The relation between color and spatial structure for interpreting colormap data visualizations

Abstract

Interpreting colormap visualizations requires determining how dimensions of color in visualizations map onto quantities in data. People have color-based biases that influence their interpretations of colormaps, such as a dark-is-more bias—darker colors map to larger quantities. Previous studies of color-based biases focused on colormaps with weak data spatial structure, but color-based biases may not generalize to colormaps with strong data spatial structure, like “hotspots” typically found in weather maps and neuroimaging brain maps. There may be a hotspot-is-more bias to infer that colors within hotspots represent larger quantities, which may override the dark-is-more bias. We tested this possibility in four experiments. Participants saw colormaps with hotspots and a legend that specified the color-quantity mapping. Their task was to indicate which side of the colormap depicted larger quantities (left/right). We varied whether the legend specified dark-more mapping or light-more mapping across trials and operationalized a dark-is-more bias as faster response time (RT) when the legend specified dark-more mapping. Experiment 1 demonstrated robust evidence for the dark-is-more bias, without evidence for a hotspot-is-more bias. Experiments 2 to 4 suggest that a hotspot-is-more bias becomes relevant when hotspots are a statistically reliable cue to “more” (i.e., the locus of larger quantities) and when hotspots are more perceptually pronounced. Yet, comparing conditions in which the hotspots were “more,” RTs were always faster for dark hotspots than light hotspots. Thus, in the presence of strong spatial cues to the locus of larger quantities, color-based biases still influenced interpretations of colormap data visualizations.

True colours or red herrings?: colour maps for finite-element analysis in palaeontological studies to enhance interpretation and accessibility

“…Parula, Polar) can work well in such a case. For the presentation on a white background, colour maps with a decreasing chroma (=colour intensity), such as Batlow, Inferno and, in particular, YlGnBu are more appropriate to convey the results [25,61,62].…”

“…This situation is further complicated in that FEA contour plots cannot be regarded in isolation but need to be considered in the context of background and environmental settings. In the simplest of cases, this could mean that the choice of background colour can influence the readability of the contour plots [25,60,61] (figure 7). Especially colour maps with a large amount of dark components can become invisible against a black background.…”

Unifying Effects of Direct and Relational Associations for Visual Communication

“…on relative lightness, rather than particular colors in visualizations. Although empirical studies of colormaps have focused on relational associations and explicitly tried to avoid potential effects of direct associations [21,35,43], direct associations likely play an important role (see Samsel et al's [34] intuitive colormaps for environmental visualizations).…”

“…Traditionally, researchers studying the role of color semantics for visual communication have treated these cases as two distinct problems. One involves mapping colors to different categories in categorical information [18,22,36,38,41] and the other involves mapping gradations of color to gradations of quantity in continuous data [8,21,35,43]. In both cases, a key goal is to understand people's expectations about the mappings between colors and concepts in visualizations (called inferred mappings) because visualizations designed to match people's expectations are easier to interpret [14,18,22,26,35,36,38,43,50,51].…”

“…Studies on visualizations of continuous data focus on relational associations-correspondences between relational properties of visual features and relational properties of concepts. For example, observers have a dark-is-more bias, inferring that darker colors map to larger quantities [4,8,21,35,43]. This bias is relational because it depends of colors to quantities (e.g., weather maps, neuroimaging maps, correlation matrices).…”