Abstract

We use psychophysical techniques to investigate the neural mechanisms subserving suprathreshold chromatic discrimination in human vision. We address two questions: (1) How are the postreceptoral detection mechanism responses combined to form suprathreshold chromatic discriminators? and (2) How do these discriminators contribute to color perception? We use a pedestal paradigm in which the subject is required to distinguish between a pedestal stimulus and the same pedestal added to a chromatic increment (the test). Our stimuli are represented in a cardinal space, in which the axes express the responses of the three postreceptoral detection mechanisms normalized relative to their respective detection thresholds. In the main experiment the test (a hue increment) was fixed in the direction orthogonal to the pedestal in our cardinal space. We found that, for high pedestal contrasts, the test threshold varied proportionally with the pedestal contrast. This result suggests the presence of a hue-increment detector dependent on the ratio of the outputs from the red–green and blue–yellow postreceptoral detection mechanisms. The exception to this was for pedestals and tests fixed along the cardinal axes. In that case detection was enhanced by direct input from the postreceptoral mechanism capable of detecting the test in isolation. Our results also indicate that discrimination in the red–green/luminance and blue–yellow/luminance planes exhibits a behavior similar to discrimination within the isoluminant plane. In the final experiment we observed that thresholds for hue-increment identification (e.g., selecting the bluer of two stimuli) are also governed by a ratio relationship. This finding suggests that our ratio-based mechanisms play an important role in color-difference perception.

© 1999 Optical Society of America

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  1. R. T. Eskew, J. S. McLellan, F. Giulianini, “Chromatic detection and discrimination,” in Color Vision: From Molecular Genetics to Perception, K. R. Gegenfurtner, L. T. Sharpe, eds. (Cambridge U. Press, Cambridge, UK, 1998).
  2. J. Krauskopf, D. R. Williams, M. B. Mandler, A. M. Brown, “Higher order color mechanisms,” Vision Res. 26, 23–32 (1986).
    [CrossRef] [PubMed]
  3. K. R. Gegenfurtner, D. C. Kiper, “Contrast detection in luminance and chromatic noise,” J. Opt. Soc. Am. A 9, 1880–1888 (1992).
    [CrossRef] [PubMed]
  4. M. J. Sankeralli, K. T. Mullen, “Postreceptoral chromatic detection mechanisms revealed by noise masking in three-dimensional cone contrast space,” J. Opt. Soc. Am. A 14, 2633–2646 (1997).
    [CrossRef]
  5. M. D’Zmura, K. Knoblauch, “Spectral bandwidths for the detection of color,” Vision Res. 38, 3117–3128 (1998).
    [CrossRef]
  6. G. Wyszecki, W. S. Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulas (Wiley, New York, 1967).
  7. D. L. MacAdam, “Visual sensitivities to color differences in daylight,” J. Opt. Soc. Am. 32, 247 (1942).
    [CrossRef]
  8. B. A. Wandell, “Color measurement and discrimination,” J. Opt. Soc. Am. A 2, 62–71 (1985).
    [CrossRef] [PubMed]
  9. J. Krauskopf, K. Gegenfurtner, “Color discrimination and adaptation,” Vision Res. 32, 2165–2175 (1992).
    [CrossRef] [PubMed]
  10. A. M. Derrington, J. Krauskopf, P. Lennie, “Chromatic mechanisms in lateral geniculate nucleus of macaque,” J. Physiol. (London) 357, 241–265 (1984).
  11. K. T. Mullen, M. J. Sankeralli, “Evidence for the stochastic independence of the blue–yellow, red–green and luminance detection mechanisms revealed by subthreshold summation,” Vision Res. 39, 733–743 (1999).
    [CrossRef] [PubMed]
  12. M. J. Sankeralli, K. T. Mullen, “Estimation of the L-, M-, and S-cone weights of the postreceptoral mechanisms,” J. Opt. Soc. Am. A 13, 906–915 (1996).
    [CrossRef]
  13. C. F. Stromeyer, A. Chaparro, A. S. Tolias, R. E. Kronauer, “Colour adaptation modifies the long-wave versus middle-wave cone weights and temporal phases in human luminance (but not red–green) mechanism,” J. Physiol. (London) 499, 227–254 (1997).
  14. P. Cavanagh, C. W. Tyler, O. E. Favreau, “Perceived velocity of moving chromatic gratings,” J. Opt. Soc. Am. A 1, 893–899 (1984).
    [CrossRef] [PubMed]
  15. To test the ratio model, the test-pedestal functions (Figs. 3,4,5) were fitted by linear regression. Each test-pedestal function consisted of a number (N) of measured hue-increment thresholds (mean μm, standard error sem) expressed in log units. Regression was applied to the data for each function at or exceeding a particular pedestal contrast (5 for MJS, 4 for KTM). The fit, constrained to pass through the origin, yielded a slope estimate λ, a 95% confidence interval [λmin, λmax], and a sum of the squares of the residual errors (∑ SEr2). An estimate of the measurement standard error (SEm) was obtained by evaluating the means 〈μm〉 and 〈sem〉 and computing the standard error in linear units with the small error approximation SEm= ln 10〈sem〉10〈μm〉. The chi-squared coefficient χ2= (∑ SEr2/SEm2)/(N-1) was used to compute a goodness-of-fit parameter Q(0<Q<1), which was the probability that the regression residuals to each test-pedestal function arose randomly. To test the uniformity of the discriminability (Δ=1/λ) over the isoluminant plane, we computed the mean μν and the standard deviation σν over ν for the M intermediate directions (>15 deg from each cardinal axis) for each subject (8 for MJS, 12 for KTM). We used the 95% confidence interval [Δmin= 1/λmax,Δmax=1/λmin] to estimate the measurement error {SEm=mean[(Δmax-Δ), (Δ-Δmin)]/2} in each pedestal direction and computed the mean measurement error 〈SEm〉 over all directions for each subject. Again, the chi-squared coefficient χ2=(σn2/〈SEm〉2) was used to compute the goodness-of-fit parameter Q for each subject.
  16. W. H. Press, S. A. Teukolsky, W. T. Vetterling, B. P. Flannery, Numerical Recipes in C: The Art of Scientific Computing, 2nd ed. (Cambridge U. Press, Cambridge, UK, 1992).
  17. C. C. Chen, J. M. Foley, D. H. Brainard, “Detecting chromatic patterns on chromatic pattern pedestals,” in Proceedings: Optics and Imaging in the Information Age (Society for Imaging Science and Technology, Springfield, Va., 1997), pp. 19–24.
  18. The pedestal contrasts for the first and third stimuli of each presentation were assigned random variables uniformly distributed about the nominal pedestal-contrast value with a distribution half-width of 20% the nominal contrast value. This contrast jitter was shown to raise contrast-increment thresholds by 54% (red–blue), 15% (green–blue), 108% (green–yellow), and 56% (red–yellow) at a nominal pedestal contrast of 15 for subject MJS.
  19. M. J. Sankeralli, K. T. Mullen, “Independent red, green, blue, and yellow submechanisms in the cone-opponent pathways,” Invest. Ophthalmol. Visual Sci. Suppl. 39, S3 (1998).
  20. A. T. Smith, G. K. Edgar, “Antagonistic comparison of temporal frequency filter outputs as a basis for speed perception,” Vision Res. 34, 253–265 (1994).
    [CrossRef] [PubMed]
  21. A. B. Metha, K. T. Mullen, “Red–green and achromatic temporal filters: a ratio model predicts contrast-dependent speed perception,” J. Opt. Soc. Am. A 14, 984–996 (1997).
    [CrossRef]
  22. M. D’Zmura, “Color in visual search,” Vision Res. 13, 951–966 (1991).
    [CrossRef]
  23. J. Krauskopf, H.-J. Wu, B. Farell, “Coherence, cardinal directions and higher-order mechanisms,” Vision Res. 36, 1235–1245 (1996).
    [CrossRef] [PubMed]
  24. G. R. Cole, C. F. Stromeyer, R. E. Kronauer, “Visual interactions with luminance and chromatic stimuli,” J. Opt. Soc. Am. A 7, 128–140 (1990).
    [CrossRef] [PubMed]
  25. E. Switkes, A. Bradley, K. K. Devalois, “Contrast dependence and mechanisms of masking interactions among chromatic and luminance gratings,” J. Opt. Soc. Am. A 5, 1149–1162 (1988).
    [CrossRef] [PubMed]
  26. K. T. Mullen, M. A. Losada, “Evidence for separate pathways for color and luminance detection mechanisms,” J. Opt. Soc. Am. A 11, 3136–3151 (1994).
    [CrossRef]
  27. R. T. Eskew, M. P. Kortick, “Unique hues in 3D color space,” Invest. Ophthalmol. Visual Sci. Suppl. 38, S454 (1997).
  28. R. L. DeValois, K. K. DeValois, E. Switkes, L. Mahon, “Hue scaling of isoluminant and cone-specific lights,” Vision Res. 37, 885–897 (1997).
    [CrossRef]
  29. P. Lennie, M. D’Zmura, “Mechanisms of color vision,” CRC Crit. Rev. Clin. Neurobiol. 3, 333–400 (1988).
  30. V. Billock, “A chaos theory approach to some intractible problems in color vision,” Invest. Ophthalmol. Visual Sci. Suppl. 38, S254 (1997).

1999 (1)

K. T. Mullen, M. J. Sankeralli, “Evidence for the stochastic independence of the blue–yellow, red–green and luminance detection mechanisms revealed by subthreshold summation,” Vision Res. 39, 733–743 (1999).
[CrossRef] [PubMed]

1998 (2)

M. D’Zmura, K. Knoblauch, “Spectral bandwidths for the detection of color,” Vision Res. 38, 3117–3128 (1998).
[CrossRef]

M. J. Sankeralli, K. T. Mullen, “Independent red, green, blue, and yellow submechanisms in the cone-opponent pathways,” Invest. Ophthalmol. Visual Sci. Suppl. 39, S3 (1998).

1997 (6)

C. F. Stromeyer, A. Chaparro, A. S. Tolias, R. E. Kronauer, “Colour adaptation modifies the long-wave versus middle-wave cone weights and temporal phases in human luminance (but not red–green) mechanism,” J. Physiol. (London) 499, 227–254 (1997).

R. T. Eskew, M. P. Kortick, “Unique hues in 3D color space,” Invest. Ophthalmol. Visual Sci. Suppl. 38, S454 (1997).

R. L. DeValois, K. K. DeValois, E. Switkes, L. Mahon, “Hue scaling of isoluminant and cone-specific lights,” Vision Res. 37, 885–897 (1997).
[CrossRef]

V. Billock, “A chaos theory approach to some intractible problems in color vision,” Invest. Ophthalmol. Visual Sci. Suppl. 38, S254 (1997).

A. B. Metha, K. T. Mullen, “Red–green and achromatic temporal filters: a ratio model predicts contrast-dependent speed perception,” J. Opt. Soc. Am. A 14, 984–996 (1997).
[CrossRef]

M. J. Sankeralli, K. T. Mullen, “Postreceptoral chromatic detection mechanisms revealed by noise masking in three-dimensional cone contrast space,” J. Opt. Soc. Am. A 14, 2633–2646 (1997).
[CrossRef]

1996 (2)

J. Krauskopf, H.-J. Wu, B. Farell, “Coherence, cardinal directions and higher-order mechanisms,” Vision Res. 36, 1235–1245 (1996).
[CrossRef] [PubMed]

M. J. Sankeralli, K. T. Mullen, “Estimation of the L-, M-, and S-cone weights of the postreceptoral mechanisms,” J. Opt. Soc. Am. A 13, 906–915 (1996).
[CrossRef]

1994 (2)

A. T. Smith, G. K. Edgar, “Antagonistic comparison of temporal frequency filter outputs as a basis for speed perception,” Vision Res. 34, 253–265 (1994).
[CrossRef] [PubMed]

K. T. Mullen, M. A. Losada, “Evidence for separate pathways for color and luminance detection mechanisms,” J. Opt. Soc. Am. A 11, 3136–3151 (1994).
[CrossRef]

1992 (2)

1991 (1)

M. D’Zmura, “Color in visual search,” Vision Res. 13, 951–966 (1991).
[CrossRef]

1990 (1)

1988 (2)

1986 (1)

J. Krauskopf, D. R. Williams, M. B. Mandler, A. M. Brown, “Higher order color mechanisms,” Vision Res. 26, 23–32 (1986).
[CrossRef] [PubMed]

1985 (1)

1984 (2)

P. Cavanagh, C. W. Tyler, O. E. Favreau, “Perceived velocity of moving chromatic gratings,” J. Opt. Soc. Am. A 1, 893–899 (1984).
[CrossRef] [PubMed]

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

1942 (1)

Billock, V.

V. Billock, “A chaos theory approach to some intractible problems in color vision,” Invest. Ophthalmol. Visual Sci. Suppl. 38, S254 (1997).

Bradley, A.

Brainard, D. H.

C. C. Chen, J. M. Foley, D. H. Brainard, “Detecting chromatic patterns on chromatic pattern pedestals,” in Proceedings: Optics and Imaging in the Information Age (Society for Imaging Science and Technology, Springfield, Va., 1997), pp. 19–24.

Brown, A. M.

J. Krauskopf, D. R. Williams, M. B. Mandler, A. M. Brown, “Higher order color mechanisms,” Vision Res. 26, 23–32 (1986).
[CrossRef] [PubMed]

Cavanagh, P.

Chaparro, A.

C. F. Stromeyer, A. Chaparro, A. S. Tolias, R. E. Kronauer, “Colour adaptation modifies the long-wave versus middle-wave cone weights and temporal phases in human luminance (but not red–green) mechanism,” J. Physiol. (London) 499, 227–254 (1997).

Chen, C. C.

C. C. Chen, J. M. Foley, D. H. Brainard, “Detecting chromatic patterns on chromatic pattern pedestals,” in Proceedings: Optics and Imaging in the Information Age (Society for Imaging Science and Technology, Springfield, Va., 1997), pp. 19–24.

Cole, G. R.

D’Zmura, M.

M. D’Zmura, K. Knoblauch, “Spectral bandwidths for the detection of color,” Vision Res. 38, 3117–3128 (1998).
[CrossRef]

M. D’Zmura, “Color in visual search,” Vision Res. 13, 951–966 (1991).
[CrossRef]

P. Lennie, M. D’Zmura, “Mechanisms of color vision,” CRC Crit. Rev. Clin. Neurobiol. 3, 333–400 (1988).

Derrington, A. M.

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

DeValois, K. K.

R. L. DeValois, K. K. DeValois, E. Switkes, L. Mahon, “Hue scaling of isoluminant and cone-specific lights,” Vision Res. 37, 885–897 (1997).
[CrossRef]

E. Switkes, A. Bradley, K. K. Devalois, “Contrast dependence and mechanisms of masking interactions among chromatic and luminance gratings,” J. Opt. Soc. Am. A 5, 1149–1162 (1988).
[CrossRef] [PubMed]

DeValois, R. L.

R. L. DeValois, K. K. DeValois, E. Switkes, L. Mahon, “Hue scaling of isoluminant and cone-specific lights,” Vision Res. 37, 885–897 (1997).
[CrossRef]

Edgar, G. K.

A. T. Smith, G. K. Edgar, “Antagonistic comparison of temporal frequency filter outputs as a basis for speed perception,” Vision Res. 34, 253–265 (1994).
[CrossRef] [PubMed]

Eskew, R. T.

R. T. Eskew, M. P. Kortick, “Unique hues in 3D color space,” Invest. Ophthalmol. Visual Sci. Suppl. 38, S454 (1997).

R. T. Eskew, J. S. McLellan, F. Giulianini, “Chromatic detection and discrimination,” in Color Vision: From Molecular Genetics to Perception, K. R. Gegenfurtner, L. T. Sharpe, eds. (Cambridge U. Press, Cambridge, UK, 1998).

Farell, B.

J. Krauskopf, H.-J. Wu, B. Farell, “Coherence, cardinal directions and higher-order mechanisms,” Vision Res. 36, 1235–1245 (1996).
[CrossRef] [PubMed]

Favreau, O. E.

Flannery, B. P.

W. H. Press, S. A. Teukolsky, W. T. Vetterling, B. P. Flannery, Numerical Recipes in C: The Art of Scientific Computing, 2nd ed. (Cambridge U. Press, Cambridge, UK, 1992).

Foley, J. M.

C. C. Chen, J. M. Foley, D. H. Brainard, “Detecting chromatic patterns on chromatic pattern pedestals,” in Proceedings: Optics and Imaging in the Information Age (Society for Imaging Science and Technology, Springfield, Va., 1997), pp. 19–24.

Gegenfurtner, K.

J. Krauskopf, K. Gegenfurtner, “Color discrimination and adaptation,” Vision Res. 32, 2165–2175 (1992).
[CrossRef] [PubMed]

Gegenfurtner, K. R.

Giulianini, F.

R. T. Eskew, J. S. McLellan, F. Giulianini, “Chromatic detection and discrimination,” in Color Vision: From Molecular Genetics to Perception, K. R. Gegenfurtner, L. T. Sharpe, eds. (Cambridge U. Press, Cambridge, UK, 1998).

Kiper, D. C.

Knoblauch, K.

M. D’Zmura, K. Knoblauch, “Spectral bandwidths for the detection of color,” Vision Res. 38, 3117–3128 (1998).
[CrossRef]

Kortick, M. P.

R. T. Eskew, M. P. Kortick, “Unique hues in 3D color space,” Invest. Ophthalmol. Visual Sci. Suppl. 38, S454 (1997).

Krauskopf, J.

J. Krauskopf, H.-J. Wu, B. Farell, “Coherence, cardinal directions and higher-order mechanisms,” Vision Res. 36, 1235–1245 (1996).
[CrossRef] [PubMed]

J. Krauskopf, K. Gegenfurtner, “Color discrimination and adaptation,” Vision Res. 32, 2165–2175 (1992).
[CrossRef] [PubMed]

J. Krauskopf, D. R. Williams, M. B. Mandler, A. M. Brown, “Higher order color mechanisms,” Vision Res. 26, 23–32 (1986).
[CrossRef] [PubMed]

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

Kronauer, R. E.

C. F. Stromeyer, A. Chaparro, A. S. Tolias, R. E. Kronauer, “Colour adaptation modifies the long-wave versus middle-wave cone weights and temporal phases in human luminance (but not red–green) mechanism,” J. Physiol. (London) 499, 227–254 (1997).

G. R. Cole, C. F. Stromeyer, R. E. Kronauer, “Visual interactions with luminance and chromatic stimuli,” J. Opt. Soc. Am. A 7, 128–140 (1990).
[CrossRef] [PubMed]

Lennie, P.

P. Lennie, M. D’Zmura, “Mechanisms of color vision,” CRC Crit. Rev. Clin. Neurobiol. 3, 333–400 (1988).

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

Losada, M. A.

MacAdam, D. L.

Mahon, L.

R. L. DeValois, K. K. DeValois, E. Switkes, L. Mahon, “Hue scaling of isoluminant and cone-specific lights,” Vision Res. 37, 885–897 (1997).
[CrossRef]

Mandler, M. B.

J. Krauskopf, D. R. Williams, M. B. Mandler, A. M. Brown, “Higher order color mechanisms,” Vision Res. 26, 23–32 (1986).
[CrossRef] [PubMed]

McLellan, J. S.

R. T. Eskew, J. S. McLellan, F. Giulianini, “Chromatic detection and discrimination,” in Color Vision: From Molecular Genetics to Perception, K. R. Gegenfurtner, L. T. Sharpe, eds. (Cambridge U. Press, Cambridge, UK, 1998).

Metha, A. B.

Mullen, K. T.

Press, W. H.

W. H. Press, S. A. Teukolsky, W. T. Vetterling, B. P. Flannery, Numerical Recipes in C: The Art of Scientific Computing, 2nd ed. (Cambridge U. Press, Cambridge, UK, 1992).

Sankeralli, M. J.

K. T. Mullen, M. J. Sankeralli, “Evidence for the stochastic independence of the blue–yellow, red–green and luminance detection mechanisms revealed by subthreshold summation,” Vision Res. 39, 733–743 (1999).
[CrossRef] [PubMed]

M. J. Sankeralli, K. T. Mullen, “Independent red, green, blue, and yellow submechanisms in the cone-opponent pathways,” Invest. Ophthalmol. Visual Sci. Suppl. 39, S3 (1998).

M. J. Sankeralli, K. T. Mullen, “Postreceptoral chromatic detection mechanisms revealed by noise masking in three-dimensional cone contrast space,” J. Opt. Soc. Am. A 14, 2633–2646 (1997).
[CrossRef]

M. J. Sankeralli, K. T. Mullen, “Estimation of the L-, M-, and S-cone weights of the postreceptoral mechanisms,” J. Opt. Soc. Am. A 13, 906–915 (1996).
[CrossRef]

Smith, A. T.

A. T. Smith, G. K. Edgar, “Antagonistic comparison of temporal frequency filter outputs as a basis for speed perception,” Vision Res. 34, 253–265 (1994).
[CrossRef] [PubMed]

Stiles, W. S.

G. Wyszecki, W. S. Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulas (Wiley, New York, 1967).

Stromeyer, C. F.

C. F. Stromeyer, A. Chaparro, A. S. Tolias, R. E. Kronauer, “Colour adaptation modifies the long-wave versus middle-wave cone weights and temporal phases in human luminance (but not red–green) mechanism,” J. Physiol. (London) 499, 227–254 (1997).

G. R. Cole, C. F. Stromeyer, R. E. Kronauer, “Visual interactions with luminance and chromatic stimuli,” J. Opt. Soc. Am. A 7, 128–140 (1990).
[CrossRef] [PubMed]

Switkes, E.

R. L. DeValois, K. K. DeValois, E. Switkes, L. Mahon, “Hue scaling of isoluminant and cone-specific lights,” Vision Res. 37, 885–897 (1997).
[CrossRef]

E. Switkes, A. Bradley, K. K. Devalois, “Contrast dependence and mechanisms of masking interactions among chromatic and luminance gratings,” J. Opt. Soc. Am. A 5, 1149–1162 (1988).
[CrossRef] [PubMed]

Teukolsky, S. A.

W. H. Press, S. A. Teukolsky, W. T. Vetterling, B. P. Flannery, Numerical Recipes in C: The Art of Scientific Computing, 2nd ed. (Cambridge U. Press, Cambridge, UK, 1992).

Tolias, A. S.

C. F. Stromeyer, A. Chaparro, A. S. Tolias, R. E. Kronauer, “Colour adaptation modifies the long-wave versus middle-wave cone weights and temporal phases in human luminance (but not red–green) mechanism,” J. Physiol. (London) 499, 227–254 (1997).

Tyler, C. W.

Vetterling, W. T.

W. H. Press, S. A. Teukolsky, W. T. Vetterling, B. P. Flannery, Numerical Recipes in C: The Art of Scientific Computing, 2nd ed. (Cambridge U. Press, Cambridge, UK, 1992).

Wandell, B. A.

Williams, D. R.

J. Krauskopf, D. R. Williams, M. B. Mandler, A. M. Brown, “Higher order color mechanisms,” Vision Res. 26, 23–32 (1986).
[CrossRef] [PubMed]

Wu, H.-J.

J. Krauskopf, H.-J. Wu, B. Farell, “Coherence, cardinal directions and higher-order mechanisms,” Vision Res. 36, 1235–1245 (1996).
[CrossRef] [PubMed]

Wyszecki, G.

G. Wyszecki, W. S. Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulas (Wiley, New York, 1967).

CRC Crit. Rev. Clin. Neurobiol. (1)

P. Lennie, M. D’Zmura, “Mechanisms of color vision,” CRC Crit. Rev. Clin. Neurobiol. 3, 333–400 (1988).

Invest. Ophthalmol. Visual Sci. Suppl. (3)

V. Billock, “A chaos theory approach to some intractible problems in color vision,” Invest. Ophthalmol. Visual Sci. Suppl. 38, S254 (1997).

M. J. Sankeralli, K. T. Mullen, “Independent red, green, blue, and yellow submechanisms in the cone-opponent pathways,” Invest. Ophthalmol. Visual Sci. Suppl. 39, S3 (1998).

R. T. Eskew, M. P. Kortick, “Unique hues in 3D color space,” Invest. Ophthalmol. Visual Sci. Suppl. 38, S454 (1997).

J. Opt. Soc. Am. (1)

J. Opt. Soc. Am. A (9)

K. R. Gegenfurtner, D. C. Kiper, “Contrast detection in luminance and chromatic noise,” J. Opt. Soc. Am. A 9, 1880–1888 (1992).
[CrossRef] [PubMed]

K. T. Mullen, M. A. Losada, “Evidence for separate pathways for color and luminance detection mechanisms,” J. Opt. Soc. Am. A 11, 3136–3151 (1994).
[CrossRef]

P. Cavanagh, C. W. Tyler, O. E. Favreau, “Perceived velocity of moving chromatic gratings,” J. Opt. Soc. Am. A 1, 893–899 (1984).
[CrossRef] [PubMed]

A. B. Metha, K. T. Mullen, “Red–green and achromatic temporal filters: a ratio model predicts contrast-dependent speed perception,” J. Opt. Soc. Am. A 14, 984–996 (1997).
[CrossRef]

B. A. Wandell, “Color measurement and discrimination,” J. Opt. Soc. Am. A 2, 62–71 (1985).
[CrossRef] [PubMed]

M. J. Sankeralli, K. T. Mullen, “Postreceptoral chromatic detection mechanisms revealed by noise masking in three-dimensional cone contrast space,” J. Opt. Soc. Am. A 14, 2633–2646 (1997).
[CrossRef]

E. Switkes, A. Bradley, K. K. Devalois, “Contrast dependence and mechanisms of masking interactions among chromatic and luminance gratings,” J. Opt. Soc. Am. A 5, 1149–1162 (1988).
[CrossRef] [PubMed]

G. R. Cole, C. F. Stromeyer, R. E. Kronauer, “Visual interactions with luminance and chromatic stimuli,” J. Opt. Soc. Am. A 7, 128–140 (1990).
[CrossRef] [PubMed]

M. J. Sankeralli, K. T. Mullen, “Estimation of the L-, M-, and S-cone weights of the postreceptoral mechanisms,” J. Opt. Soc. Am. A 13, 906–915 (1996).
[CrossRef]

J. Physiol. (London) (2)

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

C. F. Stromeyer, A. Chaparro, A. S. Tolias, R. E. Kronauer, “Colour adaptation modifies the long-wave versus middle-wave cone weights and temporal phases in human luminance (but not red–green) mechanism,” J. Physiol. (London) 499, 227–254 (1997).

Vision Res. (8)

J. Krauskopf, D. R. Williams, M. B. Mandler, A. M. Brown, “Higher order color mechanisms,” Vision Res. 26, 23–32 (1986).
[CrossRef] [PubMed]

M. D’Zmura, K. Knoblauch, “Spectral bandwidths for the detection of color,” Vision Res. 38, 3117–3128 (1998).
[CrossRef]

J. Krauskopf, K. Gegenfurtner, “Color discrimination and adaptation,” Vision Res. 32, 2165–2175 (1992).
[CrossRef] [PubMed]

K. T. Mullen, M. J. Sankeralli, “Evidence for the stochastic independence of the blue–yellow, red–green and luminance detection mechanisms revealed by subthreshold summation,” Vision Res. 39, 733–743 (1999).
[CrossRef] [PubMed]

R. L. DeValois, K. K. DeValois, E. Switkes, L. Mahon, “Hue scaling of isoluminant and cone-specific lights,” Vision Res. 37, 885–897 (1997).
[CrossRef]

A. T. Smith, G. K. Edgar, “Antagonistic comparison of temporal frequency filter outputs as a basis for speed perception,” Vision Res. 34, 253–265 (1994).
[CrossRef] [PubMed]

M. D’Zmura, “Color in visual search,” Vision Res. 13, 951–966 (1991).
[CrossRef]

J. Krauskopf, H.-J. Wu, B. Farell, “Coherence, cardinal directions and higher-order mechanisms,” Vision Res. 36, 1235–1245 (1996).
[CrossRef] [PubMed]

Other (6)

R. T. Eskew, J. S. McLellan, F. Giulianini, “Chromatic detection and discrimination,” in Color Vision: From Molecular Genetics to Perception, K. R. Gegenfurtner, L. T. Sharpe, eds. (Cambridge U. Press, Cambridge, UK, 1998).

G. Wyszecki, W. S. Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulas (Wiley, New York, 1967).

To test the ratio model, the test-pedestal functions (Figs. 3,4,5) were fitted by linear regression. Each test-pedestal function consisted of a number (N) of measured hue-increment thresholds (mean μm, standard error sem) expressed in log units. Regression was applied to the data for each function at or exceeding a particular pedestal contrast (5 for MJS, 4 for KTM). The fit, constrained to pass through the origin, yielded a slope estimate λ, a 95% confidence interval [λmin, λmax], and a sum of the squares of the residual errors (∑ SEr2). An estimate of the measurement standard error (SEm) was obtained by evaluating the means 〈μm〉 and 〈sem〉 and computing the standard error in linear units with the small error approximation SEm= ln 10〈sem〉10〈μm〉. The chi-squared coefficient χ2= (∑ SEr2/SEm2)/(N-1) was used to compute a goodness-of-fit parameter Q(0<Q<1), which was the probability that the regression residuals to each test-pedestal function arose randomly. To test the uniformity of the discriminability (Δ=1/λ) over the isoluminant plane, we computed the mean μν and the standard deviation σν over ν for the M intermediate directions (>15 deg from each cardinal axis) for each subject (8 for MJS, 12 for KTM). We used the 95% confidence interval [Δmin= 1/λmax,Δmax=1/λmin] to estimate the measurement error {SEm=mean[(Δmax-Δ), (Δ-Δmin)]/2} in each pedestal direction and computed the mean measurement error 〈SEm〉 over all directions for each subject. Again, the chi-squared coefficient χ2=(σn2/〈SEm〉2) was used to compute the goodness-of-fit parameter Q for each subject.

W. H. Press, S. A. Teukolsky, W. T. Vetterling, B. P. Flannery, Numerical Recipes in C: The Art of Scientific Computing, 2nd ed. (Cambridge U. Press, Cambridge, UK, 1992).

C. C. Chen, J. M. Foley, D. H. Brainard, “Detecting chromatic patterns on chromatic pattern pedestals,” in Proceedings: Optics and Imaging in the Information Age (Society for Imaging Science and Technology, Springfield, Va., 1997), pp. 19–24.

The pedestal contrasts for the first and third stimuli of each presentation were assigned random variables uniformly distributed about the nominal pedestal-contrast value with a distribution half-width of 20% the nominal contrast value. This contrast jitter was shown to raise contrast-increment thresholds by 54% (red–blue), 15% (green–blue), 108% (green–yellow), and 56% (red–yellow) at a nominal pedestal contrast of 15 for subject MJS.

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Figures (9)

Fig. 1
Fig. 1

Experimental paradigms. In each experiment a pedestal, P (solid circles), and a pedestal+test, P+T (arrow heads), were presented in random order. In (a) and (b) the subject was presented a temporal sequence of three stimuli: The pedestal was presented twice and the pedestal+test once. The middle stimulus (the reference) was always the pedestal only. The subject was required to report which of the other two stimuli (the first or the third) was different from the reference. In (c) the subject was required to make a color comparison, e.g., which stimulus was the bluer of the two stimuli presented. Axes represent the two isoluminant cardinal axes red–green (rg) and blue–yellow (by).

Fig. 2
Fig. 2

Fixed-pedestal discrimination contours in the isoluminant plane for two subjects. The axes represent the red–green (rg) and blue–yellow (by) cardinal directions. The open triangles encircling the origin represent the detection threshold measurements (no pedestal). The solid circles in each quadrant represent the discrimination thresholds relative to the fixed pedestal (crosses). For clarity both the detection and the discrimination measurements have been scaled by a factor of 3. The open triangles enclosed in each discrimination contour represent the translation of the detection measurements from the origin to the fixed pedestal. The results for both subjects show that discrimination contours are elongated in the pedestal direction relative to the detection contours, suggesting the presence of multiple distributed suprathreshold discriminators.

Fig. 3
Fig. 3

Hue-increment detection thresholds for subject MJS. The figure shows thresholds for nine pedestal directions in the red–blue (rb) quadrant of the isoluminant plane (top three rows) and for one (45-deg) direction in each of the green–blue (gb), green–yellow (gy), and red–yellow (ry) quadrants (bottom row). The horizontal axis represents the pedestal contrast, and the vertical axis represents the test threshold. The test was orthogonal to the pedestal in cardinal space. Horizontal dashed lines represent the prediction that the test threshold will be constant, and sloping dotted curves represent a proportional representation between test threshold and pedestal contrast. Solid curves portray a composite model: constant test thresholds at low pedestal contrasts and proportional test thresholds at high pedestal contrasts. The D value represents the inverse of the fitted slope of a linear regression, and Q represents the goodness of the regression fit (0<Q<1), a Q value exceeding 0.1 taken to be a good fit. Open squares represent measurements of test thresholds under conditions of pedestal-contrast jitter (see Section 4).

Fig. 4
Fig. 4

Hue-increment detection thresholds for subject KTM in intermediate directions of the isoluminant plane. The figure shows thresholds measured with pedestals fixed in three directions in each of the four quadrants (rb, gb, gy, ry) of the isoluminant plane. Symbols and results are the same as in Fig. 3.

Fig. 5
Fig. 5

Hue-increment detection thresholds for subject KTM in cardinal directions of the isoluminant plane. The figure shows thresholds measured with pedestals fixed along each of the four cardinal axes (red, blue, green, yellow). Symbols as in Fig. 3.

Fig. 6
Fig. 6

Hue discriminability as a function of pedestal direction in color space. The figure shows results for pedestals in the red–blue (top left), red–luminance, and blue–luminance (top right) quadrants for MJS and for pedestals in the red–blue and red–yellow (lower left) and green–blue and green–yellow (lower half of lower-right panel) quadrants for KTM. Horizontal axes represent the pedestal direction, whose end points are labeled, accordingly, r, red; b, blue; g, green; y, yellow; l, luminance. The vertical axis represents hue discriminability, which is determined as the inverse of the fitted slope of the proportional relation between test threshold and pedestal contrast. The figure shows that discriminability is constant for pedestal directions more than 15 deg from the cardinal axes, i.e., directions between 15 and 75 deg in each quadrant. The upper overlay in the bottom-right panel represents the discriminability for this condition adjusted for a 0.15 log-unit underestimation of the green cardinal-axis unit (see Section 4).

Fig. 7
Fig. 7

Discrimination zones in the isoluminant plane for two subjects. The figure shows measurements for subject MJS with pedestals in the red–blue (rb) quadrant and for subject KTM with pedestals throughout the isoluminant plane. The pedestal is represented in the appropriate direction in cardinal space (the bisecting direction of each shaded area). The data points (*) represent the hue discrimination thresholds illustrated in Figs. 35. For clarity these thresholds have been scaled by a factor of 0.5. Each line through the data points represents the fit of a proportionality relationship between hue-increment threshold and pedestal contrast. The other (clockwise) boundary of each shaded zone is the reflection of this line about the pedestal direction. The figure shows that the angular width of the discrimination zones in cardinal space is constant throughout the isoluminant plane.

Fig. 8
Fig. 8

Identification response as a function of hue increment. (a) The pedestal is fixed in the isoluminant plane, and the test is assigned by a constant-stimulus paradigm to one of 25 values. The test vector is resolved into two components: hue- and contrast-increment. (b) For each of the five contrast-increment values, the identification response (e.g., bluer) was tallied as a function of hue increment. For each contrast-increment value this variation was fitted by a cumulative Gaussian (solid curve), which provided a measure of the bias (50%-response test hue value) and the identification threshold (81.6%-response test hue value less the bias).

Fig. 9
Fig. 9

Hue-increment identification threshold as a function of net chromatic contrast. The identification threshold was measured for each test chromatic contrast value with a cumulative Gaussian fit to a constant-stimulus paradigm (see Fig. 8). Net chromatic contrast is given by the sum of the pedestal contrast and the test contrast increment. The figure shows that the identification threshold varies proportionally with net chromatic contrast at high chromatic contrast values. This similarity to hue-increment detection thresholds (Figs. 35) suggests that the mechanisms for hue-increment detection and identification are directly linked and that the latter subserves the perception of color differences.

Equations (6)

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L(x, y, t)=C exp{-[(x/sx)2+(y/sy)2+(t/st)2]},
C=(rg2+by2+lum2),
θ=sin-1[by/(rg2+by2)],
ϕ=sin-1[lum/(rg2+by2+lum2)].
T=k1(T0)+(1-k1)(P/Δ).
Ri=mipj(aijmjq)+Z,

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