Research Article

Connectionism, Artificial Neural Networks and Reading

Number: 12 October 21, 2018
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Connectionism, Artificial Neural Networks and Reading

Abstract

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. 

Keywords

References

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Details

Primary Language

English

Subjects

-

Journal Section

Research Article

Publication Date

October 21, 2018

Submission Date

June 22, 2018

Acceptance Date

October 6, 2018

Published in Issue

Year 2018 Number: 12

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, nos. 12: 276-83. https://doi.org/10.29000/rumelide.472778.
EndNote
Toprak TE (October 1, 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, no. 12, pp. 276–283, Oct. 2018, doi: 10.29000/rumelide.472778.
ISNAD
Toprak, Tuğba Elif. “Connectionism, Artificial Neural Networks and Reading”. RumeliDE Dil ve Edebiyat Araştırmaları Dergisi. 12 (October 1, 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, no. 12, Oct. 2018, pp. 276-83, doi:10.29000/rumelide.472778.
Vancouver
1.Tuğba Elif Toprak. Connectionism, Artificial Neural Networks and Reading. RumeliDE. 2018 Oct. 1;(12):276-83. doi:10.29000/rumelide.472778