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Related Experiment Video

Updated: Nov 27, 2025

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

Supervisors' Visual Attention Allocation Modeling Using Hybrid Entropy.

Haifeng Bao¹, Weining Fang¹, Beiyuan Guo¹

¹State Key Lab Rail Traff Control & Safety, School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, 100044 Beijing, China.

Entropy (Basel, Switzerland)

|December 3, 2020

Summary

Supervisors focus on fuzzy information and prioritize important data in complex systems. A new model accurately predicts this visual attention allocation, aiding interface design and cognitive optimization.

Keywords:

attention allocation attention behavior hybrid entropy information entropy

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Related Experiment Videos

Last Updated: Nov 27, 2025

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Published on: January 23, 2017

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Published on: January 18, 2020

Investigating the Deployment of Visual Attention Before Accurate and Averaging Saccades via Eye Tracking and Assessment of Visual Sensitivity

Investigating the Deployment of Visual Attention Before Accurate and Averaging Saccades via Eye Tracking and Assessment of Visual Sensitivity

Published on: March 18, 2019

Area of Science:

Human-Computer Interaction
Cognitive Science
Automation Technology

Background:

Automation advancements shift human roles to system supervision.
Understanding supervisor visual attention is crucial for effective human-machine interface (HMI) design.
Supervisors exhibit tendencies to focus on information with higher uncertainty (fuzziness) and perceived importance.

Purpose of the Study:

To develop and validate a model for visual attention allocation in human supervisors.
To integrate cognitive tendencies of fuzziness preference and information importance into an attention model.
To evaluate the model's effectiveness using a real-world HMI simulation.

Main Methods:

Quantified fuzziness tendency using hybrid entropy and probability of correct evaluation.
Defined importance tendency via a novel value priority function based on information theory.
Integrated these tendencies to formulate an informative top-down visual attention allocation mechanism.
Validated the model using a Building Automatic System (BAS) simulator in a subway environment.

Main Results:

The proposed model demonstrated strong agreement with observed supervisor attention behaviors.
Experimental results confirmed the model's ability to predict visual attention allocation.
The model proved effective and reasonable when compared to existing attention models.

Conclusions:

The developed visual attention allocation model provides a robust framework for understanding supervisor behavior.
The model is promising for applications in behavior analysis, cognitive optimization, and industrial HMI design.
This research contributes to the effective design and evaluation of complex automated systems.