Is language core to thought, or a separate process? For 15 years, the neuroscientist Ev Fedorenko has gathered evidence of a language network in the human brain — and has found some similarities to LLMs.

The cognitive neuroscientist Ev Fedorenko has identified a “language network” in the brain that stores the mappings between words and their meanings.

Katherine Taylor for Quanta Magazine

Introduction

Even in a world where large language models (LLMs) and AI chatbots are commonplace, it can be hard to fully accept that fluent writing can come from an unthinking machine. That’s because, to many of us, finding the right words is a crucial part of thought — not the outcome of some separate process.

But what if our neurobiological reality includes a system that behaves something like an LLM? Long before the rise of ChatGPT, the cognitive neuroscientist Ev Fedorenko began studying how language works in the adult human brain. The specialized system she has described, which she calls “the language network,” maps the correspondences between words and their meanings. Her research suggests that, in some ways, we do carry around a biological version of an LLM — that is, a mindless language processor — inside our own brains.

“You can think of the language network as a set of pointers,” Fedorenko said. “It’s like a map, and it tells you where in the brain you can find different kinds of meaning. It’s basically a glorified parser that helps us put the pieces together — and then all the thinking and interesting stuff happens outside of [its] boundaries.”

Fedorenko has been gathering biological evidence of this language network for the past 15 years in her lab at the Massachusetts Institute of Technology. Unlike a large language model, the human language network doesn’t string words into plausible-sounding patterns with nobody home; instead, it acts as a translator between external perceptions (such as speech, writing and sign language) and representations of meaning encoded in other parts of the brain (including episodic memory and social cognition, which LLMs don’t possess). Nor is the human language network particularly large: If all of its tissue were clumped together, it would be about the size of a strawberry. But when it is damaged, the effect is profound. An injured language network can result in forms of aphasia in which sophisticated cognition remains intact but trapped within a brain unable to express it or distinguish incoming words from others.

Fedorenko walks and talks with postdoctoral fellows Andrea de Varda (left) and Halie Olson (right) at MIT.

Katherine Taylor for Quanta Magazine

Fedorenko came by her interest in language early. In the 1980s, when she was growing up in the Soviet Union, her mother made her learn five languages (English, French, German, Spanish and Polish) in addition to her native Russian. Despite significant privations related to the fall of communism in that country — Fedorenko “lived through a few years of being hungry,” she said — she was a strong student and earned a full scholarship to Harvard University. There, she initially planned to study linguistics but later added a second major in psychology. “The [linguistics] classes were interesting, but they felt kind of like puzzle-solving, not really figuring out how things work in reality,” she said.

Three years into her graduate studies at MIT, Fedorenko pivoted again, this time into neuroscience. She began collaborating with Nancy Kanwisher, who had first identified the fusiform face area, a brain region specialized for facial recognition. Fedorenko wanted to find the same thing for language. She had her work cut out for her. “At that point, it was possible to read pretty much everything that was published [on the subject], and I thought the foundations were pretty weak,” Fedorenko said. “As you can imagine, that [assessment] was not so popular with some people. But after a while they saw I was not going away.”

Following a steady stream of findings, in 2024 Fedorenko published a comprehensive review in Nature Reviews Neuroscience defining the human language network as a “natural kind”: an integrated set of regions, exclusively specialized for language, that resides in “every typical adult human brain,” she wrote.

Growing up, Fedorenko learned six languages. She studied linguistics, then pivoted to neuroscience. In brain scans of around 1,400 people, she’s identified a common language network — “tissue that is reliably doing linguistic computations,” she said.

Katherine Taylor for Quanta Magazine

Quanta spoke to Fedorenko about how the language network is like the digestive system, what she knows about how the language decoder works, and whether she really believes that people have LLMs inside their heads. The conversation has been condensed and edited for clarity.

What is the language network?

There’s a core set of areas in adult brains that acts as an interconnected system for computing linguistic structure. They store the mappings between words and meanings, and rules for how to put words together. When you learn a language, that’s what you learn: You learn these mappings and the rules. And that allows us to use this “code” in incredibly flexible ways. You can convert between a thought and a word sequence in any language that you know.

That sounds very abstract. But you call the language network a “natural kind” — does that mean it’s something physical you can point to, like the digestive system?

That’s exactly right. These systems that people have discovered [in the brain], including the language network and some parts of the visual system, are like organs. For example, the fusiform face area is a natural kind: It’s meaningfully definable as a unit. In the language network, there are basically three areas in the frontal cortex in most people. All three of them are on the side of the left frontal lobe. There’s also a couple of areas that fall along the side of the middle temporal gyrus, this big hunk of meat that goes along the whole temporal lobe. Those are the core areas.

You can see the unity in a few different ways. For example, if you put people in an [fMRI, or functional magnetic resonance imaging], scanner, you can look at responses to language versus some control condition. Those regions always go together. We’ve now scanned about 1,400 people, and we can build up a probabilistic map, which estimates where those regions will tend to be. The topography is a little bit variable across people, but the general patterns are very consistent. Somewhere within those broad frontal and temporal areas, everybody will have some tissue that is reliably doing linguistic computations.

How is this different from other parts of brain anatomy known to be associated with language, such as Broca’s area?

Broca’s area is actually incredibly controversial. I would not call it a language region; it’s an articulatory motor-planning region. Right now, it’s being engaged to plan the movements of my mouth muscles in a way that allows me to say what I’m saying. But I could say a bunch of nonsense words, and it would be just as engaged. So it’s an area that takes some sound-level representation of speech and figures out the set of motor movements you would need [to produce it]. It’s a downstream region that the language network sends information to.

You’ve also said that language isn’t the same as thought. So if the language network isn’t producing speech, and it’s also not involved in thinking, what is it doing?

The language network is basically an interface between lower-level perceptual and motor components and the higher-level, more abstract representations of meaning and reasoning.

There are two things we do with language. In language production, you have this fuzzy thought, and then you have a vocabulary — not just of words, but larger constructions, and rules for how to connect them. You search through it to find a way to express the meaning you’re trying to convey using a structured sequence of words. Once you have that utterance, then you go to the motor system to say it out loud, write it or sign it.

Fedorenko’s isolation of a language parser in the brain suggests that language production is a separate process from high-level thought.

Katherine Taylor for Quanta Magazine

In language comprehension, it’s the inverse. It starts with sound waves hitting your ear or light hitting your retina. You do some basic perceptual crunching of that input to extract a word sequence or utterance. Then the language network parses that, finding familiar chunks in the utterance and using them as pointers to stored representations of meaning.

For both cases, the language network is a store of these form-to-meaning mappings. It’s a fluid store that we keep updating throughout our lives. But as soon as we know this code, we can flexibly use it to both take a thought and express it, and take somebody else’s word sequence and decode meaning from it.

Why do we have this system? So we can take our thoughts and share them. There’s no telepathy, right?

How far down does this biological specialization go? Are there individual cells in the language network that respond to certain utterances, akin to how concept neurons only respond to specific concepts?

I suspect it’s a bit distributed within the system because language is very contextualized. But yes, there may well be cells that respond to particular aspects of language.

There’s a preprint, from Itzhak Fried’s group at UCLA, looking at single cells and finding some of the same properties that we found with [fMRI] imaging and population-level intracranial recordings. For example, cells will respond to both written and auditory language in similar ways. And the language network is where you would look for those cells.

What kinds of patterns or features get learned?

The brain’s general object-recognition machinery is at the same level of abstractness as the language network. It’s not so different from some higher-level visual areas such as the inferotemporal cortex storing bits of object shapes, or the fusiform face area storing a basic face template. You use those representations to help you recognize objects in the world, but they’re disconnected from our world knowledge.

The language system Fedorenko has characterized is “memory-limited,” she said, and only handles “chunks of maybe eight to 10 words, max.”

Katherine Taylor for Quanta Magazine

[Linguist Noam] Chomsky’s famous example of a nonsense sentence — “Colorless green ideas sleep furiously” — comes in handy here. You kind of know what it means, but you can’t relate it to anything about the world because it doesn’t make sense. We and a few other groups have evidence that the language network will respond just as strongly to those “colorless green”–type sentences as it does to plausible sentences that tell us something meaningful. I don’t want to call it “dumb,” but it’s a pretty shallow system.

It almost sounds like you’re saying there’s essentially an LLM inside everyone’s brain. Is that what you’re saying?

Pretty much. I think the language network is very similar in many ways to early LLMs, which learn the regularities of language and how words relate to each other. It’s not so hard to imagine, right? I’m sure you’ve encountered people who produce very fluent language, and you kind of listen to it for a while, and you’re like: There’s nothing coherent there. But it sounds very fluent. And that’s with no physical injury to their brain!

Still, the idea that humans produce language with something mindless, like ChatGPT, seems counterintuitive.

Yes — including to me! When I started [this research], I thought that language is a really core part of high-level thought. There was this notion that maybe humans are just really good at representing and extracting hierarchical structures, which of course are a key signature of language, but are also present in other domains like math and music and aspects of social cognition. So I was fully expecting that some parts of this network would be these very domain-general, hierarchical processors. And that just turns out empirically not to be the case. Back in 2011, it was already clear that all parts of the system are quite specialized for language. If you’re a scientist, you just update your beliefs and roll with it.

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