Reading shakespeare sonnets: Combining quantitative narrative analysis and predictive modeling-An eye tracking study

Abstract

As a part of a larger interdisciplinary project on Shakespeare sounets' reception (Jacobs et al.. 2017; Xue et al.. 2017). the present study analyzed the eve movement behavior of participants reading three of the 154 sounets as a function of seven lexical features extracted via Quantitative Narrative Analysis (QNA). Using a maclnne learning-based predictive modellila approach five 'surface' featiires (word length, orthographic neighborhood density. word frequency. orthographic dissimilarity and sonority score) were detected as nnportant predictors of total reading time and fixation probability in poetry reading. The fact that one phonological featnre. 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).