In this lesson, let's talk about another way of thinking about this or explaining this phenomenology which is in empirical terms. And empirical terms just refers to the idea that, as I talked about before in the case of luminance, that vision is confronted with a very deep problem and that it has had to evolve a way to solve that problem and that the problem is what I described in talking about black and white vision, luminance and lightness as the inverse optics problem. But the inverse optics problem applies just as well to color as it does to black and white. The luminant that falls on object's surfaces has different spectral qualities. It can be, as I said a minute ago, it can be sunlight in the morning which is bluish sunlight, middle of the day, which is yellowish. Sunlight in the evening, which is reddish. And that's falling on object surfaces and the object surfaces, the reflective efficiency functions of object surfaces, of course, are instrumental in generating the information, the spectral information that reaches the eye, which is sitting here and the transmittance also affects the color. The spectral light that's transmitted. The short, middle, and long wavelength light that's transmitted to the eye. That's all going to be affected by the physical properties of the world. The illumination, the illuminant, the reflectors properties or object surfaces, the transmittance of the atmosphere, those are all going to affect the distribution of light intensities, spectral intensities that reach the eye. And that conflation exists just as well for these spectral stimuli that we're talking about in this topic as it did for the stimuli that we talked about in relation to luminance. You need to know what the contribution to color is to react appropriately to objects and you can't know that from the spectra or the spectral distribution energy in the stimulus. Why? Because the spectral contribution of the illumination reflectants, transmits and many other factors that lead to the spectra that make up the stimulus, they're all entangled in the stimulus and you have no way of getting back to the contributions of the physical properties of the real world that contributed to the stimulus that in some sense you need to know about to act appropriately. So again, taking the same tact that we took in trying to explain the phenomenology, the discrepancy of our perceptions of black and white from the actual luminance values that are coming to retina in an empirical way. You remember we took a database of natural scenes and we used that database to ask, What's human experience always been? And are these discrepancies in black and white, and now we're talking about discrepancies in color. Are they due to the experience that we've always had, that's been used by evolving visual systems over evolutionary time, to resolve this inverse problem to get around the fact that we don't really have the information about the physical properties of the world in retinal images? It's just, for the reasons that I've said, impossible. We don't have that information so we need somehow to get around this problem. And in color the argument is that, well, okay, we could do it in just the same way, at least in principle. That is, we take a whole variety of scenes, now spectral scenes, that are colored scenes like this and ask, what's our experience again under the assumption that over the millions of years, the few million years of human evolution the natural world hasn't really changed, what is the experience that we've always had in terms of color. And again, let me remind you that we can't take these scenes entire because we never will have seen this exact scene in the same way, more than once. I think realistically it's fair to say that we never see it more than once but at least very, very rarely. So if you want to learn something, whether it's over the course of evolution or abetted by your experience in your individual lifetime, you need to see, or you need to take advantage of, the color relationships, the spectral relationships that exist in little patches. In the same way we took little patches, more or less the size of receptive fields of retinal ganglion cells, of cells in the lateral nucleus in the primary visual cortex, we need to take little patches because those are the things we see again and again and again and can learn from. We can learn what the color relationships are by learning those relationships, again, not learning them in the sense that we have any knowledge of this as we normally think about it, but learning them in the sense of having evolution-generated characteristics in the physiology and anatomy of our visual systems based on this. That we can take these little patches, and by analyzing millions of them, see what the relationships are of the spectra that we have seen forever or at least forever, forever being the time of human evolution. And by doing that, by taking millions of these little patches and asking what are the relationships that we've seen over and over again, what are the spectral relationships that our visual systems have used to get around the inverse problem by sampling color experience? Again, in a database we can understand what our perceptual experience has been and explain, in the same way as explaining the phenomenology of black and white, we can explain the phenomenology of color contrast and color constancy in exactly the same way that I went through and described to you before for spectra just as for samples from a database of black and white images. So this is really the same general approach to solving the same problem in two different contexts. One, the context of black and white, which is just the overall intensity of the experience that we've had forever of the patches in natural scenes versus the spectral relationships in patches from natural scene, such as the ones that you see here, just as an example of applying the same approach to the same problem, in the context of color, instead of black and white.
