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

Shuwei Xue,Teresa Sylvester,Jana Lüdtke,Arthur M Jacobs

doi:10.16910/jemr.12.5.2

Shuwei Xue, Teresa Sylvester + Show 2 more

Open Access

https://doi.org/10.16910/jemr.12.5.2

Copy DOI

Abstract

As a part of a larger interdisciplinary project on Shakespeare sonnets’ reception (1, 2), 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(3).

Highlights

When was the last time you read a poem, or a piece of literature? The answer of many people might well be ‘today’ or ‘yesterday’
In the context of our Quantitative Narrative Analysis (QNA)-based predictive modeling approach, here we considered a minimalistic first attempt at introducing an already considerably more complex way of analyzing eye movements in reading poetic texts
Following up on earlier proposals (Jacobs et al, 2017), this study aimed to identify psycholinguistic surface features that shape eye movement behavior while reading Shakespeare sonnets by using a combination of QNA and predictive modeling techniques

Summary

Introduction

When was the last time you read a poem, or a piece of literature? The answer of many people might well be ‘today’ or ‘yesterday’. M. (2019) Reading Shakespeare sonnets: combining quantitative narrative analysis and predictive modeling—an eye tracking study. Within the fields of reading and eye tracking research, single words or single sentences from non-literary materials appear to be the most extensively investigated text materials (e.g., Clifton et al, 2007; Radach & Kennedy, 2013; Rayner, 2009). E.g., word length or word frequency, work differently in a connected text context (Kuperman et al, 2010, 2013; Wallot et al, 2013), empirical research using natural materials like narrative texts or poems are quite rare and the majority of studies on literary works confine to text-based qualitative aspects (e.g., ‘close reading’). Reading research seems to be experiencing difficulty to open itself for empirical studies focusing on more natural and ecologically valid reading acts, as recently admonished by several researchers (e.g., Jacobs, 2015a; Radach et al, 2008; Wallot et al, 2013)

Objectives

Methods

Results

Conclusion