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
저널정보
서울대학교 인지과학연구소 Journal of Cognitive Science Journal of Cognitive Science 제17권 제1호
발행연도
2016.1
수록면
1 - 26 (26page)

이용수

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

초록· 키워드

오류제보하기
A characterization of computation and computational explanation is important in accounting for the explanatory power of many models in cognitive neuroscience. Piccinini (2015) describes computational models as both abstract and mechanistic. This approach stands in contrast to a usual way of understanding mechanism which implies that explanation is impoverished by abstraction. I argue that in order to provide a useful account of computational explanation in cognitive neuroscience, Piccininiʼs proposal must be complemented by an abstraction criterion that fulfills two conditions: motivating abstractions enough to make a model computational and not motivating the omission of information that is constitutive of mechanistic explanation. These conditions are relevant because although there are computational and mechanistic descriptions of neural processes (Piccinini & Bahar 2013) mechanism must, as a normative theory, determine whether the abstractions that these models involve are well motivated. I argue that the abstraction criterion proposed by Levy and Bechtel (2013) is a promising candidate to fulfill these requirements. First, I show that this criterion can legitimize the omission from recently proposed neurocognitive models of all features that are non-computational according to Piccinini’s approach (although it also motivates some modifications of his characterization of neural computation). Second, I argue that this criterion legitimizes those models only if we interpret them as including all the information constitutive of mechanistic explanation.

목차

등록된 정보가 없습니다.

참고문헌 (26)

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