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Cognitive Science
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Cognitive Science
Article . 2017 . Peer-reviewed
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Models of Chinese Reading: Review and Analysis

Authors: Erik D, Reichle; Lili, Yu;

Models of Chinese Reading: Review and Analysis

Abstract

AbstractOur understanding of the cognitive processes involved in reading has been advanced by computational models that simulate those processes (e.g., see Reichle, 2015). Unfortunately, most of these models have been developed to explain the reading of English and other alphabetic languages, with relatively fewer efforts to examine whether or not the assumptions of these models also explain what has been learned from other languages and, in particular, non‐alphabetic writing systems like Chinese (e.g., see Li, Zang, Liversedge, & Pollatsek, 2015). In this article, we will review those computational models that have been developed to explain the reading of Chinese, with the goal of comparing their theoretical assumptions to those of models that explain the reading of English. Our analysis indicates that there are both points of convergence and divergence between the theoretical assumptions of Chinese versus English models, suggesting that the cognitive systems supporting reading may be differentially influenced by features of the languages and/or writing systems, or that certain theoretical assumptions developed to explain the reading of one language might be adapted to explain the reading of others.

Related Organizations
Keywords

Reading, Humans, Models, Psychological, Language

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
28
Top 10%
Top 10%
Top 10%
bronze