Analyzing gaze transition behavior using bayesian mixed effects Markov models

Abstract

The complex stochastic nature of eye tracking data calls for exploring sophisticated statistical models to ensure reliable inference in multi-trial eye-tracking experiments. We employ a Bayesian semi-parametric mixed-effects Markov model to compare gaze transition matrices between different experimental factors accommodating individual random effects. The model not only allows us to assess global influences of the external factors on the gaze transition dynamics but also provides comprehension of these effects at a deeper local level. We experimented to explore the impact of recognizing distorted images of artwork and landmarks on the gaze transition patterns. Our dataset comprises sequences representing areas of interest visited when applying a content independent grid to the resulting scan paths in a multi-trial setting. Results suggest that image recognition to some extent affects the dynamics of the transitions while image type played an essential role in the viewing behavior.