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

Luminance gradient configuration determines perceived lightness in a simple geometric illusion

Accurate perception of surface reflectance poses a significant computational problem for the visual system. The amount of light reflected by a surface is affected by a combination of factors including the surface's reflectance properties and illumination conditions. The latter are not limited by the strength of the illuminant but also include the relative placement of the light illuminating the surface, the orientation of the surface and its 3d shape, all of which result in a pattern of luminance gradients across the surface. In this study we explore how luminance gradients contribute to lightness perception. We introduce a novel, simple lightness illusion. It consists of six separate checks, organized in rows of two. Each check has a negative luminance gradient across it. The top and the bottom rows are the same: with the darker check on the left, and the lighter check on the right. Two checks in the middle row are identical; however, the check on the right appears darker than the check on the left. As there are no shared borders between the checks, simultaneous contrast cannot explain the effect. However, there are multiple possible explanations including spatial filtering (Blakeslee and McCourt, 2004) or some higher-order mechanism such as perceptual grouping or amodal completion. Here, we explore these possibilities by manipulating the luminance configurations and the gradient slopes of the checks.

Standard deviation of luminance distribution affects lightness and pupillary response

We examined whether the standard deviation (SD) of luminance distribution serves as information of illumination. We measured the lightness of a patch presented in the center of a scrambled-dot pattern while manipulating the SD of the luminance distribution. Results showed that lightness decreased as the SD of the surround stimulus increased. We also measured pupil diameter while viewing a similar stimulus. The pupil diameter decreased as the SD of luminance distribution of the stimuli increased. We confirmed that these results were not obtained because of the increase of the highest luminance in the stimulus. Furthermore, results of field measurements revealed a correlation between the SD of luminance distribution and illuminance in natural scenes. These results indicated that the visual system refers to the SD of the luminance distribution in the visual stimulus to estimate the scene illumination.

Modelling the Mach bands illusion by means of a diffusion model

First, we criticize the validity of the principle of lateral inhibition. Second, on the basis of illusory phenomena and stabilized retinal images, we point out that the retina does not code the absolute luminance; the retina forwards a relative luminance sketch towards higher levels of the visual system. However, at the level of conscious processing the perceptual counterpart of absolute luminance, brightness, is available. Therefore, it is reasonable to assume that a reconstruction process is carried out by the visual system, which recovers the inner representation that corresponds to the retinal light distribution from the coded relative luminance sketch. We provide an illustrative description of a computational model of this reconstruction process. The basis of the reconstruction is a mathematically provable theorem, according to which if image P is produced from image I by Laplacian filtering, and then P is used as the sources and sinks of a homogeneous linear diffusion process, then the equilibrium of the diffusion will be identical to the original image I. We have illustrated this by a one-dimensional heat diffusion example, and by a series of test tubes connected to each other, also in one dimension. Brightness illusions are considered as a side effect of this diffusion-based reconstruction process. If the diffusion process deviates from the principle of homogeneous linearity, then the result of the reconstruction will deviate from the original image I. We showed a concrete illustration of this with regards to the Mach bands illusion: here we violated the principle of homogeneous linearity by means of inserting a small vertical tube serving as a serial resistance between each test tube and the horizontal connecting tube. This violation resulted in a change of water level in the source and the sink test tubes corresponding to the Mach bands illusion.

Brightness induction and suprathreshold vision: effects of age and visual field

A variety of visual capacities show significant age-related alterations. We assessed suprathreshold contrast and brightness perception across the lifespan in a large sample of healthy participants (N=155; 142) ranging in age from 16 to 80 years. Experiment 1 used a quadrature-phase motion cancelation technique (Blakeslee & McCourt, 2008) to measure canceling contrast (in central vision) for induced gratings at two temporal frequencies (1 Hz and 4 Hz) at two test field heights (0.5° or 2°×38.7°; 0.052 c/d). There was a significant age-related reduction in canceling contrast at 4 Hz, but not at 1 Hz. We find no age-related change in induction magnitude in the 1 Hz condition. We interpret the age-related decline in grating induction magnitude at 4 Hz to reflect a diminished capacity for inhibitory processing at higher temporal frequencies. In Experiment 2 participants adjusted the contrast of a matching grating (0.5° or 2°×38.7°; 0.052 c/d) to equal that of both real (30% contrast, 0.052 c/d) and induced (McCourt, 1982) standard gratings (100% inducing grating contrast; 0.052 c/d). Matching gratings appeared in the upper visual field (UVF) and test gratings appeared in the lower visual field (LVF), and vice versa, at eccentricities of ±7.5°. Average induction magnitude was invariant with age for both test field heights. There was a significant age-related reduction in perceived contrast of stimuli in the LVF versus UVF for both real and induced gratings.

Mach bands explained by response normalization

Mach bands are the illusory dark and bright bars seen at the foot and knee of a luminance trapezoid. First demonstrated by Ernst Mach in the latter part of the 19th century, Mach bands are a test bed not only for models of brightness illusions but of spatial vision in general. Up until 50 years ago the dominant explanation of Mach Bands was that they were caused by lateral inhibition among retinal neurons. More recently, the dominant idea has been that Mach bands are a consequence of a visual process that generates a sparse, binary description of the image in terms of "edges" and "bars". Another recent explanation is that Mach bands result from learned expectations about the pattern of light typically found on sharply curved surfaces. In keeping with recent multi-scale filtering accounts of brightness illusions as well as current physiology, I show however that Mach bands are most simply explained by response normalization, whereby the gains of early visual channels are adjusted on a local basis to make their responses more equal. I show that a simple one-dimensional model of response normalization explains the range of conditions under which Mach bands occur, and as importantly, the conditions under which they do not occur.

A temporal window for estimating surface brightness in the Craik-O'Brien-Cornsweet effect

The central edge of an opposing pair of luminance gradients (COC edge) makes adjoining regions with identical luminance appear to be different. This brightness illusion, called the Craik-O'Brien-Cornsweet effect (COCe), can be explained by low-level spatial filtering mechanisms (Dakin and Bex, 2003). Also, the COCe is greatly reduced when the stimulus lacks a frame element surrounding the COC edge (Purves et al., 1999). This indicates that the COCe can be modulated by extra contextual cues that are related to ideas about lighting priors. In this study, we examined whether processing for contextual modulation could be independent of the main COCe processing mediated by the filtering mechanism. We displayed the COC edge and frame element at physically different times. Then, while varying the onset asynchrony between them and changing the luminance contrast of the frame element, we measured the size of the COCe. We found that the COCe was observed in the temporal range of around 600–800 ms centered at the 0 ms (from around −400 to 400 ms in stimulus onset asynchrony), which was much larger than the range of typical visual persistency. More importantly, this temporal range did not change significantly regardless of differences in the luminance contrast of the frame element (5–100%), in the durations of COC edge and/or the frame element (50 or 200 ms), in the display condition (interocular or binocular), and in the type of lines constituting the frame element (solid or illusory lines). Results suggest that the visual system can bind the COC edge and frame element with a temporal window of ~1 s to estimate surface brightness. Information from the basic filtering mechanism and information of contextual cue are separately processed and are linked afterwards.

Visual crowding illustrates the inadequacy of local vs. global and feedforward vs. feedback distinctions in modeling visual perception

Experimentalists tend to classify models of visual perception as being either local or global, and involving either feedforward or feedback processing. We argue that these distinctions are not as helpful as they might appear, and we illustrate these issues by analyzing models of visual crowding as an example. Recent studies have argued that crowding cannot be explained by purely local processing, but that instead, global factors such as perceptual grouping are crucial. Theories of perceptual grouping, in turn, often invoke feedback connections as a way to account for their global properties. We examined three types of crowding models that are representative of global processing models, and two of which employ feedback processing: a model based on Fourier filtering, a feedback neural network, and a specific feedback neural architecture that explicitly models perceptual grouping. Simulations demonstrate that crucial empirical findings are not accounted for by any of the models. We conclude that empirical investigations that reject a local or feedforward architecture offer almost no constraints for model construction, as there are an uncountable number of global and feedback systems. We propose that the identification of a system as being local or global and feedforward or feedback is less important than the identification of a system's computational details. Only the latter information can provide constraints on model development and promote quantitative explanations of complex phenomena.

Paradoxical effect of spatially homogenous transparent fields on simultaneous contrast illusions

In simultaneous brightness contrast (SBC) demonstrations, identical mid-luminance disks appear different from each other when one is placed on a black background while the other is placed on a white background. The strength of SBC effects can be enhanced by placing a semi-transparent layer on top of the display (Meyer's effect). Here, we try to separate the causes of Meyer's effect by placing a spatially homogenous transparent layer over a standard SBC display, and systematically varying the transmission level (alpha=0, clear; alpha=1, opaque) and color (black, gray, white) of the semi-transparent layer. Spatially homogenous transparent layers, which lack spatial cues, cannot be unambiguously interpreted as transparent fields. We measure SBC strength with both matching and ranking procedures. Paradoxically, with black layers, increasing alpha level weakens SBC when measured with a ranking procedure (no Meyer's effect) and strengthens SBC when measured with a matching procedure (Meyer's effect). With white and gray layers, neither procedure produces Meyer's effect. We account for the differences between white and black layers by positing that the visual system separates luminance from contrast. The results suggest that observers attend to different information in the matching and ranking procedures.

The roles of image decomposition and edge curvature in the 'snake' lightness illusion

The snake illusion is an effect in which the lightness of target patches is strongly affected by the luminance of remote patches. One explanation is that such images are decomposed into a pattern of illumination and a pattern of reflectance, involving a classification of luminance edges into illumination and reflectance edges. Based on this decomposition, perceived reflectance is determined by discounting the illumination. A problem for this account is that image decomposition is not unique, and that different decompositions may lead to different lightness predictions. One way to rule out alternative decompositions and ensure correct predictions is to postulate that the visual system tends to classify curved luminance edges as reflectance edges rather than illumination edges. We have constructed several variations of the basic snake display in order to test the proposed curvature constraint and the more general image decomposition hypothesis. Although the results from some displays have confirmed previous findings of the effect of curvature, the general pattern of data questions the relevance of the shape of luminance edges for the determination of lightness in this class of displays. The data also argue against an image decomposition mechanism as an explanation of this effect. As an alternative, a tentative neurally based account is sketched.

Scale-invariance in brightness illusions implicates object-level visual processing

Brightness illusions demonstrate that an object's perceived brightness depends on its visual context, leading to theoretical explanations ranging from simple lateral inhibition to those based on the influence of knowledge of and experience with the world. We measure the relative brightness of mid-luminance test disks embedded in gray-scale images, and show that rankings of test disk brightness are independent of viewing distance, implying that the rankings depend on the physical object size, not the size of disks subtended on the retina. A single filter that removes low spatial frequency content, adjusted to the diameters of the test disks, can account for the relative brightness of the disks. We note that the removal of low spatial frequency content is a principle common to many different approaches to brightness/lightness phenomena; furthermore, object-size representations--as opposed to retinal-size representations--inherently remove low spatial frequency content, therefore, any process that creates object representations should also produce brightness illusions.

Perceptual consequence of normalization revealed by a novel brightness induction

The human brain is renowned for its dynamic regulation of sensory inputs, which enables our brain to operate under an enormous range of physical energy with sensory neurons whose processing range is limited. Here we present a novel and strong brightness induction that reflects neural mechanisms underlying this dynamic regulation of sensory inputs. When physically identical, stationary and moving objects are viewed simultaneously, the stationary and moving objects appear largely different. Experiments reveal that normalization at multiple stages of visual processing provides a plausible account for the large shifts in perceptual experiences, observed in both the stationary and the moving objects. This novel brightness induction suggests that brightness of an object is influenced not only by variations in surrounding light (i.e. simultaneous contrast) but also by dynamically changing neural responses associated with stimulus motion.

Effect of stimulus width on simultaneous contrast

Perceived brightness of a stimulus depends on the background against which the stimulus is set, a phenomenon known as simultaneous contrast. For instance, the same gray stimulus can look light against a black background or dark against a white background. Here we quantified the perceptual strength of simultaneous contrast as a function of stimulus width. Previous studies have reported that wider stimuli result in weaker simultaneous contrast, whereas narrower stimuli result in stronger simultaneous contrast. However, no previous research has quantified this relationship. Our results show a logarithmic relationship between stimulus width and perceived brightness. This relationship is well matched by the normalized output of a Difference-of-Gaussians (DOG) filter applied to stimuli of varied widths.

A Bayesian model of lightness perception that incorporates spatial variation in the illumination

The lightness of a test stimulus depends in a complex manner on the context in which it is viewed. To predict lightness, it is necessary to leverage measurements of a feasible number of contextual configurations into predictions for a wider range of configurations. Here we pursue this goal, using the idea that lightness results from the visual system's attempt to provide stable information about object surface reflectance. We develop a Bayesian algorithm that estimates both illumination and reflectance from image luminance, and link perceived lightness to the algorithm's estimates of surface reflectance. The algorithm resolves ambiguity in the image through the application of priors that specify what illumination and surface reflectances are likely to occur in viewed scenes. The prior distributions were chosen to allow spatial variation in both illumination and surface reflectance. To evaluate our model, we compared its predictions to a data set of judgments of perceived lightness of test patches embedded in achromatic checkerboards (Allred, Radonjić, Gilchrist, & Brainard, 2012). The checkerboard stimuli incorporated the large variation in luminance that is a pervasive feature of natural scenes. In addition, the luminance profile of the checks both near to and remote from the central test patches was systematically manipulated. The manipulations provided a simplified version of spatial variation in illumination. The model can account for effects of overall changes in image luminance and the dependence of such changes on spatial location as well as some but not all of the more detailed features of the data.

Retinal mesopic adaptation model for brightness perception under transient glare

A glare source in the visual field modifies the brightness of a test patch surrounded by a mesopic background. In this study, we investigated the effect of two levels of transient glare on brightness perception for several combinations of mesopic reference test luminances (Lts) and background luminances (Lbs). While brightness perception was affected by Lb, there were no appreciable effects for changes in the Lt. The highest brightness reduction was found for Lbs in the low mesopic range. Considering the main proposal that brightness can be inferred from contrast and the Lb sets the mesopic luminance adaptation, we hypothesized that contrast gain and retinal adaptation mechanisms would act when a transient glare source was present in the visual field. A physiology-based model that adequately fitted the present and previous results was developed.

Orientation enhancement in early visual processing can explain time course of brightness contrast and White's illusion

Dynamics of orientation tuning in V1 indicates that computational model of V1 should not only comprise of bank of static spatially oriented filters but also include the contribution for dynamical response facilitation or suppression along orientation. Time evolution of orientation response in V1 can emerge due to time- dependent excitation and lateral inhibition in the orientation domain. Lateral inhibition in the orientation domain suggests that Ernst Mach's proposition can be applied for the enhancement of initial orientation distribution that is generated due to interaction of visual stimulus with spatially oriented filters and subcortical temporal filter. Oriented spatial filtering that appears much early (<70 ms) in the sequence of visual information processing can account for many of the brightness illusions observed at steady state. It is therefore expected that time evolution of orientation response might be reflecting in the brightness percept over time. Our numerical study suggests that only spatio-temporal filtering at early phase can explain experimentally observed temporal dynamics of brightness contrast illusion. But, enhancement of orientation response at early phase of visual processing is the key mechanism that can guide visual system to predict the brightness by "Max-rule" or "Winner Takes All" (WTA) estimation and thus producing White's illusions at any exposure.

A neurodynamical model of brightness induction in v1

Brightness induction is the modulation of the perceived intensity of an area by the luminance of surrounding areas. Recent neurophysiological evidence suggests that brightness information might be explicitly represented in V1, in contrast to the more common assumption that the striate cortex is an area mostly responsive to sensory information. Here we investigate possible neural mechanisms that offer a plausible explanation for such phenomenon. To this end, a neurodynamical model which is based on neurophysiological evidence and focuses on the part of V1 responsible for contextual influences is presented. The proposed computational model successfully accounts for well known psychophysical effects for static contexts and also for brightness induction in dynamic contexts defined by modulating the luminance of surrounding areas. This work suggests that intra-cortical interactions in V1 could, at least partially, explain brightness induction effects and reveals how a common general architecture may account for several different fundamental processes, such as visual saliency and brightness induction, which emerge early in the visual processing pathway.

Brightness induction magnitude declines with increasing distance from the inducing field edge

Brightness induction refers to a class of visual illusions where the perceived intensity of a region of space is influenced by the luminance of surrounding regions. These illusions are significant because they provide insight into the neural organization and processing strategies employed by the visual system. The nature of these processing strategies, however, has long been debated. Here we investigate the spatial characteristics of grating induction as a function of the distance from the inducing field edge to evaluate the viability of various competing models. In particular multiscale spatial filtering models and homogeneous filling-in models make very different predictions in regard to the magnitude of induction as a function of this distance. Filling-in explanations predict that the brightness/lightness of the filled-in region will be homogeneous, whereas multiscale filtering predicts a fall-off in induction magnitude with distance from the inducing field edge. Induction magnitude was measured using a narrow probe version of the quadrature-phase motion-cancellation paradigm (Blakeslee & McCourt, 2011) and a point-by-point brightness matching paradigm (Blakeslee & McCourt, 1997, 1999; McCourt, 1994). Both techniques reveal a decrease in the magnitude of induction with increasing distance from the inducing edge. A homogeneous filling-in mechanism cannot explain the induced structure in the test fields of these stimuli. The results argue strongly against filling-in mechanisms as well as against any mechanism that posits that induction is homogeneous. The structure of the induction is, however, well accounted for by the multiscale filtering (ODOG) model of Blakeslee and McCourt (1999). These results support models of brightness/lightness, such as filtering models, which preserve these gradients of induction.

A possible role and basis of visual pathway selection in brightness induction

It is a well-known fact that the perceived brightness of any surface depends on the brightness of the surfaces that surround it. This phenomenon is termed as brightness induction. Isotropic arrays of multi-scale DoG (Difference of Gaussians) as well as cortical Oriented DoG (ODOG) and extensions thereof, like the Frequency-specific Locally Normalized ODOG (FLODOG) functions have been employed towards prediction of the direction of brightness induction in many brightness perception effects. But the neural basis of such spatial filters is seldom obvious. For instance, the visual information from retinal ganglion cells to such spatial filters, which have been generally speculated to appear at the early stage of cortical processing, are fed by at least three parallel channels viz. Parvocellular (P), Magnocellular (M) and Koniocellular (K) in the subcortical pathway, but the role of such pathways in brightness induction is generally not implicit. In this work, three different spatial filters based on an extended classical receptive field (ECRF) model of retinal ganglion cells, have been approximately related to the spatial contrast sensitivity functions of these three parallel channels. Based on our analysis involving different brightness perception effects, we propose that the M channel, with maximum conduction velocity, may have a special role for an initial sensorial perception. As a result, brightness assimilation may be the consequence of vision at a glance through the M pathway; contrast effect may be the consequence of a subsequent vision with scrutiny through the P channel; and the K pathway response may represent an intermediate situation resulting in ambiguity in brightness perception. The present work attempts to correlate this phenomenon of pathway selection with the complementary nature of these channels in terms of spatial frequency as well as contrast.

When is spatial filtering enough? Investigation of brightness and lightness perception in stimuli containing a visible illumination component

Brightness (perceived intensity) and lightness (perceived reflectance) matching were investigated in seven well-known visual stimuli that contain a visible shadow or transparent overlay. These stimuli are frequently upheld as evidence that low-level spatial filtering is inadequate to explain brightness/lightness illusions and that additional mid- or high-level mechanisms are required. The argument in favor of rejecting low-level spatial filtering explanations has been founded on the erroneous assumption that equating test patch and near surround luminance is sufficient to control for and rule out this type of mechanism. We tested this idea by comparing the matching behavior of four observers to the predictions of the ODOG multiscale filtering model (Blakeslee & McCourt, 1999). Lightness and brightness matching differed significantly only when test patches appeared in shadow or beneath a transparency. Lightness and brightness matches were both significantly larger under these conditions; however, the lightness matches greatly exceeded the brightness matches. Lightness matches were greater for test patches in shadow or beneath a transparency because lightness matches under these conditions were based on conscious inferential (not sensory-level) judgments where observers attempted to discount the difference in illumination. The ODOG model accounted for approximately 80% of the total variance in the brightness matches (as well as in the lightness matches for targets not in shadow or beneath a transparency), and successfully predicted the relative magnitude of these matches in five of the seven stimulus sets. These results indicate that multiscale spatial filtering provides a unified and parsimonious explanation for brightness perception in these stimuli and imply that higher-level mechanisms are not required to explain them. The model was not as successful for the argyle and wall of blocks illusions in that it incorrectly rank-ordered the relative magnitude of the effects across different versions of the stimuli. It is an important question whether such model failures are due to known but corrigible limitations of the ODOG model or whether they will require other (possibly higher-level) explanations.

Microgenesis of surface completion in visual objects: evidence for filling-out

Using metacontrast masking we examined the temporal dynamics of surface completion in object vision. By varying the stimulus onset asynchrony between the target object and the flanking mask(s), we obtained estimates of the time required for the entire surface contrast to fill out within the area delimited by the contours/edges of the target. The estimated speed of the filling-out process was 36.0 deg/s. Using existing estimates of cortical magnification, the computed filling-out speed in terms of cortical distance is .385 m/s, a value that approximates the estimated cortical filling-in speed and the speed of horizontal propagation in monkey V1. We discuss our results in relation to (1) prior findings of filling-in and filling-out phenomena, using surface completion in cortical space as the unifying principle, and (2) extant computational models of object vision.

Relative Brightness in Natural Images Can Be Accounted for by Removing Blurry Content

One critical question regarding visual cognition concerns how the physical properties of the visual world are represented in early vision and then relayed to high-level vision. Here, we posit a simple theory: Processes that encode object appearance reduce their response to spatial content that is coarser than the size of the attended object. We show that a filtering procedure based on this theory can account for the relative brightness levels of test patches placed in images of natural scenes and for many hard-to-explain brightness illusions. The implication is that the perception of brightness differences in most brightness illusions actually corresponds to physical differences present in the images. Portions of the visual system may encode these physical differences by means of neural processes that adaptively reduce their response to low-spatial-frequency content.

Changing the Chevreul illusion by a background luminance ramp: lateral inhibition fails at its traditional stronghold--a psychophysical refutation

The Chevreul illusion is a well-known 19^th century brightness illusion, comprising adjacent homogeneous grey bands of different luminance, which are perceived as inhomogeneous. It is generally explained by lateral inhibition, according to which brighter areas projected to the retina inhibit the sensitivity of neighbouring retinal areas. Lateral inhibition has been considered the foundation-stone of early vision for a century, upon which several computational models of brightness perception are built. One of the last strongholds of lateral inhibition is the Chevreul illusion, which is often illustrated even in current textbooks. Here we prove that lateral inhibition is insufficient to explain the Chevreul illusion. For this aim, we placed the Chevreul staircase in a luminance ramp background, which noticeably changed the illusion. In our psychophysical experiments, all 23 observers reported a strong illusion, when the direction of the ramp was identical to that of the staircase, and all reported homogeneous steps (no illusion) when its direction was the opposite. When the background of the staircase was uniform, 14 saw the illusion, and 9 saw no illusion. To see whether the change of the entire background area or that of the staircase boundary edges were more important, we placed another ramp around the staircase, whose direction was opposite to that of the original, larger ramp. The result is that though the inner ramp is rather narrow (mean = 0.51 deg, SD = 0.48 deg, N = 23), it still dominates perception. Since all conditions of the lateral inhibition account were untouched within the staircase, lateral inhibition fails to model these perceptual changes. Area ratios seem insignificant; the role of boundary edges seems crucial. We suggest that long range interactions between boundary edges and areas enclosed by them, such that diffusion-based models describe, provide a much more plausible account for these brightness phenomena, and local models are insufficient.

Spatiotemporal analysis of brightness induction

Brightness induction refers to a class of visual illusions in which the perceived intensity of a region of space is influenced by the luminance of surrounding regions. These illusions are significant because they provide insight into the neural organization of the visual system. A novel quadrature-phase motion cancelation technique was developed to measure the magnitude of the grating induction brightness illusion across a wide range of spatial frequencies, temporal frequencies and test field heights. Canceling contrast is greatest at low frequencies and declines with increasing frequency in both dimensions, and with increasing test field height. Canceling contrast scales as the product of inducing grating spatial frequency and test field height (the number of inducing grating cycles per test field height). When plotted using a spatial axis which indexes this product, the spatiotemporal induction surfaces for four test field heights can be described as four partially overlapping sections of a single larger surface. These properties of brightness induction are explained in the context of multiscale spatial filtering. The present study is the first to measure the magnitude of grating induction as a function of temporal frequency. Taken in conjunction with several other studies (Blakeslee & McCourt, 2008; Magnussen & Glad, 1975; Robinson & de Sa, 2008) the results of this study illustrate that at least one form of brightness induction is very much faster than that reported by DeValois, Webster, DeValois, and Lingelbach (1986) and Rossi and Paradiso (1996), and are inconsistent with the proposition that brightness induction results from a slow "filling in" process.

The effect of figural manipulations on brightness differences in the Benary cross

The Benary cross is a classical demonstration showing that the perceived brightness f an area is not solely determined by its luminance, but also by the context in which it is embedded. Despite the fact that two identical grey triangles are flanked by an equal amount of black and white, one of the triangles is perceived as being lighter than the other. It has been argued that the junctions surrounding a test area are crucial in determining brightness. Here, we explored how different aspects influencing perceptual organisation influence perceived figure-background relations in the Benary cross and, with that, the perceived brightness of the triangular patches in our stimuli. The results of a cancellation task confirm that the alignment of contours at junctions indeed has a strong influence on an area's brightness. At the same time, however, the Benary effect is also influenced by the overall symmetry of the cross and its orientation.

Lightness, brightness and transparency: a quarter century of new ideas, captivating demonstrations and unrelenting controversy

The past quarter century has witnessed considerable advances in our understanding of Lightness (perceived reflectance), Brightness (perceived luminance) and perceived Transparency (LBT). This review poses eight major conceptual questions that have engaged researchers during this period, and considers to what extent they have been answered. The questions concern 1. the relationship between lightness, brightness and perceived non-uniform illumination, 2. the brain site for lightness and brightness perception, 3 the effects of context on lightness and brightness, 4. the relationship between brightness and contrast for simple patch-background stimuli, 5. brightness "filling-in", 6. lightness anchoring, 7. the conditions for perceptual transparency, and 8. the perceptual representation of transparency. The discussion of progress on major conceptual questions inevitably requires an evaluation of which approaches to LBT are likely and which are unlikely to bear fruit in the long term, and which issues remain unresolved. It is concluded that the most promising developments in LBT are (a) models of brightness coding based on multi-scale filtering combined with contrast normalization, (b) the idea that the visual system decomposes the image into "layers" of reflectance, illumination and transparency, (c) that an understanding of image statistics is important to an understanding of lightness errors, (d) Whittle's logW metric for contrast-brightness, (e) the idea that "filling-in" is mediated by low spatial frequencies rather than neural spreading, and (f) that there exist multiple cues for identifying non-uniform illumination and transparency. Unresolved issues include how relative lightness values are anchored to produce absolute lightness values, and the perceptual representation of transparency. Bridging the gap between multi-scale filtering and layer decomposition approaches to LBT is a major task for future research.

Organização espacial na percepção visual de luminosidade

Este estudo analisou a influência de variações físicas dos estímulos, com base na organização espacial de figura-fundo criada pela associação dos efeitos ilusórios de contraste simultâneo de luminosidade e de contornos subjetivos. A cada participante, no total de 64, foram apresentadas 160 matrizes de escolha, cada uma composta de um estímulo modelo e quatro estímulos de comparação, devendo ser identificado qual dos quatro estímulos de comparação correspondia ao estímulo modelo. A diferença significativa entre as médias de ajuste visual verificadas para a condição de contorno subjetivo médio e para a condição controle (sem contorno) mostrou que a formação clássica de contornos subjetivos de Kanizsa, quando associada ao efeito de contraste simultâneo de luminosidade, influenciou a percepção de luminosidade dos participantes.

Spillmann's weaves are more resilient than Hermann's grid

In a classic Hermann grid display, faint and transient (illusory) spots are produced at the intersections of a white grid superimposed on a black background (or vice versa). In a variant of the Hermann grid developed by Spillmann and Levine (Spillmann, L., & Levine, J. (1971). Contrast enhancement in a Hermann grid with variable figure-ground ratio. Experimental Brain Research, 13, 547-559), the vertical and horizontal bars have different reflectance levels. In previous studies, the illusory spots in the Hermann and Spillmann and Levine grids have been treated analogously. Here, we focus on differences by introducing two types of 'weaves': one type consists of intertwined vertical and horizontal bars with the same luminance levels (hereinafter referred to as 'equiluminant weaves'); the vertical bars in the other type of weave differ in luminance level from the horizontal bars (hereinafter referred to as 'luminance-mismatched weaves'). The Hermann grid is a type of equiluminant weave, and the portion of the Spillmann and Levine grid in which the bars have different reflectance levels is similar to the luminance-mismatched weave. We demonstrate differences between illusory spots produced by luminance-mismatched weaves (and therefore Spillmann and Levine displays) and spots produced by equiluminant weaves (and therefore the Hermann grid): (1) low-pass equiluminant weaves create scintillating patterns, whereas low-pass luminance-mismatched weaves do not; (2) unlike spots for equiluminant weaves, the spots for the luminance-mismatched weaves are not abolished by jagged bars, wavy bars, thick bars, or orientation changes; (3) unlike the spots for equiluminant weaves, the spots for luminance-mismatched weaves occur foveally; and (4) unlike the spots for equiluminant weaves, luminance-mismatched weaves can be created with contrast variation (contrast-contrast, or 2nd-order weaves). We suggest three possible explanations for these results: (1) equiluminant weaves are just a liminal case among luminance-mismatched weaves; (2) the spots arise out of the co-activation of cortical simple cells and color-selective cells, where color-selective cells represent both hue and achromatic sensations; and (3) the spots for both equiluminant and luminance-mismatched weaves are present in high spatial frequency content, but the appearance or disappearance of the spots indicates the interplay between luminance and contrast responses at multiple spatial scales.

Spatial filtering versus anchoring accounts of brightness/lightness perception in staircase and simultaneous brightness/lightness contrast stimuli

J. Cataliotti and A. Gilchrist (1995) reported that, consistent with anchoring theory, the lightness of a black step in a reflectance staircase was not altered by moving a white step from a remote to an adjacent location. Recently, E. Economou, S. Zdravkovic, and A. Gilchrist (2007) reported data supporting three additional predictions of the anchoring model (A. Gilchrist et al., 1999): 1) equiluminant incremental targets in staircase simultaneous lightness contrast stimuli appeared equally light; 2) the simultaneous lightness contrast effect was due mainly to the lightening of the target on the black surround; and 3) the strength of lightness induction was greatest for darker targets. We investigated similar stimuli using brightness/lightness matching and found, contrary to these reports, that: 1) the relative position of the steps in a luminance staircase significantly influenced their brightness/lightness; 2) equiluminant incremental targets in staircase simultaneous brightness/lightness contrast stimuli did not all appear equally bright/light; 3) an asymmetry due to a greater brightening/lightening of the target on the black surround was not general; and 4) darker targets produced larger effects only when plotted on a log scale. In addition, the ODOG model (B. Blakeslee & M. E. McCourt, 1999) did an excellent job of accounting for brightness/lightness matching in these stimuli.

Multiplicative and additive Adelson's snake illusions

Two different versions of Adelson's snake lightness illusion are quantitatively investigated. In one experiment an additive version of the illusion is investigated by varying the additive component of the atmosphere transfer function (ATF) introduced by Adelson [2000, in The New Cognitive Neuroscience Ed. M Gazzaniga (Cambridge, MA: MIT Press) pp 339-351]. In the other, a multiplicative version of the illusion is examined by varying the multiplicative component of the ATE In both experiments four observers matched the targets' lightness of the snake patterns with Munsell samples. Increasing the additive or the multiplicative component elicited an approximately equal increase in the magnitude of the lightness illusion. The results show that both components, in the absence of other kinds of information, can be used as heuristics by our visual system to anchor luminance of the object when converting it into lightness.

Low-level features determine brightness in White's and Benary's illusions

We masked White's and Benary's brightness illusions and simultaneous contrast with narrowband visual noise and measured detection thresholds and brightness. The noise was either isotropic or orientation filtered. A narrow spatial frequency tuning was found for detection and brightness for every stimulus. A narrow orientation tuning was also found: the strength of the illusions decreased (White and Benary) or increased (White) depending on the orientation of the mask. The critical borders were always of the same contrast polarity. The results suggest that the brightness in figure-ground scenes is determined by mechanisms integrating incremental and decremental borders in early visual cortices.

Spatial and temporal influences on the contrast gauge

A contrast gauge consists of a narrow bar shaded from dark on bottom to light on top [Shapiro, A. G., Charles, J. P., & Shear-Heyman, M. (2005). Visual illusions based on single-field contrast asynchronies. Journal of Vision, 5(10), 764-782]. The perceptual division between dark and light on the bar depends on the luminance level of the surround: when the surround has a high luminance level, the perceptual divider moves up the bar; when the surround has a low luminance level, the perceptual divider moves down the bar. This paper examines the extent to which the perceptual division between light and dark can be used as an indicator to mark the zero contrast level between the bar and the surround. In the experiments, the bar was surrounded by a field whose luminance modulated in time. Three observers marked the maximum and minimum levels of the perceptual divider as a function of modulation amplitude, chromaticity (R, G, B, W), temporal frequency, and width of the surround. Linear changes in the modulation amplitude of the surround produced linear changes in the observers' settings of the indicator. Observer settings matched zero luminance contrast when the surround was wide (12.5deg), was modulating at less than or equal to 1Hz, and had W or G chromaticity, but not when the surround was narrow, or was modulating faster than 1Hz, or had R or B chromaticity. The effects of surround size suggest that the perceived minimum contrast results from processes that operate over multiple spatial scales. To test this hypothesis, the paper presents a new configuration in which near and far contrast information create different perceptual signatures. Under normal viewing conditions, the motion of the indicator follows the contrast information from the nearest edge, but when high spatial frequency information is removed (through image blur), the motion follows the contrast from the far spatial edge. It is therefore likely that the setting for the indicator for the contrast gauge depends on multiple processes and is not a simple indicator of luminance contrast. The perceptual response to low spatial frequency contrast appears to be given less perceptual weight when high spatial frequencies are present in the image.

Nearly instantaneous brightness induction

Brightness induction is the modulation of the perceived intensity of a region by the luminance of surrounding regions and reveals fundamental properties of neural organization in the visual system. Grating induction affords a unique opportunity to precisely measure the temporal properties of induction using a quadrature motion technique. Contrary to previous reports that induction is a sluggish process with temporal frequency cutoffs of 2-5 Hz (R. L. DeValois, M. A. Webster, K. K. DeValois, & B. Lingelbach, 1986; A. F. Rossi & M. A. Paradiso, 1996), we find that induction is nearly instantaneous. The temporal response of induced brightness differs from that of luminance gratings by a small time lag (<1 ms), or by a small temporal phase lag (<0.016 cycle), and remains relatively constant across wide variations in test field height. These data are not easily explained by an edge-dependent, homogeneous filling-in process (A. F. Rossi & M. A. Paradiso, 1996); however, they are consistent with an explanation of brightness induction based on spatial filtering by cortical simple cells (B. Blakeslee & M. E. McCourt, 1999).

Multiresolution wavelet framework models brightness induction effects

A new multiresolution wavelet model is presented here, which accounts for brightness assimilation and contrast effects in a unified framework, and includes known psychophysical and physiological attributes of the primate visual system (such as spatial frequency channels, oriented receptive fields, contrast sensitivity function, contrast non-linearities, and a unified set of parameters). Like other low-level models, such as the ODOG model [Blakeslee, B., & McCourt, M. E. (1999). A multiscale spatial filtering account of the white effect, simultaneous brightness contrast and grating induction. Vision Research, 39, 4361-4377], this formulation reproduces visual effects such as simultaneous contrast, the White effect, grating induction, the Todorović effect, Mach bands, the Chevreul effect and the Adelson-Logvinenko tile effects, but it also reproduces other previously unexplained effects such as the dungeon illusion, all using a single set of parameters.

Retinomorphic image processing

The present work is aimed at understanding and explaining some of the aspects of visual signal processing at the retinal level while exploiting the same towards the development of some simple techniques in the domain of digital image processing. Classical studies on retinal physiology revealed the nature of contrast sensitivity of the receptive field of bipolar or ganglion cells, which lie in the outer and inner plexiform layers of the retina. To explain these observations, a difference of Gaussian (DOG) filter was suggested, which was subsequently modified to a Laplacian of Gaussian (LOG) filter for computational ease in handling two-dimensional retinal inputs. Till date almost all image processing algorithms, used in various branches of science and engineering had followed LOG or one of its variants. Recent observations in retinal physiology however, indicate that the retinal ganglion cells receive input from a larger area than the classical receptive fields. We have proposed an isotropic model for the non-classical receptive field of the retinal ganglion cells, corroborated from these recent observations, by introducing higher order derivatives of Gaussian expressed as linear combination of Gaussians only. In digital image processing, this provides a new mechanism of edge detection on one hand and image half-toning on the other. It has also been found that living systems may sometimes prefer to "perceive" the external scenario by adding noise to the received signals in the pre-processing level for arriving at better information on light and shade in the edge map. The proposed model also provides explanation to many brightness-contrast illusions hitherto unexplained not only by the classical isotropic model but also by some other Gestalt and Constructivist models or by non-isotropic multi-scale models. The proposed model is easy to implement both in the analog and digital domain. A scheme for implementation in the analog domain generates a new silicon retina model implemented on a hardware development platform.

What are lightness illusions and why do we see them?

Lightness illusions are fundamental to human perception, and yet why we see them is still the focus of much research. Here we address the question by modelling not human physiology or perception directly as is typically the case but our natural visual world and the need for robust behaviour. Artificial neural networks were trained to predict the reflectance of surfaces in a synthetic ecology consisting of 3-D “dead-leaves” scenes under non-uniform illumination. The networks learned to solve this task accurately and robustly given only ambiguous sense data. In addition—and as a direct consequence of their experience—the networks also made systematic “errors” in their behaviour commensurate with human illusions, which includes brightness contrast and assimilation—although assimilation (specifically White's illusion) only emerged when the virtual ecology included 3-D, as opposed to 2-D scenes. Subtle variations in these illusions, also found in human perception, were observed, such as the asymmetry of brightness contrast. These data suggest that “illusions” arise in humans because (i) natural stimuli are ambiguous, and (ii) this ambiguity is resolved empirically by encoding the statistical relationship between images and scenes in past visual experience. Since resolving stimulus ambiguity is a challenge faced by all visual systems, a corollary of these findings is that human illusions must be experienced by all visual animals regardless of their particular neural machinery. The data also provide a more formal definition of illusion: the condition in which the true source of a stimulus differs from what is its most likely (and thus perceived) source. As such, illusions are not fundamentally different from non-illusory percepts, all being direct manifestations of the statistical relationship between images and scenes.

Sometimes the best way to understand how the visual brain works is to understand why it sometimes does not. Thus, visual illusions have been central to the science and philosophy of human consciousness for decades. Here we explain the root cause of brightness illusions, not by modelling human perception or its assumed physiological substrate (as is more typically done), but by modelling the basic challenge that all visual animals must resolve if they are to survive: the inherent ambiguity of sensory data. We do this by training synthetic neural networks to recognise surfaces under different lights in scenes with naturalistic structure. The result is that the networks not only solve this task robustly (i.e., they exhibit “lightness constancy”), they also—as a consequence—exhibit the same illusions of lightness that humans also see. In short, these synthetic systems not only get it right like we do, but also get it wrong like we do, too. This emergent coincidence strongly provides causal evidence that illusions (and by extension all percepts) represent the probable source of images in past visual experience, which has fundamental consequences for explaining how and why we see what we do. The study also suggests the first formal definition of what an illusion is: The condition in which the actual source of a stimulus differs from its most likely source.

The lowest spatial frequency channel determines brightness perception

This study investigates the role played by individual spatial scales in determining the apparent brightness of greyscale patterns. We measured the perceived difference in brightness across an edge in the presence of notch filtering and high-pass filtering for two stimulus configurations, one that elicits the perception of transparency and one that appears opaque. For both stimulus configurations, the apparent brightness of the surfaces delimited by the border decreased monotonically with progressive (ideal) high-pass filtering, with a critical cut-off at 1 c/deg. Using two octave ideal notch filtering, the maximum detrimental effect on apparent brightness was observed at about 1c/deg. Critical frequencies for apparent brightness did not vary with contrast, viewing distance, or surface size, suggesting that apparent brightness is determined by the channel tuned at 1 c/deg. Modelling the data with the local energy model [Morrone, M. C., & Burr, D. C. (1988). Feature detection in human vision: a phase dependent energy model. Proceedings of the Royal Society (London), B235, 221-245] at 1c/deg confirmed the suggestion that this channel mediates apparent brightness for both opaque and transparent borders, with no need for pooling or integration across spatial channels.

Explaining brightness illusions using spatial filtering and local response normalization

We introduce two new low-level computational models of brightness perception that account for a wide range of brightness illusions, including many variations on White's Effect [Perception, 8, 1979, 413]. Our models extend Blakeslee and McCourt's ODOG model [Vision Research, 39, 1999, 4361], which combines multiscale oriented difference-of-Gaussian filters and response normalization. We extend the response normalization to be more neurally plausible by constraining normalization to nearby receptive fields (models 1 and 2) and spatial frequencies (model 2), and show that both of these changes increase the effectiveness of the models at predicting brightness illusions.

Separate effects of background and illumination on lightness

Four experiments attempted to establish an effect of context on lightness. Lightness is one of the dimensions of color and it varies from black to white. Most of our stimuli were inspired by simultaneous lightness contrast illusion. First two experiments contrast the size of an effect produced by the change of background color vs. the change in illumination. The third experiment deals with different type of illusions, where the effect is obtained through the appearance of multiple illumination levels. The last experiment takes into account the ratio of the target and the background. The results reveal the size of effects produced separately by the background color and illumination level and suggest the prime importance of background. Also there are other factors such as reflectance range in the scene, incremental and decremental targets, and 2D vs. 3D representation.

Lightness and perceptual transparency

To estimate intrinsic descriptors of objects in the environment, effective biological vision systems must 'discount' extrinsic image properties that arise from changes in viewing conditions. In particular, to estimate the reflectance of surfaces, human vision must discount, or 'take account of', likely differences in the illumination of surfaces between one image region and another. If human vision possesses any significant degree of lightness constancy, then we would expect a target perceived to be in low illumination to appear lighter than an identical target perceived to be in higher illumination. In this paper, I present lightness illusions that run directly counter to this expectation. I suggest that mid-level and higher-level factors such as image junction structure and perceived illumination and transparency, are ineffective for generating strong lightness illusions on their own, and that these factors are not 'stronger' than luminance contrast in determining lightness. I discuss the implications of these results for current models of lightness perception. I also suggest a statistical justification for the highest-luminance anchoring rule for lightness.

No functional magnetic resonance imaging evidence for brightness and color filling-in in early human visual cortex

The brightness and color of a surface depends on its contrast with nearby surfaces. For example, a gray surface can appear very light when surrounded by a black surface or dark when surrounded by a white surface. Some theories suggest that perceived surface brightness and color is represented explicitly by neural signals in cortical visual field maps; these neural signals are not initiated by the stimulus itself but rather by the contrast signals at the borders. Here, we use functional magnetic resonance imaging (fMRI) to search for such neural "filling-in" signals. Although we find the usual strong relationship between local contrast and fMRI response, when perceived brightness or color changes are induced by modulating a surrounding field, rather than the surface itself, we find there is no corresponding local modulation in primary visual cortex or other nearby retinotopic maps. Moreover, when we model the obtained fMRI responses, we find strong evidence for contributions of both local and long-range edge responses. We argue that such extended edge responses may be caused by neurons previously identified in neurophysiological studies as being brightness responsive, a characterization that may therefore need to be revised. We conclude that the visual field maps of human V1 and V2 do not contain filled-in, topographical representations of surface brightness and color.

Gradient representation and perception in the early visual system--a novel account of Mach band formation

Recent evidence suggests that object surfaces and their properties are represented at early stages in the visual system of primates. Most likely invariant surface properties are extracted to endow primates with robust object recognition capabilities. In real-world scenes, luminance gradients are often superimposed on surfaces. We argue that gradients should also be represented in the visual system, since they encode highly variable information, such as shading, focal blur, and penumbral blur. We present a neuronal architecture which was designed and optimized for segregating and representing luminance gradients in real-world images. Our architecture in addition provides a novel theory for Mach bands, whereby corresponding psychophysical data are predicted consistently.

Smooth Gradient Representations as a Unifying Account of Chevreul's Illusion, Mach Bands, and a Variant of the Ehrenstein Disk

Recent evidence suggests that the primate visual system generates representations for object surfaces (where we consider representations for the surface attribute brightness). Object recognition can be expected to perform robustly if those representations are invariant despite environmental changes (e.g., in illumination). In real-world scenes, it happens, however, that surfaces are often overlaid by luminance gradients, which we define as smooth variations in intensity. Luminance gradients encode highly variable information, which may represent surface properties (curvature), nonsurface properties (e.g., specular highlights, cast shadows, illumination inhomogeneities), or information about depth relationships (cast shadows, blur). We argue, on grounds of the unpredictable nature of luminance gradients, that the visual system should establish corresponding representations, in addition to surface representations. We accordingly present a neuronal architecture, the so-called gradient system, which clarifies how spatially accurate gradient representations can be obtained by relying on only high-resolution retinal responses. Although the gradient system was designed and optimized for segregating, and generating, representations of luminance gradients with real-world luminance images, it is capable of quantitatively predicting psychophysical data on both Mach bands and Chevreul's illusion. It furthermore accounts qualitatively for a modified Ehrenstein disk.

A possible explanation of the low-level brightness-contrast illusions in the light of an extended classical receptive field model of retinal ganglion cells

The low-level brightness-contrast illusions constitute a special class within visual illusions. Speculations exist that these illusions may be processed through the filtering action of the retinal ganglion cells without necessitating much intervention from higher order processes of visual perception. Concept of the classical receptive field of the ganglion cell, derived from early physiological studies, prompted the idea that a Difference of Gaussian (DoG) model might explain the low-level illusions. In spite of its many successes, the DoG model fails to explain some of these illusions. It has been shown in this paper that it is possible to simulate those illusions with a model that takes into cognizance the role of the extended classical receptive field.

Do cortical neurons process luminance or contrast to encode surface properties?

On the one hand, contrast signals provide information about surface properties, such as reflectance, and patchy illumination conditions, such as shadows. On the other hand, processing of luminance signals may provide information about global light levels, such as the difference between sunny and cloudy days. We devised models of contrast and luminance processing, using principles of logarithmic signal coding and half-wave rectification. We fit each model to individual response profiles obtained from 67 surface-responsive macaque V1 neurons in a center-surround paradigm similar to those used in human psychophysical studies. The most general forms of the luminance and contrast models explained, on average, 73 and 87% of the response variance over the sample population, respectively. We used a statistical technique, known as Akaike's information criterion, to quantify goodness of fit relative to number of model parameters, giving the relative probability of each model being correct. Luminance models, having fewer parameters than contrast models, performed substantially better in the vast majority of neurons, whereas contrast models performed similarly well in only a small minority of neurons. These results suggest that the processing of local and mean scene luminance predominates over contrast integration in surface-responsive neurons of the primary visual cortex. The sluggish dynamics of luminance-related cortical activity may provide a neural basis for the recent psychophysical demonstration that luminance information dominates brightness perception at low temporal frequencies.

A multiscale filtering explanation of gradient induction and remote brightness induction effects: a reply to Logvinenko (2003)

Grating induction is a brightness effect in which a counterphase spatial brightness variation (a grating) is induced in a homogeneous test strip that is surrounded by an inducing luminance grating (McCourt, 1982 Vision Research 22 119-134). Moulden and Kingdom (1991 Vision Research 31 1999-2008) introduced an interesting variant of grating induction (sometimes referred to as gradient induction) in which multiple strips of either a linear luminance ramp or a sine-wave grating were interlaced with strips of homogeneous luminance. We (Blakeslee and McCourt, 1999 Vision Research 39 4361-4377) demonstrated that a simple multiscale filtering explanation could account for grating induction. Recently, however, Logvinenko (2003 Perception 32 621-626) presented several arguments impugning the adequacy of spatial filtering approaches to understanding brightness induction in gradient induction stimuli. We propose that Logvinenko's arguments apply only to a limited class of filtering models, specifically those which employ only a single spatial filter. To test this hypothesis we modeled gradient induction stimuli as a function of inducing contrast, as well as Logvinenko's (2003) remote induction stimulus, using our multiscale oriented difference-of-Gaussians (ODOG) model (Blakeslee and McCourt 1999). The ODOG model successfully predicts the appearance of the inducing strips and the homogeneous test strips in the gradient induction stimuli and the appearance of the test patches in the remote induction stimuli. These results refute Logvinenko's (2003) claims, and we interpret them as providing strong evidence for a multiscale filtering approach to understanding both gradient induction and remote brightness induction effects.

Computational adaptation model and its predictions for color induction of first and second orders

The appearance of a patch of color or its contrast depends not only on the stimulus itself but also on the surrounding stimuli (induction effects-simultaneous contrast). A comprehensive computational physiological model is presented to describe chromatic adaptation of the first (retinal) and second (cortical) orders, and to predict the different chromatic induction effects. We propose that the chromatic induction of the first order that yields perceived complementary colors can be predicted by retinal adaptation mechanisms, contrary to previous suggestions. The second order of the proposed adaptation mechanism succeeds to predict the automatic perceived inhibition or facilitation of the central contrast of a texture stimulus, depending on the surrounding contrast. Furthermore, contrary to other models, this model is able to also predict the effect of variegated surrounding on the central perceived color.