Texts are often reread in everyday life, but most studies of rereading have been based on expository texts, not on literary ones such as poems, though literary texts may be reread more often than others. To correct this bias, the present study is based on two of Shakespeare's sonnets. Eye movements were recorded, as participants read a sonnet then read it again after a few minutes. After each reading, comprehension and appreciation were measured with the help of a questionnaire. In general, compared to the first reading, rereading improved the fluency of reading (shorter total reading times, shorter regression times, and lower fixation probability) and the depth of comprehension. Contrary to the other rereading studies using literary texts, no increase in appreciation was apparent. Moreover, results from a predictive modeling analysis showed that readers' eye movements were determined by the same critical psycholinguistic features throughout the two sessions. Apparently, even in the case of poetry, the eye movement control in reading is determined mainly by surface features of the text, unaffected by repetition.
As a part of a larger interdisciplinary project on Shakespeare sonnets’ reception (Jacobs et al., 2017; Xue et al., 2017), 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 (cf. Jacobs, 2015c).
In this theoretical paper, we would like to pave the ground for future empirical studies in Neurocognitive Poetics by describing relevant properties of Shakespeare’s 154 sonnets extracted via Quantitative Narrative Analysis. In the first two parts, we quantify aspects of the sonnets’ cognitive and affective-aesthetic features, as well as indices of their thematic richness, symbolic imagery, and semantic association potential. In the final part, we first demonstrate how the results of these quantitative narrative analyses can be used for generating testable predictions for empirical studies of literature. Second, we feed the quantitative narrative analysis data into a machine learning algorithm which successfully classifies the 154 sonnets into two main categories, i.e. the young man and dark lady poems. This shows how quantitative narrative analysis data can be combined with computational modeling for identifying those of the many quantifiable sonnet features that may play a key role in their reception.
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