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Related Experiment Video

Updated: May 20, 2026

Using Eye Movements to Evaluate the Cognitive Processes Involved in Text Comprehension
06:49

Using Eye Movements to Evaluate the Cognitive Processes Involved in Text Comprehension

Published on: January 10, 2014

Exploring how textual complexity affects cognitive load during reading: an eye-tracking study.

David Vaněček1, Martin Kursch2, Eyüp Şen2,3

  • 1Department of Pedagogical and Psychological Studies, Masaryk Institute of Advanced Studies, Czech Technical University in Prague, Kolejni 2637/2a, 160 00, Praha, Czechia. david.vanecek@cvut.cz.

Scientific Reports
|May 18, 2026
PubMed
Summary

Increased text complexity elevates cognitive load during reading, as evidenced by more fixations, regressions, and longer reading times. Eye-tracking metrics effectively gauge reading difficulty and mental effort, unlike pupillary responses.

Keywords:
Cognitive loadEye movementEye-trackingText complexity

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Last Updated: May 20, 2026

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06:49

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Published on: January 10, 2014

Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading
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07:36

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

Published on: November 30, 2018

Area of Science:

  • Cognitive Psychology
  • Educational Psychology
  • Human-Computer Interaction

Background:

  • Understanding cognitive load during reading is crucial for effective learning and learner engagement.
  • Eye-movement behavior provides insights into cognitive processes during text comprehension.
  • Textual complexity, encompassing lexical, syntactic, and semantic factors, is a key determinant of reading difficulty.

Purpose of the Study:

  • To investigate how manipulated text complexity influences eye-movement behavior and cognitive load.
  • To determine if key eye-tracking metrics (fixations, regressions, pupil area) and reading duration differ between easy and hard texts.
  • To assess the sensitivity of gaze-based metrics and pupillary responses to variations in cognitive demand.

Main Methods:

  • Eye-tracking technology was employed to collect detailed reading data from 33 university students.
  • Text complexity was experimentally manipulated across lexical, syntactic, and semantic dimensions.
  • Participants' perceived mental effort was measured using the NASA-TLX (NASA Task-Load Index).

Main Results:

  • Higher text complexity resulted in increased fixations, more regressions, and longer reading durations, indicating elevated cognitive load.
  • Self-reported mental effort (NASA-TLX) was significantly higher for complex texts.
  • No significant differences in pupillary metrics were observed between easy and hard text conditions.

Conclusions:

  • Gaze-based eye-tracking metrics are sensitive indicators of cognitive demand during reading.
  • Reading duration, fixations, and regressions effectively reflect cognitive load variations.
  • Pupillary responses may be less reliable indicators of cognitive load in certain reading contexts.