinations of reflectance and illumination can give rise to identical luminance values, finding the correct reflectance value of a surface is an ill-posed problem, and it is still an open question how it is solved by the brain. Here we propose a computational approach that first learns filter kernels ("receptive fields") for slow and fast variations in luminance, respectively, from achromatic real-world images. Distinguishing between luminance gradients (slow variations) and non-gradients (fast variations) could serve to constrain the mentioned ill-posed problem. The second stage of our approach successfully segregates luminance gradients and non-gradients from real-world images. Our approach furthermore predicts that visual illusions that contain luminance gradients (such as Adelson's checker-shadow display or grating induction) may occur as a consequence of this segregation process.

An adaptive scale Gaussian filter to explain White's illusion from the viewpoint of lightness assimilation for a large range of variation in spatial frequency of the grating and aspect ratio of the targets

The variation between the actual and perceived lightness of a stimulus has strong dependency on its background, a phenomena commonly known as lightness induction in the literature of visual neuroscience and psychology. For instance, a gray patch may perceptually appear to be darker in a background while it looks brighter when the background is reversed. In the literature it is further reported that such variation can take place in two possible ways. In case of stimulus like the Simultaneous Brightness Contrast (SBC), the apparent lightness changes in the direction opposite to that of the background lightness, a phenomenon often referred to as lightness contrast, while in the others like neon colour spreading or checkerboard illusion it occurs opposite to that, and known as lightness assimilation. The White's illusion is a typical one which according to many, does not completely conform to any of these two processes. This paper presents the result of quantification of the perceptual strength of the White's illusion as a function of the width of the background square grating as well as the length of the gray patch. A linear filter model is further proposed to simulate the possible neurophysiological phenomena responsible for this particular visual experience. The model assumes that for the White's illusion, where the edges are strong and quite a few, i.e., the spectrum is rich in high frequency components, the inhibitory surround in the classical Difference-of-Gaussians (DoG) filter gets suppressed, and the filter essentially reduces to an adaptive scale Gaussian kernel that brings about lightness assimilation. The linear filter model with a Gaussian kernel is used to simulate the White's illusion phenomena with wide variation of spatial frequency of the background grating as well as the length of the gray patch. The appropriateness of the model is presented through simulation results, which are highly tuned to the present as well as earlier psychometric results.

The effect of luminance differences on color assimilation

The color appearance of a surface depends on the color of its surroundings (inducers). When the perceived color shifts towards that of the surroundings, the effect is called "color assimilation" and when it shifts away from the surroundings it is called "color contrast." There is also evidence that the phenomenon depends on the spatial configuration of the inducer, e.g., uniform surrounds tend to induce color contrast and striped surrounds tend to induce color assimilation. However, previous work found that striped surrounds under certain conditions do not induce color assimilation but induce color contrast (or do not induce anything at all), suggesting that luminance differences and high spatial frequencies could be key factors in color assimilation. Here we present a new psychophysical study of color assimilation where we assessed the contribution of luminance differences (between the target and its surround) present in striped stimuli. Our results show that luminance differences are key factors in color assimilation for stimuli varying along the s axis of MacLeod-Boynton color space, but not for stimuli varying along the l axis. This asymmetry suggests that koniocellular neural mechanisms responsible for color assimilation only contribute when there is a luminance difference, supporting the idea that mutual-inhibition has a major role in color induction.

To compute lightness, illumination is not estimated, it is held constant

The light reaching the eye from a surface does not indicate the black-gray-white shade of a surface (called lightness) because the effects of illumination level are confounded with the reflectance of the surface. Rotating a gray paper relative to a light source alters its luminance (intensity of light reaching the eye) but the lightness of the paper remains relatively constant. Recent publications have argued, as had Helmholtz (1866/1924), that the visual system unconsciously estimates the direction and intensity of the light source. We report experiments in which this theory was pitted against an alternative theory according to which illumination level and surface reflectance are disentangled by comparing only those surfaces that are equally illuminated, in other words, by holding illumination level constant. A 3-dimensional scene was created within which the rotation of a target surface would be expected to become darker gray according to the lighting estimation theory, but lighter gray according to the equi-illumination comparison theory, with results clearly favoring the latter. In a further experiment cues held to indicate light source direction (cast shadows, attached shadows, and glossy highlights) were completely eliminated and yet this had no effect on the results. (PsycINFO Database Record

A parsimonious model of brightness induction

We present a parsimonious model of brightness induction which can account for various brightness illusions of both brightness-contrast and brightness-assimilation types. Our model is based on a difference of difference-of-Gaussian filter and a two-pass model of attentive vision based on the parallel channels in the central visual pathway. It overcomes some of the problems that could not be addressed by the well-known oriented difference of Gaussian model like those associated with Mach band and checkerboard illusions. This model attempts to provide insight to the mechanism of attention in brightness perception through the two major complimentary visual channels, viz. the magnocellular and the parvocellular.

Lightness matching and perceptual similarity

Lightness constancy is the ability to perceive surface reflectance correctly despite substantial changes in lighting intensity. A classic view is that lightness constancy is the result of a "discounting" of lighting intensity, and this continues to be a prominent view today. Logvinenko and Maloney (2006) have proposed an alternative approach to understanding lightness constancy, in which observers do not make explicit estimates of reflectance, and lightness constancy is instead based on a perceptual similarity metric that depends on both the reflectance and the illuminance of surfaces viewed under different lighting conditions. Here we compare these two views using a novel, free-adjustment reflectance-matching task. We test whether observers can match reflectance in a task where they are free to adjust both the illuminance and the reflectance of the match stimulus over a wide range. We find that observers can match reflectance under these conditions, which supports the view that observers make explicit estimates of reflectance. We also compare performance in this free adjustment task using physical objects and computer-rendered images as stimuli. We find that lightness constancy is good in both cases, but with some evidence of a glow-related artifact with computer-rendered stimuli.

Remote controls illusion: strange interactions across space cannot be explained by simple contrast filters

The visual system has separable visual encoding for luminance and for contrast modulation [J. Vis.8(1), B152 (2008)1534-736210.1167/8.6.1]; the two dimensions can be represented with a luminance contrast versus luminance plane. Here we use a contrast asynchrony paradigm to explore contextual effects on luminance contrast modulation: two identical rectangular bars (0.5°×2.5°) have luminance levels that modulate at 2 Hz; when one bar is placed on a bright field and the other bar on a dark field, observers perceive the bars modulating in antiphase with each other and yet becoming light and dark at the same time. The antiphase perception corresponds to the change in contrast between the bars and their surrounds (a change along the contrast axis of the plane); the in-phase perception corresponds to the luminance modulation (a change along the luminance axis of the plane). We examine spatial interaction by adding bright rectangular (0.5°×2.5°) flankers on both sides of the dark-field bar and dark flankers on both sides of the bright-field bar. Remarkably, flankers produce an in-phase appearance when separated from the bars by between 2^' and 4^' of visual angle, and produce antiphase appearance when they directly adjoin the bars or are separated by more than 8^'. To estimate the dimensions of the spatial interaction, we parametrically adjust the size of the gap between bars and flankers and the length of the flankers. We attempt to account for the results with models based on rectified difference of Gaussian filters and with rectified oriented difference of Gaussian filters. The models can account for the results when the flankers are the same height as bars, but are unable to account for the effects of increasing the flanker length. The models therefore suggest that the spatial interaction across distances requires more complex interactions of contrast filters.

Kitaoka's Tomato: Two Simple Explanations Based on Information in the Stimulus

Kitaoka’s Tomato is a color illusion in which a semitransparent blue-green field is placed on top of a red object (a tomato). The tomato appears red even though the pixels would appear green if viewed in isolation. We show that this phenomenon can be explained by a high-pass filter and by histogram equalization. The results suggest that this illusion does not require complex inferences about color constancy; rather, the tomato’s red is available in the physical stimulus at the appropriate spatial scale and dynamic range.

Bioplausible multiscale filtering in retino-cortical processing as a mechanism in perceptual grouping

Why does our visual system fail to reconstruct reality, when we look at certain patterns? Where do Geometrical illusions start to emerge in the visual pathway? How far should we take computational models of vision with the same visual ability to detect illusions as we do? This study addresses these questions, by focusing on a specific underlying neural mechanism involved in our visual experiences that affects our final perception. Among many types of visual illusion, 'Geometrical' and, in particular, 'Tilt Illusions' are rather important, being characterized by misperception of geometric patterns involving lines and tiles in combination with contrasting orientation, size or position. Over the last decade, many new neurophysiological experiments have led to new insights as to how, when and where retinal processing takes place, and the encoding nature of the retinal representation that is sent to the cortex for further processing. Based on these neurobiological discoveries, we provide computer simulation evidence from modelling retinal ganglion cells responses to some complex Tilt Illusions, suggesting that the emergence of tilt in these illusions is partially related to the interaction of multiscale visual processing performed in the retina. The output of our low-level filtering model is presented for several types of Tilt Illusion, predicting that the final tilt percept arises from multiple-scale processing of the Differences of Gaussians and the perceptual interaction of foreground and background elements. The model is a variation of classical receptive field implementation for simple cells in early stages of vision with the scales tuned to the object/texture sizes in the pattern. Our results suggest that this model has a high potential in revealing the underlying mechanism connecting low-level filtering approaches to mid- and high-level explanations such as 'Anchoring theory' and 'Perceptual grouping'.

The Café Wall Illusion: Local and Global Perception from Multiple Scales to Multiscale

Geometrical illusions are a subclass of optical illusions in which the geometrical characteristics of patterns in particular orientations and angles are distorted and misperceived as a result of low-to-high-level retinal/cortical processing. Modelling the detection of tilt in these illusions, and its strength, is a challenging task and leads to the development of techniques that explain important features of human perception. We present here a predictive and quantitative approach for modelling foveal and peripheral vision for the induced tilt in the Café Wall illusion, in which parallel mortar lines between shifted rows of black and white tiles appear to converge and diverge. Difference of Gaussians is used to define a bioderived filtering model for the responses of retinal simple cells to the stimulus, while an analytical processing pipeline is developed to quantify the angle of tilt in the model and develop confidence intervals around them. Several sampling sizes and aspect ratios are explored to model variant foveal views, and a variety of pattern configurations are tested to model variant Gestalt views. The analysis of our model across this range of test configurations presents a precisely quantified comparison contrasting local tilt detection in the foveal sample sets with pattern-wide Gestalt tilt.

Retinal Lateral Inhibition Provides the Biological Basis of Long-Range Spatial Induction

Retinal lateral inhibition is one of the conventional efficient coding mechanisms in the visual system that is produced by interneurons that pool signals over a neighborhood of presynaptic feedforward cells and send inhibitory signals back to them. Thus, the receptive-field (RF) of a retinal ganglion cell has a center-surround receptive-field (RF) profile that is classically represented as a difference-of-Gaussian (DOG) adequate for efficient spatial contrast coding. The DOG RF profile has been attributed to produce the psychophysical phenomena of brightness induction, in which the perceived brightness of an object is affected by that of its vicinity, either shifting away from it (brightness contrast) or becoming more similar to it (brightness assimilation) depending on the size of the surfaces surrounding the object. While brightness contrast can be modeled using a DOG with a narrow surround, brightness assimilation requires a wide suppressive surround. Early retinal studies determined that the suppressive surround of a retinal ganglion cell is narrow (< 100–300 μm; ‘classic RF’), which led researchers to postulate that brightness assimilation must originate at some post-retinal, possibly cortical, stage where long-range interactions are feasible. However, more recent studies have reported that the retinal interneurons also exhibit a spatially wide component (> 500–1000 μm). In the current study, we examine the effect of this wide interneuron RF component in two biophysical retinal models and show that for both of the retinal models it explains the long-range effect evidenced in simultaneous brightness induction phenomena and that the spatial extent of this long-range effect of the retinal model responses matches that of perceptual data. These results suggest that the retinal lateral inhibition mechanism alone can regulate local as well as long-range spatial induction through the narrow and wide RF components of retinal interneurons, arguing against the existing view that spatial induction is operated by two separate local vs. long-range mechanisms.

Dissecting the influence of the collinear and flanking bars in White's effect

In White's effect equiluminant test patches placed on the black and white bars of a square-wave grating appear different in brightness. The illusion has generated intense interest because the direction of the brightness effect does not correlate with the amount of black or white border in contact with the test patch, or in its general vicinity. Therefore, unlike brightness induction effects such as simultaneous contrast, White's effect is not consistent with explanations based on contrast or assimilation that depend solely on the relative amounts of black and white surrounding the test patches. We independently manipulated the luminance of the collinear and flanking bars to investigate their influence on test patch matching luminance (brightness). The inducing grating was a 0.5c/d square-wave and test patches measured 1.0° in width and either 0.5° or 3.0° in height. Test patches measuring 0.5° in height had more extensive contact with the collinear bars and test patches measuring 3.0° in height had more extensive contact with the flanking bars. The luminance of the collinear (or flanking) bars assumed twenty values from 3.2 to 124.8cd/m(2), while the luminance of the flanking (or collinear) bars remained white (124.8cd/m(2)) or black (3.2cd/m(2)). Under these conditions the influence of the collinear and flanking bars was found to be purely in the direction of contrast. The effect was dominated by contrast from the collinear bars (which results in White's effect), however, the influence of the flanking bars was also in the contrast direction. The data elucidate the luminance relationships between the collinear and flanking bars which produce the behavior associated with White's effect as well as that associated with "the inverted White effect" which is akin to simultaneous contrast.

Visual evoked potentials to an illusory change in brightness: the Craik-Cornsweet-O'Brien effect

Can brain electrical activity associated with the Craik-Cornsweet-O'Brien effect (CCOB) be identified in humans? Opposing luminance gradients met in the middle of a square image to create a luminance contrast-defined vertical border. The resulting rectangles on each side of the border were otherwise equiluminant, but appeared to differ in brightness, the CCOB effect. When the contrast gradients were swapped, the participants perceived darker and lighter rectangles trading places. This dynamic CCOB stimulus was reversed 1/s to elicit visual evoked potentials. The CCOB effect was absent in two control conditions. In one, the immediate contrast border, where the gradients met, was replaced by a dark vertical stripe; in the other, the outer segments of both rectangles, where the illusion would otherwise occur, were replaced by dark rectangles, leaving only the contrast-reversing gradients. Visual evoked potential components P1 and N2 were present for the CCOB stimuli, but not the control stimuli. Results are consistent with functional MRI and single unit evidence, suggesting that the brightness of the CCOB effect becomes dissociated from the luminance falling on the eye early in visual processing. These results favor explanations of brightness induction invoking rapid, early amplification of very low spatial-frequency information in the image to approximate natural scenes as opposed to a sluggish brightness adjustment spreading from the contrast border.

Background Stripes Affect Apparent Speed of Rotation

A gray line that rotated about its own center against a stationary background of vertical stripes appeared to double in perceptual speed as it rotated through the vertical position and thus momentarily aligned with the background. Four factors may contribute to this speed-up: (i) landmarks, in which the tip of the moving vertical line moves horizontally across the maximum number of stationary stripes; (ii) orientation repulsion of the moving line by the vertical stripes, which may distort the line's perceived position and hence its perceived speed; (iii) the orientation of an induced brightness pattern along the line; and (iv) the motion of the induced brightness pattern, which moves physically most rapidly along the line when the line is near vertical. To test these possibilities, an annulus display provided landmarks but no intersections, and this almost abolished the effect. A rotating-slit display provided an oriented, moving pattern that mimicked the induced brightness but had no landmarks, and this increased the effect. We conclude that the motion, but not the orientation, of the intersections [option (iv)] was responsible for the illusion. The fact that this motion along the length of the line affected the perceived speed of the line orthogonal to its own length indicates a failure on the part of the visual system to fully decouple tangential from radial motion.

Testing the Coplanar Ratio Hypothesis of Lightness Perception

What determines an object's lightness remains unclear, but it is generally thought that the ratios of its luminance to the luminance of other objects in a scene play a crucial role because these ratios allow the relative reflectance of each object to be estimated, providing all the objects are under the same illumination. Because objects that lie in the same plane are typically illuminated equally, it has been suggested that it is the luminance ratios between coplanar objects that primarily determine lightness (Gilchrist, 1977 Science195 185–187; Gilchrist et al, 1999 Psychological Review106 795–834). An alternative hypothesis is that perceived illumination differences can affect lightness directly. As the studies that provided evidence for the coplanar ratio hypothesis always varied the illumination and the coplanar relationships simultaneously, it is unclear which hypothesis is correct. I measured the influence of each factor separately and found that the perceived illumination differences have a greater effect on lightness.

A DOG filter model of the occurrence of Mach bands on spatial contrast discontinuities

The present work proposes a unified model to explain two previously reported properties of the Mach band illusion. The first is the frequently referenced fact that Mach bands are prominently visible at ramps, but practically vanish at intensity steps. The second property, less studied, on the other hand may also be related to the first. It concerns the fact that the width of the illusory Mach bands appears to be a function of the slope of the ramp itself. The model proposed here combines the difference of Gaussians (DOG) model of lateral inhibition in receptive fields with the models of feature detection, based on a holistic approach. The sharpness of discontinuity (SOD) concept for Mach band stimulus has been defined and is related to the slope of the ramp. It is suggested that calculation of SOD leads to an adaptive change in inhibitory surround, a notion that has the support of physiological experiments too.

Limitations of the Oriented Difference of Gaussian Filter in Special Cases of Brightness Perception Illusions

The Oriented Difference of Gaussian (ODOG) filter of Blakeslee and McCourt has been successfully employed to explain several brightness perception illusions which include illusions of both brightness-contrast type, for example, Simultaneous Brightness Contrast and Grating Induction and the brightness-assimilation type, for example, the White effect and the shifted White effect. Here, we demonstrate some limitations of the ODOG filter in predicting perceived brightness by comparing the ODOG responses to various stimuli (generated by varying two parameters, namely, test patch length and spatial frequency) with experimental observations of the same.

Theoretical approaches to lightness and perception

Theories of lightness, like theories of perception in general, can be categorized as high-level, low-level, and mid-level. However, I will argue that in practice there are only two categories: one-stage mid-level theories, and two-stage low-high theories. Low-level theories usually include a high-level component and high-level theories include a low-level component, the distinction being mainly one of emphasis. Two-stage theories are the modern incarnation of the persistent sensation/perception dichotomy according to which an early experience of raw sensations, faithful to the proximal stimulus, is followed by a process of cognitive interpretation, typically based on past experience. Like phlogiston or the ether, raw sensations seem like they must exist, but there is no clear evidence for them. Proximal stimulus matches are postperceptual, not read off an early sensory stage. Visual angle matches are achieved by a cognitive process of flattening the visual world. Likewise, brightness (luminance) matches depend on a cognitive process of flattening the illumination. Brightness is not the input to lightness; brightness is slower than lightness. Evidence for an early (< 200 ms) mosaic stage is shaky. As for cognitive influences on perception, the many claims tend to fall apart upon close inspection of the evidence. Much of the evidence for the current revival of the 'new look' is probably better explained by (1) a natural desire of (some) subjects to please the experimenter, and (2) the ease of intuiting an experimental hypothesis. High-level theories of lightness are overkill. The visual system does not need to know the amount of illumination, merely which surfaces share the same illumination. This leaves mid-level theories derived from the gestalt school. Here the debate seems to revolve around layer models and framework models. Layer models fit our visual experience of a pattern of illumination projected onto a pattern of reflectance, while framework models provide a better account of illusions and failures of constancy. Evidence for and against these approaches is reviewed.

Putting low-level vision into global context: Why vision cannot be reduced to basic circuits

To cope with the complexity of vision, most models in neuroscience and computer vision are of hierarchical and feedforward nature. Low-level vision, such as edge and motion detection, is explained by basic low-level neural circuits, whose outputs serve as building blocks for more complex circuits computing higher level features such as shape and entire objects. There is an isomorphism between states of the outer world, neural circuits, and perception, inspired by the positivistic philosophy of the mind. Here, we show that although such an approach is conceptually and mathematically appealing, it fails to explain many phenomena including crowding, visual masking, and non-retinotopic processing.

Grouping Factors and the Reverse Contrast Illusion

In simultaneous lightness contrast, two identical gray target squares lying on backgrounds of different intensities appear different in lightness. Traditionally, this illusion was explained by lateral inhibitory mechanisms operating retinotopically. More recently, spatial filtering models have been preferred. We report tests of an anchoring theory account in which the illusion is attributed to grouping rules used by the visual system to compute lightness. We parametrically varied the belongingness of two gray target bars to their respective backgrounds so that they either appeared to group with a set of bars flanking them, or they appeared to group with their respective backgrounds. In all variations, the retinal adjacency of the gray squares and their backgrounds was essentially unchanged. We report data from seven experiments showing that manipulation of the grouping rules governs the size and direction of the simultaneous lightness contrast illusion. These results support the idea that simultaneous lightness contrast is the product of anchoring within perceptual groups.

Testing the role of luminance edges in White's illusion with contour adaptation

White's illusion is the perceptual effect that two equiluminant gray patches superimposed on a black-and-white square-wave grating appear different in lightness: A test patch placed on a dark stripe of the grating looks lighter than one placed on a light stripe. Although the effect does not depend on the aspect ratio of the test patches, and thus on the amount of border that is shared with either the dark or the light stripe, the context of each patch must, in a yet to be specified way, influence their lightness. We employed a contour adaptation paradigm (Anstis, 2013) to test the contribution of each of the test patches' edges to the perceived lightness of the test patches. We found that adapting to the edges that are oriented parallel to the grating slightly increased the lightness illusion, whereas adapting to the orthogonal edges abolished, or for some observers even reversed, the lightness illusion. We implemented a temporal adaptation mechanism in three spatial filtering models of lightness perception, and show that the models cannot account for the observed adaptation effects. We conclude that White's illusion is largely determined by edge contrast across the edge orthogonal to the grating, whereas the parallel edge has little or no influence. We suggest mechanisms that could explain this asymmetry.

An exponential filter model predicts lightness illusions

Lightness, or perceived reflectance of a surface, is influenced by surrounding context. This is demonstrated by the Simultaneous Contrast Illusion (SCI), where a gray patch is perceived lighter against a black background and vice versa. Conversely, assimilation is where the lightness of the target patch moves toward that of the bounding areas and can be demonstrated in White's effect. Blakeslee and McCourt (1999) introduced an oriented difference-of-Gaussian (ODOG) model that is able to account for both contrast and assimilation in a number of lightness illusions and that has been subsequently improved using localized normalization techniques. We introduce a model inspired by image statistics that is based on a family of exponential filters, with kernels spanning across multiple sizes and shapes. We include an optional second stage of normalization based on contrast gain control. Our model was tested on a well-known set of lightness illusions that have previously been used to evaluate ODOG and its variants, and model lightness values were compared with typical human data. We investigate whether predictive success depends on filters of a particular size or shape and whether pooling information across filters can improve performance. The best single filter correctly predicted the direction of lightness effects for 21 out of 27 illusions. Combining two filters together increased the best performance to 23, with asymptotic performance at 24 for an arbitrarily large combination of filter outputs. While normalization improved prediction magnitudes, it only slightly improved overall scores in direction predictions. The prediction performance of 24 out of 27 illusions equals that of the best performing ODOG variant, with greater parsimony. Our model shows that V1-style orientation-selectivity is not necessary to account for lightness illusions and that a low-level model based on image statistics is able to account for a wide range of both contrast and assimilation effects.

What visual illusions tell us about underlying neural mechanisms and observer strategies for tackling the inverse problem of achromatic perception

Research in lightness perception centers on understanding the prior assumptions and processing strategies the visual system uses to parse the retinal intensity distribution (the proximal stimulus) into the surface reflectance and illumination components of the scene (the distal stimulus—ground truth). It is agreed that the visual system must compare different regions of the visual image to solve this inverse problem; however, the nature of the comparisons and the mechanisms underlying them are topics of intense debate. Perceptual illusions are of value because they reveal important information about these visual processing mechanisms. We propose a framework for lightness research that resolves confusions and paradoxes in the literature, and provides insight into the mechanisms the visual system employs to tackle the inverse problem. The main idea is that much of the debate and confusion in the literature stems from the fact that lightness, defined as apparent reflectance, is underspecified and refers to three different types of judgments that are not comparable. Under stimulus conditions containing a visible illumination component, such as a shadow boundary, observers can distinguish and match three independent dimensions of achromatic experience: apparent intensity (brightness), apparent local intensity ratio (brightness-contrast), and apparent reflectance (lightness). In the absence of a visible illumination boundary, however, achromatic vision reduces to two dimensions and, depending on stimulus conditions and observer instructions, judgments of lightness are identical to judgments of brightness or brightness-contrast. Furthermore, because lightness judgments are based on different information under different conditions, they can differ greatly in their degree of difficulty and in their accuracy. This may, in part, explain the large variability in lightness constancy across studies.

Contour Erasure and Filling-in: Old Simulations Account for Most New Observations

Three recent studies used similar stimulus sequences to investigate mechanisms for brightness perception. Anstis and Greenlee (2014) demonstrated that adaptation to a flickering black and white outline erased the visibility of a subsequent target shape defined by a luminance increment or decrement. Robinson and de Sa (2012, 2013) used a flickering disk or annulus to show a similar effect. Here, a neural network model of visual perception (Francis & Kim, 2012), that previously explained properties of scene fading, is shown to also explain most of the erasure effects reported by Anstis and Greenlee and by Robinson and de Sa. The model proposes that in normal viewing conditions a brightness filling-in process is constrained by oriented boundaries, which thereby define separate regions of a visual scene. Contour adaptation can weaken the boundaries and thereby allow brightness signals to merge together, which renders target stimuli indistinguishable from the background. New simulations with the stimuli used by Anstis and Greenlee and Robinson and de Sa produce model output very similar to the perceptual experience of human observers. Finally, the model predicts that adaptation to illusory contours will not produce contour erasure.

Brightness masking is modulated by disparity structure

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Brightness masking is affected by three-dimensional configuration.

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Masks with the same 3-D orientation yield a greater masking effect.

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Brightness estimation is likely to be partially mediated by mid-level mechanisms.

The luminance contrast at the borders of a surface strongly influences surface’s apparent brightness, as demonstrated by a number of classic visual illusions. Such phenomena are compatible with a propagation mechanism believed to spread contrast information from borders to the interior. This process is disrupted by masking, where the perceived brightness of a target is reduced by the brief presentation of a mask (Paradiso & Nakayama, 1991), but the exact visual stage that this happens remains unclear. In the present study, we examined whether brightness masking occurs at a monocular-, or a binocular-level of the visual hierarchy. We used backward masking, whereby a briefly presented target stimulus is disrupted by a mask coming soon afterwards, to show that brightness masking is affected by binocular stages of the visual processing. We manipulated the 3-D configurations (slant direction) of the target and mask and measured the differential disruption that masking causes on brightness estimation. We found that the masking effect was weaker when stimuli had a different slant. We suggest that brightness masking is partly mediated by mid-level neuronal mechanisms, at a stage where binocular disparity edge structure has been extracted.

The Oriented Difference of Gaussians (ODOG) model of brightness perception: Overview and executable Mathematica notebooks

The Oriented Difference of Gaussians (ODOG) model of brightness (perceived intensity) by Blakeslee and McCourt (Vision Research 39:4361-4377, 1999), which is based on linear spatial filtering by oriented receptive fields followed by contrast normalization, has proven highly successful in parsimoniously predicting the perceived intensity (brightness) of regions in complex visual stimuli such as White's effect, which had been believed to defy filter-based explanations. Unlike competing explanations such as anchoring theory, filling-in, edge-integration, or layer decomposition, the spatial filtering approach embodied by the ODOG model readily accounts for the often overlooked but ubiquitous gradient structure of induction which, while most striking in grating induction, also occurs within the test fields of classical simultaneous brightness contrast and the White stimulus. Also, because the ODOG model does not require defined regions of interest, it is generalizable to any stimulus, including natural images. The ODOG model has motivated other researchers to develop modified versions (LODOG and FLODOG), and has served as an important counterweight and proof of concept to constrain high-level theories which rely on less well understood or justified mechanisms such as unconscious inference, transparency, perceptual grouping, and layer decomposition. Here we provide a brief but comprehensive description of the ODOG model as it has been implemented since 1999, as well as working Mathematica (Wolfram, Inc.) notebooks which users can employ to generate ODOG model predictions for their own stimuli.

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.

The impact of stereoscopic depth on the Munker-White illusion

The current study investigated the impact of stereoscopic depth information on adults' perception of a coloured version of the Munker-White illusion. In one half of the illusory figure red patches were embedded in black stripes and flanked by yellow stripes. In the other half of the illusory figure red patches were embedded in yellow stripes and flanked by black stripes. The red patches either remained in the same depth plane as the black and yellow inducing stripes (zero horizontal disparity condition) or were shifted into the foreground (crossed horizontal disparity condition) or into the background (uncrossed horizontal disparity condition). According to the results, the illusory effect was robust across all viewing conditions. The illusion mainly consisted of a subjective darkening of the red patches superimposed on the yellow stripes, a perceived hue shift of the red patches superimposed on the black stripes toward yellow, and a subjective saturation decrease in both kinds of red patches. Moreover, the study established a partial confirmation of Anderson's scission theory, according to which the Munker-White illusion should be largest in the crossed horizontal disparity condition, intermediate in the zero horizontal disparity condition, and smallest in the uncrossed horizontal disparity condition.

Noise masking of White's illusion exposes the weakness of current spatial filtering models of lightness perception

Spatial filtering models are currently a widely accepted mechanistic account of human lightness perception. Their popularity can be ascribed to two reasons: They correctly predict how human observers perceive a variety of lightness illusions, and the processing steps involved in the models bear an apparent resemblance with known physiological mechanisms at early stages of visual processing. Here, we tested the adequacy of these models by probing their response to stimuli that have been modified by adding narrowband noise. Psychophysically, it has been shown that noise in the range of one to five cycles per degree (cpd) can drastically reduce the strength of some lightness phenomena, while noise outside this range has little or no effect on perceived lightness. Choosing White's illusion (White, 1979) as a test case, we replicated and extended the psychophysical results, and found that none of the spatial filtering models tested was able to reproduce the spatial frequency specific effect of narrowband noise. We discuss the reasons for failure for each model individually, but we argue that the failure is indicative of the general inadequacy of this class of spatial filtering models. Given the present evidence we do not believe that spatial filtering models capture the mechanisms that are responsible for producing many of the lightness phenomena observed in human perception. Instead we think that our findings support the idea that low-level contributions to perceived lightness are primarily determined by the luminance contrast at surface boundaries.