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

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.

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

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

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

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

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

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

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

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

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.

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.

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

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

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.

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.

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.

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

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

Paradoxical effect of spatially homogenous transparent fields on simultaneous contrast illusions

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

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

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

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

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

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

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

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

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

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

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

Variability in constancy of the perceived surface reflectance across different illumination statistics

In contrast to the classical findings of lightness constancy, recent psychophysical studies show the strong dependency of the perceived reflectance of a surface on the structure of the natural illumination. The present study examined this inconstancy for systematic variations in the light field and an image-based explanation for it. Observers matched the specular and diffuse reflectance of a three-dimensional object in a complex scene under a fixed light field to that in the scene under different light fields with variable mean, contrast, and gamma. For the both specular and diffuse components, the matched reflectance was relatively constant against changes in the mean illuminance but varied extensively with changes in the contrast and gamma of the light field. We found that the matching data were well predicted by the similarity of the subband histograms of the images. The results support the notion that early spatial filtering can provide a unified account of both the constancy in the perceived surface reflectance against mean illuminance and the inconstancy for higher-order illumination statistics.

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

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

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.

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

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

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.

A functional circuitry for edge-induced brightness perception

The identification of visual contours and surfaces is central to visual scene segmentation. One view of image construction argues that object contours are first identified and then surfaces are filled in. Although there are psychophysical and single-unit data to suggest that the filling-in view is correct, the underlying circuitry is unknown. Here we examine specific spike-timing relationships between border and surface responses in cat visual cortical areas 17 and 18. With both real and illusory (Cornsweet) brightness contrast stimuli, we found a border-to-surface shift in the relative timing of spike activity. This shift was absent when borders were absent and could be reversed with relocation of the stimulus border, indicating that the direction of information flow is highly dependent on stimulus conditions. Furthermore, this effect was seen predominantly in 17-18, and not 17-17, interactions. These results demonstrate a border-to-surface mechanism at early stages of visual processing and emphasize the importance of interareal circuitry in vision.

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.

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

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

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.