38 Constancy

DAVID H. BRAINARD AND ANA RADONJIC

Vision is useful because it informs us about the physical provide a self-contained introduction, to highlight environment. In the case of colm~ two distinct functions some more recent results, and to outline what we see are generally emphasized (e.g., Jacobs, 1981; Mollon, as important challenges for the field. We focus on key 1989). First, color helps to segment objects from each concepts and avoid much in the way of technical devel­ other and the background. A canonical task for this opment. Other recent treatments complement the one function is locating fruit in foliage (Regan et al., 2001; provided here and provide a level of technical detail Sumner & Mollon, 2000). Second, color provides infm' beyond that introduced in this chapter (Brainard, 2009; mation about object properties (e.g., fresh fish versus Brainard & Maloney, 2011; Foster, 2011; Gilchrist, 2006; old fish; figure 38.1). Using color for this second func­ Kingdom, 2008; Shevell & Kingdom, 2008; Smithson, tion is enabled to the extent that perceived object color 2005; Stockman & Brainard, 2010). correlates well with object reflectance properties. Achieving such correlation is a nontrivial requirement, MEASURING CONSTANCY however, because the spectrum of the reflected from an object confounds variation in object surface The study of constancy requires methods for measuring reflectance with variation in the illumination (figure it. The classic approach to such measurement is to 38.2; Brainard, Wandell, & Chichilnisky, 1993; Hurl­ assess the extent to which the color appearance of bert, 1998; Maloney, 1999). In particular, the spectrum objects varies as they are viewed under different illumi­ of the reflected light is the wavelength-by-wavelength nations. Helson (1938; Helson & Jeffers, 1940), for product of the spectrum of the illumination and the example, trained observers to use a color-naming system object's surface reflectance function (figure 38.2B). (Munsell notation) and then had them name the The dependence of the reflected light on illumination of surfaces viewed under different illuminants. He leads to ambiguity about object reflectance because found very good constancy as long as the illumination changes in the spectrum of the reflected light can arise was not too close to monochromatic. from changes in object reflectance, changes in illumi­ More recent work has focused on continuous mea­ nation, or both. Despite this ambiguity, color appear­ sures. For example, in a cross-illumination asymmetric ance is often quite stable across illumination changes matching experiment, the observer acljusts the color of (e.g., Helmholtz, 1896/2000; Katz, 1935; Brainard, a matching stilnulus seen under one illuminant so that 2004). The approximate invariance of object color it appears to have the same color as a reference stimulus appearance is called color constancy. VVhen color con­ presented under another illuminant (e.g., Arend & c-stan<:yfails, as in the case of Uluru (figure 38.2A), it is Reeves, 1986; Brainard, Brunt, & Speigle, 1997; prterwJmc:n

A 8 E1(A.)

S1(A.) / ~01 C(A.) = E1(A.) S1(A.)

E2(A.)

0 1

FIGURE 38.2 (A) Ulunt, a sandstone formation in the Australian outback famous for its large changes in color appearance. T hese changes occur because of variation in the spectrum of the light impinging on the rock, which in ntrn affects the reflected spectrum. What is unusual about Uluru is not the illumination changes-these are pervasive in natural viewing- but that they are perceived as large changes in object color. Photographs copy•;ght © Anya Hurlbert and reproduced with permission. (B) T he spectrum of the light reflected from an object, C(A.), is the wavelength-by-wavelength product of the illuminant spectrum E(A.) and the object's surface reflectance S(A.). The same spectrum reaching the eye can arise from many different combinations of illuminant and surface. The figure illustrates two such combinations.

Various techniques may be used to produce and manip­ reflect different light to the observer under the test ulate the stimuli for this type of experiment (for exam­ illuminant than under the . An ples, see Arend & Reeves, 1986; Brainard, Brunt, & observer who sets such a match would be considered Speigle, 1997; Delahunt & Brainard, 2004; Xiao et al. , perfectly color constant ( 100% constant, figure 38.3) 2012). because for such an observer we can infer that the ref~ To connect asymmetric matches to color constancy, erence surface retains its appearance across the change consider possible experimental outcomes. One possibil­ in illuminant. ity is that the observer will set the matching surface to A second possibility is that the observer will set the have the same reflectance as the reference surface matching surface so that it reflects the same spectrum despite the change in illuminant. This surface will to the eye under the test illuminant as does the

546 DAVID H. BRAINARD AND ANA RADONJI<~ standard test

0% 100% constancy ! \ constancy 0 0 observer A observer B FI GURE g8 .g In a cross-illumination asym metric matching task, an obse•·ver is presented with two contexts. In the figme these are shown as two coll ections of colored rectangles (so-called mond1;ans). One context is the standard, and the other is the test, with the illumination differing between the two. The experimenter presents a 1·eference surface in the standard context, and th e subject adjusts a matching surfa ce presented in the test context until its color matches that of the reference. In the figure the reference and matching surfaces are at the center of their corresponding mondrians. Below the mondrians are shown five example matches that might be set by an observer. One possibility is that the observer will set a match that has the same surface reflectance as the reference surface. This is illustrated on the right and labeled 100% constancy. Another possibility is that the observer will set a match that corresponds to a different surface refl ectance, but that reflects the same spectrum as the refer­ ence. This is shown schematically on the left and labeled 0% constancy. Observer matches typically fall between these two theoretical e ndpoint~ , and a constancy index may be assigned according to where between the endpoints the match lies. For example, if observe1· A sets the match indicated by the leftmost arrow, then A's constancy index would be approximately 25%. Similarly, observer B's constancy index would be approximately 75%.

reference surface under the standard illuminant. This matches. Such in dices summarize p erforma nce and possibility is depicted at the left-hand side of the set of allow consideration of what factors influence the degree sample squares in the figure. Note that the m atch es set of constancy exhibited by observers. A n umber of by an observer of this type are the same as those that sources discuss the quantitative analysis used to compute would be set on the basis of physical measurements of constancy indices from asymmetric matches (Arend et the reflected sp ectrum. Thus, we say that an observer al., 1991 ; Brainard , Brunt, & Speigle, 1997; Brainard & who sets this type of match has no constancy (0% Wandell, 1991; Troost & d e Weert, 1991). constant). A second method that h as been used to assess con­ In actual experiments observers' m atches ten d to lie stancy is achromatic adjustment. This method is con­ between the two possibilities described above: The ceptually similar to asymmeu-ic m atching except that matches are neither 0% con sta nt n or 100% constant. instead of adjusting a test surface to match a physical Based on this it is common to use asymmetric matching reference, the observer adjusts the chrom aticity of a test data to assign a constancy index value, typically ranging so that it appears achromatic and then repeats this between 0% and 100%, that characterizes how close the adjustment with the test e mbedded in a variety of con­ observer's matches are to the idealized endpoint texts. The resulting achromatic obtained

COLOR CONSTANCY 547 I I

in different contexts are then considered to be matched example, used real illuminated surfaces and the method in appearance, and the data can be analyzed to produce of achromatic adjustmen t and found constancy indices constancy indices in much the same way as achromatic of abou t 85%. High degrees of constancy can also be matching data (Brainard, 1998). Speiglc and Brainard observed wi th stimuli that consist of graphics simula­ (1999) established that asymmetric matching and ach­ tions of illuminated objects (Delahunt & Brainard, romati c adjustment lead to similar conclusions abou t 2004, experiment J, average constancy index 73%). constancy wh en the two tasks are compared wi th well­ Foster (2011 ) p rovides the constancy ind ices found in matched stimuli. Ach romatic adjustment allows one to a large number of studies many of which are similarly study performance across contexts presented in isola­ high . Such high d egrees of constancy arc consistent ti on without introducing a heavy memory load on the with the intuition that the color appearance of obj ects observer. does no t change much fro m one situation to ano ther, and data of this type lead to the .empirical generaliza­ BASIC OBSE RVATION S tion that human color constancy is very good when all that is changed is the scene illumination. Given an understanding of how constancy may be mea­ In most studies of constancy the experimen tal manip­ sured , we can turn to making some basic empitical ulati on is as described above- the illuminan t is changed generalizations fro m over 100 years of experimental while the surfaces surround ing the lest and reference literature. Much of the experimental literature has con­ arc held fixed. This is the manipulation that is illus­ sidered a simplified laboratory model in which a spa­ trated by the comparison between figure 38.4A and ti ally diffuse light source homogen eously illuminates a fi gu re 38.4B. Although the moti vation for studying this set of flat matte su rfaces (flat-matte-diffuse conditions; for type of manipulation is obvious, note that for a collec­ reviews see Brainard, 2004; Brainard & Malo ney, 2011 ; tion of scenes where most of the surfaces never change, Maloney, 1999; for a selection of experimen ts see Ar end constancy is not a challenging computatio nal problem. & Reeves, L986; Brainard, 1998; Brainard, Brunt, & Indeed, changing only the illuminant is not the only Speigle, 1997; Delahun t & Brainard, 2004; Grat1Zier et way to vary the context in which an object is seen. The al., 2005; B elson & J effers, 1 940; Kraft & Brainard, comparison between figure 38.4A and figure 38.4C 1 999; McCann, McKee, & Taylor, 1976; Olkkonen, illustrates a case in which the illuminant changes and H ansen, & Gegenfurtner, 2009). Such simplification is the reflectance of the surface that forms the immediate reasonable. Although one might view a characte ri zation background of the test also changes. In this example of constancy for the richer conditions of natural viewing the change in background reflectance was chosen to as the ultimate end goal, it is also true that natural exactly cancel the effect of the illuminan t change for scenes are very complicated. To m ake experimental that background surface so that the light reflected from progress some d egree of simplification is necessary, and the backgro und rem ained the same. The effect of this actual experiments represent a compromise between m anipulation is to silence local contrast as a cue to an attempt to capture key aspects of natural viewing and constan cy. For stimulus manipulations of this type con­ an attempt to p rovide enough experimental control stancy is reduced but not eliminated (Delahunt & that the data are interpretable. Brainard, 2004; Kraft & Brainard, 1999; Kraft, Malon ey, For flat-matte-diffuse conditions constancy across illu­ & Brainard, 2002; McCann, 1992; McCann, H all, & minant changes can be very good. Brainard (1998), for Land, 1977; Werner, 2006). For example, Delahu nt and

A 8 c

FIGURE g8.4 Experimental manipulations used in studying constancy. Between A and B the ill uminant has changed. Between B and C the illuminant is held constant, but the refl ectance of the background surface has been changed so that the light reOectecl from the background is the same as in A. Images depict sti muli used by Delahunt and Brainard (2004). (Reprinted with permission from Brainard & Maloney, 2011.)

548 DAVID H. BRAINARD AND ANA RADONJIC Brainard (2004, experiment 2) found an average con­ Maloney & Wandell, 1986; Webster & Mollon, 1995; stal1CY index of 22% across context changes of this type. Zaidi, 1998). Kraft and Brainard (1999) showed similarly that manip­ Brainard and colleagues (Brainard eta!., 2006) used ulations that silence the change in the most luminous the Bayesian approach to model the data of Delahunt surface in the contextual image or the change in the and Brainard (2004) and showed that it provided a spatial average of the reflected light have a similar good account of performance. The essence of their effect: Constancy is reduced but not completely model was to assume that observers, in effect, use a eliminated. specific Bayesian algorithm to estimate the illuminant The results outlined above highlight the point that and then discount the effect of this estimated illumi­ blanket statements about the degree of human con­ nant (see also Brainard, Brunt, & Speigle, 1997; Brain­ stat1Cy are not useful because the measured degree is ard & Wandell, 199!; Speigle & Brainard, 1996). highly dependent on the stimulus manipulation chosen Brainard (2009) and Brainard and Maloney (2011) for study. Any successful model of constancy must provide a conceptual review of this approach and account not only for the fact that_ constancy is some­ how it may be generalized to more complex viewing times very good but also for the systematic failures of situations. constancy that can be observed. This point appears A key feature of Brainard et al.'s (2006) Bayesian underappreciated in the literature and is critical to bear model is that it correctly predicts variation in the degree in mind when one is comparing the degree of con­ of measured constancy across different stimulus manip­ stailCY reported across studies. ulations and connects this variation to the information about the illuminant available in natural images. MODELING CONSTANCY Although the efficacy of this model has not been tested extensively even within the restricted flat-matte-diffuse One approach to understanding how constancy varies stimulus ensemble, it provides both a framework for with different stimulus manipulations is to connect thinking about constancy at Marr's (1982) computa­ human performance to a model of how an ideal visual tional level of analysis and theory that links the compu­ system would estitnate and correct for illuminant tations to measurements of human performance. In changes so as to stabilize object color appearance. Early addition, a successful computational model is impor­ efforts along these lines focused on the information tant for understanding the neural mechanisms that carried by the spatial mean of the reflected light or on subserve constancy in that such a model characterizes the information carried from the most luminous surface the phenomena a neural theory must account for. That in the scene Qudd, 1940; Land, 1964; von Kries, 1902, said, an important challenge for theories of color con­ 1905). Models of this sort capture a number of regu­ stancy remains elucidation of the mechanisms that larities in the data for experiments when only the illu­ implement it in the human visual pathways. In this minant is changed (McCann, McKee, & Taylor, 1976), regard the Bayesian analysis does not provide any direct but their predictions deviate from the data when the insight about the mechanisms that accomplish con­ cues they rely on are silenced experimentally (Kraft & stancy. Brainard, 1999). The earlier version of this chapter (Brainard, 2004; A 1nore general approach is to ernploy a Bayesian see also Stockman & Brainard, 2010) provides an over­ algorithm to estimate the illuminant (Brainard & view of mechanistic ideas as they relate to color con­ Freeman, 1997; D'Zmura, Iverson, & Singer, 1995; stancy, and we briefly review these ideas here. At a Gebler et a!., 2008) and to use this to predict perfor­ general level, 1nechanistic accounts of constancy focus mance. The Bayesian approach has the advantage that on the notion of adaptation. This is the idea that the sensitive to all the information about the illuminant transformation between the initial representation of by the initial visual representation of color. This the stimulus and responses later in the visual pathways informtation is provided by the isomerization rcttes of depends on context. As noted above, the initial repre­ >pllmtoj)i~:ment in three classes of cone photoreceptors sentation of a color stimulus is given by the isomeriza­ Brainard & Stockman, 2010). Indeed, there is tion rates of photopigment in three classes of cone information not only in the spatial average of the photoreceptors (e.g., Brainard & Stockman, 2010). isomerization rates but also in their covariance These are referred to as the L (long-wavelength-sensi­ higher-order moments. Note that the observation tive), M (middle-wavelength-sensitive), and S (short­ the covariance of the isomerization rates carries wavelength-sensitive) cones. Subsequent retinal and about the illuminant precedes Bayesian cortical processing then transforms the LMS cone rep­ l;reatrneJnts of color constancy (MacLeod & Golz, 2003; resentation into one that consists of sums and

COLOR CONSTANCY 549 differences of signals from the different cone classes. At toward incorporating a growing knowledge about the various sites along the pathways that implement this statistical structure of natural scenes as it pertains to transformation, the signals are subject to multiplicative color (surface reflectance functions:Jaaskelainen, Park­ gain control as well as a subtractive form of adaptation k.inen & Toyooka, 1990; Krinov, 1947; Nickerson, 1957; (e.g., Blakeslee & McCourt, 2004; Chen, Foley, & Brain­ Parkkinen, Hallikainen, & Jaaskelainen, 1989; Vrhel, ard, 2000; Engel & Furmanski, 2001; Jameson & Gershon, & lwan, 1994; illuminant spectral power dis­ Hurvich, 1964; Poirson & Wandell, 1993; Shevell, 1978; tributions: DiCarlo & Wandell, 2000; Judd, MacAdam, Stiles, 1967; Valeton, 1983; von I\.ries, 1902; Wade & & Wyszecki, 1964; calibrated color images: Burge & Wandell, 2002; Walraven, 1976; Webster & Mollon, Geisler, 2011; Ciurea & Funt, 2003; Gehler et al., 2008; 1994; for reviews see Smithson, 2005; Stockman & Geisler & Perry, 2011; Olmos & Kingdom, 2004; Tkacik Brainard, 2010; Walraven & Werner, 1982; Webster, et al., 2011; hyperspectral image datasets: Chakrabarti 1996). Key is that the exact values of the multiplicative & Zickler, 2011; Foster, Nascimento, & Amano, 2004; and subtractive transformations depend on the viewing Longere & Brainard, 2001; Parraga et al., 1998; Ruder­ context, so the representation corresponding to any man, Cronin, & Chiao, 1998). Nonetheless, one can triplet of LMS cone isomerization rates varies with envision how a focused research program could bring context. Adaptation of this sort supports constancy to together the computational and mechanistic ideas out­ the extent that its overall effect is to stabilize the post­ lined above and test these with experiments that are receptoral representation of the light reflected from within current technical reach. objects across changes of illumination (as well as other Thus, it is worth asking, suppose that enterprise of contextual changes). understanding color constancy for flat-matte-diffuse A challenge for connecting our mechanistic under­ conditions is brought to a successful conclusion, what standing of color adaptation to color constancy is that would remain to be done within the field of color con­ the stimuli that have been used to study constancy are stancy? Below we outline what we see as some important generally more complex than those that have been used directions. to study adaptation. For example, we know that con­ stancy as measured under flat-matte-diffuse conditions Real-World Scenes is well described by changes in multiplicative gain (e.g., Bauml, 1995; Brainard, Brunt, & Speigle, 1997; Brain­ Real scenes do not consist of flat matte surfaces under ard & Wandell, 1992; McCann, McKee, & Taylor, 1976). diffuse illumination. First, real scenes exist in three­ What we do not know is how to independently derive dimensional space so that objects differ in their depth the values of the multiplicative gain from a specification as well as their position on the retinal projection. of a spatially rich stimulus. Two lines of research that Second, real objects have three-dimensional shape and aim to close this gap are worth note. First, Webster and are made of nonmatte materials. Third, real objects Mollon (1994, 1995) analyzed how illuminant variation often have texture of some sort, so that surface reflec­ changes the mean and covariance of cone isomeriza­ tance can vary across an object. Finally, real illumina­ tion rates and showed that measured adaptation to such tion has geometric structure. changes could support color constancy with respect to Figure 38.5 illustrates richness that is introduced illumination changes. Second, Blakeslee and McCourt when we depart from flat-matte-diffuse. Figure 38.5A (2004; Blakeslee, Reetz, & McCourt, 2009) developed a shows the effect of varying the three-dimensional pose model of based on an abstracted model of of a flat matte surface when the light is dii·ectional: .· mechanistic processing from to early rather than diffuse. As illustrated by the figure, and showed that such a model can account for a variety amount of light reflected to an observer de!pt!ncls; of constancy-related lightness effects. Pushing the con­ strongly on the pose. Although the figure illustrates nections between mechanistic models, computational effect for a change in intensity, similar effects occur· models, and the empirical data represents an important color (Bloj, Kersten, & Hurlbert, 1999; Boyaci, Dc•en:cn; research frontier. ner, & Maloney, 2004). If the visual system did compensate for such effects, objects would change FUTURE DIRECTIONS appearance as a function of their pose in the dimensional environment. It is now possible to imagine a fairly complete account Constancy with respect tO surface pose has mcei.ve' of color constancy for flat-matte-diffuse stimuli, although attention in the literature, although primarily in there is still much to be done. Even within the flat­ domain of lightness (Bioj & Hurlbert, 2002; matte-diffuse stimulus ensemble, we can go further Kersten, & Hurlbert, 1999; Boyaci, Doerschner,

550 DAVID H. BRAINARD AND ANA RADONJIC Maloney, 2004; Boyaci, Maloney, & Hersh, 2003; Doer­ selm er, Boyaci, & Maloney, 2004; Epstein, 1961 ; Gil­ christ, 1980; Hochberg & Beck, 1954; Radonjic, Todorovic, & Gilchrist, 2010; Ripamonti el a!., 2004) . The bulk of this work indicates lhat the human visual system indeed compensates for changes in reflected light that arise from changes of surface pose within lhree-dimensional scenes, allhough the exact stimulus conditions lhat support such compensation are less well understood. Otlllines of th eories that could be deployed to understand these effects are also available. One line of thought seeks to connect the result of three-dimen­ sional manipulations of surface pose to results for flat­ matte-diffuse conditio ns by invoking grouping principles that segment lhe three-d imensional scene into separate frameworks and applying principles derived from studies of simplified stimuli to undersland what happens within each framework (Gilchrist, 2006; Gilch1ist et al., 1999; see also Adelson, 2000; Anderson & Winawer, 2005). A second approach is to extend the computa­ tional approach described above to include geometric aspects of the illumination (Bloj et a!., 2004; Boyaci, Doerschner, & Maloney, 2004; Boyaci, Maloney, & Hersh, 2003; Doerschner, Boyaci, & Maloney, 2004; reviewed in Brainard & Maloney, 2011). Figure 38.5B shows how the light refl ected from a three-dimensional obj ecl can vary across the surface of the object. For a fixed surface reflectance the pattern of reflected light can depend strongly on object shape. Variation in lh e pose of a three-d imensio nal object, the geo metry of the illumination, and the material out of which the object is made affeclS lhe pattern of refl ected light A challenge is thus to measure and understand FI GURt~ 38.5 (A) Photograph of the same surface in two how we judge the color of objeclS in the face of these different poses with respect to a directional light source. T he effects. The challenge arises because once variations in intensity of the light reflected from the surface varies with object shape, pose, illuminalion geometry, and object pose. (Repri nted with permission from Ripamon ti et al. , material are introduced into the experimental para­ 2004.) (B) Two o~ j ects ("blob" and "pepper") made from the same material and rende under the same spatially complex digm , the number of stimulus dimensions·available for illumination field. T he pattern of light refl ected to th e eye explorati on grows lo a point wh ere systematic study via across the obj ect va1ies with object sha pe. This varia tion is fully crossed designs is not feasible. T herefore, some shown expliciLi y by the colored squares (left), which are type of simplifying theory becomes critical for guiding drawn from individual o n the "blob." (Figure courtesy experiments. At presen t, such a theory is no l available. of Maria Olkkonen.) (C) Illustrative image pa tches from poly­ chromatic o ~j ects. Clockwise from upper left: (odgi­ One theoretical suggestion, explored primarily in th e nal photograph copyrigh t © J ames Bowe); leaf (original lightness literature, is that simple summary statistics photograph copyright© Peter Shanks); sweater ( 01;ginal pho­ extracted from the luminance histogram of light tograph copyright © "orchidgalore"); (original pho­ refl ected fro m an obj ect might provide direct predic­ tograph copyright© Liz West). Image patches obtained from tors of its lightness (and also of ilS perceived glossiness) . photographs posted at flickr.com; all were posted under the creative commons atu·ibution 2.0 genedc license (http:/ I Although initial results were promising (Motoyoshi et creativecommons.org/licenses/ by/ 2.0/ deed.en). al., 2007; Nishida & Shinya, 1998; Sharan et al., 2008), this approach has not generali zed well (Anderson & Kim, 2009; Kim & An derson, 2010; J{jm, Marlow, & Anderson, 2011 ; Olkkonen & Brainard, 2010, 2011 ). It remains possible, however, that lheories that combine

C OLOR CONSTANCY 551 such statistics with other information, such as o~ject changes in surface reflectance (Craven & Foster, 1992; shape, will be useful. Another theoretical approach has D'Zmura & Mangalick, 1994; Foster & Nascimento, been to test for simplifying empirical regularities (e.g., 1994; Gerhard & Maloney, 2010). Additional study of separability of effects of shape and illumination geom­ the relation between perceived color appearance and etry/spectra), but these efforts have not to date led to performance on color-based tasks is a priority for under­ successful accounts of the empirical data (Olkkonen & standing how color is used in the real world. Brainard, 2010,201 l; Xiao et al., 2012). Work aimed at A second challenge for understanding the role of generalizing our understanding toward the full richness color in real-world tasks is that the results of color of three-dimensional objects and scenes thus remains appearance experiments can depend on the instruc­ exploratory. tions given to subjects (Arend & Reeves, 1986; Arend et Many objects (e.g., leaves, skin, rock, fabric) exhibit al., 1991; Bauml, 1999; Reeves, Amano, & Foster, 2008; reflectance variation with varying degrees of spatial Troost & de Weert, 1991). In an influential asymmetric regularity. This is a form of texture, although the varia­ matching study Arend and Reeves ( 1986) found that tion often does not have the quasi-periodic spatial struc­ when subjects are asked to "match the , saturation, ture of stimuli typically studied in the texture literature. and brightness of the [target color], while disregarding Introspection suggests that reflectance variation need as much as possible other areas in the screen," the data not prevent us from perceiving objects as having a char­ show very little constancy across changes in illuminant. acteristic color appearance (figure 38.5C). We currently On the other hand, when they are asked to "to make know little about color constancy for such polychro­ the [match] look as if were cut from the same piece of matic objects, or indeed about how their overall color paper [as the target]," constancy is considerably is extracted even under a single illuminant, although better. interest in this question is growing (Beeckmans, 2004; There is no agreement about the fundamental nature Hurlbert, Ling, & Vurro, 2008; Hurlbert et al., 2009; of such instructional effects. Some authors (Arend & Hurlbert, Vurro, & Ling, 2008; Ling, Pietta, & Hurlbert, Spehar, 1993; Logvinenko & Maloney, 2006; Rock, 2009; Olkkonen, Hansen, & Gegenhrrtner, 2008; Vurro, 1975) posit that su~jects have available multiple percep­ Ling, & Hurlbert, 2009; Yoonessi & Zaidi, 2010). As with tual modes of color appearance and that instructions the geometric effects considered above, progress toward modulate which 1node is used when making a match. understanding color appearance and color constancy Other authors are of the view that there is a single per­ for polychromatic objects will require identification ceptual representation and that instructional effects and confirmation of simplifying regularities. modulate the degree to which subjects use explicit rea­ soning (Blakeslee, Reetz, & McCourt, 2008; MacLeod, Real-World Tasks 2011; see also Gibson, 1966; Koffka, 1935). Distinguish­ ing between these and other related accounts has been The discussion of constancy above focused on color difficult (see Wagner, 2012, for a recent review of the appearance as the key dependent measure, and indeed, broad issues in the context of size judgments). This is most of what we know about constancy is based on in part because the varying views do not make discern­ measurements of color appearance. These measure­ ibly different predictions for the outcome of most of ments, however, do not directly characterize perfor­ the relevant experiments (but see Logvinenko & mance in real-life tasks that require the use of color to Maloney, 2006, for an interesting exceptiori) and in choose among objects. As Zaidi (I 998; see also Abrams, part because delineating the precise experimental con­ Hillis, & Brainard, 2007) has emphasized, an object ditions (method, class of stimuli, and instructional could look different across a change in illumination, wording) that lead to instructional effects has proved but it still might be possible to use color effectively for elusive (Cornelissen & Brenner, 1995; Delahunt & object selection or identification. This would, for Brainard, 2004; Logvinenko & Tokunaga, 2011; example, be the case if a person perceived that the Ripamonti et al., 2004). Independent of the theoretical illumination had changed and used this fact to reason position one adopts, understanding the relation about o~ject identity. Only a few papers report studies between laboratory experiments and how color is used of how well subjects can use lightness or color to iden­ in real life clearly requires better control and character:­ tify objects across changes in illumination (Robilotto & ization of instructional (as well as related individual Zaidi, 2004, 2006; Zaidi & Bostic, 2008). Related work difference) effects. Our sense is that these issues will has considered the extent to which su~jects can dis­ become more acute as we continue to extend criminate between image changes that result from experiments and thinking to richer and more naturale, changes in illumination and those that result from istic stimuli.

552 DAVID H. BRAINARD AND ANA RADONJIC REFERENCES Brainard, D. H. (1998). Color constancy in the nearly natural image. 2. Achromatic loci. Joumal qf the Optical Society of Abrams, A. B., Hillis,]. M., & Brainard, D. H. (2007). The America. A, Optic~, Image Science, and Vision, 15, 307-325. relation between color discrimination and color constancy: Brainard, D. H. (2004). Color constancy. In L. M. Chalupa & VVhen is optimal adaptation task dependent? Neural Comjm­ ]. S. "\Nemer (Ed.s.), The visual neurosciences (pp. 948-961). tation, 19, 2610-2637. Cambridge, MA: MIT Press. Adelson, E. H. (2000). Lightness perception and lightness Brainard, D. H. (2009). Bayesian approaches to . illusions. In M. Gazzaniga (Ed.), The new cognitive newvsci­ In M. S. Gazzaniga (Ed.), The cognitive neumsr:iences (4th ed., ences (2nd ed., pp. 339-351). Cambridge, MA: MIT Press. pp. 395-408). Cambridge, MA: MIT Press. Anderson, B. L., & Kim, J. (2009). Image statistics do not Brainard, D. H., Brunt, W. A, & Speigle,J. M. (1997). Color explain the perception of gloss and lightness . .Journal ~f constancy in the nearly natural image. 1. Asymmetric Vision, 9, 10-26. doi:10.1167/9.11.10. matches. joumal of the Optical Society of America. A, Optics, Anderson, B. L., & Winawcr, J. (2005). Image segmentation Image Science, and l'ision, 14, 2091-2110. and lightness perception. Natum, 434, 79-83. Brainard, D. H., & Freeman, W. T. (1997). Bayesian color L. & A. Arend, E., Reeves, (1986). Simultaneous color con­ constancy. journal of the Optical Society of America. A, Optics, stancy. Journal ~f the Optical Society of America. A, OjJtics and Image Science, and Vision, 14, 1393-1411. Image Science, 3, 1743-1751. Brainard, D. H., Longere, P., Delahunt, P. B., Freeman, W. T., Arend, L. E., Reeves, A., Schirillo,J., & Goldstein, R. (1991). Kraft,]. M., & Xiao, B. (2006). Bayesian model of human Simultaneous color constancy: Papers with diverse Munsell color constancy. Journal qf Vision, 6, 1267-1281. doi:IO. values. Journal of the Optical Suciet;1 of America. A, Optics and 1167/6.11.10. Image Science, 8, 661-672. Brainard, D. H., & Maloney, L. T. (2011). Surface color per­ Arend, L. E., & Spehar, B. (1993). Lightness, brightness, and ception and equivalent illumination models. joumal of brightness contrast. 1. Illuminance variation. Perception & Vi1ion, 11, 1-18. doi:IO.ll67/11.5.L PsyclwjJhysics, 54, 446-456. Brainard, D. H., & Stockman, A. (2010). . In M. Bauml, K H. ( 1995). Illuminant changes under different Bass, C. DeCu.satis, J. Enoch, V. Lakshminarayanan, G. Li, surface collections: Examining some principles of color C. Macdonald, V. Mahajan, & E. van Stryland (Eds.), The appearance. Journal of the Optical Sudety ojAmerica. A, Optics, Optical Society ofAmerica handboo/( of optics, 3rd eel., Volume III· Image Science, mtd Vision, 12, 261-271. Vision and vision optics (pp. 10.11-10.56). New York: McGraw­ Bauml, K H. (1 999). Simultaneous color constancy: How Hill. .surface color perception varies with the illuminant. Vision Brainard, D. H., & Wandell, B. A. (1991). A bilinear model of Research, 39, 1531-1550. the illuminant's effect on color appearance. In M.S. Landy Beeckmans,]. (2004). Chromatically rich phenomenal per­ & J. A. Movshon (Eds.), Computational model~ of visual process­ cept<;. Philosophical PsycholOf!JI, 17( 1), 27-44. ing (pp. 171-186). Cambridge, MA: MIT Press. Blakeslee, B., & McCourt, M. E. (2004). A unified theory of Brainard, D. H., & Wandell, B. A. (1992). Asymmetric color­ brightness contrast and assimilation incorporating oriented matching: How color appearance depends on the i11umi­ multiscale filtering and contrast normalization. Vision nant. journal of the Optical Society of Amnica. A, Optics and Research, 44, 2483-2503. doi:l0.1016/j.visres.2004.05.015. Image Science, 9(9), 1433-1448. Blakeslee, B., Reetz, D., & McCourt, M. E. (2008). Coming to Brainard, D. H., Wandell, B. A., & Chichilnisky, E.]. (1993). terms with lightness and brightness: Effects of stimulus con­ Color constancy: From physics to appearance. Current Direc­ figuration and instructions on brightness and lightness tions in P~J~clwlogical Science, 2, 165-170. judgments.]ottnwl of Vision, 8, 1-14. doi:l0.1167/8.11.3. Burge,]., & Geisler, W. S. (2011). Optimal defocus estimation Blakeslee, B., Reetz, D., & McCourt, M. E. (2009). Spatial in individual natural images. Pmceedings of the National filtering versus anchoring accounts of brightness/lightness Academy of Sciences of the United States qfAmerica, 108, 16849- perception in .staircase and .simultaneous brightness/ 16854. lightness contrast stimuli. journal qf Vision, 9, 1-17. Burnham, R. W., Evans, R. M., & Newhall, S.M. (1957). Pre­ doi:IO.ll67 /9.3.22. diction of color appearance with differe~t adaptation & An Bloj, M. G., Hurlbert, A C. (2002). empirical study of illuminations. journal of the Optical Society of America, 47, the traditional Mach card effect. PercejJtiun, 31, 233-246. 35-42. Bloj, M., Kersten, D., & Hurlbert, A. C. (1999). Perception of Chakrabarti, A., & Zickler, T. (2011). Statistics of real-world three-dimensional shape influences colour perception hyperspectral images. Paper presented at Proceedings of through mutual illumination. Natun;, 402, 877-879. the IEEE Computer Society Conference on Computer Bloj, M., Ripamonti, C., Mitha, K., Greenwald, S., Hauck, R., Vision and Pattern Recognition (pp. 193-200). & Brainard, D. H. (2004). An eq11ivalent illuminant model Chen, C. C., Foley,J. M., & Brainard, D. H. (2000). Detection for the effect of surhtce slant on perceived Iightness.]ou·mal of chromoluminance patterns on chromoluminance pedes­ ojV"1ion, 4, 735-746. doi:IO.ll67/4.9.6. tals II: model. Vision Resean:h, 40, 789-803. H., Doerschne1~ K., & Maloney, L. T. (2004). Perceived Ciurea, E, & Funt, B. (2003). A large image database for color surface color in binocularly viewed scenes with two light constancy research. Paper presented at Proceedings of sources differing in . Jo·urnal qf' Vision, 4, 664- the IS&T lith Color Imaging Conference, Scottsdale, AZ 679. doi:IO.l!67/4.9.1. (pp. 160-164). , H., Maloney, L. T., & Hersh, S. (2003). The effect of Cornelissen, F. V\'., & Brenner, E. (1995). Simultaneous colour . P'en:ei'ved surface orientation on perceived surface albedo constancy revisited-an analysis of viewing strategies. Vision uuw•cutarty viewed scenes. Journal of Vision, 3, 541-553. Research, 35(17), 2431-2448. doi:JO.JOI6/0042-6989(94) 2. 00318-1.

COLOR CONSTANCY 553 Craven, B.J., & Foster, D. H. (1992). An operational approach Gregory, R. L. (1978). Eye and brain. New York: McGraw­ to colour constancy. Vision Research, 32, 1359-1366. Hill. doi: 10.1016/0042-6989 (92) 90228B. Helmholtz, H. von. (2000). Handbuch der Physiologischen Opt£k Delahunt, P. B., & Brainard, D. H. (2004). Does human color (J. P. C. Southall, Trans., I 924). Reprinted London: constancy incorporate the statistical regularity of natural Thoemmes Press. (Original work published I896.) daylight? Journal of Vision, 4, 57-81. doi:l0.1167/4.2.1. Helson, H. ( 1938). Fundamental problems in color vision. I. DiCarlo,]. M., & Wandell, B. A. (2000).11luminant estimation: The principle governing changes in hue, saturation beyond the bases. Paper presented at IS&T/SID Eighth and lightness of non-selective samples in chromatic Color Imaging Conference, Scottsdale, AZ (pp. 91-96). illumination. journal of Experimental PS)IChology, 23, 439- Doerschner, K, Boyaci, H., & Maloney, L. T. (2004). Human 476. observers compensate for secondary illumination oliginat­ Helson, H., &Jeffers, V. B. (1940). Fundamental problems in ing in nearby chromatic surfaces. Journal of Vision, 4, 92- color vision. II. Hue, lightness, and saturation of selective 105. doi:l0.1167/4.2.3. samples in chromatic illumination . .Journal of Experimental D'Zmura, M., Iverson, G., & Singer, B. (1995). Probabilistic 1\yc!wlog:y, 26, l-27. doi:10.1037/h0060515. color constancy. In R. D. Luce, M. D'Zmura, D. Hoffman, Hochberg,]. E., & Beck,]. (1954). Apparent spatial arrange­ G. Iverson, & A. K. Romney (Eds.), Geometric representations ment and perceived brightness. journal of Expe1imental Psy­ of percejJttwl phenomena: Papers in honor of Tarow lndow 's 70th chology, 47, 263-266. birthda)1 (pp. 187-202). Mahwah, !'{}: Lawrence Erlbaum Hurlbert, A. C. (1998). Computational models of color con­ Associates. stancy. In V. Walsh & J. Kulikowski (Eds.), PercejJtual con­ D'Zmura, M., & Mangalick, A. (1 994). Detection of contrary stancy: VVhy things look as they do (pp. 283---322). Cambridge: chromatic change. Journal of the Optical So(iety of America. A, Cambridge University Press. Optics, Image Science, and Vision, 11 (2), 543-546. Hurlbert, A., Ling, Y. Z., & Vurro, M. (2008). Polychromatic Engel, S. A., & Furmanski, C. S. (2001). Selective adaptation colour constancy [Abstract]. Perception, 37(2), 3Il. to color contrast in human primary visual cortex. .Journal of Hurlbert, A., Pi etta, I., Vurro, M., & Ling, Y. (2009). Surface Newvscience, 2./(11), 3949-3954. discrimination of natural o~jects: VVhen is a kiwi off­ Epstein, W. (1961). Phenomenal orientation and perceived colour? journal of Vision, 9, 330. doi:lO.I167/9.8.330. achromatic color. Journal of P~ychology, 52, 51-53. Hurlbert, A., Vurro, M., & Ling, Y. (2008). Colour constancy Foder,J. A., Bever, T. G., & Garrett, M. F. (1974). The psychol­ of polychromatic surfaces. journal of Vision, 8, 1101. ogy of language. New York: McGraw-HilL doi:lO.l167 /8.6.1101. Foster, D. H. (2011). Color constancy. Vision Resem·ch, 51, Jaaskelainen, T., Parkkinen,J., & Toyooka, S. (1990). A vector­ 674-700. subspace model for color representation . .Journal of the Foster, D. H., & Nascimento, S. M. C. (1994). Relational Optical Societ)l of America. A, OjJtics and Irnage Science, 7, 725- colour constancy from invariant cone-excitation ratios. Pro­ 730. ceedings of the .!Wyal Society of London. Series B, Biological Jacobs, G. H. (1981). Compamtive color vision. New York: Sciences, 257, 115-121. Academic Press. Foster, D. H., Nascimento, S. M. C., & Amana, K. (2004). Jameson, D., & Hurvich, L. M. (1964). Theory of brightness InfOrmation limits on neural identification of colored sur­ and color contrast in human vision. Vision Research, 4, 135- faces in natural scenes. Visual Neumscience, 21, 331-336. 154. doi:10.l017/50952523804213335. Judd, D. B. (1940). Hue saturation and lightness of surface Gehler, P., Rother, C., Blake, A., Minka, T., & Sharp, T. (2008). colors lvith chromatic illumination. journal of the Optical Bayesian color constancy revisited. Paper presented at IEEE Society ofAmerica, 30, 2-32. doi:10.1364(JOSA.30.000002. Computer Society Conference on and Judd, D. B., MacAdam, D. L., & Wyszecki, G. W. (1964). Spec­ Pattern Recognition, Anchorage, Alaska. tral distribution of typical daylight as a function of corre­ Geisler, W. S., & Perry,]. S. (2011). Statistics for optimal point lated . journal of the Optical Society of prediction in natural images . .Joumal of Vision, 11(12), I4, America, 54, 1031-1040. 1-17. doi:10.1167/1l.l2.14. Katz, D. (1935). The world of colour. London: Kegan, Paul, Gerhard, H. E., & Maloney, L. T. (2010). Detection of light Trench, Trlibner & Co. transformations and concomitant changes in surface Kim, J., & Anderson, B. L. (2010). Image statistics and the albedo. journal of Vision, 10, 1-14. doi:10.1167/10.9.l. percepton of surface gloss and lightness. journal of llisionJ Gibson, J. J. (1 966). T'he senses considered as perceptual systems. 10, 3. doi:10.1167/10.9.3. Boston: Houghton Mifflin. Kim,]., Marlow, P., & Anderson, B. L. (2011). The perception Gilchrist, A. L. (I 980). V\lhen does perceived lightness depend of gloss depends on highlight congruence ·with surface on perceived spatial arrangement? PercejJtion & Prychophys­ . Journal of Vision, 11, 4. doi:10.I167 /11.9.4. ics, 28, 527-538. Kingdom, F. A. A. (2008). Perceiving light versus material.<\ Gilchrist, A. (2006). Seeing black and . Oxford: Oxford Vision Research, 48, 2090-2105. University Press. Knill, D. C., & Richards, W. (1996). Perce-ption as Bayesian Gilchrist, A., Kossyfidis, C., Bonato, F., Agostini, T., Cataliotti, ence. Cambridge: Cambridge University Press. ]., Li, X., et al. (1999). An anchoring theory of lightness Koffka, K. (1935). Pfinciples of Gestalt jJsyclwlogy. New perception. Psychological Review, 106, 795-834. Harcourt, Brace. Granzier,J.J. M., Brenner, E., Cornelissen, F. W., & Smeets,J. Kraft, J. M., & Brainard, D. H. (1999). Mechanisms of B. J. (2005). Luminance-color correlation is not used to constancy under nearly natural viewing. Proceedings estimate the color of the illumination. Journal of Vision, 5, National Academy of Sciences of the United States of America, 20-27. doi:10.ll67/5.l.2. 307-312.

554 DAVID H. BRAINARD AND ANA RADONJIC Kraft,]. M., Maloney, S.l., & Brainard, D. H. (2002). Surlace­ of the Optical Society of America. A, Optics, Image Science, illuminant ambiguity and color constancy: Effects of scene and Vision, 15, 2951-2965. doi:10.1364/JOSAA.15.00295l. complexity and depth cues. Perception, 31, 247-263. Olkkonen, M., & Brainard, D. H. (2010). Perceived glossiness Krinov, E. L. (1947). SuTjace Reflectance Properties of Natural and lightness under real-world illumination. Journal of .Formations. National Research Council of Canada: Techni­ Vision, 10, 5. doi:10.ll67/10.9.5 . cal Translation, TT-439. Olkkonen, M., & Brainard, D. H. (2011). Joint effects of Land, E. H. (1964). The retinex. In A. V. S. de Reuck &J. illumination geometry and object shape in the perception Knight (Eds.), CIBA Fmtndation S)>mposium on colour ·oision of surface reflectance. Perception, 2, 1014--1034. (pp. 217-227). Boston: Little, and Company. Olkkonen, M., Hansen, T., & Gegenfurtner, K R. (2008). Ling, Y., Pietta, 1., & Hurlbert, A (2009). The interaction of Color appearance of familiar objects: Effects of object colour and texture in an object classification task. Journal shape, texture, and illumination changes. Journal of Vision, of Vision, 9, 788. doi:10.ll67/9.8.788. 8, 13-28. doi:10.ll67/8.5.13. Logvinenko, A. D., & Maloney, L. T. (2006). The proximity Olkkonen, M., Hansen, T., & Gegenfurtner, K. R. (2009). structure of achromatic surface colors and the impossibility Categorical color constancy for simulated surfaces. Journal of asymmetric lightness matching. Perception & Psychophys­ of Vision, 9, 6-23. doi:10.ll67/9.12.6. ics, 68, 76-83. Olmos, A., & Kingdom, F. A. A. (2004). A biologically inspired Logvinenko, A. D., & Tokunaga, R. (2011). Lightness con­ algorithm for the recovery of shading and reflectance stancy and illumination discounting. Attention, Perception & images. Perception, 33, 1463~1473. Psychophysics, 73, 1886-1902. Parkkinen,J. P. S., Hallikainen,J., &Jaaskelainen, T. (1989). Longf:re, P., & Brainard, D. H. (2001). Simulation of digital Characteristic spectra of Munsell colors. Journal of the optical camera images from hyperspectral input. In C. J. van den Society of America, 6, 318-322. Branden Lambrecht (Ed.), Vision modeL~ and ajJjJlications to Parraga, C. A., Brelstaff, G., Troscianko, T., & Moorehead, image and video processing (pp. 123-150). Boston: Kluwer I. R. (1998). Color and luminance information in natural Academic. scenes. journal of the Optical Society ofAmerica. A, Optics, Irnage MacLeod, D. I. A. (2011). Into the neural maze. In]. Cohen Science, and Vision, 15, 563-569. & M. Matthen (Eds.), Color ontology and color science (pp. Poirson,A. B., & Wandell, B. A. (1993). Appearance of colored 151-178). Cambridge, MA: MIT Press. patterns-pattern color separability. Journal of the Optical MacLeod, D. I. A., & Golz,J. (2003). A computational analysis Society of America. A, Optics and Irnage Science, 10(12), 2458-- of colour constancy. In R. Mausfeld & D. Heyer (Eds.), 2470. Colou-r perception: lVIind and the physical wo-rld (pp. 205-242). Purves, D., & Lotto, R. B. (2003). Why we see what we do: Oxford: Oxford University Press. An empirical theory of vision. Sunderland, MA: Sinauer Maloney, L. T. (1999). Physics-based approaches to modeling Associates. surface color perception. In K R. Gegenfurtner & L. T. RadonjiC, A., TodoroviC, D., & Gilchrist, A. (2010). Adjacency Sharpe (Eds.), Color vision: }'rom genes to perception (pp. 387- and surroundedness in the depth effect on lightness. Journal 416). Cambridge: Cambridge University Press. of Vision, 10, 12-27. doi:10.ll67/10.9.12. Maloney, L. T., & Wandell, B. A. (1986). Color constancy: A Reeves, A., Amana, K, & Foster, D. H. (2008). Color method for recovering surface spectral reflectances. Jou·rnal constancy: Phenomenal or projective? Perception & Psycho­ of the Optical Society of America. A, Optics and Image Science, 3, physics, 70, 219-228. 29-33. Regan, B. C.,Julliot, C., Simmen, B., Vienot, F., Charles-Dom­ Marr, D. (1982). Vision. San Francisco: W. H. Freeman. inique, P., & Mollon, J. D. (2001). Fruits, foliage and the McCann, J.]. (1992). Rules for colour constancy. Ophthalmic evolution of primate color vision. Philosophical Transactions & Physiological Optics, 12, 175-179. of the Royal Society qf London. Series B, Biological Sciences, 356, McCann, J. ]., Hall, J. A., & Land, E. H. (1977). Color 229-283. Mondrian experiments: The study of average spectral Ripamonti, C., Bloj, M., Hauck, R., Mitha, K, Greenwald, S., distributions. Jou·rnal of the Optical Society of America, 67, Maloney, S. 1., eta!. (2004). Measurements of the effect of 1380. surface slant on perceived lightness . .Journal of Vision, 4, 7- McCann,].]., McKee, S. P., & Taylor, T. H. (1976). Quantita­ 23. doi:10.ll67/4.9.4. tive studies in retinex theory: A comparison between theo­ Robilotto, R., & Zaidi, Q. (2004). Perceived transparency of retical predictions and observer responses to the "Color neutral density filters across dissimilar backgrounds. Journal Mondrian" experiments. Vision Research, 16, 445-458. of Vision, 4, 5-17. doi:10.ll67/4.3.5. Miller, G. A. (1973). Communication, language, and meaning: Robilotto, R., & Zaidi, Q. (2006). Lightness identification of New York: Basic Books. patterned three-dimensional, real oqjects. Journal of Vision, Mollon,J. D. (1989). "Tho' she kneel'd in that place where 6, 3-21. doi:10.ll67/6.l.3. they grew ... " The uses and origins of primate color vision. Rock, I. ( 1975). An introduction to perception. New York: Journal of Experimental Biology, 146, 21-38. Macmillan . . Motoyoshi, I., Nishida, S., Sharan, L., & Adelson, E. H. (2007). Ruderman, D. L., Cronin, T. W., & Chiao, C. C. (1998). Image statistics and the perception of surface qualities. Statistics of cone responses to natural images: Implica­ Nature, 447, 206-209. tions for visual coding. Journal of the Optical Society of D. (1957). Spectrophotometric data for a. collection America. A, Optics, Image Science, and Vision, 15, 2036- samples. Washington, D.C.: U.S. Department of 2045. Rust, N.C., & Stocker, A. A. (2010). Ambiguity and invariance: S., & Shinya, M. (1998). Use of image-based informa­ Two fundamental challenges for visual processing. Current tion in judgments of surface-reflectance properties. Journal Opinion in Neumbiology, 20, 382-388.

COLOR CONSTANCY 555 Sources of color science (1970 reprint, pp. 120-127. Cam­ Sharan, L., Li, Y., Motoyoshi, 1., Nishida, S., & Adelson, E. H. (2008). Image statistics for surface reflectance perception. biidge, MA: MIT Press. Vrhcl, M.J., Gershon, R., & Iwan, L. S. (1994). Measurement journal of the Optical Society ofAmerica. A, OjJtics, Image Science, and analysis of object reflectance spectra. Color Research and and Vision, 25, 846-865. Shevcll, S. K. (1978). The dual role of chromatic backgrounds Application, 19, 4-9. Vurro, M., Ling, Y, & Hurlbert, A (2009). Memory colours of in color perception. Vision Resea·rch, 18, 1649-1661. polychromatic objecL<;. joumal of Vision, 9, 333. doi: 10.1016/0048-6989 ( 78) 9025 7-2. Shevell, S. K., & Kingdom, F. A. A (2008). Color in complex doi:l0.1167 /9.8.333. ·wade, A. R, & Wandell, B. A. (2002). Chromatic light adapta­ scenes. Annual Review of P:-.yclwlogy, 59, 143-166. tion measured using functional magnetic resonance Smithson, H. E. (2005). Sensory, computational, and cogni­ imaging. Journal of Neumscience, 22, 8148-8157. tive componenL~ of human color constancy. Philosophical Wagner, M. (2012). Sensory and cognitive explanations for a Transactions of the Royal Society of London. Series B, Biological century of size constancy research. In S. R. Allred & G. Sciences, 360, 1329-1346. Hatfield (Eds.), Visual experience: Sensation, cognition, and Speigle,J. M., & Brainard, D. H. (1996). Luminosity thresh­ constancy (pp. 63-86). Oxford: Oxford University Press. olds: Effects of test chromaticity and ambient illumination. VValraven,J. ( 1976). Discounting the background: The missing journal of the Optical Society ofAmerica. A, OjJtics, Image Science, link in the explanation of chromatic induction. Vision and Vision, 13, 436-451. Spcigle,J. M., & Brainard, D. H. (1999). Predicting color from Resea·rch, 16, 289-295. Walraven, J., & Werner, S. (1982). Chromatic adaptation gray: The relationship between achromatic adjustment and J. and 1t mechanisms. Color Research and Application, 7, 50-53, asymmetric matching . .Jou·mal of the Optical Society ofAmerica. Webster, M. A. (1996). Human colour perception and its A, Optics, Image Science, and Vision, 16, 2370-2376. adaptation. Network (Bristol, England), 7, 587-634. Stiles, W. S. (1967). Mechanism concepts in colour theory. VVebster, M.A., & Mollon,J. D. (1994). The influence of con­ Journal of the Colou·r Group, 11, 106-123. trast adaptation on color appearance. Vision Research, 34, Stockman, A., & Brainard, D. H. (2010). Color vision mecha­ nisms. In M. Bass, C. DeCusatis,J. Enoch, V. Lakshminaray­ 1993-2020. VVcbster, M. A, & Mollon, J. D. (1995). Colour constancy anan, G. Li, C. Macdonald, V. Mahajan, & E. van Stryland influenced by contrast adaptation. Natu·re, 373, 694-698. (Eels.), The Optical Societ), ofAmel'ica handbook of optics, 3nl ed, Werner, A. (2006). The influence of depth segregation on Volume III: Vision and vision optics (pp. 11.11-11.104). New colour constancy. Perception, 35, 1171-1184. York: McGraw-Hill. Wolfe, J. M., Kluender, K. R., Levi, D. M., Bartoshuk, L. M. 1 Sumner, P., & Mollon,J. D. (2000). Catarrhine photopigments Herz, R. S., Klatzky, R L., et al. (2006). Sensation and percep­ are optimized for detecting targets against a foliage back­ tion. Sunderland, MA: Sinauer A~sociates. ground. Journal of Experirnental Biology, 203, 1963-1986. Xiao, B., Hurst, B., Macintyre, L., & Brainard, D. H. (2012). Tkacik, G., Garrigan, P., Ratliff, C., Milcinski, G., Klein,J. M., The color constancy of three-dimensional objects . .Journal Sterling, P., et al. (2011). Natural images from the birth­ place of the . PLuS One, 6, e20409). doi:10.1371/ of Vision, 12, 6. doi:IO.ll67/12.4.6. Yoonessi, A., & Zaidi, Q. (2010). The role of color in recogniz­ journal.pone.0020409. ing material changes. Ophthalmic & Physiological Optics, 30, Troost, J. M., & de Weert, C. M. M. (1991). Naming versus matching in color constancy. Perception & Psychophysics, 50, 626-631. Zaidi, Q. (1998). Identification of illmninant and object 591-602. colors: Heuristic-based algorithms . .Journal of the Optical Valeton,J. M. (1983). Photoreceptor light adaptation models: Society of Ameriw. A, Optics, Image Science, and \fision, 15, An evaluation. Vision Research, 23, 1549-1554. doi: 10.1016/0042-6989 (83) 90168-2. 1767-1776. Zaidi, Q., & Bostic, M. (2008). Color strategies for object von Kries, J. (1902). Chromatic adaptation. In Sources of color identification. Vision ReseaTch, 48, vision (pp. 109-119), reprinted 1970, Cambridge, !viA: MIT 2673-26~1. Press. von Kries, J. (1905). Influence of adaptation on the effects produced by luminous stimuli. In D. L. MacAdam (Ed.),

556 DAVID H. BRAINARD AND ANA RADONJIC