Computational Method for Predicting Visual Attention in Older Adults with Age-Related Features

Age-related changes in visual perception alter attentional deployment, yet computational models of visual attention have been validated almost exclusively on younger populations. This limits both the theoretical investigation of age-specific mechanisms and practical applications in age-inclusive design, where researchers depend on specialised eye-tracking equipment to observe such differences. Therefore, we present the Elderly Visual Attention Estimation (EVAE) model, a computational framework that predicts early visual attentional orienting in older adults by combining stimulus-driven image features with age-specific top-down priors. The framework models six dimensions of elderly visual attention from cross-age eye-tracking data: colour brightness sensitivity, centre bias, foreground–background differentiation, depth detection, early attentional prior, and sustained-attention spatial prior. On public datasets, EVAE achieves an AUC-Judd of 0.92, which outperforms existing saliency models and deep learning approaches such as DeepGaze II. The framework is optimised for an input resolution of 128 × 96 pixels, producing fixation probability maps that are upsampled to match the original stimulus resolution for practical interface evaluation. Cross-age validation confirms the model’s specificity, as EVAE predicts attentional behaviour in older adults but does not generalise to younger adults. An ablation study shows that image features and top-down spatial priors each contribute independently to prediction accuracy, and that bottom-up saliency alone cannot account for age-related attentional patterns. Centre bias and early attentional prior are the strongest predictors, indicating that visual ageing involves greater reliance on spatial strategies and compensatory processing. As an alternative to hardware-based eye-tracking, EVAE widens the scope of empirical research into older adults’ visual attention and informs the design of accessible digital interfaces.