Araştırma Makalesi

Connectionism, Artificial Neural Networks and Reading

Sayı: 12 21 Ekim 2018
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Connectionism, Artificial Neural Networks and Reading

Öz

Connectionism, which is a novel approach to human intellectual abilities, has challenged the basic assumptions and tenets of top-down and interactive approaches of the 1960s and 1970s to human cognitive processing and reading. Connectionism has specifically dealt with reading in order to understand and model the cognitive processes and intellectual properties underlying this significant skill. It has also embraced a more bottom-up approach to reading, an orientation which attaches great importance to pattern recognition governed by parameters, weights, connections and constraints in lieu of rules and symbols. Although the great majority of studies which applied connectionism have concentrated on how words are recognized, a considerable amount of scholarly work also has targeted at understanding syntactic parsing and pronouncing words. To date, connectionism has contributed to the understanding and modeling human reading and attracted the attention of researchers working in various fields such as linguistics, psychology, and artificial intelligence to a considerable extent. This paper aims to provide fundamental information about the connectionist approaches and neural network modeling that suggest an alternative to the classical theory of the mind while accounting for the cognitive processes that underlie human reading.  The paper also compares the connectionist approaches to traditional approaches to reading, such as bottom-up, top-down and interactive approaches. Finally, it reviews several connectionist models that have proved to be highly influential in the relevant literature. 

Anahtar Kelimeler

Kaynakça

  1. Carver, R. P. (1977). Toward a theory of reading comprehension and raiding. Reading Research Quarterly, 13, 8-63. Eysenck, M. W., & Keane, M. T. (2010). Cognitive psychology: A student's handbook (6th ed.). New York: Psychology Press. Foorman, B. R. (1994). The relevance of a connectionist model of reading for “The great debate”. Educational Psychology Review, 6(1), 25-47. Gao, L. (2006). Toward a cognitive processing model of MELAB reading test item performance. Spaan Fellow Working Papers in Second or Foreign Language Assessment, 4, 1-40. Goodman, K. S. (1967). Reading: A psycholinguistic guessing game. Journal of the Reading Specialist, 6, 126-135. Gough, P. B. (1972). One second of reading. Visible Language, 6(4), 291-320. Hebb, D. O. (1949). The organization of behavior. New York: John Wiley & Sons. Hinton, G. E., & Shallice, T. (1991). Lesioning an attractor network: Investigations of acquired dyslexia. Psychological Review, 98(1), 74-95. Hulme, C., Snowling, M., & Quinlan, P. (1991). Connectionism and learning to read: Steps towards a psychologically plausible model. Reading and Writing, 3(2), 159-168. 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), 273-296. LaBerge, D., & Samuels, S. (1974). Toward a theory of automatic information processing in reading. Cognitive Psychology, 6, 293-323. McClelland, J. L., & Rumelhart, D. E. (1981). An interactive activation model of context effects in letter perception: Part 1. An account of basic findings. Psychological Review, 88(5), 375-407. McNeil, L. (2012). Extending the compensatory model of second language reading. System, 40, 64-76. Perfetti, R. (1991). A neural network to design neural networks. IEEE transactions on circuits and systems, 38(9), 1099-1103. Plaut, D. C. (2005) Connectionist approaches to reading, Eds. M. J. Snowling and C. Hulme. The Science of Reading: A Handbook. Oxford: Blackwell Publishing, 24–38. Plaut, D. C. (1996). Relearning after damage in connectionist networks: Toward a theory of rehabilitation. Brain and Language, 52, 25-82. 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, 56-115. Rumelhart, D. E. (1977). Toward and interactive model of reading. Ed. S. Dornic. Attention and performance. Hillsdale, NJ: Erlbaum. Seidenberg, M. S., & McClelland, J. L. (1989). A distributed, developmental model of word recognition and naming. Psychological Review, 96, 523-568. Sejnowski, T. J., & Rosenberg, C. R. (1987). Parallel networks that learn to pronounce English text. Complex Systems, 1, 145-168. Smith, F. (1971). Understanding reading. New York: Holt, Rinehart & Winston. Stanovich, K. E. (1980). Towards an interactive compensatory model of individual differences in the development of reading fluency. Reading Research Quarterly, 16, 32-71.

Ayrıntılar

Birincil Dil

İngilizce

Konular

-

Bölüm

Araştırma Makalesi

Yayımlanma Tarihi

21 Ekim 2018

Gönderilme Tarihi

22 Haziran 2018

Kabul Tarihi

6 Ekim 2018

Yayımlandığı Sayı

Yıl 2018 Sayı: 12

Kaynak Göster

APA
Toprak, T. E. (2018). Connectionism, Artificial Neural Networks and Reading. RumeliDE Dil ve Edebiyat Araştırmaları Dergisi, 12, 276-283. https://doi.org/10.29000/rumelide.472778
AMA
1.Toprak TE. Connectionism, Artificial Neural Networks and Reading. RumeliDE. 2018;(12):276-283. doi:10.29000/rumelide.472778
Chicago
Toprak, Tuğba Elif. 2018. “Connectionism, Artificial Neural Networks and Reading”. RumeliDE Dil ve Edebiyat Araştırmaları Dergisi, sy 12: 276-83. https://doi.org/10.29000/rumelide.472778.
EndNote
Toprak TE (01 Ekim 2018) Connectionism, Artificial Neural Networks and Reading. RumeliDE Dil ve Edebiyat Araştırmaları Dergisi 12 276–283.
IEEE
[1]T. E. Toprak, “Connectionism, Artificial Neural Networks and Reading”, RumeliDE, sy 12, ss. 276–283, Eki. 2018, doi: 10.29000/rumelide.472778.
ISNAD
Toprak, Tuğba Elif. “Connectionism, Artificial Neural Networks and Reading”. RumeliDE Dil ve Edebiyat Araştırmaları Dergisi. 12 (01 Ekim 2018): 276-283. https://doi.org/10.29000/rumelide.472778.
JAMA
1.Toprak TE. Connectionism, Artificial Neural Networks and Reading. RumeliDE. 2018;:276–283.
MLA
Toprak, Tuğba Elif. “Connectionism, Artificial Neural Networks and Reading”. RumeliDE Dil ve Edebiyat Araştırmaları Dergisi, sy 12, Ekim 2018, ss. 276-83, doi:10.29000/rumelide.472778.
Vancouver
1.Tuğba Elif Toprak. Connectionism, Artificial Neural Networks and Reading. RumeliDE. 01 Ekim 2018;(12):276-83. doi:10.29000/rumelide.472778