• Korean Journal of Cognitive Science
• /
• v.8 no.1
• /
• pp.7-36
• /
• 1997

Beginning with the historical background under the light of which the program of naturalization proves to presuppose the mechanistic mechanistic materilalism, this paper first exposes what is the problem about intentionality, and then explains and examines the approaches to the program of naturalization of intentionality. Churchland's eliminativism and Dennett's instrumentalism are examined critically. Intentional realism is argued for;however, Dretaske's theory is preferred to than Foder's because the former but not the latter reveals least one of the various possible mechanisms of misrepresentations.

• Korean Journal of Cognitive Science
• /
• v.6 no.1
• /
• pp.5-30
• /
• 1995

Fodor and his followers argue that connectionist model does not have compositional structure,or else it is merely an implementation of a classicist model.'Classical constityency'is used importantly to show connectionist model does not have compositional structure.However,in contrast Fodor and his followers,I maintain that it is possible for connectionist model to have compositional structure without attributing classical constituency to cognitive model,and the very classical consitiuency makes the connectionist model nonimplementational.

• Proceedings of the KIEE Conference
• /
• 2007.10a
• /
• pp.201-202
• /
• 2007

본 논문에서는 RFID 인식기술을 이용한 소지품 추적 시스템을 설계하였고 실험 환경은 가로 1.19m, 세로 0.9m의 아파트 모형을 제작하였다. 하드웨어 구성은 트랜스포더, 리더, 안테나이며, 사용 주파수는 13.56MHz대역이다. 안테나 간의 간섭은 실딩으로 처리 하여 태그 인식률을 높이고 상호 간섭은 최소화 시켰다. 소지품 정보의 처리는 XML 데이터로 표현하였고 데이터베이스에 저장한다. 설정된 약 5개의 설정한 시나리오의 약 $20{\sim}30$여개의 소지품에 대해 유용성을 테스트하였으며 개인 일정정보와 연결되면 높은 응용성 및 효용성을 기대할 수 있다.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
• /
• v.9 no.8
• /
• pp.783-790
• /
• 2019

Cognitive science attempts to explain human intelligence on the basis of success of artificial neural network, which is called connectionism. The neural network, e.g., deep learning, seemingly promises connectionism to go beyond what it is. But those(Fodor & Pylyshyn, Fodor, & McLaughlin) who advocate classical computationalism, or symbolism claim that connectionism must fail since it cannot represent the relation between human thoughts and human language. The neural network lacks systematicity, so any output of neural network is at best association or accidental combination of data plugged in input units. In this paper, I first introduce structure of artificial neural network and what connectionism amounts to. Second, I shed light on the problem of systematicity the classical computationalists pose for the connectionists. Third, I briefly introduce how those who advocate connectionism respond to the criticism while noticing Smolensky's theory of vector product. Finally, I examine the debate of computationalism and connectionism on systematicity, and show how the problem of systematicity contributes to the development of connectionism and computationalism both.