Michael Raschke

State-of-the-Art of Visualization for Eye Tracking Data

Eye tracking technology is becoming easier and cheaper to use, resulting in its increasing application to numerous fields of research. The data collected during an eye tracking experiment can be analyzed by statistical methods and/or with visualization techniques. Visualizations can reveal characteristics of fixations, saccades, and scanpath structures. In this survey, we present an overview of visualization techniques for eye tracking data and describe their functionality. We classify the visualization techniques using nine categories. The categories are based on properties of eye tracking data, including aspects of the stimuli and the viewer, and on properties of the visualization techniques. The classification of about 90 publications including technical as well as application papers with modifications of common visualization techniques are described in more detail. We finally present possible directions for further research in the field of eye tracking data visualization.

Parallel scan-path visualization

Eye tracking analysis is the state of the art technique to study questions of usability and cognition of graphical user interfaces. This paper presents a new technique for the visualization of eye tracking data, the Parallel Scan-Path Visualization. A key feature is the visualization of eye movements of many subjects on a single screen in a parallel layout. The visualization presents various properties of scan-paths, such as fixations, gaze durations and eye shift frequencies at one glance. The paper concludes with an example of use of the Parallel Scan-Path Visualization technique.

Visual task solution strategies in tree diagrams

We investigate visual task solution strategies when exploring traditional, orthogonal, and radial node-link tree layouts, four orientations of the non-radial layouts, as well as varying difficulty of the task. The strategies are identified by examining eye movement data recorded in a controlled user study previously conducted by Burch et al. For detailed analysis of the spatio-temporal structures and patterns in the eye tracking data, we employ visual analytics techniques adopted from related methodology for geographic movement data by Andrienko et al. In this way, we complement the statistical analysis of task completion times and error rates reported by Burch et al. with spatio-temporal strategies that explain the variation in completion times. We identify differences between task solution strategies dependent on layout type, orientation, and task difficulty. Furthermore, we examine differences between groups of participants split according to completion time. Our analysis identifies that for all layouts it took nearly the same time to find the task solution node, but in the radial layout the solution was not confirmed directly. Instead, a more frequent cross-checking occurs afterwards, which is the main reason for the impaired performance of radial layouts.

Indented pixel tree plots

We introduce Indented Pixel Tree Plots (IPTPs): a novel pixel-based visualization technique for depicting large hierarchies. It is inspired by the visual metaphor of indented outlines, omnipresent in graphical file browsers and pretty printing of source code. Inner vertices are represented as vertically arranged lines and leaf groups as horizontally arranged lines. A recursive layout algorithm places parent nodes to the left side of their underlying tree structure and leaves of each subtree grouped to the rightmost position. Edges are represented only implicitly by the vertically and horizontally aligned structure of the plot, leading to a sparse and redundant-free visual representation. We conducted a user study with 30 subjects in that we compared IPTPs and node-link diagrams as a within-subjects variable. The study indicates that working with IPTPs can be learned in less than 10 minutes. Moreover, IPTPs are as effective as node-link diagrams for accuracy and completion time for three typical tasks; participants generally preferred IPTPs. We demonstrate the usefulness of IPTPs by understanding hierarchical features of huge trees such as the NCBI taxonomy with more than 300,000 nodes.

A visual approach for scan path comparison

Several algorithms, approaches, and implementations have been developed to support comparison of scan paths and finding of interesting scan path structures. In this work we contribute a visual approach to support scan path comparison. A key feature of this approach is the combination of a clustering algorithm using Levenshtein distance with the parallel scan path visualization technique. The combination of computational methods with an interactive visualization allows us to use both the power of pattern finding algorithms and the human ability to visually recognize patterns. To use the concept in practice we implemented the approach in a prototype and show its application in two scan path analysis scenarios from automobile usability testing and visualization research.

Circular heat map transition diagram

Eye tracking experiments are the state-of-the-art technique to study questions of usability of graphical interfaces. Visualizations help to analyse eye tracking data by presenting it in a graphical way. In this paper we contribute a new visualization technique combining features of state-of-the-art visualizations for eye tracking data like heat maps, and transition matrices with a circular layout. The circular heat map transition diagram uses areas of interest (AOIs) and orders them alphabetically on a circular layout to show transitions between AOIs visually. The AOIs are colour coded segments on a circle where the colour is mapped with respect to the fixation count in each AOI. The segment size corresponds to the fixation duration within an AOI. Furthermore, the transitions between and within the AOIs of a participant are drawn as arrow lines. Key features of the circular heat map transition diagram are extraction of similar eye movement patterns of different participants, graphical representation of transitions between AOIs, finding an appropriate AOI sequence, and investigating inefficient search behaviour of participants. To be able to use the visualization technique in practice, we have implemented three variants, the AOI transition diagram, the AOI transition and completion time diagram, and the fixation transition diagram. We will show their application in an exemplary analysis of an eye tracking experiment.

Challenges and Perspectives in Big Eye-Movement Data Visual Analytics

Eye tracking has become an important technology to understand where and when people pay visual attention to a scene. Nowadays, eye tracking technology is moving from the laboratory to the real-world, producing more data at higher rates with extensive amounts of different data types. If this trend continues, eye tracking moves into the direction of big data. This requires developing new evaluation approaches beyond statistical analysis and visual inspection to find patterns in the data. As in big data analysis, visual analytics is one possible direction for eye movement data analysis. We look at current visual analytics methods and discuss how they can be applied to big eye-movement data. In this position paper we describe challenges for big eye-movement data visual analytics and discuss which techniques may be useful to address these challenges. Finally, we describe a number of potential scenarios for big eye-movement data.

AOI hierarchies for visual exploration of fixation sequences

In eye tracking studies a complex visual stimulus requires the definition of many areas of interest (AOIs). Often these AOIs have an inherent, nested hierarchical structure that can be utilized to facilitate analysis tasks. We discuss how this hierarchical AOI structure in combination with appropriate visualization techniques can be applied to analyze fixation sequences on differently aggregated levels. An AOI View, AOI Tree, AOI Matrix, and AOI Graph enable a bottom-up and top-down evaluation of fixation sequences. We conducted an expert review and compared our techniques to current state-of-the-art visualization techniques in eye movement research to further improve and extend our approach. To show how our approach is used in practice, we evaluate fixation sequences collected during a study where 101 AOIs are organized hierarchically.

Saccade plots

Visualization by heat maps is a powerful technique for showing frequently visited areas in displayed stimuli. However, by aggregating the spatio-temporal data, heat maps lose the information about the transitions between fixations, i.e., the saccades. In gaze plots, instead, trajectories are shown as overplotted polylines, leading to much visual clutter, which makes those diagrams difficult to read. In this paper, we introduce Saccade Plots as a novel technique that combines the benefits of both approaches: it shows the gaze frequencies as a heat map and the saccades in the form of color-coded triangular matrices that surround the heat map. We illustrate the usefulness of our technique by applying it to a representative example from a previously conducted eye tracking study.

A dynamic graph visualization perspective on eye movement data

During eye tracking studies, vast amounts of spatio-temporal data in the form of eye gaze trajectories are recorded. Finding insights into these time-varying data sets is a challenging task. Visualization techniques such as heat maps or gaze plots help find patterns in the data but highly aggregate the data (heat maps) or are difficult to read due to overplotting (gaze plots). In this paper, we propose transforming eye movement data into a dynamic graph data structure to explore the visualization problem from a new perspective. By aggregating gaze trajectories of participants over time periods or Areas of Interest (AOIs), a fair trade-off between aggregation and details is achieved. We show that existing dynamic graph visualizations can be used to display the transformed data and illustrate the approach by applying it to eye tracking data recorded for investigating the readability of tree diagrams.