Advances in eye-tracking technology have promoted its widespread use to understand and improve target search in psychology, industrial engineering, human factors, medical diagnostics, and marketing. Eye movements are the realization of a complex unobserved spatiotemporal attention process with many sources of variation.
Eye-tracking data often have been aggregated and/or summarized descriptively, few adequate statistical models being available for their analysis. This article proposes a model that may serve to uncover the latent attention processes of people searching for targets in complex scenes. It recognizes the spatial nature of eye movements and represents two latent attention states: a localization and an identification state, between which people may switch over time according to a Markov process. […]