Evidence map›Paper›PMID 39240533›Full record

ReviewPsychonomic bulletin & review2025

Towards a model of eye-movement control in Chinese reading.

Yanping Liu, Lili Yu, Erik D Reichle

Abstract readReview
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In one paragraph

Review in Psychonomic bulletin & review, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Yanping LiuDepartment of Psychology, Sun Yat-sen University, No. 135, Xingang Xi Road, Guangzhou, 510275, P. R. China. liuyp33@mail.sysu.edu.cn.ORCID http://orcid.org/0000-0002-6487-3738
Lili YuSchool of Psychological Sciences, Macquarie University, Sydney, Australia.
Erik D ReichleSchool of Psychological Sciences, Macquarie University, Sydney, Australia.

Funding

Australian Research Council Discovery Projects DP190100719Australian Research Council Discovery Projects DP200100311National Social Science Fund of China 21BYY105
6 · The paper itself

Abstract

The Chinese writing system has several features that make it markedly different from the alphabetic systems that have most often been examined in reading research, including the fact that individual words consist of various uniformly sized, box-shaped characters whose boundaries are not clearly demarcated (e.g., by blank spaces). These features raise the question: How do readers of Chinese "know" where to move their eyes for the purpose of efficiently segmenting and/or identifying words? To answer this question, we used the E-Z Reader model of eye-movement control in reading to run an 'experiment' involving a series of simulations in which two saccade-targeting assumptions (i.e., directing the eyes towards default targets vs. adjusting saccade length as a function of parafoveal processing difficulty) were factorially manipulated with three word-segmentation heuristics (i.e., ideal-observer knowledge of word boundaries vs. probabilistic guessing vs. familiarity-based segmentation) to examine which combination of assumptions provide the best quantitative account of eye-movement control during the reading of Chinese. Based on these simulations, we conclude the best account is one in which readers use relative differences in the familiarity of groups of parafoveal characters to dynamically adjust the lengths of saccades in a manner that affords efficient word identification. We discuss the broader theoretical implications of these conclusions for models of Chinese reading and for models of reading more generally.

Indexed as

Eye MovementsLanguagePattern Recognition, VisualReadingHumansSaccadesChineseComputational modelsEye-movement controlReading

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.