Web pages are typically comprised of different kinds of visual elements such as menus, headers and footers. To improve user experience, eye tracking has been widely used to investigate how users interact with such elements. In particular, eye movement sequences, called scanpaths, have been analysed to understand the path that people follow in terms of these elements. However, individual scanpaths are typically complicated and they are related to specific users, and therefore any processing done with those scanpaths will be specific to individuals and will not be representative of multiple users. Therefore, those scanpaths should be clustered to provide a general direction followed by users. This direction will allow researchers to better understand user interactions with web pages, and then improve the design of the pages accordingly. Existing research tends to provide a very short scanpath which is not representative for understanding user behaviours. This thesis introduces a new algorithm for clustering scanpaths, called Scanpath Trend Analysis (STA). In contrast to existing research, in STA, if a particular element is not shared by all users but it gets at least the same attention as the fully shared elements, it is included in the resulting scanpath. Thus, this algorithm provides a richer understanding of how users interact with web pages. The STA algorithm was evaluated with a series of eye tracking studies where the web pages used were automatically segmented into their visual elements by using different approaches. The results show that the outputs of the STA algorithm are significantly more similar to the inputted scanpaths in comparison with the outputs of other existing work, and this is not limited to a particular segmentation approach. The effects of the number of users were also investigated on the STA algorithm as the number of users required for scanpath analysis has not been studied in depth in the literature. The results show the possibility to reach the same results with a smaller group of users. The research presented in this thesis should be of value to eye tracking researchers, to whom the STA algorithm has been made available to analyse scanpaths, and to behaviour analysis researchers, who can use the algorithm to understand user behaviours on web pages, and then design, develop and present the pages accordingly.

The aim of the present thesis was to investigate the relationship between individuals' allocation of overt visual attention during action observation and their consequent pickup of information. Four interrelated studies were conducted to achieve this. In Study 1 we examined the effects of visual guidance - colour highlighting of relevant aspects of the action - on observational learning of the golf swing. The results showed that the visual guides facilitated novices' intake of information pertaining to the model's posture, which was reflected in faster learning. In the remaining studies, transcranial magnetic stimulation and eye tracking data were acquired concurrently to measure the interaction between gaze behaviour and motor resonance - a neurophysiological index of the motor system's engagement with a viewed action, and thus a correlate of information extraction. In Study 2, we directed observers' gaze to distinct locations of the display while they viewed thumb adduction/abduction movements. The results showed that, by directing gaze to a location that maximised the amount of thumb motion across the fovea, motor resonance was maximised relative to a free viewing condition. In Study 3 we examined the link between gaze and motor resonance during the observation of transitive actions. Participants viewed reach-to-grasp actions with natural gaze, or while looking at a target- or an effector- based visual guide. The results showed that the effector-based guide disrupted natural gaze behaviour, and this was associated with a reversal of the motor resonance response. In Study 4 we showed novice and skilled golfers videos of the golf swing and of a reach-grasp-lift action. The results revealed that, for both actions, the extent of motor resonance was related to the location of participants' fixations. The present work provides the first evidence of a relationship between gaze and motor resonance and highlights the importance of appropriate gaze behaviour for observational learning.

During collaboration, people communicate using verbal and non-verbal cues, including gaze cues. Spoken language is usually the primary medium of communication in these interactions, yet despite this co-occurrence of speech and gaze cueing, most experiments have used paradigms without language. Furthermore, previous research has shown that myriad social factors influence behaviour during interactions, yet most studies investigating responses to gaze have been conducted in a lab, far removed from any natural interaction. It was the aim of this thesis to investigate the relationship between language and gaze cue utilisation in natural collaborations. For this reason, the initial study was largely observational, allowing for spontaneous natural language and gaze. Participants were found to rarely look at their partners, but to do so strategically, with listeners looking more at speakers when the latter were of higher social status. Eye movement behaviour also varied with the type of language used in instructions, so in a second study, a more controlled (but still real-world) paradigm was used to investigate the effect of language type on gaze utilisation. Participants used gaze cues flexibly, by seeking and following gaze more when the cues were accompanied by distinct featural verbal information compared to overlapping spatial verbal information. The remaining three studies built on these findings to investigate the relationship between language and gaze using a much more controlled paradigm. Gaze and language cues were reduced to equivalent artificial stimuli and the reliability of each cue was manipulated. Even in this artificial paradigm, language was preferred when cues were equally reliable, supporting the idea that gaze cues are supportive to language. Typical gaze cueing effects were still found, however the size of these effects was modulated by gaze cue reliability. Combined, the studies in this thesis show that although gaze cues may automatically and quickly affect attention, their use in natural communication is mediated by the form and content of concurrent spoken language.

The growth of a child’s early vocabulary is one of the most salient indicators of progress in language development, but measuring a young child’s comprehension of words is non-trivial. Parental checklists are prone to underestimation of a child’s vocabulary (Houston-Price et al., 2007; Brady et al. 2014), so it may be that more direct measures, such as measuring a child’s eye movements during comprehension, may provide a better assessment of children’s vocabulary. Prior research has found relationships between gaze patterns and vocabulary development (Fernald et al. 2006), and the present exploratory study investigates these relationships with verbs, along with a number of methodological considerations. In addition, recent research supports the idea that verbs may differ in difficulty of acquisition based on word class, with manner verbs being easier to learn than result verbs (Horvath et al. 2015). The present study has two aims: 1) investigate the effect of dynamic stimuli on correlations with vocabulary scores and 2) experimentally investigate the notion that manner verbs are easier to learn than result verbs. Forty children (Mean age = 22.97 months) were recruited for participation and shown a vocabulary test. While no significant correlations were found between vocabulary measures and accuracy and latency, several experimental measures proved to be related to vocabulary development, including fixation density and length of first fixation to the non-target. Additionally, results indicate that children knew the same number of manner and result verbs. Finally, these results could inform vocabulary tests using eye-tracking measures that specifically target verb knowledge.

abstract: Previous research has shown that training visual attention can improve golf putting performance. A technique called the Quiet Eye focuses on increasing a player’s length of fixation between the ball and the hole. When putting, the final fixation is made on the ball before executing the stroke leaving players to rely on their memory of the hole’s distance and location. The present study aimed to test the effectiveness of Quiet Eye training for final fixation on the hole. Twelve Arizona State University (ASU) students with minimal golf experience putted while wearing eye tracking glasses under the following conditions: from three feet with final fixation on the ball, from six feet with final fixation on the ball, from three feet with final fixation on the hole and from six feet with final fixation on the hole. Participant’s performance was measured before training, following quiet eye training, and under simulated pressure conditions. Putting performance was not significantly affected by final fixation for all conditions. The number of total putts made was significantly greater when putting from three feet for all conditions. Future research should test the effects of this training with expert golfers whose processes are more automatic compared to novices and can afford to look at the hole while putting. / Dissertation/Thesis / Masters Thesis Human Systems Engineering 2019

Tese de mestrado. Multimédia. Faculdade de Engenharia. Universidade do Porto. 2009

This master thesis report describes the work of evaluating the approach of using an eye-tracker and machine learning to generate an interaction model for clicks. In the study, recordings were done from 10 participants using a quiz application, and machine learning was then applied. Models were created with varying quality from a machine learning view, although most models did not work well for interaction. One model was created that enable correct interaction 80\% of the time, although the specific circumstances for success were not identified. The conclusion of the thesis is that the approach works in some cases, but that more research needs to be done to evaluate general suitability, and approaches to make it work reliably.

Data resulting from eye-tracking experiments allows researchers to analyze the decision making process as study participants consider alternative items prior to the ultimate end point selection. The aim of such an analysis is to extract the underlying cognitive decision making process that develops throughout the experiment. Resulting data can be difficult to analyze, however, as eye-tracking curves have very high autocorrelation values which consists of measurements that are milliseconds apart, as mandated by the nature of eye movements. We propose an analytic approach to eye-tracking data that tests for statistically significant differences at every time point along the curve while calculating an appropriate familywise error rate correction which is based upon an autoregressive correlation assumption of the test statistics. Our technique has been implemented in the R package BDOTS with various extensions relevant to the real-world analysis of highly correlated nonlinear data. A popular alternative approach to analyzing eye-tracking data is to fit mixed models to the area under the curve. Through simulation studies we provide evidence for the benefit of using information criterion measures in selection of the random effects structure and make an argument against current industry-standard approaches such as sequential likelihood ratio tests or always using a maximal random effects structure.

With the development of these new interfaces, such as Next Generation Air Transportation System (NextGen), and the evolution of the United States National Air System (NAS) from a ground-based system of Air Traffic Control (ATC) to a satellite-based system of air traffic management (FAA, 2009), new evaluations for efficiency and safety are required. Therefore, these tasks require visual behaviors such as search, fixation, tracking, and grouping. Therefore, designing and implementing a virtual eye movement application that generates gaze and action visualizations could provide detailed data on the allocation of visual attention across interface entities.The goal is to develop state-machine representations of straight-and-level flight, turns, climbs and descents within the Pilot Eye Flight Deck Application to simulate pilots' eye movement.

E-Learning is transforming the delivery of education. Today, millions of students take selfpaced online courses. However, the content and language complexity often hinders comprehension, and that with lack of immediate help from the instructor leads to weaker learning outcomes. Ability to predict difficult content in real time enables eLearning systems to adapt content as per students' level of learning. The recent introduction of lowcost eye trackers has opened a new class of applications based on eye response. Eye tracking devices can record eye response on the visual element or concept in real time. The response and the variations in eye response to the same concept over time may be indicative of the levels of learning. In this study, we have analyzed reading patterns using eye tracker and derived 12 eye response features based on psycholinguistics, contextual information processing, anticipatory behavior analysis, recurrence fixation analysis, and pupils' response. We use eye responses to predict the level of learning for a term/concept. One of the main contribution is the spatio-temporal analysis of the eye response on a term/concept to derive relevant first pass (spatial) and reanalysis (temporal) eye response features. A spatiotemporal model, built using these derived features, analyses slide images, extracts words (terms), maps the subject's eye response to words, and prepares a term-response map. A parametric baseline classifier, trained with labeled data (term-response maps) classifies a term/concept as a novel (positive class) or familiar (negative class), using majority voting method. On using, only first pass features for prediction, the baseline classifier shows 61% prediction accuracy, but on adding reanalysis features, baseline achieves 66.92% accuracy for predicting difficult terms. However, all proposed features do not have the same response to learning difficulties for all subjects, as we consider reading as an individual characteristic. Hence, we developed a non-parametric, feature weighted linguistics classifier (FWLC), which assigns weight to features based on their relevance. The FWLC classifier achieves a prediction accuracy of 90.54% an increase of 23.62% over baseline and 29.54% over the first-pass variant of baseline. Predicting novel terms as familiar is more expensive because content adapts by using this information. Hence, our primary goal is to increase the prediction rate of novel terms by minimizing the cost of false predictions. On comparing the performance of FWLC with other frequently used machine learning classifiers, FWLC achieves highest true positive rate (TPR) and lowest ratio of false negative rate (FNR) to false positive rate (FPR). The higher prediction performance of proposed spatio-temporal eye response model to predict levels of learning builds a strong foundation for eye response driven adaptive e-Learning. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2017. / FAU Electronic Theses and Dissertations Collection