Maurice Koch

Abstract

Technical progress in hardware and software enables us to record gaze data in everyday situations and over long time spans. Among a multitude of research opportunities, this technology enables visualization researchers to catch a glimpse behind performance measures and into the perceptual and cognitive processes of people using visualization techniques. The majority of eye tracking studies performed for visualization research are limited to the analysis of gaze distributions and aggregated statistics, thus only covering a small portion of insights that can be derived from gaze data. We argue that incorporating theories and methodology from psychology and cognitive science will benefit the design and evaluation of eye tracking experiments for visualization. This book chapter provides an overview of how eye tracking can be used in a variety of study designs. Furthermore, we discuss the potential merits of cognitive models for the evaluation of visualizations. We exemplify these concepts on two scenarios, each focusing on a different eye tracking study. Lastly, we identify several call for actions.

Abstract

Gaze-based analysis of areas of interest (AOI) is widely used in information visualisation research to understand how people explore visualisations or assess the quality of visualisations concerning key characteristics such as memorability. However, nearby AOIs in visualisations amplify the uncertainty caused by the gaze estimation error, which strongly influences the mapping between gaze samples or fixations and different AOIs. We contribute a novel investigation into gaze uncertainty and quantify its impact on AOI-based analysis on visualisations using two novel metrics: the Flipping Candidate Rate (FCR) and Hit Any AOI Rate (HAAR). Our analysis of 40 real-world visualisations, including human gaze and AOI annotations, shows that uncertainty commonly appears in visualisations, which significantly impacts the analysis conducted in AOI-based studies. Moreover, we analysed four visualisation types and found that bar and scatter plots are commonly designed in a way that causes more uncertainty than line and pie plots in gaze-based analysis.

Abstract

Comparing mobile eye tracking data from multiple participants without information about areas of interest (AOIs) is challenging because of individual timing and coordinate systems. We present a technique, the gaze spiral, that visualizes individual recordings based on image content of the stimulus. The spiral layout of the slitscan visualization is used to create a compact representation of scanpaths. The visualization provides an overview of multiple recordings even for long time spans and helps identify and annotate recurring patterns within recordings. The gaze spirals can also serve as glyphs that can be projected to 2D space based on established scanpath metrics in order to interpret the metrics and identify groups of similar viewing behavior. We present examples based on two egocentric datasets to demonstrate the effectiveness of our approach for annotation and comparison tasks. Our examples show that the technique has the potential to let users compare even long-term recordings of pervasive scenarios without manual annotation.

Abstract

We propose a new technique for visual analytics and annotation of long-term pervasive eye tracking data for which a combined analysis of gaze and egocentric video is necessary. Our approach enables two important tasks for such data for hour-long videos from individual participants: (1) efficient annotation and (2) direct interpretation of the results. Exemplary time spans can be selected by the user and are then used as a query that initiates a fuzzy search of similar time spans based on gaze and video features. In an iterative refinement loop, the query interface then provides suggestions for the importance of individual features to improve the search results. A multi-layered timeline visualization shows an overview of annotated time spans. We demonstrate the efficiency of our approach for analyzing activities in about seven hours of video in a case study and discuss feedback on our approach from novices and experts performing the annotation task.

Abstract

The comparison of scanpaths between multiple participants is an important analysis task in eye tracking research. Established methods typically inspect recorded gaze sequences based on geometrical trajectory properties or strings derived from annotated areas of interest (AOIs). We propose a new approach based on image similarities of gaze-guided slit-scans: For each time step, a vertical slice is extracted from the stimulus at the gaze position. Placing the slices next to each other over time creates a compact representation of a scanpath in the context of the stimulus. These visual representations can be compared based on their image similarity, providing a new measure for scanpath comparison without the need for annotation. We demonstrate how comparative slit-scan visualization can be integrated into a visual analytics approach to support the interpretation of scanpath similarities in general.