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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
PubMed
Summary
This summary is machine-generated.

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 allocationattention behaviorhybrid entropyinformation entropy

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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.