Ординатура / Офтальмология / Английские материалы / Seeing_De Valois_2000

124 Wilson S. Geisler and Duane G. Albrecht

Carandini, M., & Heeger, D. J. (1994). Summation and division by neurons in primate visual cortex.

Science, 264, 1333–1336.

Carpenter, R. H. S. (Ed.). (1991). Eye movements. London: MacMillan Press.

Casagrande, V. A. (1994). A third parallel visual pathway to primate area V1. Trends in Neuroscience, 17, 305–310.

Charman, W. N. (1993). Optics of the eye. In M. Bass (Ed.), Handbook of optics (Vol. I, pp. 24.3–24.54). Washington: Optical Society of America.

Croner, L. J., & Kaplan, E. (1995). Receptive fields of P and M ganglion cells across the primate retina.

Vision Research, 35, 7–24.

Croner, L. J., Purpura, K., & Kaplan, E. (1993). Response variability in retinal ganglion cells of primates.

Proceedings of the National Academy of Sciences, 90, 8128–8130.

Curcio, C. A., & Allen, K. A. (1990). Topography of ganglion cells in human retina. The Journal of Comparative Neurology, 300, 5–25.

Dacey, D. M. (1996). Circuitry for color coding in the primate retina. Proceedings of the National Academy of Sciences, 93, 582–588.

DeAngelis, G. C., Ohzawa, I., & Freeman, R. D. (1993). Spatiotemporal organization of simple-cell receptive fields in the cat’s striate cortex. II. Linearity of temporal and spatial summation. Journal of Neurophysiology, 69,1118–1135.

DeAngelis, G. C., Freeman, R. D., & Ohzawa, I. (1994). Length and width tuning of neurons in the cat’s primary visual cortex. Journal of Neurophysiology, 71, 347–374.

Derrington, A. M., & Henning, G. B. (1989). Some observations on the masking e ects of two-dimen- sional stimuli. Vision Research, 28, 241–246.

Derrington, A. M., & Lennie, P. (1984). Spatial and temporal contrast sensitivities of neurones in lateral geniculate nucleus of macaque. Journal of Physiology, 357, 219–240.

De Valois, K. K. (1994). Spatial vision based upon color di erences. In T. Lawton (Ed.), Computational vision based on neurobiology (Vol. 2054, pp. 95–103). Bellingham, WA: SPIE.

De Valois, R. I., Abramov, I., & Mead, W. R. (1967). Single cell analysis of wavelength discrimination at the lateral geniculate nucleus in the macaque. Journal of Neurophysiology, 30, 415–433.

De Valois, R. L., Albrecht, D. G., & Thorell, L. G. (1982). Spatial frequency selectivity of cells in macaque visual cortex. Vision Research, 22, 545–559.

De Valois, R. L., & De Valois, K. K. (1990). Spatial vision. New York: Oxford University Press.

De Valois, R. L., Thorell, L. G., & Albrecht, D. G. (1985). Periodicity of striate-cortex-cell receptive

fields. Journal of the Optical Society of America A, 2, 1115–1123.

De Valois, R. L., Yund, E. W., & Hepler, N. (1982). The orientation and direction selectivity of cells in macaque visual cortex. Vision Research, 22, 531–544.

Enroth-Cugell, C., & Robson, J. G. (1966). The contrast sensitivity of retinal ganglion cells of the cat.

Journal of Physiology, 187, 517–552.

Felleman, D. J., & Van Essen, D. C. (1991). Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex, 1, 1–47.

Field, D. J. (1987). Relations between the statistics of natural images and the response properties of cortical cells. Journal of the Optical Society of America A, 4, 2379–2394.

Foley, J. M., & Boynton, G. M. (1994). A new model of human luminance pattern vision mechanisms: Analysis of the e ects of pattern orientation, spatial phase and temporal frequency. SPIE, 2054, 32–42.

Foley, J. M., & Legge, G. E. (1981). Contrast detection and near-threshold discrimination in human vision. Vision Research, 21, 1041–1053.

Foster, K. H., Gaska, J. P., Nagler, M., & Pollen, D. A. (1985). Spatial and temporal frequency selectivity of neurones in visual cortical areas V1 and V2 of the macaque monkey. Journal of Physiology, 365, 331–363.

Frazor, R. A., Albrecht, D. G., Geisler, W. S., & Crane, A. M. (1998). Discrimination performance of V1 neurons in monkeys and cats for di erent intervals of integration. Investigative Ophthalmology & Visual Science Supplement, 39/4, S238.

Geisler, W. S. (1984). Physical limits of acuity and hyperacuity. Journal of the Optical Society of America A, 1, 775–782.

Geisler,W. S. (1989). Sequential ideal-observer analysis of visual discriminations. Psychological Review, 96, 267–314.

Geisler,W. S. (1995). Discrimination information in natural radiance spectra. In E. Peli (Ed.), Vision models for target detection and recognition (pp. 117–131). New York: World Scientific Publishing Co. Inc.

Geisler,W. S., & Albrecht, D. G. (1995). Bayesian analysis of identification in monkey visual cortex: Nonlinear mechanisms and stimulus certainty. Vision Research, 35, 2723–2730.

Geisler, W. S., & Albrecht, D. G. (1997). Visual cortex neurons in monkeys and cats: Detection, discrimination, and identification. Visual Neuroscience, 14, 897–919.

Geisler, W. S., Albrecht, D. G., Salvi, R. J., & Saunders, S. S. (1991). Discrimination performance of single neurons: Rate and temporal-pattern information. Journal of Neurophysiology, 66, 334–361.

Gilbert, C. D., & Wiesel, T. N. (1990). The influence of contextual stimuli on the orientation selectivity of cells in the primary visual cortex of the cat. Vision Research, 30, 1689–1701.

Graham, N. (1989). Visual pattern analyzers. New York: Oxford.

Graham, N., Robson, J. G., & Nachmias, J. (1978). Grating summation in fovea and periphery. Vision Research, 18, 815–825.

Hamilton, D. B., Albrecht, D. G., & Geisler, W. S. (1989). Visual cortical receptive fields in monkey and cat: Spatial and temporal phase transfer function. Vision Research, 29, 1285–1308.

Hartline, H. K. (1974). Studies on excitation and inhibition in the retina. New York: The Rockefeller University Press.

Hawken, M. J., & Parker, A. J. (1990). Detection and discrimination mechanisms in the striate cortex of the Old-World monkey. In C. Blakemore (Ed.), Vision: Coding and e ciency (pp. 103–116). Cambridge: Cambridge University press.

Hawken, M. J., Shapley, R. M., & Grosof, D. H. (1996). Temporal frequency selectivity in monkey lateral geniculate nucleus and striate cortex. Visual Neuroscience, 13, 477–492.

Heeger, D. J. (1991). Computational model of cat striate physiology. In M. S. Landy & J. A. Movshon (Eds.), Computational models of visual perception (pp. 119–133). Cambridge: The MIT Press.

Heeger, D. J. (1992a). Half-squaring in responses of cat striate cells. Visual Neuroscience, 9, 427–443. Heeger, D. J. (1992b). Normalization of cell responses in cat striate cortex. Visual Neuroscience, 9, 191–

197.

Hochstein, S., & Shapley, R. M. (1976). Quantitative analysis of retinal ganglion cell classifications. Journal of Physiology, 262, 237–264.

Hood, D. C. (1998). Lower-level visual processing and models of light adaptation. Annual Review of Psychology, 49, 503–535.

Hubel, D. H., & Wiesel, T. N. (1962). Receptive fields, binocular interaction, and functional architecture in the cat’s visual cortex. Journal of Physiology, 160, 106–154.

Hubel, D. H., & Wiesel,T. N. (1968). Receptive fields and functional architecture of monkey striate cortex. Journal of Physiology, 195, 215–243.

Kaas, J. H., Huerta, M. F., Weber, J. T., & Harting, J. K. (1978). Patterns of retinal terminations and laminar organization of the lateral geniculate nucleus of primates. The Journal of Comparative Neurology, 182, 517–554.

Kaplan, E., & Shapley, R. (1986). The primate retina contains two types of ganglion cells, with high and low contrast sensitivity. Proceedings of the National Academy of Sciences, 83, 125–143.

Legge, G. E., & Foley, J. M. (1980). Contrast masking in human vision. Journal of the Optical Society of America, 70, 1458–1470.

Levitt, J., & Lund, J. S. (1997). Contrast dependence of contextual e ects in primate visual cortex. Nature, 387, 73–76.

Li, C., & Creutzfeldt, O. (1984). The representation of contrast and other stimulus parameters by single neurons in area 17 of the cat. Pflugers Archiv, 401, 304–314.

Li, C.-Y., & Li, W. (1994). Extensive integration field beyond the classical receptive field of cat’s striate cortical neurons—classification and tuning properties. Vision Research, 34, 2337–2355.

126 Wilson S. Geisler and Duane G. Albrecht

McCormick, D. A., Connors, B. W., Lighthall, J. W., & Prince, D. A. (1985). Comparative electrophysiology of pyramidal and sparsely spiny stellate neurons of the neocortex. Journal of Neurophysiology, 54, 782–806.

McLean, J., & Palmer, L. A. (1989). Contribution of linear spatiotemporal receptive field structure to velocity selectivity of simple cells in area 17 of cat. Vision Research, 29, 675–679.

McLean, J., & Palmer, L. A. (1994). Organization of simple cell responses in the three-dimensional (3- D) frequency domain. Visual Neuroscience, 11, 295–306.

McLean, J., Raab, S., & Palmer, L. A. (1994). Contribution of linear mechanisms to the specification of local motion by simple cells in areas 17 and 18 of the cat. Visual Neuroscience, 11, 271–294.

Merigan, W. H., & Maunsell, J. H. R. (1993). How parallel are the primate visual pathways? Annual Review of Neuroscience, 16, 369–402.

Movshon, J. A.,Thompson, I. D., & Tolhurst, D. J. (1978a). Receptive field organization of complex cells in the cat’s striate cortex. Journal of Physiology, 283, 79–99.

Movshon, J. A., Thompson, I. D., & Tolhurst, D. J. (1978b). Spatial and temporal contrast sensitivity of neurones in area 17 and 18 of cat’s visual cortex. Journal of Physiology, 283, 101–120.

Movshon, J. A., Thompson, I. D., & Tolhurst, D. J. (1978c). Spatial summation in the receptive fields of simple cells in the cat’s striate cortex. Journal of Physiology, 283, 53–77.

Mullen, K.T. (1985).The contrast sensitivity of human colour vision to red-green and blue-yellow chromatic gratings. Journal of Physiology, 359, 381–400.

Newsome, W. T., Britten, K. H., & Movshon, J. A. (1989). Neuronal correlates of a perceptual decision.

Nature, 341, 52–54.

Olshausen, B. A., & Field, D. J. (1997). Sparse coding with an overcomplete basis set: A strategy by V1?

Vision Research, 37, 3311–3325.

Palmer, L. A., Jones, J. P., & Stepnoski, R. A. (1991). Striate receptive fields as linear filters: Characterization in two dimensions of space. In A. G. Leventhal (Ed.), The neural basis of visual function (pp. 246–265). Boston: CRC Press.

Parker, A. J., & Newsome, W. T. (1998). Sense and the single neuron: Probing the physiology of perception. Annual Review of Neuroscience, 21, 227–277.

Pelli, D. G. (1990). The quantum e ciency of vision. In C. Blakemore (Ed.), Vision: coding and e ciency (pp. 3–24). Cambridge: Cambridge University Press.

Purpura, K., Tranchina, D., Kaplan, E., & Shapley, R. M. (1990). Light adaptation in the primate retina: Analysis of changes in gain and dynamics of monkey retinal ganglion cells. Visual Neuroscience, 4, 75–93.

Regan, D. (Ed.). (1991). Spatial vision. New York: Macmillan.

Reid, R. C.,Victor, J. D., & Shapley, R. M. (1992). Broadband temporal stimuli decrease the integration time of neurons in cat striate cortex. Visual Neuroscience, 9, 39–45.

Robson, J. G. (1966). Spatial and temporal contrast sensitivity functions of the visual system. Journal of the Optical Society of America, 56, 1141–1142.

Robson, J. G. (1991). Neural coding of contrast in the visual system. Journal of the Optical Society of America, Technical Digest Series, 17, 152.

Robson, J. G., & Graham, N. (1981). Probability summation and regional variation in contrast sensitivity across the visual field. Vision Research, 21, 409–418.

Rodieck, R. W. (1965). Quantitative analysis of cat retinal ganglion cell response to visual stimuli. Vision Research, 5, 583–601.

Rodieck, R. W. (1998). The first steps in seeing. Sunderland, MA: Sinauer.

Sclar, G. (1987). Expression of “retinal” contrast gain control by neurons of the cat’s lateral geniculate nucleus. Experimental Brain Research, 66, 589–596.

Sclar, G., & Freeman, R. D. (1982). Orientation selectivity in the cat’s striate cortex is invariant with contrast. Experimental Brain Research, 46, 457–461.

Sclar, G., Maunsell, J. H. R., & Lennie, P. (1990). Coding of image contrast in central visual pathways of macaque monkey. Vision Research, 30, 1–10.

Sekiguchi, N., Williams, D. R., & Brainard, D. H. (1993). E ciency in detection of isoluminant and isochromatic interference fringes. Journal of the Optical Society of America A, 10, 2118–2133.

Sekuler, R., Pantle, A., & Levinson, E. (1978). Physiological basis of motion perception. In R. Held, H. W. Leibowitz, & H.-L. Teuber (Eds.), Handbook of sensory physiology (Vol. VIII: Perception, pp. 67– 96). Berlin: Springer-Verlag.

Sengpiel, F., Baddeley, R. J., Freeman, T. C. B., Harrad, R., & Blakemore, C. (1998). Di erent mechanisms underlie three inhibitory phenomena in cat area 17. Vision Research, 38, 2067–2080.

Shapley, R. M., & Lennie, P. (1985). Spatial frequency analysis in the visual system. Annual Review of Neuroscience, 8, 547–583.

Shapley, R. M., & Victor, J. D. (1978). The e ect of contrast on the transfer properties of cat retinal ganglion cells. Journal of Physiology, 285, 275–298.

Sillito, A. M., Grieve, K. L., Jones, H. E., Cudiero, J., & Davis, J. (1995). Visual cortical mechanisms detecting focal orientation discontinuities. Nature, 378, 492–496.

Skottun, B. C., De Valois, R. L., Grosof, D. H., Movshon, J. A., Albrecht, D. G., & Bonds, A. B. (1991). Classifying simple and complex cells on the basis of response modulation. Vision Research, 31(7/ 8), 1079–1086.

Snowden, R. J., Treue, S., & Andersen, R. A. (1992). The response of neurons in areas V1 and MT of the alert rhesus monkey to moving random dot patterns. Experimental Brain Research, 88, 389– 400.

Softky, W. R., & Koch, C. (1993). The highly irregular firing of cortical cells is inconsistent with temporal integration of random epsps. The Journal of Neuroscience, 13, 334–350.

Sterling, P. (1998). Retina. In G. M. Shepherd (Ed.), The synaptic organization of the brain (pp. 205–253). New York: Oxford University Press.

Talbot, W. P., Darian-Smith, I., Kornhuber, H. H., & Mountcastle, V. B. (1968). The sense of fluttervibration: Comparison of human capacity with response patterns of mechano-receptive a erents from the monkey hand. Journal of Neurophysiology, 31, 301–334.

Teller, D. Y. (1984). Linking propositions. Vision Research, 24, 1233–1246.

Teo, P. C., & Heeger, D. J. (1994). Perceptual image distortion. SPIE Proceedings, 2179, 127–139. Thomas, J. R., & Olzak, L. A. (1997). Contrast gain control and fine spatial discriminations. Journal of

the Optical Society of America A, 14, 2392–2405.

Tolhurst, D. J., & Heeger, D. J. (1997). Comparison of contrast-normalization and threshold models of the responses of simple cells in cat striate cortex. Visual Neuroscience, 14, 293–309.

Tolhurst, D. J., Movshon, J. A., & Dean, A. F. (1983). The statistical reliability of signals in single neurons in the cat and monkey visual cortex. Vision Research, 23, 775–785.

Ullman, S. (1996). High-level vision. Cambridge, MA: MIT Press.

Van Essen, D. C., & DeYoe, E. A. (1994). Concurrent processing in the primate visual cortex. In M. S. Gazzaniga (Ed.), The cognitive neurosciences (pp. 383–400). Cambridge, MA: MIT Press.

Virsu,V., & Lee, B. B. (1983). Light adaptation in cells of macaque lateral geniculate nucleus and its relation to human light adaptation. Journal of Neurophysiology, 50, 864–878.

Vogels, R., Spileers, W., & Orban, G. A. (1989). The response variability of striate cortical neurons in the behaving monkey. Experimental Brain Research, 77, 432–436.

Walraven, J., Enroth-Cugell, C., Hood, D. C., MacLeod, D. I. A., & Schnapf, J. L. (1990). The control of visual sensitivity: Receptoral and postreceptoral processes. In L. Spillman & J. S. Werner (Eds.),

Visual perception: The neurophysiological foundations (pp. 53–101). San Diego: Academic Press. Wandell, B. A. (1995). Foundations of vision. Sunderland, MA: Sinauer.

Wandell, B. (1999). Functional imagery of human visual cortex. Annual Review of Neuroscience, 22, 145– 173.

Watson, A. B. (1983). Detection and recognition of simple spatial forms. In O. J. Braddick & A. C. Sleigh (Eds.), Physical and biological processing of images (pp. 110–114). Berlin: Springer-Verlag.

Watson, A. B., & Ahumada, A. J. (1985). Model of human visual-motion sensing. Journal of the Optical Society of America A, 2, 322–342.

128 Wilson S. Geisler and Duane G. Albrecht

Watson, A. B., & Solomon, J. A. (1997). Model of visual contrast gain control and pattern masking. Journal of the Optical Society of America A, 14, 2379–2391.

Wertheim, T. (1894). Über die indirekte Sehschärfe. Zeitschrift für Psychologie und Physiologie der Sinnesorgane, 7, 172–189.

Westheimer, G., & Campbell, F. W. (1962). Light distribution in the image formed by the living human eye. Journal of the Optical Society of America, 52, 1040–1045.

Williams, D. R. (1988). Topography of the foveal cone mosaic in the living human eye. Vision Research, 28, 433–454.

Wilson, H. R. (1980). A transducer function for threshold and suprathreshold human vision. Biological Cybernetics, 38, 171–178.

Wilson, H. R., & Bergen, J. R. (1979). A four mechanism model for threshold spatial vision. Vision Research, 19, 19–32.

Wilson, H. R., & Gelb, D. J. (1984). Modified line-element theory for spatial-frequency and width discrimination. Journal of the Optical Society of America A, 1, 124–131.

Wilson, H. R., & Humanski, R. (1993). Spatial frequency adaptation and contrast gain control. Vision Research, 33, 1133–1149.

Wilson, H. R., Levi, D., Ma ei, L., Rovamo, J., & De Valois, R. (1990). The perception of form: Retina to striate cortex. In L. Spillmann & J. S. Werner (Eds.), Visual perception: The neurophysiological foundations (pp. 231–272). San Diego: Academic Press.

Zipser, K., Lamme, V. A. F., & Schiller, P. H. (1996). Contextual modulation in primary visual cortex.

The Journal of Neuroscience, 16(22), 7376–7389.

130 Karen K. De Valois and Russell L. De Valois

trichromatic nature of color vision was postulated as early as the 18th century (Palmer, 1777; Young, 1802), and it was empirically demonstrated during the 19th century (Maxwell, 1860). Trichromacy implies that at some processing stage, color information is limited by being transmitted through a three-channel pathway. It does not identify the point at which the three-channel bottleneck occurs, despite many suggestions that trichromacy implies the existence of only three cone types. Indeed, recent evidence (vide infra) clearly implicates not the receptors, but rather a later, postreceptoral site as the source of the trichromatic limitation.

The three variable lights which, when mixed, can produce a match for any test light, can be selected from an infinite set of possible primaries. The only restriction is that no two primaries can be mixed (in any proportion) to match the third. Thus, the three primaries can be all drawn from similar colors or from very di erent colors. There is no requirement that they be red, blue, and yellow, or any other particular set. In color matching, one of the primaries must often be added to the test light to be matched, while the other two are combined.

In a standard color-matching experiment, the test light (which the subject is asked to match) is displayed on one side of a bipartite field, usually a circle divided into halves. The subject may combine any or all of the three primaries in the other half field, or he may add a primary to the half field containing the test light. In this case, of course, the appearance of the test light itself will generally be altered. The subject controls the radiance as well as the half-field position of each of the three primaries. The results are described in the form of an equation, such as:

A B C I T

where A, B, and C are the three primaries; , , and are the radiances of primaries A, B, and C, respectively; T is the test light to be matched; the symbol I denotes a complete visual match; and a minus sign indicates that the primary so designated was added to the half field containing the test light. The strong form of the trichromatic generalization (Grassmann, 1853) also states that adding any other light to both sides of a color match will not disturb the match (the additivity property), and that multiplying the radiances of both sides of a color match by the same factor will not disturb the match (the proportionality property).

The quantitative data generated by color-matching experiments and the strong implication of a three-channel limiting stage combined to drive research and theory in color vision for many decades. Models proposing three largely independent channels that began at the receptor stage and continued with little or no interaction through most of the visual system were based on the undeniable demonstrations of trichromacy. Among the best known of such models was that of Helmholtz (1867), who postulated three receptors with somewhat overlapping spectral absorption functions but widely separated peaks centered in the regions of the spectrum typically identified as red, green, and blue by a normal observer adapted to an achromatic background. We now know that both of these postulates—widely separated spectral peaks and independent channels from the di erent cone types to the

brain—are quite wrong. Nonetheless, attention to the trichromatic nature of color vision is essential to an understanding of many anomalies of color vision, as well as of normal color matching. For example, the reduced chromatic discrimination of a dichromat can be understood as the behavior of a system in which only two, not three, channels are present.

B. Color Spaces and the Representation of Color

Because color vision is trichromatic, all visible colors can be adequately represented in a three-dimensional space. There are an infinite number of possible spaces, and insofar as color mixing in the visual system is linear (and it is), any one space can be linearly transformed to any other. Thus the characteristics of the space used can be selected for convenience or for their ability to represent more or less directly the processing stages in the neural color system. Several color spaces have been devised over the years. We briefly describe three of the most useful.

1. Perceptual Color Space

A consideration of the perceptual dimensions of color led to the representation of color in a three-dimensional space in which each dimension corresponds to one of the three main perceptual dimensions of color. It is most commonly illustrated (in a nonquantitative, diagrammatic way) as two cones of equal size with their round bases abutting, as illustrated in Figure 1. The vertical axis (running lengthwise through the two cones) is the locus of all achromatic lights and corresponds to lightness or brightness, with bright, achromatic lights being represented near the peak, intermediate grays arrayed in order along the middle of the axis, and dark, achromatic stimuli near the bottom. The lateral distance of a point from the achromatic axis indicates its saturation, the perceived amount of hue in the stimulus. Thus, the light pastel colors would plot to points above the middle and near the vertical axis; the dark desaturated colors such as navy and brown would be represented near the vertical axis and below the middle; and the most highly saturated colors would be arranged around the perimeter of the midlevel of the double cone. At any vertical level, the hues vary in sequence around any horizontal plane through the space. The hues are arranged in such a way that nearest neighbors are those hues that appear most similar. Thus green, for example, would gradually shade into yellowish-green, which would gradually turn into yellow, and so on. Generally, such an ordering re- flects spectral ordering, with adjacent wavelengths appearing most similar. The one exception results from the fact that while wavelength varies along a single dimension and can be represented along a straight line, the color appearance of spectral lights has a circular organization, with the long wavelengths curling back to meet the shortest visible wavelengths. Thus very short-wavelength violets appear reddish. This space also illustrates the fact that greatly increasing the intensity of a light reduces its saturation, so that the brightest lights appear white (Parsons, 1915). Sim-

132 Karen K. De Valois and Russell L. De Valois

A perceptually based color space, in which the vertical axis represents lightness or brightness, the angle around a circular cross-section at any level represents hue, and the distance along any radius represents saturation. All colors that lie along the central vertical axis, thus, are achromatic, and all colors that lie along any vertical axis have the same hue.

ilarly, even monochromatic stimuli much darker than the adaptation level appear black.

2. CIE xyz Color Space

Although a perceptually defined color space well represents the relationships in appearance across the broad gamut of visible colors, it has proven less useful as a scientific tool, though a modified version of such a space, the Munsell color space, is still widely used in industry. A more widely used color space is the 1931 xyz chromaticity diagram defined by the Commission Internationale de l’Eclairage (CIE). This is based upon color-matching data generated early in the 20th century using several observers in di erent laboratories, making color matches using di erent sets of primaries. The data were combined and averaged to produce color-matching functions for a standard observer.

Recall that in color-matching experiments, one primary often must be added to the test light, and that the amount of this primary is represented by a negative coe - cient. If any set of three physically realizable primaries were used to define the three dimensions of the space, many points would plot outside the first quadrant. In order