Abstract

Many perceptual dimensions are thought to be represented relative to an average value or norm. Models of norm-based coding assume that the norm appears psychologically neutral because it reflects a neutral response in the underlying neural code. We tested this assumption in human color vision by asking how judgments of “white” are affected as neural responses are altered by adaptation. The adapting color was varied to determine the stimulus level that did not bias the observer’s subjective white point. This level represents a response norm at the stages at which sensitivity is regulated by the adaptation, and we show that these response norms correspond to the perceptually neutral stimulus and that they can account for how the perception of white varies both across different observers and within the same observer at different locations in the visual field. We also show that individual differences in perceived white are reduced when observers are exposed to a common white adapting stimulus, suggesting that the perceptual differences are due in part to differences in how neural responses are normalized. These results suggest a close link between the norms for appearance and coding in color vision and illustrate a general paradigm for exploring this link in other perceptual domains.

© 2008 Optical Society of America

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2008 (1)

N. Davidenko, D. Remus, M. Ramscar, and K. Grill-Spector, “Stronger face-selective responses to typical versus distinctive faces when stimulus variability is controlled (abstract),” J. Vision 8, 531a (2008).
[CrossRef]

2007 (3)

A. Kohn, “Visual adaptation: Physiology, mechanisms, and functional benefits,” J. Neurophysiol. 97, 3155-3164 (2007).
[CrossRef] [PubMed]

B. Wark, B. N. Lundstrum, and A. Fairhall, “Sensory adaptation,” Curr. Opin. Neurobiol. 17, 423-429 (2007).
[CrossRef] [PubMed]

J. M. Stringham and B. R. J. Hammond, “Compensation for light loss due to filtering by macular pigment: relation to hue cancellation,” Ophthalmic Physiol. Opt. 27, 232-237 (2007).
[CrossRef] [PubMed]

2006 (5)

R. D. Beer, A. Dinca, and D. I. A. MacLeod, “Ideal white can be yellowish or bluish, but not reddish or greenish,” J. Vision 6, 417a (2006).
[CrossRef]

J. Mollon, “Monge: the Verriest lecture, Lyon, July 2005,” Visual Neurosci. 23(3-4), 297-309 (2006).
[CrossRef]

I. Kuriki, “The loci of achromatic points in a real environment under various illuminant chromaticities,” Vision Res. 46, 3055-3066 (2006).
[CrossRef] [PubMed]

D. A. Leopold, I. V. Bondar, and M. A. Giese, “Norm-based face encoding by single neurons in the monkey inferotemporal cortex,” Nature 442, 572-575 (2006).
[CrossRef] [PubMed]

A. Stockman, M. Langendorfer, H. E. Smithson, and L. T. Sharpe, “Human cone light adaptation: from behavioral measurements to molecular mechanisms,” J. Vision 6, 1194-1213 (2006).
[CrossRef]

2005 (4)

G. Loffler, G. Yourganov, F. Wilkinson, and H. R. Wilson, “fMRI evidence for the neural representation of faces,” Nat. Neurosci. 8, 1386-1390 (2005).
[CrossRef] [PubMed]

L. Steels and T. Belpaeme, “Coordinating perceptually grounded categories through language: a case study for colour,” Behav. Brain Sci. 28, 469-489 (2005).
[CrossRef] [PubMed]

R. D. Beer, J. Wortman, G. Horwitz, and D. MacLeod, “Compensation of white for macular filtering,” J. Vision 5, 282a (2005).
[CrossRef]

J. L. Hardy, C. M. Frederick, P. Kay, and J. S. Werner, “Color naming, lens aging, and grue: what the optics of the aging eye can teach us about color language,” Psychol. Sci. , 321-327 (2005).
[CrossRef] [PubMed]

2004 (2)

P. B. Delahunt, M. A. Webster, L. Ma, and J. S. Werner, “Long-term renormalization of chromatic mechanisms following cataract surgery,” Visual Neurosci. 21, 301-307 (2004).
[CrossRef]

M. A. Webster, D. Kaping, Y. Mizokami, and P. Duhamel, “Adaptation to natural facial categories,” Nature 428, 558-561 (2004).
[CrossRef]

2002 (2)

J. Neitz, J. Carroll, Y. Yamauchi, M. Neitz, and D. R. Williams, “Color perception is mediated by a plastic neural mechanism that is adjustable in adults,” Neuron 35, 783-792 (2002).
[CrossRef] [PubMed]

V. Ekroll, F. Faul, R. Niederee, and E. Richter, “The natural center of chromaticity space is not always achromatic: a new look at color induction,” Proc. Natl. Acad. Sci. U.S.A. 99, 13352-13356 (2002).
[CrossRef] [PubMed]

2001 (1)

D. A. Leopold, A. J. O'Toole, T. Vetter, and V. Blanz, “Prototype-referenced shape encoding revealed by high-level aftereffects,” Nat. Neurosci. 4, 89-94 (2001).
[CrossRef] [PubMed]

2000 (4)

1999 (2)

M. A. Webster and O. H. MacLin, “Figural after-effects in the perception of faces,” Psychon. Bull. Rev. 6, 647-653 (1999).
[CrossRef]

B. Lee, D. Dacey, S. VC, and J. Pokorny, “Horizontal cells reveal cone type-specific adaptation in primate retina,” Proc. Natl. Acad. Sci. U.S.A. 96, 14611-14616 (1999).
[CrossRef] [PubMed]

1996 (1)

S. M. Wuerger, “Color appearance changes resulting from iso-luminant chromatic adaptation,” Vision Res. 36, 3107-3118 (1996).
[CrossRef] [PubMed]

1995 (3)

M. A. Webster and J. D. Mollon, “Colour constancy influenced by contrast adaptation,” Nature 373, 694-698 (1995).
[CrossRef] [PubMed]

A. Chaparro, C. F. I. Stromeyer, G. Chen, and R. E. Kronauer, “Human cones appear to adapt at low light levels: measurements on the red-green detection mechanism,” Vision Res. 35, 3103-3118 (1995).
[CrossRef] [PubMed]

E.-J. Chichilnisky and B. A. Wandell, “Photoreceptor sensitivity changes explain color appearance shifts induced by large uniform backgrounds in dichoptic matching,” Vision Res. 35, 239-254 (1995).
[CrossRef] [PubMed]

1993 (3)

R. L. De Valois and K. K. De Valois, “A multi-stage color model,” Vision Res. 33, 1053-1065 (1993).
[CrossRef] [PubMed]

S. J. Ahn and D. I. A. MacLeod, “Link-specific adaptation in the luminance and chromatic channels,” Vision Res. 33, 2271-2286 (1993).
[CrossRef] [PubMed]

J. S. Werner and B. E. Schefrin, “Loci of achromatic points throughout the life span,” J. Opt. Soc. Am. A 10, 1509-1516 (1993).
[CrossRef] [PubMed]

1992 (3)

D. H. Brainard and B. A. Wandell, “Asymmetric color matching: how color appearance depends on the illuminant,” J. Opt. Soc. Am. A 9, 1433-1448 (1992).
[CrossRef] [PubMed]

H. Hibino, “Red-green and yellow-blue opponent-color responses as a function of retinal eccentricity,” Vision Res. 32, 1955-1964 (1992).
[CrossRef] [PubMed]

D. I. A. MacLeod, D. R. Williams, and W. Makous, “A visual nonlinearity fed by single cones,” Vision Res. 32, 347-363 (1992).
[CrossRef] [PubMed]

1991 (1)

J. Walraven and J. S. Werner, “The invariance of unique white; a possible implication for normalizing cone action spectra,” Vision Res. 31, 2185-2193 (1991).
[CrossRef] [PubMed]

1990 (2)

J. L. Schnapf, B. J. Nunn, M. Meister, and D. A. Baylor, “Visual transduction in the cones of the monkey Macaca fascicularis,” J. Physiol. (London) 427, 681-713 (1990).

C. M. Cicerone, M. M. Hayhoe, and D. I. A. MacLeod, “The spread of adaptation in human foveal and parafoveal cone vision,” Vision Res. 30, 1603-1615 (1990).
[CrossRef] [PubMed]

1987 (3)

1984 (2)

A. M. Derrington, J. Krauskopf, and P. Lennie, “Chromatic mechanisms in lateral geniculate nucleus of macaque,” J. Physiol. (London) 357, 241-265 (1984).

D. M. Snodderly, J. D. Auran, and F. C. Delori, “The macular pigment. II. Spatial distribution in primate retinas,” Invest. Ophthalmol. Visual Sci. 25, 674-685 (1984).

1982 (1)

J. Krauskopf, D. R. Williams, and D. W. Heeley, “Cardinal directions of color space,” Vision Res. 22, 1123-1131 (1982).
[CrossRef] [PubMed]

1979 (1)

1970 (1)

R. M. Boynton and D. N. Whitten, “Visual adaptation in monkey cones: recordings of late receptor potentials,” Science 170, 1423-1426 (1970).
[CrossRef] [PubMed]

1959 (1)

W. S. Stiles, “Color vision: the approach through increment-threshold sensitivity,” Proc. Natl. Acad. Sci. U.S.A. 45, 100-114 (1959).
[CrossRef]

1957 (1)

L. M. Hurvich and D. Jameson, “An opponent-process theory of color vision,” Psychol. Rev. 64, 384-404 (1957).
[CrossRef] [PubMed]

1937 (1)

J. J. Gibson and M. Radner, “Adaptation, after-effect and contrast in the perception of tilted lines. I. Quantitative studies,” J. Exp. Psychol. 20, 453-467 (1937).
[CrossRef]

Ahn, S. J.

S. J. Ahn and D. I. A. MacLeod, “Link-specific adaptation in the luminance and chromatic channels,” Vision Res. 33, 2271-2286 (1993).
[CrossRef] [PubMed]

Anstis, S. M.

Atkinson, J.

O. Braddick, F. W. Campbell, and J. Atkinson, “Channels in vision: basic aspects,” in Handbook of Sensory Physiology VIII, R.Held, H.W.Leibowitz, and H.Teuber, eds. (Springer-Verlag, 1978), pp. 3-38.

Auran, J. D.

D. M. Snodderly, J. D. Auran, and F. C. Delori, “The macular pigment. II. Spatial distribution in primate retinas,” Invest. Ophthalmol. Visual Sci. 25, 674-685 (1984).

Baylor, D. A.

J. L. Schnapf, B. J. Nunn, M. Meister, and D. A. Baylor, “Visual transduction in the cones of the monkey Macaca fascicularis,” J. Physiol. (London) 427, 681-713 (1990).

Beer, R. D.

R. D. Beer, A. Dinca, and D. I. A. MacLeod, “Ideal white can be yellowish or bluish, but not reddish or greenish,” J. Vision 6, 417a (2006).
[CrossRef]

R. D. Beer, J. Wortman, G. Horwitz, and D. MacLeod, “Compensation of white for macular filtering,” J. Vision 5, 282a (2005).
[CrossRef]

Belpaeme, T.

L. Steels and T. Belpaeme, “Coordinating perceptually grounded categories through language: a case study for colour,” Behav. Brain Sci. 28, 469-489 (2005).
[CrossRef] [PubMed]

Blanz, V.

D. A. Leopold, A. J. O'Toole, T. Vetter, and V. Blanz, “Prototype-referenced shape encoding revealed by high-level aftereffects,” Nat. Neurosci. 4, 89-94 (2001).
[CrossRef] [PubMed]

Bondar, I.

D. A. Leopold and I. Bondar, “Adaptation to complex visual patterns in humans and monkeys,” in Fitting the Mind to the World: Adaptation and Aftereffects in High Level Vision, C.W. G.Clifford and G.Rhodes, eds. (Oxford U. Press, 2005), pp. 189-211.

Bondar, I. V.

D. A. Leopold, I. V. Bondar, and M. A. Giese, “Norm-based face encoding by single neurons in the monkey inferotemporal cortex,” Nature 442, 572-575 (2006).
[CrossRef] [PubMed]

Boynton, R. M.

D. I. A. MacLeod and R. M. Boynton, “Chromaticity diagram showing cone excitation by stimuli of equal luminance,” J. Opt. Soc. Am. 69, 1183-1186 (1979).
[CrossRef] [PubMed]

R. M. Boynton and D. N. Whitten, “Visual adaptation in monkey cones: recordings of late receptor potentials,” Science 170, 1423-1426 (1970).
[CrossRef] [PubMed]

Boynton, R. M. B.

P. Kaiser and R. M. B. Boynton, Human Color Vision (Optical Society of America, 1996).

Braddick, O.

O. Braddick, F. W. Campbell, and J. Atkinson, “Channels in vision: basic aspects,” in Handbook of Sensory Physiology VIII, R.Held, H.W.Leibowitz, and H.Teuber, eds. (Springer-Verlag, 1978), pp. 3-38.

Brainard, D. H.

Calderone, J. B.

Campbell, F. W.

O. Braddick, F. W. Campbell, and J. Atkinson, “Channels in vision: basic aspects,” in Handbook of Sensory Physiology VIII, R.Held, H.W.Leibowitz, and H.Teuber, eds. (Springer-Verlag, 1978), pp. 3-38.

Carroll, J.

J. Neitz, J. Carroll, Y. Yamauchi, M. Neitz, and D. R. Williams, “Color perception is mediated by a plastic neural mechanism that is adjustable in adults,” Neuron 35, 783-792 (2002).
[CrossRef] [PubMed]

Cavanagh, P.

Chaparro, A.

A. Chaparro, C. F. I. Stromeyer, G. Chen, and R. E. Kronauer, “Human cones appear to adapt at low light levels: measurements on the red-green detection mechanism,” Vision Res. 35, 3103-3118 (1995).
[CrossRef] [PubMed]

Chen, G.

A. Chaparro, C. F. I. Stromeyer, G. Chen, and R. E. Kronauer, “Human cones appear to adapt at low light levels: measurements on the red-green detection mechanism,” Vision Res. 35, 3103-3118 (1995).
[CrossRef] [PubMed]

Chichilnisky, E.-J.

E.-J. Chichilnisky and B. A. Wandell, “Photoreceptor sensitivity changes explain color appearance shifts induced by large uniform backgrounds in dichoptic matching,” Vision Res. 35, 239-254 (1995).
[CrossRef] [PubMed]

Cicerone, C. M.

C. M. Cicerone, M. M. Hayhoe, and D. I. A. MacLeod, “The spread of adaptation in human foveal and parafoveal cone vision,” Vision Res. 30, 1603-1615 (1990).
[CrossRef] [PubMed]

Dacey, D.

B. Lee, D. Dacey, S. VC, and J. Pokorny, “Horizontal cells reveal cone type-specific adaptation in primate retina,” Proc. Natl. Acad. Sci. U.S.A. 96, 14611-14616 (1999).
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Figures (5)

Fig. 1
Fig. 1

Commonly proposed schemes in visual coding. In (a) different levels along the stimulus dimension are signaled by the degree of excitation or inhibition within a single opponent channel, with the norm coded by a response null. In (b) the dimension is represented by the population response of multiple channels each narrowly tuned to different stimulus levels. In this case the norm occurs when the responses across the channels are equal. (c) If the stimulus is narrowly tuned, then the same multiple channels result in a central-tendency code in which the level is signaled by which subset of channels respond. Such models lack a unique response norm. Upper and lower panels illustrate how the channel responses change with adaptation. In the central-tendency model, adapting to any level reduces sensitivity to that stimulus and biases appearance away from the adapting level, with no change in the perceived level of the adapter [(c) and (f)]. In the norm-based codes, adaptation to a biased stimulus renormalizes the responses so that the norm shifts toward the adapting level [(d) and (e)], while adapting to the norm itself leaves the neutral point unaltered. Thus in the norm-based models there is an asymmetry between adaptation to the norm and any other stimulus level: adapting to the norm should not bias the perceived level of other stimuli, while adapting to these stimuli does bias the norm.

Fig. 2
Fig. 2

Changes in perceptual norms following adaptation. Panels show predictions for two observers (S1 and S2) who select different norms because they have (a) the same sensitivity but different criteria or (b) the same criteria but different sensitivities. Note that a criterion difference cannot be distinguished from a sensitivity difference subsequent to the site of the mechanisms affected by the adaptation. In the first case, adaptation will shift their norms in the same way and thus the criterion differences persist as the adapting level varies (solid lines). In the second case, adapting to the common stimulus will renormalize both observers to a common value so that their norms converge.

Fig. 3
Fig. 3

Norms in color vision. (a) “White” is assumed to be represented by balanced activity across the S, M, and L cone receptors and by a null response in postreceptoral opponent channels. Responses to a biased spectrum could be renormalized by compensatory adjustments (b) in the sensitivity of the receptors, (c) in the strength of inputs to the opponent channels, or (d) by changing the criterion for white.

Fig. 4
Fig. 4

(a) achromatic settings for a single observer (MY) before (solid symbols) or after (open) adapting to chromaticities (crosses) that varied along the LvsM or SvsLM axes of the color space [shown in panel (b)]. Circles plot the settings for centrally fixated colors, while triangles show settings at the 8 deg eccentricity. Arrow and diamond show the shift in the 8 deg settings predicted by the difference in macular pigment at the two locations. (c) Level along the LvsM axis that appeared neutral as a function of the LvsM adapting level for colors viewed in the fovea (solid circles) or at 8 deg (triangles). Lines through the measured points are the best-fitting fourth-order polynomials. Dashed lines show the dark-adapted settings and the peripheral setting predicted by the macular pigment difference. Response norms were determined by estimating which adaptation level produced the same achromatic response as the dark-adapted perceptual norm. (d) Similar results for the SvsLM axis.

Fig. 5
Fig. 5

(a) Perceptual nulls and (b) response nulls for all observers. Circles show foveal settings, Triangles the settings at 8 deg . Diamonds show the differences between the central and peripheral settings predicted by the macular pigment densities estimated for individual observers (with peak densities noted next to the symbols). (c) Comparison of individual perceptual norms and response norms along the LvsM axis. (d) Similar results for the norm levels along the SvsLM axis.

Tables (1)

Tables Icon

Table 1 Comparisons of Interobserver Variations in White Settings Measured during Dark Adaptation (Pre) or after Adapting to a Common Stimulus with the Chromaticity of an Equal-Energy Spectrum (Post) a

Equations (2)

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LvsM = 2754 * ( r 0.6657 ) and
SvsLM = 4099 * ( b 0.01545 )

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