Color Nameability Predicts Inference Accuracy in Spatial Visualizations

Abstract

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

Color Coding of Large Value Ranges Applied to Meteorological Data

“…Rogowitz and Treinish [17] criticize the Rainbow-colormap for the fact that the boundaries of the different colors can be perceived as boundaries in the data. This is countered by the findings of Reda and Szafir [16] and Reda et al [15] that the more unique colors represented, the better the plot.…”

“…2). To achieve a better color nameability [15,16], we chose unique hues instead of different saturation tones of one specific hue.…”

psudo: Exploring Multi-Channel Biomedical Image Data with Spatially and Perceptually Optimized Pseudocoloring

“…Our ability to distinguish between colors is intrinsically linked to the names we ascribe to each color, the distinctiveness of these names, and their distance from one another [HLX*19, TAW*09, HS12]. This, in turn, impacts our graphical perception in spatial data [RSGP21, Red22]. In L 2 , we attempt to improve graphical perception by evaluating the distinctiveness of color names in a palette (Fig.…”

Color Maker: a Mixed-Initiative Approach to Creating Accessible Color Maps

“…Future work could incorporate an empirical aesthetics model into the objective scoring function. Moreover, there is room to optimize for additional cognitive factors, like color nameability [24,46], thus enabling the generation of designs with more distinct and recognizable color names.…”

“…Despite the importance of this channel to visualization, designing quantitative colormaps is challenging. Visualization designers need to carefully balance various factors, such as perceptual uniformity [63], color nameability [46], and personal preferences. Given the complexity of the task, most color advice tools provide a limited set of manually crafted color scales to select from.…”