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의미범주 지식의 다양성과 보편성: 동물 범주 유창성 과제를 활용하여The Diversity and Universality of the Knowledge in Semantic Categories: Insights from the Animal Category Fluency Task

Other Titles
The Diversity and Universality of the Knowledge in Semantic Categories: Insights from the Animal Category Fluency Task
Authors
정유진홍정하황유미
Issue Date
2014
Publisher
담화·인지언어학회
Keywords
diversity; universality; category fluency task; animal category; semantic knowledge; subcategorization; dice analysis; statistical methods using R; multidimensional scaling; correspondence analysis; perceptual map
Citation
담화와 인지, v.21, no.3, pp.209 - 232
Indexed
KCI
Journal Title
담화와 인지
Volume
21
Number
3
Start Page
209
End Page
232
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/99989
DOI
10.15718/discog.2014.21.3.209
ISSN
1226-5691
Abstract
This study discusses semantic knowledge and subcategorization of animals for Koreans based on a semantic verbal fluency task. Compared to many previous studies which focused on the number of animals in response or the switches between clusters, we aim to identify diversity and universality of semantic knowledge about animals and to propose subcategories of animals. Instead of using pre-existing semantic categories proposed by previous studies, we argue for Korean specific semantic knowledge and categories of animals based on healthy Korean adults’ semantic knowledge. We evaluated 275 cognitively healthy volunteers, aged 45 to 86 years. An educational average level ± standard deviation for the whole sample is 9.89 ± 4.2 years. As a result of the fluency task, the participants mentioned 193 different types of animals with the number of 4,047. They named an animal with the following order of preferences: ‘a tiger, a dog, a cow, a pig, a chicken, a lion, an elephant, a horse, a duck, a rabbit, a giraffe, etc.’ The top ranked animals among 193 types in naming can be regarded as a “typical” animal among Korean people. With respect to category-based induction, typicality can be a case of a more universal tendency. In order to examine subcategories of animals, we extracted a bigram from the response list. We derived dice distances to measure similarity between two co-occurring items and provided a perceptual map based on multidimensional scaling with statistical methods using R.
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