The where, what and when of gaze allocation in the lab and the natural environment

Purpose: Reasons for the development and progression of myopia remain unclear. Some studies show a high prevalence of myopia in certain occupational groups. This might imply that certain head and eye movements lead to ocular elongation, perhaps as a result of forces from the extraocular muscles, lids or other structures. The present study aims to analyse head and eye movements in myopes and non‐myopes for near‐vision tasks.Methods: The study analysed head and eye movements in a cohort of 14 myopic and 16 non‐myopic young adults. Eye and head movements were monitored by an eye tracker and a motion sensor while the subjects performed three near tasks, which included reading on a screen, reading a book and writing. Horizontal eye and head movements were measured in terms of angular amplitudes. Vertical eye and head movements were analysed in terms of the range of the whole movement during the recording. Values were also assessed as a ratio based on the width of the printed text, which changed between participants due to individual working distances.Results: Horizontal eye and head movements were significantly different among the three tasks (p = 0.03 and p = 0.014, for eye and head movements, respectively, repeated measures ANOVA). Horizontal and vertical eye and head movements did not differ significantly between myopes and non‐myopes. As expected, eye movements preponderated over head movements for all tasks and in both meridians. A positive correlation was found between mean spherical equivalent and the working distance for reading a book (r = 0.41; p = 0.025).Conclusions: The results show a similar pattern of eye movements in all participating subjects, although the amplitude of these movements varied considerably between the individuals. It is likely that some individuals when exposed to certain occupational tasks might show different eye and head movement patterns.

Retinal motion patterns during locomotion are shaped by gait, gaze location, and the terrain, and these motion patterns may influence the way motion sensitivity and receptive field properties vary across the visual field.

Research investigating scene perception normally involves laboratory experiments using static images. Much has been learned about how observers look at pictures of the real world and the attentional mechanisms underlying this behaviour. However, the use of static, isolated pictures as a proxy for studying everyday attention in real environments has led to the criticism that such experiments are artificial. We report a new study that tests the extent to which the real world can be reduced to simpler laboratory stimuli. We recorded the gaze of participants walking on a university campus with a mobile eye tracker, and then showed static frames from this walk to new participants, in either a random or sequential order. The aim was to compare the gaze of participants walking in the real environment with fixations on pictures of the same scene. The data show that picture order affects interobserver fixation consistency and changes looking patterns. Critically, while fixations on the static images overlapped significantly with the actual real-world eye movements, they did so no more than a model that assumed a general bias to the centre. Remarkably, a model that simply takes into account where the eyes are normally positioned in the head-independent of what is actually in the scene-does far better than any other model. These data reveal that viewing patterns to static scenes are a relatively poor proxy for predicting real world eye movement behaviour, while raising intriguing possibilities for how to best measure attention in everyday life. (PsycINFO Database Record

The effects of age on the contributions of head and eye movements to scanning behavior at intersections

Animals selectively direct their visual attention toward relevant aspects of their environments. They can shift their attention using a combination of eye, head and body movements. While we have a growing understanding of eye and head movements in mammals, we know little about these processes in birds. We therefore measured the eye and head movements of freely behaving Indian peafowl (Pavo cristatus) using a telemetric eye-tracker. Both eye and head movements contributed to gaze changes in peafowl. When gaze shifts were smaller, eye movements played a larger role than when gaze shifts were larger. The duration and velocity of eye and head movements were positively related to the size of the eye and head movements, respectively. In addition, the coordination of eye and head movements in peafowl differed from that in mammals; peafowl exhibited a near-absence of the vestibulo-ocular reflex, which may partly result from the peafowl's ability to move their heads as quickly as their eyes.

BackgroundThe anti-saccade task, when people must respond in the direction opposite to a visual stimulus, has been used as a marker of operation of the frontal cortical oculomotor area. However, early development of oculomotor control has been little studied with the infant anti-saccade paradigm, and a few studies did not recognize anti-saccades in infants in light of the results of adult anti-saccade. Since the characteristics of infant eye movements are little known, applying the criteria used in adult study is by no means the best way to study infant anti-saccade. As it is indicated that coordinated eye and head movements often enable infants to control the direction of their gaze, head movements should be examined as an infant orienting response. The aim of this study was to address how infants used eye and head movements during the anti-saccade paradigm. To distinguish infants' responses, we also investigated eye and head movements during a task for an inhibition of return. Inhibition of return, in which delayed responses occur in the direction to which attention had previously been oriented, has been thought to mark activity of the superior colliculus. Since the superior colliculus is thought to develop much earlier in life than the frontal lobes, we thought it useful to compare these task performances during infancy.MethodsInfants were divided into three groups according to age. Anti-saccade and inhibition-of-return tasks were given. Their eye and head movements during tasks were independently recorded by the corneal reflection method in the head-free condition.ResultsYounger infants tended to initiate eye movement less than older ones in both tasks. In the anti-saccade task, responses opposite to the cue tended to show longer latency than responses to the cue. Infants made faster responses toward the side opposite the cue when it was to the right than when it was left of fixation. Regarding the comparison of responses toward the side opposite the cue between two tasks, the leftward eye movement was faster than the leftward head movements in the inhibition-of-return task, while no difference of latency was observed between eye and head movements in the anti-saccade task. A qualitative analysis of the trajectory of these responses revealed that head movement trajectories were steeper in the anti-saccade than in the inhibition-of-return task.ConclusionYounger infants move head and eyes together, with head movements frequently starting first. On the other hand, both the leftward latency difference between eye and head and gentle trajectories of head in inhibition of return indicate that eye movements are more predominant over head movements in the inhibition-of-return task than in the anti-saccade task. This would suggest an earlier developing inhibition-of-return mechanism.

ObjectiveSmartphones have shown promise in the assessment of neuro-ophthalmologic and vestibular disorders. We have shown that the head impulse test results recorded using our application are comparable with measurements from clinical video-oculography (VOG) goggles. The smartphone uses ARKit's capability to acquire eye and head movement positions without the need of performing a calibration as in most eye-tracking devices. Here, we measure the accuracy and precision of the eye and head position recorded using our application.MethodsWe enrolled healthy volunteers and asked them to direct their eyes, their heads, or both to targets on a wall at known eccentricities while recording their head and eye movements with our smartphone application. We measured the accuracy as the error between the eye or head movement measurement and the location of each target and the precision as the standard deviation of the eye or head position for each of the target positions.ResultsThe accuracy of head recordings (15% error) was overall better than the accuracy of eye recordings (23% error). We also found that the accuracy for horizontal eye movements (17% error) was better than for vertical (27% error). Precision was also better for head movement (0.8 degrees) recordings than eye movement recordings (1.3 degrees) and variability tended to increase with eccentricity.ConclusionOur results provide basic metrics evaluating the utility of smartphone applications in the quantitative assessment of head and eye movements. While the new method may not replace the more accurate dedicated VOG devices, they provide a more accessible quantitative option. It may be advisable to include a calibration recording together with any planned clinical test to improve the accuracy.

One’s “attention” is said to appear in the visual behavior, i.e. eye and head movements and human brain is thought to set visual attention area in the sight to get visual information efficiently. In head-eye coordination head movements are thought to be controlled to fit the sight to visual attention area. Hence, setting of the attention area can appear in head movements. In this study head and eye movements were measured using a wearable head and eye tracker system newly developed in this study, and were studied whether the visual attention could be known from head movements or not. Results show that, head-movements toward new attended area occur when human wants to switch the visual attention.

We used event-related fMRI to measure brain activity while subjects performed saccadic eye, head, and gaze movements to visually presented targets. Two distinct patterns of response were observed. One set of areas was equally active during eye, head, and gaze movements and consisted of the superior and inferior subdivisions of the frontal eye fields, the supplementary eye field, the intraparietal sulcus, the precuneus, area MT in the lateral occipital sulcus and subcortically in basal ganglia, thalamus, and the superior colliculus. These areas have been previously observed in functional imaging studies of human eye movements, suggesting that a common set of brain areas subserves both oculomotor and head movement control in humans, consistent with data from single-unit recording and microstimulation studies in nonhuman primates that have described overlapping eye- and head-movement representations in oculomotor control areas. A second set of areas was active during head and gaze movements but not during eye movements. This set of areas included the posterior part of the planum temporale and the cortex at the temporoparietal junction, known as the parieto-insular vestibular cortex (PIVC). Activity in PIVC has been observed during imaging studies of invasive vestibular stimulation, and we confirm its role in processing the vestibular cues accompanying natural head movements. Our findings demonstrate that fMRI can be used to study the neural basis of head movements and show that areas that control eye movements also control head movements. In addition, we provide the first evidence for brain activity associated with vestibular input produced by natural head movements as opposed to invasive caloric or galvanic vestibular stimulation.

Most humans can walk effortlessly across uniform terrain even when they do not pay much attention to it. However, most natural terrain is far from uniform, and we need visual information to maintain stable gait. Recent advances in mobile eye-tracking technology have made it possible to study, in natural environments, how terrain affects gaze and thus the sampling of visual information. However, natural environments provide only limited experimental control, and some conditions cannot safely be tested. Typical laboratory setups, in contrast, are far from natural settings for walking. We used a setup consisting of a dual-belt treadmill, 240 projection screen, floor projection, three-dimensional optical motion tracking, and mobile eye tracking to investigate eye, head, and body movements during perturbed and unperturbed walking in a controlled yet naturalistic environment. In two experiments (N = 22 each), we simulated terrain difficulty by repeatedly inducing slipping through accelerating either of the two belts rapidly and unpredictably (Experiment 1) or sometimes following visual cues (Experiment 2). We quantified the distinct roles of eye and head movements for adjusting gaze on different time scales. While motor perturbations mainly influenced head movements, eye movements were primarily affected by the presence of visual cues. This was true both immediately following slips and—to a lesser extent—over the course of entire 5-min blocks. We find adapted gaze parameters already after the first perturbation in each block, with little transfer between blocks. In conclusion, gaze–gait interactions in experimentally perturbed yet naturalistic walking are adaptive, flexible, and effector specific.

Purpose: To compare eye and head movements, lane keeping and vehicle control of drivers with hemianopic and quadrantanopic field defects with controls, and to identify differences in these parameters between hemianopic and quadrantanopic drivers rated safe to drive by a clinical driving rehabilitation specialist (CDRS) compared to those rated as unsafe.Methods: Eye and head movements and lane-keeping were rated in 22 persons with homonymous hemianopic defects and 8 with quadrantanopic defects (M=53 yrs) who were ≥ 6 months post injury and 30 persons with normal fields (M=53 yrs).All were licensed to drive and were current drivers or aimed to resume driving.Participants drove a 6.3-mile route along non-interstate city roads under in-traffic conditions.Vehicle control was assessed objectively by vehicle instrumentation for speed, braking, acceleration, and cornering.Results: As a group drivers with hemianopic or quadrantanopic defects drove slower, exhibited less excessive cornering or acceleration, and executed more shoulder movements than the controls.Those drivers with hemianopic or quadrantanopic defects rated as safe also made more head movements into their blind field, received superior ratings regarding eye movement extent and lane position stability, and exhibited less sudden braking and drove faster than those rated unsafe. Conclusions:Persons with hemianopic and quadrantanopic defects rated as safe to drive compensated by making more head movements into their blind field combined with more stable lane keeping and less sudden braking.Future research should evaluate whether these characteristics could be trained in rehabilitation programs aimed at improving driving safety in this population.

Driving simulation is widely used to answer important applied research questions, however, it is vital for specific driving tasks to undergo appropriate behavioural validation testing. Many previous validation studies have used simple driving tasks and measured relatively low-level vehicle control. The purpose of the current study was to investigate whether drivers’ visual attention at intersections with different levels of demand, are similar in the simulator and on the road. Unlike simpler driving tasks, crossing intersections requires complex interactions with other vehicles governed by sequences of head and eye movements that may not be accurately captured in a simulated environment.In the current study we directly compare performance at simulated junctions with the same participants' behaviour in a real car. We compared drivers’ visual attention in a high-fidelity driving simulator (instrumented car, 360-degree screen) and on-road in both low and medium demand driving situations. The low and medium demand driving situations involved the same motor movements, containing straight on, right turn and left turn manoeuvres. The low demand situations were controlled by the road environment and traffic lights, whereas medium demand situations required the driver to scan the environment and decide when it was safe to pull out into the junction. Natural junctions in Nottingham were used for the on-road phase and the same junctions were recreated in the simulator with traffic levels matched to those that were encountered on the real roads.The frequency and size of drivers' head movements were not significantly different between manoeuvres performed in the simulator and those conducted when driving on real roads. This suggests that drivers' broad search strategies in the simulator are representative of real-world driving. These strategies did change as a function of task demand - compared to low demand situations, behaviour at the medium demand junctions was characterised by longer junction crossing times, more head movements, shorter fixation durations and larger saccadic amplitudes. Although patterns of head movements were equivalent on road and in the simulator, there were differences in more fine-grained measures of eye-movements. Mean fixation durations were longer in the simulator compared to on-road, particularly in low-demand situations. We interpret this as evidence for lower levels of visual engagement with the simulated environment compared to the real world, at least when the task demands are low. These results have important implications for driving research. They suggest that high fidelity driving simulators can be useful tools for investigating drivers’ visual attention at junctions, particularly when the driving task is of at least moderate demand.

How do we explore the visual environment around us, and how are head and eye movements coordinated during our exploration? To investigate this question, we had observers look at omnidirectional panoramic scenes, composed of both landscape and fractal images, using a virtual reality viewer while their eye and head movements were tracked. We analyzed the spatial distribution of eye fixations and the distribution of saccade directions and the spatial distribution of head positions and the distribution of head shifts, as well as the relation between eye and head movements. The results show that, for landscape scenes, eye and head behavior best fit the allocentric frame defined by the scene horizon, especially when head tilt (i.e., head rotation around the view axis) is considered. For fractal scenes, which have an isotropic texture, eye and head movements were executed primarily along the cardinal directions in world coordinates. The results also show that eye and head movements are closely linked in space and time in a complementary way, with stimulus-driven eye movements predominantly leading the head movements. Our study is the first to systematically examine eye and head movements in a panoramic virtual reality environment, and the results demonstrate that a virtual reality environment constitutes a powerful and informative research alternative to traditional methods for investigating looking behavior.

There is little information regarding the characteristics of head movements during reading. This study was undertaken to investigate horizontal and vertical head movements during two different reading tasks. Head and eye movements were monitored with an infrared pupil and head tracker in 15 subjects during repeated reading of text from an A4-sized card and a card 90 degrees wide. In addition, head and eye movements were recorded in 45 subjects to compare head movement propensity during an A4 text-reading task and a saccadic task of an equivalent gaze shift. During the A4 standard reading task, horizontal and vertical head movements accounted for 4.7% and 28.7% of the gaze shift, respectively. During the 90 degrees text reading, horizontal head movements accounted for 40.3% of the gaze amplitude, and vertical head movements accounted for 28.4%. Horizontal gaze velocities increased significantly on repeated A4 and 90 degrees text readings, as did horizontal head velocities and amplitudes. Reading head movement propensities were significantly smaller than saccadic head movement propensities (P < 0.001). Head movement strategies are rapidly switched between the A4 and 90 degrees text-reading paradigms. They are minimized during A4 text reading but actively assist the gaze strategy during 90 degrees text reading. Horizontal head movement is reduced during A4 reading compared to the equivalent saccadic task and may be suppressed to improve fixation stability. The results support the view that the head and eye movement system is a highly coupled but extremely flexible system.

Spatial familiarity has seen a long history of interest in wayfinding research. To date, however, no studies have been done which systematically assess the behavioral correlates of spatial familiarity, including eye and body movements. In this study, we take a step towards filling this gap by reporting on the results of an in-situ, within-subject study with pedestrian wayfinders that combines eye-tracking and body movement sensors. In our study, participants were required to walk both a familiar route and an unfamiliar route by following auditory, landmark-based route instructions. We monitored participants’ behavior using a mobile eye tracker, a high-precision Global Navigation Satellite System receiver, and a high-precision, head-mounted Inertial Measurement Unit. We conducted machine learning experiments using Gradient-Boosted Trees to perform binary classification, testing out different feature sets, i.e., gaze only, Inertial Measurement Unit data only, and a combination of the two, to classify a person as familiar or unfamiliar with a particular route. We achieve the highest accuracy of using exclusively Inertial Measurement Unit data, exceeding gaze alone at , and gaze and Inertial Measurement Unit data together at . For the highest accuracy achieved, yaw and acceleration values are most important. This finding indicates that head movements (“looking around to orient oneself”) are a particularly valuable indicator to distinguish familiar and unfamiliar environments for pedestrian wayfinders.