Reading Shakespeare sonnets: Combining quantitative narrative analysis and predictive modeling - an eye tracking study

  • Shuwei Xue Department of Experimental and Neurocognitive Psychology, Freie Universität Berlin, Germany
  • Jana Lüdtke Department of Experimental and Neurocognitive Psychology, Freie Universität Berlin, Germany
  • Teresa Sylvester Department of Experimental and Neurocognitive Psychology, Freie Universität Berlin, Germany
  • Arthur M. Jacobs Department of Experimental and Neurocognitive Psychology, Freie Universität Berlin, Germany; Dahlem Institute for Neuroimaging of Emotion (D.I.N.E.), Berlin, Germany; Center for Cognitive Neuroscience Berlin (CCNB), Berlin, Germany
Keywords: Literary reading, eye movements, eye tracking, QNA, predictive modeling

Abstract

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).

Published
27-03-2019
How to Cite
Xue, S., Lüdtke, J., Sylvester, T., & Jacobs, A. (2019). Reading Shakespeare sonnets: Combining quantitative narrative analysis and predictive modeling - an eye tracking study. Journal of Eye Movement Research, 12(5). https://doi.org/10.16910/jemr.12.5.2