The role of history in how efficient color names evolve

Suppose two speakers of the same language are playing a guessing game where each has the same color swatches, and Player 1 tries to get Player 2 to guess a hue by naming the color. If the second player consistently guesses correctly as often as possible, that indicates their language has an efficient color naming system.

Past research has shown that efficient color vocabularies are constrained both by how people perceive colors and by how much they want or need to communicate about a given color. For example, Penn researchers found in a 2021 study that the need to communicate about reds and yellows is high across languages, while greens are more important in some languages.

Now, Penn scientists have identified another constraint: history. Colin R. Twomey, interim executive director of the Data Driven Discovery Initiative in the School of Arts & Sciences, worked with psychology professor David H. Brainard and biology professor Joshua B. Plotkin in a new paper showing how a language's past color vocabulary shapes its ability to evolve, shedding light on how the number and meaning of color words have changed over time.

Their work is published in Proceedings of the National Academy of Sciences.

"The main takeaway is that, once you as a linguistic community have an efficient vocabulary, that starting point restricts the next possible efficient vocabulary that you could have when you introduce a new term," says Twomey, the first author. "As the vocabulary grows, the number of different vocabularies that you could move to is increasingly constrained."

This takeaway shares a principle with evolutionary biology. The existence of an evolutionary pathway limits available future pathways, compared to the number of options available if a species were to arise anew. Plotkin and Twomey come from a background in evolutionary theory for biology, and here they are trying to better understand how some of the same theory applies to cultural evolution.

The authors used the publicly available World Color Survey (WCS), in which about 25 speakers from each of 110 languages were asked to name the same set of 330 color stimuli. This dataset provides the likelihood that a speaker from each of the languages would use a certain term to describe a color.

The WCS was the work of anthropologist Brent Berlin and linguist Paul Kay, who identified 11 color categories in English: red, orange, yellow, green, blue, purple, pink, black, white, brown, and gray. While English uses 11 color categories, the most common color vocabulary size among WCS languages is six words.

Using the WCS, the Penn researchers explored the introduction of new terms and the probability that a given word will change meaning as the size of the color vocabulary increases. For example, a term identified as green-blue could shift in meaning to "green" if a new term is established as "blue." Green-blue and blue are quite susceptible to changes in meaning as new terms are added, whereas red, black, and yellow remain relatively stable in meaning.