Psycholinguistics & the Brain: How a Mind Handles a Sentence

Someone says, "She spread the warm bread with…" and you already know the next word, before it arrives. When they say "butter", nothing happens. When they say "socks", something does: about 400 milliseconds after the word begins, your brain gives off a small electrical signal that a scalp electrode can pick up. Nobody told your brain to do this. It was running a few words ahead of the speaker and found itself wrong.

Psycholinguistics is the study of what the mind does in real time with language: recognising a word among tens of thousands in a few hundred milliseconds, building a sentence structure word by word, reading a writing system that has existed for a tiny fraction of human history, and, at the edges, asking whether the language you speak shapes how you think. Neurolinguistics asks where in the brain it happens. This piece is descriptive. It explains research on how language works in healthy brains and after injury, and it is not medical advice.

Not medical advice. This explains research on language and the brain. If you or someone you know has had a stroke or injury, or has trouble reading or speaking, a doctor or speech-language pathologist is the person to ask. In an emergency call your local emergency number.

Broca, Wernicke and what imaging changed

In 1861 the French physician Paul Broca met a patient, Louis Victor Leborgne, known as "Tan" because that was nearly the only syllable he could say, although he seemed to understand speech. After Leborgne died Broca found damage in the left frontal lobe. In 1874 the German neurologist Carl Wernicke described patients with the opposite pattern: fluent, grammatical-sounding speech that made little sense, and poor understanding, after damage farther back in the left temporal lobe. Put together, they gave the classic picture, later developed by Lichtheim (1885) and Norman Geschwind (1960s): a front area for producing speech, a back area for understanding it, and a fibre bundle (the arcuate fasciculus) joining them.

That picture still organises how clinicians describe the aphasias, language disorders after brain damage. Three questions sort most patients: is speech fluent, is comprehension good, can they repeat what they hear? Here is the standard (Boston) classification as a decision table.

Without JavaScript: fluent, poor comprehension, poor repetition is Wernicke’s; effortful, good comprehension, poor repetition is Broca’s; fluent, good comprehension, poor repetition is conduction aphasia; effortful and poor on both is global; fluent with good understanding and repetition is anomic aphasia.

Modern imaging made the map more complicated. In 2007 Nina Dronkers and colleagues scanned the preserved brain of Leborgne with high-resolution MRI. The damage reached well beyond Broca's area into deeper structures and white-matter tracts, which fits a later finding that damage to the underlying connections may predict lasting speech problems better than damage to the cortical area alone. Functional MRI and diffusion imaging show language using a network, not two spots: regions in left frontal, temporal and parietal cortex, linked by several pathways. Gregory Hickok and David Poeppel's dual-stream model (2007) proposes a ventral route from sound to meaning and a dorsal route from sound to articulation. Evelina Fedorenko and colleagues find that, in each person, a set of regions responds strongly to language and much less to maths, music or general effort. Aphasia and the language network also look separate from thought: Rosemary Varley and colleagues (2005) showed that people with severe grammar loss could still solve algebra problems. And for most people, the left hemisphere is dominant for language, but not for everyone; a minority, more often left-handers, have language on the right or both sides. Broca and Wernicke were right that language has a biological address, and the address has turned out to be a neighbourhood.

Hearing a word: a race among competitors

An adult knows a lot of words. Brysbaert and colleagues (2016) estimated that a typical 20-year-old American English speaker knows about 42,000 lemmas (dictionary forms), and a speaker hears several words every second. Yet word recognition is very quick, often complete within about a quarter of a second of the word starting, sometimes before the speaker finishes. How can the mind pick one word out of so many that fast?

The answer in cohort models (William Marslen-Wilson, 1987) is that it does not wait. As soon as the first sound arrives, every word beginning that way is activated, the cohort. Each new sound knocks out the words that no longer fit. The point at which only one word remains is the uniqueness point, and recognition tends to happen close to it. The TRACE model (McClelland and Elman, 1986) adds competition: active words inhibit each other. Frequent words win faster, and words with many similar-sounding neighbours (cat, cap, cab…) are slower to recognise than isolated ones. Step through a word below.

Without JavaScript: in the toy lexicon all 26 words begin with the sound K; after K AE thirteen remain; after K AE P four (cap, captain, capital, captive); captain becomes unique only at its fifth sound, when AH separates it from captive. Cap and cat never become unique by sound alone because longer words begin with them.

Notice what the toy shows. Cap is a prefix of other words, so it is never "unique" at its end by sound alone. Listeners use the pause after it, stress, and the sentence. Context is not a luxury in recognition; it is part of how the race ends. Brain-imaging studies place much of this in the left temporal cortex, with the signature of "word found" appearing quickly after the point of recognition.

Garden paths: the parser commits early

Read: The horse raced past the barn fell. Most readers stumble at "fell". Nothing is ungrammatical. The sentence means "the horse (that was) raced past the barn fell", but your mind took "raced" as the main verb of the sentence as soon as it appeared, and had to rebuild when it met a second verb. Thomas Bever described such garden-path sentences in 1970. Others: While Anna dressed the baby slept. (people commit to "Anna dressed the baby", and Christianson and colleagues, 2001, found that readers often keep the wrong reading even after they reread: asked "Did Anna dress the baby?", many say yes.) And The old man the boats. (the old, as a noun, man the boats.)

These sentences matter because they show that sentences are not read all at once, and that the parser is incremental: it builds a structure word by word and commits. Two families of theory disagree on how it chooses. The garden-path model (Lyn Frazier and Keith Rayner, 1982) says that a first-pass process picks the simplest structure, using only syntactic categories; meaning corrects it afterwards. Constraint-based accounts (Maryellen MacDonald, Neal Pearlmutter and Mark Seidenberg, 1994; John Trueswell and Michael Tanenhaus) say that many sources of information, such as verb preferences, meaning and context, compete from the start. Evidence for the second includes eye-tracking: "The defendant examined by the lawyer" tricks readers, but "The evidence examined by the lawyer" doesn't, since evidence cannot examine anything (Trueswell, Tanenhaus and Garnsey, 1994). And in the visual-world method (Tanenhaus and colleagues, 1995), listeners' eyes dart to objects as they are named, which shows parsing in action. Also, Fernanda Ferreira has argued comprehension is often "good enough": we settle for a plausible meaning, not a fully worked-out structure. Try the effect on yourself.

Without JavaScript: in "The horse raced past the barn fell" readers slow down at "fell"; the version "The horse that was raced past the barn fell" removes the trap. Likewise a comma in "While Anna dressed, the baby slept" removes the garden path.

Reading is not natural: two routes and dyslexia

Speaking is learned without lessons. Reading is not. Alphabets are a few thousand years old, and most human languages have never been written. Reading has to be built on brain systems that evolved for something else. Stanislas Dehaene's "neuronal recycling" idea is that a patch of left visual cortex (the visual word form area, identified by Laurent Cohen and colleagues in 2000) is recruited for recognising letter strings; Dehaene and colleagues (2010) found that adults who learn to read late show changes in visual and language regions.

How do we get from print to sound? The dual-route model (Max Coltheart and colleagues) proposes two pathways. A lexical route looks up whole words in memory, which is the only way to read irregular words like pint or have. A sub-lexical route converts letters into sounds by rules, which is the only way to read a made-up word like frib. Competing "triangle" models (Seidenberg and McClelland, 1989; Plaut and colleagues, 1996) use one network trained on spelling, sound and meaning. Brain damage in adults has produced patterns that fit the two-route picture: surface dyslexia (irregular words regularised, as if pint rhymed with mint) and phonological dyslexia (real words fine, invented words impossible). Switch routes off in the tool and see.

Without JavaScript: with only the rule route, pint is read like "mint", have like "gave" and love like "cove"; with only the lookup route, invented words such as frib cannot be read.

Developmental dyslexia is a difficulty with accurate and fluent word reading and spelling that persists despite normal instruction and ordinary intelligence. Estimates of how common it is vary with the definition, commonly about 5 to 10 percent of people. Its most replicated core is a weakness in phonological processing, seeing the sounds inside words (the phonological-deficit account, associated with Margaret Snowling, among others), and it runs in families. Some myths are worth retiring: dyslexic people do not see letters backwards (young children of all kinds reverse letters), it is not a sign of low intelligence, and coloured lenses and vision therapy are not supported as treatments (a joint statement of the American Academy of Pediatrics and ophthalmology bodies, 2009, reaffirmed 2011). What does help is explicit, structured teaching of the link between sounds and letters, started early. Writing systems matter too: Philip Seymour and colleagues (2003) found that children learning regular alphabets such as Finnish, Italian and German learn to decode accurately within about a year, while English, with its irregular spelling (a legacy of the sound changes in Historical Linguistics, 36/25), takes much longer; in regular systems dyslexia shows up more as slow reading than as inaccurate reading. Chinese, which writes meaning-bearing characters, taxes different brain systems. Whether to seek an assessment, and what support is available, depends on the country and school; that is a question for a qualified professional.

Prediction: the brain runs ahead of the words

Back to the bread and the socks. In 1980 Marta Kutas and Steven Hillyard recorded electrical activity from people's scalps as they read sentences one word at a time and found that a semantically odd final word ("He spread the warm bread with socks") evoked a negative wave peaking about 400 milliseconds after the word began, the N400. The wave is graded: the less expected a word, the bigger it is, and it appears even for words that are perfectly sensible but unlikely. Researchers use cloze probability, the share of people who would fill in a given word, to measure expectation, and information theory to describe it: the surprisal of a word is −log₂ of its probability. Lower probability means more bits of surprise. Studies (including Stefan Frank and colleagues, 2015) find N400 size tracks surprisal, though what the wave reflects (difficulty of integrating a word, ease of retrieving it, or both) is debated by Kutas, Federmeier and others.

Without JavaScript: "butter" after "spread the warm bread with" is expected (about 0.3 bits of surprisal); "jam" is less so (about 3 bits); "socks" is almost never said (about 11 bits), so the N400 is large.

Prediction is not an optional extra. It explains why a noisy phone line is still understandable, why you can finish a friend's sentence, and why a garden path hurts: the parser bet and lost. It is also the training task of today's language models, which learn by predicting the next word. That similarity is real; whether it means the models and brains do the same thing is argued (see Computational Linguistics, 36/30).

Does language shape thought? Weak, strong and the evidence

Benjamin Lee Whorf, writing in the 1930s and 1940s, and his teacher Edward Sapir, are credited with the idea of linguistic relativity: the language you speak affects how you think. The strong version, that language determines thought, so people cannot think what they cannot say, is rejected by nearly everyone. Whorf's own best-known case, that the Hopi had no concept of time, was challenged by Ekkehart Malotki's 1983 book, which documented Hopi time expressions. The weak version, that language nudges attention, memory and speed of categorisation, has real evidence. Three areas of that evidence are worth knowing.

Colour. Berlin and Kay (1969) argued that colour terms across languages grow in a near-fixed order: dark and light first, then red, then green or yellow, then blue, then brown, then purple, pink, orange and grey. That suggests biology constrains the vocabulary (the later World Color Survey, covering 110 unwritten languages, found the pattern as a strong tendency, not a law). But the vocabulary then affects perception a little. Winawer and colleagues (2007) found that Russian speakers, whose language has separate basic words for light blue (goluboy) and dark blue (siniy), were faster at telling apart two blues that fall on opposite sides of that line, and the advantage vanished when they had to hold a string of digits in mind at the same time, which suggests the effect works through language in the moment. Roberson and colleagues (2005) found a similar boundary effect in speakers of Himba, whose language does not split blue from green. The effects are modest, measured in milliseconds, and some replications are mixed.

Without JavaScript: two terms (dark and light), three adds red, four adds green or yellow, five both, six adds blue, seven brown, and eight to eleven purple, pink, orange and grey.

Space. Speakers of Guugu Yimithirr, in northern Queensland, do not say "left" or "right". They give compass directions for everything, even small things nearby: "there is an ant on your southwest leg". Stephen Levinson and colleagues showed that speakers of such languages keep track of compass directions even in unfamiliar surroundings, and that people laying out picture cards for time put them in order from east to west whatever way they face (Boroditsky and Gaby, 2010, for Kuuk Thaayorre). Critics, such as Li and Gleitman (2002), found that English speakers' choices flip if the environment gives cues, and argued that the language is not the cause; Levinson's group replied. The jury is still out, but the difference in everyday description is real.

Without JavaScript: the cup is always due east. A speaker who uses "left" and "right" must change the description every time they turn; a speaker of a compass-direction language does not.

Number. The Pirahã of the Amazon and the Mundurukú of Brazil have few or no exact number words. Peter Gordon (2004) and Pierre Pica and colleagues (2004) found that their speakers estimate small quantities well but become inaccurate for exact larger ones, which suggests that number words are cultural tools that help exactness. Claims about grammatical gender affecting how people describe objects have been harder to replicate. A fair summary: language does not set the limits of thought, but it can shift attention, speed and memory in specific tasks, and each claim has to be tested one by one.

What a graduate just sees

• Language lives in a left-hemisphere network, not in two spots; Broca and Wernicke named regions of a larger system.
• Aphasia types can be sorted by three questions: fluency, comprehension, repetition.
• You recognise a word before it ends because you track a shrinking cohort of candidates.
• Parsing is incremental: the mind commits early, and a garden path is the cost of that bet.
• Reading is a recycled skill; irregular words need lookup, new words need rules, and dyslexia is a phonological difficulty, not a vision problem.
• The brain predicts the next word constantly; an unexpected one costs about 400 ms of extra effort.
• Language nudges thought in specific, modest ways; it does not cage it.

Where it shows up next

• Computational Linguistics (36/30): surprisal, parsers and next-word models are the same ideas as programs.
• Clinical Psychology (35/27) and Biological Psychology (35/20): aphasia, stroke recovery and the limits of brain scans.
• Language Acquisition (36/27) and Sociolinguistics (36/26): how the system gets built, and how it varies between groups.
• In the world: speech-language therapy, reading instruction, accessible text design, brain-computer interfaces for speech, and the design of user interfaces that avoid garden paths.

If you want to go deeper

• 📘 Textbook. Trevor Harley, The Psychology of Language: From Data to Theory (Psychology Press / Routledge, 4th ed. 2014); Matthew Traxler, Introduction to Psycholinguistics (Wiley-Blackwell, 2nd ed. 2022).
• 📘 Gentler. Stanislas Dehaene, Reading in the Brain (2009); Guy Deutscher, Through the Language Glass (2010) on language and thought; Maryanne Wolf, Proust and the Squid (2007) on the reading brain.
• 🌐 Free text. The National Aphasia Association (aphasia.org) and the British Dyslexia Association (bdadyslexia.org.uk) publish plain-language guides; the Noba Project has free modules on language.
• 🌐 Free course. MIT OpenCourseWare 24.900 Introduction to Linguistics (Spring 2022) covers the structure of language this subject builds on; for the brain side, MIT OpenCourseWare also hosts courses in Brain and Cognitive Sciences (course 9).