Perceiving light versus material

Color and luminance processing in V1 complex cells and artificial neural networks

Object recognition by natural and artificial visual systems benefits from the identification of object boundaries. A useful cue for the detection of object boundaries is the superposition of luminance and color edges. To gain insight into the suitability of this cue for object recognition, we examined convolutional neural network models that had been trained to recognize objects in natural images. We focused specifically on units in the second convolutional layer whose activations are invariant to the spatial phase of a sinusoidal grating. Some of these units were tuned for a nonlinear combination of color and luminance, which is broadly consistent with a role in object boundary detection. Others were tuned for luminance alone, but very few were tuned for color alone. A literature review reveals that V1 complex cells have a similar distribution of tuning. We speculate that this pattern of sensitivity provides an efficient basis for object recognition, perhaps by mitigating the effects of lighting on luminance contrast polarity. The absence of a contrast polarity-invariant representation of chromaticity alone suggests that it is redundant with other representations.

Perceiving the representative surface color of real-world materials

Natural surfaces such as soil, grass, and skin usually involve far more complex and heterogenous structures than the perfectly uniform surfaces assumed in studies on color and material perception. Despite this, we can easily perceive the representative color of these surfaces. Here, we investigated the visual mechanisms underlying the perception of representative surface color using 120 natural images of diverse materials and their statistically synthesized images. Our matching experiments indicated that the perceived representative color revealed was not significantly different from the Portilla-Simoncelli-synthesized images or phase-randomized images except for one sample, even though the perceived shape and material properties were greatly impaired in the synthetic stimuli. The results also showed that the matched representative colors were predictable from the saturation-enhanced color of the brightest point in the image, excluding the high-intensity outliers. The results support the notion that humans judge the representative color and lightness of real-world surfaces depending on simple image measurements.

Luminance contrast provides metric depth information

The perception of depth from retinal images depends on information from multiple visual cues. One potential depth cue is the statistical relationship between luminance and distance; darker points in a local region of an image tend to be farther away than brighter points. We establish that this statistical relationship acts as a quantitative cue to depth. We show that luminance variations affect depth in naturalistic scenes containing multiple cues to depth. This occurred when the correlation between variations of luminance and depth was manipulated within an object, but not between objects. This is consistent with the local nature of the statistical relationship in natural scenes. We also showed that perceived depth increases as contrast is increased, but only when the depth signalled by luminance and binocular disparity are consistent. Our results show that the negative correlation between luminance and distance, as found under diffuse lighting, provides a depth cue that is combined with depth from binocular disparity, in a way that is consistent with the simultaneous estimation of surface depth and reflectance variations. Adopting more complex lighting models such as ambient occlusion in computer rendering will thus contribute to the accuracy as well as the aesthetic appearance of three-dimensional graphics.

Crossmodal Texture Perception Is Illumination-Dependent

Visually perceived roughness of 3D textures varies with illumination direction. Surfaces appear rougher when the illumination angle is lowered resulting in a lack of roughness constancy. Here we aimed to investigate whether the visual system also relies on illumination-dependent features when judging roughness in a crossmodal matching task or whether it can access illumination-invariant surface features that can also be evaluated by the tactile system. Participants ( N = 32) explored an abrasive paper of medium physical roughness either tactually, or visually under two different illumination conditions (top vs oblique angle). Subsequently, they had to judge if a comparison stimulus (varying in physical roughness) matched the previously explored standard. Matching was either performed using the same modality as during exploration (intramodal) or using a different modality (crossmodal). In the intramodal conditions, participants performed equally well independent of the modality or illumination employed. In the crossmodal conditions, participants selected rougher tactile matches after exploring the standard visually under oblique illumination than under top illumination. Conversely, after tactile exploration, they selected smoother visual matches under oblique than under top illumination. These findings confirm that visual roughness perception depends on illumination direction and show, for the first time, that this failure of roughness constancy also transfers to judgements made crossmodally.

Distinguishing shadows from surface boundaries using local achromatic cues

In order to accurately parse the visual scene into distinct surfaces, it is essential to determine whether a local luminance edge is caused by a boundary between two surfaces or a shadow cast across a single surface. Previous studies have demonstrated that local chromatic cues may help to distinguish edges caused by shadows from those caused by surface boundaries, but the information potentially available in local achromatic cues like contrast, texture, and penumbral blur remains poorly understood. In this study, we develop and analyze a large database of hand-labeled achromatic shadow edges to better understand what image properties distinguish them from occlusion edges. We find that both the highest contrast as well as the lowest contrast edges are more likely to be occlusions than shadows, extending previous observations based on a more limited image set. We also find that contrast cues alone can reliably distinguish the two edge categories with nearly 70% accuracy at 40x40 resolution. Logistic regression on a Gabor Filter bank (GFB) modeling a population of V1 simple cells separates the categories with nearly 80% accuracy, and furthermore exhibits tuning to penumbral blur. A Filter-Rectify Filter (FRF) style neural network extending the GFB model performed at better than 80% accuracy, and exhibited blur tuning and greater sensitivity to texture differences. We compare human performance on our edge classification task to that of the FRF and GFB models, finding the best human observers attaining the same performance as the machine classifiers. Several analyses demonstrate both classifiers exhibit significant positive correlation with human behavior, although we find a slightly better agreement on an image-by-image basis between human performance and the FRF model than the GFB model, suggesting an important role for texture.

Distinguishing edges caused by changes in illumination from edges caused by surface boundaries is an essential computation for accurately parsing the visual scene. Previous psychophysical investigations examining the utility of various locally available cues to classify edges as shadows or surface boundaries have primarily focused on color, as surface boundaries often give rise to more significant change in color than shadows. However, even in grayscale images we can readily distinguish shadows from surface boundaries, suggesting an important role for achromatic cues in addition to color. We demonstrate using statistical analysis of natural shadow and surface boundary edges that locally available achromatic cues can be exploited by machine classifiers to reliably distinguish these two edge categories. These classifiers exhibit sensitivity to blur and local texture differences, and exhibit reasonably good agreement with humans classifying edges as shadows or surface boundaries. As trichromatic vision is relatively rare in the animal kingdom, our work suggests how organisms lacking rich color vision can still exploit other cues to avoid mistaking illumination changes for surface changes.

Lightness Perception in Complex Scenes

Lightness perception is the perception of achromatic surface colors: black, white, and shades of grey. Lightness has long been a central research topic in experimental psychology, as perceiving surface color is an important visual task but also a difficult one due to the deep ambiguity of retinal images. In this article, I review psychophysical work on lightness perception in complex scenes over the past 20 years, with an emphasis on work that supports the development of computational models. I discuss Bayesian models, equivalent illumination models, multidimensional scaling, anchoring theory, spatial filtering models, natural scene statistics, and related work in computer vision. I review open topics in lightness perception that seem ready for progress, including the relationship between lightness and brightness, and developing more sophisticated computational models of lightness in complex scenes. Expected final online publication date for the Annual Review of Vision Science, Volume 7 is September 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

The Fork-and-Knife Illusion

We describe a transparency illusion that can be observed with an ordinary metal knife and fork. Placed in the correct configuration relative to the fork, the metal knife appears transparent, with some observers experiencing a bistable percept in which transparency alternates with reflective appearance. The effect is related to other illusory percepts that follow from careful placement of mirrored surfaces, but to our knowledge, it is unique in that the key feature of the illusion is how the mirrored surface (in this case, the knife) is perceived rather than how a mirror induces altered perception of other objects and surfaces. We describe conditions that do and do not affect the strength of the illusion and point out its connections to previously reported phenomena.

Brightness matching in optical see-through augmented reality

A visual experiment using a beam-splitter-based optical see-through augmented reality (OST-AR) setup tested the effect of the size and alignment of AR overlays with a brightness-matching task using physical cubes. Results indicate that more luminance is required when AR overlays are oversized with respect to the cubes, showing that observers discount the AR overlay to a greater extent when it is more obviously a transparent layer. This is not explained by conventional color appearance modeling but supports an AR-specific model based on foreground-background discounting. The findings and model will help determine parameters for creating convincing AR manipulation of real-world objects.

Infants distinguish and represent pattern as an object feature from externally generated patterns superimposed on real, 3-dimensional objects' surfaces

As infants form object representations, the patterns viewed on objects' surfaces may be challenging to decipher because these patterns may be created from the surface reflectance of an object (an object property) or from an external source, such as a cast shadow. We tested 7 ½-month-old infants' use of cues that specify the source of patterns seen on the surfaces of real, 3-dimensional objects to individuate those objects. Results suggest that when forming object representations based on patterns, 7½-month-olds rely heavily on temporal and depth cues to distinguish patterns inherent to the object from other types of patterns.

A sparkle in the eye: Illumination cues and lightness constancy in the perception of eye contact

In social interactions, our sense of when we have eye contact with another person relies on the distribution of luminance across their eye region, reflecting the position of the darker iris within the lighter sclera of the human eye. This distribution of luminance can be distorted by the lighting conditions, consistent with the fundamental challenge that the visual system faces in distinguishing the nature of a surface from the pattern of light falling upon it. Here we perform a set of psychophysics experiments in human observers to investigate how illumination impacts on the perception of eye contact. First, we find that simple changes in the direction of illumination can produce systematic biases in our sense of when we have eye contact with another person. Second, we find that the visual system uses information about the lighting conditions to partially discount or 'explain away' the effects of illumination in this context, leading to a significantly more robust sense of when we have eye contact with another person. Third, we find that perceived eye contact is affected by specular reflections from the eye surface in addition to shading patterns, implicating eye glint as a potential cue to gaze direction. Overall, this illustrates how our interpretation of social signals relies on visual mechanisms that both compensate for the effects of illumination on retinal input and potentially exploit novel cues that illumination can produce.

Color ensembles: Sampling and averaging spatial hue distributions

Color serves both to segment a scene into objects and background and to identify objects. Although objects and surfaces usually contain multiple colors, humans can readily extract a representative color description, for instance, that tomatoes are red and bananas yellow. The study of color discrimination and identification has a long history, yet we know little about the formation of summary representations of multicolored stimuli. Here, we characterize the human ability to integrate hue information over space for simple color stimuli varying in the amount of information, stimulus size, and spatial configuration of stimulus elements. We show that humans are efficient at integrating hue information over space beyond what has been shown before for color stimuli. Integration depends only on the amount of information in the display and not on spatial factors such as element size or spatial configuration in the range measured. Finally, we find that observers spontaneously prefer a simple averaging strategy even with skewed color distributions. These results shed light on how human observers form summary representations of color and make a link between the perception of polychromatic surfaces and the broader literature of ensemble perception.

Transparent layer constancy is improved by motion, stereo disparity, highly regular background pattern, and successive presentation

The visual system uses figural and colorimetric regularities in the retinal image to recognize optical filters and to discern the properties of the transparent overlay from properties of the background. Previous work suggests that the perceived color and transmittance of the transparent layer vary less under illumination changes than it would be expected from corresponding changes in the input. Here, we tested how the degree of this approximate transparent layer constancy (TLC) depends on factors that presumably facilitate the decomposition into a filter and a background layer. Using an asymmetric filter matching task, we found that motion, stereo disparity, and a highly regular background pattern each contribute to the vividness of the transparency impression and the degree of TLC. Combining these cues led to a cumulative increase in TLC, suggesting a "strong fusion" cue integration process. We also tested objects with invalid figural conditions for transparency (T-junctions). The tendency to perceive these objects as opaque and to establish a proximal match increased the more conspicuous the violation of this figural condition was. Furthermore, we investigated the gain in TLC due to alternating presentation. Alternating presentation enhanced TLC and color constancy to a comparable degree, and our results suggest that adaptation contributes to this effect.

Color improves edge classification in human vision

Despite the complexity of the visual world, humans rarely confuse variations in illumination, for example shadows, from variations in material properties, such as paint or stain. This ability to distinguish illumination from material edges is crucial for determining the spatial layout of objects and surfaces in natural scenes. In this study, we explore the role that color (chromatic) cues play in edge classification. We conducted a psychophysical experiment that required subjects to classify edges into illumination and material, in patches taken from images of natural scenes that either contained or did not contain color information. The edge images were of various sizes and were pre-classified into illumination and material, based on inspection of the edge in the context of the whole image from which the edge was extracted. Edge classification performance was found to be superior for the color compared to grayscale images, in keeping with color acting as a cue for edge classification. We defined machine observers sensitive to simple image properties and found that they too classified the edges better with color information, although they failed to capture the effect of image size observed in the psychophysical experiment. Our findings are consistent with previous work suggesting that color information facilitates the identification of material properties, transparency, shadows and the perception of shape-from-shading.

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.

Neural Mechanisms of Material Perception: Quest on Shitsukan

In recent years, a growing body of research has addressed the nature and mechanism of material perception. Material perception entails perceiving and recognizing a material, surface quality or internal state of an object based on sensory stimuli such as visual, tactile, and/or auditory sensations. This process is ongoing in every aspect of daily life. We can, for example, easily distinguish whether an object is made of wood or metal, or whether a surface is rough or smooth. Judging whether the ground is wet or dry or whether a fish is fresh also involves material perception. Information obtained through material perception can be used to govern actions toward objects and to make decisions about whether to approach an object or avoid it. Because the physical processes leading to sensory signals related to material perception is complicated, it has been difficult to manipulate experimental stimuli in a rigorous manner. However, that situation is now changing thanks to advances in technology and knowledge in related fields. In this article, we will review what is currently known about the neural mechanisms responsible for material perception. We will show that cortical areas in the ventral visual pathway are strongly involved in material perception. Our main focus is on vision, but every sensory modality is involved in material perception. Information obtained through different sensory modalities is closely linked in material perception. Such cross-modal processing is another important feature of material perception, and will also be covered in this review.

Deformation-induced transparency resolves color scission

Dynamic image deformation produces the perception of a transparent material that appears to deform the background image by light refraction. Since past studies on this phenomenon have mainly used subjective judgment about the presence of a transparent layer, it remains unsolved whether this is a real perceptual transparency effect in the sense that it forms surface representations, as do conventional transparency effects. Visual computation for color and luminance transparency, induced mainly by surface-contour information, can be decomposed into two components: surface formation to determine foreground and background layers, and scission to assign color and luminance to each layer. Here we show that deformation-induced perceptual transparency aids surface formation by color transparency and consequently resolves color scission. We asked observers to report the color of the front layer in a spatial region with a neutral physical color. The layer color could be seen as either reddish or greenish depending on the spatial context producing the color transparency, which was, however, ambiguous about the order of layers. We found that adding to the display a deformation-induced transparency that could specify the front layer significantly biased color scission in the predicted way if and only if the deformation-induced transparency was spatially coincident with the interpretation of color transparency. The results indicate that deformation-induced transparency is indeed a novel type of perceptual transparency that plays a role in surface formation in cooperation with color transparency.

Sensual Light? Subjective Dimensions of Ambient Illumination

This work concerns the subjective impression of perceived illumination. The purpose of the study is to test attributes expressing qualitative experiences referring to ambient lighting that can be applied as descriptors. Seventy participants viewed an actual model room, with the fourth wall removed (viewing booth). Walls, floor, and ceiling were achromatic. Two achromatic cubes were placed inside the room: One was a reflectance increment to the walls, the other a decrement. The room was illuminated by two different light sources, Artificial Daylight (D65) or Tungsten Filament (F), the order of which was randomized across participants. The participants' task was to evaluate ambient illumination for each light source. A semantic differential method was employed with 27 pairs of adjectives on 1 to 7 rating scales, categorized in three groups: characteristics of atmosphere, time, and cross-modal. Only the ratings of nine pairs of adjectives were not influenced by the type of illumination. The most differentiated couples under different illuminants were hot/cold and modern/old, but large differences also appeared with the following couples: hard/soft, technological/primitive, summery/wintry, warm/cool, sensual/frigid, natural/artificial, and hospitable/inhospitable. The hypothesis that there would be consistency among the subjects in evaluations of the characteristics tested and that these would be differently perceived under different illuminants was confirmed. The results show that it is possible to identify subjective perceived illumination as a phenomenon endowed with specific filling-in qualities and that as a perceptual experience it can be categorized, with implications for application in architecture and design.

Colour, contours, shading and shape: flow interactions reveal anchor neighbourhoods

Two dilemmas arise in inferring shape information from shading. First, depending on the rendering physics, images can change significantly with (even) small changes in lighting or viewpoint, while the percept frequently does not. Second, brightness variations can be induced by material effects-such as pigmentation-as well as by shading effects. Improperly interpreted, material effects would confound shading effects. We show how these dilemmas are coupled by reviewing recent developments in shape inference together with a role for colour in separating material from shading effects. Aspects of both are represented in a common geometric (flow) framework, and novel displays of hue/shape interaction demonstrate a global effect with interactions limited to localized regions. Not all parts of an image are perceptually equal; shape percepts appear to be constructed from image anchor regions.

Lightness Constancy in Surface Visualization

Color is a common channel for displaying data in surface visualization, but is affected by the shadows and shading used to convey surface depth and shape. Understanding encoded data in the context of surface structure is critical for effective analysis in a variety of domains, such as in molecular biology. In the physical world, lightness constancy allows people to accurately perceive shadowed colors; however, its effectiveness in complex synthetic environments such as surface visualizations is not well understood. We report a series of crowdsourced and laboratory studies that confirm the existence of lightness constancy effects for molecular surface visualizations using ambient occlusion. We provide empirical evidence of how common visualization design decisions can impact viewers' abilities to accurately identify encoded surface colors. These findings suggest that lightness constancy aids in understanding color encodings in surface visualization and reveal a correlation between visualization techniques that improve color interpretation in shadow and those that enhance perceptions of surface depth. These results collectively suggest that understanding constancy in practice can inform effective visualization design.

Material properties from contours: New insights on object perception

In this work we explored phenomenologically the visual complexity of the material attributes on the basis of the contours that define the boundaries of a visual object. The starting point is the rich and pioneering work done by Gestalt psychologists and, more in detail, by Rubin, who first demonstrated that contours contain most of the information related to object perception, like the shape, the color and the depth. In fact, by investigating simple conditions like those used by Gestalt psychologists, mostly consisting of contours only, we demonstrated that the phenomenal complexity of the material attributes emerges through appropriate manipulation of the contours. A phenomenological approach, analogous to the one used by Gestalt psychologists, was used to answer the following questions. What are contours? Which attributes can be phenomenally defined by contours? Are material properties determined only by contours? What is the visual syntactic organization of object attributes? The results of this work support the idea of a visual syntactic organization as a new kind of object formation process useful to understand the language of vision that creates well-formed attribute organizations. The syntax of visual attributes can be considered as a new way to investigate the modular coding and, more generally, the binding among attributes, i.e., the issue of how the brain represents the pairing of shape and material properties.

Texture variations suppress suprathreshold brightness and colour variations

Discriminating material changes from illumination changes is a key function of early vision. Luminance cues are ambiguous in this regard, but can be disambiguated by co-incident changes in colour and texture. Thus, colour and texture are likely to be given greater prominence than luminance for object segmentation, and better segmentation should in turn produce stronger grouping. We sought to measure the relative strengths of combined luminance, colour and texture contrast using a suprathreshhold, psychophysical grouping task. Stimuli comprised diagonal grids of circular patches bordered by a thin black line and contained combinations of luminance decrements with either violet, red, or texture increments. There were two tasks. In the Separate task the different cues were presented separately in a two-interval design, and participants indicated which interval contained the stronger orientation structure. In the Combined task the cues were combined to produce competing orientation structure in a single image. Participants had to indicate which orientation, and therefore which cue was dominant. Thus we established the relative grouping strength of each cue pair presented separately, and compared this to their relative grouping strength when combined. In this way we observed suprathreshold interactions between cues and were able to assess cue dominance at ecologically relevant signal levels. Participants required significantly more luminance and colour compared to texture contrast in the Combined compared to Separate conditions (contrast ratios differed by about 0.1 log units), showing that suprathreshold texture dominates colour and luminance when the different cues are presented in combination.

A unified account of perceptual layering and surface appearance in terms of gamut relativity

When we look at the world--or a graphical depiction of the world--we perceive surface materials (e.g. a ceramic black and white checkerboard) independently of variations in illumination (e.g. shading or shadow) and atmospheric media (e.g. clouds or smoke). Such percepts are partly based on the way physical surfaces and media reflect and transmit light and partly on the way the human visual system processes the complex patterns of light reaching the eye. One way to understand how these percepts arise is to assume that the visual system parses patterns of light into layered perceptual representations of surfaces, illumination and atmospheric media, one seen through another. Despite a great deal of previous experimental and modelling work on layered representation, however, a unified computational model of key perceptual demonstrations is still lacking. Here we present the first general computational model of perceptual layering and surface appearance--based on a boarder theoretical framework called gamut relativity--that is consistent with these demonstrations. The model (a) qualitatively explains striking effects of perceptual transparency, figure-ground separation and lightness, (b) quantitatively accounts for the role of stimulus- and task-driven constraints on perceptual matching performance, and (c) unifies two prominent theoretical frameworks for understanding surface appearance. The model thereby provides novel insights into the remarkable capacity of the human visual system to represent and identify surface materials, illumination and atmospheric media, which can be exploited in computer graphics applications.

A theory divided: current representations of the anchoring theory of lightness contradict the original's core claims

The anchoring theory of lightness perception (Gilchrist et al., Psychological Review 106 (1999) 795-834) has been described as one of the most successful approaches to lightness perception. Yet, not only does the original proposal contain serious gaps and inconsistencies, later expressions of the theory, which was never formally revised, seem to contradict the original claims while leaving the gaps unresolved. These problems call into question the theory's viability.

A unified account of gloss and lightness perception in terms of gamut relativity

A recently introduced computational theory of visual surface representation, termed gamut relativity, overturns the classical assumption that brightness, lightness, and transparency constitute perceptual dimensions corresponding to the physical dimensions of luminance, diffuse reflectance, and transmittance, respectively. Here I extend the theory to show how surface gloss and lightness can be understood in a unified manner in terms of the vector computation of "layered representations" of surface and illumination properties, rather than as perceptual dimensions corresponding to diffuse and specular reflectance, respectively. The theory simulates the effects of image histogram skewness on surface gloss/lightness and lightness constancy as a function of specular highlight intensity. More generally, gamut relativity clarifies, unifies, and generalizes a wide body of previous theoretical and experimental work aimed at understanding how the visual system parses the retinal image into layered representations of surface and illumination properties.

Intrinsic Image Decomposition Using Optimization and User Scribbles

In this paper, we present a novel high-quality intrinsic image recovery approach using optimization and user scribbles. Our approach is based on the assumption of color characteristics in a local window in natural images. Our method adopts a premise that neighboring pixels in a local window having similar intensity values should have similar reflectance values. Thus, the intrinsic image decomposition is formulated by minimizing an energy function with the addition of a weighting constraint to the local image properties. In order to improve the intrinsic image decomposition results, we further specify local constraint cues by integrating the user strokes in our energy formulation, including constant-reflectance, constant-illumination, and fixed-illumination brushes. Our experimental results demonstrate that the proposed approach achieves a better recovery result of intrinsic reflectance and illumination components than the previous approaches.

Perceptual learning of second order cues for layer decomposition

Luminance variations are ambiguous: they can signal changes in surface reflectance or changes in illumination. Layer decomposition-the process of distinguishing between reflectance and illumination changes-is supported by a range of secondary cues including colour and texture. For an illuminated corrugated, textured surface the shading pattern comprises modulations of luminance (first order, LM) and local luminance amplitude (second-order, AM). The phase relationship between these two signals enables layer decomposition, predicts the perception of reflectance and illumination changes, and has been modelled based on early, fast, feed-forward visual processing (Schofield et al., 2010). However, while inexperienced viewers appreciate this scission at long presentation times, they cannot do so for short presentation durations (250 ms). This might suggest the action of slower, higher-level mechanisms. Here we consider how training attenuates this delay, and whether the resultant learning occurs at a perceptual level. We trained observers to discriminate the components of plaid stimuli that mixed in-phase and anti-phase LM/AM signals over a period of 5 days. After training, the strength of the AM signal needed to differentiate the plaid components fell dramatically, indicating learning. We tested for transfer of learning using stimuli with different spatial frequencies, in-plane orientations, and acutely angled plaids. We report that learning transfers only partially when the stimuli are changed, suggesting that benefits accrue from tuning specific mechanisms, rather than general interpretative processes. We suggest that the mechanisms which support layer decomposition using second-order cues are relatively early, and not inherently slow.

Dealing with illumination in visual scenes: effects of ageing and Alzheimer's disease

Various visual functions decline in ageing and even more so in patients with Alzheimer's disease (AD). Here we investigated whether the complex visual processes involved in ignoring illumination-related variability (specifically, cast shadows) in visual scenes may also be compromised. Participants searched for a discrepant target among items which appeared as posts with shadows cast by light-from-above when upright, but as angled objects when inverted. As in earlier reports, young participants gave slower responses with upright than inverted displays when the shadow-like part was dark but not white (control condition). This is consistent with visual processing mechanisms making shadows difficult to perceive, presumably to assist object recognition under varied illumination. Contrary to predictions, this interaction of "shadow" colour with item orientation was maintained in healthy older and AD groups. Thus, the processing mechanisms which assist complex light-independent object identification appear to be robust to the effects of both ageing and AD. Importantly, this means that the complexity of a function does not necessarily determine its vulnerability to age- or AD-related decline.We also report slower responses to dark than light "shadows" of either orientation in both ageing and AD, in keeping with increasing light scatter in the ageing eye. Rather curiously, AD patients showed further slowed responses to "shadows" of either colour at the bottom than the top of items as if they applied shadow-specific rules to non-shadow conditions. This suggests that in AD, shadow-processing mechanisms, while preserved, might be applied in a less selective way.

Basic characteristics of simultaneous color contrast revisited

In this article, we present evidence supporting the hypothesis that the local mechanism of simultaneous color contrast is the same as the mechanism responsible for the crispening effect and the gamut expansion effect. A theoretically important corollary of this hypothesis is that the basic characteristics of simultaneous contrast are at odds with traditional laws. First, this hypothesis implies that the direction of the simultaneous contrast effect in color space is given by the vector from surround to target and not--as traditionally assumed--by the hue complementary to that of the surround. Second, it implies that the size of the simultaneous contrast effect depends on the difference between the target and surround colors in a way that challenges Kirschmann's fourth law. The widespread belief in the traditional laws, we argue, is due to the confounding influence of temporal adaptation.

Role of eye movements in chromatic induction

There exist large interindividual differences in the amount of chromatic induction [Vis. Res. 49, 2261 (2009)]. One possible reason for these differences between subjects could be differences in subjects' eye movements. In experiment 1, subjects either had to look exclusively at the background or at the adjustable disk while they set the disk to a neutral gray as their eye position was being recorded. We found a significant difference in the amount of induction between the two viewing conditions. In a second experiment, subjects were freely looking at the display. We found no correlation between subjects' eye movements and the amount of induction. We conclude that eye movements only play a role under artificial (forced looking) viewing conditions and that eye movements do not seem to play a large role for chromatic induction under natural viewing conditions.

Cast shadows in wide perspective

We investigated the apparent spatial layout of cast shadows up to very wide fields of view. We presented up to 130 degrees wide images in which two 'flat poles' were standing on a green lawn under a cloudless blue sky on a sunny day. The poles threw sharp cast shadows on the green, of which one was fixed. The observer's task was to adjust the azimuth of the shadow of the other pole such that it fitted the scene. The source elevation was kept constant. The two cast shadows are, of course, parallel in physical space, but generically not in the picture plane because of the wide perspective. We found that observers made huge systematic errors, indicating that, generically, they fail to account for these perspective effects. The systematic deviations could be well described by a weighted linear combination of the directions in the picture plane and in the physical space, with weights that depended on the positions of, and distance between, the poles.

Measuring the Breathing Light Illusion by means of induced simultaneous contrast

By blurring the margins of a surface, both its brightness and the perceived contrast against a superimposed figure with sharp boundaries increase. Also, if one approaches a blurred white spot on a grey background, this spot will appear wider and brighter: this phenomenon is known as the Breathing Light Illusion (BLI) (Gori and Stubbs, 2006 Perception 35 1573-1577). We studied the increment of the achromatic contrast of a grey sharp-boundary disk when it was superimposed on the BLI. This augmentation of the perceived contrast in the dynamic presentation of the BLI was significantly stronger than the effect that Agostini and Galmonte (2002a Psychonomic Bulletin and Review 9 264-269) obtained in static presentation. Our study leads to an indirect quantification of the BLI. Two control experiments showed that the increment of the achromatic contrast depends on the blurred spot and is independent of the dynamic increment in angular size. These results argue for a causal relationship between the increase in brightness due to the BLI and the darkening of the superimposed disk.

Hue manifold

It is generally accepted that hues can be arranged so as to make a circle. The circular representation of hue has been supported by multidimensional scaling, which allows for the representation of a set of colored papers as a configuration in a Euclidean space where the distances between the papers correspond to the perceptual dissimilarities between them. In particular, when papers of various hues are evenly illuminated, they are arranged in a one-dimensional circular configuration. However, under variegated illumination we show that the same papers in fact make a two-dimensional configuration that resembles a torus.

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.