Hostname: page-component-586b7cd67f-gb8f7 Total loading time: 0 Render date: 2024-11-30T21:30:25.120Z Has data issue: false hasContentIssue false

Macular pigment and color discrimination

Published online by Cambridge University Press:  06 September 2006

J.D. MORELAND
Affiliation:
MacKay Institute, Keele University, United Kingdom
S. WESTLAND
Affiliation:
School of Design, University of Leeds, United Kingdom

Abstract

An earlier modeling study of the effect of changes in macular pigment optical density (MPOD) on a wide range of surface colors is re-examined. That study reported changes in local chromaticity variance and in color spacing, some of which were incompatible with tritan-like confusions in normals associated with high-simulated MPOD. This disagreement might have arisen through the use of the von Kries correction for adaptation. The analysis is repeated, using 1782 reflectance spectra of natural and man-made colors. These colors are segregated into an array of 25 equally populated cells in an analogue of the MacLeod-Boynton cone excitation diagram. Removing the von Kries correction restores compatibility with other experimental data. Differences between the results for normal and anomalous trichromats, noted in the earlier study, are confirmed. An analysis of local chromaticity variance across color space indicates the presence of systematic patterns. The earlier study also reported differences in results across observer types (for example, between normals and protanomals) and this is addressed here by utilizing fundamentals defined by a variable photopigment template. Chromaticities are computed for the same 1782 reflectance spectra for normals and for a set of protanomals (for whom the anomalous L pigment is shifted between the normal L and M spectral locations). Colors are segregated into an array of 100 cells in an analogue of the MacLeod-Boynton cone excitation diagram. Changes in chromaticity variance with MPOD for these cells are mapped for normals and protanomals. Variance along the L/(L + M) axis is sensitive to the number of cells used for segmentation. It also increases with MPOD for normal observers but this trend reverses as the wavelength of maximum sensitivity of the L cone shifts towards shorter wavelengths (protanomalous locations).

Type
COLOR CONSTANCY
Copyright
© 2006 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Beatty, S., Boulton, M., Henson, D., Koh, H.H., & Murray, I.J. (1999). Macular pigment and age related macular degeneration. British Journal of Ophthalmology 83, 867877.Google Scholar
Bone, R.A. & Sparrock, J.M.B. (1971). Comparison of macular pigment densities in human eyes. Vision Research 11, 10571064.Google Scholar
DeMarco, P., Pokorny, J., & Smith, V.C. (1992). Full-spectrum cone sensitivity functions for X-chromosome-linked anomalous trichromats. Journal of the Optical Society of America A 9, 14651476.Google Scholar
Hammond, B.R., Wooten, B.R., & Snodderley, D.M. (1998). Preservation of visual sensitivity of older individuals: association with macular pigment density. Investigative Ophthalmology and Vision Science 39, 397406.Google Scholar
MacLeod, D.I.A. & Boynton, R.M. (1979). Chromaticity diagram showing cone excitation by stimuli of equal luminance. Journal of the Optical Society of America 69, 11831186.Google Scholar
Mollon, J.D. & Regan, B.C. (1999). The spectral distribution of primate cones and of the macular pigment: Matched to properties of the world? Journal of Optical Technology 66, 847852.Google Scholar
Moreland, J.D. & Dain, S.L. (1995). Macular pigment contributes to variance in 100 hue tests. Documenta Ophthalmologica Proceedings Series 57, 517522.Google Scholar
Moreland, J.D. & Westland, S. (2003). Macular pigment: Nature's notch filter. In Normal and Defective Color Vision, eds. Mollon, J.D., Pokorny, J. & Knoblauch, K., pp. 273278. Oxford: Oxford University Press.
Owens, H.C. (2002), Spatiochromatic processing in humans and machines. PhD Thesis, University of Derby (UK).
Parkkinen, J.P.S, Hallikainen, J., & Jaaskelainen, T. (1989). Characteristic spectra of Munsell colors. Journal of the Optical Society of America A 3, 16731683.Google Scholar
Schalch, W., Dayhaw-Barker, P., & Barker II, F.M. (1999). The carotenoids of the human retina. In Nutritional and Environmental Influences on Vision, ed. Taylor, A.J., pp. 397406. CRC Press.
Smith, V.C. & Pokorny, J. (1995). Chromatic-discrimination axes, CRT phosphor spectra, and individual variation in color vision. Journal of the Optical Society of America A 12, 2735.Google Scholar
Stockman, A., Sharpe, L.T., & Fach, C. (1999). The spectral sensitivity of the human short-wavelength sensitive cones derived from thresholds and color matches. Vision Research 39, 29012927.Google Scholar
Stockman, A. & Sharpe, L.T. (2000). Spectral sensitivities of the middle- and long-wavelength sensitive cones derived from measurements in observers of known genotype. Vision Research 40, 17111737.Google Scholar
von Schelling, H. (1950). A method for calculating the effect of filters on color vision. Journal of the Optical Society of America 40, 419423.Google Scholar
Wright, W.D. (1947). Characteristics of normal and defective color vision. London: Kimpton.
Wyszecki, G. & Stiles, W.S. (1982). Color Science—Concepts and Methods, Quantitative Data and Formulae. New York: Wiley.