The visual display of quantitative information
The visual display of quantitative information
A cognitive model for the perception and understanding of graphs
CHI '91 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Information Graphics: A Comprehensive Illustrated Reference: Visual Tools for Analyzing, Managing, and Communicating
Cognitive measurements of graph aesthetics
Information Visualization
APVis '05 proceedings of the 2005 Asia-Pacific symposium on Information visualisation - Volume 45
Information Dashboard Design: The Effective Visual Communication of Data
Information Dashboard Design: The Effective Visual Communication of Data
Eye Tracking Methodology: Theory and Practice
Eye Tracking Methodology: Theory and Practice
Exploring the role of individual differences in information visualization
AVI '08 Proceedings of the working conference on Advanced visual interfaces
Scanpath clustering and aggregation
Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications
Measuring effectiveness of graph visualizations: a cognitive load perspective
Information Visualization
Comparing information graphics: a critical look at eye tracking
Proceedings of the 3rd BELIV'10 Workshop: BEyond time and errors: novel evaLuation methods for Information Visualization
Proceedings of the 2013 international conference on Intelligent user interfaces
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
EuroVis '13 Proceedings of the 15th Eurographics Conference on Visualization
| Hi-index | 0.00 |
An eye tracking methodoLogy can help uncover subtLe cognitive processing stages that are otherwise difficult to observe in visuaLization evaLuation studies. Pros and cons of eye tracking methods are discussed here, incLuding common analysis metrics. One example metric is the initial time at which all elements of a visualization that are required to complete a task have been viewed. An illustrative eye tracking study was conducted to compare how radial and Linear graphs support value Lookup tasks for both one and two data-dimensions. Linear and radial versions of bar, Line, area, and scatter graphs were presented to 32 participants, who each completed a counterbalanced series of tasks. Tasks were completed more quickly on Linear graphs than on those with a radial Layout. Scanpath analysis revealed that a three-stage processing] find desired data dimension, [2] find its datapoint, and [3] map the datapoint to its value. Mapping a datapoint to its value was slower on radial than Linear graphs, probably because eyes need to follow a circular, as opposed to a Linear path. Finding a datapoint within a dimension was harder using Line and area graphs than bar and scatter graphs, possibly due to visual confusion of the Line representing a data series. ALthough few errors were made, eye tracking was also used here to classify error strategies. As a result of these analyses, guidelines are proposed for the design of radial and Linear graphs.