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Dissociation and Association of Chunks

Year: 2023
Type: article
Abstract: Chunks, or patterns of sequences, have been attracting attention in studies of second language acquisition.For example, Ellis (2001) stated that "language acquisition is essentially a sequence learning problem: the acquisition of word form, collocations, and grammatical class information all results from predominantly unconscious (or implicit) processes of analysis of sequence information" (p.41).Chunks have been investigated under different terms with slightly different interests.For example, there are numerous studies on formulaic sequences, which includes the notion of nativelikeness and pragmatics.Use of multiword sequences improves fluency of spoken performance in a second language, and studying such relationship can provide insights into language production (Tavakoli & Uchida, 2020).However, they can be considered chunks from the perspective of processing (Bybee, 2010).Although the data-base of chunks is a precious source for fast and light-burden processing, learners of a second language tend to have little of this, as they start learning a second language by remembering and applying rules, without the data-base that is supposed to be at hand in the case of their first language.Filling in this missing database may lead to more easy processing of a second language.Considering that chunking is unconscious processing by nature (Ellis, 2002), incorporation of implicit learning of chunks may be one possible way to provide a data-base of chunks.This study defines chunks, followed by a description of chunking and segmentation to spotlight chunking as association.First language development is then briefly described to demonstrate there is a lack of data-base of chunks in learners of a second language.Finally, the study argues that encouraging implicit learning of chunks can be a solution to fill in the missing data-base. Dissociation and Association of ChunksKoichi YAMAOKA
Language: English
Field: Psychology
Open Access status: green
OpenAlex ID: W7146380496
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Cites: 10
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Related to: 10