Understanding the opaque-is-more bias and saturated-is-more bias for colormap data visualizations

Area of Science:

• Cognitive Psychology
• Information Visualization
• Human-Computer Interaction

Background:

• People rely on color-quantity mapping biases (e.g., dark-is-more, opaque-is-more) for data interpretation.
• Previous research on the opaque-is-more bias confounded opacity with lightness variations.
• The perceptual conditions for activating these biases remain unclear.

Purpose of the Study:

• To investigate if the opaque-is-more bias can be activated without substantial lightness variation.
• To explore the role of color saturation in quantity perception within data visualizations.
• To identify new biases influencing color-quantity mappings.

Main Methods:

• Manipulated color saturation to vary perceived opacity, while controlling for lightness contrast (L* in CIELAB).
• Presented participants with visualizations and assessed their interpretations of quantity based on color properties.
• Analyzed responses to determine the influence of saturation and opacity on perceived magnitude.

Main Results:

• The opaque-is-more bias was activated even with minimal lightness variation.
• Evidence emerged for a novel "saturated-is-more" bias, where higher saturation implies greater magnitude.
• Color saturation independently influences perceived quantity, distinct from lightness.

Conclusions:

• The opaque-is-more bias is not solely dependent on substantial lightness variation.
• Color saturation is a key visual feature that can drive quantity-magnitude inferences.
• Findings inform the design of more effective and intuitive data visualizations by leveraging color properties.