Talking in a Bubble: Using Physics to Explain Dialects

When you know the laws of the universe, many things become predictable—the next full moon, the trajectory of a bullet, and even the fate of the Earth. Physics can be an excellent tool for predicting how objects behave under certain conditions. It turns out that physics may also be a valuable tool for predicting where dialects emerge, according to research published this week in the American Physical Society journal Physical Review X.

By applying concepts developed to describe the physics of bubbles and magnetics, University of Portsmouth researcher James Burridge created a spatial model of how language changes. His model shows that geography and population distribution play surprisingly important roles in determining where dialects emerge. Tests of the model are a good match to reality and to predictions by dialectologists (people who study the local evolution of languages), even though Burridge’s model is based on a completely different approach.

These images of the island of Great Britain show how languages evolves over time according to Burridge’s model. The colors represent different dialects. What begins as a patchwork of many dialects in small regions (top left) evolves into fewer dialects as time passes (left to right, top to bottom). You can see how the boundaries of dialects straighten and how they fix to indented coastlines.
Image Credit: J. Burridge, Spatial Evolution of Human Dialects, Phys. Rev. X (2017).

You might not expect that the physics of magnets and bubbles can provide insight on something as complicated as language evolution. After all, the differences in speech patterns between someone from Baltimore and Southern Appalachia seem more likely to be the result of history, chance, and time than anything else. However, the atoms in magnets are like people in one important way—they want to fit in with their neighbors.

On a simplified but fundamental level, you can visualize a refrigerator magnet as a grid of atoms. Each atom behaves like a tiny magnet that wants to be magnetically aligned with its neighbors. Magnets consist of regions, called domains, in which this peer pressure causes all of the atoms to magnetically align in the same direction.

It turns out that under some conditions, the domains form a striped pattern of alternating magnetic orientation (for example, up-down-up-down). The formation of these stripes is controlled by an effect similar to surface tension, the tendency of a fluid to minimize its surface area. Surface tension causes oil and water to separate into different layers, bubbles to form in a bubble bath, and small bubbles in the bath to merge into larger ones.

These magnetic stripes caught Burridge’s attention, and he began to wonder whether they offered any insight into human and animal behavior. “I’ve always been interested in spatial patterns,” he says, “and in the idea that humans and animals behave and evolve predictably in some ways.”

Like the atoms in a magnet, human social behavior is linked to physical arrangement. In other words, people close to one another tend to copy each other’s style of dress, social norms, and way of talking. Physicists have explored this connection between social and atomic alignment in many contexts, including models of language. With the support of a Leverhulme fellowship, and a burning curiosity to see whether this surface tension effect in language interactions could explain the geographical patterns observed by linguists, Burridge set out to create a simple, physics-based model of how dialects evolve.

The model assumes that people interact most frequently with other people who live nearby, and that those who live near a town or city interact with people from the city more often than with people from outside of the city (given their relative populations). Finally, it assumes that speakers change their speech over time to conform to the local norms—to sound more like other people in their area.

“Dialectologists use the term ‘isogloss’ to describe a line across which there is a transition in language use, and these are like the surfaces of bubbles,” says Burridge. “The edges of the geographical area in which a language (including all its dialects) is spoken are very important… It turns out that if you evolve bubbles on country-shaped domains, accounting for where people live, then the pattern which emerges looks remarkably like the pattern of language use in those countries.”

In other words, this simple, physics-based model leads to spatial patterns of dialects that reflect reality and the predictions of dialectologists. For example, in one test Burridge applied the model to the island of Great Britain. He input data on the population distribution and average interaction distances over time, as well as the shape of the island. The outcome shows the emergence of dialects similar to those that exist now, and it shows that dialect boundaries get smoother and straighter over time. Dialects tend to move outward from population centers, and are more likely to form in regions bounded by indented coastlines or another language. These are all features documented by dialectologists.

Burridge points out that the model isn’t all-encompassing. “The physics of bubbles won’t always capture everything that’s going on, but the point is that country shape and population distribution seem to play a surprisingly important role,” says Burridge. He points out that this model doesn’t account for invasions or political upheaval, the creation of new kinds of speech, or the changes in our ability to interact frequently with people in distant places. Yet the model appears to be a good one, and its simplicity suggests that it may apply to other forms of culture beyond language. That’s not bad for the physics that governs refrigerator magnets and bubble baths.

Kendra Redmond

Mathematical modeling doesn’t just help us predict how language is going to evolve, it can help us look backward in time as well—giving insights into how accents and styles of speaking evolved: check out our 2015 piece, Hunting Language Sounds Through History, to learn how scientists are using statistical techniques from physics to reconstruct the earliest human languages.

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