Tanja Blascheck

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.

VA 2 : A Visual Analytics Approach for // Evaluating Visual Analytics Applications

Evaluation has become a fundamental part of visualization research and researchers have employed many approaches from the field of human-computer interaction like measures of task performance, thinking aloud protocols, and analysis of interaction logs. Recently, eye tracking has also become popular to analyze visual strategies of users in this context. This has added another modality and more data, which requires special visualization techniques to analyze this data. However, only few approaches exist that aim at an integrated analysis of multiple concurrent evaluation procedures. The variety, complexity, and sheer amount of such coupled multi-source data streams require a visual analytics approach. Our approach provides a highly interactive visualization environment to display and analyze thinking aloud, interaction, and eye movement data in close relation. Automatic pattern finding algorithms allow an efficient exploratory search and support the reasoning process to derive common eye-interaction-thinking patterns between participants. In addition, our tool equips researchers with mechanisms for searching and verifying expected usage patterns. We apply our approach to a user study involving a visual analytics application and we discuss insights gained from this joint analysis. We anticipate our approach to be applicable to other combinations of evaluation techniques and a broad class of visualization applications.

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.

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.

Visual Analysis of Eye Tracking Data

Eye tracking has become a valuable approach to evaluate visualization techniques in a user centered design process. Apart from just relying on task accuracies and completion times, eye movements can additionally be recorded to later study visual task solution strategies and the cognitive workload of study participants. During an eye tracking experiment many data sets are recorded. Standard techniques to analyze this eye tracking data are heat map and scan path visualizations. However, it still requires a high effort to analyze scan path trajectory data to find common task solution strategies among the study participants. In this chapter we discuss three existing methodologies for analyzing the vast amount of eye tracking data from a visualization and visual analytics perspective. These three approaches are a classical static visualization, visual analytics techniques and finally a software prototype, which helps the user to manage, view and analyze the recorded data in a simple interactive way.

Triangulating user behavior using eye movement, interaction, and think aloud data

In information visualization, evaluation plays a crucial role during the development of a new visualization technique. In recent years, eye tracking has become one means to analyze how users perceive and understand a new visualization system. Since most visualizations are highly interactive nowadays, a study should take interaction, in terms of user-input, into account as well. In addition, think aloud data gives insights into cognitive processes of participants using a visualization system. Typically, researchers evaluate these data sources separately. However, we think it is beneficial to correlate eye tracking, interaction, and think aloud data for deeper analyses. In this paper, we present challenges and possible solutions in triangulating user behavior using multiple evaluation data sources. We describe how the data is collected, synchronized, and analyzed using a string-based and a visualization-based approach founded on experiences from our current research. We suggest methods how to tackle these issues and discuss benefits and disadvantages. Thus, the contribution of our work is twofold. On the one hand, we present our approach and the experiences we gained during our research. On the other hand, we investigate additional methods that can be used to analyze this multi-source data.

Towards analyzing eye tracking data for evaluating interactive visualization systems

Eye tracking can be a suitable evaluation method for determining which regions and objects of a stimulus a human viewer perceived. Analysts can use eye tracking as a complement to other evaluation methods for a more holistic assessment of novel visualization techniques beyond time and error measures. Up to now, most stimuli in eye tracking are either static stimuli or videos. Since interaction is an integral part of visualization, an evaluation should include interaction. In this paper, we present an extensive literature review on evaluation methods for interactive visualizations. Based on the literature review we propose ideas for analyzing eye movement data from interactive stimuli. This requires looking critically at challenges induced by interactive stimuli. The first step is to collect data using different study methods. In our case, we look at using eye tracking, interaction logs, and thinking-aloud protocols. In addition, this requires a thorough synchronization of the mentioned study methods. To analyze the collected data new analysis techniques have to be developed. We investigate existing approaches and how we can adapt them to new data types as well as sketch ideas how new approaches can look like.