A Survey of Perception-Based Visualization Studies by Task

@article{Quadri2021ASO,
  title={A Survey of Perception-Based Visualization Studies by Task},
  author={Ghulam Jilani Quadri and Paul Rosen},
  journal={IEEE transactions on visualization and computer graphics},
  year={2021},
  volume={PP}
}
Knowledge of human perception has long been incorporated into visualizations to enhance their quality and effectiveness. The last decade, in particular, has shown an increase in perception-based visualization research studies. With all of this recent progress, the visualization community lacks a comprehensive guide to contextualize their results. In this report, we provide a systematic and comprehensive evaluation of research studies on perception related to visualization. This survey reviews… 

Figures and Tables from this paper

Automatic Scatterplot Design Optimization for Clustering Identification

TLDR
This paper proposes an automatic tool to optimize the design factors of scatterplots to reveal the most salient cluster structure, which leverages the merge tree data structure to identify the clusters and optimize the choice of subsampling algorithm, sampling rate, marker size, and marker opacity used to generate a scatterplot image.

Rethinking the Ranks of Visual Channels

TLDR
The results point to the need for a body of empirical studies that move beyond two-value ratio judgments as a baseline for reliably ranking the quality of a visual channel, including testing new tasks, timescales (immediate computation, or later comparison), and the number of values (from a handful, to thousands).

Where did my Lines go? Visualizing Missing Data in Parallel Coordinates

TLDR
Three missing value representation concepts to represent missing values in parallel coordinates are identified: removing line segments where values are missing, adding a separate, horizontal axis onto which missing values are projected, and using imputed values as a replacement for missing values.

Color Coding of Large Value Ranges Applied to Meteorological Data

TLDR
A novel color scheme designed to address the challenge of visualizing data series with large value ranges, where scale transformation provides limited support is presented, and significantly outperforms the others in interpretation tasks, while showing comparable performances in discrimination tasks.

References

SHOWING 1-10 OF 160 REFERENCES

Human factors in visualization research

TLDR
This work aims to review known methodology for doing human factors research, with specific emphasis on visualization, and review current human factor research in visualization to provide a basis for future investigation, and identify promising areas for future research.

A Design Space of Vision Science Methods for Visualization Research

TLDR
This paper introduces a design space of experimental methods for empirically investigating the perceptual processes involved with viewing data visualizations to ultimately inform visualization design guidelines and highlights a history of collaborative success between visualization and vision science research.

Ranking Visualizations of Correlation Using Weber's Law

TLDR
A large scale crowdsourced experiment is conducted to investigate whether the perception of correlation in nine commonly used visualizations can be modeled using Weber's law, establishing that for all tested visualizations, the precision of correlation judgment could be modeled by Weber'sLaw.

Attention and Visual Memory in Visualization and Computer Graphics

  • C. HealeyJ. Enns
  • Art
    IEEE Transactions on Visualization and Computer Graphics
  • 2012
TLDR
This paper surveys research on attention and visual perception, with a specific focus on results that have direct relevance to visualization and visual analytics.

How Capacity Limits of Attention Influence Information Visualization Effectiveness

TLDR
The results show that the severe capacity limits of attention strongly modulate the effectiveness of information visualizations, particularly the ability to detect unexpected information.

Evaluating the Impact of User Characteristics and Different Layouts on an Interactive Visualization for Decision Making

TLDR
Key findings are that performance with low and high‐level tasks is affected by different user characteristics, and users with low visual working memory perform better with a horizontal layout, and how these findings can inform the provision of personalized support to visualization processing is discussed.

Toward Understanding Representation Methods in Visualization Recommendations through Scatterplot Construction Tasks

TLDR
This work investigates the effectiveness of three representation methods under scatterplot construction tasks and shows how different representations individually and cooperatively help users understand and choose recommended visualizations, for example, by supporting their expect‐and‐confirm process.

Task-Based Effectiveness of Basic Visualizations

TLDR
It is found the effectiveness of these visualization types significantly varies across task, suggesting that visualization design would benefit from considering context-dependent effectiveness, and recommendations on which visualizations to choose based on different tasks are derived.

Quality Metrics for Information Visualization

TLDR
This survey attempts to report, categorize and unify the diverse understandings and aims to establish a common vocabulary that will enable a wide audience to understand their differences and subtleties.

Correlation Judgment and Visualization Features: A Comparative Study

TLDR
The hypothesis that people attend to a small number of visual features when discriminating correlation in scatterplots is investigated, and it is discussed how visual features provide a baseline for future model-based approaches in visualization evaluation and design.
...