AOI hierarchies for visual exploration of fixation sequences

Blascheck, Tanja; Kurzhals, Kuno; Raschke, Michael; Strohmaier, Stefan; Weiskopf, Daniel; Ertl, Thomas

doi:10.1145/2857491.2857524

Cited by 33 publications

(17 citation statements)

References 17 publications

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“…For example, a densely filled matrix indicates poor search behaviour since all AOIs were focused on for several times. Finding visual search patterns is improved when cells in the transition matrix are coloured to show the transition count [LPS*12, KSK13, BKR*16].…”

Section: Aoi‐based Visualization Techniquesmentioning

confidence: 99%

Visualization of Eye Tracking Data: A Taxonomy and Survey

Blascheck

Kurzhals

Raschke

et al. 2017

Computer Graphics Forum

214

115

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This survey provides an introduction into eye tracking visualization with an overview of existing techniques. Eye tracking is important for evaluating user behaviour. Analysing eye tracking data is typically done quantitatively, applying statistical methods. However, in recent years, researchers have been increasingly using qualitative and exploratory analysis methods based on visualization techniques. For this state‐of‐the‐art report, we investigated about 110 research papers presenting visualization techniques for eye tracking data. We classified these visualization techniques and identified two main categories: point‐based methods and methods based on areas of interest. Additionally, we conducted an expert review asking leading eye tracking experts how they apply visualization techniques in their analysis of eye tracking data. Based on the experts' feedback, we identified challenges that have to be tackled in the future so that visualizations will become even more widely applied in eye tracking research.

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Section: Aoi‐based Visualization Techniquesmentioning

confidence: 99%

Visualization of Eye Tracking Data: A Taxonomy and Survey

Blascheck

Kurzhals

Raschke

et al. 2017

Computer Graphics Forum

214

115

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show abstract

“…An option to overcome this issue is to only show selected AOIs. If a semantic or spatial relation of AOIs is given, another possibility is to depict the parent‐AOI of a hierarchical class of AOIs [BKR*16]. Concerning the arrangement of AOIs, in our current version we order the AOIs alphabetically or an analyst can assign them an explicit order.…”

Section: Discussionmentioning

confidence: 99%

“…In the scope of this paper, we define an eye movement pattern as a set of AOI transitions recurring in the same or similar way across different participants or within the temporal sequence of a single participant. Current techniques only show single AOI transition graphs without providing details of differences between graphs [BKR*16, HHBL03, KW15, TAK*05]. Others contrast AOI sequences as part of a hierarchy or timeline representation that either aggregates the individual recordings or do not explicitly hint at differences and similarities [BKW13, KW15, RCE12, TTS10].…”

Section: Introductionmentioning

confidence: 99%

Visual Comparison of Eye Movement Patterns

Blascheck

Schweizer

Beck

et al. 2017

Computer Graphics Forum

Self Cite

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In eye tracking research, finding eye movement patterns and similar strategies between participants’ eye movements is important to understand task solving strategies and obstacles. In this application paper, we present a graph comparison method using radial graphs that show Areas of Interest (AOIs) and their transitions. An analyst investigates a single graph based on dwell times, directed transitions, and temporal AOI sequences. Two graphs can be compared directly and temporal changes may be analyzed. A list and matrix approach facilitate the analyst to contrast more than two graphs guided by visually encoded graph similarities. We evaluated our approach in case studies with three eye tracking and visualization experts. They identified temporal transition patterns of eye movements across participants, groups of participants, and outliers.

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“…Hence, Blascheck et. al [15] utilizes hierarchical visualization approaches that take into account the inherent hierarchies in AoI clusters over time. Multiple visualization tools such as transition trees, graphs, transition matrix and hierarchy diagrams have been used to collectively portray the sequential, relational and temporally evolving nature of areas of interest in eye gaze data.…”

Section: Visualization Of Eye Tracking Datamentioning

confidence: 99%