Fan, Xi (2020) A developmental exploration of Chinese reading in a population of early readers: from eye movement control to textual coherence. PhD thesis, National University of Ireland Maynooth.

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Abstract

The research described in this thesis explores the developmental characteristic of Chinese reading in a population of young readers with different reading ability. There are two in-depth explorations described: (1) an exploration of the processes underlying eye movement control in Chinese reading; and (2) the use of semantic similarity measures based on distributed representations of words, sentences, and paragraphs to assess the impact of supra-lexical constraints on eye-movements in beginning readers of Chinese. The main results show that the most likely account of processes underlying eye movement control in Chinese reading is a two-factor process whereby the character is the main driver for longer saccades and that the word plays a role in shorter ones. A small-scale extension to the Glenmore model of eye movement control in reading is proposed to account for the saccade targeting patterns of readers. It provides an integrated account of the dynamic interaction of the two factors and demonstrates that a model architecture facilitating a dynamic interaction between top down lexical and orthographic constrains and bottom-up visual inputs is suited to account for Chinese readers’ eye movement. Results also showed that text similarity measures have a significant impact on the moment-to moment processing of words in reading. Consequently, these factors need to be incorporated into any realistic model of Chinese reading. An additional study is described where a character confusion matrix was generated that can be used as a resource for both pedagogical and psycholinguistic studies of Chinese reading and other studies interested in character recognition.

Item Type:	Thesis (PhD)
Keywords:	developmental exploration; Chinese; reading; population; early readers; eye movement control; textual coherence;
Academic Unit:	Faculty of Science and Engineering > Computer Science
Item ID:	13586
Depositing User:	IR eTheses
Date Deposited:	16 Nov 2020 16:31
URI:
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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