Connectionism, artificial neural networks and reading
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Tarih
2018
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Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Connectionism, which is a novel approach to human intellectual abilities, has challenged the basicassumptions and tenets of top-down and interactive approaches of the 1960s and 1970s to humancognitive processing and reading. Connectionism has specifically dealt with reading in order tounderstand and model the cognitive processes and intellectual properties underlying this significantskill. It has also embraced a more bottom-up approach to reading, an orientation which attaches greatimportance to pattern recognition governed by parameters, weights, connections and constraints inlieu of rules and symbols. Although the great majority of studies which applied connectionism haveconcentrated on how words are recognized, a considerable amount of scholarly work also has targetedat understanding syntactic parsing and pronouncing words. To date, connectionism has contributedto the understanding and modeling human reading and attracted the attention of researchers workingin 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 neuralnetwork modeling that suggest an alternative to the classical theory of the mind while accounting forthe cognitive processes that underlie human reading. The paper also compares the connectionistapproaches to traditional approaches to reading, such as bottom-up, top-down and interactiveapproaches. Finally, it reviews several connectionist models that have proved to be highly influentialin the relevant literature.
