The human visual system evolved in environments with statistical regularities. Binocular vision is adapted to these such that depth perception and eye movements are more precise, faster, and performed comfortably in environments consistent with the regularities. We measured the statistics of eye movements and binocular disparities in virtual-reality (VR) - gaming environments and found that they are quite different from those in the natural environment. Fixation distance and direction are more restricted in VR, and fixation distance is farther. The pattern of disparity across the visual field is less regular in VR and does not conform to a prominent property of naturally occurring disparities. From this we predict that double vision is more likely in VR than in the natural environment. We also determined the optimal screen distance to minimize discomfort due to the vergence-accommodation conflict, and the optimal nasal-temporal positioning of head-mounted display (HMD) screens to maximize binocular field of view. Finally, in a user study we investigated how VR content affects comfort and performance. Content that is more consistent with the statistics of the natural world yields less discomfort than content that is not. Furthermore, consistent content yields slightly better performance than inconsistent content.

A large dataset that contains the eye movements of N=216 programmers of different experience levels captured during two code comprehension tasks is presented. Data are grouped in terms of programming expertise (from none to high) and other demographic descriptors. Data were collected through an international collaborative effort that involved eleven research teams across eight countries on four continents. The same eye tracking apparatus and software was used for the data collection. The Eye Movements in Programming (EMIP) dataset is freely available for download. The varied metadata in the EMIP dataset provides fertile ground for the analysis of gaze behavior and may be used to make novel insights about code comprehension.

Current eye-tracking techniques rely primarily on video-based tracking of components of the anterior surfaces of the eye. However, these trackers have several limitations. Their limited resolution precludes study of small fixational eye motion. Furthermore, many of these trackers rely on calibration procedures that do not offer a way to validate their eye motion traces. By comparison, retinal-image-based trackers can track the motion of the retinal image directly, at frequencies greater than 1kHz and with subarcminute accuracy. The retinal image provides a way to validate the eye position at any point in time, offering an unambiguous record of eye motion as a reference for the eye trace. The benefits of using scanning retinal imaging systems as eye trackers, however, comes at the price of different problems that are not present in video-based systems, and need to be solved to obtain robust eye traces. The current abstract provides an overview of retinal-image-based eye tracking methods, provides preliminary eye-tracking results from a tracking scanning-laser ophthalmoscope (TSLO), and proposes a new binocular line-scanning eye-tracking system.

In stereoscopic vision, the ability of perceiving the three-dimensional structure of the surrounding environment is subordinated to a precise and effective motor control for the binocular coordination of the eyes/cameras. If, on the one side, the binocular coordination of camera movements is a complicating factor, on the other side, a proper vergence control, acting on the binocular disparity, facilitates the binocular fusion and the subsequent stereoscopic perception process. In real-world situations, an effective vergence control requires further features other than real time capabilities: real robot systems are indeed characterized by mechanical and geometrical imprecision that affect the binocular vision, and the illumination conditions are changeable and unpredictable. Moreover, in order to allow an effective visual exploration of the peripersonal space, it is necessary to cope with different gaze directions and provide a large working space. The proposed control strategy resorts to a neuromimetic approach that provides a distributed representation of disparity information. The vergence posture is obtained by an open-loop and a closed-loop control, which directly interacts with saccadic control. Before saccade, the open-loop component is computed in correspondence of the saccade target region, to obtain a vergence correction to be applied simultaneously with the saccade. At fixation, the closed-loop component drives the binocular disparity to zero in a foveal region. The obtained vergence servos are able to actively drive both the horizontal and the vertical alignment of the optical axes on the object of interest, thus ensuring a correct vergence posture. Experimental tests were purposely designed to measure the performance of the control in the peripersonal space, and were performed on three different robot platforms. The results demonstrated that the proposed approach yields real-time and effective vergence camera movements on a visual stimulus in a wide working range, regardless of the illumination in the environment and the geometry of the system.

We show how a cortical model of early disparity detectors is able to autonomously learn effective control signals in order to drive the vergence eye movements of a binocular active vision system. The proposed approach employs early binocular mechanisms of vision and basic learning processes such as synaptic plasticity and reward modulation. The computational substrate consists of a population of modeled V1 complex cells, that provides a distributed representation of binocular disparity information. The population response also provides a global signal to describe the state of the system and thus its deviation from the desired vergence position. The proposed network, by taking into account the modification of its internal state as a consequence of the action performed, evolves following a differential Hebbian rule. Furthermore, the weights update is driven by an intrinsic signal derived by the overall activity of the population. Exploiting this signal implies a maximization of the population activity itself, thus providing an highly effective reward for the developing of a stable and accurate vergence behaviour. The efficacy of the proposed intrinsic reward signal is comparatively assessed against the ground-truth signal (the actual disparity) providing equivalent results, and thus validating the approach. Experimental tests in a simulated environment demonstrate that the proposed network is able to cope with vergent geometry and thus to learn effective vergence movements for static and moving visual targets in realistic situations.

We present two neural models for vergence angle control of a robotic head, a simplified and a more complex one. Both models work in a closed-loop manner and do not rely on explicitly computed disparity, but extract the desired vergence angle from the post-processed response of a population of disparity tuned complex cells, the actual gaze direction and the actual vergence angle. The first model assumes that the gaze direction of the robotic head is orthogonal to its baseline and the stimulus is a frontoparallel plane orthogonal to the gaze direction. The second model goes beyond these assumptions, and operates reliably in the general case where all restrictions on the orientation of the gaze, as well as the stimulus position, type and orientation, are dropped.