Literature DB >> 31944807

Repetition causes confusion: Insights to word segmentation during Chinese reading.

Jingwen Wang1, Bernhard Angele2, Guojie Ma3, Xingshan Li1.   

Abstract

Since there are no spaces between words to mark word boundaries in Chinese, it is common to see 2 identical neighboring characters in natural text. Usually, this occurs when there are 2 adjacent words containing the same character (we will call such a coincidental sequence of 2 identical characters repeated characters). In the present study, we examined how Chinese readers process words when there are repeated characters. In 3 experiments, we compared how Chinese readers process 4-character strings including 2 repeated characters (e.g. , pinyin: xíngdòng dòngjī, meaning behavioral motivation) with a control condition where none of the characters repeat (e.g. , pinyin: xíngdòng yùwàng, meaning behavioral desire). In Experiment 1, the 4-character strings were presented for 40 ms and participants were asked to report as many characters as possible. Participants reported the second and third characters less accurately in the repeated condition than the control condition. In Experiments 2A and 2B, we embedded 2 different types of 4-character strings, compound Chinese characters and simple Chinese characters, into the same sentence frames, and asked participants to read these sentences normally. Gaze duration and total time on the second word were significantly longer in the repeated condition. These results suggest that the repeated characters increased the difficulty of word processing. Moreover, the results are consistent with the predictions of serial models, which assumes that words are processed serially in reading. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Entities:  

Year:  2020        PMID: 31944807     DOI: 10.1037/xlm0000817

Source DB:  PubMed          Journal:  J Exp Psychol Learn Mem Cogn        ISSN: 0278-7393            Impact factor:   3.051


  1 in total

1.  A hybrid Chinese word segmentation model for quality management-related texts based on transfer learning.

Authors:  Peihan Wen; Linhan Feng; Tian Zhang
Journal:  PLoS One       Date:  2022-10-07       Impact factor: 3.752

  1 in total

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