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Title: Modeling human comprehension of data visualizations

Abstract

This project was inspired by two needs. The first is a need for tools to help scientists and engineers to design effective data visualizations for communicating information, whether to the user of a system, an analyst who must make decisions based on complex data, or in the context of a technical report or publication. Most scientists and engineers are not trained in visualization design, and they could benefit from simple metrics to assess how well their visualization's design conveys the intended message. In other words, will the most important information draw the viewer's attention? The second is the need for cognition-based metrics for evaluating new types of visualizations created by researchers in the information visualization and visual analytics communities. Evaluating visualizations is difficult even for experts. However, all visualization methods and techniques are intended to exploit the properties of the human visual system to convey information efficiently to a viewer. Thus, developing evaluation methods that are rooted in the scientific knowledge of the human visual system could be a useful approach. In this project, we conducted fundamental research on how humans make sense of abstract data visualizations, and how this process is influenced by their goals and prior experience. Wemore » then used that research to develop a new model, the Data Visualization Saliency Model, that can make accurate predictions about which features in an abstract visualization will draw a viewer's attention. The model is an evaluation tool that can address both of the needs described above, supporting both visualization research and Sandia mission needs.« less

Authors:
 [1];  [1];  [1];  [1]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1398333
Report Number(s):
SAND-2017-10543
657407
DOE Contract Number:
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Matzen, Laura E., Haass, Michael Joseph, Divis, Kristin Marie, and Wilson, Andrew T.. Modeling human comprehension of data visualizations. United States: N. p., 2017. Web. doi:10.2172/1398333.
Matzen, Laura E., Haass, Michael Joseph, Divis, Kristin Marie, & Wilson, Andrew T.. Modeling human comprehension of data visualizations. United States. doi:10.2172/1398333.
Matzen, Laura E., Haass, Michael Joseph, Divis, Kristin Marie, and Wilson, Andrew T.. Fri . "Modeling human comprehension of data visualizations". United States. doi:10.2172/1398333. https://www.osti.gov/servlets/purl/1398333.
@article{osti_1398333,
title = {Modeling human comprehension of data visualizations},
author = {Matzen, Laura E. and Haass, Michael Joseph and Divis, Kristin Marie and Wilson, Andrew T.},
abstractNote = {This project was inspired by two needs. The first is a need for tools to help scientists and engineers to design effective data visualizations for communicating information, whether to the user of a system, an analyst who must make decisions based on complex data, or in the context of a technical report or publication. Most scientists and engineers are not trained in visualization design, and they could benefit from simple metrics to assess how well their visualization's design conveys the intended message. In other words, will the most important information draw the viewer's attention? The second is the need for cognition-based metrics for evaluating new types of visualizations created by researchers in the information visualization and visual analytics communities. Evaluating visualizations is difficult even for experts. However, all visualization methods and techniques are intended to exploit the properties of the human visual system to convey information efficiently to a viewer. Thus, developing evaluation methods that are rooted in the scientific knowledge of the human visual system could be a useful approach. In this project, we conducted fundamental research on how humans make sense of abstract data visualizations, and how this process is influenced by their goals and prior experience. We then used that research to develop a new model, the Data Visualization Saliency Model, that can make accurate predictions about which features in an abstract visualization will draw a viewer's attention. The model is an evaluation tool that can address both of the needs described above, supporting both visualization research and Sandia mission needs.},
doi = {10.2172/1398333},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Sep 01 00:00:00 EDT 2017},
month = {Fri Sep 01 00:00:00 EDT 2017}
}

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