How many colors are there?
If you’re manufacturing crayons, this is an important question. A standard 8-pack of Crayola crayons, for example, comes with Red, Black, Blue, Green, Yellow, Orange, Purple, and Brown. That’s probably about as many as you would have memorized as a toddler, minus gray and white. But Crayola also offers a pack of 120 crayons, and even they wouldn’t claim that it includes every color.
Someone had to deal with this when computer screens started displaying color. It might be common knowledge at this point, but just so we’re on the same page, the pixels that make up your screen are each comprised of three smaller units of light: one red, one green, and one blue. It looks like this:
Fig. 1, Picture of pixels (photo by Umberto Gnocchi at Unsplash)
Each pixel will emit a certain amount of red, green, or blue depending on which color needs to be displayed. Combine an even amount of red and blue, for example, and you’ll get an expected shade of purple.
Fig. 2, Expected Purple
And then throw in a splash of green and you get something closer to lavender.
Fig. 3, Something Closer to Lavender
We call this system RGB (for, presumably, obvious reasons). The science behind this model is called additive color, and it’s surprisingly effective at recreating a portion of the visible spectrum. You can make up your own colors with a site like this, or just use the color finding tool in MS Paint. You’ll be hard pressed to find a color that doesn’t have a close approximation in RGB!
But “approximation” is a keyword here. There’s a couple of caveats to this model of coloration. First, RGB isn’t able to produce all shades and hues of visible light. Look at this chart you can find on Wikipedia:
Fig.4, Not Enough Color (chart by Dick Lyon on Wikipedia)
The gray space in that oblong shape represents (basically) the entire visible spectrum. The little rainbow triangle includes all the colors that RGB screens can display. Notice the disparity of real estate. In plain terms, you can combine these three colors in a lot of ways, but you’ll still come up short of the whole visible spectrum. If you’d like to know what colors are in the gray space outside that triangle, you’ll have to look somewhere else! Your computer screen is literally incapable of displaying it. So RGB fails our “all the colors” test on this simple principle.
RGB also suffers another, more subtle limitation. I mentioned that the three base colors (red, green, and blue) are mixed in “certain amounts.” The amount of any one base color—called its intensity—is denoted by integers between the minimum 0 (totally off) and the maximum 255 (full blast). That means you can combine 256 possible intensities of red with 256 possible intensities of green and 256 possible intensities of blue. To save you the calculation, that means the RGB system allows for 16,777,216 distinct colors. That’s a pretty big number! A box of crayons with all those colors would weigh over 165 tons, and I’ll bet you could grab handfuls of minutely different shades without being able to distinguish between them.
But . . . that’s still not all the colors, right? Even barring the colors that RGB can’t express, the issue of increments arises. RGB intensity is limited to integers—no fractions, no decimals. Say you’ve got a shade of lime green with a red value of 93. If you bump that red value up to 94, you’ll get another very similar shade of lime green. But what if you used 93.5? Or 93.25? It would be hard to see the difference between them, but those are strictly different colors! And because RGB is expressed in integers those colors don’t exist in that system. Instead, RGB approximates colors by rounding to the nearest integer.
And that’s not how visible light works! Real color isn’t divided up into tiny, discrete units. The light we see is continuous, almost organic compared to the digital rendering of RGB computer screens. If you zoom into the RGB spectrum (like you can at a site like this), you’ll find millions of tiny squares. But you won’t find that kind of composition if you magnify the spectrum of real visible light. The problem is that real light is not a system of numbers. You can use numbers to describe real light, but because light is not comprised of numbered units, you will invariably settle with mere approximations in your description. In other words, a system like RGB will always have gaps. You can use it to generate a lot of colors, but you won’t be able to make them all.
At this point, it must look like I have a political axe to grind about RGB, as if we are victim to some flagrant color fraud. But the point I actually want to make is quite removed from issues of color. It has to do with our perception of color, yes, but more broadly it has to do with our perception of everything. And not just how we perceive everything but how we think about everything and communicate what we’re thinking. Just like color systems like RGB are limited to units to display light, our perception and thinking and expression only occurs in units.
I don’t mean to say we’re digital beings. We don’t see in pixels, and we don’t think in megabytes. But what we do see and think will always come in finite chunks. For example, you’re almost certainly reading this sentence one word at a time. That’s really the only way to do it. Our eyes can only focus on a limited portion of space, especially when it comes to identifying intricate objects like text on a screen. As you read, you gradually absorb sentences one word—one unit—at a time. And those sentences, of course, are smaller units of whole paragraphs, and those paragraphs are smaller units of whole works of writing.
Pointing out that sentences are units of speech may sound more like a definition than a groundbreaking observation. What’s important here is the very fact that speech is divided into units. You can never say anything all at once and expect to be understood. If you’ve got something to say, you have to break it down into words and sentences. And more than just breaking it down, you’re really translating it from thoughts—you’re internal, personal understanding—into units of language. I call it a translation because thought and knowledge and understanding aren’t made up of words. It’s hard to say what thought or knowledge is “made of,” but it isn’t language. You can tell because you don’t have to actively talk yourself through most of the things you do every day. Think about your own regular morning routine. Do you have to narrate it to yourself as you go about your business? Of course not! Somehow, you just know what to do even if you don’t carry on an internal monologue for instruction. Now try to explain that same routine to a friend. Suddenly, words are a necessity! Something simple like the idea of making breakfast now requires a step-by-step exposition, full of easily-forgotten words you never need to think to yourself, like “waffle iron” or “dish rack,” and encumbered by tedious grammatical terms, like “and then” or “so that.” Splitting your contiguous thoughts into fragmented units of language is no trivial task!
But more than being cumbersome, breaking thoughts into units of language always entails a net loss. Think back to the RGB system and its gamut of units. In between the 16,777,216 possible colors, there’s an infinite array of visible frequencies of light that cannot be expressed, all because visible light isn’t actually divided up into 16,777,216 units (or any number of units, for that matter). That same principle applies to units like words and sentences. Words and sentences divide thoughts up about as well as RGB divides visible light up. They’re all surprisingly effective, but they’re never comprehensive.
A classic example of this limitation to language is the challenge of writing out instructions to make a peanut butter and jelly sandwich. You tell a kid to compose a step-by-step guide to making a sandwich, and then you follow that guide, taking every opportunity to misinterpret what is written. This forces the kid to rewrite the guide in increasingly precise detail, most of which is completely needless for real-life, practical purposes. It’s a hilarious exercise, and it demonstrates how language can always be a little more precise. There are always informational gaps in between sentences that our conversation partner or reader has to fill in.
In short, simple terms, things that are translated and broken into units of a different kind are never comprehensively expressed. Colors can be represented by numbers, but, since real color is not a system of numbers, that representation cannot be 100% accurate. Thoughts can be represented by words, but, since thoughts are not comprised of words, there will always be a loss of information along the way. It’s not that these modes of expression are ineffective; we simply need to be aware of the gaps they invariably entail.
But the more profound challenge is this: what are the other ways our conscious lives are affected by translating-fragmenting processes like these? I can only focus on one fraction of my field of vision. What does that say about my ability to perceive the world around me? Where is the loss? Even my own internal attention is limited this way. I might say that I have a good understanding of how to drive my car, but I can only actively think about one or two aspects of that at a time. How does that affect my ability to make quick decisions on the road? And how does that affect the way I would teach someone to drive? That kind of task requires me to break my knowledge and muscle memory into thoughts and then break those thoughts into words! Somehow, our minds are able to accommodate most of these informational gaps. But for the times when communication breaks down entirely, it’s good to look closely at the way we’ve broken things up.
Thumbnail picture by Anthony Poynton at Pexels.