As a native Turkish speaker who lives in Germany and works mostly in English, I switch languages many times a day. So a study asking whether the brain handles Turkish, Norwegian or Mandarin differently from English immediately caught my attention.
The question behind the study
Neuroscientists have mapped the brain’s “language network” in great detail over the past decades: a set of mostly left-hemisphere regions in the frontal and temporal lobes that become active when we understand language. The catch is that almost all of this mapping was done with English speakers. That leaves an open question: are we learning how the human brain processes language, or just how it processes English?
What the researchers did
A team led by Evelina Fedorenko at MIT, with Saima Malik-Moraleda and Dima Ayyash as lead authors, scanned native speakers of 45 languages from 12 language families using fMRI. Each participant listened to passages from Alice in Wonderland in their own language, chosen because it is one of the most widely translated books in the world. As controls, they also heard distorted speech and completed non-linguistic tasks such as spatial working memory exercises.
What they found
The result was remarkably consistent. In every language, the network appeared in roughly the same brain regions, it was dominant in the left hemisphere, and it stayed quiet during non-language tasks. The differences between speakers of different languages were no bigger than the normal differences between two English speakers. In short, the basic architecture of the language network appears to be universal.
Why this matters to me as an engineer
- The English bias is not only a neuroscience problem. Many AI systems, from speech recognition to large language models, are still trained and evaluated mostly on English. Just like the researchers, we should ask whether our technology works for language in general or only for English.
- Turkish is a great stress test. Turkish builds meaning by stacking suffixes onto words, while English relies more on word order. The follow-up research includes Turkish, Mandarin and Finnish to test whether the brain processes words in similarly sized chunks across languages. These are exactly the kinds of features that also challenge language technology.
- Validate across diverse conditions. When I worked on camera calibration for vehicles, an algorithm that performed well on one set of roads was not yet proven. It had to work across different environments before it could go into production. Good science and good engineering share the same principle: a result is only general if it has been tested on diverse data.
The takeaway
Our brains seem to share the same language machinery, whether we speak Turkish, Norwegian or English. Now that this baseline is established, the more interesting work begins: understanding the differences. For engineers building products for a global audience, the lesson is the same: design for diversity from the start, not as an afterthought.