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Related Experiment Video

Updated: Oct 3, 2025

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects
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BayesGaze: A Bayesian Approach to Eye-Gaze Based Target Selection.

Zhi Li1, Maozheng Zhao1, Yifan Wang1

  • 1Stony Brook University.

Proceedings. Graphics Interface (Conference)
|February 21, 2022
PubMed
Summary
This summary is machine-generated.

BayesGaze, a Bayesian method, enhances eye-gaze target selection accuracy and speed. It improves upon dwell-based and Center of Gravity Mapping methods by using posterior probabilities and target priors.

Keywords:
Human-centered computing—Human computer interaction (HCI)Human-centered computing—Human computer interaction (HCI)—HCI design and evaluation methods—User studiesHuman-centered computing—Human computer interaction (HCI)—Interaction techniques

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Area of Science:

  • Human-Computer Interaction
  • Biomedical Engineering
  • Machine Learning

Background:

  • Accurate and rapid target selection using eye-gaze input is a significant challenge in human-computer interaction.
  • Existing methods like dwell-based selection and Center of Gravity Mapping (CM) have limitations in speed and accuracy.

Purpose of the Study:

  • To introduce BayesGaze, a novel Bayesian approach for determining target selection from eye-gaze trajectories.
  • To evaluate the performance of BayesGaze against established eye-gaze selection techniques.

Main Methods:

  • BayesGaze utilizes Bayes' theorem to compute posterior probabilities of target selection based on eye-gaze sampling points.
  • It accumulates these probabilities, weighted by sampling intervals, to identify the selected target.
  • A categorical distribution models target priors, which are updated based on selection outcomes.

Main Results:

  • BayesGaze demonstrated significant improvements in both target selection accuracy and speed compared to dwell-based and CM methods.
  • The study confirmed the effectiveness of accumulating posterior probabilities in enhancing performance.
  • Incorporating prior target distributions also proved beneficial for improving eye-gaze selection outcomes.

Conclusions:

  • BayesGaze offers a more accurate and faster method for eye-gaze based target selection.
  • The Bayesian framework, combining posterior probabilities and prior distributions, effectively addresses limitations in current eye-gaze control systems.
  • This approach has the potential to advance assistive technologies and human-computer interaction interfaces.