Guidelines
Suggest edit

Prefer a perceptually uniform multi-hue sequential palette over a rainbow palette

For retrieve tasks, prefer a perceptually uniform multi-hue sequential color scale on quantitative color encodings to improve fidelity and mitigate slower, more error-prone relative-value judgments for viewers comparing values against a legend.

  • purpose:refine
  • basis:empirical
  • task:retrieve
  • data:quantitative
  • quality:fidelity
  • lever:encoding
  • reading-mode:lookup
  • polish:palette

advice

Sequential palette replacement

Replace a rainbow quantitative palette with a perceptually uniform multi-hue sequential palette when people must retrieve values from color. For example, switch from a rainbow ramp such as jet to a perceptually uniform sequential ramp such as viridis when readers must judge which encoded value is closer to a reference.

reason

Why the sequential replacement works

A perceptually uniform multi-hue sequential palette preserves an ordered scale while giving readers cleaner distance cues across the legend. A rainbow palette made the same retrieval task slower and more error-prone.

Mechanism: The palette change improves how reliably readers map color distance back to value distance, so they make fewer wrong closeness judgments and spend less time resolving the comparison.

Evidence: In the assorted-palette comparison, viridis had the best accuracy and significantly outperformed jet, while jet was the slowest and most error-prone option overall; the review collates the same pattern from this study into an actionable ranking for quantitative color retrieval (Liu & Heer, 2018; Zeng & Battle, 2023).

context

Use when this is the task

  • User Goal: Retrieve which of two encoded values is closer to a reference.
  • Task: Value retrieval from a continuous quantitative color scale.
  • Data: Scalar quantitative values mapped to color.
  • Chart Setting: A static chart with a visible color legend.
  • Audience: Readers who must decode value differences from color rather than another channel.
  • Success Criterion: Lower retrieval error and faster response time.

exceptions

When not to use this palette swap

Break it when: The color scale is not a single ordered quantitative ramp and instead must encode a different structure such as a midpoint-centered scheme. Why: This guideline is supported by the study’s sequential-versus-rainbow retrieval comparison, not by all possible color-scale semantics.

costs

What this costs

Sacrifice: You give up the familiar rainbow look. Risk: Treating every non-rainbow palette as equally good can overstate the evidence. Mitigation: Compare the current rainbow palette directly against a perceptually uniform sequential alternative on the same retrieval question.

mistakes

Common failure around this change

Mistake: Replacing a rainbow palette with another non-sequential-looking palette and assuming the retrieval problem is solved. Why it fails: The supported benefit is tied to an ordered perceptually uniform sequential ramp, not to any arbitrary palette change.

check

How to test the palette change

Failure Sign: Readers hesitate or miss closest-value judgments when using the rainbow scale. Quick Check: Show the same chart once with the rainbow palette and once with a perceptually uniform sequential palette, then ask which of two colored values is closer to a reference. Stronger Test: Compare both error rate and response time across several legend locations before keeping the new palette.

fix

What to change

  • Replace the rainbow ramp with a perceptually uniform multi-hue sequential ramp.
  • Keep the same legend and rerun the same closest-to-reference check on the revised palette.
  • Keep the new palette only if it reduces retrieval errors or time on the same chart task.

References

Liu, Y., & Heer, J. (2018). Somewhere Over the Rainbow: An Empirical Assessment of Quantitative Colormaps. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 1–12. https://doi.org/10.1145/3173574.3174172
Zeng, Z., & Battle, L. (2023). A Review and Collation of Graphical Perception Knowledge for Visualization Recommendation. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 1–16. https://doi.org/10.1145/3544548.3581349