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Related Experiment Video

Updated: May 11, 2026

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects
07:36

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects

Published on: November 30, 2018

A framework for modeling the interaction of syntactic processing and eye movement control.

Felix Engelmann1, Shravan Vasishth, Ralf Engbert

  • 1Department of Linguistics, University of Potsdam, Germany. felix.engelmann@uni-potsdam.de

Topics in Cognitive Science
|May 18, 2013
PubMed
Summary

This study models how eye movements interact with language processing during reading. The model successfully predicts word re-reading based on parsing difficulty, advancing our understanding of reading behavior.

Keywords:
Computational modelingEye movementsParsing difficultyReadingSentence comprehensionSurprisalWorking memory

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Area of Science:

  • Cognitive Science
  • Computational Linguistics
  • Psycholinguistics
  • Cognitive Neuroscience

Background:

  • Reading involves complex interactions between oculomotor control and language comprehension.
  • Surprisal and cue-based memory retrieval are established predictors of reading time.
  • The precise interplay between sentence processing and eye movement control remains an open research question.

Purpose of the Study:

  • To computationally model the interaction between language comprehension and oculomotor control at the sentence level.
  • To investigate how parsing difficulty influences eye movement patterns, specifically re-reading.
  • To extend existing eye movement models with cognitive architecture components to simulate reading behavior.

Main Methods:

  • Integrated computational accounts of parsing difficulty (surprisal, cue-based retrieval) with the EMMA eye movement model within the ACT-R cognitive architecture.
  • Developed a reading model incorporating a linking hypothesis to simulate 'Time Out regressions' for postlexical processing.
  • Evaluated the model's predictive accuracy on the Potsdam Sentence Corpus, focusing on frequency effects and re-reading patterns.

Main Results:

  • The extended eye movement model successfully predicted word re-reading based on parsing difficulty.
  • The integration of postlexical processing within the EMMA model significantly improved predictions of re-reading rates and durations.
  • The model demonstrated a reasonable fit to empirical data, validating the proposed interaction mechanisms.

Conclusions:

  • The study provides a foundational computational model for understanding the interaction between high-level language processing and eye movement control.
  • Simple, independently motivated assumptions can effectively explain complex reading phenomena like re-reading.
  • This work paves the way for more precise investigations into the cognitive mechanisms underlying reading.