Multiresolution wavelet framework models brightness induction effects

A model of colour appearance based on efficient coding of natural images

An object's colour, brightness and pattern are all influenced by its surroundings, and a number of visual phenomena and "illusions" have been discovered that highlight these often dramatic effects. Explanations for these phenomena range from low-level neural mechanisms to high-level processes that incorporate contextual information or prior knowledge. Importantly, few of these phenomena can currently be accounted for in quantitative models of colour appearance. Here we ask to what extent colour appearance is predicted by a model based on the principle of coding efficiency. The model assumes that the image is encoded by noisy spatio-chromatic filters at one octave separations, which are either circularly symmetrical or oriented. Each spatial band's lower threshold is set by the contrast sensitivity function, and the dynamic range of the band is a fixed multiple of this threshold, above which the response saturates. Filter outputs are then reweighted to give equal power in each channel for natural images. We demonstrate that the model fits human behavioural performance in psychophysics experiments, and also primate retinal ganglion responses. Next, we systematically test the model's ability to qualitatively predict over 50 brightness and colour phenomena, with almost complete success. This implies that much of colour appearance is potentially attributable to simple mechanisms evolved for efficient coding of natural images, and is a well-founded basis for modelling the vision of humans and other animals.

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.

A Neurodynamic Model of Saliency Prediction in V1

Lateral connections in the primary visual cortex (V1) have long been hypothesized to be responsible for several visual processing mechanisms such as brightness induction, chromatic induction, visual discomfort, and bottom-up visual attention (also named saliency). Many computational models have been developed to independently predict these and other visual processes, but no computational model has been able to reproduce all of them simultaneously. In this work, we show that a biologically plausible computational model of lateral interactions of V1 is able to simultaneously predict saliency and all the aforementioned visual processes. Our model's architecture (NSWAM) is based on Penacchio's neurodynamic model of lateral connections of V1. It is defined as a network of firing rate neurons, sensitive to visual features such as brightness, color, orientation, and scale. We tested NSWAM saliency predictions using images from several eye tracking data sets. We show that the accuracy of predictions obtained by our architecture, using shuffled metrics, is similar to other state-of-the-art computational methods, particularly with synthetic images (CAT2000-Pattern and SID4VAM) that mainly contain low-level features. Moreover, we outperform other biologically inspired saliency models that are specifically designed to exclusively reproduce saliency. We show that our biologically plausible model of lateral connections can simultaneously explain different visual processes present in V1 (without applying any type of training or optimization and keeping the same parameterization for all the visual processes). This can be useful for the definition of a unified architecture of the primary visual cortex.

Chromatic Induction in Migraine

The human visual system is not a colorimeter. The perceived colour of a region does not only depend on its colour spectrum, but also on the colour spectra and geometric arrangement of neighbouring regions, a phenomenon called chromatic induction. Chromatic induction is thought to be driven by lateral interactions: the activity of a central neuron is modified by stimuli outside its classical receptive field through excitatory-inhibitory mechanisms. As there is growing evidence of an excitation/inhibition imbalance in migraine, we compared chromatic induction in migraine and control groups. As hypothesised, we found a difference in the strength of induction between the two groups, with stronger induction effects in migraine. On the other hand, given the increased prevalence of visual phenomena in migraine with aura, we also hypothesised that the difference between migraine and control would be more important in migraine with aura than in migraine without aura. Our experiments did not support this hypothesis. Taken together, our results suggest a link between excitation/inhibition imbalance and increased induction effects.

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.

Contrasting a Misinterpretation of the Reverse Contrast

The reverse contrast is a perceptual phenomenon in which the effect of the classical simultaneous lightness contrast is reversed. In classic simultaneous lightness contrast configurations, a gray surrounded by black is perceived lighter than an identical gray surrounded by white, but in the reverse contrast configurations, the perceptual outcome is the opposite: a gray surrounded by black appears darker than the same gray surrounded by white. The explanation provided for the reverse contrast (by different authors) is the belongingness of the gray targets to a more complex configuration. Different configurations show the occurrence of these phenomena; however, the factors determining this effect are not always the same. In particular, some configurations are based on both belongingness and assimilation, while one configuration is based only on belongingness. The evidence that different factors determine the reverse contrast is crucial for future research dealing with achromatic color perception and, in particular, with lightness induction phenomena.

Visual illusions via neural dynamics: Wilson-Cowan-type models and the efficient representation principle

We reproduce suprathreshold perception phenomena, specifically visual illusions, by Wilson-Cowan (WC)-type models of neuronal dynamics. Our findings show that the ability to replicate the illusions considered is related to how well the neural activity equations comply with the efficient representation principle. Our first contribution consists in showing that the WC equations can reproduce a number of brightness and orientation-dependent illusions. Then we formally prove that there cannot be an energy functional that the WC dynamics are minimizing. This leads us to consider an alternative, variational modeling, which has been previously employed for local histogram equalization (LHE) tasks. To adapt our model to the architecture of V1, we perform an extension that has an explicit dependence on local image orientation. Finally, we report several numerical experiments showing that LHE provides a better reproduction of visual illusions than the original WC formulation, and that its cortical extension is capable also to reproduce complex orientation-dependent illusions.NEW & NOTEWORTHY We show that the Wilson-Cowan equations can reproduce a number of brightness and orientation-dependent illusions. Then we formally prove that there cannot be an energy functional that the Wilson-Cowan equations are minimizing, making them suboptimal with respect to the efficient representation principle. We thus propose a slight modification that is consistent with such principle and show that this provides a better reproduction of visual illusions than the original Wilson-Cowan formulation. We also consider the cortical extension of both models to deal with more complex orientation-dependent illusions.

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.

Which tone-mapping operator is the best? A comparative study of perceptual quality

Tone-mapping operators (TMOs) are designed to generate perceptually similar low-dynamic-range images from high-dynamic-range ones. We studied the performance of 15 TMOs in two psychophysical experiments where observers compared the digitally generated tone-mapped images to their corresponding physical scenes. All experiments were performed in a controlled environment, and the setups were designed to emphasize different image properties: in the first experiment we evaluated the local relationships among intensity levels, and in the second one we evaluated global visual appearance among physical scenes and tone-mapped images, which were presented side by side. We ranked the TMOs according to how well they reproduced the results obtained in the physical scene. Our results show that ranking position clearly depends on the adopted evaluation criteria, which implies that, in general, these tone-mapping algorithms consider either local or global image attributes but rarely both. Regarding the question of which TMO is the best, KimKautz ["Consistent tone reproduction," in Proceedings of Computer Graphics and Imaging (2008)] and Krawczyk ["Lightness perception in tone reproduction for high dynamic range images," in Proceedings of Eurographics (2005), p. 3] obtained the better results across the different experiments. We conclude that more thorough and standardized evaluation criteria are needed to study all the characteristics of TMOs, as there is ample room for improvement in future developments.

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'.

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

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

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

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

Brightness masking is modulated by disparity structure

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Brightness masking is affected by three-dimensional configuration.

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

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

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

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

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

The impact of stereoscopic depth on the Munker-White illusion

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

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

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

Mach bands explained by response normalization

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

From image processing to computational neuroscience: a neural model based on histogram equalization

There are many ways in which the human visual system works to reduce the inherent redundancy of the visual information in natural scenes, coding it in an efficient way. The non-linear response curves of photoreceptors and the spatial organization of the receptive fields of visual neurons both work toward this goal of efficient coding. A related, very important aspect is that of the existence of post-retinal mechanisms for contrast enhancement that compensate for the blurring produced in early stages of the visual process. And alongside mechanisms for coding and wiring efficiency, there is neural activity in the human visual cortex that correlates with the perceptual phenomenon of lightness induction. In this paper we propose a neural model that is derived from an image processing technique for histogram equalization, and that is able to deal with all the aspects just mentioned: this new model is able to predict lightness induction phenomena, and improves the efficiency of the representation by flattening both the histogram and the power spectrum of the image signal.

Reversing the Reversed Contrast

Galmonte and Agostini (1998 Investigative Ophthalmology & Visual Science, 39(4), S158), Agostini and Galmonte (2002 Psychological Science, 13, 89–93), Bressan (2001 Perception, 30, 1031–1046), and Gilchrist and Annan (2002 Perception, 31, 141–150) reported three different lightness contrast configurations in which grouping factors make a gray target totally surrounded by black appear darker than an equal gray target surrounded by white, reversing the classical contrast effect. In this paper we demonstrate that the three configurations known as ‘reversed contrast’ are based on different mechanisms. Sixteen participants judged the lightness of the gray targets of the original and modified versions of the three configurations. Our results highlight that the Agostini and Galmonte effect is reversed when the global grouping factors are removed, while in a number of variations of Bressan's and Gilchrist and Annan's displays the direction of the effect does not change, even in absence of global grouping factors. Our results indicate that the factors determining the Agostini and Galmonte effect are different from those acting on the other two configurations, in which the lightness change is also due to factors other than belongingness.

Retinal mesopic adaptation model for brightness perception under transient glare

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

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.

The Boldface Illusion: A New Visual Assimilation

This article introduces a new visual illusion. A thick line induces boldface appearance to adjacent letters or geometric figures. This boldface illusion suggests that visual features assimilate in terms of thickness.

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

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