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Reading Shakespeare Sonnets: Combining Quantitative Narrative Analysis and Predictive Modeling -an Eye Tracking

Shuwei Xue1, Jana Lüdtke1, Teresa Sylvester1

  • 1Freie Universität Berlin, Germany.

Journal of Eye Movement Research
|April 8, 2021
PubMed
Summary
This summary is machine-generated.

Researchers analyzed eye movements during Shakespearean sonnet reading. Lexical features like word length and sonority score predicted reading time and fixation, advancing computational literary studies.

Keywords:
Literary readingQNAeye movementseye trackingpredictive modeling

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Area of Science:

  • Cognitive Psychology
  • Computational Linguistics
  • Literary Studies

Background:

  • Shakespearean sonnets have a rich reception history.
  • Understanding the cognitive processes of reading poetry is an ongoing challenge.
  • Quantitative Narrative Analysis (QNA) offers tools for literary text analysis.

Purpose of the Study:

  • To investigate the influence of lexical features on eye movement behavior during poetry reading.
  • To identify key predictors of reading time and fixation probability in Shakespearean sonnets.
  • To explore the application of machine learning in analyzing literary texts and reader responses.

Main Methods:

  • Participants read three Shakespearean sonnets.
  • Eye movement data (total reading time, fixation probability) were collected.
  • Machine learning predictive modeling was used to analyze seven lexical features extracted via QNA.

Main Results:

  • Five 'surface' lexical features (word length, orthographic neighborhood density, word frequency, orthographic dissimilarity, sonority score) were significant predictors.
  • Sonority score, a phonological feature, was identified as an important predictor, aligning with current theories.
  • The study successfully modeled reading behavior based on textual characteristics.

Conclusions:

  • Lexical and phonological features significantly influence the cognitive processing of poetry.
  • This approach provides novel methods for eye movement research in literary studies.
  • The findings contribute to understanding the interplay between text properties and reading behavior in complex literary works.