@article{Xue_Lüdtke_Sylvester_Jacobs_2019, title={Reading Shakespeare sonnets: Combining quantitative narrative analysis and predictive modeling - an eye tracking study}, volume={12}, url={https://bop.unibe.ch/JEMR/article/view/4460-Xue-final-sub}, DOI={10.16910/jemr.12.5.2}, abstractNote={<div class="page" title="Page 1"> <div class="section"> <div class="layoutArea"> <div class="column"> <p><span style="font-size: 9.000000pt; font-family: ’TimesNewRomanPSMT’;">As a part of a larger </span><span style="font-size: 9.000000pt; font-family: ’TimesNewRomanPSMT’;">interdisciplinary project on Shakespeare sonnets’ reception (Jacobs et al., 2017; Xue et al., </span><span style="font-size: 9.000000pt; font-family: ’TimesNewRomanPSMT’;">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- </span><span style="font-size: 9.000000pt; font-family: ’TimesNewRomanPSMT’;">based predictive modeling approach five ‘surface’ features (word length, orthographic neighborhood density, word </span><span style="font-size: 9.000000pt; font-family: ’TimesNewRomanPSMT’;">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). </span></p> </div> </div> </div> </div>}, number={5}, journal={Journal of Eye Movement Research}, author={Xue, Shuwei and Lüdtke, Jana and Sylvester, Teresa and Jacobs, Arthur M.}, year={2019}, month={Mar.} }