A major unresolved question in understanding visually guided locomotion in humans is whether actions are driven solely by the immediately available optical information (model-free online control mechanisms), or whether internal models have a role in anticipating the future path. We designed two experiments to investigate this issue, measuring spontaneous gaze behaviour while steering, and predictive gaze behaviour when future path information was withheld. In Experiment 1 participants (N = 15) steered along a winding path with rich optic flow: gaze patterns were consistent with tracking waypoints on the future path 1–3 s ahead. In Experiment 2, participants (N = 12) followed a path presented only in the form of visual waypoints located on an otherwise featureless ground plane. New waypoints appeared periodically every 0.75 s and predictably 2 s ahead, except in 25% of the cases the waypoint at the expected location was not displayed. In these cases, there were always other visible waypoints for the participant to fixate, yet participants continued to make saccades to the empty, but predictable, waypoint locations (in line with internal models of the future path guiding gaze fixations). This would not be expected based upon existing model-free online steering control models, and strongly points to a need for models of steering control to include mechanisms for predictive gaze control that support anticipatory path following behaviours.
In this paper we present and qualitatively analyze an expert driver's gaze behavior in natural driving on a real road, with no specific experimental task or instruction. Previous eye tracking research on naturalistic tasks has revealed recurring patterns of gaze behavior that are surprisingly regular and repeatable. Lappi (2016) identified in the literature seven “qualitative laws of gaze behavior in the wild”: recurring patterns that tend to go together, the more so the more naturalistic the setting, all of them expected in extended sequences of fully naturalistic behavior. However, no study to date has observed all in a single experiment. Here, we wanted to do just that: present observations supporting all the “laws” in a single behavioral sequence by a single subject. We discuss the laws in terms of unresolved issues in driver modeling and open challenges for experimental and theoretical development.
We present a computational model of intermittent visual sampling and locomotor control in a simple yet representative task of a car driver following another vehicle. The model has a number of features that take it beyond the current state of the art in modelling natural tasks, and driving in particular. First, unlike most control theoretical models in vision science and engineering—where control is directly based on observable (optical) variables—actions are based on a temporally enduring internal representation. Second, unlike the more sophisticated engineering driver models based on internal representations, our model explicitly aims to be psychologically plausible, in particular in modelling perceptual processes and their limitations. Third, unlike most psychological models, it is implemented as an actual simulation model capable of full task performance (visual sampling and longitudinal control). The model is developed and validated using a dataset from a simplified car-following experiment (N = 40, in both three-dimensional virtual reality and a real instrumented vehicle). The results replicate our previously reported connection between time headway and visual attention. The model reproduces this connection and predicts that it emerges from control of action uncertainty. Implications for traffic psychological models and future developments for psychologically plausible yet computationally rigorous models of full natural task performance are discussed.
It is well-established how visual stimuli and self-motion in laboratory conditions reliably elicit retinalimage-stabilizing compensatory eye movements (CEM). Their organization and roles in natural-task gaze strategies is much less understood: are CEM applied in active sampling of visual information in human locomotion in the wild? If so, how? And what are the implications for guidance? Here, we directly compare gaze behavior in the real world (driving a car) and a fixed base simulation steering task. A strong and quantifiable correspondence between self-rotation and CEM counter-rotation is found across a range of speeds. This gaze behavior is "optokinetic", i.e. optic flow is a sufficient stimulus to spontaneously elicit it in naïve subjects and vestibular stimulation or stereopsis are not critical. Theoretically, the observed nystagmus behavior is consistent with tracking waypoints on the future path, and predicted by waypoint models of locomotor control-but inconsistent with travel point models, such as the popular tangent point model. Consider traveling in a textured environment at very high speed-for example rushing down a race track or an autobahn at 120+ mph; sledding down an olympic luge chute; a crazy headfirst plummet in a bungee jump. The visual field a blur, eyes and concentration totally focused on where you are going. But at moderate speeds-the more sedate pace in which our sensory physiology evolved and where most of our lives are still spent-our experience is of a sharp, "unblurred", and visually stable world. What is the basis of this visual stability? There are central mechanisms for visual stability, such as saccadic suppression 1,2 and remapping of receptive fields 3 , but active gaze strategies structuring the retinal input can also play a part. Our oculomotor system has a suite of reflexes and action patterns such as optokinetic response (OKR), vestibulo-ocular response (VOR), smooth pursuit and vergence that potentially could be used to stabilize the retinal image-and thus directly contribute to the experience of stability and focus. Indeed, in the laboratory studies of slow eye movements (SEM), it is commonly taken as given-if not definitional-that "in the wild" the job of these mechanisms is to prevent the retinal image from blurring and keep (relevant portions of) the visual world sharp and in focus. For reviews on these "compensatory eye movements" (CEM) see 4-7. We investigated the presence and parametric behavior of CEM in real and simulated high-speed locomotion (steering a bend). The theoretical rationale is that different visual steering models in the literature assume different gaze strategies. A travel-point strategy means the you hold gaze fixed, relative to the locomotor reference frame of the torso (or vehicle). Gaze lands on a point, say 20 m ahead on the path of travel, which travels along with you, so that the point your gaze lands on sweeps along the road as you move. A waypoint strategy, on the other hand, means the you fixate a point on the road, a specific location in t...
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