With help from an
artificial language network, MIT neuroscientists have discovered what kind of
sentences are most likely to fire up the brain’s key language processing
centers.
The new study reveals that sentences
that are more complex, either because of unusual grammar or unexpected meaning,
generate stronger responses in these language processing centers. Sentences
that are very straightforward barely engage these regions, and nonsensical
sequences of words don’t do much for them either.
For example, the researchers found this
brain network was most active when reading unusual sentences such as “Buy sell
signals remains a particular,” taken from a publicly available language dataset
called C4. However, it went quiet when reading something very straightforward,
such as “We were sitting on the couch.”
“The input has to be language-like
enough to engage the system,” says Evelina Fedorenko, Associate Professor of
Neuroscience at MIT and a member of MIT’s McGovern Institute for Brain
Research. “And then within that space, if things are really easy to process,
then you don’t have much of a response. But if things get difficult, or
surprising, if there’s an unusual construction or an unusual set of words that
you’re maybe not very familiar with, then the network has to work harder.”
Fedorenko is the senior author of the
study, which appears today in Nature Human Behavior. MIT graduate student Greta Tuckute is the lead author of the paper.
Processing
language
In this study, the researchers focused
on language-processing regions found in the left hemisphere of the brain, which
includes Broca’s area as well as other parts of the left frontal and temporal
lobes of the brain.
“This language network is highly
selective to language, but it’s been harder to actually figure out what is
going on in these language regions,” Tuckute says. “We wanted to discover what
kinds of sentences, what kinds of linguistic input, drive the left hemisphere
language network.”
The researchers began by compiling a set
of 1,000 sentences taken from a wide variety of sources — fiction,
transcriptions of spoken words, web text, and scientific articles, among many
others.
Five human participants read each of the
sentences while the researchers measured their language network activity using
functional magnetic resonance imaging (fMRI). The researchers then fed those
same 1,000 sentences into a large language model — a model similar to ChatGPT,
which learns to generate and understand language from predicting the next word
in huge amounts of text — and measured the activation patterns of the model in
response to each sentence.
Once they had all of those data, the
researchers trained a mapping model, known as an “encoding model,” which
relates the activation patterns seen in the human brain with those observed in
the artificial language model. Once trained, the model could predict how the
human language network would respond to any new sentence based on how the
artificial language network responded to these 1,000 sentences.
The researchers then used the encoding
model to identify 500 new sentences that would generate maximal activity in the
human brain (the “drive” sentences), as well as sentences that would elicit
minimal activity in the brain’s language network (the “suppress” sentences).
In a group of three new human
participants, the researchers found these new sentences did indeed drive and
suppress brain activity as predicted.
“This ‘closed-loop’ modulation of brain
activity during language processing is novel,” Tuckute says. “Our study shows
that the model we’re using (that maps between language-model activations and
brain responses) is accurate enough to do this. This is the first demonstration
of this approach in brain areas implicated in higher-level cognition, such as
the language network.”
Linguistic
complexity
To figure out what made certain
sentences drive activity more than others, the researchers analyzed the
sentences based on 11 different linguistic properties, including
grammaticality, plausibility, emotional valence (positive or negative), and how
easy it is to visualize the sentence content.
For each of those properties, the
researchers asked participants from crowd-sourcing platforms to rate the
sentences. They also used a computational technique to quantify each sentence’s
“surprisal,” or how uncommon it is compared to other sentences.
This analysis revealed that sentences
with higher surprisal generate higher responses in the brain. This is
consistent with previous studies showing people have more difficulty processing
sentences with higher surprisal, the researchers say.
Another linguistic property that
correlated with the language network’s responses was linguistic complexity,
which is measured by how much a sentence adheres to the rules of English
grammar and how plausible it is, meaning how much sense the content makes,
apart from the grammar.
Sentences at either end of the spectrum
— either extremely simple, or so complex that they make no sense at all —
evoked very little activation in the language network. The largest responses
came from sentences that make some sense but require work to figure them out,
such as “Jiffy Lube of — of therapies, yes,” which came from the Corpus of
Contemporary American English dataset.
“We found that the sentences that elicit
the highest brain response have a weird grammatical thing and/or a weird
meaning,” Fedorenko says. “There’s something slightly unusual about these
sentences.”
The researchers now plan to see if they
can extend these findings in speakers of languages other than English. They
also hope to explore what type of stimuli may activate language processing
regions in the brain’s right hemisphere.
Source: https://news.mit.edu/2024/complex-unfamiliar-sentences-brains-language-network-0103
Paper: https://www.nature.com/articles/s41562-023-01783-7
Open-access paper: https://dspace.mit.edu/handle/1721.1/153265
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