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The relation between color and spatial structure for interpreting colormap data visualizations.

Shannon C Sibrel1,2, Ragini Rathore1,3, Laurent Lessard4,5

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|November 17, 2020
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Summary
This summary is machine-generated.

People exhibit a "dark-is-more" bias when interpreting colormaps, where darker colors are perceived as representing larger quantities. This bias persists even when spatial cues like "hotspots" are present in data visualizations.

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Area of Science:

  • Data Visualization
  • Cognitive Psychology
  • Human-Computer Interaction

Background:

  • Interpreting colormaps relies on mapping color dimensions to data quantities.
  • Color-based biases, like "dark-is-more," affect perception.
  • Previous research focused on colormaps with weak spatial structure.

Purpose of the Study:

  • To investigate if a "hotspot-is-more" bias overrides the "dark-is-more" bias in colormaps with strong spatial structure.
  • To examine the influence of spatial cues on color-based biases in data visualization interpretation.
  • To test the generalizability of color-based biases to complex visualizations.

Main Methods:

  • Four experiments were conducted using colormap visualizations with defined hotspots and legends.
  • Participants indicated which side of the colormap represented larger quantities.
  • The mapping of dark/light colors to quantities was varied, and response times (RT) were measured.

Main Results:

  • A "dark-is-more" bias was consistently observed, indicated by faster RTs when legends specified dark-more mappings.
  • A "hotspot-is-more" bias was not consistently found and depended on statistical reliability and perceptual prominence of hotspots.
  • Even with strong spatial cues, darker hotspots led to faster interpretations of larger quantities.

Conclusions:

  • The
  • dark-is-more
  • bias remains influential in interpreting colormap data visualizations, even when strong spatial cues like hotspots are present.
  • Spatial cues can modulate the "hotspot-is-more" bias, but color-based biases continue to impact interpretation.
  • Understanding these biases is crucial for designing effective and accurate data visualizations.