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

• Annual Conference on Human and Language Technology
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• 1992.10a
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• pp.615-627
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• 1992

연결주의적 선회'(the connectionistic turn)를 말할 만큼 학문적 방법론과 이론적 관점에 중요한 변화를 가져오고 있는 연결주의를 철학에서의 원자주의와 대비시켜서 고찰한다. 특히 원인론, 실재론 및 의미론의 문제에 관하여 연결주의적 관점이 어떤 의미를 갖는지 검토해 본다.

• Korean Journal of Cognitive Science
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• v.7 no.4
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• pp.141-162
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• 1996

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

• Korean Journal of Cognitive Science
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• v.10 no.4
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• pp.71-83
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• 1999

Conventional symbolic inference systems lack flexibility because they do not well reflect flexible semantic structure of knowledge and use symbolic logic for their basic inference mechanism. For solving this problem. we have recently proposed the 'Connectionist Semantic Network(CSN)' as a model for flexible knowledge representation and inference based on neural networks. The CSN is capable of carrying out both approximate reasoning and commonsense reasoning based on similarity and association. However. we have difficulties in representing general and structured high-level knowledge and variable binding using the connectionist framework of the CSN. In this paper. we propose a hybrid system called SymCSN(Symbolic CSN) that combines a symbolic module for representing general and structured high-level knowledge and a connectionist module for representing and learning low-level semantic structure Simulation results show that the SymCSN is a plausible model for human-like flexible knowledge representation and inference.

• Journal of Science and Technology Studies
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• v.1 no.2 s.2
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• pp.399-422
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• 2001

Models for human cognition currently discussed in cognitive science cannot be appropriate ones. The symbolic model of the traditional artificial intelligence works for reasoning and problem-solving tasks, but doesn't fit for pattern recognition such as letter/sound cognition. Connectionism shows the contrary phenomena to those of the traditional artificial intelligence. Connectionist systems has been shown to be very strong in the tasks of pattern recognition but weak in most of logical tasks. Brooks' situated action theory denies the. notion of representation which is presupposed in both the traditional artificial intelligence and connectionism and suggests a subsumption model which is based on perceptions coming from real world. However, situated action theory hasn't also been well applied to human cognition so far. In emphasizing those characteristics of models I refer those models 'left-brain model', 'right-brain model', and 'robot model' respectively. After I examine those models in terms of substantial items of cognitions- mental state, mental procedure, basic element of cognition, rule of cognition, appropriate level of analysis, architecture of cognition, I draw three arguments of embodiment. I suggest a way of unifying those existing models by examining their theoretical compatability which is found in those arguments.

• Journal of the Korean Society for Railway
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• v.4 no.1
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• pp.24-32
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• 2001

동북아 지역은 경제적으로 선진국과 신흥공업국, 개발도상국이란 서로 상이한 경제발전 수준을 가지고 있으며, 정치적으로는 자본주의와 사회주의라는 다른 체제가 공존하고 있다. 이러한 상황 속에서 한반도에는 남북정상회담 이후 남북 교류의 새로운 장을 열게된 경의선 복원사업이 실시됨과 동시에 러시아가 경원선 연결을 통하여 시베리아횡단철도와 한국철도의 연결을 제의하여 철도 연결을 둘러싼 동북아의 상황은 빠르게 변모하고 있다. (중략)

• Korean Journal of Cognitive Science
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• v.28 no.2
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• pp.111-131
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• 2017

The connectionist model is one approach to studying language processing from a computational perspective. And building a representation in the connectionist model study is just as important as making the structure of the model in that it determines the level of learning and performance of the model. The connectionist model has been constructed in two different ways: localist representation and distributed representation. However, the localist representation used in the previous studies had limitations in that the unit of the output layer having a rare target activation value is inactivated, and the past distributed representation has the limitation of difficulty in confirming the result by the opacity of the displayed information. This has been a limitation of the overall connection model study. In this paper, we present a new method to induce distributed representation with local representation using abstraction of information, which is a feature of restricted Boltzmann machine, with respect to the limitation of such representation of the past. As a result, our proposed method effectively solves the problem of conventional representation by using the method of information compression and inverse transformation of distributed representation into local representation.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.3-5
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• 1998

본 연구팀에서는 기존의 기호주의 전문가 시스템의 경우 지식표현 체계가 의미구조를 반영하지 못함으로써 발생하는 경직성문제를 해결하기 위해 CSN(Connectionist Semantic Network) 모델을 제안하였다. 그러나 CSN모델은 상위개념간의 관계를 표현하기 위해 단순한 전향 신경망을 사용함으로써 상위개념간의 일반적이고 구조화된 지식표현 및 추론에 어려움이 있었다. CSN 모델의 이런 문제점을 위해 본 논문에서는 상위개념간의 일반적이고 구조화된 지식표현과 추론이 용이한 기호주의 표현 체계와 이 표현 체계 안에 효과적으로 의미구조를 반영할 수 있는 연결주의 학습 모델인 CSN을 결합한 하이브리드 구조를 제안하고, 실험을 통하여 제안된 하이브리드 구조의 타당성을 보인다.

• The Journal of Korean Academy of Prosthodontics
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• v.55 no.1
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• pp.9-17
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• 2017

Purpose: The purpose of this study was to evaluate the fracture strength of straight and angled zirconia abutments for internal hex and external hex implants. Materials and methods: Twenty internal hex implants and 20 external hex implants were prepared. The prefabricated straight zirconia abutments and 17-degree-angled zirconia abutments were connected to those 40 implants. The specimens were classified into 4 groups depending on the connection type and abutment angulation; internal hex implant/straight abutment, group INS; internal hex implant/angled abutment, group INA; external hex implant/straight abutment, group EXS; external hex implant/angled abutment, group EXA. All specimens were loaded at a 30-degree angle with a crosshead speed of 1 mm/min using universal testing machine. The fracture loads were analyzed using 2-way ANOVA and independent t-test (${\alpha}=.05$). Results: The mean fracture load for INS was 955.91 N, 933.65 N for INA, 1267.20 N for EXS, and 1405.93 N for EXA. External hex implant showed a significantly higher fracture load, as compared to internal hex implant (P < .001). No significant differences in fracture loads were observed between the straight and angled abutment in internal hex implants (P = .747) and external hex implants (P = .222). Internal hexes of abutments were fractured horizontally in internal connection implants, while lingual cervical neck portions were fractured in external connection implants. Conclusion: The zirconia abutments with external hex implants showed significantly higher fracture strength than those with internal hex implants. However there was no difference in fracture strength between the straight and 17-degree-angled zirconia abutment connected to both implant systems.