title: Modeling drivers' visual attention allocation while interacting with in-vehicle technologies.
abstract: In 2 experiments, the authors examined how characteristics of a simulated traffic environment and in-vehicle tasks impact driver performance and visual scanning and the extent to which a computational model of visual attention (SEEV model) could predict scanning behavior. In Experiment 1, the authors manipulated task-relevant information bandwidth and task priority. In Experiment 2, the authors examined task bandwidth and complexity, while introducing infrequent traffic hazards. Overall, task priority had a significant impact on scanning; however, the impact of increasing bandwidth was varied, depending on whether the relevant task was supported by focal (e.g., in-vehicle tasks; increased scanning) or ambient vision (e.g., lane keeping; no increase in scanning). The computational model accounted for approximately 95% of the variance in scanning across both experiments.
authors.affiliation: Department of Psychology, University of Illinois at Urbana-Champaign, IL, USA. william.horrey@libertymutual.com
authors.forename: William J
authors.initials: WJ
authors.lastname: Horrey
authors.forename: Christopher D
authors.initials: CD
authors.lastname: Wickens
authors.forename: Kyle P
authors.initials: KP
authors.lastname: Consalus
mesh_terms: D000328:Adult; D001288:Attention; D001334:Automobile Driving; D005260:Female; D006801:Humans; D008297:Male; D018986:Motor Vehicles; D011930:Reaction Time; D013672:Technology; D014796:Visual Perception