Visualizing temperature trends: Higher sensitivity to trend direction with single-hue palettes

Design plays a key role in the interpretability of complex visualizations. Many applied domains utilize large quantities of data to make predictions, ranging from maps showing the spread of infectious disease to line graphs displaying global temperature changes. These visualizations tap into the visual system's ability to extract information from groups of similar objects, a process known as ensemble processing, and the cognitive system's ability to relate visual features such as color to meaningful concepts such as disease or temperature. Visualizations must consider both perceptual and cognitive abilities. It remains unclear which best improves comprehension: visualizations designed to exploit ensemble processes or that use semantically resonant colors that align with the underlying data. To address this question, participants were shown visualizations designed for ensemble processes in that they used color encodings with only a single hue or designed for semantic processes in that they prioritized color alignment with the meaning of the data. Participants viewed stripplots using these colors and judged whether the temperature depicted in the graphs was increasing or decreasing. As quantified using the signal detection measure d', participants' sensitivity to trend information was higher with the single-hue palettes than with more semantically expressive multihue palettes. Our results suggest that visualizations may convey trend information more effectively by selecting colors that exploit ensemble processes rather than selecting semantically compatible colors. Moreover, our results showed semantic compatibility had no effect on sensitivity to trend direction. (PsycInfo Database Record (c) 2023 APA, all rights reserved).