53  Gestalt Principles

Gestalt is a German word that can be variously translated as design, form or shape. It encapsulates all of these ideas - the complete essence of an entity. Gestalt psychology took root in 1920s Berlin and is based on the principle that we see objects first in their entirety and second as individual parts. Hence, “the whole is greater than the sum of its parts”. Gestalt psychologists identified several visual principles that our minds intuitively recognize, some of which Figure 53.1 are valuable in the context of data visualization. Remember, here, we are mainly interested in explanatory plots. They should be fast and communicate a clear message.

Figure 53.1: Some Gestalt principles are particularly relevant for data visualization.

Gestalt principles dictate the first and immediate response to an image. Taking advantage of gestalt principles allows us to optimize visualizations to effectively communicate a clear message.

Gestalt principles relevant for data visualizations
Principle Description Use case
Similarity Objects similar in appearance belong to the same group Encode groups using distinct, easily distinguishable elements
Proximity Objects physically close together belong to the same group Arrange sub-items according to relationship
Connection Objects that are physically connected with lines belong to the same group Highlight patterns with lines where appropriate
Enclosure Objects contained within borders belong to the same group Highlight regions of interest with circles or boxes
Continuity Objects continue as they are perceived Make trends explicit so they are not misinterpreted
Closure Missing parts are mentally filled in Remove extraneous visual elements

Similarity and Proximity are common place every multivariate plot. A very common construction is shown in Figure 53.2. Even without any background knowledge, we can immediately see that we have a continuous measure described by two color groups (red versus blue) and three x axis categories.

Exactly what the variables are requires further input, but we already know the experimental design without any introduction. This plot depicts the mean length of odontoblasts (the cells responsible for tooth growth) from 60 guinea pigs is described by two variables. Dose levels of vitamin C (3: 0.5, 1, and 2 mg/day) and delivery method (2: orange juice or ascorbic acid, a form of vitamin C).

Figure 53.2: Gestalt principles of similarity and proximity. One of the most frequent plot types in science is the dodged bar plot with error bars, which make use of the two most common gestalt principles.

In terms of structure, Figure 53.2 is great. Dose increases as we move along the x axis, the colors are easily distinguishable, and the error bars are clearly labeled. Unfortunately, this is where many biologists begin and end their data visualization journey. The problem here is two fold. This is not an exploratory plot since we are already viewing summarized data. Bar plots with error bars, although very common, are poor representations data. Second, as an explanatory plot, this plot should make use of the the next most common gestalt principle: connection. We can add a line to connect mean values across doses for each supplement. Although it is technically not paired or continuous data, since they are distance individuals, they are a progression in increasing dose. The use of lines for ordinal data is somewhat debatable. I make the distinction between ordinal and interval data. The distance between the categories holds information and it’s reasonable to connect the values with a line, which is what we’re asking the reader to do anyways.

There’s one more thing. On the x axis, each dose is a doubling of the previous value, but the categories are evenly spaced. This is misleading, since the visual doesn’t match the empirical $mdash; spacing should reflect the actual values. Figure 53.3 contains a revised plot making these adjustments. Notice that the dots on each dose are dodged so that we avoid overlap between values as the same dose. Despite not sitting directly on the tick marks, there is no confusion about which dose each value represents. The error bars have also been simplified; point ranges don’t depict the meaningless crossbar at the tips of the error bar. In the background individual values are presented, and are not only dodged but also jittered. I’ll address jittering in section ?sec-ScatterPlots.

Figure 53.3: Gestalt principle of connection to show the trend in a data set.

Enclosure is another common gestalt principle implemented in scientific plots. A common example of an enclosure is the use of background elements to highlight regions of a plot, as seen in Figure 53.4. Here we have the unemployment rate in the US from 1967 - 2014. Enclosure can take many forms, here, the shaded backgrounds highlight recession periods, which correspond to an increase in the unemployment rate.

Figure 53.4: A line plot with a shaded background is an exmaple of the gestalt principles of connection and enclosure

As an example of continuity, consider the plot of two curves shown in Figure 53.5. Many viewers expect that the two lines will converge somewhere outside of the plotting space. Our mind fills in the blanks given the trend we see, we expect that the lines will continue as we see them on the page. In reality, the only difference between the lines is their y-intercept — the distance between the two lines is constant along the x-axis! Our mind tricks us into seeing a trend that is not there. William Cleveland summarizes this phenomenon nicely: “… the minimum distances lie along perpendiculars to the tangents of the curves. As the slope increases, the distance along the perpendicular decreases, so the curves look closer as the slope increases … we cannot force our visual system to process the right segments without using slow sequential search.”

Figure 53.5: An example of the gestalt principle of connection and continuity.

In this case if we wanted to overcome the fast form of visual perception, we have to invest a lot of work, adding embellishments or redrawing the plot. For example in Figure 53.6 the two lines are separated to make it difficult to draw false conclusions.

Figure 53.6: Separate plots make it more difficult to make incorrect comparisons.

Figure 53.7 line segments are added between each line to highlight that they are equidistant apart over the entire x range.

Figure 53.7: An example of the gestalt principle of connection and continuity.

There is an underlying issue with all these solutions. Do we really need to show two lines when what we really want the viewer to know is that they are the same distance apart? If the difference between two lines is the message, then we should just show that! Figure 53.8 depicts this. It’s a pretty boring plot, but also the most honest and meaningful of the series.

Figure 53.8: Alternatively, plotting the actual difference between the lines reduces any confusion and makes the message even easier to convey.

Remember, fast forms of visual perception are typically used in explanatory plots, but we are constantly implementing gestalt principles, even when producing exploratory plots.