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Drivers of L3 automated vehicles (AVs) are not required to continuously monitor the AV system. However, they must be prepared to take over when requested. Therefore, it is necessary to design an in-vehicle environment that allows drivers to adapt their levels of preparedness to the likelihood of control transition. This study evaluates ambient in-vehicle lighting that continuously communicates the current level of AV reliability, specifically on how it could influence drivers’ take-over performance and mental workload (MW). We conducted an experiment in a driving simulator with 42 participants who experienced 10 take-over requests (TORs). The experimental group experienced a four-stage ambient light display that communicated the current level of AV reliability, which was not provided to the control group. The experimental group demonstrated better take-over performance, based on lower vehicle jerks. Notably, perceived MW did not differ between the groups, and the EEG indices of MW (frontal theta power, parietal alpha power, Task–Load Index) did not differ between the groups. These findings suggest that communicating the current level of reliability using ambient light might help drivers be better prepared for TORs and perform better without increasing their MW.

Blinking is a natural user-induced response which paces visual information processing. This study investigates whether blinks are viable for segmenting continuous electroencephalography (EEG) activity, for inferring cognitive demands in ecologically valid work environments. We report the blink-related EEG measures of participants who performed auditory tasks either standing, walking on grass, or whilst completing an obstacle course. Blink-related EEG activity discriminated between different levels of cognitive demand during walking. Both behavioral parameters (e.g., blink duration or head motion) and blink-related EEG activity varied with walking conditions. Larger occipital N1 was observed during walking, relative to standing and traversing an obstacle course, which reflects differences in bottom-up visual perception. In contrast, the amplitudes of top-down components (N2, P3) significantly decreased with increasing walking demands, which reflected narrowing attention. This is consistent with blink-related EEG, specifically in Theta and Alpha power that, respectively, increased and decreased with increasing demands of the walking task. This work presents a novel and robust analytical approach to evaluate the cognitive demands experienced in natural work settings, which precludes the use of artificial task manipulations for data segmentation.

This work presents a visual analytics approach to explore microsaccade distributions in high-frequency eye tracking data. Research studies often apply filter algorithms and parameter values for microsaccade detection. Even when the same algorithms are employed, different parameter values might be adopted across different studies. In this paper, we present a visual analytics system (VisME) to promote reproducibility in the data analysis of microsaccades. It allows users to interactively vary the parametric values for microsaccade filters and evaluate the resulting influence on microsaccade behavior across individuals and on a group level. In particular, we exploit brushing-and-linking techniques that allow the microsaccadic properties of space, time, and movement direction to be extracted, visualized, and compared across multiple views. We demonstrate in a case study the use of our visual analytics system on data sets collected from natural scene viewing and show in a qualitative usability study the usefulness of this approach for eye tracking researchers. We believe that interactive tools such as VisME will promote greater transparency in eye movement research by providing researchers with the ability to easily understand complex eye tracking data sets; such tools can also serve as teaching systems. VisME is provided as open source software.

There is a growing interest in eye tracking technologies applied to support traditional visualization techniques like diagrams, charts, maps, or plots, either static, animated, or interactive ones. More complex data analyses are required to derive knowledge and meaning from the data. Eye tracking systems serve that purpose in combination with biological and computer vision, cognition, perception, visualization, human-computer-interaction, as well as usability and user experience research. The 10 articles collected in this thematic special issue provide interesting examples how sophisticated methods of data analysis and representation enable researchers to discover and describe fundamental spatio-temporal regularities in the data. The human visual system, supported by appropriate visualization tools, enables the human operator to solve complex tasks, like understanding and interpreting three-dimensional medical images, controlling air traffic by radar displays, supporting instrument flight tasks, or interacting with virtual realities. The development and application of new visualization techniques is of major importance for future technological progress.

The current study investigates the demands that steering places on mental resources. Instead of a conventional dual-task paradigm, participants of this study were only required to perform a steering task while task-irrelevant auditory distractor probes (environmental sounds and beep tones) were intermittently presented. The event-related potentials (ERPs), which were generated by these probes, were analyzed for their sensitivity to the steering task’s demands. The steering task required participants to counteract unpredictable roll disturbances and difficulty was manipulated either by adjusting the bandwidth of the roll disturbance or by varying the complexity of the control dynamics. A mass univariate analysis revealed that steering selectively diminishes the amplitudes of early P3, late P3, and the re-orientation negativity (RON) to task-irrelevant environmental sounds but not to beep tones. Our findings are in line with a three-stage distraction model, which interprets these ERPs to reflect the post-sensory detection of the task-irrelevant stimulus, engagement, and re-orientation back to the steering task. This interpretation is consistent with our manipulations for steering difficulty. More participants showed diminished amplitudes for these ERPs in the ‘hard’ steering condition relative to the ‘easy’ condition. To sum up, the current work identifies the spatiotemporal ERP components of task-irrelevant auditory probes that are sensitive to steering demands on mental resources. This provides a non-intrusive method for evaluating mental workload in novel steering environments.

Video-based gaze-tracking systems are typically restricted in terms of their effective tracking space. This constraint limits the use of eyetrackers in studying mobile human behavior. Here, we compare two possible approaches for estimating the gaze of participants who are free to walk in a large space whilst looking at different regions of a large display. Geometrically, we linearly combined eye-in-head rotations and head-in-world coordinates to derive a gaze vector and its intersection with a planar display, by relying on the use of a head-mounted eyetracker and body-motion tracker. Alternatively, we employed Gaussian process regression to estimate the gaze intersection directly from the input data itself. Our evaluation of both methods indicates that a regression approach can deliver comparable results to a geometric approach. The regression approach is favored, given that it has the potential for further optimization, provides confidence bounds for its gaze estimates and offers greater flexibility in its implementation. Open-source software for the methods reported here is also provided for user implementation.