When Luisa Maffi was a graduate student at the University of California, Berkeley in the late 1980s and early 1990s, she helped redefine what we talk about when we talk about color. Together with her advisors Brent Berlin and Paul Kay, she analyzed how people around the world name and categorize colors. The team reached a striking conclusion: Despite the obvious differences in sounds, words and syntax, the world’s languages tend to carve up the vast color spectrum into universal categories. Even languages with just a handful of color terms normally contain words that refer to black, white and red, while those with larger color vocabularies divide the rainbow into fairly predictable terms.
These seminal findings expanded previous work by Berlin, Kay, and others, and were later compiled into a monograph called the World Color Survey (WCS). This “universalist” framework stood in stark contrast to reigning “relativist” theories that color words are uniquely shaped by cultural context. Decades later, researchers equipped with new computational tools continue to draw from the WCS to answer some of the same fundamental questions posed by Berlin, Kay, Maffi and their colleagues: What explains the shared patterns in how languages compress the vast color spectrum? What drives the subtle differences — and is there a way to predict that variability?
