Similar mechanisms underlie simultaneous brightness contrast and grating induction

Neural correlates of dynamic lightness induction

The lightness of a surface depends not only on the amount of light reflected off, it but also on the context in which it is embedded. Despite a long history of research, neural correlates of context-dependent lightness perception remain a topic of ongoing debate. Here, we seek to expand on the existing literature by measuring functional magnetic resonance imaging (fMRI) responses to lightness variations induced by the context. During the fMRI experiment, we presented 10 participants with a dynamic stimulus in which either the luminance of a disk or its surround is modulated at four different frequencies ranging from 1 to 8 Hz. Behaviorally, when the surround luminance is modulated at low frequencies, participants perceive an illusory change in the lightness of the disk (lightness induction). In contrast, they perceive little or no induction at higher frequencies. Using this frequency dependence and controlling for long-range responses to border contrast and luminance changes, we found that activity in the primary visual cortex (V1) correlates with lightness induction, providing further evidence for the involvement of V1 in the processing of context-dependent lightness.

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.

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.

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.

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.

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.

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.

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.

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.

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'.

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.

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.

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.

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.

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.

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.

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

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

Low-level spatiochromatic grouping for saliency estimation

We propose a saliency model termed SIM (saliency by induction mechanisms), which is based on a low-level spatiochromatic model that has successfully predicted chromatic induction phenomena. In so doing, we hypothesize that the low-level visual mechanisms that enhance or suppress image detail are also responsible for making some image regions more salient. Moreover, SIM adds geometrical grouplets to enhance complex low-level features such as corners, and suppress relatively simpler features such as edges. Since our model has been fitted on psychophysical chromatic induction data, it is largely nonparametric. SIM outperforms state-of-the-art methods in predicting eye fixations on two datasets and using two metrics.

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 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.

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.

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.

Depth propagation and surface construction in 3-D vision

In stereo vision, regions with ambiguous or unspecified disparity can acquire perceived depth from unambiguous regions. This has been called stereo capture, depth interpolation or surface completion. We studied some striking induced depth effects suggesting that depth interpolation and surface completion are distinct stages of visual processing. An inducing texture (2-D Gaussian noise) had sinusoidal modulation of disparity, creating a smooth horizontal corrugation. The central region of this surface was replaced by various test patterns whose perceived corrugation was measured. When the test image was horizontal 1-D noise, shown to one eye or to both eyes without disparity, it appeared corrugated in much the same way as the disparity-modulated (DM) flanking regions. But when the test image was 2-D noise, or vertical 1-D noise, little or no depth was induced. This suggests that horizontal orientation was a key factor. For a horizontal sine-wave luminance grating, strong depth was induced, but for a square-wave grating, depth was induced only when its edges were aligned with the peaks and troughs of the DM flanking surface. These and related results suggest that disparity (or local depth) propagates along horizontal 1-D features, and then a 3-D surface is constructed from the depth samples acquired. The shape of the constructed surface can be different from the inducer, and so surface construction appears to operate on the results of a more local depth propagation process.

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.

Perceiving colour at a glimpse: The relevance of where one fixates

We used classification images to examine whether certain parts of a surface are particularly important when judging its colour, such as its centre, its edges, or where one is looking. The scene consisted of a regular pattern of square tiles with random colours from along a short line in colour space. Targets defined by a square array of brighter tiles were presented for 200ms. The colours of the tiles within the target were biased by an amount that led to about 70% of the responses being correct. Subjects fixated a point that fell within the target's lower left quadrant and reported each target's colour. They tended to report the colour of the tiles near the fixation point. The influence of the tiles' colour reversed at the target's border and was weaker outside the target. The colour at the border itself was not particularly important. When coloured tiles were also presented before (and after) target presentation they had an opposite (but weaker) effect, indicating that the change in colour is important. Comparing the influence of tiles outside the target with that of tiles at the position at which the target would soon appear suggests that when judging surface colours during the short "glimpses" between saccades, temporal comparisons can be at least as important as spatial ones. We conclude that eye movements are important for colour vision, both because they determine which part of the surface of interest will be given most weight and because the perceived colour of such a surface also depends on what one looked at last.

Contrast polarity and edge integration in achromatic color perception

Previous work has shown that the achromatic color of a target patch embedded in simple two-dimensional display depends not only on the luminance contrast between the target and its immediate surround but also on the contrasts of other nearby edges. Quantitative models have been proposed in which the target color is modeled as a spatially weighted sum of edge contrasts in which the target edge receives the largest weight. Rudd and Arrington [Vision Res.41, 3649 (2001)] elaborated on this idea to include an additional mechanism whereby effects of individual color-inducing edges are "partially blocked" by edges lying along the path between the inducing edge and the target. We tested the blockage model in appearance matching experiments performed with disk-and-single-ring stimuli having all four possible combinations of inner and outer ring edge contrast polarities. Evidence was obtained for both "blockage" (attenuation) and "antiblockage" (amplification) of achromatic color induction signals, depending on the contrast polarities of the inner and outer ring edges. A neural model is proposed to account for our data on the basis of the contrast gain control occurring between cortical edge detector neurons.

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.

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 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.

Induction effects for heterochromatic brightness matching, heterochromatic flicker photometry, and minimally distinct border: implications for the neural mechanisms underlying induction

Brightness induction refers to the finding that the apparent brightness of a stimulus changes when surrounded by a black versus a white stimulus. In the current study, we investigated the effects of black/white surrounding stimuli on settings made between red and green stimuli on three different tasks: heterochromatic brightness matching (HBM), heterochromatic flicker photometry (HFP), and minimally distinct border (MDB). For HBM, subjects varied the relative luminance between the red and green stimuli so that the brightness of the two colors appeared equal. For the two other tasks, matches were made based on minimizing red/green flicker (HFP) or the saliency of a red/green border (MDB). For all three tasks, the presence of black/white surrounding stimuli significantly altered red/green settings, demonstrating the existence of induction effects. These results are discussed in terms of which underlying color pathways (L+ M versus L-M) may contribute to induction effects for the different tasks.

Comparing the spatial-frequency response of first-order and second-order lateral visual interactions: grating induction and contrast-contrast

The magnitudes of two suprathreshold lateral spatial-interaction effects--grating induction and contrast--contrast--were compared with regard to their dependence upon inducing-grating spatial frequency. Both effects cause the contrast of target stimuli embedded in surrounding patterns to be matched nonveridically. The magnitudes of each effect were measured in a common unit that indexed the degree of nonveridical contrast matching across a large range of target-grating contrasts (+/- 0.80). Grating induction was a low-pass effect with respect to spatial frequency, whereas contrast-contrast was bandpass, peaking at approximately 4.0 cycles deg(-1). The magnitude of grating induction exceeded that of contrast--contrast, both overall and at their optimal frequencies (0.03125 and 4.0 cycles deg(-1), respectively); the two effects are equipotent at an inducing-grating spatial frequency of 1.0 cycle deg(-1). A significant negative correlation between the magnitudes of the two effects suggests a link whereby activation of second-order normalization mechanisms may inhibit first-order mechanisms.

Brightness induction: Unequal spatial integration with increments and decrements

Modern theories of brightness induction include an influence from regions that do not share a border with the target. This study tested whether the spatial range of neural integration is the same with incremental versus decremental contrast edges in relatively remote parts of the background. Using an asymmetric matching task, observers set the brightness of a comparison ring, within its own uniform surround, to match the brightness of a test ring within a contiguous surround and a noncontiguous background. The measurements showed that the area of integration depended on the incremental versus decremental contrast polarity at the edge between the surround and background. This implies that brightness induction from an inhomogeneous background must consider the polarity of contrast edges within the whole scene.

A unified theory of brightness contrast and assimilation incorporating oriented multiscale spatial filtering and contrast normalization

Brightness induction includes both contrast and assimilations effects. Brightness contrast occurs when the brightness of a test region shifts away from the brightness of adjacent regions. Brightness assimilation refers to the opposite situation in which the brightness of the test region shifts toward that of the surrounding regions. Interestingly, in the White effect [Perception 8 (1979) 413] the direction of the induced brightness change does not correlate with the amount of black or white border in contact with the gray test patch. This has led some investigators to reject spatial filtering explanations not only for the White effect but for brightness perception in general. Instead, these investigators have offered explanations based on a variety of junction analyses and/or perceptual organization schemes. Here, these approaches are challenged with a critical set of new psychophysical measurements that determined the magnitude of the White effect, the shifted White effect [Perception 10 (1981) 215] and the checkerboard illusion [R.L. DeValois, K.K. DeValois, Spatial Vision, Oxford University Press, NY, 1988] as a function of inducing pattern spatial frequency and test patch height. The oriented difference-of-Gaussians (ODOG) computational model of Blakeslee and McCourt [Vision Res. 39 (1999) 4361] parsimoniously accounts for the psychophysical data, and illustrates that mechanisms based on junction analysis or perceptual inference are not required to explain them. According to the ODOG model, brightness induction results from linear spatial filtering with an incomplete basis set (the finite array of spatial filters in the human visual system). In addition, orientation selectivity of the filters and contrast normalization across orientation channels are critical for explaining some brightness effects, such as the White effect.

Mechanisms of contrast induction in heterogeneous displays

This study examines how judgments of a region's contrast are influenced by components of a heterogeneous surround. Each stimulus comprised a 5x5 grid of squares in a homogeneous background of fixed mean luminance, with the central square the target. On a given trial, the task was to judge (with feedback) whether the (Weber) contrast of the target was 0.04 or -0.04 (relative to the background); the contrasts assigned (in random order) to the 24 surrounding squares were drawn from the values -0.98, -0.33, 0.33, 0.98 in conformity to one of nine pre-chosen histograms. Presentations were brief (80 ms) in one condition and long (800 ms) in another. A novel psychophysical method was used to estimate the impact exerted on judged target contrast (JTC) by a given contrast in a given grid position. Results were similar for four observers. For both display durations, the four squares sharing an edge with the target influenced JTC 2.4-9 times more than any other surrounding squares. In long presentations, abutting squares of extreme contrast repelled target contrast: squares of contrast -0.98 (0.98) increased (decreased) JTC. However, lower contrast abutting squares attracted target contrast: squares of contrast -0.33 (0.33) decreased (increased) JTC. This central finding can be explained by supposing that: (a) JTC is strongly correlated with the average boundary contrast from surround to target, as registered by linear, edge-selective neurons, and, crucially, (b) the responses of these neurons are themselves subject to lateral inhibition from the rectified responses of other similarly tuned neurons. Finally, in brief presentations, a polarity-specific asymmetry was observed: the two positive abutting-square contrasts continued to influence JTC as they did in long presentations, but contrasts -0.33 and -0.98 ceased to exert much impact, suggesting that lateral influences on target appearance propagate more quickly from positive than from negative contrast abutting regions.

Quantitative properties of achromatic color induction: an edge integration analysis

Edge integration refers to a hypothetical process by which the visual system combines information about the local contrast, or luminance ratios, at luminance borders within an image to compute a scale of relative reflectances for the regions between the borders. The results of three achromatic color matching experiments, in which a test and matching ring were surrounded by one or more rings of varying luminance, were analyzed in terms of three alternative quantitative edge integration models: (1) a generalized Retinex algorithm, in which achromatic color is computed from a weighted sum of log luminance ratios, with weights free to vary as a function of distance from the test (Weighted Log Luminance Ratio model); (2) an elaboration of the first model, in which the weights given to distant edges are reduced by a percentage that depends on the log luminance ratios of borders lying between the distant edges and the target (Weighted Log Luminance Ratio model with Blockage); and (3) an alternative modification of the first model, in which Michelson contrasts are substituted for log luminance ratios in the achromatic color computation (Weighted Michelson Contrast model). The experimental results support the Weighted Log Luminance Ratio model over the other two edge integration models. The Weighted Log Luminance Ratio model is also shown to provide a better fit to the achromatic color matching data than does Wallach's Ratio Rule, which states that the two disks will match in achromatic color when their respective disk/ring luminance ratios are equal.

Brightening prospects for early cortical coding of perceived luminance: a high-density electrical mapping study

Establishing the computational rules and neural substrates of brightness coding is a topic of both historical and contemporary interest. Two major classes of explanations for brightness illusions, such as brightness contrast, can be traced to Hering and Helmholtz. Hering's legacy is a low-level account in which brightness contrast results from obligatory lateral inhibitory interactions occurring at some level(s) in the visual system. Helmholtz offered a high-level account, positing a causal role for factors such as perceptual grouping, inferred illumination, and the extraction of surface properties such as orientation and reflectance. The tension between these theoretical viewpoints persists unabated to date. Intracranial electrophysiological recordings have revealed that brightness is represented in the firing rates of striate neurons, a fact consistent with low-level explanations. However, since the time-course of brightness-related responses relative to the onset of striate activity is undisclosed, it remains possible that striate activation might be temporally and causally secondary to higher-level computational processes. Knowledge of the timing of brightness-related neural activity is thus crucial to both constrain and adjudicate between these competing theories. We utilize high-density electrophysiological recording and a tachistoscopic brightness discrimination task to measure the time-course and scalp topography of brightness-related electrical potentials in human observers. Brightness perception is correlated with electrical activity at the earliest stages of visual cortical processing. These findings are interpreted to support Hering's low-level account of brightness for White's effect, and the results are discussed in the context of current theories of brightness perception.

Perceiving the intensity of light

The relationship between luminance (i.e., the photometric intensity of light) and its perception (i.e., sensations of lightness or brightness) has long been a puzzle. In addition to the mystery of why these perceptual qualities do not scale with luminance in any simple way, "illusions" such as simultaneous brightness contrast, Mach bands, Craik-O'Brien-Cornsweet edge effects, and the Chubb-Sperling-Solomon illusion have all generated much interest but no generally accepted explanation. The authors review evidence that the full range of this perceptual phenomenology can be rationalized in terms of an empirical theory of vision. The implication of these observations is that perceptions of lightness and brightness are generated according to the probability distributions of the possible sources of luminance values in stimuli that are inevitably ambiguous.

Brightness assimilation in bullseye displays

In simultaneous brightness contrast displays, a gray target square G(B) bordered by black appears brighter than an identical gray target square G(W) bordered by white. Here we demonstrate that this effect can be reversed if G(B) is surrounded by bands that alternate outward from black to white, while G(W) is surrounded by bands that alternate outward from white to black. With these simple "bullseye" displays assimilation generally occurs--G(B) appears darker than G(W). Experiments 1 and 2 used a 2AFC design with a 2.2 s display duration. The results of these experiments indicate that (i) substantial assimilation occurs for target Weber contrasts (relative to the gray background) of -0.25, 0, and 0.25, but assimilation was maximal when target contrast was -0.25 and decreased as target contrast increased, (ii) assimilation effects were the same whether the width of the four surround bands was 20% of the target or 40% of the target, and (iii) assimilation occurs with as few as 2 surround-bands and the magnitude of the effect increases slightly as the number of bands increase. When experiment 1 was re-run using the method of matching (experiment 3), however, the results changed dramatically: (moderate) assimilation effects were found only when target contrast was -0.25; when target contrast was 0.25, there was a brightness contrast effect; when target contrast was 0, there was no illusion. Assimilation effects in bullseye displays are not predicted by the CSF model described in DeValois and DeValois [Spatial Vision, Oxford University Press, New York, 1988], the anchoring model of Gilchrist et al. [Psychological Review, 106(4) (1999) 795], or Blakeslee and McCourt's [Vision Research 39 (1999) 4361] ODOG model. We propose that this assimilation effect is the result of a contrast inhibition mechanism similar to that proposed by Chubb et al. [Proceedings for the National Academy of Science, vol. 86, 1989, p. 9631] to underlie contrast effects.

Brightness contrast and assimilation from patterned inducing backgrounds

Theories of induction propose that the brightness of a test patch within a complex surround is explained by local contrast or by integrating contrasts from various regions within the surround, weighted inversely with the distance from the test. Results here corroborate that brightness induction from a patterned background depends on both contiguous and non-contiguous surrounding light, but the measurements were inconsistent with any linear integration of contrast at edges within the scene. In some conditions, assimilation rather than contrast to contiguous surrounding light was observed, depending on the luminance of the light in non-contiguous regions. This finding implies that brightness induction from patterned backgrounds depends on neural processes that can cause contrast and/or assimilation, depending on the luminance relation between contiguous and non-contiguous regions. A model in the literature postulating that the influence of a non-contiguous edge is regulated by the amount of contrast at the contiguous edge can accommodate brightness induced by these patterned backgrounds.

Natural scene statistics as the universal basis of color context effects

The color context effects referred to as color contrast, constancy, and assimilation underscore the fact that color percepts do not correspond to the spectral characteristics of the generative stimuli. Despite a variety of proposed theories, these phenomena have resisted explanation in a single principled framework. Using a hyperspectral image database of natural scenes, we here show that color contrast, constancy, and assimilation are all predicted by the statistical organization of spectral returns from natural visual environments.

A fair test of the effect of a shadow-incompatible luminance gradient on the simultaneous lightness contrast

Shadow-compatibility of simultaneous lightness contrast is discussed by Alexander D Logvinenko and Paola Bressan, with examples claiming to provide a test of the hypothesis.

A multiscale spatial filtering account of the Wertheimer-Benary effect and the corrugated Mondrian

Blakeslee and McCourt [Blakeslee, B., & McCourt, M.E. (1997). Similar mechanisms underlie simultaneous brightness contrast and grating induction. Vision Research, 37, 2849-2869] demonstrated that a multiscale array of two-dimensional difference-of-Gaussian (DOG) filters provided a simple but powerful model for explaining a number of seemingly complex features of grating induction (GI), while simultaneously encompassing salient features of brightness induction in simultaneous brightness contrast (SBC), brightness assimilation and Hermann Grid stimuli. The DOG model (and isotropic contrast models in general) cannot, however, account for another important group of brightness effects including the White effect [White, M. (1997). A new effect of pattern on perceived lightness. Perception, 8, 413-416] and a variant of SBC [Todorovic, D. (1997). Lightness and junctions. Perception, 26, 379-395]. Blakeslee and McCourt [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] developed a modified version of the model, an oriented (ODOG) model, which differed from the DOG model in that the filters were anisotropic and their outputs were pooled nonlinearly. Using this model, they were able to account for both groups of induction effects. The present paper examines two additional sets of brightness illusions that cannot be explained by isotropic contrast models. Psychophysical brightness matching is employed to quantitatively measure the size of the brightness effect for two Wertheimer-Benary stimuli [Benary, W. (1924). Beobachtungen zu einem experiment uber helligkeitskontrast. Psychologische Forschung, 5, 131-142; Todorovic, D. (1997). Lightness and junctions. Perception, 26, 379-395] and for low- and high-contrast versions of corrugated Mondrian stimuli [Adelson, E.H. (1993). Perceptual organization and the jugdement of brightness. Science, 262, 2042-2044; Todorovic, D. (1997). Lightness and junctions. Perception, 26, 379-395]. Brightness matches are obtained on both homogeneous and checkerboard matching backgrounds. The ODOG model qualitatively predicts the appearance of the test patches in the Wertheimer-Benary stimuli and corrugated Mondrian stimuli. In addition, it quantitatively predicts the relative magnitudes of the corrugated Mondrian effects in the various conditions. In general, the psychophysical results and ODOG modeling argue strongly that like SBC, GI, the White effect and Todorovic's SBC demonstration, induced brightness in Wertheimer-Benary stimuli and in the corrugated Mondrian primarily reflects early-stage filtering operations in the visual system.