Lightness induction revisited

Assessment of associations between clinical and immune microenvironmental factors and tumor mutation burden in resected nonsmall cell lung cancer by applying machine learning to whole-slide images

Background

It is unclear whether clinical factors and immune microenvironment (IME) factors are associated with tumor mutation burden (TMB) in patients with nonsmall cell lung cancer (NSCLC).

Materials and methods

We assessed TMB in surgical tumor specimens by performing whole exome sequencing. IME profiles, including PD‐L1 tumor proportion score (TPS), stromal CD8 tumor‐infiltrating lymphocyte (TIL) density, and stromal Foxp3 TIL density, were quantified by digital pathology using a machine learning algorithm. To detect factors associated with TMB, clinical data, and IME factors were assessed by means of a multiple regression model.

Results

We analyzed tumors from 200 of the 246 surgically resected NSCLC patients between September 2014 and September 2015. Patient background: median age (range) 70 years (39‐87); male 37.5%; smoker 27.5%; pathological stage (p‐stage) I/II/III, 63.5/22.5/14.0%; histological type Ad/Sq, 77.0/23.0%; primary tumor location upper/lower, 58.5/41.5%; median PET SUV 7.5 (0.86‐29.8); median serum CEA (sCEA) level 3.4 ng/mL (0.5‐144.3); median serum CYFRA 21‐1 (sCYFRA) level 1.2 ng/mL (1.0‐38.0); median TMB 2.19/ Mb (0.12‐64.38); median PD‐L1 TPS 15.1% (0.09‐77.4); median stromal CD8 TIL density 582.1/mm² (120.0‐4967.6);, and median stromal Foxp3 TIL density 183.7/mm² (6.3‐544.0).

The multiple regression analysis identified three factors associated with higher TMB: smoking status: smoker, increase PET SUV, and sCEA level: >5 ng/mL (P < .001, P < .001, and P = .006, respectively).

Conclusions

The IME factors assessed were not associated with TMB, but our findings showed that, in addition to smoking, PET SUV and sCEA levels may be independent predictors of TMB. TMB and IME factors are independent factors in resected NSCLC.

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.

Infants' perception of lightness changes related to cast shadows

When humans perceive the lightness of an object’s surface in shadows there is an implicit assumption that cast shadows dim the surface. In two experiments, we investigated whether 5- to 8-month-old infants make this assumption about shadows. According to this shadow assumption, the apparent change in lightness produced by shadows on an object’s surface are attributed to blocked light sources. If infants can use the shadow assumption to perceive the object’s lightness in shadows, they will also be able to detect unnatural lightness changes in shadows. We compared the infants’ looking times to the unnatural and the natural lightness changes in the shadow when an object (duck) goes through the cast shadow. In Experiment 1, we examined whether infants could detect the unnatural lightness changes of the object’s surface in shadows. We created computer-graphic movies of unnatural and natural lightness changes to the duck’s surface. Our results showed that 7- to 8-month-olds but not 5- to 6-month-olds significantly preferred the movie with the unnatural changes in lightness, indicating that only the older infants could detect these changes. In Experiment 2, we confirmed that the infants’ preference was based on the detection of unnatural lightness changes according to the shadow assumption. The natural and the unnatural lightness changes of Experiment 1 were presented without cast shadows. Under these conditions, neither younger nor older infants showed a significant preference. Taken together, the experiments showed that 7- to 8-month-old infants could detect the unnaturalness of a surface’s lightness changes produced by shadows. In conclusion, our findings suggest that 7- to 8-month-old infants can perceive an object’s lightness in shadows by using an assumption that cast shadows dim the surface of an object.

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.

What visual illusions tell us about underlying neural mechanisms and observer strategies for tackling the inverse problem of achromatic perception

Research in lightness perception centers on understanding the prior assumptions and processing strategies the visual system uses to parse the retinal intensity distribution (the proximal stimulus) into the surface reflectance and illumination components of the scene (the distal stimulus—ground truth). It is agreed that the visual system must compare different regions of the visual image to solve this inverse problem; however, the nature of the comparisons and the mechanisms underlying them are topics of intense debate. Perceptual illusions are of value because they reveal important information about these visual processing mechanisms. We propose a framework for lightness research that resolves confusions and paradoxes in the literature, and provides insight into the mechanisms the visual system employs to tackle the inverse problem. The main idea is that much of the debate and confusion in the literature stems from the fact that lightness, defined as apparent reflectance, is underspecified and refers to three different types of judgments that are not comparable. Under stimulus conditions containing a visible illumination component, such as a shadow boundary, observers can distinguish and match three independent dimensions of achromatic experience: apparent intensity (brightness), apparent local intensity ratio (brightness-contrast), and apparent reflectance (lightness). In the absence of a visible illumination boundary, however, achromatic vision reduces to two dimensions and, depending on stimulus conditions and observer instructions, judgments of lightness are identical to judgments of brightness or brightness-contrast. Furthermore, because lightness judgments are based on different information under different conditions, they can differ greatly in their degree of difficulty and in their accuracy. This may, in part, explain the large variability in lightness constancy across studies.

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.

Brightness/darkness induction and the genesis of a contour

Visual contours often result from the integration or interpolation of fragmented edges. The strength of the completion increases when the edges share the same contrast polarity (CP). Here we demonstrate that the appearance in the perceptual field of this integrated unit, or contour of invariant CP, is concomitant with a vivid brightness alteration of the surfaces at its opposite sides. To observe this effect requires some stratagems because the formation in the visual field of a contour of invariant CP normally engenders the formation of a second contour and then the rise of two streams of induction signals that interfere in different ways. Particular configurations have been introduced that allow us to observe the induction effects of one contour taken in isolation. I documented these effects by phenomenological observations and psychophysical measurement of the brightness alteration in relation to luminance contrast. When the edges of the same CP complete to form a contour, the background of homogeneous luminance appears to dim at one side and to brighten at the opposite side (in accord with the CP). The strength of the phenomenon is proportional to the local luminance contrast. This effect weakens or nulls when the contour of the invariant CP separates surfaces filled with different gray shades. These conflicting results stimulate a deeper exploration of the induction phenomena and their role in the computation of brightness contrast. An alternative perspective is offered to account for some brightness illusions and their relation to the phenomenal transparency. The main assumption asserts that, when in the same region induction signals of opposite CP overlap, the filling-in is blocked unless the image is stratified into different layers, one for each signal of the same polarity. Phenomenological observations document this “solution” by the visual system.

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.

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.

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.

Organização espacial na percepção visual de luminosidade

Este estudo analisou a influência de variações físicas dos estímulos, com base na organização espacial de figura-fundo criada pela associação dos efeitos ilusórios de contraste simultâneo de luminosidade e de contornos subjetivos. A cada participante, no total de 64, foram apresentadas 160 matrizes de escolha, cada uma composta de um estímulo modelo e quatro estímulos de comparação, devendo ser identificado qual dos quatro estímulos de comparação correspondia ao estímulo modelo. A diferença significativa entre as médias de ajuste visual verificadas para a condição de contorno subjetivo médio e para a condição controle (sem contorno) mostrou que a formação clássica de contornos subjetivos de Kanizsa, quando associada ao efeito de contraste simultâneo de luminosidade, influenciou a percepção de luminosidade dos participantes.

Multiplicative and additive Adelson's snake illusions

Two different versions of Adelson's snake lightness illusion are quantitatively investigated. In one experiment an additive version of the illusion is investigated by varying the additive component of the atmosphere transfer function (ATF) introduced by Adelson [2000, in The New Cognitive Neuroscience Ed. M Gazzaniga (Cambridge, MA: MIT Press) pp 339-351]. In the other, a multiplicative version of the illusion is examined by varying the multiplicative component of the ATE In both experiments four observers matched the targets' lightness of the snake patterns with Munsell samples. Increasing the additive or the multiplicative component elicited an approximately equal increase in the magnitude of the lightness illusion. The results show that both components, in the absence of other kinds of information, can be used as heuristics by our visual system to anchor luminance of the object when converting it into lightness.

When figure-ground segmentation modulates brightness: the case of phantom illumination

In the phantom illumination illusion, luminance ramps ranging from black to white induce a brightness enhancement on an otherwise homogeneous dark background. The strength of the illusion was tested with regard to the extension of the brightness inducing perimeter, surrounding the target area by manipulating the number of inducers (exp. 1) and the size of the inducers (exp. 2). Participants' task was to rate the difference in brightness between the target area and the background. Results show that the illusion occurs only when the target area is not completely segregated from the background by luminance ramps; vice versa, when the target area is delimited by a continuous gradient, it appears darker than the background. These findings suggest a major role of figure-ground organization in the appearance of the illusion. This hypothesis was tested in a rating task experiment with three types of target area shapes circumscribed by four types of edges: luminance contours, illusory contours, no contours, and ambiguous contours. Illusory contours, just as luminance contours, hinder the illusion and produce a darkening of the target area. A control experiment measured the brightness of the previous stimuli without luminance ramps: all configurations resulted in a darkening of the target area. Results from all experiments suggest that figure-ground segmentation plays a major role in the determination of both illumination and lightness in stimuli with luminance gradients.

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.

Responses to lightness variations in early human visual cortex

Lightness is the apparent reflectance of a surface, and it depends not only on the actual luminance of the surface but also on the context in which the surface is viewed [1-10]. The cortical mechanisms of lightness processing are largely unknown, and the role of early cortical areas is still a matter of debate [11-17]. We studied the cortical responses to lightness variations in early stages of the human visual system with functional magnetic resonance imaging (fMRI) while observers were performing a demanding fixation task. The set of dynamically presented visual stimuli included the rectangular version of the classic Craik-O'Brien stimulus [3, 18, 19] and a variant that led to a weaker lightness effect, as well as a pattern with actual luminance variations. We found that the cortical activity in retinotopic areas, including the primary visual cortex (V1), is correlated with context-dependent lightness variations.

The discoloration illusion

The discoloration illusion, a new visual phenomenon, is described. This phenomenon originates from the juxtaposition of eight chromatic parallel contours on a white background, creating a luminance gradient and enclosing a light red region. Under these conditions, the inner region appears white: the light red discolors and appears white with both surface color and luminous qualities. In two experiments, the discoloration illusion was (i) compared with the coloration effect of the watercolor illusion, obtained when the number of adjacent contours was reduced to at least two, and (ii) tested under several conditions useful for understanding the roles of the luminance gradient profile. The results suggest that discoloration is not a lightness illusion and does not depend on simultaneous contrast or on achromatic mechanisms, but more likely on chromatic mechanisms that, through the luminance chromatic gradient, provide cues about the interactions of light and surface and model the volume by depicting lights and shades. The discoloration illusion suggests a possible neural scenario where multiple juxtaposed contours may stimulate neurons, selective for different asymmetric luminance profiles and signaling not only the unilateral belongingness of the boundaries and the coloration effect but also the volumetric and the illumination effects.

The place of white in a world of grays: a double-anchoring theory of lightness perception

The specific gray shades in a visual scene can be derived from relative luminance values only when an anchoring rule is followed. The double-anchoring theory I propose in this article, as a development of the anchoring theory of Gilchrist et al. (1999), assumes that any given region (a) belongs to one or more frameworks, created by Gestalt grouping principles, and (b) is independently anchored, within each framework, to both the highest luminance and the surround luminance. The region's final lightness is a weighted average of the values computed, relative to both anchors, in all frameworks. The new model accounts not only for all lightness illusions that are qualitatively explained by the anchoring theory but also for a number of additional effects, and it does so quantitatively, with the support of mathematical simulations.

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.

Glowing greys and surface-white: the photo-geometric factors of luminosity perception

The perception of luminosity is thought to depend upon the intensity of the stimulus: a surface begins to appear self-luminous when it emits or reflects a certain amount of light. This is known as the luminosity threshold. It is a common opinion among vision scientists that such a threshold is correlated to the intensity of a perceptually white surface, in the sense that only an area of the visual field with luminance higher than perceived surface-white will appear luminous. Here we show grey colours that appear luminous in virtue of surrounding luminance ramps. These ramps are intended to mimic halos seen around light sources in natural environments. The results of three experiments indicate that the phenomenon is in direct contradiction to the aforementioned assumptions and suggest the existence of separate perceptual pathways for self-luminosity perception and for surface-colour perception.

The phantom illumination illusion

A novel brightness illusion in planar patterns is reported. The illusion occurs, for example, when surfaces with a luminance ramp shaded from black to white are positioned on a black homogeneous background, so that each white end of the surfaces faces a single point of the plane of the pattern. The illusion consists of the enhancement of the brightness of the background in a relatively wide area around the white ends of the surfaces. A parametric study was conducted in which participants were asked to rate the difference in brightness between the parts of the background inside and outside a virtual circle formed by disks with different luminance ramps. The results show that mean ratings of brightness depended on the luminance of the background, the luminance range of ramps, and the kind of ramp. Discussion of these results with reference to other brightness illusions (assimilation, neon color spreading, anomalous surfaces, visual phantoms, grating induction, and the glare effect) shows that t hephantom illumination illusion derives from processes producing the perception of ambient illumination.

Straightness as a cue for luminance edge interpretation

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

Mechanisms of contrast induction in heterogeneous displays

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

Brightness assimilation in bullseye displays

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

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

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

Does the bandpass linear filter response predict gradient lightness induction? A reply to Fred Kingdom

It is argued that lightness illusions induced by luminance gradient cannot be accounted for by the response of a bandpass linear filter as recently claimed by Kingdom (Perception 28 929-934).

Olive green or chestnut brown?

Reflectance and spectral power functions are poor predictors of color experiences. Only in completely relativized conditions (single observer, non-metameric set of stimuli, and single set of viewing conditions) is the relationship close. Variation in reflectance of Munsell chips experienced by color-normal observers as having a unique green hue encompasses approximately sixty percent of the complete range of hues falling under the category “green”; and in recent determinations of unique hues, ranges of yellow and green as well as green and blue unique hues overlap.

Why not color physicalism without color absolutism?

We make three points. First, the concept of productance value that the authors propose in their defense of color physicalism fails to do the work for which it is intended. Second, the authors fail to offer an adequate physicalist account of what they call the hue-magnitudes. Third, their answer to the problem of individual differences faces serious difficulties.

Color realism and color illusions

As demonstrated by several example displays, color illusions challenge color realism, because they involve a one-to-many reflectance-to-color mapping. Solving this problem by differentiating between veridical and illusory colors corresponding to the same reflectance is hampered because of the lack of an appropriate criterion. However, the difference between veridical and illusory color perception can still be maintained.

Have Byrne & Hilbert answered Hardin's challenge?

I argue that Byrne & Hilbert (B&H) have not answered Hardin's objection to physicalism about color concerning the unitary-binary structure of the colors for two reasons. First, their account of unitary-binary structure seems unsatisfactory. Second, pace B&H, there are no physicalistically acceptable candidates to be the hue-magnitudes. I conclude with a question about the justification of physicalism about color.

Perceptual variation, realism, and relativization, or: How I learned to stop worrying and love variations in color vision

In many cases of variation in color vision, there is no nonarbitrary way of choosing between variants. Byrne & Hilbert insist that there is an unknown standard for choosing, whereas eliminativists claim that all the variants are erroneous. A better response relativizes colors to perceivers, thereby providing a color realism that avoids the need to choose between variants.

Can a physicalist notion of color provide any insight into the nature of color perception?

Byrne & Hilbert (B&H) conceive of color perception as the representation of a physical property “out there.” In our view, their approach does not only have various internal problems, but is also apt to becloud both the intricate and still poorly understood role that “color” plays within perceptual architecture, and the complex coupling to the “external world” of the perceptual system as an entirety. We propose an alternative perspective, which avoids B&H's misleading dichotomy between a purely subjective and a realist conception of “color.”

An account of color without a subject?

While color realism is endorsed, Byrne & Hilbert's (B&H's) case for it stretches the notion of “physical property” beyond acceptable bounds. It is argued that a satisfactory account of color should do much more to respond to antirealist intuitions that flow from the specificity of color experience, and a pointer to an approach that does so is provided.

Color realism redux

Our reply is in three parts. The first part concerns some foundational issues in the debate about color realism. The second part addresses the many objections to the version of physicalism about color (“productance physicalism”) defended in the target article. The third part discusses the leading alternative approaches and theories endorsed by the commentators.

Orange laser beams are not illusory: The need for a plurality of “real” color ontologies

Reflectance physicalism only provides a partial picture of the ontology of color. Byrne & Hilbert’ account is unsatisfactory because the replacement of reflectance functions by productance functions is ad hoc, unclear, and only leads to new problems. Furthermore, the effects of color contrast and differences in illumination are not really taken seriously: Too many “real” colors are tacitly dismissed as illusory, and this for arbitrary reasons. We claim that there cannot be an all-embracing ontology for color.

Color nominalism, pluralistic realism, and color science

Byrne & Hilbert are right that it might be an objective fact that a particular tomato is unique red, but wrong that it cannot simultaneously be yellowish-red (not only objectively, but from somebody else's point of view). Sensory categorization varies among organisms, slightly among conspecifics, and sharply across taxa. There is no question of truth or falsity concerning choice of categories, only of utility and disutility. The appropriate framework for color categories is Nominalism and Pluralistic Realism.

Perceptual variation and access to colors

To identify the set of reflectances that constitute redness, the authors must first determine which surfaces are red. They do this by relying on widespread agreement among us. However, arguments based on the possible ways in which humans would perceive colors show that mere widespread agreement among us is not a satisfactory way to determine which surfaces are red.

Luminance gradient can break background-independent lightness constancy

A display with a luminance gradient was shown to induce a strong lightness illusion (Logvinenko, 1999 Perception 28 803-816). However, a 3-D cardboard model of this display was found to produce a much weaker illusion (less than half that in the pictorial version) despite the fact that its retinal image is practically the same. This is in line with the hypothesis that simultaneous lightness contrast is solely a phenomenon of pictorial perception (Logvinenko et al, 2002 Perception 31 73-82). The residual lightness illusion in the 3-D model can be accounted for by the fact that this model is a hybrid display. Specifically, while it is a real object, a pictorial representation (of the illumination gradient) is superimposed on it. Thus, lightness in the 3-D display is a compromise between two opposite tendencies: the background-independent lightness constancy and the lightness illusory shift induced by the luminance gradient.

Parallels between hearing and seeing support physicalism

There are 2,000 hair cells in the cochlea, but only three cones in the retina. This disparity can be understood in terms of the differences between the physical characteristics of the auditory signal (discrete excitations and resonances requiring many narrowly tuned receptors) and those of the visual signal (smooth daylight excitations and reflectances requiring only a few broadly tuned receptors). We argue that this match supports the physicalism of color and timbre.

Surreptitious substitution

In this commentary I argue that Byrne & Hilbert commit a number of philosophical solecisms: They beg the question of “realism,” they take the phenomenon and the theoretical model to be the same thing, and they surreptitiously substitute data sets for the life-world.

Clarifying the problem of color realism

“The problem of color realism” as defined in the first section of the target article, is crucial to the argument laid out by Byrne & Hilbert. They claim that the problem of color realism “does not concern, at least in the first instance, color language or color concepts” (sect. 1.1). I argue that this claim is misconceived and that a different characterisation of the problem, and some of their preliminary assumptions makes their positive proposal less appealing.

Ecological considerations support color physicalism

We argue that any theory of color physicalism must include consideration of ecological interactions. Ecological and sensorimotor contingencies resulting from relative surface motion and observer motion give rise to measurable effects on the spectrum of light reflecting from surfaces. These contingencies define invariant manifolds in a sensory-spatial space, which is the physical underpinning of all subjective color experiences.

Confusion of sensations and their physical correlates

The authors favor a “color realism” theory that considers colors to be physical properties residing in objects that reflect, emit, or transmit light. It is opposed to the theory that colors are sensations or visual experiences. This commentary suggests that both theories are correct, and that context usually indicates which of these dual aspects is being considered.

True color only exists in the eye of the observer

The colors we perceive are the outcome of an attempt to meaningfully order the spectral information from the environment. These colors are not the result of a straightforward mapping of a physical property to a sensation, but arise from an interaction between our environment and our visual system. Thus, although one may infer from a surface’ reflectance characteristics that it will be perceived as “colored,” true colors only arise by virtue of the interaction of the reflected light with the eye (and brain) of an observer.

Color and content

Those who identify colours with physical properties need to say how the content of colour experiences relate to their favoured identifications. This is because it is not plausible to hold that colour experiences represent things as having the physical properties in question. I sketch how physical realists about colour might tackle this item of unfinished business.

“Color realism” shows a subjectivist' mode of thinking

Byrne & Hilbert (B&H) assert that reflectances embody the reality of color, but metamerism smears the authors' “real” color categories into uselessness. B&H ignore this problem, possibly because they implicitly adopt a sort of subjectivism, whereby an object is defined by the percepts (or more generally by the measurements) it engenders. Subjectivism is unwieldy, and hence prone to such troubles.

Spatial position and perceived color of objects

Visual percepts are called veridical when a “real” object can be identified as their cause, and illusions otherwise. The perceived position and color of a flashed object may be called veridical or illusory depending on which viewpoint one adopts. Since “reality” is assumed to be fixed (independent of viewpoint) in the definition of veridicality (or illusion), this suggests that “perceived” position and color are not properties of “real” objects.

Reflectance-to-color mappings depend critically on spatial context

In visual science, color is usually regarded as a subjective phenomenon. The relationship between the specific color experiences that are evoked by a visual scene and the physical properties of the surfaces viewed in that scene are complex and highly dependent on spatial context. There is no simple correspondence between experienced color and a stable class of physical reflectances.