메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색

논문 기본 정보

자료유형
학술저널
저자정보
정원일 (동국대학교) 김세영 (한양대학교) 박명관 (동국대학교)
저널정보
현대문법학회 현대문법연구 현대문법연구 제111호
발행연도
2021.9
수록면
117 - 142 (26page)

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색

초록· 키워드

오류제보하기
This paper reports an ERP-based study concerning the limited productivity of the ACC + ACC subcategorization frame with Korean dative and causative verbs. This frame is compared to the unmarkedly productive DAT + ACC frame in the ERP experiment and the acceptability rating task. The results show that the double Accusatives with the two types of dative verbs expressing caused possession or caused motion recorded N400, followed by P600, while those with morphological causative verbs registered N400 only. Likewise, the double Accusatives with both dative and causative verbs were consistently rated as unacceptable in the acceptability task. We take the disconfirmed expectation of a certain Case morphology to act as an etiology of the N400 modulation. The reader expects to encounter a dative or causative verb after the DAT + ACC sequence, but the preceding ACC + ACC sequence is not compatible with such a verb after it, evoking N400 because Case encodes morpho-lexical information. Meanwhile, reduced P600 represents a severe disruption of semantic analysis, reflected by N400; dative verbs differ from causative verbs in that the former employ covert lexical feature for causation, but the latter a morphologically-overt morpheme. The upshot of this paper is that Case as an apparently grammatical relation-encoding morpho-syntactic marker serves as a cue for predicting the following word associated with it, and ERP responses to a failure in such a Case-related prediction are not confined to a late positivity but are also detected evoking negativity at posterior regions in the 250 ∼ 500 ms interval.

목차

등록된 정보가 없습니다.

참고문헌 (0)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

최근 본 자료

전체보기

댓글(0)

0