Abstract

I report the results of a set of experiments designed to study whether the visual system’s adjustments to illuminant changes vary with the surface collection in a scene. Simulations of flat matte surfaces rendered under diffuse illumination were presented on a CRT monitor. Under several surface collections subjects set asymmetric color matches between a standard surface and a test surface that were rendered under illuminants with different spectral power distributions. The three subjects’ data span 28 different illuminant × surface collection conditions. Five different standard surfaces were used. Two results stand out. First, a change in surface collection did not induce a substantial change in the effect of illuminant changes on the subjects’ settings. In this sense the results are consistent with the hypothesis that the visual system’s adjustments to illuminant changes do not depend on the surface collection. Second, the illuminant-induced changes in the subjects’ settings for a given surface collection were well approximated by a von Kries model, in which the change in the von Kries coefficients is a linear function of the illuminant change. In addition, I tested the hypothesis that the gain of the signal from each cone class is regulated by the photopigment absorptions originating entirely within that cone class. I found some clear deviations from this hypothesis, which indicates interactions among the cone classes. A first-order quantification of these interactions is provided.

© 1995 Optical Society of America

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References

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  1. G. W. Wyszecki, W. S. Stiles, Color Scence, 2nd ed. (Wiley, New York, 1982).
  2. D. H. Brainard, B. A. Wandell, “Asymmetric color-matching: how color appearance depends on the illuminant,” J. Opt. Soc. Am. A 9, 1433–1488 (1992).
    [Crossref] [PubMed]
  3. K.-H. Bäuml, “Color appearance: effects of illuminant changes under different surface collections,” J. Opt. Soc. Am. A 11, 531–543 (1994).
    [Crossref]
  4. J. von Kries, “Die Gesichtsempfindungen,” in Handbuch der Physiologie des Menschen, W. Nagel, ed. (Vieweg-Verlag, Braunschweig, 1905), Vol. 3, pp. 109–282.
  5. R. M. Boynton, Human Color Vision (Holt, Rhinehart & Winston, New York, 1979).
  6. G. W. Wyszecki, “Color appearance,” in Handbook of Perception and Human Performance, K. R. Boff, L. Kaufman, J. P. Thomas, eds. (Wiley, New York, 1986), Vol. 1, pp. 9-1–9-57.
  7. D. H. Krantz, “A theory of context effects based on cross-context matching,”J. Math. Psychol. 5, 1–48 (1968).
    [Crossref]
  8. H. Fuchs, “Color constancy in non-grey average surrounds,” presented at the 14th European Conference on Visual Perception, Vilnius, Lithuania, August 26–30, 1991.
  9. J. Walraven, “Discounting the background—the missing link in the explanation of chromatic adaptation,” Vision Res. 16, 289–295 (1976).
    [Crossref]
  10. J. Werner, J. Walraven, “Effect of chromatic adaptation on the achromatic locus: the role of contrast, luminance and background color,” Vision Res. 22, 929–943 (1982).
    [Crossref] [PubMed]
  11. R. W. Burnham, R. M. Evans, S. M. Newell, “Prediction of color appearance with different adaptation illuminations,”J. Opt. Soc. Am. 47, 35–42 (1957).
    [Crossref]
  12. S. K. Shevell, “The dual role of chromatic backgrounds in color perception,” Vision Res. 18, 1649–1661 (1978).
    [Crossref] [PubMed]
  13. E. H. Land, J. J. McCann, “Lightness and retinex theory,”J. Opt. Soc. Am. 61, 1–11 (1971).
    [Crossref] [PubMed]
  14. J. L. Dannemiller, “Computational approaches to color constancy: adaptive and ontogenetic considerations,” Psychol. Rev. 96, 255–266 (1989).
    [Crossref] [PubMed]
  15. D. B. Judd, D. L. MacAdam, G. Wyszecki, “Spectral distribution of typical daylight as a function of correlated color temperature,”J. Opt. Soc. Am. 54, 1031–1040 (1964).
    [Crossref]
  16. D. H. Brainard, “Understanding the illuminant’s effect on color appearance,” Ph.D. dissertation (Stanford University, Stanford, Calif., 1989).
  17. K. L. Kelly, K. S. Gibson, D. Nickerson, “Tristimulus specification of the Munsell Book of Colorfrom spectrophotometric measurements,”J. Opt. Soc. Am. 33, 355–376 (1943).
    [Crossref]
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    [Crossref] [PubMed]
  19. W. L. Sachtler, Q. Zaidi, “Chromatic and luminance signals in visual memory,” J. Opt. Soc. Am. A 9, 877–894 (1992).
    [Crossref] [PubMed]
  20. V. Smith, J. Pokorny, “Spectral sensitivity of the foveal cone photopigments between 400 and 700 nm,” Vision Res. 15, 161–171 (1975).
    [Crossref] [PubMed]
  21. A. B. Poirson, B. A. Wandell, “Appearance of colored patterns: pattern–color separability,” J. Opt. Soc. Am. A 10, 2458–2470 (1993).
    [Crossref]
  22. K. R. Gegenfurtner, “praxis: Brent’s algorithm for function minimization,” Behav. Res. Methods Instrum. Computers 24, 560–564 (1992).
    [Crossref]
  23. The models were also evaluated with other error measures. For instance, I minimized the differences between the observed and the predicted settings by using one single global covariance matrix estimated for all surfaces simultaneously or by using the CIE LUV metric space.1 The conclusions drawn about the quality of the models did not depend on the choice of error measure.
  24. For illuminant linearity and the von Kries principle the linearly transformed illuminant changes, or standard surfaces, were used for the prediction. For collection invariance the mean of the illuminant-induced change in the test surfaces under the two surface collections was used for the prediction of the individual changes observed under the two surface collections.
  25. D. Jameson, L. Hurvich, “Some quantitative aspects of an opponent colors theory. III. Changes in brightness, saturation, and hue with chromatic adaptation,”J. Opt. Soc. Am. 45, 546–552 (1955).
    [Crossref]
  26. This pattern of results did not change with use of some other opponent color matrices, such as those used in the study of Poirson and Wandell.21
  27. A version of the summary model was fitted to the data that includes the more general von Kries principle, illuminant linearity, and collection invariance. The same 18 parameters were used to describe each subject’s data set. With the restriction of using the same parameters for the different subjects, the rmse of the model is 1.836, compared with 1.446 when the model is fitted individually to each subject’s data.
  28. The rmse’s of the general version of the summary model (including the free color matrix C) and the cone-based version of the summary model (with Cbeing the identity matrix) are practically identical. The rmse of the general version is 1.836, and that of the cone-based version is 1.840.
  29. D. H. Marimont, B. A. Wandell, “Linear models of surface and illuminant spectra,” J. Opt. Soc. Am. A 9, 1905–1913 (1992).
    [Crossref] [PubMed]
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    [Crossref]
  31. S. K. Shevell, R. A Humanski, “Color perception under chromatic adaptation: red/green equilibria with adapted short-wavelength-sensitive cones,” Vision Res. 28, 1345–1356 (1988).
    [Crossref] [PubMed]
  32. A. Reeves, “Transient desensitization of a red–green opponent site,” Vision Res. 21, 453–460 (1981).
    [Crossref]
  33. E. Zrenner, I. Abramov, M. Akita, A. Cowey, M. Livingstone, A. Valberg, “Color perception: retinex to cortex,” in Visual Perception, L. Spillmann, S. Werner, eds. (Academic, San Diego, Calif., 1990), pp. 163–204.
  34. L. Arend, A. Reeves, “Simultaneous color constancy,” J. Opt. Soc. Am. A 3, 1743–1751 (1986).
    [Crossref] [PubMed]
  35. L. Arend, A. Reeves, J. Schirillo, R. Goldstein, “Simultaneous color constancy: patterns with diverse Munsell values,” J. Opt. Soc. Am. A 8, 661–672 (1991).
    [Crossref] [PubMed]
  36. L. Arend, B. Spehar, “Lightness, brightness, and brightness contrast: 1. Illuminance variation,” Percept. Psychophys. 54, 446–456 (1993).
    [Crossref] [PubMed]
  37. I exclude the technical possibility that one of the diagonal entries of Ptor Puis precisely zero.

1994 (1)

1993 (2)

A. B. Poirson, B. A. Wandell, “Appearance of colored patterns: pattern–color separability,” J. Opt. Soc. Am. A 10, 2458–2470 (1993).
[Crossref]

L. Arend, B. Spehar, “Lightness, brightness, and brightness contrast: 1. Illuminance variation,” Percept. Psychophys. 54, 446–456 (1993).
[Crossref] [PubMed]

1992 (4)

1991 (1)

1989 (1)

J. L. Dannemiller, “Computational approaches to color constancy: adaptive and ontogenetic considerations,” Psychol. Rev. 96, 255–266 (1989).
[Crossref] [PubMed]

1988 (1)

S. K. Shevell, R. A Humanski, “Color perception under chromatic adaptation: red/green equilibria with adapted short-wavelength-sensitive cones,” Vision Res. 28, 1345–1356 (1988).
[Crossref] [PubMed]

1986 (2)

1982 (1)

J. Werner, J. Walraven, “Effect of chromatic adaptation on the achromatic locus: the role of contrast, luminance and background color,” Vision Res. 22, 929–943 (1982).
[Crossref] [PubMed]

1981 (1)

A. Reeves, “Transient desensitization of a red–green opponent site,” Vision Res. 21, 453–460 (1981).
[Crossref]

1979 (1)

E. N. Pugh, J. D. Mollon, “A theory of the π1and π3colour mechanisms of Stiles,” Vision Res. 19, 293–312 (1979).
[Crossref]

1978 (1)

S. K. Shevell, “The dual role of chromatic backgrounds in color perception,” Vision Res. 18, 1649–1661 (1978).
[Crossref] [PubMed]

1976 (1)

J. Walraven, “Discounting the background—the missing link in the explanation of chromatic adaptation,” Vision Res. 16, 289–295 (1976).
[Crossref]

1975 (1)

V. Smith, J. Pokorny, “Spectral sensitivity of the foveal cone photopigments between 400 and 700 nm,” Vision Res. 15, 161–171 (1975).
[Crossref] [PubMed]

1971 (1)

1968 (1)

D. H. Krantz, “A theory of context effects based on cross-context matching,”J. Math. Psychol. 5, 1–48 (1968).
[Crossref]

1964 (1)

1957 (1)

1955 (1)

1943 (1)

Abramov, I.

E. Zrenner, I. Abramov, M. Akita, A. Cowey, M. Livingstone, A. Valberg, “Color perception: retinex to cortex,” in Visual Perception, L. Spillmann, S. Werner, eds. (Academic, San Diego, Calif., 1990), pp. 163–204.

Akita, M.

E. Zrenner, I. Abramov, M. Akita, A. Cowey, M. Livingstone, A. Valberg, “Color perception: retinex to cortex,” in Visual Perception, L. Spillmann, S. Werner, eds. (Academic, San Diego, Calif., 1990), pp. 163–204.

Arend, L.

Bäuml, K.-H.

Boynton, R. M.

R. M. Boynton, Human Color Vision (Holt, Rhinehart & Winston, New York, 1979).

Brainard, D. H.

D. H. Brainard, B. A. Wandell, “Asymmetric color-matching: how color appearance depends on the illuminant,” J. Opt. Soc. Am. A 9, 1433–1488 (1992).
[Crossref] [PubMed]

D. H. Brainard, “Understanding the illuminant’s effect on color appearance,” Ph.D. dissertation (Stanford University, Stanford, Calif., 1989).

Burnham, R. W.

Cowey, A.

E. Zrenner, I. Abramov, M. Akita, A. Cowey, M. Livingstone, A. Valberg, “Color perception: retinex to cortex,” in Visual Perception, L. Spillmann, S. Werner, eds. (Academic, San Diego, Calif., 1990), pp. 163–204.

Dannemiller, J. L.

J. L. Dannemiller, “Computational approaches to color constancy: adaptive and ontogenetic considerations,” Psychol. Rev. 96, 255–266 (1989).
[Crossref] [PubMed]

Evans, R. M.

Fuchs, H.

H. Fuchs, “Color constancy in non-grey average surrounds,” presented at the 14th European Conference on Visual Perception, Vilnius, Lithuania, August 26–30, 1991.

Gegenfurtner, K. R.

K. R. Gegenfurtner, “praxis: Brent’s algorithm for function minimization,” Behav. Res. Methods Instrum. Computers 24, 560–564 (1992).
[Crossref]

Gibson, K. S.

Goldstein, R.

Humanski, R. A

S. K. Shevell, R. A Humanski, “Color perception under chromatic adaptation: red/green equilibria with adapted short-wavelength-sensitive cones,” Vision Res. 28, 1345–1356 (1988).
[Crossref] [PubMed]

Hurvich, L.

Jameson, D.

Judd, D. B.

Kelly, K. L.

Krantz, D. H.

D. H. Krantz, “A theory of context effects based on cross-context matching,”J. Math. Psychol. 5, 1–48 (1968).
[Crossref]

Land, E. H.

Livingstone, M.

E. Zrenner, I. Abramov, M. Akita, A. Cowey, M. Livingstone, A. Valberg, “Color perception: retinex to cortex,” in Visual Perception, L. Spillmann, S. Werner, eds. (Academic, San Diego, Calif., 1990), pp. 163–204.

MacAdam, D. L.

Maloney, L. T.

Marimont, D. H.

McCann, J. J.

Mollon, J. D.

E. N. Pugh, J. D. Mollon, “A theory of the π1and π3colour mechanisms of Stiles,” Vision Res. 19, 293–312 (1979).
[Crossref]

Newell, S. M.

Nickerson, D.

Poirson, A. B.

Pokorny, J.

V. Smith, J. Pokorny, “Spectral sensitivity of the foveal cone photopigments between 400 and 700 nm,” Vision Res. 15, 161–171 (1975).
[Crossref] [PubMed]

Pugh, E. N.

E. N. Pugh, J. D. Mollon, “A theory of the π1and π3colour mechanisms of Stiles,” Vision Res. 19, 293–312 (1979).
[Crossref]

Reeves, A.

Sachtler, W. L.

Schirillo, J.

Shevell, S. K.

S. K. Shevell, R. A Humanski, “Color perception under chromatic adaptation: red/green equilibria with adapted short-wavelength-sensitive cones,” Vision Res. 28, 1345–1356 (1988).
[Crossref] [PubMed]

S. K. Shevell, “The dual role of chromatic backgrounds in color perception,” Vision Res. 18, 1649–1661 (1978).
[Crossref] [PubMed]

Smith, V.

V. Smith, J. Pokorny, “Spectral sensitivity of the foveal cone photopigments between 400 and 700 nm,” Vision Res. 15, 161–171 (1975).
[Crossref] [PubMed]

Spehar, B.

L. Arend, B. Spehar, “Lightness, brightness, and brightness contrast: 1. Illuminance variation,” Percept. Psychophys. 54, 446–456 (1993).
[Crossref] [PubMed]

Stiles, W. S.

G. W. Wyszecki, W. S. Stiles, Color Scence, 2nd ed. (Wiley, New York, 1982).

Valberg, A.

E. Zrenner, I. Abramov, M. Akita, A. Cowey, M. Livingstone, A. Valberg, “Color perception: retinex to cortex,” in Visual Perception, L. Spillmann, S. Werner, eds. (Academic, San Diego, Calif., 1990), pp. 163–204.

von Kries, J.

J. von Kries, “Die Gesichtsempfindungen,” in Handbuch der Physiologie des Menschen, W. Nagel, ed. (Vieweg-Verlag, Braunschweig, 1905), Vol. 3, pp. 109–282.

Walraven, J.

J. Werner, J. Walraven, “Effect of chromatic adaptation on the achromatic locus: the role of contrast, luminance and background color,” Vision Res. 22, 929–943 (1982).
[Crossref] [PubMed]

J. Walraven, “Discounting the background—the missing link in the explanation of chromatic adaptation,” Vision Res. 16, 289–295 (1976).
[Crossref]

Wandell, B. A.

Werner, J.

J. Werner, J. Walraven, “Effect of chromatic adaptation on the achromatic locus: the role of contrast, luminance and background color,” Vision Res. 22, 929–943 (1982).
[Crossref] [PubMed]

Wyszecki, G.

Wyszecki, G. W.

G. W. Wyszecki, W. S. Stiles, Color Scence, 2nd ed. (Wiley, New York, 1982).

G. W. Wyszecki, “Color appearance,” in Handbook of Perception and Human Performance, K. R. Boff, L. Kaufman, J. P. Thomas, eds. (Wiley, New York, 1986), Vol. 1, pp. 9-1–9-57.

Zaidi, Q.

Zrenner, E.

E. Zrenner, I. Abramov, M. Akita, A. Cowey, M. Livingstone, A. Valberg, “Color perception: retinex to cortex,” in Visual Perception, L. Spillmann, S. Werner, eds. (Academic, San Diego, Calif., 1990), pp. 163–204.

Behav. Res. Methods Instrum. Computers (1)

K. R. Gegenfurtner, “praxis: Brent’s algorithm for function minimization,” Behav. Res. Methods Instrum. Computers 24, 560–564 (1992).
[Crossref]

J. Math. Psychol. (1)

D. H. Krantz, “A theory of context effects based on cross-context matching,”J. Math. Psychol. 5, 1–48 (1968).
[Crossref]

J. Opt. Soc. Am. (5)

J. Opt. Soc. Am. A (8)

Percept. Psychophys. (1)

L. Arend, B. Spehar, “Lightness, brightness, and brightness contrast: 1. Illuminance variation,” Percept. Psychophys. 54, 446–456 (1993).
[Crossref] [PubMed]

Psychol. Rev. (1)

J. L. Dannemiller, “Computational approaches to color constancy: adaptive and ontogenetic considerations,” Psychol. Rev. 96, 255–266 (1989).
[Crossref] [PubMed]

Vision Res. (7)

S. K. Shevell, “The dual role of chromatic backgrounds in color perception,” Vision Res. 18, 1649–1661 (1978).
[Crossref] [PubMed]

V. Smith, J. Pokorny, “Spectral sensitivity of the foveal cone photopigments between 400 and 700 nm,” Vision Res. 15, 161–171 (1975).
[Crossref] [PubMed]

J. Walraven, “Discounting the background—the missing link in the explanation of chromatic adaptation,” Vision Res. 16, 289–295 (1976).
[Crossref]

J. Werner, J. Walraven, “Effect of chromatic adaptation on the achromatic locus: the role of contrast, luminance and background color,” Vision Res. 22, 929–943 (1982).
[Crossref] [PubMed]

E. N. Pugh, J. D. Mollon, “A theory of the π1and π3colour mechanisms of Stiles,” Vision Res. 19, 293–312 (1979).
[Crossref]

S. K. Shevell, R. A Humanski, “Color perception under chromatic adaptation: red/green equilibria with adapted short-wavelength-sensitive cones,” Vision Res. 28, 1345–1356 (1988).
[Crossref] [PubMed]

A. Reeves, “Transient desensitization of a red–green opponent site,” Vision Res. 21, 453–460 (1981).
[Crossref]

Other (13)

E. Zrenner, I. Abramov, M. Akita, A. Cowey, M. Livingstone, A. Valberg, “Color perception: retinex to cortex,” in Visual Perception, L. Spillmann, S. Werner, eds. (Academic, San Diego, Calif., 1990), pp. 163–204.

I exclude the technical possibility that one of the diagonal entries of Ptor Puis precisely zero.

G. W. Wyszecki, W. S. Stiles, Color Scence, 2nd ed. (Wiley, New York, 1982).

D. H. Brainard, “Understanding the illuminant’s effect on color appearance,” Ph.D. dissertation (Stanford University, Stanford, Calif., 1989).

This pattern of results did not change with use of some other opponent color matrices, such as those used in the study of Poirson and Wandell.21

A version of the summary model was fitted to the data that includes the more general von Kries principle, illuminant linearity, and collection invariance. The same 18 parameters were used to describe each subject’s data set. With the restriction of using the same parameters for the different subjects, the rmse of the model is 1.836, compared with 1.446 when the model is fitted individually to each subject’s data.

The rmse’s of the general version of the summary model (including the free color matrix C) and the cone-based version of the summary model (with Cbeing the identity matrix) are practically identical. The rmse of the general version is 1.836, and that of the cone-based version is 1.840.

H. Fuchs, “Color constancy in non-grey average surrounds,” presented at the 14th European Conference on Visual Perception, Vilnius, Lithuania, August 26–30, 1991.

J. von Kries, “Die Gesichtsempfindungen,” in Handbuch der Physiologie des Menschen, W. Nagel, ed. (Vieweg-Verlag, Braunschweig, 1905), Vol. 3, pp. 109–282.

R. M. Boynton, Human Color Vision (Holt, Rhinehart & Winston, New York, 1979).

G. W. Wyszecki, “Color appearance,” in Handbook of Perception and Human Performance, K. R. Boff, L. Kaufman, J. P. Thomas, eds. (Wiley, New York, 1986), Vol. 1, pp. 9-1–9-57.

The models were also evaluated with other error measures. For instance, I minimized the differences between the observed and the predicted settings by using one single global covariance matrix estimated for all surfaces simultaneously or by using the CIE LUV metric space.1 The conclusions drawn about the quality of the models did not depend on the choice of error measure.

For illuminant linearity and the von Kries principle the linearly transformed illuminant changes, or standard surfaces, were used for the prediction. For collection invariance the mean of the illuminant-induced change in the test surfaces under the two surface collections was used for the prediction of the individual changes observed under the two surface collections.

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Figures (9)

Fig. 1
Fig. 1

Visual display. Subjects saw CRT simulations of a collection of 24 flat matte surfaces rendered under a spatially uniform illuminant (rectangular regions) and a test surface (oval region). A detailed description of the stimulus is given in Section 2.

Fig. 2
Fig. 2

Experimental surface collections. Each plot shows for one of the four experimental surface collections the CIE xy coordinates of the collection’s single surface reflectances when the surfaces are rendered under the standard illuminant D0 (see Table 2 in Appendix B). To include some rough information in this figure about the luminance variation within the collections, I categorized the luminance of the single surfaces into three classes: open circles, surfaces with a relatively low luminance; filled circles, surfaces with a moderate luminance; crosses, surfaces with a relatively high luminance. In surface collections S1, S2, and S3 the luminance ratio of the surface with the highest luminance to the surface with the lowest luminance was approximately 13/1. In surface collection S4 the surfaces were nearly isoluminant.

Fig. 3
Fig. 3

Test surfaces set by the three subjects. The upper row shows the illuminant effects for collection S1 separately for the three subjects. The lower row shows the illuminant effects for collections S2, S3, and S4. The effects are shown with CIE xy chromaticity coordinates. The seven diamonds in each of the six plots represent the xy coordinates of the seven experimental illuminants. Each plot also shows the mean xy coordinates of a subject’s (two) test surface(s) set under each of the seven illuminants. Circles, achromatic test surfaces; crosses, chromatic surfaces.

Fig. 4
Fig. 4

Collection invariance, illuminant linearity, and the von Kries principle. Scatterplots compare observed mean changes in the test surfaces with the predictions of these changes. The quality of fit of the three principles is shown separately for the three cone classes. The data are merged over subjects and test surfaces. If a principle held perfectly, all data points would fall on the diagonal line. L, long-wavelength-sensitive; M, middle-wavelength-sensitive; S, short-wavelength-sensitive.

Fig. 5
Fig. 5

Quality of fit of collection invariance, illuminant linearity, and the von Kries principle. The quality of fit of the three principles is compared with the subjects’ precision and the size of the effect (no model). The error measurements are root-mean-square errors (rmse’s).

Fig. 6
Fig. 6

Summary model. Scatterplot comparing the observed mean changes in the test surfaces with the model’s predictions of these changes (see Fig. 4).

Fig. 7
Fig. 7

Site of adaptation. Comparison of quality of fit of the hypotheses that (1) adaptation is sited at the cones, (2) adaptation is sited at an opponent stage, and (3) adaptation is sited at a putative stage that linearly combines the outputs from the three cone classes. This putative stage was estimated individually from a subject’s data set. The error measurements are rmse’s.

Fig. 8
Fig. 8

Color-sensitivity-function estimates of the three putative von Kries mechanisms (solid curves). The estimates are based on fitting a version of the summary model to the experiment’s whole data set that includes a general von Kries principle, illuminant linearity, and collection invariance (see text). The estimates are compared with the Smith–Pokorny cone fundamentals (dashed curves).

Fig. 9
Fig. 9

Regulation of the three cone classes. Visual comparison of four nested hypotheses about the regulation of the gain of the three cones classes. Scatterplots compare observed versus predicted S-cone coordinates for subject BM (upper row) and observed versus predicted L-cone coordinates for subject KHB (lower row). Assumptions are, in the first column, that the gain of each cone class does not depend on the signal from the other two classes; in the second column, that the L and the M cones have an effect on the gain of the S cones; and in the third column, that the S cones have an additional effect on the gain of the L and M cones. In the fourth column a complete interaction among the cone classes is assumed.

Tables (3)

Tables Icon

Table 1 Regulation of the Gain of the Three Cone Classesa

Tables Icon

Table 2 Experimental Illuminantsa

Tables Icon

Table 3 Subjects’ Standard Test Surfaces under the Two Surface Collectionsa

Equations (6)

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i j e i j t Δ i - 1 e i j ,
Δ t i j = Δ t i k .
Δ t i j = M t j Δ d i .
Δ t i j = K i j t 0 j .
Δ t i j = P t M Δ d i .
Δ t i j = C - 1 K i j Ct 0 j ,

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