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Related Experiment Video

Updated: Jul 9, 2025

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

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

Published on: October 18, 2018

Human attention during goal-directed reading comprehension relies on task optimization.

Jiajie Zou^1,2, Yuran Zhang¹, Jialu Li³

¹Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Sciences, Zhejiang University, Hangzhou, China.

|November 30, 2023

Summary

Computational models predict attention during goal-directed reading. Deep neural networks (DNNs) optimized for reading tasks explain word reading times, mirroring human attention patterns for text features and question relevance.

Keywords:

computational neuroscience deep neural network eye movements human neuroscience reading comprehension visual attention

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Last Updated: Jul 9, 2025

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

Published on: October 18, 2018

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Published on: January 10, 2014

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

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

Published on: November 30, 2018

Area of Science:

Cognitive Science
Computational Neuroscience
Artificial Intelligence

Background:

Understanding attention allocation in complex, goal-directed tasks is a significant challenge in cognitive science.
Reading to answer a question is a common real-world task that requires focused attention.

Purpose of the Study:

To investigate computational models that can explain attention distribution during goal-directed reading.
To determine how task optimization influences attention allocation in reading.

Main Methods:

Utilized transformer-based deep neural networks (DNNs) optimized for a reading comprehension task.
Correlated DNN attention weights with human word reading times obtained through eye-tracking.
Compared DNNs trained on reading comprehension versus word prediction tasks.

Main Results:

Reading time on each word was accurately predicted by attention weights in DNNs trained for the same reading task.
Eye-tracking showed distinct attention to text features (first-pass) and question relevance (rereading) in humans.
Shallow DNN layers modulated attention by text features, while deep layers modulated by question relevance.
DNNs trained for word prediction, not reading comprehension, predicted reading times when no question was present.

Conclusions:

Transformer-based DNNs provide a viable computational account for attention distribution in goal-directed reading.
Task optimization significantly modulates how attention is allocated, both in DNNs and human readers.
The findings offer insights into the interplay between low-level text features and high-level task goals in attention.