Why You Can’t Just Download A Perfect Relationship: The Science Behind 7000 Human Languages
Why our brains prefer complex feelings over digital shortcuts.
Look, we all know that a perfect relationship is not something you can just download from the app store. We are messy, emotional creatures. And while we often wish we could just code our partners to understand us instantly, nature seems to have taken a different approach. A fascinating piece of research recently published in Nature Human Behaviour dives into exactly why we are not built to speak like computers. Linguist Michael Hahn from Saarbrücken teamed up with Richard Futrell from the University of California, Irvine to figure out why human language looks the way it does. They wanted to know why we don't just communicate in ones and zeros like a robot.
Their findings are pretty eye-opening if you stop to think about it. You see, there are actually around 7,000 languages spoken across the globe right now. Some of these languages are used by just a handful of people, while others, like Chinese, English, Spanish, and Hindi, are spoken by billions. Despite these massive differences, every single language out there serves the exact same core function. They all communicate meaning by combining words into phrases. These phrases are then arranged into sentences. Each piece of that puzzle carries its own meaning, and when you put them all together, a clear message is created.
From the standpoint of information theory, this seems a bit complicated. The natural world generally tends to maximize efficiency and conserve resources. So, you might wonder why the human brain encodes linguistic information in such a way that seems complicated instead of digital. In theory, encoding speech as binary sequences of ones and zeros would be much more efficient because it compresses information much more tightly than spoken language. It is a valid question. Why do we not communicate like R2-D2 from Star Wars?
Hahn and Futrell believe they have found the answer, and it has to do with real-world experience. Human language is actually shaped by the realities of life around us. If I were to talk about half a cat paired with half a dog and I used an abstract term like 'gol,' nobody would know what I meant because it is pretty certain that no one has ever seen a gol. It simply does not reflect anyone's lived experience. It makes no sense to blend the words 'cat' and 'dog' into a string of characters that uses the same letters but is impossible to interpret. A scrambled form such as 'gadcot' technically contains letters from both words, but it is meaningless to listeners. By contrast, the phrase 'cat and dog' is instantly understandable because both animals are concepts we all share.
This brings us to the concept of predictive processing. Hahn summarizes their findings by saying that it is easier for our brain to take what might seem to be the more complicated route. Although natural language is not maximally compressed, it places far less strain on the brain. This is because the brain processes words in constant interaction with what we already know about the world. A purely digital code might transmit information faster, but it would be totally detached from everyday experience.
Hahn compares this to commuting to work in a really interesting way. He says that on our usual commute, the route is so familiar to us that the drive is almost like being on autopilot. Our brain knows exactly what to expect, so the effort it needs to make is much lower. However, taking a shorter but less familiar route feels much more tiring. The new route demands that we be far more attentive during the drive. From a mathematical perspective, he adds that the number of bits the brain needs to process is far smaller when we speak in familiar, natural ways.
In other words, speaking and understanding binary code would require much more mental effort from both the speaker and the listener. Instead, the brain constantly estimates how likely certain words and phrases are to appear next. Because we use our native language daily over decades, these patterns become deeply embedded, making communication smoother and less demanding.
Hahn offers a clear illustration to explain this predictive nature. When I say the German phrase 'Die fünf grünen Autos,' the phrase will almost certainly make sense to another German speaker. However, the scrambled version 'Grünen fünf die Autos' will not. When someone hears 'Die fünf grünen Autos,' the brain starts interpreting meaning immediately. The word 'Die' signals certain grammatical possibilities. A German listener can instantly narrow the options, ruling out masculine or neuter singular nouns. The next word, 'fünf,' suggests something countable, which excludes abstract ideas such as love or thirst. Then 'grünen' indicates that the noun will be plural and green in color. At that point, the object could be cars, bananas or frogs. Only when the final word, 'Autos,' is spoken does the meaning fully settle into place. With each word, the brain reduces uncertainty until only one interpretation remains.
On the flip side, 'Grünen fünf die Autos' disrupts this predictable pattern. The expected grammatical signals appear in the wrong order, so the brain cannot easily build meaning from the sequence. Hahn and Futrell were even able to demonstrate these patterns mathematically. Their findings show that human language prioritizes reducing cognitive load over maximizing compression. These insights may also inform improvements in large language models. These are the systems behind generative AI tools such as ChatGPT or Microsoft's Copilot. By better understanding how the human brain processes language, researchers could design AI systems that align more closely with natural communication patterns.
So, the next time you find yourself struggling to find the right words in a conversation, remember that your brain is working hard to keep you connected to reality. It is choosing a complex path that requires less energy than a digital shortcut because it is the path that actually means something to us. We are not computers, and that is a very good thing.
First reported by ScienceDaily: Relationships.
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