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Activity Number: 421
Type: Contributed
Date/Time: Tuesday, August 11, 2015 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #317062 View Presentation
Title: A Bayesian Model for Saliency-Based Analysis of Eye-Tracking Data
Author(s): Daniel Campbell* and Frederick Shic and Joseph Chang
Companies: Boston Scientific and Yale University and Yale University
Keywords: Bayesian ; Hidden Markov model ; Eye-tracking ; Saliency
Abstract:

Eye-tracking is an experimental method of recording the point of gaze of a person while viewing an image. Most analytic approaches for eye-tracking data focus on dynamic properties of viewing, such as identification of fixations and saccades, or on semantic properties of the image being viewed, through a topographical representation of visual attention called a saliency map, but not both. This work presents a method that incorporates both aspects into a single Bayesian model that jointly estimates dynamic properties and a saliency map, providing a comprehensive strategy for eye-tracking analysis. The model is implemented using a Gibbs sampler and assessed on simulated data, and estimation of model parameters is highly accurate. The model is also applied to eye-tracking data from children with autism spectrum disorder and typically-developing controls. Saliency differences between autism and control groups were found in both social images (faces) and non-social images (blocks), but differences in dynamic features were evident in only some images. The results in this clinical sample are consistent with previous region-based analyses as well as previous fixation parameter models.


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