Rainbows Revisited: Modeling Effective Colormap Design for Graphical Inference.

Area of Science:

• Data visualization
• Cognitive science
• Perceptual psychology

Background:

• Traditional colormap design prioritizes perceptual uniformity (e.g., luminance variation).
• Existing guidelines are primarily based on perceptual tasks, limiting generalizability to cognitive inference tasks.
• Conventional ramp designs may overlook other critical design strategies.

Purpose of the Study:

• To investigate the impact of color name variation, a cognitive metric, on model-based judgments.
• To compare the efficacy of color name variation against established colormap design principles.
• To explore the potential of "colorful" colormaps for visual inference tasks.

Main Methods:

• Conducted two graphical inference experiments involving participants assessing relationships between models using color-coded scalar fields.
• Modeled participant performance using color name variation as a predictive metric.
• Evaluated colormaps based on their ability to support cognitive judgments and compared this to perceptual metrics.

Main Results:

• Contrary to conventional wisdom, participants performed more accurately with colormaps featuring high color name variation (e.g., rainbow colormaps).
• Color name variation provided a better fit to experimental data than existing colormap design principles.
• No evidence was found that high color categorization leads to the inference of false data features.

Conclusions:

• Cognitive advantages exist for "colorful" colormaps that exhibit high color categorization, challenging traditional design guidelines.
• Color name variation offers an empirically grounded metric for predicting colormap performance in inference tasks.
• Results suggest alternative guidelines for designing quantitative colormaps to enhance visual inference.