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속성적 관점에 기반한단어 재인시의 의미 처리 가능성 연구: 연결주의 모델링

Semantic Process Possibility Research in Featural View: Connectionist Modeling

한국심리학회지: 인지 및 생물 / The Korean Journal of Cognitive and Biological Psychology, (P)1226-9654; (E)2733-466X
2015, v.27 no.4, pp.613-638
https://doi.org/10.22172/cogbio.2015.27.4.002
유희조 (고려대학교)
남기춘 (고려대학교)
남호성 (고려대학교)
  • 다운로드 수
  • 조회수

초록

단어의 의미처리는 개별 단어가 나타내는 개념(concept)들 간의 의미적 관련성을 효과적으로 표상할 수 있어야 한다. 본 연구는 의미 처리의 모델로서, 속성적 관점에 기반한 연결주의 모델을 제시하고, 단어와 의미 간의 관계를 학습하였다. 학습 과정 이후 모델용의 어휘 판단 과제를 수행하여, 종래 행동실험에서 의미 처리와 관련되어 나타나는 것으로 언급되었던 효과들이 제대로 모사되었는가 통계적으로 검증하였다. 모델링 수행 결과, 모델은 빈도효과(frequency effect), 단어 유사성 효과(word similarity effect), 의미 충실도 효과(semantic richness effect)와 의미 점화 효과(semantic priming effect)를 정상적으로 모사하는데 성공함으로써, 모델이 정상적인 의미의 학습 및 처리가 가능함을 보였다. 본 연구의 결과는, 속성적 관점 기반의 의미 표상(representation) 구조가 충분히 실제 사람의 언어처리를 반영할 가능성을 제시한다.

keywords
개념 표상, 의미 처리, 속성적 관점, 연결주의 모델링, conceptual representation, semantic process, featural view, connectionist modeling

Abstract

Semantic processing should effectively encode the meaning of a word and represent semantic relationship among individual words. This study proposed a connectionist model employing features as basic units for semantic processing among words and learning the relationship between the words and the associated meanings. The model statistically proved the capability to effectively simulate behavioral results from lexical decision tasks. In addition, the model successfully simulated the frequency effect, the word similarity effect, the semantic richness effect, and the semantic priming effect, which have been observed in behavioral studies. These results suggest that features are possibly basic units for human’s semantic processing.

keywords
개념 표상, 의미 처리, 속성적 관점, 연결주의 모델링, conceptual representation, semantic process, featural view, connectionist modeling

참고문헌

1.

김선경, 이혜원 (2007). 한글단어재인에서 청년과 노인의 의미점화효과. 한국심리학회지: 인지 및 생물, 19(4), 279-297.

2.

김지혜 (2010). 한국어 시각 단어 재인에서 음운, 표기, 의미 이웃 크기 효과와 과제에 따른 양상. 미발표 고려대학교 대학원 석사학위 청구논문.

3.

나윤혜 (2014). Behavioral and ERP Correlates of Korean Homographs in Visual Word Recognition by Department of Psychology. 미발표 고려대학교 대학원 석사학위 청구논문.

4.

남기춘, 서광준, 최기선, 이경인, 김태훈, 이만영 (1997). 한글 단어 재인에서의 단어 길이 효과. 한국심리학회지: 인지 및 생물, 9(2), 1-18.

5.

박권생 (1997). 단어의 의미 파악에 관여하는 음운 정보의 역할. 한국심리학회지: 인지 및 생물, 9(2), 131-152.

6.

박태진 (2003). 자료: 한국어 단어의 주관적 빈도 추정치 및 단어 재인에 미치는 빈도 효과. 한국심리학회지: 인지 및 생물, 15(2), 349-366.

7.

송진영, 남기춘, 구민모 (2012). 단어 빈도와 음절 이웃 크기가 한국어 명사의 음성 분절에 미치는 영향. 말소리와 음성과학, 4(2), 3-20.

8.

이광오, 박현수 (2009). 언어심리학. 서울: 박학사.

9.

이창환, 김연희, 강봉경 (2003). 한글 단어 재인에 있어서 음운정보와 시각정보의 역할. 한국심리학회지: 인지 및 생물, 15(1), 1-17.

10.

임형욱 (2004). 신경망 모형에 의한 어휘판단과제의 어휘빈도효과와 철자조합적합성효과 모사 연구. 미발표 고려대학교 대학원 석사학위 청구논문.

11.

한국과학기술원 전문용어언어공학연구센터 (2005). 다국어 어휘의미망제1권: 어휘의미망구축론. KAIST Press.

12.

Bock, K., & Levelt, W. (2002). Language production. In Psycholinguistics: Critical concepts in psychology (Vol. 5, pp.945-984). Taylor & Francis.

13.

Collins, A. M., & Loftus, E. F. (1975). A spreading-activation theory of semantic processing. Psychological Review, 82(6), 407-428.

14.

Collins, A. M., & Quillian, M. R. (1972). How to make a language user.

15.

Cree, G. S., McNorgan, C., & McRae, K. (2006). Distinctive features hold a privileged status in the computation of word meaning: Implications for theories of semantic memory. Journal of Experimental Psychology. Learning, Memory, and Cognition, 32(4), 643-658.

16.

Cree, G. S., McRae, K., & McNorgan, C. (1999). An attractor model of lexical conceptual processing: simulating semantic priming. Cognitive Science, 23(3), 371-414.

17.

Dell, G. S. (1986). A spreading-activation theory of retrieval in sentence production. Psychological Review, 93(3), 283-321.

18.

Frank, S. L., Haselager, W. F. G., & Van Rooij, I. (2009). Connectionist semantic systematicity. Cognition, 110(3), 358-379.

19.

Fujihara, N., Nageishi, Y., Koyama, S., & Nakajima, Y. (1998). Electrophysiological evidence for the typicality effect of human cognitive categorization. International Journal of Psychophysiology, 29(1), 65-75.

20.

Harm, M. W. (2002). Parallel Distributed Processing and Cognitive Neuroscience Center for the Neural Basis of Cognition. Design, (January).

21.

Hinton, G. E., & Shallice, T. (1991). Lesioning an attractor network: investigations of acquired dyslexia. Psychological Review, 98(1), 74-95.

22.

Hudson, P., & Bergman, M. (1985). Lexical knowledge in word recognition: Word length and word frequency in naming and lexical decision tasks. Journal of Memory and Language, 24, 46-58.

23.

Hutzler, F., Ziegler, J. C., Perry, C., Wimmer, H., & Zorzi, M. (2004). Do current connectionist learning models account for reading development in different languages? Cognition, 91(3), 273-96.

24.

Johnson-Laird, P. N., Herrmann, D. J., & Chaffin, R. (1984). Only connections: A critique of semantic networks. Psychological Bulletin, 96(2), 292-315.

25.

Jacobs, R. A., & Jordan, M. (1991). A modular connectionist architecture for learning piecewise control strategies. In American Control Conference 1991 (pp.1597-1602).

26.

Kwon, Y., & Lee, Y. (2014). Time course of Word Frequency and Word Length Effect in Visual Word Recognition: Evidence from Event-Related Brain Potential Study. The Journal of Linguistic Science, 69, 43-62.

27.

Kwon, Y., Park, K., Lim, H., Jung, S., & Nam, K. (2006). Word frequency effect and word similarity effect in korean lexical decision task and their computational model. Neural Information Processing, 4234, 331-340.

28.

Lei, Y., Li, F., Long, C., Li, P., Chen, Q., Ni, Y., & Li, H. (2010). How does typicality of category members affect the deductive reasoning? An ERP study. Experimental Brain Research, 204(1), 47-56.

29.

McRae, K., Cree, G. S., Seidenberg, M. S., & McNorgan, C. (2005). Semantic feature production norms for a large set of living and nonliving things. Behavior Research Methods, 37(4), 547-559.

30.

Morris, R. G. M. (2004). Long-term potentiation and memory. In T. V. P. Bliss, G. L. Collingridge, & R. G. M. Morris (Eds.), Long-term potentiation: enhancing neuroscience for 30 years (1st ed., pp.53-61). Oxford University Press.

31.

Park, K., Jung, S., Lee, Y., Lee, C.-H., & Lim, H. (2011). A computational model for simulating korean visual word recognition. Information -An International Interdisciplinary Journal, 14(8), 2669-2684.

32.

Park, K., & Lim, H. (2014). A computational model explaining language phenomena on Korean visual word recognition. Cognitive Systems Research.

33.

Pexman, P. M. (2012). Meaning-based influences on visual word recognition. In J. S. Adelman (Ed.), Visual Word Recognition: Volume 2 (1st ed., pp.24-43). Psychology Press.

34.

Pexman, P. M., Hargreaves, I. S., Siakaluk, P. D., Bodner, G. E., & Pope, J. (2008). There are many ways to be rich: effects of three measures of semantic richness on visual word recognition. Psychonomic Bulletin & Review, 15(1), 161-167.

35.

Plaut, D. C. (1996). Relearning after damage in connectionist networks: toward a theory of rehabilitation. Brain and Language, 52(1), 25-82.

36.

Plaut, D. C., McClelland, J. L., Seidenberg, M. S., & Patterson, K. (1996). Understanding normal and impaired word reading: computational principles in quasi-regular domains. Psychological Review, 103(1), 56-115.

37.

Plaut, D. C. (1997). Structure and Function in the Lexical System: Insights from Distributed Models of Word Reading and Lexical Decision. Language and Cognitive Processes, 12(5-6), 765-806.

38.

Plaut, D. C. (1999). A Connectionist Approach to Word Reading and Acquired Dyslexia: Extension to Sequential Processing. Cognitive Science, 23(4), 543-568.

39.

Plaut, D. C., & Shallice, T. (1993). Deep dyslexia: A case study of connectionist neuropsychology. Cognitive Neuropsychology, 10(5), 377-500.

40.

Reed, S. K. (2000). Cognition: Theory and Applications (5th ed.). Wadsworth Publishing.

41.

Rips, L. J., Shoben, E. J., & Smith, E. E. (1973). Semantic distance and the verification of semantic relations. Journal of Verbal Learning and Verbal Behavior, 12(1), 1-20.

42.

Seidenberg, M. S. (2005). Connectionist Models of Word Reading. Current Directions in Psychological Science, 14(5), 238-242.

43.

Seidenberg, M. S., & McClelland, J. L. (1989). A distributed, developmental model of word recognition and naming. Psychological Review, 96(4), 523-68.

44.

Siegelbaum, S. a, & Kandel, E. R. (2013). Prefrontal Cortex, Hippocampus, and the Biology of Explizit Memory Storage. In E. R. Kandel, J. H. Schwartz, T. M. Jessell, S. A. Siegelbaum, & A. J. Hudspeth (Eds.), Principles of Neural Science (5th ed., pp.1486-1521). McGraw-Hill Education.

45.

Thomas, M. J., & Malenka, R. C. (2004). Synaptic plasticity in the mesolimbic dopamine system. In T. V. P. Bliss, G. L. Collingridge, & R. G. M. Morris (Eds.), Long-term Potentiation: Enhancing Neuroscience for 30 Years (1st ed., pp. 349-356). Oxford University Press.

46.

Vigliocco, G., & Vinson, D. P. (2007). Semantic Representation. In The Oxford handbook of psycholinguistics (pp.195-215).

47.

Yap, M. J., Tan, S. E., Pexman, P. M., & Hargreaves, I. S. (2011). Is more always better? Effects of semantic richness on lexical decision, speeded pronunciation, and semantic classification. Psychonomic Bulletin & Review, 18(4), 742-750.

48.

Yermolayeva, Y., & Rakison, D. H. (2014). Connectionist modeling of developmental changes in infancy: Approaches, challenges, and contributions. Psychological bulletin, 140(1), 224.

49.

Yim, H., Lim, H., Park, K., & Nam, K. (2005). A Computation Model of Korean Lexical. In Advances in Natural Computation (pp.844-849).

50.

Ziegler, J. C., Perry, C., & Coltheart, M. (2000). The DRC model of visual word recognition and reading aloud: An extension to German. European Journal of Cognitive Psychology, 12(3), 413-430.

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