ORIGINAL RESEARCH article
Front. Psychol.
Sec. Psychology of Language
Volume 16 - 2025 | doi: 10.3389/fpsyg.2025.1576247
Assessing the rereading effect of digital reading through eye movements using artificial neural networks
Provisionally accepted
- 1Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences (CAS), Beijing, Beijing Municipality, China
- 2University of Chinese Academy of Sciences, Beijing, China
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Objective: This study aimed to investigate the differences in eye movement characteristics between first reading and rereading and to develop a neural network model for classifying these reading practices. The primary goal was to enhance the understanding of rereading identification and provide insights into assessing students' text familiarity.Methods: We compared eye movement metrics during first reading and rereading, focusing on parameters such as total reading time, fixation duration, regression size, regression count, and local eye movement behaviors within areas of interest (AOIs).Pupil size, the proportion of fixation duration, and regression duration within and across lines were also examined. A neural network model was constructed to classify the reading practices based on these metrics.Results: During rereading, students exhibited shorter total reading time, fixation durations, and fewer regression counts compared to first reading. Regression size was longer during rereading. Local eye movement behaviors within AOIs were also reduced.However, pupil size, the proportion of fixation duration, and regression duration within and across lines were not useful in identifying rereading. The neural network model achieved an accuracy of 0.769, precision of 0.774, recall of 0.788, and an F1-score of 0.781.The findings demonstrate distinct eye movement patterns between first reading and rereading, highlighting the effectiveness of certain metrics in differentiating these practices. The neural network model provides a promising tool for rereading identification. These results expand our understanding of rereading behavior and offer valuable insights for assessing students' text familiarity.
Keywords: Rereading effect, Digital reading, EYE MOVEMENT, saccade, Neural network model Kastrati, A., Plomecka, M. B., Wattenhofer, R., & Langer, N. (2021). Using Deep Learning to Classify Saccade Direction from Brain Activity. ACM Symposium
Received: 13 Feb 2025; Accepted: 23 Jul 2025.
Copyright: © 2025 XU, Liang, Jin, Wang, Gao and Tao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Ligang Wang, Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences (CAS), Beijing, 100101, Beijing Municipality, China
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