Saliency-based Bayesian modeling of dynamic viewing of static scenes

Abstract

Most analytic approaches for eye-tracking data focus either on identification of fixations and saccades, or on estimating saliency properties. Analyzing both aspects of visual attention simultaneously provides a more comprehensive view of strategies used to process information. This work presents a method that incorporates both aspects in a unified Bayesian model to jointly estimate dynamic properties of scanpaths and a saliency map. Performance of the model is assessed on simulated data and on eye-tracking data from 15 children with autism spectrum disorder and 13 control children. Saliency differences between ASD and TD groups were found for both social and non-social images, but differences in dynamic gaze features were evident in only a subset of social images. These results are consistent with previous region-based analyses as well as previous fixation parameter models, suggesting that the new approach may provide synthesizing and statistical perspectives on eye-tracking analyses.