ArticleJournal of eye movement research2019
Reading Shakespeare Sonnets: Combining Quantitative Narrative Analysis and Predictive Modeling -an Eye Tracking Study.
Article in Journal of eye movement research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- How Do Stress Situations Affect Higher-Level Text Processing in L1 and L2 Readers? An Eye-Tracking Study.Journal of eye movement research · 2025Article
- Assessing the rereading effect of digital reading through eye movements using artificial neural networks.Frontiers in psychology · 2025Article
- The Effects of Emotional Design on Multimedia Learning and Appreciation of Chinese Poetry.Frontiers in psychology · 2021Article
- Fluctuation in Pupil Size and Spontaneous Blinks Reflect Story Transportation.Journal of eye movement research · 2020Article
- Eye movements and mental imagery during reading of literary texts with different narrative styles.Journal of eye movement research · 2020Article
- What Is the Difference? Rereading Shakespeare's Sonnets -An Eye Tracking Study.Frontiers in psychology · 2020Article
- Rhythmic subvocalization: An eye-tracking study on silent poetry reading.Journal of eye movement research · 2020Article
- Sentiment Analysis of Children and Youth Literature: Is There a Pollyanna Effect?Frontiers in psychology · 2020Article
- Following in Jakobson and Lévi-Strauss' footsteps: A neurocognitive poetics investigation of eye movements during the reading of Baudelaire's 'Les Chats'.Journal of eye movement research · 2020Article
- Reading Russian poetry: An expert-novice study.Journal of eye movement research · 2020Article
- From Abstract Symbols to Emotional (In-)Sights: An Eye Tracking Study on the Effects of Emotional Vignettes and Pictures.Frontiers in psychology · 2020Article
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Authors and funding
4 authors.
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Abstract
As a part of a larger interdisciplinary project on Shakespeare sonnets' reception (1, 2), the present study analyzed the eye movement behavior of participants reading three of the 154 sonnets as a function of seven lexical features extracted via Quantitative Narrative Analysis (QNA). Using a machine learning-based predictive modeling approach five 'surface' features (word length, orthographic neighborhood density, word frequency, orthographic dissimilarity and sonority score) were detected as important predictors of total reading time and fixation probability in poetry reading. The fact that one phonological feature, i.e., sonority score, also played a role is in line with current theorizing on poetry reading. Our approach opens new ways for future eye movement research on reading poetic texts and other complex literary materials(3).
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