Maybe if you’re an artist or an interior
designer, you know specific meanings for as many as
50 or 100 different words for colors – like
turquoise, amber, indigo or taupe. But this is still
a tiny fraction of the colors that we can
distinguish.
Interestingly, the ways that languages categorize
color vary widely. Nonindustrialized cultures
typically have far fewer words for colors than
industrialized cultures. So while English has 11
words that everyone knows, the Papua-New Guinean
language Berinmo has only five, and the Bolivian
Amazonian language Tsimane’ has only three words
that everyone knows, corresponding to black, white
and red.
The most widely accepted explanation for the
differences goes back to two linguists, Brent
Berlin and Paul Kay. In their early work in
the 1960s, they gathered color-naming data from
20 languages. They observed some commonalities
among sets of color terms across languages: If a
language had only two terms, they were always
black and white; if there was a third, it was
red; the fourth and fifth were always green and
yellow (in either order); the sixth was blue;
the seventh was brown; and so on.
Based on this order, Berlin and Kay argued
that certain colors were more salient. They
suggested that cultures start by naming the most
salient colors, bringing in new terms one at a
time, in order. So black and white are the most
salient, then red, and so on.
While this approach seemed promising, there
are several problems with this innate
vision-based theory.
Berlin, Kay and their colleagues went on to
gather a
much larger data set, from 110
nonindustrialized languages. Their original
generalization isn’t as clear in this larger
data set: there are many exceptions, which Kay
and his colleagues have tried to explain in a
more complicated vision-based theory.
What’s more, this nativist theory doesn’t
address why industrialization, which introduced
reliable, stable and standardized colors on a
large scale, causes more color words to be
introduced. The visual systems of people across
cultures are the same: in this model,
industrialization should make no difference on
color categorization, which was clearly not the
case.
**********
Our research groups therefore
explored a completely different idea: Perhaps
color words are developed for efficient
communication. Consider the task of simply
naming a color chip from some set of colors. In
our study, we used 80 color chips, selected
from Munsell colors to be evenly spaced
across the color grid. Each pair of neighboring
colors is the same distance apart in terms of
how different they appear. The speaker’s task is
to simply label the color with a word (“red,”
“blue” and so on).
Participants had to communicate one of the
80 color chip choices from across the color
grid. (Richard Futrell
and Edward Gibson, CC BY)
To evaluate the communication-based idea, we
need to think of color-naming in simple
communication terms, which can be formalized by information
theory. Suppose the color I select at random
is N4. I choose a word to label the color that I
picked. Maybe the word I choose is “blue.” If I
had picked A3, I would have never said “blue.”
And if I had picked M3, maybe I would have said
“blue,” maybe “green” or something else.
Now in this thought experiment, you as a
listener are trying to guess which physical
color I meant. You can choose a whole set of
color chips that you think corresponds to my
color “blue.” Maybe you pick a set of 12 color
chips corresponding to all those in columns M, N
and O. I say yes, because my chip is in fact one
of those. Then you split your set in half and
guess again.
The number of guesses it takes the ideal
listener to zero in on my color chip based on
the color word I used is a simple score for the
chip. We can calculate this score – the number
of guesses or “bits” – using some simple math
from the way in which many people label the
colors in a simple color-labeling task. Using
these scores, we can now rank the colors across
the grid, in any language.
In English, it turns out that people can
convey the warm colors – reds, oranges and
yellows – more efficiently (with fewer guesses)
than the cool colors – blues and greens. You can
see this in the color grid: There are fewer
competitors for what might be labeled “red,”
“orange” or “yellow” than there are colors that
would be labeled “blue” or “green.” This is true
in spite of the fact that the grid itself is
perceptually more or less uniform: The colors
were selected to completely cover the most
saturated colors of the Munsell color space, and
each pair of neighboring colors looks equally
close, no matter where they are on the grid.
We found that this generalization is true in
every language in the entire World Color Survey
(110 languages) and in three more that we did
detailed experiments on: English, Spanish and
Tsimane’.
Each row orders the color chips for one
language: Colors farther left are easier to
communicate, those farther to the right are
harder to communicate.
(Richard Futrell, CC BY)
It’s clear in a visual representation,
where each row is an ordering of the color
chips for a particular language. The
left-to-right ordering is from easiest to
communicate (fewest guesses needed to get
the right color) to hardest to communicate.
The diagram shows that all languages have
roughly the same order, with the warm colors
on the left (easy to communicate) and the
cool ones on the right (harder to
communicate). This generalization occurs in
spite of the fact that languages near the
bottom of the figure have few terms that
people use consistently, while languages
near the top (like English and Spanish) have
many terms that most people use
consistently.
**********
In addition to discovering this
remarkable universal across languages, we
also wanted to find out what causes it.
Recall that our idea is that maybe we
introduce words into a language when there
is something that we want to talk about. So
perhaps this effect arises because objects –
the things we want to talk about – tend to
be warm-colored.
We evaluated this hypothesis in a
database of 20,000 photographs of objects
that people at Microsoft had decided
contained objects, as distinct from
backgrounds. (This
data set is available to train and test
computer vision systems that are trying to
learn to identify objects.) Our colleagues
then determined the specific boundaries of
the object in each image and where the
background was.
We mapped the colors in the images onto
our set of 80 colors across the color space.
It turned out that indeed objects are more
likely to be warm-colored, while backgrounds
are cool-colored. If an image’s pixel fell
within an object, it was more likely to
correspond to a color that was easier to
communicate. Objects’ colors tended to fall
further to the left on our ranked ordering
of communicative efficiency.
When you think about it, this doesn’t
seem so surprising after all. Backgrounds
are sky, water, grass, trees: all
cool-colored. The objects that we want to
talk about are warm-colored: people,
animals, berries, fruits and so on.
Our hypothesis also easily explains why
more color terms come into a language with
industrialization. With increases in
technology come improved ways of purifying
pigments and making new ones, as well as new
color displays. So we can make objects that
differ based only on color – for instance,
the new iPhone comes in “rose
gold” and “gold” – which makes
color-naming even more useful.
So contrary to the earlier nativist
visual salience hypothesis, the
communication hypothesis helped identify a
true cross-linguistic universal – warm
colors are easier to communicate than cool
ones – and it easily explains the
cross-cultural differences in color terms.
It also explains why color words often come
into a language not as color words but as
object or substance labels. For instance,
“orange” comes from the fruit; “red” comes
from Sanskrit for blood. In short, we label
things that we want to talk about.
This article was originally published on
The Conversation.
image:
https://counter.theconversation.edu.au/content/84117/count.gif?distributor=republish-lightbox-advanced
Ted Gibson, Professor of Cognitive
Science, Massachusetts Institute of Technology
Bevil R. Conway, Investigator at the
National Eye Institute's Sensation, Cognition,
Action Unit, National Institutes of Health
Read more:
http://www.smithsonianmag.com/science-nature/why-different-languages-name-different-colors-180964945/#SSTlUsyehC7lemv2.99
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