Assessing the rereading effect of digital reading through eye movements using artificial neural networks
ObjectiveThis 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 insigh...
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Psychology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyg.2025.1576247/full |
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| author | Ying Xu Ying Xu Mingzhen Liang Mingzhen Liang Yuanyuan Jin Yuanyuan Jin Ligang Wang Ligang Wang Wenbin Gao Wenbin Gao Ting Tao Ting Tao |
| author_facet | Ying Xu Ying Xu Mingzhen Liang Mingzhen Liang Yuanyuan Jin Yuanyuan Jin Ligang Wang Ligang Wang Wenbin Gao Wenbin Gao Ting Tao Ting Tao |
| author_sort | Ying Xu |
| collection | DOAJ |
| description | ObjectiveThis 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.MethodsWe 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.ResultsDuring 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.ConclusionThe 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. |
| format | Article |
| id | doaj-art-e64ccf4e540d4ee286b00604122ef963 |
| institution | Kabale University |
| issn | 1664-1078 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj-art-e64ccf4e540d4ee286b00604122ef9632025-08-21T10:54:28ZengFrontiers Media S.A.Frontiers in Psychology1664-10782025-08-011610.3389/fpsyg.2025.15762471576247Assessing the rereading effect of digital reading through eye movements using artificial neural networksYing Xu0Ying Xu1Mingzhen Liang2Mingzhen Liang3Yuanyuan Jin4Yuanyuan Jin5Ligang Wang6Ligang Wang7Wenbin Gao8Wenbin Gao9Ting Tao10Ting Tao11CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, ChinaDepartment of Psychology, University of Chinese Academy of Sciences, Beijing, ChinaCAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, ChinaDepartment of Psychology, University of Chinese Academy of Sciences, Beijing, ChinaCAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, ChinaDepartment of Psychology, University of Chinese Academy of Sciences, Beijing, ChinaCAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, ChinaDepartment of Psychology, University of Chinese Academy of Sciences, Beijing, ChinaCAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, ChinaDepartment of Psychology, University of Chinese Academy of Sciences, Beijing, ChinaCAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, ChinaDepartment of Psychology, University of Chinese Academy of Sciences, Beijing, ChinaObjectiveThis 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.MethodsWe 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.ResultsDuring 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.ConclusionThe 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.https://www.frontiersin.org/articles/10.3389/fpsyg.2025.1576247/fullrereading effectdigital readingeye movementsaccadeneural network model |
| spellingShingle | Ying Xu Ying Xu Mingzhen Liang Mingzhen Liang Yuanyuan Jin Yuanyuan Jin Ligang Wang Ligang Wang Wenbin Gao Wenbin Gao Ting Tao Ting Tao Assessing the rereading effect of digital reading through eye movements using artificial neural networks Frontiers in Psychology rereading effect digital reading eye movement saccade neural network model |
| title | Assessing the rereading effect of digital reading through eye movements using artificial neural networks |
| title_full | Assessing the rereading effect of digital reading through eye movements using artificial neural networks |
| title_fullStr | Assessing the rereading effect of digital reading through eye movements using artificial neural networks |
| title_full_unstemmed | Assessing the rereading effect of digital reading through eye movements using artificial neural networks |
| title_short | Assessing the rereading effect of digital reading through eye movements using artificial neural networks |
| title_sort | assessing the rereading effect of digital reading through eye movements using artificial neural networks |
| topic | rereading effect digital reading eye movement saccade neural network model |
| url | https://www.frontiersin.org/articles/10.3389/fpsyg.2025.1576247/full |
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