OSA's Digital Library

Journal of the Optical Society of America A

Journal of the Optical Society of America A


  • Vol. 16, Iss. 11 — Nov. 1, 1999
  • pp: 2625–2637

Ratio model for suprathreshold hue-increment detection

Marcel J. Sankeralli and Kathy T. Mullen  »View Author Affiliations

JOSA A, Vol. 16, Issue 11, pp. 2625-2637 (1999)

View Full Text Article

Acrobat PDF (244 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



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

OCIS Codes
(330.1720) Vision, color, and visual optics : Color vision
(330.5510) Vision, color, and visual optics : Psychophysics
(330.6180) Vision, color, and visual optics : Spectral discrimination

Marcel J. Sankeralli and Kathy T. Mullen, "Ratio model for suprathreshold hue-increment detection," J. Opt. Soc. Am. A 16, 2625-2637 (1999)

Sort:  Author  |  Year  |  Journal  |  Reset


  1. R. T. Eskew, Jr., J. S. McLellan, and F. Giulianini, “Chromatic detection and discrimination,” in Color Vision: From Molecular Genetics to Perception, K. R. Gegenfurtner and L. T. Sharpe, eds. (Cambridge U. Press, Cambridge, UK, 1998).
  2. J. Krauskopf, D. R. Williams, M. B. Mandler, and A. M. Brown, “Higher order color mechanisms,” Vision Res. 26, 23–32 (1986).
  3. K. R. Gegenfurtner and D. C. Kiper, “Contrast detection in luminance and chromatic noise,” J. Opt. Soc. Am. A 9, 1880–1888 (1992).
  4. M. J. Sankeralli and 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).
  5. M. D’Zmura and K. Knoblauch, “Spectral bandwidths for the detection of color,” Vision Res. 38, 3117–3128 (1998).
  6. G. Wyszecki and 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).
  8. B. A. Wandell, “Color measurement and discrimination,” J. Opt. Soc. Am. A 2, 62–71 (1985).
  9. J. Krauskopf and K. Gegenfurtner, “Color discrimination and adaptation,” Vision Res. 32, 2165–2175 (1992).
  10. A. M. Derrington, J. Krauskopf, and P. Lennie, “Chromatic mechanisms in lateral geniculate nucleus of macaque,” J. Physiol. (London) 357, 241–265 (1984).
  11. K. T. Mullen and 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).
  12. M. J. Sankeralli and 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).
  13. C. F. Stromeyer III, A. Chaparro, A. S. Tolias, and 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, and O. E. Favreau, “Perceived velocity of moving chromatic gratings,” J. Opt. Soc. Am. A 1, 893–899 (1984).
  15. To test the ratio model, the test-pedestal functions (Figs. 345) 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/〈SEm2) was used to compute the goodness-of-fit parameter Q for each subject.
  16. W. H. Press, S. A. Teukolsky, W. T. Vetterling, and 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, and 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 and 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 and G. K. Edgar, “Antagonistic comparison of temporal frequency filter outputs as a basis for speed perception,” Vision Res. 34, 253–265 (1994).
  21. A. B. Metha and 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).
  22. M. D’Zmura, “Color in visual search,” Vision Res. 13, 951–966 (1991).
  23. J. Krauskopf, H.-J. Wu, and B. Farell, “Coherence, cardinal directions and higher-order mechanisms,” Vision Res. 36, 1235–1245 (1996).
  24. G. R. Cole, C. F. Stromeyer III, and R. E. Kronauer, “Visual interactions with luminance and chromatic stimuli,” J. Opt. Soc. Am. A 7, 128–140 (1990).
  25. E. Switkes, A. Bradley, and K. K. Devalois, “Contrast dependence and mechanisms of masking interactions among chromatic and luminance gratings,” J. Opt. Soc. Am. A 5, 1149–1162 (1988).
  26. K. T. Mullen and M. A. Losada, “Evidence for separate pathways for color and luminance detection mechanisms,” J. Opt. Soc. Am. A 11, 3136–3151 (1994).
  27. R. T. Eskew, Jr. and 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, and L. Mahon, “Hue scaling of isoluminant and cone-specific lights,” Vision Res. 37, 885–897 (1997).
  29. P. Lennie and 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).

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited