A recent study published in Nature Human Behaviour provides a scientific explanation for why human language remains rich, structured, and far from the compressed digital codes used by computers. Despite the theoretical efficiency of binary sequences—ones and zeros—that could transmit information more compactly, the brain favors natural language because it minimizes cognitive effort rather than maximizing data compression.
The research, conducted by Michael Hahn, Professor of Computational Linguistics at Saarland University in Saarbrücken, Germany, and Richard Futrell from the University of California, Irvine, explores this apparent paradox. As Hahn explains: “This is actually a very complex structure. Since the natural world tends to maximize efficiency and conserve resources, it’s perfectly reasonable to ask why the brain encodes linguistic information in such an apparently complicated way instead of doing it digitally, like a computer.”
The key insight lies in how the human brain processes language. Unlike computers, which handle abstract binary data efficiently, human communication is deeply anchored in real-world experience and familiar patterns. Hahn illustrates this with a hypothetical example: if someone described a hybrid creature as “half cat paired with half dog” using an abstract term like “gol,” it would convey nothing because no one has encountered such a “gol” in lived experience. In contrast, saying “half cat and half dog” is immediately understandable, as it draws on known concepts and everyday realities.
Mixing words arbitrarily, such as creating “gaperroto” from “gato” and “perro,” fails to communicate meaning, while structured phrases like “the five red cars” are effortlessly processed by speakers due to ingrained grammatical and predictive patterns. Altering the order to “red five the cars” disrupts these expectations, making comprehension much harder.
The researchers argue that natural language, though less compressed than digital code, reduces the mental workload for both speaker and listener. The brain relies on probabilistic sequences and predictions based on frequent exposure to familiar structures. Hahn summarizes: “Put simply, it’s easier for our brain to take what might seem to be the more complicated route.” A purely digital code might pack more bits of information quickly, but it would demand far greater cognitive resources from humans, as it lacks grounding in shared real-world experiences and predictable linguistic patterns.
Hahn further notes: “The amount of bits that the brain needs to process is much lower when we speak in familiar and natural ways.” This optimization prioritizes ease of prediction and comprehension over raw informational density. While computers excel at handling disconnected, abstract data, the human mind thrives on language that mirrors lived realities—patterns shaped by the surrounding world.
The findings highlight a fundamental difference between biological and artificial information processing. Human language, with its approximately 7,000 spoken varieties worldwide, evolved not for maximum efficiency in bits but for minimal cognitive strain in social, experiential contexts. What appears overly complex from a digital perspective is, in fact, the simplest path for the brain.
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