Inside the Brain’s Language Engine: What a Polyglot Neuroscientist Reveals About How We Think
How the Brain Parses Language: Insights From a Polyglot Neuroscientist
Imagine effortlessly switching among several languages while also running a brain scanner and debugging a large language model (LLM). That combination comes surprisingly close to the daily life of neuroscientist Ev Fedorenko, who has spent more than 15 years probing how our brains make sense of words, grammar and meaning. Her work suggests that the brain’s language network is a specialized system — more like the digestive system than a vague, all‑purpose “intelligence” — and that it shares intriguing parallels with modern AI language models.
In this article, we’ll unpack what Fedorenko and colleagues are discovering about how the brain parses language, why it matters for the classic question “Is language core to thought?”, and what these findings mean for the way we design and relate to LLMs such as ChatGPT, Claude and others.
We’ll balance the big scientific ideas with practical takeaways: how this research can change the way you learn languages, communicate more clearly, and think about the strengths and limits of AI.
The Big Question: Is Language the Core of Thought?
Philosophers and scientists have argued for centuries about whether language is the “stuff” of thought, or whether it’s a separate tool layered on top of more basic mental processes like perception and spatial reasoning. Fedorenko’s work approaches this question by asking a precise version of it: Does the neural machinery that handles sentences and words also support other kinds of thinking?
Using functional MRI (fMRI) and carefully designed experiments, her lab finds that the language network — a set of regions mostly in the left frontal and temporal lobes — lights up robustly when people read or listen to meaningful sentences. But when people do demanding nonlinguistic tasks, like solving logic puzzles or complex math problems, these same areas often stay surprisingly quiet.
“Language is not the workspace for all thought. It’s a specialized system for encoding and decoding linguistic signals.” — Paraphrasing findings from Ev Fedorenko’s research program
This doesn’t mean language is unimportant. It suggests that:
- Thought can proceed without language (for example, in visual reasoning or music).
- Language adds a powerful “interface” for sharing thoughts, planning with others, and reflecting on our own reasoning.
- Damage to the language network can impair communication while leaving other types of problem‑solving more intact, and vice versa.
Mapping the Brain’s Language Network: Like a Digestive System for Words
One of Fedorenko’s signature contributions is a highly individualized map of the language network. Instead of averaging across many brains, her team often localizes language‑selective regions in each participant using “contrast” tasks such as:
- Reading or listening to meaningful sentences.
- Reading or listening to lists of nonwords or gibberish.
Comparing brain responses across these conditions highlights areas that respond specifically to structured meaning, not just sound or visual input. These regions cluster around:
- The left inferior frontal gyrus (often linked to syntax and complex structure).
- The left superior and middle temporal gyri (involved in decoding word meaning and combinatorics).
- Additional patches in the temporal and parietal cortex that help integrate context.
Fedorenko sometimes compares this setup to the digestive system: just as we have organs specialized for breaking down food, we seem to have interconnected cortical “organs” specialized for processing words and sentences.
“Thinking of language as an organ system makes it easier to look for its inputs, outputs and interfaces, rather than treating it as a vague ‘faculty.’”
Brain vs. Large Language Models: Surprising Similarities and Clear Differences
As LLMs like GPT‑4, Claude, and others have become more capable, neuroscientists naturally ask: Do these systems capture anything real about the brain’s language computations? Fedorenko’s lab has been at the forefront of comparing brain responses to the internal activity of such models.
A recurring finding across multiple groups is that representations from advanced LLMs correlate surprisingly well with activity in human language areas when both are exposed to the same sentences. In other words, parts of the brain that care about language seem to “agree,” statistically, with layers of a transformer model that predicts the next word.
This doesn’t mean brains are transformers. But the alignment suggests that:
- Predictive processing — anticipating the next word or phrase — may be central to how our brains parse language.
- Training on massive text data can produce internal “maps” of syntax and semantics that echo human representations.
- LLMs could serve as computational models for hypothesis‑driven neuroscience, helping researchers generate and test new ideas about language encoding.
Fedorenko has also emphasized that humans bring much more to the table than text statistics, including:
- Rich embodied experience (seeing, acting, feeling in the world).
- Social and pragmatic knowledge about other minds.
- Strong domain‑general control systems that steer when and how we use language.
Inside the Brain’s “Language Decoder”: What We Know So Far
Fedorenko sometimes talks about a language decoder — shorthand for the set of operations that turn acoustic or visual input into an internal representation of meaning, and then into an output (speech, writing, or silent understanding). Unlike LLMs, the brain’s decoder is built from neurons and shaped by evolution, but scientists can still try to infer its algorithms.
Current evidence points to a few key principles:
- Incremental parsing: The brain doesn’t wait for a full sentence; it updates its interpretation word by word, predicting and revising as new input comes in.
- Context sensitivity: Prior sentences, world knowledge and speaker identity all modulate how strongly different meanings are activated.
- Robustness to noise: Even with missing words, accents or typos, the system usually recovers intended meaning.
- Interface with memory: Language regions work closely with memory systems to keep track of who did what to whom, especially in long or complex sentences.
We don’t yet have a full wiring diagram of this decoder, and Fedorenko is careful not to overclaim. Still, progress has been steady: better imaging, large datasets, and cross‑talk with AI research are refining our picture of how neural population codes represent syntax and semantics.
A Polyglot’s Brain: What Multiple Languages Reveal
Fedorenko herself is multilingual, and her lab has studied how multiple languages live in the same brain. One consistent result is that different languages of the same person rely on largely overlapping language networks, rather than distinct “modules” for each tongue.
This aligns with the everyday experience of polyglots: once you’re fluent, switching languages often feels like swapping vocabularies and sound patterns, not like jumping between separate mental silos. The same underlying decoder appears to handle:
- Different grammars and word orders (e.g., English vs. Russian).
- Different scripts (e.g., Latin alphabet vs. Cyrillic).
- Different sound systems and phonotactics.
Case studies of bilingual individuals with brain damage also support this view: although impairment patterns can differ by language, lesions often affect all of a person’s languages to some degree, consistent with a shared neural infrastructure.
Common Obstacles in Studying Language and How Researchers Overcome Them
Building a detailed map of the language network hasn’t been easy. Fedorenko’s group has had to confront several persistent challenges:
- Individual variability: People’s brains are wired a bit differently. Averaging across subjects can blur out important patterns.
- Limited temporal resolution: Standard fMRI is slow compared with the speed of language, which unfolds in milliseconds.
- Task design: Many older studies mixed language processing with other cognitive demands, making it hard to isolate truly language‑selective areas.
To address these, her lab often:
- Identifies language areas per person using localizer tasks.
- Combines methods (fMRI, intracranial recordings when available, behavioral testing).
- Collaborates with computer scientists to analyze complex neural and model data.
“We moved from drawing a fuzzy blob called ‘Broca’s area’ to tracing a detailed, distributed circuit that behaves differently across individuals and contexts.”
Practical Takeaways: How This Science Can Inform Everyday Life
While Fedorenko’s work is primarily basic science, it carries several down‑to‑earth implications for how we learn, teach and communicate.
1. Treat Language as a Skill, Not as General Intelligence
Because language relies on a specific neural network, people can be strong in language and weaker in other domains, or vice versa. This argues against using verbal ability as a one‑size‑fits‑all proxy for intelligence.
- In education, value nonverbal reasoning, spatial skills and creativity alongside verbal fluency.
- In the workplace, avoid equating articulate speech with overall competence.
2. For Language Learning, Context and Prediction Matter
The brain’s decoder thrives on pattern prediction in rich context. To leverage this:
- Read and listen to authentic materials just slightly above your level.
- Pause occasionally to guess the next word or phrase before revealing it.
- Engage in real conversations, where turn‑taking forces rapid prediction and adaptation.
3. Use AI as a Complement, Not a Replacement
Since LLMs mimic some aspects of the language network, they can be powerful tools for:
- Generating practice sentences and personalized reading materials.
- Providing on‑the‑fly feedback on clarity and grammar.
- Exploring multiple paraphrases of the same idea.
But they should complement, not substitute, human interaction, which provides the social and emotional cues that brains are deeply tuned to.
Before and After: How This Research Shifts Our Mental Model
To appreciate Fedorenko’s impact, it helps to contrast older, fuzzier views of language in the brain with the emerging picture.
| Earlier View | Updated View Informed by Fedorenko’s Work |
|---|---|
| Language is a single “faculty” centered in Broca’s and Wernicke’s areas. | Language is a distributed network of multiple regions with distinct but coordinated roles. |
| Language is basically the same as thought. | Language is a specialized system that interfaces with, but is separable from, other forms of cognition. |
| Multiple languages might occupy separate “modules.” | Multiple languages typically share the same core language network. |
| AI language tools are unrelated to brain function. | Advanced LLMs provide useful, though incomplete, models of how language representations might be organized. |
Looking Ahead: How You Can Engage With the Science of Language
The story of how the brain parses language is still being written, and Fedorenko’s work is a major chapter. As imaging methods improve and collaborations with AI deepen, we’re likely to see sharper models of the brain’s language decoder — models that respect both the power of predictive learning and the richness of human experience.
If this topic resonates with you, a few concrete next steps:
- Explore accessible summaries on sites like Quanta Magazine that profile researchers including Fedorenko.
- Skim open‑access papers on the language network through services like Google Scholar.
- Notice, in your own life, how language supports your thinking — and when your thoughts feel more visual, musical or spatial.
Understanding that your brain has a specialized, hard‑working network just for language can be surprisingly empowering. You don’t need to treat every inner experience as a sentence; instead, you can let language do what it does best: connect your mind to other minds, whether through conversation, writing, or careful collaboration with AI tools.
