Lightness from contrast: a selective integration model

Empirical evaluation of computational models of lightness perception

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

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.

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

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

Layer and framework theories of lightness

Lightness (the perceived dimension running from black to white) represents a problem for vision science because the light coming to the eye from an object totally fails to specify the shade of gray of the object, due to the confounding of surface gray and illumination intensity. The two leading approaches, decomposition theories and anchoring theories, split the retinal image into overlapping layers and adjacent frameworks, respectively. Because each approach has important strengths and some weaknesses, an integration of them would mark an important step forward for the lightness theory. But the problem remains how this integration can actually be realized.

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 Influence of Physical Illumination on Lightness Perception in Simultaneous Contrast Displays

Three experiments investigated the role of physical illumination on lightness perception in simultaneous lightness contrast (SLC). Four configurations were employed: the classic textbook version of the illusion and three configurations that produced either enhanced or reduced SLC. Experiment 1 tested the effect of ambient illumination on lightness perception. It simulated very dark environmental conditions that nevertheless still allowed perception of different shades of gray. Experiment 2 tested the effect of the intensity of Gelb lighting on lightness perception. Experiment 3 presented two conditions that integrated illumination conditions from Experiments 1 and 2. Our results demonstrated an illumination effect on both lightness matching and perceived SLC contrast: As the intensity of illumination increased, the target on the black background appeared lighter, while the target on the white background was little affected. We hypothesize the existence of two illumination ranges that affect lightness perception differently: low and normal. In the low range, the SLC contrast was reduced and targets appeared darker. In the normal range, the SLC contrast and lightness matchings for each background were little changed across illumination intensities.

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'.

Lines That Induce Phenomenal Transparency

Three neighbouring opaque surfaces may appear split into two layers, one transparent and one opaque beneath, if an outline contour is drawn that encompasses two of them. The phenomenon was originally observed by Kanizsa [1955 Rivista di Psicologia69 3–19; 1979 Organization in Vision: Essays on Gestalt Psychology (New York: Praeger)], for the case where an outline contour is drawn to encompass one of the two parts of a bicoloured figure and a portion of a background of lightest (or darkest) luminance. Preliminary observations revealed that the outline contour yields different effects: in addition to the stratification into layers described by Kanizsa, a second split, opposite in depth order, may occur when the outline contour is close in luminance to one of the three surfaces. An initial experiment was designed to investigate what conditions give rise to the two phenomenal transparencies: this led to the conclusion that an outline contour superimposed on an opaque surface causes this surface to emerge as a transparent layer when the luminances of the contour and the surface differ, in absolute value, by no more than 13.2 cd m−2. We have named this phenomenon ‘transparency of the intercepted surface’, to distinguish it from the phenomenal transparency arising when the contour and surface are very different in luminance. When such a difference exists, the contour acts as a factor of surface definition and grouping: the portion of the homogeneous surface it bounds emerges as a fourth surface and groups with a nearby surface if there is one close in luminance. The transparency phenomena (‘transparency of the contoured surface’) perceived in this context conform to the constraints of Metelli's model, as demonstrated by a second experiment, designed to gather ‘opacity’ ratings of stimuli. The observer judgments conformed to the values predicted by Metelli's formula for perceived degree of transparency, a. The role of the outline contour in conveying figural and intensity information is discussed.

Testing the Coplanar Ratio Hypothesis of Lightness Perception

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

Theoretical approaches to lightness and perception

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

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

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

The impact of stereoscopic depth on the Munker-White illusion

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

Kanizsa illusory contours appearing in the plasmodium pattern of Physarum polycephalum

The plasmodium of Physarum polycephalum is often used in the implementation of non-linear computation to solve optimization problems, and this organismal feature was not used in this analysis to compute perception and/or sensation in humans. In this paper, we focused on the Kanizsa illusion, which is a well-known visual illusion resulting from the differentiation-integration of the visual field, and compared the illusion with the adaptive network in the plasmodium of P. polycephalum. We demonstrated that the network pattern mimicking the Kanizsa illusion can be produced by an asynchronous automata-fashioned model of the foraging slime mold and by the real plasmodia of P. polycephalum. Because the protoplasm of the plasmodium is transported depending on both local and global computation, it may contain differentiation-integration processes. In this sense, we can extend the idea of perception and computation.

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.

The Brightness of Diamonds

A brightness illusion and transparency effects are described for configurations comprising alternating black and white bars on which are superimposed mid-luminance shapes (diamonds and hexagons). The resulting phenomena are predictable by Anderson's ‘scission theory’, which is thus given further support.

Linking depth to lightness and anchoring within the differentiation-integration formalism

Recently we developed a model that reproduces the Kanizsa square illusion based on two principles: (1) a spatial 2-D integration of luminance ratio and differentiated depth signals creates a "primary" lightness map and a depth map, respectively, which is then followed by (2) a modification of the primary lightness values under influence of the perceived depth (Kogo, Strecha, Van Gool, & Wagemans, 2010). Within this model, the process of the spatial integration inevitably introduced an arbitrary offset. In order to obtain absolute values of depth and lightness, the offset values needed to be determined by other constraints. This is the anchoring problem of the depth and lightness measurements. Here we report the anchoring rules that were established by investigating the model's responses to the Kanizsa square and its wide range of variations. For the primary lightness map, the highest value rule was applied, while the area rule appeared most plausible for the depth map. By applying the same principles to simple figures consisting of black and white areas of different size ratios, the model succeeded in reproducing published empirical results on lightness anchoring (Li & Gilchrist, 1999).

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.

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.

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.

Spatial frequency difference between textures interferes with brightness perception

Abrupt changes in luminance trigger and restrict brightness filling-in. If brightness was actively filled-in and mediated by cells signaling both luminance borders and surface brightness, then brightness spreading could also get disrupted by changes in texture. We measured psychophysically the brightness of a uniform luminance disk, which was segmented into two parts by different textures. The brightness of the central part of the disk was substantially reduced, and the reduction depended on spatial frequency, but not on the orientation difference between the textures. The results show that texture borders are able to block brightness filling-in. The bandwidth of brightness spreading was estimated to be approximately 1.5 octaves. This suggests that brightness information spreads only between neurons of similar spatial frequency characteristics.

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.

White's effect: removing the junctions but preserving the strength of the illusion

White's effect (also known as the Munker White effect) is a lightness illusion in which, contrary to expectations based on simultaneous contrast and Wallach's rule, a gray rectangle predominantly surrounded by white appears lighter than an identical rectangle that is mainly surrounded by black. The illusion is often explained in terms of T-junctions that are formed by the three-way intersection of the gray rectangle, a black stripe, and a white stripe. I present a circular variant of White's effect in which all the junctions have been removed without significantly affecting the strength of the illusion, suggesting that junctions are not an important consideration in all versions of White's effect.

Recovering real-world images from single-scale boundaries with a novel filling-in architecture

Filling-in models were successful in predicting psychophysical data for brightness perception. Nevertheless, their suitability for real-world image processing has never been examined. A unified architecture for both predicting psychophysical data and real-world image processing would constitute a powerful theory for early visual information processing. As a first contribution of the present paper, we identified three principal problems with current filling-in architectures, which hamper the goal of having such a unified architecture. To overcome these problems we propose an advance to filling-in theory, called BEATS filling-in, which is based on a novel nonlinear diffusion operator. BEATS filling-in furthermore introduces novel boundary structures. We compare, by means of simulation studies with real-world images, the performance of BEATS filling-in with the recently proposed confidence-based filling-in. As a second contribution we propose a novel mechanism for encoding luminance information in contrast responses ('multiplex contrasts'), which is based on recent neurophysiological findings. Again, by simulations, we show that 'multiplex contrasts' at a single, high-resolution filter scale are sufficient for recovering absolute luminance levels. Hence, 'multiplex contrasts' represent a novel theory addressing how the brain encodes and decodes luminance information.

Oriented multiscale spatial filtering and contrast normalization: a parsimonious model of brightness induction in a continuum of stimuli including White, Howe and simultaneous brightness contrast

The White effect [Perception 8 (1979) 413] cannot be simply explained as due to either brightness contrast or brightness assimilation because 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 abandon spatial filtering explanations not only for the White effect but for brightness perception in general. Offered instead are explanations based on a variety of junction analyses and/or perceptual organization schemes which in the case of the White effect are usually based on T-junctions. Recently, Howe [Perception 30 (2001) 1023] challenged T-junction based explanations with a novel variation of White's effect in which the T-junctions were constant while the brightness effect was eliminated or reversed, and proposed an alternative explanation in terms of illusory contours. The present study argues that an analysis at the level of illusory contours is not necessary and that a much simpler spatial filtering based explanation is sufficient. Brightness induction was measured in a set of stimuli chosen to illustrate the relationship between the Howe stimulus [Perception 30 (2001) 1023], the White stimulus [Perception 8 (1979) 413] and the classical simultaneous brightness contrast (SBC) stimulus. The White stimulus and the SBC stimulus occupy opposite ends of a continuum of stimuli in which the Howe stimulus is the mid-point. The psychophysical measurements were compared with the predictions of the oriented difference-of-Gaussians (ODOG) computational model of Blakeslee and McCourt [Vision Research 39 (1999) 4361]. The ODOG model parsimoniously accounted for both the direction and relative magnitude of the brightness effects suggesting that more complex mechanisms are not required to explain them.

Lightness perception: seeing one color through another

A newly described and dramatic visual illusion suggests that the retinal image is decomposed by the brain into overlapping layers, not into contiguous frameworks of illumination.

Straightness as a cue for luminance edge interpretation

In order to determine the reflectance of a surface, it is necessary to discount luminance changes produced by illumination variation, a process that requires the visual system to respond differently to luminance changes that are due to illumination and reflectance. It is known that various cues can be used in this process. By measuring the strength of lightness illusions, we find evidence that straightness is, used as a cue: When a boundary is straight rather than curved, it has a greater tendency to be discounted, as if it were an illumination edge. The strongest illusions occur when a boundary has high contrast and has multiple X-junctions that preserve a consistent contrast ratio.

Influence of target size and luminance on the White-Todorovic effect

Variants of a lightness effect described by [Todorovic's, D. (1997). Lightness and junctions. Perception, 26, 379] were studied to quantify the failure of lightness constancy as a function of target luminance and target size. Todorovic's effect is similar to White's effect. Simultaneous lightness contrast appears to operate selectively between stimuli belonging to the same perceptual group, and not between stimuli of equal proximity belonging to different perceptual groups. We found that mid-gray targets grouped with a white contextual stimulus were matched on average to a darker-than-veridical gray. Those grouped with a black contextual stimulus were matched on average veridically. This is consistent with 'anchoring' effects observed in simple two-stimulus displays. However, target luminance had an effect that was not captured by mid-level target luminance data or data averaged across target luminances. For both white and black contextual stimuli, light-gray targets were matched to a darker-than-veridical gray and the direction of this error shifted toward the lighter-than-veridical direction as the luminance of the target was lowered. The result was a constant difference between the perceived lightnesses of targets presented with white and black contextual stimuli. Target size had no effect on perceived lightness. These data imply that the Todorovic-White effect can be characterized as lightness assimilation rather than as lightness contrast. By accounting for compression as well as the Todorovic-White effect, assimilation is the more general explanation.

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.

The effects of global grouping laws on surface lightness perception

Previous studies of lightness perception have shown that local surface grouping laws such as proximity and T junction were powerful determinants of target surface lightness. Recent lightness theories also emphasize the importance of local grouping of surfaces. In this study, we further examined the effects of three global grouping laws--symmetry, repetition, and alternation--on lightness perception. Local surface grouping laws such as proximity and good continuation were controlled across all of our stimulus displays. Participants' lightness perception consistently depended on a given surface's belongingness as determined by these laws--that is, global grouping laws affected a target surface's lightness perception. Our results indicate that global grouping laws determine a target surface's lightness when local surface grouping does not produce any distinct surface belongingness. Implications of our basic results are discussed in terms of a recent lightness theory.

The statistical structure of natural light patterns determines perceived light intensity

The same target luminance in different contexts can elicit markedly different perceptions of brightness, a fact that has long puzzled vision scientists. Here we test the proposal that the visual system encodes not luminance as such but rather the statistical relationship of a particular luminance to all possible luminance values experienced in natural contexts during evolution. This statistical conception of vision was validated by using a database of natural scenes in which we could determine the probability distribution functions of co-occurring target and contextual luminance values. The distribution functions obtained in this way predict target brightness in response to a variety of challenging stimuli, thus explaining these otherwise puzzling percepts. That brightness is determined by the statistics of natural light patterns implies that the relevant neural circuitry is specifically organized to generate these probabilistic responses.

On the determinants of surface brightness

The brightness of an achromatic surface with luminance S on an achromatic background with luminance B varies with S, with B, and with the luminance step deltaL at the border of the surface. In agreement with previous findings indicating that the visual system can perform as a photometer, the results of the two experiments reported here show that S and B determined surface brightness independently of deltaL when the surface was adjacent to and when it was separated from the background. This finding suggests that surface brightness depends on the integration of neural signals representing magnitudes of absolute luminance. A weighted-average model of this integration is proposed.

Perception of brightness and brightness illusions in the macaque monkey

Recent physiological studies show that neural responses correlated with the perception of brightness are found in cortical area V1 but not earlier in the visual pathway (Kayama et al., 1979; Reid and Shapley, 1989; Squatrito et al., 1990; Komatsu et al., 1996; Rossi et al., 1996; MacEvoy et al., 1998; Rossi and Paradiso, 1999; Hung et al., 2001; Kinoshita and Komatsu, 2001; MacEvoy and Paradiso, 2001). However, these studies are based on comparisons of neural responses in animals with brightness perception in humans. Very little is known about the perception of brightness in animals typically used in physiological experiments. In this study, we quantify brightness discrimination, brightness induction, and White's effect in macaque monkeys. The results show that, qualitatively and quantitatively, the perception of brightness in macaques and humans is quite similar. This similarity may be an indication of common underlying neural computations in the two species.

Is lightness induction a pictorial illusion?

Lightness induction, or simultaneous lightness contrast (we prefer the term lightness induction since contrast has another meaning in the visual literature, namely, the relative intensity of the stimulation), was studied for a 3-D object (Adelson's wall of blocks) and its 2-D pictorial representations. A statistically significant lightness induction effect was found only for the pictures but not for the 3-D object. No lightness induction effect was found for the 3-D object under either monocular or binocular viewing conditions.

Induction in variants of White's effect: common or separate mechanisms?

In White's display the gray target surrounded more by black than white appears darker than the target of the same physical luminance surrounded more by white than black. Several subsequent studies have shown that this effect occurs only when the luminance of the test regions lies between the minimum and maximum luminance values of the inducing stripes. With targets either lighter or darker than both inducing stripes, the direction of the effect is reversed and the effect is known as the 'inverted' White's effect. Views differ on whether the classical and inverted White's effects are mediated by common or separate underlying mechanisms. We varied the aspect ratio of the test and inducing regions in the classical and inverted White's effects. Consistent with previously reported findings, we found that the direction of the classical effect did not depend on the amount of black or white border in immediate contact with the test patch. On the other hand, perceived lightness in the inverted White's effect was affected by such variations, suggesting that induction in classical and inverted White's configurations is governed by different mechanisms. These results confirm the critical importance of the interaction between luminance and geometric relationships in induced brightness.

Perceptual organization overcomes the effects of local surround in determining simultaneous lightness contrast

Lightness induction can occur on the basis of the immediate surround of a region (local interactions) and also on the basis of global factors of perceptual organization. The experiments reported in this article used novel displays that made it possible to differentiate the contributions of these two kinds offactors. The experiments demonstrated, for the first time, that when higher-level factors act contemporaneously with lower-level factors, the contrast effect induced by the global-organization principle of perceptual belongingness overcomes the effect due to retinal lateral inhibition.

Classical and inverted White's effects

In classical White's effect, intermediate-luminance targets appear lighter when they interrupt the dark stripes of a grating and darker when they interrupt the light stripes. The effect is reversed when targets are of double-increment or double-decrement luminance, relative to the luminances of grating stripes. To find a common explanation for classical and inverted effects, we ran two experiments. In experiment 1, we utilised intermediate-target displays to show that perceived transparency dominates over occlusion only when the target luminance is close to the luminances of top regions. This result weakens transparency-based accounts of White's effect. In experiment 2, we varied grating contrast and target luminance to measure the classical effect in seven intermediate-target cases, as well as the inverted effect in four double-increment and four double-decrement cases. Both types of effect are explained by a common model, based on assimilation to the top region and contrast with the interrupted region, weighted by adjacency along the luminance continuum.