Gaze Strategies in Driving-An Ecological Approach.

Human performance in natural environments is deeply impressive, and still much beyond current AI. Experimental techniques, such as eye tracking, may be useful to understand the cognitive basis of this performance, and "the human advantage." Driving is domain where these techniques may deployed, in tasks ranging from rigorously controlled laboratory settings through high-fidelity simulations to naturalistic experiments in the wild. This research has revealed robust patterns that can be reliably identified and replicated in the field and reproduced in the lab. The purpose of this review is to cover the basics of what is known about these gaze behaviors, and some of their implications for understanding visually guided steering. The phenomena reviewed will be of interest to those working on any domain where visual guidance and control with similar task demands is involved (e.g., many sports). The paper is intended to be accessible to the non-specialist, without oversimplifying the complexity of real-world visual behavior. The literature reviewed will provide an information base useful for researchers working on oculomotor behaviors and physiology in the lab who wish to extend their research into more naturalistic locomotor tasks, or researchers in more applied fields (sports, transportation) who wish to bring aspects of the real-world ecology under experimental scrutiny. Part of a Research Topic on Gaze Strategies in Closed Self-paced tasks, this aspect of the driving task is discussed. It is in particular emphasized why it is important to carefully separate the visual strategies driving (quite closed and self-paced) from visual behaviors relevant to other forms of driver behavior (an open-ended menagerie of behaviors). There is always a balance to strike between ecological complexity and experimental control. One way to reconcile these demands is to look for natural, real-world tasks and behavior that are rich enough to be interesting yet sufficiently constrained and well-understood to be replicated in simulators and the lab. This