This article first appeared in Issue 7 of our free digital magazine CURIOUS.
In science, we often stumble upon profound questions about what is real and what is perceived. Quantum mechanics and the uncertainty principle is a fan favorite, for example. A more subtle question we might encounter is about naturally occurring patterns. From snowflakes to Northern Ireland’s Giant’s Causeway, Romanesco broccoli to Saturn’s polar clouds, why does nature keep arranging itself in patterns?
Humans are so good at spotting patterns that we sometimes trick ourselves into seeing things that are not there. This phenomenon, called pareidolia, lets us see meaningful images in random or ambiguous visual patterns, and may have been vital for our survival. There’s a reason we see shapes in clouds, faces in sockets, and mice on Mars.
So is nature actually full of patterns or are we simply too good at seeing them, regardless of if they’re there or not? As is always the case for these juicy scientific dilemmas, the answer is far from simple and far from definitive.
From mighty hexagons to molecular crystals
A lot of natural structures, to us, have distinct patterns so we give them names. Take the six-sided hexagonal form, for example. Once it has a recognizable name and shape it starts appearing everywhere, though there may be many different causes. Saturn’s polar vortex famously takes a hexagonal shape. The six-sided storm covering the top of the planet’s north pole is believed to be caused by smaller storms interacting with bigger cyclones, which find a sort of equilibrium in this hexagonal shape.
Closer to home are the hexagonal columns that make up the Giant’s Causeway in Northern Ireland. Their natural shape is due to the cooling of these structures from when they were still molten lava. As they cooled, the first cracks happened at a 90-degree angle but evolved quickly to 120 degrees, which releases more energy, creating the patterns we see today.
The most recognizable hexagonal pattern is probably that of snowflakes, which, in their six-branch versions, is due to the angle in water molecules between the oxygen atom and the hydrogen atoms. It’s not just hexagons, though, there are plenty of geometrical patterns that emerge from small chemical bonds that end up creating large complex crystals. Pyrite is a pretty and extremely cubic example of that.
“Molecular crystals form like that because that's the way they minimize their overall energy,” Professor Andrew Croll from North Dakota State University told IFLscience. “They just fit in better, like eggs in an egg cart. And they kind of roll into these little holes and they sit there.”
But Croll stresses that there are multiple ways to create structures in nature, even things that at first glance don’t look like they’ll be good for patterns at all. An example is a type of molecule called block copolymers. Polymers are long molecular chains made by repeating sub-units. They are used to make plastic and can often be in disordered layers.
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But in block copolymers, two or more polymers that repel each other are tethered together, which creates some interesting patterns. Two of the same length might end up in an alternating layer structure, but by varying the length, it is possible to create structures with curvature with familiar or less familiar geometrical patterns. And they can have surprisingly common applications too.
“In terms of actual market applications, I know for block copolymers there are things like shoe rubber. So, the rubbers in your shoes tend to be a triblock copolymer, so you’d have maybe a polystyrene block and then a big elastic kind of chain, and then another polystyrene block,” Croll explained. Another application includes the absorbent material in nappies.
It takes a computer genius to see the patterns in randomness
There are certain natural patterns that do not follow neat geometrical rules of repeating shapes but they are distinctive nonetheless. We can recognize the stripes on a tiger or a zebra or the spots on a leopard as specific natural patterns even though they are not the regular patterns of geometry.
These patterns are named Turing patterns after the English mathematical genius, father of the field of computing, who in a 1952 paper titled "The Chemical Basis of Morphogenesis" worked out how the spots and stripes structures can naturally form without input from a homogenous and uniform state.
“When you think about the spots on a leopard or the stripes on a tiger, those are patterns where there is this element, the stripe or the spot, that's repeated, but it's not repeated perfectly. There's not that perfect regularity you get from, say, a checkerboard. But there's still this shape, this feature that is repeated across space,” Ben Balas, a professor of psychology at North Dakota State University, told IFLScience.
Balas’s work focuses on understanding how humans look at these and other types of natural patterns and recognize them for what they are, something he says is a “huge question”. It’s trying to understand not only if you can tell the difference between a zebra pattern or a leopard pattern, but also if a texture is smooth or rough, shiny or dull, etc.
“We think a lot in my lab about this idea we call summary statistics,” Balas explained. “When you think about recognizing something like a face or an object, we often think about descriptions that care a lot about exactly where things were in the picture. For a face, it matters that there's an eye here, an eye here, and a nose and a mouth.
“Textures are different because those things are spread out over the image. It sort of doesn't matter exactly where you saw the spot on a leopard. It matters that you saw spots, spread out across this image.”
Summary statistics is about having the gist of the pattern rather than the specifics. Balas’s team is looking at measuring what makes patterns distinctive and using the measured quantities to create new patterns to understand how our brains relate to them.
We see what we see and what we want to see
Our ability as humans to understand textures and patterns has an important function in terms of survival. For example, by looking at the things that we eat, from their glossiness to their prickliness, we can make a snap judgment on their safety for consumption.
“Our world is not made up of just random images. There are these regularities to what we see. And what we understand now is there's lots of ways that the human visual system is adapted to those regularities,” Balas told IFLScience.
Pattern recognition likely saved our ancestors on more than one occasion and what they needed for survival, we now use daily far beyond what they might have dreamt.
“Our brain is constantly trying to make sense of the outside world. One way the brain accomplishes this goal is by detecting and learning patterns, which are essentially statistical regularities in the environment, because these patterns help the brain decide how to react or behave in order to survive,” Dr Jess Taubert, from the University of Queensland, told IFLScience.
But such a powerful tool for finding patterns can also be fooled into seeing things that are not there. This is the visual phenomenon known as pareidolia. One type that we experience frequently is face pareidolia, where we see faces in inanimate objects such as houses, rocks, and vegetables.
“The reason we believe the experience of face pareidolia is so common is that our visual system is optimized for detecting faces. This is because knowing when people are around (and whether they are friends or enemies) has been so important for our survival as social primates. But a side effect of this hypersensitivity to faces is that we sometimes see faces where there are none,” Dr Taubert explained.
Nature is rich in patterns, from the nanoscopic to vast celestial structures, and we are perfectly suited to appreciate those patterns. But this excellent skill of ours is also sometimes too good, making us see patterns when they are not really there.
CURIOUS magazine is a digital magazine from IFLScience featuring interviews, experts, deep dives, fun facts, news, book excerpts, and much more. Issue 10 is out now.