Ординатура / Офтальмология / Английские материалы / Seeing_De Valois_2000

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(or hue) in a stimulus as compared to the amount of white, black, or gray. Thus, both pastel blue and navy blue are less saturated than royal blue, and pink and maroon are both less saturated versions of red. A highly saturated light can be desaturated by the addition of white or by the addition of black (produced, for example, by surrounding the patch by a brighter annulus, thus inducing blackness into the patch itself). The physical measure that corresponds most closely to the perceptual variable of saturation is purity, the relative amount of monochromatic light in a mixture of a monochromatic light and a reference white. (White is produced by the superposition of multiple monochromatic lights. Two appropriately chosen wavelengths added in proper proportions can produce white, but whites more commonly contain many, often all, the visible wavelengths). For any given photopic light under otherwise invariant conditions, increasing purity results in an increase in saturation. Above we discussed the purity discrimination function, which shows a sharp minimum at about 570 nm. This part of the spectrum, which appears greenish-yellow under neutral adaptation, appears most similar to an achromatic stimulus.

D. Brightness or Lightness

The third perceptual dimension of color is brightness or lightness. A light of fixed hue and saturation in an invariant surround can be made brighter or darker, respectively, by increasing or decreasing its radiance. Brightness refers to the perceptual dimension corresponding most closely to a change in radiometric intensity of a light viewed in isolation, a condition termed aperture viewing. In this circumstance, the light appears almost to float in space that is otherwise empty. When the stimulus patch is viewed in the presence of other stimuli, such as an annular surround, its appearance is a ected by the presence of the other stimuli, and it is more likely to look like a reflective surface rather than a disembodied light. Under these conditions, the term lightness is more appropriate, and the stimulus may be referred to as having surface color. In this mode, changing the intensity of the object’s surround can dramatically a ect its lightness, even when the stimulus patch is itself invariant. For example, greatly decreasing the intensity of the surround can produce a signi- ficant increase in the lightness of the test stimulus. With both aperture and surface colors, if all other factors remain constant, increasing the radiometric intensity of the light will result in an increase in its brightness or lightness, and decreasing the intensity produces a decrease in brightness or lightness.

Each of the three chromatic perceptual dimensions of hue, saturation, and brightness is associated most closely with a single physical dimension (wavelength, purity, and intensity, respectively), but the relationships are not exclusive. Hue, for example, can be a ected by intensity and by purity, as well as by wavelength. When the intensity of a photopic monochromatic light is increased over a range of perhaps two log units, the associated hue is invariant in certain spectral regions, particularly those around the unique hues. At other wavelengths, however, as the light

becomes increasingly intense, it appears to shift in hue towards blue or yellow. This is known as the Bezold-Brücke E ect (see Purdy, 1931, or Boynton & Gordon, 1965, for representative data). Similarly, the Abney E ect is a change in hue associated with a change in purity.

E. Similitude and Contrast

The hue, saturation, and lightness of a particular region in visual space are determined not just by the stimulation within that region at that instant, but by stimuli in neighboring regions and at previous times as well. These lateral interactions between two spatial or temporal regions can be in either of two opposite directions, depending on the stimulus conditions. In the contrast direction, two regions appear more di erent from each other than if they were seen in isolation. In the similitude direction, they appear more similar to each other than if each were seen in isolation. Simultaneous contrast and similitude refer to interactions between an area and its surround or other objects in the field of view; successive contrast and similitude refer to interactions between a region and what had been present there previously.

Similitude is also known as assimilation or the spreading e ect of von Bezold (von Bezold, 1876). Although several factors can influence whether and how strongly similitude occurs (e.g., relative luminance, de Weert & Spillman, 1995), the most significant determining factor is probably the size of the test target (Fach & Sharpe, 1986). Optical or neural processes that lead to summation in space or time produce spatial or temporal similitude. Blur in the optics of the eye produces spatial similitude, by smearing the light from adjacent regions in the retinal image. The limit of resolution is that point at which there is complete similitude, with the black and white, or red and green lines in a grating pattern appearing the same uniform color. Neural summation produces the same e ect: a bipolar cell that sums the outputs of a group of receptors may just average out any di erence between the outputs of the individual receptors to produce spatial similitude within that small region; a cortical cell that sums together the outputs of several LGN cells would produce similitude over a somewhat larger area.Thus, a thin red line on a large white background would appear desaturated, and on a large blue background it would appear slightly bluish. Both of these changes could have been produced by either optical or neural spreading from the background.

The slow random nature of the cascade of reactions within a receptor similarly smears out temporal di erences to produce temporal similitude (e.g., the overhead lights in a room may change from very bright to dark 60 times a second, but they appear to be a uniform intermediate brightness). Any temporal smearing (or lowpass filtering) that occurs at later synaptic levels would have the same e ect as the slow cascade within the receptors.

Changes in color appearance in the contrast direction depend on inhibitory interactions in space or time.The responses in two regions with mutually inhibitory

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connections will be more di erent from each other than they would otherwise be. The antagonism between the center and surround within the RFs of LGN cells for luminance-varying patterns, and that between di erent subregions of cortical cells, are examples of interactions that produce spatial contrast over short distances. Spatial color contrast e ects that require longer distance interactions take place at prestriate cortical loci, such as V4 (Schein & Desimone, 1990).

One factor that produces temporal color contrast is neural rebound in a spectrally opponent cell. Consider a cell that fires to long wavelengths and inhibits to middle wavelengths. After prolonged firing to a red stimulus, the cell will rebound into inhibition at the termination of the stimulus. Since inhibition in this cell may signal “green,” this could produce a momentary appearance of a green afterimage, thus temporal contrast. Another major factor producing temporal contrast e ects is receptor-specific adaptation, as discussed below.

Contrast and similitude e ects can be related to the spatial and temporal contrast sensitivity functions. High-contrast sensitivity means that the di erent bars in the grating appear very di erent from each other, that is, in the contrast direction. In a region of low contrast sensitivity, the bars appear similar to each other, thus in the similitude direction. Similitude corresponds to the attenuation of the luminance CSF at very high and low temporal and spatial frequencies, and of the color CSF at high temporal and spatial frequencies. Color and brightness contrast corresponds to the increased sensitivity to middle spatial and temporal frequencies in the luminance CSF, and at low spatial and temporal frequencies in the color CSF. The di ering color and luminance CSFs account for the fact that there are many situations in which we see color similitude but brightness contrast. The explanation for the di erent CSFs for color and luminance was discussed earlier.

F. Adaptation and Afterimages

Both the presence of other stimuli in the field of view and the observer’s state of adaptation can dramatically a ect the hue of a stimulus. The intensity of the illuminant in visual scenes varies over a huge range, and the visual system has several adaptational mechanisms that adjust the sensitivity of the system to the ambient illumination level. The average chromaticity of the world does not change so dramatically, but nonetheless the visual system must adjust for it in order to keep color appearance at least approximately constant across a day, a result known as color constancy. An adaptational system that is cone-type-specific would go far towards accomplishing this. It has been shown, for instance by Werner and Walraven (1982), that a wide range of desaturated colors all appear white when one adapts to them. Consider a shift from uniform white light that stimulated all the cone types equally to a yellowish background light. When the yellow light first appeared it would activate the L more than the M cones, and one would see it as quite yellowish. But the L cones would adapt more than the M cones, and after a brief period, the two cone

types would again be responding equally to the light. The formerly yellowish light would now look white.

Another familiar example of the e ects of adaptation can be found in many introductory psychology textbooks. The viewer is asked to adapt for a minute or so while fixating a point on a vividly colored picture. Immediately after the end of the adaptation, the viewer is instructed to look at a neutral, unpatterned surface such as a white wall, on which she or he will see an image of the object in colors complementary to those of the adaptation stimulus. Such negative afterimages reflect the e ects of localized receptor adaptation as well as neural rebound.

V. THE ROLE OF COLOR IN SPATIAL VISION

The traditional separation between studies of color vision and studies of spatial vision might be taken to imply that color plays little role in visual pattern analysis. That assumption is reinforced by the fact that we can clearly capture much of the important information in a scene with an image that contains only luminance variations, such as a black-and-white photograph. Nonetheless, color di erences can be used to analyze the spatial pattern in a scene. Indeed, in some cases color may be more useful for this purpose than luminance di erences.

In considering the role of color in spatial vision, an obvious question is how well the visual system can discriminate patterns that di er along the basic spatial dimensions of spatial frequency and orientation when only color di erences are present. Webster, De Valois, and Switkes (1990) determined the minimum di erences in both orientation and spatial frequency for gratings defined along the three fundamental MBDKL axes. They found that when all gratings were equated in terms of equal multiples of their respective detection thresholds, spatial discrimination thresholds for the two isoluminant chromatic axes were very similar but were always somewhat higher than those for the luminance axis. The critical observation, however, was that discrimination was still very good even at isoluminance. Thus, spatial vision based solely upon color di erences should be respectable, though not as precise as that supported by luminance variations.

A related question concerns the organization of spatial information in the color system. There is now a wealth of evidence demonstrating the presence of multiple parallel spatial channels in the luminance system (see De Valois & De Valois, 1988, and Geisler & Albrecht, chapter 3, this volume). The spatial channels are selective for, or bandpass along, the dimensions of spatial frequency and orientation. It is not obvious that the color system should be organized in the same manner as the luminance system, but it appears to be remarkably similar. The presence and characteristics of the spatial frequency channels in the luminance system were initially demonstrated by adaptation and masking experiments. Adaptation to a luminance grating of a particular spatial frequency produces a short-term reduction in contrast sensitivity for test gratings near in frequency to the adaptation grating. The loss

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in sensitivity decreases as the di erence between adaptation and test frequencies increases, producing a bandpass function with an average spatial frequency bandwidth of a little more than an octave at half amplitude (Pantle & Sekuler, 1968; Blakemore & Campbell, 1969), though it may also depend upon factors such as spatial frequency and contrast. Similarly, adaptation to a grating of a single orientation produces an orientation-selective loss in contrast sensitivity (Blakemore & Campbell, 1969; Gilinsky, 1968), with no loss in sensitivity when adaptation and test gratings di er by about 45 or more.

Adaptation to an isoluminant color-varying grating also produces losses in contrast sensitivity that are bandpass in both spatial frequency and orientation (Bradley, Switkes, & De Valois, 1988). Along both dimensions, the spread of the adaptation e ect is somewhat greater for isoluminant color-varying patterns than for comparable patterns that vary in luminance. The noteworthy observation, however, is that both adaptation functions are clearly bandpass, suggesting that the color system, like the luminance system, analyzes patterns using a set of tuned filters that are selective on the dimensions of spatial frequency and orientation. Far from merely reporting locally on color, the color vision system is capable of analyzing and characterizing the spatial patterns it encounters.

In nature, of course, color di erences rarely occur in isolation from luminance di erences. Most commonly, color and luminance changes are highly, though not perfectly, correlated.This observation raises several questions of interest. One is how, if at all, color and luminance di erences interact when both are present. Does the existence of color contrast, for example, a ect an observer’s ability to detect luminance contrast? It can. Under some circumstances, the presence of a color-varying pattern can make the detection of a superimposed luminance-varying pattern dramatically more di cult, whereas a similar luminance-varying pattern can make a color pattern more detectable (De Valois & Switkes, 1983; Switkes, Bradley, & De Valois, 1988).The interactions between color and luminance variations can be complex (Cole, Stromeyer, & Kronauer, 1990; Mullen, Cropper, & Losada, 1997), however, and not easily predictable.

Another question of interest is whether luminance-varying stimuli are analyzed in a strictly color-blind fashion. Heterochromatic flicker photometry and other measures that show linear additivity between lights that di er in hue are tasks in which the visual system operates in a strictly color-blind fashion. As long as two lights are equated in sensation luminance, one can be substituted for the other without disturbing the e ective intensity of a flickering light, for example. There is evidence that retinal ganglion cells in the Mc pathway exhibit the indi erence to stimulus hue and the spectral luminous e ciency function that would be expected of the mechanism that underlies heterochromatic flicker photometry, but that the neurons in the Pc pathway do not (Lee, Martin, & Valberg, 1988). Although Pc pathway neurons respond vigorously to luminance contrast, they do so in a manner that is dependent upon the hue of the stimulus.To the extent that these cells are involved in the analysis of luminance-varying patterns, then, one might well expect color-

selective responses even when the pattern is defined by luminance di erences.There is evidence for the existence and operation of hue-selective, intensity-coding mechanisms (Rovamo, Hyvaerinen & Hari, 1982; Virsu & Haapasalo, 1973; Yamamoto, 1997).

A. Color Motion

In the natural world, objects typically di er from their surroundings in both chromaticity and luminance. In the laboratory, however, it is possible to produce a stimulus in which luminance remains invariant across a pattern defined only by color di erences. As noted above, spatial and temporal contrast sensitivity functions for such stimuli are markedly di erent from those for patterns that contain luminance variations. There are certain striking perceptual anomalies associated with isoluminant patterns, as well.The most dramatic of these is the peculiar appearance of moving isoluminant patterns. Imagine an extended vertical sinusoidal grating that contains luminance variations (black and white, say) over the lower half of its extent, then turns into an isoluminant pattern (red and green, perhaps) over the upper half. The bars that are black in the lower half become red in the upper half, and those that are white in the lower half become green in the upper half. If such a grating is made to drift in a horizontal direction (so the bars move smoothly to the right, say), a strange perceptual dissociation occurs. The black–white half of the grating will appear to move smoothly to the right at a speed that is a function of the actual speed of the pattern. The red-green half, however, may appear not to move at all, to move only by occasional shifts in its position, or to move but exceedingly slowly (Cavanaugh,Tyler, & Favreau, 1984;Teller & Lindsey, 1993).This occurs despite the fact that the red bars clearly abut the black bars, and the green bars abut the white bars at every moment and across the entire grating. There is no point at which the attachment between the two seems to break, yet the two halves appear clearly to be moving at markedly di erent speeds. The perceptual slowing of moving isoluminant stimuli can be observed with discrete objects, as well as with drifting gratings (Troscianko & Fahle, 1988). It appears to be a ubiquitous feature of moving stimuli that are defined only by color di erences. We have also observed that isolated moving gaussian blobs that di er from the background only in color may not appear to move along a smooth trajectory. From when their real movement path is straight, they may appear to move along some more complex trajectory.

To explain the perceptual slowing of color motion, it is necessary first to have a model of the way in which the visual system encodes speed. Despite the large number of models aimed at explaining direction selectivity in the visual system, there are relatively few models of speed encoding. An interesting and useful attempt to model the slowing of perceived speed at isoluminance has been presented by Metha and Mullen (1997).

The peculiarities associated with isoluminance should not blind us to the important role color can play. For example, color di erences can strongly influence the

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way in which an observer perceptually segregates the objects in a complex scene, such as determining whether a moving plaid pattern is seen as a coherent whole or as separate components moving transparently (Kooi, De Valois, Switkes, & Grosof, 1992).

References

Abramov, I. (1968). Further analysis of the responses of LGN cells. Journal of the Optical Society of America, 58, 574–579.

Abramov, I., Gordon, J., & Chan, H. (1990). Using hue scaling to specify color appearance and to derive color di erences. Perceiving, measuring, and using color. Proceedings of the SPIE, 1250, 40–51.

Anhelt, P. K., & Kolb, H. (1994). Horizontal cells and cone photoreceptors in human retina: a Golgielectron microscope study of spectral connectivity. Journal of Comparative Neurology, 343, 406–427.

Anhelt, P. K., Kolb, H., & Pflug, R. (1987). Identification of a subtype of cone photoreceptor, likely to be blue-sensitive, in the human retina. Journal of Comparative Neurology, 255, 18–34.

Baylor, D., Nunn, B., & Schnapf, J. (1987). Spectral sensitivity of cones of the monkey Macaca fascicularis. Journal of Physiology, 390, 145–60.

Bedford, R. E., & Wyszecki, G. W. (1958). Luminosity functions for various field sizes and levels of retinal illuminance. Journal of the Optical Society of America, 48, 406–411.

Berlin, B., & Kay, P. (1969). Basic color terms, their universality and evolution. Berkeley: University of California Press.

Blakemore, C., & Campbell, F. W. (1969). On the existence of neurones in the human visual system selectively sensitive to the orientation and size of retinal images. Journal of Physiology (London), 203, 237–260.

Boynton, R. M., & Gordon, J. (1965). Bezold-Brücke hue shift measured by color-naming technique.

Journal of the Optical Society of America, 55, 78–86.

Boynton, R. M., & Kaiser, P. K. (1968). Vision: The additivity law made to work for heterochromatic photometry with bipartite fields. Science, 161, 366–368.

Bradley, A., Switkes, E., & De Valois, K. K. (1988). Orientation and spatial frequency selectivity of adaptation to color and luminance gratings. Vision Research, 28, 841–856.

Cavanaugh, P., Tyler, C. W., & Favreau, O. E. (1984). Perceived velocity of moving chromatic gratings.

Journal of the Optical Society of America A, 1, 893–899.

Cavonius, C. R., & Estévez, O. (1975). Contrast sensitivity of individual colour mechanisms of human vision. Journal of Physiology (London), 248, 649–662.

Cole, G., & Hine, T. (1992). Computation of cone contrasts for color vision research. Behavioral Research Methods, Instruments, & Computers, 24, 22–27.

Cole, G. R., Stromeyer, C. F., & Kronauer, R. E. (1990). Visual interactions with luminance and chromatic stimuli. Journal of the Optical Society of America A, 7: 128–140.

Cottaris, N. P., & De Valois, R. L. (1998). Temporal dynamics of chromatic tuning in macaque primary visual cortex. Nature, 395, 896–900.

Curcio, C. A., Sloan, K. R., Kalina, R. E., & Hendrickson, A. E. (1990). Human photoreceptor topography. Journal of Comparative Neurology, 292, 497–523.

Curcio, C. A., Allen, K. A., Sloan, K. R., Lerea, C. L., Hurley, J. B., Klock, I. B., & Milam, A. H. (1991). Distribution and morphology of human cone photoreceptors stained with anti-blue opsin. Journal of Comparative Neurology, 312, 610–624.

Dacey, D. M., & Lee, B. B. (1994). The ‘blue-on’ opponent pathway in primate retina originates from a distinct bistratified ganglion cell type. Nature, 367, 731–735.

de Monasterio, F. M. (1979). Asymmetry of onand o -pathways of blue-sensitive cones of the retina of macaques. Brain Research, 166, 39–48.

de Monasterio, F. M., McCrane, E. P., Newlander, J. K., & Schein, S. J. (1985). Density profile of bluesensitive cones along the horizontal meridian of macaque retina. Investigative Ophthalmology and Visual Science, 26, 289–302.

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

De Valois, K. K., & Switkes, E. (1983). Simultaneous masking interactions between chromatic and luminance gratings. Journal of the Optical Society of America, 73, 11–18.

De Valois, R. L. (1965). Analysis and coding of color vision in the primate visual system. Cold Spring Harbor Symposia on Quantitative Biology, 567–579.

De Valois, R. L., Abramov, I., & Jacobs, G. H. (1966). Analysis of response patterns of LGN cells. Journal of the Optical Society of America, 56, 966–977.

De Valois, R. L., Cottaris, N. P., & Elfar, S. (1997). S-cone inputs to striate cortex cells. Investigative Ophthalmology and Visual Science, 38, S15.

De Valois, R. L., & De Valois, K. K. (1975). Neural coding of color. In E. C. Carterette & M. P. Friedman (Eds.), Handbook of perception: Seeing (Vol. 5, pp. 117–166). New York: Academic Press.

De Valois, R. L., & De Valois, K. K. (1988). Spatial vision. New York: Oxford University Press.

De Valois, R. L., & De Valois, K. K. (1993). A multi-stage color model. Vision Research, 33, 1053–1065. De Valois, R. L., De Valois, K. K., Switkes, E., & Mahon, L. (1997). Hue scaling of isoluminant and cone-

specific lights. Vision Research, 37, 885–897.

De Valois, R. L., Jacobs, G. H., & Abramov, I. (1964). Responses of single cells in visual system to shifts in the wavelength of light. Science, 146, 1184–1186.

De Valois, R. L., & Marrocco, R. T. (1973). Single cell analysis of saturation discrimination in the macaque. Vision Research, 13, 701–711.

De Valois, R. L., Morgan, H., Polson, M. C., Mead, W. R., & Hull, E. M. (1974). Psychophysical studies of monkey vision. I. Macaque luminosity and color vision tests. Vision Research, 14, 53–67.

De Valois, R. L., Snodderly, D. M., Yund, E. W., Jr., & Hepler, N. (1977). Responses of macaque lateral geniculate cells to luminance and color figures. Sensory Processes, 1, 244–259.

de Weert, C., & Spillmann, L. (1995). Assimilation: Asymmetry between brightness and darkness? Vision Research, 35, 1413–1419.

Ding,T., & Casagrande,V. A. (1997).The distribution and morphology of LGN K pathway axons within the layers and CO blobs of owl monkey. Vision Neuroscience, 14, 691–704.

Fach, C., & Sharpe, L. T. (1986). Assimilative hue shifts in color gratings depend on bar width. Perception and Psychophysics, 40, 412–418.

Famiglietti, E. V., & Kolb, H. (1976). Structural basis for Onand O -center responses in retinal ganglion cells. Science, 194, 193–195.

Gilinsky, A. S. (1968). Orientation-specific e ects of patterns of adapting light on visual acuity. Journal of the Optical Society of America, 58, 13–18.

Granger, E. M., & Heurtley, J. C. (1973). Visual chromaticity-modulation transfer function. Journal of the Optical Society of America, 63, 1173–1174.

Grassman, H. (1853). Zur Theorie der Farbenmischung. Annals of Physics und Chemistry, 89, 69–84; English translation: (1854). On the theory of compound colours. Phil. Mag., 7, 254–264.

Green, D. G. (1972). Visual acuity in the blue cone monochromat. Journal of Physiology (London), 222, 419–426.

Guth, S. L. (1967). Non-additivity and inhibition among chromatic luminances at threshold. Vision Research, 7, 319–328.

Hässler, R. (1967). Comparative anatomy of central visual systems in dayand night-active primates. In R. Hässler & H. Stephan (Eds.), Evolution of the forebrain (pp. 419–434). New York: Plenum Press.

Helmholtz, H. von (1867). Handbuch der Physiologischen Optik (1st ed.). Hamburg: Voss. English translation: J. P. C. Southall (Ed.). (1924). Handbook of physiological optics (3 vols.). Rochester, NY: Optical Society of America.

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

Hendry, S. H. C., & Yoshioka, T. (1994). A neurochemically distinct third channel in the macaque dorsal lateral geniculate nucleus. Science, 264, 575–577.

Hering, E. (1878). Zur Lehre vom Lichtsinne. Wien: Carl Gerolds Sohn. English translation: L. M. Hurvich & D. Jameson (Trans.). (1964). Outlines of a theory of the light sense. Cambridge, MA: Harvard University Press.

Hurvich, L. M., & Jameson, D. (1955). Some quantitative aspects of an opponent-colors theory. II. Brightness, saturation, and hue in normal and dichromatic vision. Journal of the Optical Society of America, 45, 602–616.

Hurvich, L. M., & Jameson, D. (1956). Some quantitative aspects of an opponent-colors theory. IV. A psychological color specifications system. Journal of the Optical Society of America, 46, 416–421.

Jacobs, G. H. (1981). Comparative color vision. New York: Academic Press.

Jameson, D., & Hurvich, L. M. (1955). Some quantitative aspects of an opponent-colors theory. 1. Chromatic responses and spectral saturation. Journal of the Optical Society of America, 45, 546–552.

Jameson, D., & Hurvich, L. M. (1956). Some quantitative aspects of an opponent-colors theory. III. Changes in brightness, saturation, and hue with chromatic adaptation. Journal of the Optical Society of America, 46, 405–415.

Kaiser, P. K. (1988). Sensation luminance: A new name to distinguish CIE luminance from luminance dependent on an individual’s spectral sensitivity. Vision Research, 28, 455–456.

Kaiser, P. K., & Boynton, R. M. (1997). Human color vision. Washington, DC: Optical Society of America.

Kay, P., Berlin, B., Ma , L., & Merrifield, W. (1997). Color naming across languages. In C. L. Hardin & L. Ma (Eds.), Color categories in thought and language (pp. 21–56). Cambridge, UK: Cambridge University Press.

Kelly, D. H. (1983). Spatio-temporal variations of chromatic and achromatic contrast thresholds. Journal of the Optical Society of America, 73, 742–750.

Kolb, H., Boycott, B. B., & Dowling, J. E. (1969). Primate retina: Light microscopy. (Appendix). A second type of midget bipolar cell in the primate retina. Philosophical Transactions of the Royal Society of London (Biology), 255, 177–184.

Kooi, F., De Valois, K. K., Switkes, E., & Grosof, D. (1992). Higher-order factors influencing the perception of sliding and coherence of a plaid. Perception, 21, 583–598.

Kouyama, N., & Marshak, D. W. (1992). Bipolar cells specific for blue cones in the macaque retina. Journal of Neuroscience, 12, 1233–1252.

Lee, B. B., Martin, P. R., & Valberg, A. (1988). The physiological basis of heterochromatic flicker photometry demonstrated in the ganglion cells of the macaque retina. Journal of Physiology (London), 404, 323–347.

Lennie, P., Haake, P.W., & Williams, D. R. (1991).The design of chromatically opponent receptive fields. In M. S. Landy & J. A. Movshon (Eds.), Computational models of visual processing (pp. 71–82). Cambridge, MA: MIT Press.

Lennie, P., Krauskopf, J., & Sclar, G. (1990). Chromatic mechanisms in striate cortex of macaque. Journal of Neuroscience, 10, 649–669.

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, 1183–1186.

Malpeli, J. G., & Schiller, P. H. (1978). Lack of blue o -center cells in the visual system of the monkey.

Brain Research, 141, 385–389.

Mariani, A. P. (1981). A di use, invaginating cone bipolar cell in primate retina. Journal of Comparative Neurology, 197, 661–671.

Mariani, A. P. (1984). Bipolar cells in monkey retina selective for the cones likely to be blue-sensitive.

Nature, 308, 184–186.

Martin, P. R., White, A. J. R., Goodchild, A. K., Wilder, H. D., & Sefton, A. E. (1997). Evidence that blue-on cells are part of the third geniculocortical pathway in primates. European Journal of Neuroscience, 9, 1536–1541.

Maxwell, J. C. (1860). On the theory of compound colours, and the relations of the colours of the spectrum. Philosophical Transactions of the Royal Society (London), 150, 57–84.

Meadows, J. C. (1974). Disturbed perception of colours associated with localized cerebral lesions. Brain, 97, 615–632.

Metha, A. B., & Mullen, K. T. (1997). Red-green and achromatic temporal filters: A ratio model predicts contrast-dependent speed perception. Journal of the Optical Society of America A, 14, 984–996.

Missotten, L. (1965). The ultrastructure of the human retina. Bruxelles: Arscia.

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

Mullen, K.T., Cropper, S. J., & Losada, M. A. (1997). Absence of linear subthreshold summation between red-green and luminance mechanisms over a wide range of spatio-temporal conditions. Vision Research, 37, 1157–1165.

Nagy, A. L., MacLeod, D. I. A., Heyneman, N. E., & Eisner, A. (1981). Four cone pigments in women heterozygous for color deficiency. Journal of the Optical Society of America, 71, 719–722.

Nathans, J., Thomas, D., & Hogness, D. S. (1986). Molecular genetics of human color vision: the genes encoding blue, green and red pigments. Science, 232, 193–202.

Neitz, J., & Jacobs, G. H. (1990). Polymorphism in normal human color vision and its mechanism. Vision Research, 30, 621–636.

Neitz, J., & Neitz, M. (1998). Molecular genetics and the biological basis of color vision. In W. G. K. Backhaus, R. Kliegl, & J. S. Werner (Eds.), Color vision (pp. 101–119). Berlin: De Gruyter.

Neitz, M., Neitz, J., & Jacobs, G. H. (1991). Spectral tuning of pigments underlying red-green color vision. Science, 252, 971–974.

Østerberg, G. (1935). Topography of the layer of rods and cones in the human retina. Acta Ophthalmologic Kbh., Suppl. 6, 1–102.

Palmer, G. (1777). Theory of colours and vision. London: S. Leacroft.

Pantle, A., & Sekuler, R. (1968). Size-detecting mechanisms in human vision. Science, 162, 1146–1148. Parsons, J. H. (1915). An introduction to the study of colour vision. Cambridge, UK: Cambridge University

Press.

Priest, I. G., & Brickwedde, F. G. (1938). The minimum perceptible colorimetyric purity as a function of dominant wavelength. Journal of the Optical Society of America, 28, 133–139.

Purdy, D. McL. (1931). Spectral hue as a function of intensity. American Journal of Psychology, 43, 541– 559.

Reid, R. C., & Shapley, R. M. (1992). Spatial structure of cone inputs to receptive fields in primate lateral geniculate nucleus. Nature, 356, 716–718.

Rodieck, R. W., Binmoeller, K. F., & Dineen, J. (1985). Parasol and midget ganglion cells of the human retina. Journal of Comparative Neurology, 233, 115–132.

Rovamo, J., Hyvaerinen, & Hari, R. (1982). Human vision without luminance-contrast system: selective recovery of the red-green colour-contrast system from acquired blindness. Documenta Ophthalmologica. Proceedings Series, 33, 457–466.

Schein, S. J., & Desimone, R. (1990). Spectral properties of V4 neurons in the macaque. Journal of Neuroscience, 10, 3369–3389.

Schein, S. J., Marrocco, R. T., & de Monasterio, F. M. (1982). Is there a high concentration of colorselective cells in area V4 of monkey visual cortex? Journal of Neurophysiology, 47, 193–213.

Schiller, P. H. (1993). The e ects of V4 and middle temporal (MT) lesions on visual performance in the rhesus monkey. Vision Neuroscience, 10, 717–746.

Schiller, P. H., & Malpeli, J. G. (1978). Functional specificity of lateral geniculate nucleus laminae of the rhesus monkey. Journal of Neurophysiology, 41, 788–797.

Schnapf, J. L., Kraft, T. W., & Baylor, D. A. (1987). Spectral sensitivity of human cone photoreceptors.

Nature, 325, 439–441.

Schnapf, J. L., Kraft, T. W., Nunn, B. J., & Baylor, D. A. (1988). Spectral sensitivity of primate photoreceptors. Visual Neuroscience, 1, 255–261.