Annual Review of Neuroscience,
Journal Year:
2024,
Volume and Issue:
47(1), P. 277 - 301
Published: April 26, 2024
It
has
long
been
argued
that
only
humans
could
produce
and
understand
language.
But
now,
for
the
first
time,
artificial
language
models
(LMs)
achieve
this
feat.
Here
we
survey
new
purchase
LMs
are
providing
on
question
of
how
is
implemented
in
brain.
We
discuss
why,
a
priori,
might
be
expected
to
share
similarities
with
human
system.
then
summarize
evidence
represent
linguistic
information
similarly
enough
enable
relatively
accurate
brain
encoding
decoding
during
processing.
Finally,
examine
which
LM
properties—their
architecture,
task
performance,
or
training—are
critical
capturing
neural
responses
review
studies
using
as
silico
model
organisms
testing
hypotheses
about
These
ongoing
investigations
bring
us
closer
understanding
representations
processes
underlie
our
ability
comprehend
sentences
express
thoughts
Physics of Life Reviews,
Journal Year:
2023,
Volume and Issue:
46, P. 92 - 118
Published: June 5, 2023
We
advance
a
novel
active
inference
model
of
the
cognitive
processing
that
underlies
acquisition
hierarchical
action
repertoire
and
its
use
for
observation,
understanding
imitation.
illustrate
in
four
simulations
tennis
learner
who
observes
teacher
performing
shots,
forms
representations
observed
actions,
imitates
them.
Our
show
agent's
oculomotor
activity
implements
an
information
sampling
strategy
permits
inferring
kinematic
aspects
movement,
which
lie
at
lowest
level
hierarchy.
In
turn,
this
low-level
supports
higher-level
inferences
about
deeper
actions:
proximal
goals
intentions.
Finally,
inferred
can
steer
imitative
responses,
but
interfere
with
execution
different
actions.
provides
unified
account
understanding,
learning
imitation
helps
explain
neurobiological
underpinnings
visuomotor
cognition,
including
multiple
routes
dorsal
ventral
streams
mirror
mechanisms.
Journal of Neuroscience,
Journal Year:
2023,
Volume and Issue:
43(17), P. 3144 - 3158
Published: March 27, 2023
The
meaning
of
words
in
natural
language
depends
crucially
on
context.
However,
most
neuroimaging
studies
word
use
isolated
and
sentences
with
little
Because
the
brain
may
process
differently
from
how
it
processes
simplified
stimuli,
there
is
a
pressing
need
to
determine
whether
prior
results
generalize
language.
fMRI
was
used
record
human
activity
while
four
subjects
(two
female)
read
conditions
that
vary
context:
narratives,
sentences,
blocks
semantically
similar
words,
words.
We
then
compared
signal-to-noise
ratio
(SNR)
evoked
responses,
we
voxelwise
encoding
modeling
approach
compare
representation
semantic
information
across
conditions.
find
consistent
effects
varying
First,
stimuli
more
context
evoke
responses
higher
SNR
bilateral
visual,
temporal,
parietal,
prefrontal
cortices
Second,
increasing
increases
at
group
level.
In
individual
subjects,
only
consistently
widespread
information.
Third,
affects
voxel
tuning.
Finally,
models
estimated
using
do
not
well
These
show
has
large
quality
data
brain.
Thus,
regime.
SIGNIFICANCE
STATEMENT
Context
an
important
part
understanding
language,
but
Here,
examined
out-of-context
improves
neuro-imaging
changes
where
represented
suggest
findings
daily
life.
Journal of Cognitive Neuroscience,
Journal Year:
2024,
Volume and Issue:
36(7), P. 1427 - 1471
Published: Jan. 1, 2024
Abstract
Human
language
is
expressive
because
it
compositional:
The
meaning
of
a
sentence
(semantics)
can
be
inferred
from
its
structure
(syntax).
It
commonly
believed
that
syntax
and
semantics
are
processed
by
distinct
brain
regions.
Here,
we
revisit
this
claim
using
precision
fMRI
methods
to
capture
separation
or
overlap
function
in
the
brains
individual
participants.
Contrary
prior
claims,
find
distributed
sensitivity
both
throughout
broad
frontotemporal
network.
Our
results
join
growing
body
evidence
for
an
integrated
network
human
within
which
internal
specialization
primarily
matter
degree
rather
than
kind,
contrast
with
influential
proposals
advocate
different
areas
types
linguistic
functions.
Annual Review of Neuroscience,
Journal Year:
2024,
Volume and Issue:
47(1), P. 277 - 301
Published: April 26, 2024
It
has
long
been
argued
that
only
humans
could
produce
and
understand
language.
But
now,
for
the
first
time,
artificial
language
models
(LMs)
achieve
this
feat.
Here
we
survey
new
purchase
LMs
are
providing
on
question
of
how
is
implemented
in
brain.
We
discuss
why,
a
priori,
might
be
expected
to
share
similarities
with
human
system.
then
summarize
evidence
represent
linguistic
information
similarly
enough
enable
relatively
accurate
brain
encoding
decoding
during
processing.
Finally,
examine
which
LM
properties—their
architecture,
task
performance,
or
training—are
critical
capturing
neural
responses
review
studies
using
as
silico
model
organisms
testing
hypotheses
about
These
ongoing
investigations
bring
us
closer
understanding
representations
processes
underlie
our
ability
comprehend
sentences
express
thoughts
