How does image noise affect actual and predicted human gaze allocation in assessing image quality?

A central research question in natural vision is how to allocate fixation to extract informative cues for scene perception. With high quality images, psychological and computational studies have made significant progress to understand and predict human gaze allocation in scene exploration. However, it is unclear whether these findings can be generalised to degraded naturalistic visual inputs. In this eye-tracking and computational study, we methodically distorted both man-made and natural scenes with Gaussian low-pass filter, circular averaging filter and Additive Gaussian white noise, and monitored participants’ gaze behaviour in assessing perceived image qualities. Compared with original high quality images, distorted images attracted fewer numbers of fixations but longer fixation durations, shorter saccade distance and stronger central fixation bias. This impact of image noise manipulation on gaze distribution was mainly determined by noise intensity rather than noise type, and was more pronounced for natural scenes than for man-made scenes. We furthered compared four high performing visual attention models in predicting human gaze allocation in degraded scenes, and found that model performance lacked human-like sensitivity to noise type and intensity, and was considerably worse than human performance measured as inter-observer variance. Furthermore, the central fixation bias is a major predictor for human gaze allocation, which becomes more prominent with increased noise intensity. Our results indicate a crucial role of external noise intensity in determining scene-viewing gaze behaviour, which should be considered in the development of realistic human-vision-inspired attention models

How does image noise affect actual and predicted human gaze allocation in assessing image quality?

A central research question in natural vision is how to allocate fixation to extract informative cues for scene perception. With high quality images, psychological and computational studies have made significant progress to understand and predict human gaze allocation in scene exploration. However, it is unclear whether these findings can be generalised to degraded naturalistic visual inputs. In this eye-tracking and computational study, we methodically distorted both man-made and natural scenes with Gaussian low-pass filter, circular averaging filter and Additive Gaussian white noise, and monitored participants’ gaze behaviour in assessing perceived image qualities. Compared with original high quality images, distorted images attracted fewer numbers of fixations but longer fixation durations, shorter saccade distance and stronger central fixation bias. This impact of image noise manipulation on gaze distribution was mainly determined by noise intensity rather than noise type, and was more pronounced for natural scenes than for man-made scenes. We furthered compared four high performing visual attention models in predicting human gaze allocation in degraded scenes, and found that model performance lacked human-like sensitivity to noise type and intensity, and was considerably worse than human performance measured as inter-observer variance. Furthermore, the central fixation bias is a major predictor for human gaze allocation, which becomes more prominent with increased noise intensity. Our results indicate a crucial role of external noise intensity in determining scene-viewing gaze behaviour, which should be considered in the development of realistic human-vision-inspired attention models

Comparing infrared and webcam eye tracking in the Visual World Paradigm

Visual World eye tracking is a temporally fine-grained method of monitoring attention, making it a popular tool in the study of online sentence processing. Recently, while infrared eye tracking was mostly unavailable, various web-based experiment platforms have rapidly developed webcam eye tracking functionalities, which are now in urgent need of testing and evaluation. We replicated a recent Visual World study on the incremental processing of verb aspect in English using ‘out of the box’ webcam eye tracking software (jsPsych; de Leeuw, 2015) and crowdsourced participants, and fully replicated both the offline and online results of the original study. We furthermore discuss factors influencing the quality and interpretability of webcam eye tracking data, particularly with regards to temporal and spatial resolution; and conclude that remote webcam eye tracking can serve as an affordable and accessible alternative to lab-based infrared eye tracking, even for questions probing the time-course of language processing

Measuring gaze and pupil in the real world: object-based attention,3D eye tracking and applications

This dissertation contains studies on visual attention, as measured by gaze orientation, and the use of mobile eye-tracking and pupillometry in applications. It combines the development of methods for mobile eye-tracking (studies II and III) with experimental studies on gaze guidance and pupillary responses in patients (studies IV and VI) and healthy observers (studies I and V). Object based attention / Study I What is the main factor of fixation guidance in natural scenes? Low-level features or objects? We developed a fixation-predicting model, which regards preferred viewing locations (PVL) per object and combines these distributions over the entirety of existing objects in the scene. Object-based fixation predictions for natural scene viewing perform at par with the best early salience model, that are based on low-level features. However, when stimuli are manipulated so that low-level features and objects are dissociated, the greater prediction power of saliency models diminishes. Thus, we dare to claim, that highly developed saliency models implicitly obtain object-hood and that fixation selection is mainly influenced by objects and much less by low-level features. Consequently, attention guidance in natural scenes is object-based. 3D tracking / Study II The second study focussed on improving calibration procedures for eye-in-head positions with a mobile eye-tracker.We used a mobile eye-tracker prototype, the EyeSeeCam with a high video-oculography (VOG) sampling rate and the technical gadget to follow the users gaze direction instantaneously with a rotatable camera. For a better accuracy in eye-positioning, we explored a refinement in the implementation of the eye-in-head calibration that yields a measure for fixation distance, which led to a mobile eye-tracker 3D calibration. Additionally, by developing the analytical mechanics for parametrically reorienting the gaze-centred camera, the 3D calibration could be applied to reliably record gaze-centred videos. Such videos are suitable as stimuli for investigating gaze-behaviour during object manipulation or object recognition in real worlds point-of-view (PoV) perspective. In fact, the 3D calibration produces a higher accuracy in positioning the gaze-centred camera over the whole 3D visual range. Study III, eye-tracking methods With a further development on the EyeSeeCam we achieved to record gaze-in-world data, by superposing eye-in-head and head-in-world coordinates. This novel approach uses a combination of few absolute head-positions extracted manually from the PoV video and of relative head-shifts integrated over angular velocities and translational accelerations, both given by an inertia measurement unit (IMU) synchronized to the VOG data. Gaze-in-world data consist of room-referenced gaze directions and their origins within the environment. They easily allow to assign fixation targets by using a 3D model of the measuring environment – a strong rationalisation regarding fixation analysis. Applications Study III Daylight is an important perceptual factor for visual comfort, but can also create discomfort glare situations during office work, so we developed to measure its behavioural influences. We achieve to compare luminance distributions and fixations in a real-world setting, by also recording indoor luminance variations time-resolved using luminance maps of a scenery spanning over a 3pi sr. Luminance evaluations in the workplace environment yield a well controlled categorisation of different lighting conditions and a localisation as well as a brightness measure of glare sources.We used common tasks like reading, typing on a computer, a phone call and thinking about a subject. The 3D model gives the possibility to test for gaze distribution shifts in the presence of glare patches and for variations between lighting conditions. Here, a low contrast lighting condition with no sun inside and a high contrast lighting condition with direct sunlight inside were compared. When the participants are not engaged in any visually focused task and the presence of the task support is minimal, the dominant view directions are inclined towards the view outside the window under the low contrast lighting conditions, but this tendency is less apparent and sways more towards the inside of the room under the high contrast lighting condition. This result implicates an avoidance of glare sources in gaze behaviour. In a second more extensive series of experiments, the participants’ subjective assessments of the lighting conditions will be included. Thus, the influence of glare can be analysed in more detail and tested whether visual discomfort judgements are correlated in differences in gaze-behaviour. Study IV The advanced eye-tracker calibration found application in several following projects and included in this dissertation is an investigation with patients suffering either from idiopathic Parkinson’s disease or from progressive supranuclear palsy (PSP) syndrome. PSP’s key symptom is the decreased ability to carry out vertical saccades and thus the main diagnostic feature for differentiating between the two forms of Parkinson’s syndrome. By measuring ocular movements during a rapid (< 20s) procedure with a standardized fixation protocol, we could successfully differentiate pre-diagnosed patients between idiopathic Parkinson’s disease and PSP, thus between PSP patients and HCs too. In PSP patients, the EyeSeeCam detected prominent impairment of both saccade velocity and amplitude. Furthermore, we show the benefits of a mobile eye-tracking device for application in clinical practice. Study V Decision-making is one of the basic cognitive processes of human behaviours and thus, also evokes a pupil dilation. Since this dilation reflects a marker for the temporal occurrence of the decision, we wondered whether individuals can read decisions from another’s pupil and thus become a mentalist. For this purpose, a modified version of the rock-paper-scissors childhood game was played with 3 prototypical opponents, while their eyes were video taped. These videos served as stimuli for further persons, who competed in rock-paper-scissors. Our results show, that reading decisions from a competitor’s pupil can be achieved and players can raise their winning probability significantly above chance. This ability does not require training but the instruction, that the time of maximum pupil dilation was indicative of the opponent’s choice. Therefore we conclude, that people could use the pupil to detect cognitive decisions in another individual, if they get explicit knowledge of the pupil’s utility. Study VI For patients with severe motor disabilities, a robust mean of communication is a crucial factor for well-being. Locked-in-Syndrome (LiS) patients suffer from quadriplegia and lack the ability of articulating their voice, though their consciousness is fully intact. While classic and incomplete LiS allows at least voluntary vertical eye movements or blinks to be used for communication, total LiS patients are not able to perform such movements. What remains, are involuntarily evoked muscle reactions, like it is the case with the pupillary response. The pupil dilation reflects enhanced cognitive or emotional processing, which we successfully observed in LiS patients. Furthermore, we created a communication system based on yes-no questions combined with the task of solving arithmetic problems during matching answer intervals, that yet invokes the most solid pupil dilation usable on a trial-by-trial basis for decoding yes or no as answers. Applied to HCs and patients with various severe motor disabilities, we provide the proof of principle that pupil responses allow communication for all tested HCs and 4/7 typical LiS patients. Résumé Together, the methods established within this thesis are promising advances in measuring visual attention allocation with 3D eye-tracking in real world and in the use of pupillometry as on-line measurement of cognitive processes. The two most outstanding findings are the possibility to communicate with complete LiS patients and further a conclusive evidence that objects are the primary unit of fixation selection in natural scenes

Automated Gaze-Based Mind Wandering Detection during Computerized Learning in Classrooms

We investigate the use of commercial off-the-shelf (COTS) eye-trackers to automatically detect mind wandering—a phenomenon involving a shift in attention from task-related to task-unrelated thoughts—during computerized learning. Study 1 (N = 135 high-school students) tested the feasibility of COTS eye tracking while students learn biology with an intelligent tutoring system called GuruTutor in their classroom. We could successfully track eye gaze in 75% (both eyes tracked) and 95% (one eye tracked) of the cases for 85% of the sessions where gaze was successfully recorded. In Study 2, we used this data to build automated student-independent detectors of mind wandering, obtaining accuracies (mind wandering F1 = 0.59) substantially better than chance (F1 = 0.24). Study 3 investigated context-generalizability of mind wandering detectors, finding that models trained on data collected in a controlled laboratory more successfully generalized to the classroom than the reverse. Study 4 investigated gaze- and video- based mind wandering detection, finding that gaze-based detection was superior and multimodal detection yielded an improvement in limited circumstances. We tested live mind wandering detection on a new sample of 39 students in Study 5 and found that detection accuracy (mind wandering F1 = 0.40) was considerably above chance (F1 = 0.24), albeit lower than offline detection accuracy from Study 1 (F1 = 0.59), a finding attributable to handling of missing data. We discuss our next steps towards developing gaze-based attention-aware learning technologies to increase engagement and learning by combating mind wandering in classroom contexts

Gaze Guidance, Task-Based Eye Movement Prediction, and Real-World Task Inference using Eye Tracking

The ability to predict and guide viewer attention has important applications in computer graphics, image understanding, object detection, visual search and training. Human eye movements provide insight into the cognitive processes involved in task performance and there has been extensive research on what factors guide viewer attention in a scene. It has been shown, for example, that saliency in the image, scene context, and task at hand play significant roles in guiding attention. This dissertation presents and discusses research on visual attention with specific focus on the use of subtle visual cues to guide viewer gaze and the development of algorithms to predict the distribution of gaze about a scene. Specific contributions of this work include: a framework for gaze guidance to enable problem solving and spatial learning, a novel algorithm for task-based eye movement prediction, and a system for real-world task inference using eye tracking. A gaze guidance approach is presented that combines eye tracking with subtle image-space modulations to guide viewer gaze about a scene. Several experiments were conducted using this approach to examine its impact on short-term spatial information recall, task sequencing, training, and password recollection. A model of human visual attention prediction that uses saliency maps, scene feature maps and task-based eye movements to predict regions of interest was also developed. This model was used to automatically select target regions for active gaze guidance to improve search task performance. Finally, we develop a framework for inferring real-world tasks using image features and eye movement data. Overall, this dissertation naturally leads to an overarching framework, that combines all three contributions to provide a continuous feedback system to improve performance on repeated visual search tasks. This research has important applications in data visualization, problem solving, training, and online education

Optimizations and applications in head-mounted video-based eye tracking

Video-based eye tracking techniques have become increasingly attractive in many research fields, such as visual perception and human-computer interface design. The technique primarily relies on the positional difference between the center of the eye\u27s pupil and the first-surface reflection at the cornea, the corneal reflection (CR). This difference vector is mapped to determine an observer\u27s point of regard (POR). In current head-mounted video-based eye trackers, the systems are limited in several aspects, such as inadequate measurement range and misdetection of eye features (pupil and CR). This research first proposes a new `structured illumination\u27 configuration, using multiple IREDs to illuminate the eye, to ensure that eye positions can still be tracked even during extreme eye movements (up to ±45° horizontally and ±25° vertically). Then eye features are detected by a two-stage processing approach. First, potential CRs and the pupil are isolated based on statistical information in an eye image. Second, genuine CRs are distinguished by a novel CR location prediction technique based on the well-correlated relationship between the offset of the pupil and that of the CR. The optical relationship of the pupil and CR offsets derived in this thesis can be applied to two typical illumination configurations - collimated and near-source ones- in the video-based eye tracking system. The relationships from the optical derivation and that from an experimental measurement match well. Two application studies, smooth pursuit dynamics in controlled static (laboratory) and unconstrained vibrating (car) environments were conducted. In the first study, the extended stimuli (color photographs subtending 2° and 17°, respectively) were found to enhance smooth pursuit movements induced by realistic images, and the eye velocity for tracking a small dot (subtending \u3c0.1°) was saturated at about 64 deg/sec while the saturation velocity occurred at higher velocities for the extended images. The difference in gain due to target size was significant between dot and the two extended stimuli, while no statistical difference existed between the two extended stimuli. In the second study, twovisual stimuli same as in the first study were used. The visual performance was impaired dramatically due to the whole body motion in the car, even in the tracking of a slowly moving target (2 deg/sec); the eye was found not able to perform a pursuit task as smooth as in the static environment though the unconstrained head motion in the unstable condition was supposed to enhance the visual performance

Influence of Low-Level Stimulus Features, Task Dependent Factors, and Spatial Biases on Overt Visual Attention

Visual attention is thought to be driven by the interplay between low-level visual features and task dependent information content of local image regions, as well as by spatial viewing biases. Though dependent on experimental paradigms and model assumptions, this idea has given rise to varying claims that either bottom-up or top-down mechanisms dominate visual attention. To contribute toward a resolution of this discussion, here we quantify the influence of these factors and their relative importance in a set of classification tasks. Our stimuli consist of individual image patches (bubbles). For each bubble we derive three measures: a measure of salience based on low-level stimulus features, a measure of salience based on the task dependent information content derived from our subjects' classification responses and a measure of salience based on spatial viewing biases. Furthermore, we measure the empirical salience of each bubble based on our subjects' measured eye gazes thus characterizing the overt visual attention each bubble receives. A multivariate linear model relates the three salience measures to overt visual attention. It reveals that all three salience measures contribute significantly. The effect of spatial viewing biases is highest and rather constant in different tasks. The contribution of task dependent information is a close runner-up. Specifically, in a standardized task of judging facial expressions it scores highly. The contribution of low-level features is, on average, somewhat lower. However, in a prototypical search task, without an available template, it makes a strong contribution on par with the two other measures. Finally, the contributions of the three factors are only slightly redundant, and the semi-partial correlation coefficients are only slightly lower than the coefficients for full correlations. These data provide evidence that all three measures make significant and independent contributions and that none can be neglected in a model of human overt visual attention