Abstract: 60 words

Main Text: 848 words

References: 372 words

Total Text: 1307

Trichromacy may result from an adaptation to the regularities in terrestrial illumination. However, we suggest that a complete characterization of the challenges faced by colour perception must include changes in surface surround and illuminant changes due to inter-reflections between surfaces in cluttered scenes. Furthermore, our trichromatic system may have evolved to allow the detection of brownish-reddish edibles against greenish backgrounds.

Human colour perception has evolved as a trichromatic system with specific receptoral sensitivities and post-receptoral transformations. Shepard is almost certainly right in proposing that ecological forces have played a crucial role in shaping such a system. However, he may be wrong in characterizing these forces in terms of the inherent three-dimensionality of variations in the power spectra of natural illumination. Two arguments are raised against Shepard’s view. First, a complete characterization of the challenges faced by colour perception must include not only illuminant changes, but also changes in surface surround, and illuminant changes due to inter-reflections between surfaces in cluttered environments. We claim that the sole ability to compensate for variations in illumination power spectra is probably inadequate to produce adaptive surface colours. Second, a number of recent results on the statistics of natural reflectance spectra and their relationship to human spectral sensitivities suggest that cone sensitivities optimize surface discriminations that were biologically important to our progenitors, most notably, those involving red-green discriminations. Under this view, the approximate colour constancy of the human visual system derives from the need to guarantee that such discriminations can be performed, rather than being a major evolutionary goal that required the internalization of the global statistics of reflectance and illumination variations.

The environment in which our progenitors evolved was likely to be cluttered with natural formations of various kinds and was subjected to circadian variations in the spectral composition of daylight. To detect edible materials, such as fruit or roots, our species evolved the ability to use information in colour signals (Mollon, 1989; Osorio & Vorobyev, 1996) and the spatial relationships between colour signals (Foster & Nascimento, 1994; Nascimento & Foster, 1997). This ability amounts to solving three related challenges: achieving colour descriptors for surface materials despite changes in phases of illuminaton (Type-1 constancy), achieving constant descriptors despite changes in the surrounds (Type-2 constancy), and properly treating changes in intensity and spectral composition of the illumination due to mutual inter-reflections, shadowing, and transparency effects. The first challenge could conceivably be solved by exploiting statistical constraints on the variability of the phases of daylight (Shepard, 1994). However, it is doubtful that the other two could. In fact, there is some consensus that solving the Type-2 constancy problem entails exploiting regularities in the distribution of surface reflectances, possibily using maxima in the distribution of colour signals (e.g. McCann, 1992) or their variability (Brown & MacLeod, 1997). In addition, there is a growing consensus that colour constancy will eventually require taking into account spatial structure (Schirillo, 1999). In this respect, a standing problem for the field of colour vision is to connect a number of important facts that have emerged from the study of such effects of spatial structure in the perception of achromatic colours (Agostini & Galmonte, 1999; Bruno, Bernardis, & Schirillo, 1997; Cataliotti & Gilchrist, 1995).

Since the pioneering contributions of Cohen (1964) and Maloney (1986), attempts at measuring the statistics of natural reflectance spectra have been performed in several laboratories (Parkkinen, Hallikainen & Jaaskelainen, 1989; Westland, Shaw & Owens, 2000). Two crucial questions have been raised: how many basis functions are practically necessary to fully capture the variability of natural reflectances, and how does the abstract space defined by such bases relate to the coding of colour signals by the cones and the chromatically-opponent channels in the visual system? Answers to the first question have varied from three (Cohen, 1964) to as many as twelve (Westland, Shaw & Owens, 2000), depending on the intepretation one gives to the word "practically". Early answers to the second question (e.g. Buchsbaum & Gottschalk, 1984) suggested that the first three basis functions are closely related to a luminance channel, red-green opponency, and yellow-blue opponency. Underlying this characterization of the mutuality of bases and opponent coding is the implicit assumption that chromatic coding is optimized to recover surface reflectance from the image intensity equation. However, whether this early answer is true in general is presently not clear. In a recent set of measurements (Castellarin, 2000) on a large sample of natural reflectance spectra collected in Italy and the UK, we consistently found the first basis function to be an approximately increasing monotonic function of wavelength, which closely mirrors the sample average of our measured reflectances, not luminance. On the other hand, we find the second base to be highly similar to a red-green opponent signal; whereas the third and the fourth base show a much weaker relation to luminance signals and to yellow-blue opponent code. Our findings seem consistent with the notion that chromatic coding is optimized to capture a single dimension of variation in natural spectra, the red -green dimension, rather than fully reconstructing spectra reflectances. A similar proposal has been advanced by Nagle & Osorio (1993) using different statistical techniques and a different sample. The ability to perform most accurate discriminations along the red-green dimension may reflect pressures from the terrestrial environment of our progenitors, who hunted and gathered to detect brownish-reddish edibles against greenish backgrounds.

Agostini, T. & Galmonte, A. (1999). Spatial articulation affects lightness. Perception & Psychophysics, 61, 1345-1355.

Brown, R. O., & MacLeod, D. I.A. (1997). Color appearance depends on the variance of surround colors. Current Biology, 7, 844-849.

Bruno, N., Bernardis, P., & Schirillo, J. (1997). Lightness, equivalent backgrounds, and anchoring. Perception & Psychophysics, 59, 643-654.

Buchsbaum, G. & Gottschalk, A. (1984). Chromaticity coordinates of frequency-limited functions. J. Opt. Soc. Am., A 1, 885-887.

Cataliotti, J., & Gilchrist, A. (1995). Local and global processes in surface lightness perception. Perception & Psychophysics, 57, 125-135.

Castellarin, I. (2000). Le proprietà statistiche della riflettanza in un campione di superfici naturali. Unpublished Thesis, University of Trieste, Trieste, Italy.

Cohen, J., (1964). Dependency of the spectral reflectance curves of Munsell color chips. Psychon. Sci., 1, 369-370.

Foster, D. H. & Nascimento, S. M. C. (1994). Relational colour constancy from invariant cone excitation ratios. Proc. R. Soc. London, B 257, 115-121.

Maloney, L.T. (1986). Evaluation of linear models of surface spectral reflectance with small numbers of parameters. J. Opt. Soc. Am., A 3, 1673-1683.

McCann, J. (1992). Rules for colour constancy. Ophtalmological & Physiological Optics, 12, 175-177.

Mollon, J.D. (1989). "Tho’ she kneel’d in that place where they grew …" The uses and origins of primate colour vision", Journal of Experimental Biology, 146, 21-38.

Nagle, M.G., & Osorio, D. (1993). The tuning of human photopigments may minimize red-green chromatic signals in natural conditions. Proceedings of the Royal Society of London, B, 252, 209-213.

Parkkinen, J.P.S., Hallikainen, J. & Jaaskelainen, T. (1989). Characteristic spectra of surface Munsell colors. J. Opt. Soc. Am., A 6, 318-322.

Schirillo, J. A. (1999). Surround articulation. II. Lightness judgements. Journal of the Optical Society of America A, 16, 804-811.

Osorio, D. & Vorobyev M. (1996). Colour vision as an adaptation to frugivory in primates, Proc. R. Soc. London, B 263, 593-599.

Shepard, R. (1994). Perceptual-Cognitive Universals as Reflections of the World. Psychonomic Bulletin & Review, 1, 2-28.

Westland, S., Shaw, J. & Owens, H. (2000). Colour statistics of natural and man-made surfaces. Sensor Review, 20, 50-55.