- [Instructor] The earliest forms of written language were visual. They were symbology, right? The image, the picture, represented the concepts being communicated. As communications evolved, our written language became more nuanced to communicate more complex ideas. But that hasn't helped us when communicating numbers. We can actually parse numbers much more quickly and easily in visual form, not text. Now, why is that? There are a bunch of reasons.
One thing is that 30 to 50% of our brain is devoted to visual processing, and 70% of our sensory receptors are in our eyes. It's also been shown that it takes a tenth of a second to make sense of a visual scene, really just at the glance of an eye. It's also been shown that you have 323% better performance on tasks when you learn them accompanied by images versus just text. There's also something known as the picture superiority effect, that you'll remember 60% of something when you learn it with imagery versus 6% without, and this effect increases with age.
There's also something called gestalt psychology, and these gestalt principles have a deep effect in how we perceive visual information. This helps immensely with data visualization to understand how it works. The first one is called figure/ground. It's most often used in logo design. So for instance, here you have the FedEx logo, one of the more famous examples of the use of figure/ground. You have the mark, FedEx, the letters in the foreground, and in the background, the negative space, you have that arrow, that white arrow subliminal effect, characterizing motion, which of course, is what FedEx is all about.
In data visualization, figure/ground is the least actively used aspect of visual perception, but it is important to understand. What's interesting is that the bottom object, the bottom portion of whatever it is that you're looking at, so in this case we're looking at an area chart, the bottom is always perceived as being the figure, meaning in the foreground, and the top as the background, no matter what the color. So if someone were to look at the area chart on the left, they're going to perceive the white as being the data and the black as being the background.
And the same thing on the right-hand side, the black would be the data and the white is the background. So you can't rely on color or contrast or anything other than bottom versus top. It's an important principle to understand. The next gestalt principle to talk about is proximity. So when you have items next to each other or near each other, so in this case we have three columns of dots on the left and two columns of dots on the right, they are perceived as being grouped, and therefore similar and together.
So I immediately sense that all the dots on the left-hand side are together and with each other, yet separate from the two columns of dots to the right. Proximity is one reason why we recognize patterns in a chart like this scatter plot. The next principle is called a similarity, kind of an obvious one. Objects that are similar, the black dots, are different from the other objects, the white dots, straightforward. The similarity of things makes us categorize them, whether the similarity is in shape or size or color.
The next principle is called parallelism. So the idea here is that when you have things in parallel, like the three lines in the middle, we assume that they're together and different from the other objects that aren't in parallel. So in data visualization, you might see patterns. So for instance, the overall trend lines for these different portions of this area chart, we see that parallelism and make judgments about these shapes because of that aspect of the shape. The next principle is called common fate, and I just love this title.
I just like the name of this one. So here's how it works. Here you have a bunch of dots and there's no discernible pattern. But as soon as they start moving, I can see that these dots have a common fate, right? They're all moving in the same direction, so I can tell that they belong together. The ones that are moving are one group and the ones that aren't moving are in another group. That's common fate. Of course, you can see this in animated visualizations, or interactive visualizations where you have a rollover effect.
You won't see this in static visualizations. Then there are the principles referred to as closure and continuity. These are interesting, although they're less relevant specifically for data visualization, but they play a role in really all design. So the first idea here is closure, the fact that we see things that aren't there. Our brain completes the picture. So for instance, in this image, in addition to seeing three Pac-Man, or three wheels of cheese, you probably also see the triangle in the middle.
That's closure. Or here you have what's known as continuity, where you see the S. You don't just see two random arcs, you see a full S shape. Your brain just does that for you. Culture can play a role in continuity. So for instance, here you don't just see a random sine wave pattern, even though you do end up seeing that full pattern. But what you probably really see is the Loch Ness Monster. That's where culture can have an influence. So as I said, this isn't specific to visualization, but it's something to be aware of.
As you visualize and as you're creating shapes, you don't want them to be misperceived or seen as something unintended by your audience. It could affect how your data is understood. The gestalt principles are key to how people perceive things visually. And there are lots of techniques to use to draw the eye, understanding how these principles work, and to help make your data stand out, whether it's tilting a line or making lines shorter or thicker, or dots fatter, using different shapes or hash marks, color and hue, et cetera.
You don't need to understand this brain scan or every principle for how brains process information to do a visualization, of course. But isn't it nice to know a bit about it? I know it helps me think when I'm designing how to trigger the brain instantly to maximize effect.
- Describe the process by which individuals’ interests are incorporated into data visualizations.
- Differentiate the use of the Ws in data visualization.
- Explain techniques involved in defining your narrative when visualizing data.
- Identify the factors that make data visualizations relatable to an audience’s interests and needs.
- Review the appropriate use of charts in data visualizations.
- Define the process involved in applying interactivity to data visualizations.