Visual analytics methodology for eye movement studies

Eye movement analysis is gaining popularity as a tool for evaluation of visual displays and interfaces. However, the existing methods and tools for analyzing eye movements and scanpaths are limited in terms of the tasks they can support and effectiveness for large data and data with high variation. We have performed an extensive empirical evaluation of a broad range of visual analytics methods used in analysis of geographic movement data. The methods have been tested for the applicability to eye tracking data and the capability to extract useful knowledge about users' viewing behaviors. This allowed us to select the suitable methods and match them to possible analysis tasks they can support. The paper describes how the methods work in application to eye tracking data and provides guidelines for method selection depending on the analysis tasks

Exploring Eye Tracking Data on Source Code via Dual Space Analysis

Eye tracking is a frequently used technique to collect data capturing users\u27 strategies and behaviors in processing information. Understanding how programmers navigate through a large number of classes and methods to find bugs is important to educators and practitioners in software engineering. However, the eye tracking data collected on realistic codebases is massive compared to traditional eye tracking data on one static page. The same content may appear in different areas on the screen with users scrolling in an Integrated Development Environment (IDE). Hierarchically structured content and fluid method position compose the two major challenges for visualization. We present a dual-space analysis approach to explore eye tracking data by leveraging existing software visualizations and a new graph embedding visualization. We use the graph embedding technique to quantify the distance between two arbitrary methods, which offers a more accurate visualization of distance with respect to the inherent relations, compared with the direct software structure and the call graph. The visualization offers both naturalness and readability showing time-varying eye movement data in both the content space and the embedded space, and provides new discoveries in developers\u27 eye tracking behaviors. Adviser: Hongfeng Y

Comparison of Attention Behaviour Across User Sets through Automatic Identification of Common Areas of Interest

Eye tracking is used to analyze and compare user behaviour across diverse domains, but long duration eye tracking experiments across multiple users generate millions of eye gaze samples, making the data analysis process complex. Usually the samples are labelled into Areas of Interest (AoI) or Objects of Interest (OoI), where the AoI approach aims to understand how a user monitors different regions of a scene, while OoI identification uncovers distinct objects in the scene that attract user attention. Using scalable clustering and cluster merging that is not constrained by input parameters, we label AoIs across multiple users in long duration eye tracking experiments. Using the common AoI labels then allows direct comparison of the users as well as the use of such methods as Hidden Markov Models and Sequence mining to uncover interesting behaviour across the users which, until now, has been prohibitively difficult to achieve

Possibilities of eye tracking and EEG integration for visual search on 2D maps

This on-going research paper explores (the possibilities to integrate eye tracking (ET) and electroencephalogram (EEG) for cartographic usability research. While ET, on one hand, provides observations and measurements related to gaze movements, EEG, on the other hand, helps to monitor and measure electrical activity occurring at different locations in the brain with a high temporal resolution. Therefore, combining ET and EEG introduces a holistic approach enabling to measure both overt and covert attention, and additionally, may reveal insights on individual’s different strategies of spatial cognition, if there is any. In this context, we introduce the experimental design settings for visual search task on simplified 2D static maps considering expert and novice participants, outlining methodological proposal and possible analyses. The paper mainly discusses the technical and theoretical issues of ET-EEG integration and mentions potential benefits of implementing EEG in cartographic usability research to indicate its value for future studies

EEG & Eye Tracking user experiments for spatial memory task on maps

The aim of this research is to evaluate the use of ET and EEG for studying the cognitive processes of expert and novice map users and to explore these processes by comparing two types of spatial memory experiments through cognitive load measurements. The first experiment consisted of single trials and participants were instructed to study a map stimulus without any time constraints in order to draw a sketch map afterwards. According to the ET metrics (i.e., average fixation duration and the number of fixations per second), no statistically significant differences emerged between experts and novices. A similar result was also obtained with EEG Frontal Alpha Asymmetry calculations. On the contrary, in terms of alpha power across all electrodes, novices exhibited significantly lower alpha power, indicating a higher cognitive load. In the second experiment, a larger number of stimuli were used to study the effect of task difficulty. The same ET metrics used in the first experiment indicated that the difference between these user groups was not statistically significant. The cognitive load was also extracted using EEG event-related spectral power changes at alpha and theta frequency bands. Preliminary data exploration mostly suggested an increase in theta power and a decrease in alpha power

What User Behaviors Make the Differences During the Process of Visual Analytics?

The understanding of visual analytics process can benefit visualization researchers from multiple aspects, including improving visual designs and developing advanced interaction functions. However, the log files of user behaviors are still hard to analyze due to the complexity of sensemaking and our lack of knowledge on the related user behaviors. This work presents a study on a comprehensive data collection of user behaviors, and our analysis approach with time-series classification methods. We have chosen a classical visualization application, Covid-19 data analysis, with common analysis tasks covering geo-spatial, time-series and multi-attributes. Our user study collects user behaviors on a diverse set of visualization tasks with two comparable systems, desktop and immersive visualizations. We summarize the classification results with three time-series machine learning algorithms at two scales, and explore the influences of behavior features. Our results reveal that user behaviors can be distinguished during the process of visual analytics and there is a potentially strong association between the physical behaviors of users and the visualization tasks they perform. We also demonstrate the usage of our models by interpreting open sessions of visual analytics, which provides an automatic way to study sensemaking without tedious manual annotations.Comment: This version corrects the issues of previous version

Interpreting maps through the eyes of expert and novice users

The experiments described in this article combine response time measurements and eye movement data to gain insight into the users' cognitive processes while working with dynamic and interactive maps. Experts and novices participated in a user study with a 'between user' design. Twenty screen maps were presented in a random order to each participant, on which he had to execute a visual search. The combined information of the button actions and eye tracker reveals that both user groups showed a similar pattern in the time intervals needed to locate the subsequent names. From this pattern, information about the users' cognitive load could be derived: use of working memory, learning effect and so on. Moreover, the response times also showed that experts were significantly faster in finding the names in the map image. This is further explained by the eye movement metrics: experts had significantly shorter fixations and more fixations per second meaning that they could interpret a larger part of the map in the same amount of time. As a consequence, they could locate objects in the map image more efficiently and thus faster

Designing Attentive Information Dashboards

Information dashboards are a critical capability in contemporary business intelligence and analytics systems. Despite their strong potential to support better decision-making, the massive amount of information they provide challenges users performing data exploration tasks. Accordingly, dashboard users face difficulties in managing their limited attentional resources when processing the presented information on dashboards. Also, studies have shown that the amount of concentrated time humans can spend on a task has dramatically decreased in recent years; thus, there is a need for designing user interfaces that support users attention management. In this design science research project, we propose attentive information dashboards that provide individualized visual attention feedback (VAF) as an innovative artifact to solve this problem. We articulate theoretically grounded design principles and instantiate a software artifact leveraging users eye movement data in real time to provide individualized VAF. We evaluated the instantiated artifact in a controlled lab experiment with 92 participants. The results from analyzing users eye movement after receiving individualized VAF reveal that our proposed design has a positive effect on users attentional resource allocation, attention shift rate, and attentional resource management. We contribute a system architecture for attentive information dashboards that support data exploration and two theoretically grounded design principles that provide prescriptive knowledge on how to provide individualized VAF. Practitioners can leverage the prescriptive knowledge derived from our research to design innovative systems that support users information processing by managing their limited attentional resources