What is the color of money? Of love? Of the ocean? In the United States, most people respond that money is green, love is red and the ocean is blue. Many concepts evoke related colors — whether due to physical appearance, common metaphors, or cultural conventions. When colors are paired with the concepts that evoke them, we call these “semantically resonant color choices.”

Artists and designers regularly use semantically resonant colors in their work. And in the research we conducted with Julie Fortuna, Chinmay Kulkarni, and Maureen Stone, we found they can be remarkably important to data visualization.

Consider these charts of (fictional) fruit sales:

The only difference between the charts is the color assignment. The left-hand chart uses colors from a default palette. The right-hand chart has been assigned semantically resonant colors. (In this case, the assignment was computed automatically using an algorithm that analyzes the colors in relevant images retrieved from Google Image Search using queries for each data category name.)

Now, try answering some questions about the data in each of these charts. Which fruit had higher sales: blueberries or tangerines? How about peaches versus apples? Which chart do you find easier to read?

If you answered the chart on the right, you’re not alone. To determine the impact of semantically resonant colors on graph analysis, we ran experiments to measure how quickly people can complete data-comparison tasks on bar charts using either default colors or semantically resonant colors. On average, people took a full second less to complete a single comparison task when they were looking at semantically resonant colors (whether chosen by our algorithm or by an expert designer). That may not sound like a lot but it’s about 10% of the total task time. These time savings can add up, particularly for data analysts making untold numbers of such comparisons throughout their work day.

What’s going on here? We see a number of ways in which semantically resonant colors could be helping improve graph-reading performance. First, semantically resonant colors can enable you to take advantage of familiar existing relationships, thus requiring you to use less conscious thought and speeding recall. Non-resonant colors, on the other hand, can cause semantic interference: the colors and concepts interfere with each other (as anyone familiar with the famous Stroop test from psychology knows – the one in which you’re asked to name the text colors of color names printed in conflicting colors: green, red, and so on). Second, because your recall of the concept-color relationship is improved when looking at semantically resonant data, you may not need to repeatedly look at the legend to remember which column is which, and so can focus more on the data itself.

To make effective visualization color choices, you need to take a number of factors into consideration. To name just two: All the colors need to be suitably different from one another, for instance, so that readers can tell them apart – what’s called “discriminability.” You also need to consider what the colors look like to the color blind — roughly 8% of the U.S. male population! Could the colors be distinguished from one another if they were reprinted in black and white?

One easy way to assign semantically resonant colors is to use colors from an existing color palette that has been carefully designed for visualization applications (ColorBrewer offers some options) but assign the colors to data values in a way that best matches concept color associations. This is the basis of our own algorithm, which acquires images for each concept and then analyzes them to learn concept color associations. However, keep in mind that color associations may vary across cultures. For example, in the United States and many western cultures, luck is often associated with green (four-leaf clovers), while red can be considered a color of danger. However, in China, luck is traditionally symbolized with the color red.

There are a few other factors to consider when using semantically resonant colors:

Type of data: So far, we have only discussed data that represent discrete categories. Other data may be numerical or rank-ordered (“poor,” “fair,” “good” for example). In these cases, a diverging or sequential color scheme may be preferred, in which a single color becomes darker or lighter depending on the relative order of the values.

Similar color associations: Some concepts map to very similar colors. For example, “magazine” and “newspaper” might both map to gray. We could consider assigning two different shades of gray to both concepts, but then it may be more difficult to remember which shade of gray maps to which one in the visualization. In this case, we might prefer less-resonant colors that ensure discriminability.

Concept-color association strength: Some concepts are simply more colorable than others. For example, people generally agree on the colors of asset categories such as “gold,” “silver,” “cash.” However, what is the color of “social security,” “national defense,” or “income security”? Overall, we found that using semantically resonant colors for categories that were more colorable unsurprisingly tends to provide greater performance improvements.

Semantically resonant colors can reinforce perception of a wide range of data categories. We believe similar gains would likely be seen for other forms of visualizations like maps, scatterplots, and line charts. So when designing visualizations for presentation or analysis, consider color choice and ask yourself how well the colors resonate with the underlying data.