Static Visualization of Temporal Eye-Tracking Data

Räihä, Kari-Jouko; Aula, Anne; Majaranta, Päivi; Rantala, Harri; Koivunen, Kimmo

doi:10.1007/11555261_76

Cited by 44 publications

(16 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For analysing the evaluation styles, we developed a static visualization that presents the order in which each AOI is visited [8]. To enable comparison of the evaluation styles of different individuals and the effects of good and poor result lists, all the visualizations (280 visualizations in total) were printed out on paper and visually inspected by each author, first separately and then in face-to-face meetings.…”

Section: Data Analysis and Resultsmentioning

confidence: 99%

Eye-Tracking Reveals the Personal Styles for Search Result Evaluation

Aula

Majaranta

Räihä

2005

Human-Computer Interaction - INTERACT 2005

Self Cite

View full text Add to dashboard Cite

Abstract. We used eye-tracking to study 28 users when they evaluated result lists produced by web search engines. Based on their different evaluation styles, the users were divided into economic and exhaustive evaluators. Economic evaluators made their decision about the next action (e.g., query re-formulation, following a link) faster and based on less information than exhaustive evaluators. The economic evaluation style was especially beneficial when most of the results in the result page were relevant. In these tasks, the task times were significantly shorter for economic than for exhaustive evaluators. The results suggested that economic evaluators were more experienced with computers than exhaustive evaluators. Thus, the result evaluation style seems to evolve towards a more economic style as the users gain more experience.

show abstract

Section: Data Analysis and Resultsmentioning

confidence: 99%

Eye-Tracking Reveals the Personal Styles for Search Result Evaluation

Aula

Majaranta

Räihä

2005

Human-Computer Interaction - INTERACT 2005

Self Cite

View full text Add to dashboard Cite

show abstract

“…The visualisation techniques are typically designed for analysing scanpaths in an exploratory and qualitative way [Räihä et al 2005;Blascheck et al 2014] whereas the algorithms are mainly designed for comparing a pair of scanpaths quantitatively, computing transition probabilities between web page elements, detecting patterns within scanpaths and identifying a general scanpath for multiple scanpaths [Eraslan et al 2016a;Holmqvist et al 2011]. Although some of these algorithms analyse these scanpaths as geometric figures [Jarodzka et al 2010;Mathôt et al 2012;Le Meur and Baccino 2013], most of them analyse scanpaths which are represented as a sequence of Areas of Interest (AoIs) [Sutcliffe and Namoun 2012;Le Meur and Baccino 2013;Eraslan et al 2014].…”

Section: Related Workmentioning

confidence: 99%

Scanpath Trend Analysis on Web Pages

2016

View full text Add to dashboard Cite

Eye tracking studies have widely been used in improving the design and usability of web pages, and in the research of understanding how users navigate them. However, there is limited research in clustering users' eye movement sequences (i.e., scanpaths) on web pages to identify a general direction they follow. Existing research tends to be reductionist which means the resulting path is so short that is not useful. Moreover, there is little work on correlating users' scanpaths with visual elements of web pages and the underlying source code which means the result cannot be used for further processing. In order to address these limitations, we introduce a new concept in clustering scanpaths called Scanpath Trend Analysis (STA) which does not only consider the visual elements visited by all users, but also considers the visual elements visited by the majority in any order. We present an algorithm which automatically does this trend analysis to identify a trending scanpath of multiple web users in terms of visual elements of a web page. In contrast to existing research, the STA algorithm first analyses mostly visited visual elements in given scanpaths, then clusters the scanpaths by using these visual elements based on their overall positions in the individual scanpaths, and then constructs a trending scanpath in terms of these visual elements. This algorithm was experimentally evaluated by an eye tracking study on six web pages for two different kinds of tasks, and therefore 12 cases in total. Our experimental results show that the STA algorithm generates a trending scanpath that addresses the reductionist problem of existing work by preventing the loss of commonly visited visual elements for all the cases. Based on the statistical tests, the STA algorithm also generates a trending scanpath which is significantly more similar to the inputted scanpaths compared to other existing work in 10 out of 12 cases. In the rest of the cases, the STA algorithm still performs significantly better than some of other existing work. This algorithm contributes to behaviour analysis research on the web which can be used for different purposes, for example re-engineering web pages guided by the trending scanpath to improve users' experience or guiding designers to improve their design.

show abstract

“…For example, scarfplots [53], and to some extent AOI rivers [54], identify AOIs via distinctive colors, and are thus limited in the number of DOIs they can show by the number of colors that users can distinguish. Alternatively, scanpaths [55] or heatmaps [2] identify DOIs through labels, and could thus encode as many DOIs as necessary. However, by listing DOIs vertically and time horizontally, scanpaths and heatmaps assign visual space to each time × DOI combination.…”

Section: Visualizing Many Dois Viewed Over Long Periods Of Timementioning

confidence: 99%

A Data Model and Task Space for Data of Interest (DOI) Eye-Tracking Analyses

Jianu

Alam

2018

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract-Eye-tracking data is traditionally analyzed by looking at where on a visual stimulus subjects fixate, or, to facilitate more advanced analyses, by using area-of-interests (AOI) defined onto visual stimuli. Recently, there is increasing interest in methods that capture what users are looking at rather than where they are looking. By instrumenting visualization code that transforms a data model into visual content, gaze coordinates reported by an eye-tracker can be mapped directly to granular data shown on the screen, producing temporal sequences of data objects that subjects viewed in an experiment. Such data collection, which is called gaze to object mapping (GTOM) or data-of-interest analysis (DOI), can be done reliably with limited overhead and can facilitate research workflows not previously possible. Our paper contributes to establishing a foundation of DOI analyses by defining a DOI data model and highlighting its differences to AOI data in structure and scale; by defining and exemplifying a space of DOI enabled tasks; by describing three concrete examples of DOI experimentation in three different domains; and by discussing immediate research challenges in creating a framework of visual support for DOI experimentation and analysis. Permanent repository link

show abstract

Static Visualization of Temporal Eye-Tracking Data

Cited by 44 publications

References 3 publications

Eye-Tracking Reveals the Personal Styles for Search Result Evaluation

Eye-Tracking Reveals the Personal Styles for Search Result Evaluation

Scanpath Trend Analysis on Web Pages

A Data Model and Task Space for Data of Interest (DOI) Eye-Tracking Analyses

Contact Info

Product

Resources

About