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Updated: Aug 29, 2025

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

Towards tacit knowledge mining within context: Visual cognitive graph model and eye movement image interpretation.

Weiwei Yu¹, Dian Jin², Wenfeng Cai²

¹School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, China; Unmanned System Research Institute, Northwestern Polytechnical University, Xi'an, China.

Computer Methods and Programs in Biomedicine

|September 12, 2022

Summary

This summary is machine-generated.

This study introduces a visual cognitive graph model to quantify expert tacit knowledge in visual attention, aiding novice training and system design. The model effectively integrates task context, revealing hidden operational insights.

Keywords:

Eye movement image Tacit knowledge Visual attention Visual cognitive graph

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Area of Science:

Cognitive Science
Human-Computer Interaction
Data Science

Background:

Visual attention is crucial for managing cognitive resources but expert tacit knowledge is difficult to transfer.
Traditional models lack task context and explicit quantification of this hidden knowledge.
This limits novice training and the design of adaptive interaction systems.

Purpose of the Study:

To propose a novel visual cognitive graph model for representing visual attention within task contexts.
To develop a data mining approach for quantitatively analyzing operator tacit knowledge.
To demonstrate the model's utility in training and adaptive system design.

Main Methods:

Developed a visual cognitive graph model using graph theory to incorporate task context.
Applied data mining techniques to identify attention patterns and quantify tacit knowledge.
Introduced three graph-theory-derived physical quantities to describe tacit knowledge.

Main Results:

The visual cognitive graph model effectively incorporates task context, outperforming traditional eye-movement models.
The tacit knowledge mining method successfully reveals underlying operational knowledge, surpassing statistical analysis.
Demonstrated practical applications in flight operations, operator training, and adaptive interaction systems.

Conclusions:

The proposed model provides a quantitative method for understanding and transferring expert tacit knowledge.
This approach enhances operator training and enables the development of more intelligent adaptive systems.
The method uncovers deeper insights into visual information processing and decision-making.