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Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
Published on: April 11, 2025
Gaze distribution analysis and saliency prediction across age groups.
Onkar Krishna1, Andrea Helo2,3, Pia Rämä2,4
1Dept. of Information and Communication Engineering, The University of Tokyo, Tokyo, Japan.
Computational models of visual attention can be improved by considering age-related differences. This study developed an age-adapted framework to predict visual saliency across diverse age groups, enhancing prediction accuracy.
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Area of Science:
- Computational neuroscience
- Human-computer interaction
- Visual perception
Background:
- Current computational models of visual attention primarily focus on young adults.
- Age-related variations in visual scene processing are largely overlooked.
- Understanding these variations is crucial for developing universally applicable attention models.
Purpose of the Study:
- To investigate age-related changes in visual scene processing.
- To propose an age-adapted framework for computational saliency models.
- To enhance the prediction of regions of interest across different age groups.
Main Methods:
- Analysis of eye movement data from 82 observers across four age groups exploring visual scenes.
- Quantification of explorativeness using saliency map entropy.
- Utilized Area Under the Curve (AUC) metrics for agreement and center bias analysis.
Main Results:
- Identified age-specific variations in observer explorativeness.
- Quantified the agreement between saliency maps and fixation points across age groups.
- Determined the influence of age on center bias tendency in visual exploration.
Conclusions:
- The developed age-adapted saliency model demonstrates superior performance in predicting regions of interest compared to existing models.
- The proposed framework effectively accounts for age-related differences in visual attention.
- This research contributes to more accurate and inclusive computational models of visual attention.

