Color Nameability Predicts Inference Accuracy in Spatial Visualizations

Color encoding is foundational to visualizing quantitative data. Guidelines for colormap design have traditionally emphasized perceptual principles, such as order and uniformity. However, colors also evoke cognitive linguistic associations whose role in data interpretation remains underexplored. We study how two factors, name salience variation, affect people's ability draw inferences from spatial visualizations. In experiments, we found that participants are better at interpreting visualizations when viewing with more salient names (e.g., prototypical ‘blue’, ‘yellow’, ‘red’ over ‘teal’, ‘beige’, ‘maroon’). The effect was robust across four visualization types, but pronounced continuous smooth geographical maps) than similar discrete representations choropleths). Participants' accuracy improved the number of nameable increased, although latter had a less effect. Our findings suggest color nameability an important consideration colormaps, may even outweigh traditional metrics. particular, predictor performance properties those colors. discuss implications outline research opportunities. materials this available https://osf.io/asb7n