Data and code relating to Husain, Narayanan, Vasishth, 2015. Integration and prediction difficulty in Hindi sentence comprehension: Evidence from an eye-tracking corpus. Journal of Eye Movement Research, 8(2):1-12, 2015.
This repository contains the Potsdam Allahabad Eyetracking Corpus.
@article{HusainVasishthNarayanan2015,
title={Integration and prediction difficulty in {H}indi sentence comprehension: {E}vidence from an eye-tracking corpus},
author={Samar Husain and Shravan Vasishth and Narayanan Srinivasan},
volume = {8(2)},
issue = {3},
pages = {1--12},
journal = {Journal of Eye Movement Research},
year={2015},
abstract = {This is the first attempt at characterizing reading difficulty in Hindi using naturally occurring sentences.
We created the Potsdam-Allahabad Hindi Eyetracking Corpus by recording eye-movement data from 30 participants at the
University of Allahabad, India. The target stimuli were 153 sentences selected from the beta version of the Hindi-Urdu
treebank. We find that word- or low-level predictors (syllable length, unigram and bigram frequency) affect first-pass
reading times, regression path duration, total reading time, and outgoing saccade length. An increase in syllable length
results in longer fixations, and an increase in word unigram and bigram frequency leads to shorter fixations. Longer
syllable length and higher frequency lead to longer outgoing saccades. We also find that two predictors of sentence
comprehension diffi- culty, integration and storage cost, have an effect on reading difficulty. Integration cost
(Gibson, 2000) was approximated by calculating the distance (in words) between a dependent and head; and storage cost
(Gibson, 2000), which measures difficulty of maintaining predictions, was estimated by counting the number of predicted
heads at each point in the sentence. We find that integration cost mainly affects outgoing saccade length, and storage
cost affects total reading times and outgoing saccade length. Thus, word-level predictors have an effect in both early
and late measures of reading time, while predictors of sentence comprehension difficulty tend to affect later measures.
This is, to our knowledge, the first demonstration using eye-tracking that both integration and storage cost influence
reading difficulty.},
pdf = {http://www.ling.uni-potsdam.de/~vasishth/pdfs/HusainEtAlETHindiJEMR2015.pdf}
}