title: Predicting human visuomotor behaviour in a driving task.
abstract: The sequential deployment of gaze to regions of interest is an integral part of human visual function. Owing to its central importance, decades of research have focused on predicting gaze locations, but there has been relatively little formal attempt to predict the temporal aspects of gaze deployment in natural multi-tasking situations. We approach this problem by decomposing complex visual behaviour into individual task modules that require independent sources of visual information for control, in order to model human gaze deployment on different task-relevant objects. We introduce a softmax barrier model for gaze selection that uses two key elements: a priority parameter that represents task importance per module, and noise estimates that allow modules to represent uncertainty about the state of task-relevant visual information. Comparisons with human gaze data gathered in a virtual driving environment show that the model closely approximates human performance.
authors.affiliation: Department of Computer Science, University of Texas at Austin, , TX, USA.
authors.forename: Leif
authors.initials: L
authors.lastname: Johnson
authors.forename: Brian
authors.initials: B
authors.lastname: Sullivan
authors.forename: Mary
authors.initials: M
authors.lastname: Hayhoe
authors.forename: Dana
authors.initials: D
authors.lastname: Ballard
mesh_terms: D001334:Automobile Driving; D003198:Computer Simulation; D006801:Humans; D008962:Models, Theoretical; D011597:Psychomotor Performance / Q000502:physiology; D014785:Vision, Ocular / Q000502:physiology*
keywords: eye movements; reward; state uncertainty; top-down control; visual attention