these models are not just useful for
proposing the word “want” after you
have typed “do you”—or for allowing
computers to complete any number
of tasks from behind the scenes.
They may also offer a window into
how your brain makes sense of the
flood of words coming out of your
friend’s mouth.
The researchers contend that this
study marks the first time that a
machine-learning algorithm has been
matched to brain data to explain the
workings of a high-level cognitive
task. (Neural networks have been
used in visual and auditory research
for years.) The finding suggests
that predictive processing is central
to how we comprehend language
and demonstrates how artificial
neural networks can offer key
insights into cognition.
“I did not think this would happen in
my lifetime,” says Evelina Fedorenko,
a cognitive neuroscientist at M.I.T.
and co-senior author of the paper.
“These models fare much better than
I would have predicted, relative to
human neural data. That just opens
up all sorts of doors.”
The researchers examined models
based on 43 artificial neural net-
works—a technology that consists of
thousands or millions of intercon-
nected nodes, similar to neurons in
the brain. Each node processes data
and feeds them to other nodes.
Some of the models the M.I.T. team
looked at were optimized for next-
word prediction, including the
well-known Generative Pre-trained
Transformer (GPT-2), which has
made an impression because of its
ability to create humanlike text.
The researchers found that the
activity of the neural network nodes
was similar to brain activity in humans
reading text or listening to stories.
They also translated the neural
networks’ performance into predic-
tions of how brains would perform—
such as how long it would take them
to read a certain word.
The work lays a foundation for
studying higher-level brain tasks.
“We view this as a sort of a template
or a guideline of how one can take
this entire approach of relating
models to data,” says Martin Schrimpf,a Ph.D. student in brain and cognitive
sciences at M.I.T. and lead author
of the paper.
The researchers found that the
models that were best at guessing
the next word were also best at
predicting how a human brain would
respond to the same tasks. This was
especially true for processing single
sentences and short paragraphs. The
models were significantly worse at
predicting words or human respons-
es when it came to longer blocks of
text. None of the other tasks reflect-
ed what was going on in the brain.
The authors argue this is strong
evidence that next-word prediction,
or something like it, plays a key role
in understanding language. “It tells
you that, basically, something like
optimizing for predictive representa-
tion may be the shared objective for
both biological systems and these
in silico models,” Fedorenko says.
To this point, Dan Yamins, a
computational neuroscientist atStanford University, who was not
involved with the research, remarks,
“A sort of convergent evolution has
happened between the engineering
and the real biological evolution.”
Computer science has independently
come up with a solution for a sophis-
ticated cognitive task that the brain
had devised over many millennia.
“I’m superimpressed by what [the
M.I.T. team] achieved,” says Noah
Goodman, a psychologist at Stanford,
who was also not involved with the
research. But he adds that he
suspects that the data are not
sufficient to explain how people
derive meaning from language.
Despite these reservations, Goodman
says the method is “still vastly better
than anything we’ve had in the past.”
While neural networks and compu-
tational science more generally are
only rough analogies for the brain,
their role in helping us understand our
own mind may be substantial. Integra-
tive modeling used by Fedorenko and
her colleagues demonstrates that
neural networks and computational
science might, in fact, be critical tools
in providing insight into the great
mystery of how the brain processes
information of all kinds.
—Anna BlausteinNEWS
“I did not think this would happen in my lifetime.
These models fare much better than I would
have predicted, relative to human neural data.
That just opens up all sorts of doors.”
—Evelina Fedorenko