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We conducted an eye-tracking user study with 13 participants to investigate the influence of stimulus-question ordering and question modality on participants using visual question-answering (VQA) tasks. We examined cognitive load, task performance, and gaze allocations across five distinct experimental designs, aiming to identify setups that minimize the cognitive burden on participants. The collected performance and gaze data were analyzed using quantitative and qualitative methods. Our results indicate a significant impact of stimulus-question ordering on cognitive load and task performance, as well as a noteworthy effect of question modality on task performance. These findings offer insights for the experimental design of controlled user studies in visualization research., Comment: Accepted at ETVIS 2024

We present a novel, web-based visual eye-tracking analytics tool called Gazealytics. Our open-source toolkit features a unified combination of gaze analytics features that support flexible exploratory analysis, along with annotation of areas of interest (AOI) and filter options based on multiple criteria to visually analyse eye tracking data across time and space. Gazealytics features coordinated views unifying spatiotemporal exploration of fixations and scanpaths for various analytical tasks. A novel matrix representation allows analysis of relationships between such spatial or temporal features. Data can be grouped across samples, user-defined AOIs or time windows of interest (TWIs) to support aggregate or filtered analysis of gaze activity. This approach exceeds the capabilities of existing systems by supporting flexible comparison between and within subjects, hypothesis generation, data analysis and communication of insights. We demonstrate in a walkthrough that Gazealytics supports multiple types of eye tracking datasets and analytical tasks.

We investigate reading strategies for node-link diagrams that wrap around the boundaries in a flattened torus topology by examining eye tracking data recorded in a previous controlled study. Prior work showed that torus drawing affords greater flexibility in clutter reduction than traditional node-link representations, but impedes link-and-path exploration tasks, while repeating tiles around boundaries aids comprehension. However, it remains unclear what strategies users apply in different wrapping settings. This is important for design implications for future work on more effective wrapped visualizations for network applications, and cyclic data that could benefit from wrapping. We perform visual-exploratory data analysis of gaze data, and conduct statistical tests derived from the patterns identified. Results show distinguishable gaze behaviors, with more visual glances and transitions between areas of interest in the non-replicated layout. Full-context has more successful visual searches than partial-context, but the gaze allocation indicates that the layout could be more space-efficient.

Traditional visualisations are designed to be shown on a flat surface (screen or page) but most data is not "flat". For example, the surface of the earth exists on a sphere, however, when that surface is presented on a flat map, key information is hidden, such as geographic paths on the spherical surface being wrapped across the boundaries of the flat map. Similarly, cyclical time-series data has no beginning or end. When such cyclical data is presented on a traditional linear chart, the viewer needs to perceive continuity of the visualisation across the chart's boundaries. Mentally reconnecting the chart across such a boundary may induce additional cognitive load. More complex data such as a network diagram with hundreds or thousands of links between data points leads to a densely connected structure that is even less "flat" and needs to wrap around in multiple dimensions. To improve the usability of these visualisations, this thesis explores a novel class of interactive wrapped data visualisations, i.e., visualisations that wrap around continuously when interactively panned on a two-dimensional projection of surfaces of 3D shapes, specifically, cylinder, torus, or sphere. We start with a systematic exploration of the design space of interactive wrapped visualisations, characterising the visualisations that help people understand the relationship within the data. Subsequently, we investigate a series of wrappable visualisations for cyclical time series, network, and geographic data. We show that these interactive visualisations better preserve the spatial relations in the case of geospatial data, and better reveal the data's underlying structure in the case of abstract data such as networks and cyclical time series. Furthermore, to assist future research and development, we contribute layout algorithms and toolkits to help create pannable wrapped visualisations.

There are many methods for projecting spherical maps onto the plane. Interactive versions of these projections allow the user to centre the region of interest. However, the effects of such interaction have not previously been evaluated. In a study with 120 participants we find interaction provides significantly more accurate area, direction and distance estimation in such projections. The surface of 3D sphere and torus topologies provides a continuous surface for uninterrupted network layout. But how best to project spherical network layouts to 2D screens has not been studied, nor have such spherical network projections been compared to torus projections. Using the most successful interactive sphere projections from our first study, we compare spherical, standard and toroidal layouts of networks for cluster and path following tasks with 96 participants, finding benefits for both spherical and toroidal layouts over standard network layouts in terms of accuracy for cluster understanding tasks.