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Abstract

<jats:p>This study contrastively analyzes the cognitive categorization and semantic expansion patterns of the representative basic color terms: Korean ‘norang’ and Mongolian ‘shar’. Grounded in the Sapir-Whorf Hypothesis, which emphasizes linguistic relativity, and cognitive linguistic theories of metaphor and metonymy, this research identifies distinct conceptual systems and cultural schemas between the two languages. To ensure empirical validity and overcome the limitations of literature-centered analysis, the study utilizes a comprehensive multi-genre dataset: the Mongolian-Korean Grand Dictionary (2023), a literary corpus from The Clear Tamir by Ch. Lodoidamba, and modern digital corpora including news portals (Gogo.mn, Ikon.mn), SNS, and daily conversation logs. The analysis incorporates a robust dataset including 98 lexicographical entries and an extensive corpus-based collection. The findings reveal that Korean ‘yellow’ has evolved into a sophisticated social signifier, representing community safety (e.g., School Zones), social remembrance (e.g., Yellow Ribbon), and labor solidarity (e.g., Yellow Envelope Law). In contrast, Mongolian ‘shar’ and its sacred variant ‘altan’ (gold) form a unique cognitive model reflecting nomadic ecological experiences and traditional physiological views. This model encompasses: (1) cultural schemas of sacredness and origin (e.g., altan urag), (2) physiological usages based on a metonymic link to bile (e.g., hangovers, shar tailakh), (3) ecological categories for livestock, and (4) modern media-driven metaphors for “Yellow Journalism” and national-level systemic warnings (e.g., yellow alert levels in disaster management). By combining lexicographical definitions with diverse empirical corpus data, this study provides foundational cognitive evidence that color metaphors are active cognitive tools integrated into modern social systems, offering crucial insights for bilingual education and cross-cultural communication.</jats:p>

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Keywords

cognitive yellow study shar modern

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