\(CM_R\), Ridgway

Some of the most ambitious attempts at mapping colors to their names, or naming colors, came from the natural sciences. American ornithologist Robert Ridgway (1850-1929), for example, authored two influential works of color naming systems: A Nomenclature of Colors for Naturalists in 1886, and Color Standards and Color Nomenclature in 1912 (Ridgway, A Nomenclature of Colors for Naturalists; Ridgway, Color Standards and Color Nomenclature). In the preface of the earlier work, Ridgway names as his problem that “the author has in collection considerably over three hundred water-colors, each bearing a different name” (Ridgway, A Nomenclature of Colors for Naturalists X). This volume contains over a thousand colors, although the color names are less metaphorical (lemon-yellow), and more descriptive (bright yellow).

To extract the colors from this book, I create a method whereby I extract images from the digitized pages of the book, computationally crop color swatches from the pages, and compute the dominant color of each swatch.The code for extracting material from \(CM_R\) may be found here in this project’s repository.

Fig. 12 shows a page from the latter book, from which I extracted this data.

As seen here, Ridgway’s scientific approach to color description, which prefers regular pale, buff, deep, and dusky variations of colors, sacrifices the specificity we find in other color manuals for a system which aligns colors into a matrix. Often a row in one of Ridgway’s plates will contain colors that all have the light modifier, or the dusky modifier. Ridgway himself apologizes for this:

The selection of appropriate names for the colors depicted on the Plates has been in some cases a matter of considerable difficulty. With regard to certain ones it may appear that the names adopted are not entirely satisfactory; but, to forestall such criticism, it may be explained that the purpose of these Plates is not to show the color of the particular objects or substances which the names suggest, but to provide appropriate, or at least approximately appropriate, names for the colors which it has seemed desirable to represent. (Ridgway, Color Standards and Color Nomenclature 10)

For this reason, I assign a lower weight to these colors, but include them to increase the breadth of the master color word mapping.

\(CM_S\), Saccardo

A continental work of the same decade is the ambitious and polyglot volume from Italian botanist Pier Andrea Saccardo bearing the formidable Latin title, Chromotaxia Seu Nomenclator Colorum, Polyglottus, Additis Specimibus Coloratis ad Usam Botanicorum et Zoologorum (1894) (Saccardo). Although containing only fifty colors, it features an index of several hundred “synonyms” for these colors in Latin, Italian, French, English, and German. While some of these are recognizable to modern Anglophone readers, others seem strangely specific, such as Murinus (mousey) or Fuligineus (“sooty”). Saccardo provides two supplementary colors: achrous, or colorless, glassy; and sordidus, or “sordid,” “dirty,” which he describes as a modifier rather than a color. “non est color definitus sed indicat inquinamentum aliorum colorum. Exempla: sordide albus, luride ruber” (Saccardo 16).

Saccardo’s decision to write in Latin seems to affect his color nomenclature: since abstractions are less stable, and less translatable across languages (see the discussion below on color terms in the linguistic relativity debate), his taxonomy prefers lemmas with metaphorical origins: fumosus or smoky, rather than, griseus, or gray; sanguineus or blood-colored alongside ruber or red.

As with Ridgway, I parse the color swatches included in digitized editions of this book, normalize the colors, and extract the dominant color of each swatch. Then, using his thesaurus, I associate each color with its English synonyms. This contributes a number of new colors: eye-blue and eye-grey (from cæsius); egg-yellow (from luteus), and livid from lividus. I assign these colors the same weights as those in Ridgway.

\(CM_M\), Maerz and Paul

Maerz and Paul’s 1930 A Dictionary of Color provides the largest number of colors, and was itself meticulously compiled from a number of prior manuals. This volume claims to contain “the most extensive range of colors as yet published, together with a list of practically all recorded color names in use up to this time in the English language” (Maerz and Paul v). The dictionary contains over three thousand colors.

Maerz and Paul recognize that there is a wide gulf between those of their source materials produced for naturalists, like those of Saccardo and Ridgway, and those produced for marketing purposes, that is, to sell pigments or the commercial products they color. They lament that “our present language of color is, to a certain extent, composed of words that are somewhat meaningless in themselves as color terms. This refers to words such as ‘folly,’ ‘Westminster,’ and ‘elephant’s breath’; they are lacking entirely in descriptive color value, yet their continued use earns them a place among the accepted color names that custom has decreed should compose our vocabulary” (Maerz and Paul v).

I am inclined to disagree somewhat: if I were to guess, I’d imagine “elephant’s breath,” despite breath itself being achromatic, to be close to the gray of elephants, and “Westminster” to be a grayish, brickish color, like that used in the eponymous Abbey of London. Folly is harder to imagine, but it likely wouldn’t be a subdued beige.

I assign an identical weight to \(CM_M\) as I do the manuals of Saccardo and Ridgway.

\(CM_P\) Pantone

The Pantone set, one of the most common among designers and artists today, contains over two thousand colors, although the colors have names which are much more commercially-based than others. Thus, these are biased towards food-related words, flower-related words, or anything else that would seem like a pleasant marketing term. Bridal blush (#eee2dd) and bridal rose (#d69fa2) seem marketed towards wedding decorations; and margarita (#b5c38e), martini olive (#716a4d) towards the cocktail set. The list is also remarkably upper-class in flavor. Ski patrol (#bb1237) and billowing sail (#d8e7e7) represent leisure-class sports, while spa blue (#d3dedf), stretch limo (#2b2c30) (why not just limo?), and caviar recall other activities associated with the wealthy. The logic must be: who wouldn’t pay a little extra for an article of clothing with a color like caviar, instead of just dark gray?

To find these trends more systematically, I vectorized their color names with GloVe vectors, and then clustered them using the K-means clustering technique, using six clusters.(Glove vectors are described in Pennington et al.; ; see Jain for a historical overview and summary of K-means.)

The categories here are very human-legible. Categories 0-3 are natural metaphors of various sorts (flora, fauna, landscape features). Category 4 is mostly varieties of white, as well as other light colors. Category 5 is colors derived from food.

Category 0: gardenia #f1e8df, dew #eeded1, ecru #f3dfca, delicacy #f5e3e2, icicle #dadcd0, twill #a79b82,

Category 1: white-asparagus #e1dbc8, vanilla-ice #f0eada, cloud-cream #e6ddc5, vanilla-custard #f3e0be,

Category 2: turtledove #ded7c8, papyrus #f5edd6, seedpearl #e6dac4, sandshell #d8ccbb, navajo #efdcc3,

Category 3: egret #f3ece0, pristine #f2e8da, birch #ddd5c7, angora #dfd1bb, fog #d0c5b1, ice #e0e4d9, frost

Category 4: snow-white #f2f0eb, bright-white #f4f5f0, cloud-dancer #f0eee9, blanc-de-blanc #e7e9e7,

Category 5: marshmallow #f0eee4, vanilla #f4e1c1, rutabaga #ecddbe, tapioca #dccdbc, parchment #dfd1be,

I derive this color map from a web-scraped dataset, itself derived from Pantone’s own website, but assign half the weight of the previous manuals (Pantone). This dataset nicely balances the scientific modes of the previous ones, yet is still highly subjective. Still, I include it in order to train the imagination algorithm to imagine more concrete color metaphor. Now, it may be able to imagine moonbeam (#696156) and skyway (#adbed3).

\(CM_X\), XKCD

The antidote to the Pantone set is one from Russell Monroe, an American author, former NASA engineer, and cartoonist best known for his webcomic XKCD. Monroe surveyed his wide readership, asking them to name colors they were shown at random on his website. He also took demographic data from them, logged their locations via their computers’ addresses, and asked them whether they were colorblind, or used a CRT (cathode ray tube) monitor. The survey results, which represent five million color mappings from 220,500 users, show a consensus for many color names. A sample of these is shown in table 1.

This mapping presents a useful counterpoint to commercial mappings such as that of Pantone, or to more systematic mappings like Ridgway’s. In the sample presented in fig. ¿fig:xkcdBlocks?, we see a mix of naming metaphors. The usual food metaphors (melon, mocha, coffee) appear next to animal metaphors (camel, canary yellow) and creative compounds indicating a small amount of one color mixed into another (purplish, bluey, purply, preyish). The informality of the “-ish” suffix suggests extemporaneous description, as if colors are mixing in the imaginations of these survey respondants, in the absence of a ready-made metaphor. For comparison, greyish pink in this color map is blush in Pantone, and darkish green translates to online lime. And of course, one would expect that sickly green would not be an easily marketable name for a commodity, especially if it were food, so in Pantone the color is lime green.If an exact match for a hex value does not exist in a color map, I find the closest color to it using \(\Delta E^{*}_{ab}-76\). This is described in more detail in the section on categorization below.

There are some issues with this mapping, however. One is that, since this is crowdsourced data, it relies on the crowd’s spelling abilities, and virtually no one, according to Monroe, spelled fuchsia correctly (“Color Survey Results”).

Summary and Comparison

I assign weights to colors in each of these mappings as follows:

To compare the tendencies, or biases, of these color maps, and to better know how to balance them, I calculate the average of their 300-dimensional GloVe vectors (Stanford’s Global vectors for word representation, trained on English-language websites), and derived the cosine similarity to the vectors of a number of seed words, shown in [eq. 1]:

\[similarity(\vec{A}, \vec{B}) = \frac{ \vec{A} \cdot \vec{B} }{\|\vec{A}\| \times \| \vec{B} \|}\qquad{(1)}\]

Or, the dot product of the two vectors, normalized by the product of their two \(L_2\) (Euclidean) norms.

Fig. 14 shows a series of word vectors chosen to illustrate the vector similarity with the average vectors of each color map. \(CM_P\), the Pantone map, has a higher similarity to positive, marketable-sounding words, and words evoking leisure, whereas \(CM_X\) has a higher similarity for snot, a decidedly unmarketable word, discussed later, and Jaffer’s aggregation of \(CM_S\), \(CM_R\), and \(CM_M\) shows a slightly higher similarity for blood, also not a decidedly marketable term.

Given these tendencies, I weight these color maps as described in table 2, and combine them into one large master color mapping, which will become the basis of the imagination model below.

The resulting color map, \(CM_A\) (A, for all), is the combination of the above maps. This color map is then fed into the deep imaginer, as described below.

Collocation-based Inference, \(M_P\)

One of the simplest methods of color inference is to calculate the syntactic distance from a known color word to a target word. Given a large enough corpus, it is quite likely that, for example, green will appear within several words of grass, and so by measuring the distances from green to grass, and noticing that these distances are much shorter than for the pair red and grass, we might infer that grass is green: that is, the literary imagination of grass has the color category green.

Another example might be inferring the color of a gull. Anyone who has visited the north Atlantic shore knows that gulls tend to be white and gray. Of the 170 times that the word gull appears in \(C_{PG}\), we see white appear within about ten words of it nineteen times. The lemma grey appears six times. Red, however, appears not once. Of course, both green and yellow appear twice, although not with the same relations in the dependency graph. Given this collocation data, we can write a model that guesses that gull is mostly white, a little gray, and with hints of green and yellow.

However, syntactic proximity is preferable to raw proximity itself, and so I developed an algorithm to score relations between two neighboring words, which uses both linear word distances and syntactic distances. I calculate syntactic distances by traversing the dependency trees of their containing sentences. By way of illustration, take these lines from Arthur Conan Doyle’s 1908 novel Sir Nigel:

Next morning they found themselves in a dangerous rock studded sea with a small island upon their starboard quarter. It was girdled with high granite cliffs of a reddish hue, and slopes of bright green grassland lay above them. (Doyle 250)

The syntax dependency graph of the clause, “slopes of bright green grassland lay above them” is parsed as shown in fig. 15:

Here, bright and green are syntactic descendants of grassland. This might even more accurately be parsed with bright and green together as one semantic unit.

This model infers color associations \(W_C\) from target words, \(W_T\), by traversing the syntax tree, and calculating weights accordingly.

However, modifiers are not always direct descendants of their modified words, since they might cross sentences. (Imagine a passage that were to read: “The slopes of grassland. How bright green they were!”.) So to account for these types, I also compute weights based on the raw distances of these words from each other. The full algorithm is this:

1. It begins by identifying a color word, \(W_C\) from color map \(CM_X\) in the target text.
2. It then parses the containing sentence, and determines its syntactic dependencies.
3. Starting from \(W_C\), it navigates through parent words and parent noun chunks \(W_T\) to the root of the sentence.
4. If \(W_T\) is a noun or adjective, it is assigned a score: 2 if it is a direct parent of \(W_C\), or 1 if it is a grandparent of \(W_C\), at two steps’ removal in the syntactic tree.
5. All other words nouns and adjectives are now candidate \(W_T\), and are assigned a score: \(1/i\) where \(i\) is the distance, in number of tokens, from \(W_C\). Thus, it gets a score of 1 if it is a directly adjacent word, or 0.5 if it is two tokens away.
6. These scores are then averaged for each token that shares the same lemma.

The resulting data structure looks like fig. 16, for grass:

There are plenty of colors, like blue, which are uncommon for grass (except for the Appalachian style of music). Yet these other colors represent only a small proportion of that for green. I blend these colors together, by finding their hex values in \(CM_X\), and then averaging their RGB values, after weighting them, so given RGB values \(R_i ... R_j\), \(G_i\), and \(B_i\), and weights \(W_i\), averaging them proportionally:

\[blended RGB = \frac{ W \sum_{R_i}^{R_j} }{ \sum_{i}^{j} W } , \frac{ W \sum_{G_i}^{G_j} }{ \sum_{i}^{j} W} , \frac{ W \sum_{B_i}^{B_j} }{\sum_{i}^{j} W}\qquad{(2)}\]

Or, for all colors, weighting and summing each component of RGB space, and then dividing by the total sum of all weights. For grass above, we get #6b9c56, a pleasantly grassy color.

This model works reasonably well—that is, meets our modern English-speaking expectations of the archetypal colors of many nouns. But there are quite a few notable differences. One is the principle of color description exceptionalism I outline above: color descriptions are anomalies. Although sheep are typically white, and black sheep comparatively rare, the probability of encountering the phrase black sheep in British fiction is about five times greater than that of encountering white sheep, according to bigram data from \(C_{NG}\).

Fig. 17 shows a portion of the model’s inferences for sheep.

The blended color from \(CM_X\) is #5B5441, a dark greenish, and hardly the color one would expect of a sheep. Part of this is because, as you can see from the inferences above, \(CM_X\) contains many metaphoric colors like grass, heather, and stone. Although these are almost certainly not used as color words in their original contexts, this model counts them as colors. This is perhaps not a bug, however, but a feature of the program: by picking up on elements like stone and grass, we might fail to imagine the sheep itself, but we succeed in imagining the hue of its context, and in a sense, this is more information than we would get from simply imagining a sheep. And after all, when we ourselves imagine a sheep, we might very likely imagine it in its pastoral context, among grass and stones, rather than floating in a colorless void, away from everything else.

But the converse of that phenomenon is also present. We might not expect to see white and gray occur so often close to gull, because these are obvious descriptors, yet we do. When a writer wants to underscore the visual properties of an object that already has well-known color properties, this contributes to the impressionism of the piece: it allows the reader to see through the writer’s eyes.

This phenomenon is brilliantly at play in another Katherine Mansfield story, “Bliss,” written two years earlier in 1918. It is even more colorful than “The Garden Party,” and its colors much more variegated. This reflects the mood of its protagonist, Bertha, who is so happy that she is almost manic. Her attentions are everywhere, and glittering, reflecting off of everything.

There is a very particular quality of light in this story: it is not a categorical color, but a phenomenological one, describing not what Bertha knows the color to be, but how it seems. Bertha sees and feels bright sparks. The narrator tells us that “in her bosom there was still that bright glowing place—that shower of little sparks coming from it” (Mansfield 145). This moment is mirrored in a description of a fruit bowl which Mary at that moment brings in, and she perceives as “a glass bowl, and a blue dish, very lovely, with a strange sheen on it as though it had been dipped in milk.” Here, Mansfield contrasts the color category of the dish—it is blue—with its perceptual reality: it appears white.

Bertha sees the fruit in this bowl also with an acute sense of newness: not quite estrangement, in Shklovsky’s formulation, but a newly intimate familiarization. “There were tangerines and apples stained with strawberry pink. Some yellow pears, smooth as silk; some white grapes covered with a silver bloom and a bug cluster of purple ones. These last she had bought to tone in with the new dining-room carpet” (146).

“Apples stained with strawberry pink,” is an uncommon metaphor for apples, which \(M_P\) models as in fig. 18, and for which the blended color is #AC883B.

In other words, apples are usually golden, green, red, gold, or yellow, but rarely strawberry pink. (There is an apple cultivar called the “pink lady,” but it wasn’t cultivated until 1976.) As with milk, this is a food metaphor, one which uses these colors, as the foods themselves seem to do, as a marker for something delicious or desirable.

Grapes, too, typically belong to the categories red or white, like their wine. Yet white grapes, and white wine, are not white at all, but a pale green.Anders Steinvall points this out, noting that the white of white wine is an instance of type modification (Steinvall, “Basic Colour Terms and Type Modification” 57)

Red grapes and red wine are not red, either, but are usually a deep purple. This illustrates the lemon-yellow position I have outlined earlier: color designations are more a matter of convention than faithful representation of the visual world. Mansfield would have been aware of this quality of grapes, and has Bertha call them “purple” to show that she is attune to the phenomenon of their hue. Mansfield further highlights this by showing the chromatic harmony Bertha expected, and sees, between the grapes and the carpet.

So by modeling imagination in this way, we’re able to take advantage of the ways writers show us the perceptual experience of words, rather than just their semantic categories. But this model is only one component of a larger system.

Dictionary-based Inference, \(M_D\)

To short-circuit the problem of color exceptionalism, I mine color inference data from more straightforward definitions, like those found in dictionaries and encyclopedias. There, sheep are more likely to be described as white. Project Gutenberg provides a copy of Chambers’s Twentieth Century Dictionary of the English Language, published in London in 1908. If I did not already have color mappings for grass (\(CM_X\): grassy green, #419c03; grass, #5cac2d; grass green, #3f9b0b), we would find this entry for grassy: “covered with or resembling grass, green.” Since words and their definitions are regularly formatted in this dictionary, it becomes possible to parse the dictionary entry into word/definition pair, and load them into a lookup table. From there, I construct an graph, where nodes are dictionary words and their colors, and edges accrue weight each time a color word from the color maps appears in the definition of one of the defined words. Thus, if we see grassy appear on the left of the page, and green in its definition, we give it a score of one. If green were to appear twice in its definition, we’d give it a score of two, and so forth. I repeat this process with an encyclopedia: The Nuttall Encyclopædia, first published in London in 1900, and still in print by 1966. Other dictionary and encyclopedia-like works are available in the usual plain-text sources, but few meet the criteria of (a) British, (b) out-of-copyright, (c) in plain-text, and (d) in an easily parsable format.

This model does not seem to perform as well as \(M_P\), however. Here is an entry for grass, for example.

The model triangulates between the color word and entry/definition pairs, but only finds a few single instances each. Possibly as a happy coincidence, however, the resulting aggregate here is #89bc6e, a very grassy color.

When merging these models, I weight \(M_D\) the lowest, since with a lack of data (dictionaries), and an inconsistent level of descriptive detail, it’s less reliable.

Image-based: \(M_I\)

A third solution mitigates some of the problems described above, by escaping comparison between colors and objects, and looking to images as a source of color information. Given a database of images that are correctly labeled, it should be possible to extract color values from those images, and build a pipeline that infers a color given a word. Luckily, two somewhat newly-created web services provide such a database: Unsplash and Pexels are stores of open-licensed stock photos and illustrations, which provide open APIs (Application Programming Interfaces) which allow users to retrieve images based on a given keyword. This follows methods used by other projects that attempt to map words and colors (Guilbeault et al.).

However, an image of a given object, such as a sheep, rarely contains only sheep. There is almost always a background to the image which is of a different color. I try to control for this difficulty by disregarding the most frequent color of these images, and only working with the second and lesser frequent colors. From there, I average the resulting colors of all the images, to retrieve an imagined (inferred) hue for the lemma.

The resulting algorithm may be summarized as follows:

1. Scan through the text, looking for nouns, adjectives, or similar words (words with potential visual content).
2. For each matching word, find its lemma.
3. Query the Pexels / Unsplash APIs, giving the lemma as a search term, and ask for ten image addresses. Download them.
4. For each downloaded image, find the second to Nth most frequent color.
5. Average all these colors proportionally, using the algorithm described above.
6. Return a pairing of a word with a corresponding RGB hex value.

Fig. 19 is a sample of the model’s inferences for three fairly concrete words, wheat, butterfly, and tennis, and with three more abstract words, deposit, pure, and travelling. I’ve included a few images used in the creation of each color, to allow for some model introspection.

Wheat the model predicts reasonably well: the resulting color is a golden brown. Butterfly is more uncertain, in part because there are a very large number of butterfly species, and so there is an incredible diversity of color patterns between them. Tennis one might have expected to be more greenish, since most tennis courts are green. The averaged color here reflects the inclusion of the image where the court is red, and the image of the racket alone. Deposit is curious: probably because it would be a rare word to use to tag a photo, the first four images are likely from the same photographer and shoot. The last is an image of bars of gold, however. This is interesting, since it is not a stack of paper money, but of an archetypal idea of money—the gold standard—which is no longer in widespread use. The inferred color for pure, interestingly, is anything but—it is a dirty grayish. Even though most of these images are water-related, one is of honey. This seems to show one of the pitfalls of computationally imagining an abstract concept. Travelling, on the other hand, is a very distinct blue, owing to the color of the beach scenes that have become emblematic of travel today. Coincidentally, however, blue seas would have been a common feature of international travel in the early twentieth century.

A broader view of this model, shown in fig. 20, gives one a sense of a few trends. First, almost all of these colors are very desaturated: they appear as if they began as pure pigments, and were mixed with a titanium white. This is owing to the way each of these is by nature an average of several colors: they are mixed. At a glance, the legal-sounding words power and possession appear darkest. The two words dealing with negative emotion, rudeness and objection, appear redder than the others. And words that evoke comfort, reliance, liberty, and amusement, appear bluish. Neighbour and neighbourhood, meanwhile, appear green, probably owing to stock images that involve green lawns.

When projected in HSL space, certain patterns emerge, as well, as shown in fig. 21.

For the problems cited above, I weigh this model higher than \(M_D\) but still lower than \(M_P\). In the absence of an evaluation metric—and I doubt one is possible—I feel like our intuition as readers, and imaginers, is enough to evaluate and weigh these methods of imagining.

Comparison

I combine these three models according the weights given in table 3, and produce a master mapping I’m calling a deep imaginer. This I then integrate with the shallow color maps described above, cascading all the models together. This model I then use to train a named entity recognizer.

The Rose Problem

Early iterations of this engine found a staggering incidence of the color word rose in the literature of this period. It didn’t take much introspection to determine that this was not exclusively the color rose, but either a woman’s given name, Rose (my parser is case-insensitive), an equivalent surname, or the past tense of the verb rise. One could remove all tokens where the first letter is capitalized, but then that would also eliminate the color rose which appears at the beginning of a sentence. One could run a part-of-speech tagger over the text, and throw out all the verbs, but this only solves half the problem.

A method exists which, if properly extended, mitigates this problem. Named Entity Recognition, or NER, is a sub-field of natural language processing which computes the probability that a string of words is a named entity. Usually, these are people, places, organizations, languages, ethnicities, and so on, and detecting them has been useful in commercial applications of text analysis, where a corporation might be interested in finding all instances of Apple, the computer company, but ignore all instances of apple, the fruit.

NER has been practiced, in one form or another, since at least Lisa Rau’s 1991 paper, Extracting Company Names from Text (Rau). But whereas early techniques like Rau’s were heuristic, modern methods use computational neural networks to achieve this end. Among the most accurate NER engines now is Explosion AI’s SpaCy library, which uses residual convolutional neural networks along with specialized word embeddings to achieve reasonably accurate predictions of entities like personal names, organization names, as well as disambiguate numeric tokens into quantities, cardinal numbers, and so on (Honnibal).

NER becomes useful to my study in two ways: first, it allows me to discard those entities like “Rose,” (a given name), as well as “Mrs. Brown,” and “Mr. Green.” (Although there is a case to be made that the writer’s choices of these names, where they are fictional, are as deliberate as choice as a visual descriptor.) Second, since this general-purpose NER doesn’t detect color expressions, descriptions, or any literary features, I train it to do so.

I train two NER models, which I’ll use to detect text with visual properties: a shallow model, \(NER_S\), and a deep model \(NER_D\). The seed data set for the more restricted model, \(NER_S\), is trained on raw color entities from \(CM_X\), while that for \(NER_D\) is trained on the set of more general visual expressions spans generated from the deep imaginer.

The Beret Test

These are not the only training data sets I feed to the model, however. I use SpaCy’s Prodigy tool to dynamically correct the model’s most uncertain guesses, across a corpus of edge-cases. In training \(NER_S\), I aim for a more restricted definition of a color: one which may be parsed as a color without much context. For example, in the famous hook from the Prince song, “Raspberry Beret,” we understand that in the words “she wore a raspberry beret,” “raspberry” is the color of her beret, which is made of cloth, and not of one or more raspberries. But if we replace raspberry with a more uncommon color, say, electric, or cyberspace (color names from \(CM_P\)), it is not still clear that the word is a color description, and does not describe some other attribute.

The process of training this model was instructive. \(NER_S\) found instances of the word salmon often, and since the word appears in nearly every color map (e.g., \(CM_R\): #D9A6A9; \(CM_X\): #FF796C), it identifies any use of salmon, regardless of whether it refers to a color. It is important to note here that salmon, the color, refers to the color of salmon meat, rather than the color of the fish itself. In other words, the color refers to the inside of the fish, rather than the outside. Disambiguating between these two senses of the word is a non-trivial task for this model. Nonetheless, given a large number of training examples, the model is able to perform better than chance.

What we are left with, after training, are probabilistic models capable of identifying explicit color expressions, in the case of \(NER_S\), and anything that may be imagined, in the case of \(NER_D\). So, to employ Mansfield again, \(NER_S\) finds instances of green baize, and assigns it an RGB value, whereas \(NER_D\) finds instances of tennis lawn, and also assigns it a green value (following color inference, described below).

But first, we would need a way of categorizing the color values generated by these models. Much in the same way that a text analysis project needs a lemmatization system to group together words like sky and skies, go and went, we need a way to bring together variations, shades, and other common properties of colors.

Color Spaces and Color Difference

We now move from discrete description of color to continuous—i.e., from that is blue to that is 80% blue. Now that we’ve mapped color words and other color-containing text to the model’s guesses of their mappings, we need a way to organize them, in relation to one another, and in relation to our perception of them. This is problematic, since we are dealing with two ontological domains: a linguistic domain, and an analogous psycho-physical. Furthermore, there are a multitude of ways to quantify color properties, and to organize colors by those properties.

Relations between colors—color difference—is a long-standing problem. Colors are typically categorized in relation to one another by embedding them in a color space: a vector space in which each color is a point in its coordinate system. These spaces are very precisely described in technical literature (Fairchild, for instance). The biggest problem they attempt to solve is that hues themselves, due to the anatomy of the eye, do not have linear relationships—this is why three-dimensional projections of these color spaces are often conical, or even asymmetrical. Furthermore, each color space must account for ocular physiology across individuals; differences in ambient reflectors, illuminants, and other lighting conditions; and differences in reference points (white values used as anchors for other color properties). We will not need all of these details, but a summary of these color spaces is necessary, since I will be using many of them below.

It is useful to pause for a moment, however, and consider that color spaces are themselves descriptions, in the literary sense, of color—they can approximate their object, but only asymptotically. And yet so much of our modern world is made up of these approximations. Every screen we use—including the one I’m using to type this now, and the one you’re using to read it—is composed of millions of tiny picture elements—pixels—each with three lights: a red, a green, and a blue. The more specific colors, and the images, that we see on these screens are only mixtures of those elements.

The most common color spaces in use today include RGB, which stands for red, green, and blue; CMYK, or cyan, magenta, yellow, and white; HSL, or hue, saturation, and luminosity; and CIELAB, the newest and most accurate of these spaces. RGB is most common among light-producing devices like computer monitors, and generates colors additively, by mixing red, green, and blue light. These values are often expressed in hexidecimal, with the marker #, such that #ff0000, red, indicates the highest value for red (ff), along with the lowest value for green (00), and the lowest value for blue (00). CMYK is the most common for print media, on the other hand, since it describes colors subtractively, combining cyan, magenta, and yellow. HSL is a useful derivative of RGB, meanwhile, which allows for numeric manipulation of colors according to these values of hue, saturation, and luminosity.

The current standard colorspace, CIE \(L^* a^* b^*\), usually abbreviated CIELAB, is a product of a century’s long effort by the Commission Internationale de l’Eclairage [International Commission on Illumination], or CIE, an organization formed in 1913 to solve problems of chromaticity standardization, among others. A 1973 meeting of the CIE Colorimetry Committee, having evaluated a number of previously used color difference formulae, produced the first iteration of the LAB colorspace, intended to model human color perception. Here, \(L^*\) represents luminosity, \(a^*\) represents a spectrum of hues between green and magenta, and \(b^*\) represents hues between blue and yellow (l’EClairage).

Relations between colors may then be calculated with respect to this coordinate system. The Euclidean distance between two colors in a LAB vector is therefore the square root of the differences of each of its components. The CIE calls this formula \(\Delta E\) (Robertson 167).

\[\Delta E = \sqrt{(\Delta L^*)^2 + (\Delta a^*)^2 + (\Delta b^*)^2}\qquad{(3)}\]

Since CIELAB space best represents human perception of color, I’ll use it wherever possible, and calculate color distances using \(\Delta E\).I implement this function here, in the color categorization module of my color analyzer.

However, I have to translate frequently between LAB space and RGB space, since most of the color maps I’ve derived, are either scanned using digital photography, or, in the case of the XKCD map, produced using computer monitors.

Debates in Color Categories and Nomenclature

If we aim to quantify the occurrences of certain color concepts, and not just the color words, then there must be a way to categorize visual experiences. For instance, if we encounter the expression light blue, we must be able to categorize this as a variety of blue, or else we will need to process and compare thousands of variables, instead of just a few. Yet the epistemological problems of the color/word interchange make this a difficult task. To begin with, since we are dealing with spectra, the boundaries of these categories are not well-defined. But the very existence of the categories themselves should not be assumed, either. While, to a painter or interior designer, the differences between ecru and eggshell may be crucial, these words may not be in the working vocabularies of some novelists. I say “working” here because they might be recognizable, and even familiar, to a writer, but they might not be the operative metaphors he or she chooses when describing a scene, or allowing a literary persona to describe it. So the color spectrum of a writer’s idiolect is always a subset of his or her dialect.

For instance, we might consider light blue to be a subcategory of blue, since the word blue is contained within it. However, is pink necessarily its own category, or is it simply a shade of red? And if so, is light pink a subcategory of pink or of red? We might categorize these colors differently if we were to use the hues rather than their written expressions.

We might look to other languages to see how these concepts are expressed, and learn about our own by comparison. Some languages lack a monolexemic term for pink, and others still have additional pink-like lexemes in other hue spectra. The Russian language, for instance, has the color-categories, or monolexemic color terms, синий [sínij], usually translated as “blue” or “dark blue,” and Голубо́й [golubój] which we might gloss as “light blue,” or “sky blue.” The image-based color mapping model, described below, predicts similar, but not identical colors for these English and Russian words, as well as their most common French translations:

Semantically and chromatically, these color categories are not synonymous. Just in the way that every translation requires some compromise, some reshaping, colors do not always cleanly map across languages. Some do: English blue and French bleu, as etymological kin, are not only morphologically closer than the English/Russian pair, but semantically, as well, and the model predicts this kinship.

The differences in color terminology between languages are important for us to bear in mind, even when the primary analysis below deals only with texts in English, because these differences are analogues for the gaps, and communications, between language and vision. Furthermore, most of the writers I’ll be discussing here speak more than one language: either from birth, as with Conrad and his native Polish, or through study, as with James Joyce, who was fluent in at least five languages. And some experiments in psychology show semantic shifts in color categorization among speakers of more than one language (Ervin; Caskey-Sirmons and Hickerson; Athanasopoulos et al.).

More importantly, however, in order to categorize color words, we must first decide what our base color categories will be. This is no easy matter, and has long been the subject of debate. By comparing languages, linguists have often tried to ascertain what fundamental colors are, irrespective of their respective cultures.

One side of this debate calls into question the basis of fundamental colors, instead positing that color nomenclature, along with other phenomena, is in fact a cultural or linguistic construct. Probably the most well-known of these theories of linguistic relativism is that independently promoted, starting around the 1930s, by linguists Edward Sapir and Benjamin Whorf. Whorf’s 1940 summary of this view puts it succinctly: “the categories and types that we isolate from the world of phenomena we do not find there because they stare every observer in the face. On the contrary the world is presented in a kaleidoscopic flux of impressions which have to be organized in our minds. This means, largely, by the linguistic system in our minds” (Whorf 212).

On the other side of the debate, usually termed universalism, is an influential study of cross-linguistic color terminology, in a 1969 monograph of Brent Berlin and Paul Kay, Basic Color Terms: Their Universality and Evolution (Berlin and Kay 2). In particular, they name eleven categories: “white, black, red, green, yellow, blue, brown, purple, pink, orange, and grey,” and suggest that these categories develop in roughly that order—that all languages have words for white and black, that if they have a third, it is red, and so on. Graphically, Berlin and Kay present this sequence as in the following diagram, where languages that have red must have both white and black, and so on. There is no order between yellow and green, but languages that develop a word for green would then develop a word for yellow, and vice-versa.

\[[\substack{white \\ black }] < [red] < [\substack{green \\ yellow}] < [blue] < [brown] < [\substack{purple \\ pink \\ orange \\ grey}]\qquad{(4)}\]

Berlin and Kay see this sequence as a linguistic evolution in more than one sense—a dangerous term, in that it suggests a linear progression of simple to complex languages. The reasons they give for this are “increasing technological and cultural advancement” among the languages they compare. By way of explanation, they suppose that,

… to a group whose members have frequent occasion to contrast fine shades of leaf color and who possess no dyed fabrics, color-coded electrical wires, and so forth, it may not be worthwhile to rote-learn labels for gross perceptual discriminations such as green/blue, despite the psychophysical salience of such contrasts. (Berlin and Kay 16)

While the contrasts might have a physical basis, their linguistic categories do not map evenly to them, as Berlin and Kay themselves show. And as one might predicted, since 1969, their arguments of universal categories—and to a larger extent those of language evolution—have been either denounced as Anglocentric, or at least treated with a healthy skepticism. For instance, in 2006, Anna Wierzbicka argues that even the notion of color itself is not universal. Citing decades of research within the subfield of Natural Semantic Metalanguage, Wierzbicka argues that, “while many languages do not have a word for ‘colour,’ all languages have a word for seeing,’” and that “it makes more sense to ask about the universals of seeing rather than any putative ‘universals of colour’” (Wierzbicka 3).

I take no sides in this debate, but present it as evidence of the bond between language and perception. While there are few hard-line Whorfians remaining in linguistics, or universalists, both camps seem to agree that there are exceptions pulling at their theoretical sweaters, and colors are frequently the axis along which that pulling happens.

Is blood red?

To further complicate our conception of color/word translation, let’s return to the discussion of color word conventionality begun in the section on lemon-yellow above. As I have argued, although red is the conventional color of blood, blood itself is rarely red. This phenomenon presents itself in process of computational color categorization attempted here. While categorizing colors using CIELAB \(\Delta E\), which model human perception, I find that the category for the \(CM_X\) color word blood (#770001) gets categorized as brown, instead of red, as one might have predicted. Incidentally, blood red (#980002) is an entirely different color in the \(CM_X\), which is redder (i.e., contains a higher R value in its RGB representation) than blood. And dried blood (#4b0101) also exists, and is mapped to a darker red.

My initial feeling was that blood was miscategorized as a brown, and should instead be categorized as red. We all know blood is red–the term blood red itself proves it, right? But to look through images of blood, we may, in fact, discover that it is not red, but at best, a reddish brown. This is seemingly confirmed by the deep imaginer’s image-based imagined color (described below), which is #915b47. An image search at a stock photo provider like Unsplash or Pexels seems to confirm this, as well. However, crucially, the same searches for illustrations, rather than photos, depict blood as a bright red, instead of reddish brown—this seems to show that the linguistic-cognitive concept of blood is aligned with the concept of red, even though they aren’t visually equivalent. So when the OED editors, however meticulously they document the usages of blood-red, which date back to early Old English, gloss the term disappointingly literally as “red like blood; blood-coloured,” they do not account for the discrepancy between the color of “blood-red” and the actual color of blood (“Blood-Red, Adj.”).

In British literature of this period, blood-red is often used to evoke other qualities of blood itself, although not necessarily its true color. In the hell-sermon that is the pivotal scene in Joyce’s A Portrait of the Artist as a Young Man, it is used to underscore the apocalyptic scene that Father Arnall is trying to describe: “the doomsday was at hand. The stars of heaven were falling upon the earth … The sun, … had become as sackcloth of hair. The moon was bloodred” (Joyce, Portrait 99). Lunar eclipses, in which the sun’s light on the moon is eclipsed, leaving only the earth’s light, make the moon appear dark red. These have long been described in English as a blood moon, but this is not just a color comparison: it is a metaphor which anthropomorphizes the moon in this state, comparing the moon’s face to one whose face has filled with blood, out of anger or another heightened emotional state. Father Arnall’s use of this metaphor, along with his simile for the sun, anthropomorphize heaven as a way to dramatize the wrath of God.

In Thomas Hardy’s Tess of the d’Urbervilles, Tess is described, in an early foreshadowing scene, as “not divining” that Alec d’Urberville, “one who stood fair to be the blood-red ray in the spectrum of her young life,” would come to be “the tragic mischief of her drama” (Hardy 73). As in Joyce, “blood-red” allows for polysemy. First, it is “red … in the spectrum of her life”: red is the first, highest-frequency, and longest-wavelength band of a prismatic or spectrographic projection of Tess’s life, which implies that Alec will be for her among the first and most striking bands of her life. Spectroscopy—a kind of scientific “divining” of the material composition of matter, based on the spectral composition of its light—had come of age as a science in the 1870s and 80s, only a decade or two before Tess’s publication.

Second, “blood red” here implies a more literal red which comes from blood: a blushing which is seen in human faces, as well as, by extension of the metaphor, flowers, and fruit. This is the culmination of a chapter’s worth of red imagery, since Tess and Alec have just been picking strawberries and roses, and it is intertwined with imagery of Tess’s coming-of-age, or blossoming as the floral metaphor often has it.

When blood-red is understood as blushing, however, Berlin and Kay note that many words for red are derived from blood (Berlin and Kay 38). Yet this is not always the color of external, disembodied blood, which we have already established is more akin to brown, but often refers to pinkish, blood-rich skin. In the Hungarian language, to choose one cross-cultural example, there are famously two words for red, vörös, derived from the word for blood, and piros, of similar etymology, but referring instead to, as Wierzbicka posits, “the color of blood inside a person’s body (visible sometimes in an open wound and in a person’s ‘red’ face)” (Wierzbicka). A red face, Wierzbicka suggests, is not an attempt at accurately describing the color of someone’s face, but only that it has become more pink, i.e., taken on a more reddish hue than before. The red in question, then, is more of a reference to the concept of red, via blood and blood-red, than to the color phenomenon itself.

This red—again, not really the color red, but the concept—is the same red of rouge, the cosmetic used to emulate blushing, and whose name is derived from the French word for red. Rouge itself is often not red, but a somewhat reddish, pinkish, or purplish tint of another color.

P.A. Saccardo’s taxonomy does not place the color of blood with red at all, however, but with purple: he gives sanguineus as a Latin synonym of purpureus, along with the Greco-latin hæmatochrous, hæmatinus, and hæmatites (Saccardo 8). This is the traditional categorization of classical antiquity: the mapping appears in Homer, where in the Iliad, the earth is wet with purple blood. A. T. Murray’s English translation of Homer gives “thus mighty Aias charged them, and the earth grew wet with dark blood,” [αἵματι δὲ χθὼν δεύετο πορφυρέῳ] although πορφυρέῳ, which is translated as dark, is an etymological ancestor of purple (Homer). This categorization continues through Vergil, Ovid, and Horace. In fact, as Jacquiline Clarke points out, Horace plays with the traditional Homeric association of πορφύρεος with the sea and with death (πορφύρεος θάνατος, purple death or dark death, appears thrice in the Iliad), by juxtaposing the two in a purple blood-stained sea (Clarke 132). However, Liddell and Scott are quick to warn that “Homer seems not to have known the πορφύρα, [a purple fish, or purple dye] so that the word does not imply any definite colour” (Liddell and Scott).

To further complicate matters, Saccardo’s purpureus, while certainly on a spectrum that seems to range from red, to purple, and finally to brown, has a color of #8D0202, at least as it appears in the scanned edition from archive.org, however faded its original pigments may be. Some may rightly call this color red. So blood is not really red; it’s purple. But purple is red.

We may add blood to the long list of things called red which aren’t: the Red Sea (it’s blue), red wine and red grapes (they’re purple), red hair, red pandas, and Mars, the red planet (they’re orange). It comes as no surprise to report that red hair appears close to 900 times in \(C_{PG}\), but that orange hair, ginger hair, and copper hair are used only thrice each. Or that red wine appears 160 times, but purple wine only thrice. These are simply English-language conventions. But they prove that we must be especially careful while modeling our imagination of these terms. When we read red wine, do we imagine something red, or purple?

Similarly, when we read red hair, are we imagining red? The persistence of the villainous red-haired minor character trope in sensational literature of this period is evidence that the associations we’ve so far catalogued for red—blood, violence, ferocity, and so on—seep subconsciously into the characters’ depictions in fiction.Depictions of red-haired people in fiction could easily be the subject of another chapter, but are a little too far afield for this one. A concordance of \(C_{PG}\) for red-haired and similar terms, however, shows a multitude of unflattering accompanying personal descriptions.

We do not see those same stereotyped character attributes among more conscious and nuanced descriptions of hair color.

I want to reiterate here that these difficulties of textual color are not, as they may seem, merely background linguistic components of a literary art that is unconscious of them. Rather, they are fundamental to the process of literary description. Some writers are more explicit than others about these optical mechanics. But the modernist writers I choose to study the deepest in this chapter foreground color epistemologies in a way that, while it may not be a new literary device, is stronger, and brighter, and more variegated than before.

The wine-dark sea

Let’s return again to the works of James Joyce, one of the writers that deals most explicitly with the phenomenology of color. The first scene of Ulysses introduces a motif that recurs throughout the novel: the color of the sea. There, Buck Mulligan is gazing out onto the Irish sea from the crenellated parapets of Martello tower, in Sandycove, south of Dublin, and musing at once irreverently and reverently:

God! he said quietly. Isn’t the sea what Algy calls it: a great sweet mother? The snotgreen sea. The scrotumtightening sea. Epi oinopa ponton. Ah, Dedalus, the Greeks! I must teach you. You must read them in the original. Thalatta! Thalatta! She is our great sweet mother. Come and look. (Joyce, Ulysses 2)

How is the sea “snotgreen”? \(CM_X\) contains several colors for sea, as shown in table 4 below, as well as two mappings for snot. (Snot is not present in other color maps—unsurprisingly, since it would not very likely be a marketable name for a paint.)

And this list does not even include the many seafoam and seaweed variations. The variety of sea-like colors is an interesting problem, because seas themselves have a very wide range of colors among them, and even within any given sea. As suggested here in the name deep sea blue, the depth of the sea changes its apparent color. For comparison, the image-based color model predicts #98B8B3 for irish sea —a somewhat snot-green color.

Epi oinopa ponton, according to Don Gifford’s notes for Ulysses, is Homeric Greek for “upon the wine-dark sea,” a classic Homeric epithet that occurs throughout The Odyssey (Gifford and Seidman 15). It has long been a puzzle of Homeric scholarship as to why the sea is not blue, or green, but “wine-dark.” We should remember, however, that “dark” is an artifact of this translation convention, for in the Greek, which Mulligan advisedly does not gloss, “ἐπὶ οἴνοπα πόντον” could more accurately be rendered “over the vinaceous sea” or “over the wine-like sea,” since οἴνοπα itself, despite clearly being used as a visual metaphor elsewhere in Homer, does not explicitly contain a signifier for dark, which would be closer to μέλας in Homeric Greek—in fact, elsewhere in Homer, wine itself is described as μέλας (Gladstone 472; Liddell and Scott).

One of the more well-known works of scholarship on this topic, however dated it may be considered now, is that put forth in William Gladstone’s 1858 Studies on Homer and the Homeric Age. Among the more interesting theses of this work is his catalog and interpretation of color words in the Homeric epics. After a thorough concordance of visual terminology in Homer—which what one might call an analogue quantitative literary analysis—Gladstone concludes that Homer’s color expressions are relatively few. He lists as Homer’s only color words—excepting color metaphors—as λευκός (white), μέλας (black), ξανθός (yellow), έρυθρός (red), πορφύρεος (violet), κυάνεος (indigo), φοίνιξ (a phoenix, or Phoenician, purple or indigo), and πόλιος, (gray, grizzled) (Gladstone 459). His color metaphors, though, number thirteen, among which is οἴνοπα, vinaceous. Gladstone notes that Homer applies οἴνοψ to only two objects, oxen and the sea. This puzzles him, however, since:

… there is no small difficulty in combining these two uses by reference to the idea of a common colour. The sea is blue, grey, or green. Oxen are black, bay, or brown. … It is remarkable that, among colours properly so called, Homer has none whatever, derived from the name of an object, that are light, unless it be in the case of the rose. (Gladstone 472)

οἴνοπα functions just as πορφύρεος does: as a visual descriptor of the sea, in the sense of “blood-red”: by comparing the sea to wine, it is not just the color that is compared, but other aspects, as well. We might imagine a tumultuous sea, for instance, which causes the ships upon it to sway as if drunken, as in Arthur Rimbaud’s poem “Le bateau ivre.” This same motion of the sea might also cause sailors on it to vomit as if they’d had too much wine.

The blood/wine/sea metaphoric trinity was not lost on Joyce, either: in the “Proteus” episode of Ulysses, we see Stephen daydream the following, looking again out at the Irish sea:

A tide westering, moondrawn, in her wake. Tides, myriadislanded, within her, blood not mine, oinopa ponton, a winedark sea. Behold the handmaid of the moon. In sleep the wet sign calls her hour, bids her rise. Bridebed, childbed, bed of death, ghostcandled. Omnis caro ad te veniet. He comes, pale vampire, through storm his eyes, his bat sails bloodying the sea, mouth to her mouth’s kiss.

Here, Stephen’s poetically free-associating imagination conjures a nighttime sea as “the handmaid of the moon,” because it is “pulled” by it in its tides. He extends this feminine analogy, via the conventional euphemism for menstruation, to a series of blood-soaked bedsheets, with their analogue in the bloodied sea, and a recollection of a sexual episode with a prostitute that Stephen will remember more fully later. A common connection in this stream—or sea?—of consciousness is the purple color. Color is, here and elsewhere in Ulysses, the fulcrum of poetic associations which align along a visual axis.

What’s important to recall here is that this purple is closer to how the sea appears than how it is categorized. Again, conventional associations have it that the sea is blue, and that blood is red, and that red wine is of course red, but to read Homeric descriptions of the sea and blood and wine as purple, we are more reminded of the perceptual phenomenon than the linguistic category. And that, I argue, is a strong force in the literary description of this period.

Bildung

The genre and subject matter of a literary work has a strong influence on its color and brightness. There are obvious genres, like painters’ novels, which deal directly with pigments, and so should be expected to contain lots of color words and color descriptions. But textual color is correlated with a number of other surprising genres, as well.

A defining genre of the novels of this period is the Bildungsroman, a genre which Franco Moretti famously calls a “symbol” of modernity, in that modernity itself is a kind of society-level youth, self-mythologizing (Moretti 4). I argue that visuality, and color in particular, are crucial to the modernist Bildungsroman. After all, the literal meaning of Bild is image, painting, or figure, and Bildung, usually translated education, means literally image creation or formation. It is no mere coincidence, then, that portraiture and self-portraiture are common themes of these novels, appearing even in the titles of Joyce’s A Portrait of the Artist as a Young Man, Wilde’s The Picture of Dorian Gray, and Pater’s Imaginary Portraits, to name a few. If we count Henry James, then we might add The Portrait of a Lady, and T. S. Eliot’s poem of the same name. Gregory Castle calls these “portrait” novels “portraits of aesthetic life,” and underscores their roots in the aestheticist literary movement, of which Pater and Wilde were visible figures, and which took place in the Yellow Nineties (Castle).

It would make quick work of this study if it turned out that almost all highly colorful works in this corpus come from a single genre, like painters’ romances. So by grouping these text by subject or genre, I can begin to see trends among colorful texts. I first examine correlations between (a) texts’ Library of Congress Subject Headings, and (b) the number of different unique colors the model detects. This is not measuring the number of colors, but their breadth: the creativity with which writers relate their visual domains. Table 7 below shows this correlation.

The LCSH love stories has the highest number of unique colors in this corpus, by far. This is a large category of mostly novels, containing a number of well-known works. Among these are three novels by D.H. Lawrence, three by Wells (Marriage, Ann Veronica and Love and Mr. Lewisham), and two by Woolf (The Voyage Out and Night and Day).

Love stories are colorful because time (durée in Bergsonian formulation) is softer in love stories. The French literary theorist Philippe Hamon observes that literary descriptions tend to happen when the describing character is “‘absorbed’, ‘fascinated’, ‘loses track of time’, because of what he is looking at,” traits that would apply equally as well to lovers (Hamon 149). The describer “has been able to abstract himself for a while from the plot; the ‘delay’ in the text is justified by a ‘delay’ invoked by the text: an ‘idle period’ in an activity, a ‘breather’, a ‘pause’” (ibid.). This is why love stories among stock brokers or auctioneers are not as common as those among cowboys, or gardeners, since love, like description, is something that grows in ease and leisure—with care, rather than hurry; with the rhythm of the daydream. There are fast-paced sections of Ann Veronica, without a doubt, but it is the slower ones, the ones that deal with the couple’s European vacation, in which descriptions are allowed the freedom to polychromatically shimmer, as in the excerpt shown below:

It is ironic that Wells’s slightly caricatured portrait of Peter, Ann Veronica’s father, describes him reading “chiefly healthy light fiction with chromatic titles, The Red Sword, The Black Helmet, The Purple Robe, … in order ‘to distract his mind.’” (Wells, Ann Veronica 57), given that the novel is otherwise so colorful. In one scene, Manning professes his love to Ann Veronica by saying: “I want my life to be beaten gold just in order to make it a fitting setting for yours. … Forgive me if a certain warmth creeps into my words! The Park is green and gray to-day, but I am glowing pink and gold. It is difficult to express these things” (252). Wells paints a very bright, colorful scene, in which the lover’s pink skin is glowing with excitement, and his feelings—as shiny and as valuable as gold—emanate from him as if they were colors, and he were a source of light. This all contrasts with the “green and gray” of the inert vegetation against which it is set. Manning’s apology that “it is difficult to express” this shows how this kind of color description is so often rooted in reaching for fresh, unconventional ways of relating one’s visual experience.

That love and vision are close interlocutors is a view shared by H.D., in her theoretical work, Notes on Thought and Vision (H. D. (Hilda Doolittle) and H. D. (Hilda Doolittle)). There, she reminds us that “Socrates’s whole doctrine of vision was a doctrine of love. We must be ‘in love’ before we can understand the mysteries of vision” (22). She illustrates this with an imagined story of “the Galilean,” never explicitly named as Jesus, who

fell in love with things as well as people. He would fall in love with a sea-gull or some lake-heron that would dart up from the coarse lake grass … He looked at the blue grass-lily and the red-brown sand-lily that grew under the sheltered hot sand-banks in the southern winter, for hours and hours. If he closed his eyes, he saw every vein or fleck of blue or vermilion. (28)

This attention to color is that which we expect to read of lovers, in love stories.

But we should remember that love stories are not disproportionately bright—that is, they don’t have more numbers of colors, simply more unique colors. If we quantify the total proportions of colors, we see a different story. Fig. 24 shows a subset of base color proportions for each LCSH. Here, psychological fiction is the most prominent, overall, although largely due to the incidence of black and white. The subject heading psychological fiction contains no fewer than ten works from Conrad, two each from Wells, Stevenson, Sinclair, Lawrence, Gissing, and individual novels from Woolf, West, and Maugham. James Joyce’s Ulysses is also notably present here. Conrad’s novels in this category are Heart of Darkness, The Secret Sharer, An Outcast of the Islands, Almayer’s Folly, Chance, Lord Jim, Victory, and The Nigger of the Narcissus. Gissing’s are New Grub Street and The Odd Women. Lawrence’s are Women in Love and The Lost Girl, Stevenson’s are The Strange Case of Dr. Jekyll and Mr. Hyde and The Master of Ballantrae. Wells’s are The Secret Places of the Heart and The Invisible Man. Sinclair’s are Life and Death of Harriett Frean and The Three Sisters. Also included are Joyce’s Ulysses, David Lindsay’s A Voyage to Arcturus, Lucas Malet’s The History of Sir Richard Calmady, Maugham’s The Moon and Sixpense, Neil Monro’s Bud: a Novel, Wests’s The Return of the Soldier, and Woolf’s Jacob’s Room.

Table 7 shows that the LCSH is somewhat hierarchical: double hyphens separate time periods (nineteenth century), locations (England), and genres (Fiction). Genres also include “drama,” “poetry,” and “juvenile fiction.” I split out these commonly-occurring genre designations, programmatically, to form a new metadata column called “genre”. Then, I compute the same proportions of base colors, and group by these genres. Fig. 25 shows the result of the grouping based on this genre inference.

While fiction and drama have roughly the same proportions of colors, juvenile texts and poetry show nearly twice those numbers. This leads me to a theory that the most colorful fiction actually shows something we might call prose poetry. Alternatively, colorful fiction could be evidence of a childlike perceptual state on the part of the narrator.

Mark Doty, poet and author of The Art of Description, writes about what he calls “lyric time,” as a temporal element of the descriptive mode. Here, he notes how it’s a childlike state, in that it evokes a state in which causality and responsibility have not yet been eroded:

Lyric is concerned neither with the impingement of the past nor with anticipation of events to come. It represents instead a slipping out of story and into something still more fluid, less linear: the interior landscape of reverie. This sense of time originates in childhood, before the conception of causality and the solidifying of our temporal sense into an orderly sort of progression. (Doty 30)

Of course, writers of juvenile literature are not themselves children, but are writing to, and from, this state of mind. This is a state which attempts to convey the awe of early visual experiences. Before the names, purposes, and dangers of our immediate surroundings are known to us, they are first colors, sensations.

Milton Bradley argued that “color is one of the earliest subjects which should be taught in any education course,” since “bright color is the first thing to attract the infant’s eye, winning his notice before he pays any attention to form.” (Blaszczyk and Spiekermann 58).

Impression(ism)s

A second stylistic signal, just as important, is one that critics have called literary impressionism. Impressionism proper, the art movement born in 1870s Paris, was a revolutionary style in painting, more concerned with light, visual perception, and the interaction of color, than with strict representation. In fact, one story has it that Roland Rood, on a trip to Paris, found that his father’s book, Ogden Rood’s Modern Chromatics, was considered by many to be “the impressionists’ Bible” (Rossi 24). Modern Chromatics: With Applications to Art and Industry describes a variety of color phenomena: the interaction of colors with each other and their environment, their reflection, their refraction by water, and their absorption by certain materials, to name a few. There is no history of Impressionism that does not describe its love affair with color—especially violet.

As impressionism spread throughout the world, it began to appear in literature, as well. Critics began using the term literary impressionism to describe literature, chiefly modernist, which dealt with sensory perceptions in a fashion analogous to that of the painters. Some of its first definitions explain it just in terms of the painting style. One of its earliest definitions, for example, is from Ferdinand Brunetière, in Le roman naturaliste: “nous pourrons définir déjà l’impressionnisme littéraire une transposition systématique des moyens d’expression d’un art, qui est l’art de peindre, dans le domaine d’une autre art, qui est l’art d’écrire” (Brunetière 88).“We could already define literary impressionism as a systematic transposition from means of expression in one art, painting, into another art, the art of writing.”

In discussing a novel by Alphonse Daudet, Brunetière uses analogies from painting: “chaque scène ainsi devient un tableau, qui s’arrange comme dans une toile suspendue sous les yeux du lexteur, complète en elle-même, isolée des autres, comme dans une galerie, par sa bordure, par son cadre, par un large pan de mur vide” (Brunetière 89).“Each scene becomes a tableau which arranges itself like a canvas suspended under the eyes of the reader, complete in itself, isolated from others, as if in a gallery, by its border, by its frame, by a large expanse of blank wall.”

But by the time the style reaches Britain, literary impressionism has departed from its analogue in painting. It has since been defined in a number of ways. Michael Winkler defines it thus:

One of several stylistic tendencies that emerged during the 1880s from the decline of realistic writing and its reliance on the mimetic function of lit. … Impressionism thus came to share a penchant for subtle nuances, refined perception, and the ornamental use of ‘precious’ images … with poetic Jugendstil and the decorative arts around 1900; its preference for the openness of allusive hints over conceptually fixed meaning and its fluid suggestion of atmosphere through a quick succession of sensory impressions were made possible at the expense of a clearly delineated world of concrete objects and have also be called ‘neoromantic’. (Winkler 579–80)

This definition could certainly apply to a number of modernist writers. Literary impressionism as a style or distinction came to be used, in the first decades of the twentieth century, of a litany of writers who deal with color, or the ocular. Rebecca Fowler’s Literary Impressionism treats visual and ocular aspects of the works of Dorothy Richardson, Ford Madox Ford, H.D., and May Sinclair (Bowler). Jesse Matz’s Literary Impressionism and Modernist Aesthetics deals with James, Hardy, Proust, Conrad, Ford, and Woolf (Matz). Maria Kronegger’s Literary Impressionism reads French writers such as Gide, Flaubert, Proust, and Sartre (Kronegger). The term was used so often for writers of the day that it seems to have annoyed Skard, who defines the style thus:

The third movement which strove to replace Naturalism, Impressionism, was strictly speaking no movement of reaction but tried to deepen and intensify the Realistic means of expression and at the same time sublimate them. Impressionism has been en vogue in the literary research of recent decades; one early writer after another has been registered as Impressionist, from La Fontaine down. Many of these attempts—for instance, Carlowitz’ efforts to make Goethe an Impressionist … doubtlessly are over-strained … (Skard 202)

There is a strong subjectivity implied in the term impression: it is a sensory perception as perceived. Thus, much of literary impressionism, and thus its textual color, takes place within a certain point of view. We have just seen a strong example of this in Joyce’s Portrait, which ends with Stephen’s journal. Journals and diaries abound in fiction of this period, where the form is used to the the writer’s advantage. Maria Kronegger argues that this form is crucial to literary impressionism:

Writers of diaries, notebooks, and memoirs have the advantage of making everything proceed from a certain instant: according to the moment, they can change writing styles, that is the manner of suggesting reality; they can change point of view in order to capture thee most volatile moments of life together with nuances of color and tone, and to seize in passing the variations in aspect which the same scene assumes at different moments; they can reveal the development of awareness of the protagonist. (Kronegger 51)

This changing point of view—perhaps more analogous to painterly cubism than to impressionism—is a style we see in Joyce’s Portrait and Ulysses, Woolf’s Mrs Dalloway. Before all of those, however, there was Dorothy Richardson’s Pointed Roofs, an exemplar of literary impressionism, remarkable in its subjectivity and its sensory detail. It exhibits very high proportions of color expressions throughout.

J.D. Beresford, who writes the introduction to the first edition, doesn’t quite know what to make of its point of view, at first. When he reads the novel in manuscript, he decides it “was realism, was objective” (Richardson et al. 257). The second time, however, when he reads the novel in typescript, he decides “it is the most subjective thing I have ever read.” He imagines that “the writer of this has gone through life with eyes that looked inward; she has known every person and experience solely by her own sensations and reactions” (Richardson et al. 258). The third time he reads it, however, he is enlightened: he entirely suspends his judgment about its objectivity or subjectivity.

May Sinclair’s introduction to Pointed Roofs agrees with Beresford, and polemicizes his view. It reads almost like a manifesto for literary impressionism. Sinclair writes:

It seems to me that the first step towards life is to throw off the philosophic cant of the nineteenth century. I don’t mean that there is no philosophy of Art, or that if there has been there is to be no more of it; I meant that it is absurd to go on talking about realism and idealism, or objective and subjective art, as if the philosophies were sticking where they stood in the eighties. (Richardson v)

Sinclair’s introduction will make literary history, as it is the first time the term “stream of consciousness” appears. Describing Richardon’s Pilgrimage series, which Pointed Roofs begins, she writes: “in this series there is no drama, no situation, no set scene. Nothing happens. It is just life going on and on. It is Miriam Henderson’s stream of consciousness going on and on” (Richardson ix).

As with Joyce’s coloured narrative, the point of view is not entirely interior, or exterior. Here is a section from the first pages:

The song, and its usage of green, triggers, like Proust’s madeleine, a flash flood of memories. Miriam’s nostalgic reverie recalls for her the sights, sounds, and temperatures of her youth. Light is a crucial ingredient here, in the “shady north room,” with the “sun-blinds,” the “fire,” its “flames” and its “shadows.” Color pairs link the past and the present: the green of the song with the green of the classroom; Lilla’s amber-flecked eyes and the flames of the fire; Lilla’s black hair and the dark windows.

Love is at play here, again, creating a mood of wonder which allows these sensory perceptions to come to the fore. After quoting an exceptionally colorful passage from Richardson’s Honeycomb, Sinclair writes: “it is as if no other writers had ever used their senses so purely and with so intense a joy in their use” (Richardson xii).