Two hours in Hollywood: A manually annotated ground truth data set of eye movements during movie clip watching

An Eye-Tracking-Based Investigation on the Principle of Closure in Logo Design

This study employs subjective evaluation and eye movement experiments to explore the application and conveyance of logo graphics design, which conforms to the Gestalt principle of closure, to understand the psychological process of this principle in the perception of a logo. The study found that there is no significant difference between completely enclosed logos and unenclosed ones that conform to the principle of closure in their influence on sightline behavior due to the effects of closure, but the subjective evaluation favors unenclosed logos as more attractive and comfortable to perceive, which agrees with modern logo design trends. In addition, the sightline distribution of the image-type logos is more scattered and has the most extended fixation duration. In contrast, the sightline distribution of text-type logos is more concentrated. Designers who understand the principle of closure can intentionally incorporate imperfection into logo design, triggering the automatic mental filling of gaps and instilling new meaning and visual effect into a design.

Outlier-Robust Gaze Signal Filtering Framework Based on Eye-Movement Modality Recognition and Set-Membership Approach

OBJECTIVE

High-quality gaze signals are crucial in many biomedical fields that utilize them. However, the limited studies on gaze signal filtering can hardly address the outliers and non-Gaussian noise in gaze data simultaneously. Our objective is to design a generic filtering framework capable of reducing the noise and eliminating outliers of the gaze signal.

METHODS

In this study, we design an eye-movement modality-based zonotope set-membership filtering framework (EM-ZSMF) to suppress the noise and outliers of the gaze signal. This framework consists of an eye-movement modality recognition model (EG-NET), an eye-movement modality-based gaze movement model (EMGM), and a zonotope set-membership filter (ZSMF). The eye-movement modality determines the EMGM, and the ZSMF combined with the EMGM completes the filtering of the gaze signal. Moreover, this study establishes an eye-movement modality and gaze filtering dataset (ERGF) that can be utilized for the evaluation of future work integrating eye-movement modality with gaze signal filtering.

RESULTS

The eye-movement modality recognition experiments demonstrated that our proposed EG-NET achieved the best Cohen's kappa compared with previous studies. The gaze data filtering experiments showed that the proposed EM-ZSMF reduced the gaze signal noise and eliminated outliers effectively, and achieved the best performance (RMSEs and RMS) compared with previous methods.

CONCLUSION

The proposed EM-ZSMF effectively recognizes eye-movement modalities, reduces gaze signal noise and, eliminates outliers.

SIGNIFICANCE

To the best of the authors' knowledge, this is the first attempt to simultaneously solve the problem of non-Gaussian noise and outliers in gaze signals. The proposed framework has the potential for application in any eye image-based eye trackers and contributes to the development of eye-tracking technology.

Factors affecting inter-rater agreement in human classification of eye movements: a comparison of three datasets

Manual classification of eye-movements is used in research and as a basis for comparison with automatic algorithms in the development phase. However, human classification will not be useful if it is unreliable and unrepeatable. Therefore, it is important to know what factors might influence and enhance the accuracy and reliability of human classification of eye-movements. In this report we compare three datasets of human manual classification, two from earlier datasets and one, our own dataset, which we present here for the first time. For inter-rater reliability, we assess both the event-level F1-score and sample-level Cohen's κ, across groups of raters. The report points to several possible influences on human classification reliability: eye-tracker quality, use of head restraint, characteristics of the recorded subjects, the availability of detailed scoring rules, and the characteristics and training of the raters.

Evaluating Eye Movement Event Detection: A Review of the State of the Art

Detecting eye movements in raw eye tracking data is a well-established research area by itself, as well as a common pre-processing step before any subsequent analysis. As in any field, however, progress and successful collaboration can only be achieved provided a shared understanding of the pursued goal. This is often formalised via defining metrics that express the quality of an approach to solving the posed problem. Both the big-picture intuition behind the evaluation strategies and seemingly small implementation details influence the resulting measures, making even studies with outwardly similar procedures essentially incomparable, impeding a common understanding. In this review, we systematically describe and analyse evaluation methods and measures employed in the eye movement event detection field to date. While recently developed evaluation strategies tend to quantify the detector's mistakes at the level of whole eye movement events rather than individual gaze samples, they typically do not separate establishing correspondences between true and predicted events from the quantification of the discovered errors. In our analysis we separate these two steps where possible, enabling their almost arbitrary combinations in an evaluation pipeline. We also present the first large-scale empirical analysis of event matching strategies in the literature, examining these various combinations both in practice and theoretically. We examine the particular benefits and downsides of the evaluation methods, providing recommendations towards more intuitive and informative assessment. We implemented the evaluation strategies on which this work focuses in a single publicly available library: https://github.com/r-zemblys/EM-event-detection-evaluation .

Eye tracking applied to tobacco smoking: current directions and future perspectives

Over the years the general awareness of the health costs associated with tobacco smoking has motivated scientists to apply the measurement of eye movements to this form of addiction. On one hand they have investigated whether smokers attend and look preferentially at smoking related scenes and objects. In parallel, on the other hand eye tracking has been used to test how smokers and nonsmokers interact with the different types of health warning that policymakers have mandated in tobacco advertisements and packages. Here we provide an overview of the main findings from the different lines of research, such as the evidence related to the attentional bias for smoking cues in smokers and the evidence that graphic warning labels and plain packages measurably increase the salience of the warning labels. We point to some open questions, such as the conditions that determine whether heavy smokers exhibit a tendency to actively avoid looking at graphic warning labels. Finally we argue that the research applied to gaze exploration of warning labels would benefit from a more widespread use of the more naturalistic testing conditions (e.g. mobile eye tracking or virtual reality) that have been introduced to study the smokers' attentional bias for tobacco-related objects when freely exploring the surrounding environment.