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Perceptual proxies for extracting averages in data visualizations.

Lei Yuan1, Steve Haroz2, Steven Franconeri3

  • 1Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA. leiyuan@indiana.edu.

Psychonomic Bulletin & Review
|September 22, 2018
PubMed
Summary
This summary is machine-generated.

The established hierarchy of data visualization precision breaks down for comparing averages. Viewers rely on basic visual cues, not precise encodings, when analyzing multi-value data, impacting data interpretation.

Keywords:
Data visualizationGraph comprehensionMagnitude perceptionVisual perception

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Area of Science:

  • Data visualization research
  • Human-computer interaction
  • Cognitive psychology

Background:

  • A hierarchy of perceptual precision for data encoding (e.g., position vs. size) is a key finding in visualization research.
  • This hierarchy has primarily been validated for single-value comparisons.

Purpose of the Study:

  • To investigate whether the established hierarchy of perceptual precision extends to multi-value data comparisons.
  • To identify the perceptual cues viewers use when comparing averages across multiple data points.

Main Methods:

  • Experimental study comparing different visual encodings for average value representation.
  • Analysis of viewer perception and reliance on specific visual cues during comparison tasks.

Main Results:

  • The hierarchy of precision significantly diminishes when comparing averages, even for pairs of data points.
  • Viewers utilize more primitive perceptual cues, such as summed area in bar graphs, for multi-value comparisons.
  • Established assumptions about precise visual encoding do not hold for average comparisons.

Conclusions:

  • The findings challenge the universal applicability of the precision hierarchy in data visualization.
  • There is a critical need to explore a wider range of visual cues and their impact on data interpretation across diverse visualization types and tasks.
  • Future research should focus on understanding how viewers process multi-value data to improve visualization design and effectiveness.