Abstract
Although people perceive countless different colors, they typically use 11 discrete ‘basic’ or ‘universal’ terms to categorize hues. However, multiple languages (e.g. Russian, Greek, Italian, Lithuanian; Bimler & Uusküla, 2014, 2017; Paramei, 2005, 2007) use different terms for different blues, suggesting additional basic-level distinctions (e.g., Italian: blu, azzurro, celeste). Experiments show no such distinction for blue in English (Uusküla & Bimler, 2016), but perhaps speakers use the words ‘light’ and ‘dark’ (and other specifiers) with ‘blue’ far more frequently than with other basic colors, thus identifying multiple blues in practice. To examine the use of ‘light’ blue, ‘dark’ blue, and other chromatic specifiers in daily common language, we used corpus linguistics. From the Corpus of American English (COCA, a balanced corpus with >550 million words from newspapers, magazines, academic texts, literature, spoken language) we extracted all occurrences of 11 basic color terms, and their 100 most common preceding words (collocates). We calculated the relative frequency of ‘light’ and ‘dark’ and marked all other potential specifiers of the colors. ‘Light’ and ‘dark’ indeed appeared frequently with blue (1.19, 2.15% of selected cases). There could be cases where ‘light blue’ referred to something both lightweight and blue, but subsets of unambiguous cases showed similar patterns. ‘Light’ also specified brown (2.18%), whereas ‘dark’ collocated with brown (2.60%), gray (1.71%), green (1.87%) and purple (2.05%), with much lower percentages for other colors (~0-0.82%). Finally, blue was preceded by many more unique chromatic specifiers than other colors. Results suggest: (1) American English speakers distinguish between different blue categories, resembling explicit distinctions of blues in some other languages (the pattern was even stronger for brown); (2) the separation between basic and non-basic color categories might be gradual rather than distinct in English. Issues related to developing corpus linguistics to investigate human perception and categorization are discussed.