Neuron,
Journal Year:
2024,
Volume and Issue:
112(10), P. 1531 - 1552
Published: March 5, 2024
How
is
conscious
experience
related
to
material
brain
processes?
A
variety
of
theories
aiming
answer
this
age-old
question
have
emerged
from
the
recent
surge
in
consciousness
research,
and
some
are
now
hotly
debated.
Although
most
researchers
so
far
focused
on
development
validation
their
preferred
theory
relative
isolation,
article,
written
by
a
group
scientists
representing
different
theories,
takes
an
alternative
approach.
Noting
that
various
often
try
explain
aspects
or
mechanistic
levels
consciousness,
we
argue
do
not
necessarily
contradict
each
other.
Instead,
several
them
may
converge
fundamental
neuronal
mechanisms
be
partly
compatible
complementary,
multiple
can
simultaneously
contribute
our
understanding.
Here,
consider
unifying,
integration-oriented
approaches
been
largely
neglected,
seeking
combine
valuable
elements
theories.
Proceedings of the National Academy of Sciences,
Journal Year:
2021,
Volume and Issue:
118(45)
Published: Nov. 4, 2021
Significance
Language
is
a
quintessentially
human
ability.
Research
has
long
probed
the
functional
architecture
of
language
in
mind
and
brain
using
diverse
neuroimaging,
behavioral,
computational
modeling
approaches.
However,
adequate
neurally-mechanistic
accounts
how
meaning
might
be
extracted
from
are
sorely
lacking.
Here,
we
report
first
step
toward
addressing
this
gap
by
connecting
recent
artificial
neural
networks
machine
learning
to
recordings
during
processing.
We
find
that
most
powerful
models
predict
behavioral
responses
across
different
datasets
up
noise
levels.
Models
perform
better
at
predicting
next
word
sequence
also
measurements—providing
computationally
explicit
evidence
predictive
processing
fundamentally
shapes
comprehension
mechanisms
brain.
Annals of the New York Academy of Sciences,
Journal Year:
2020,
Volume and Issue:
1464(1), P. 242 - 268
Published: March 1, 2020
Abstract
For
many
years,
the
dominant
theoretical
framework
guiding
research
into
neural
origins
of
perceptual
experience
has
been
provided
by
hierarchical
feedforward
models,
in
which
sensory
inputs
are
passed
through
a
series
increasingly
complex
feature
detectors.
However,
long‐standing
orthodoxy
these
accounts
recently
challenged
radically
different
set
theories
that
contend
perception
arises
from
purely
inferential
process
supported
two
distinct
classes
neurons:
those
transmit
predictions
about
states
and
signal
information
deviates
predictions.
Although
predictive
processing
(PP)
models
have
become
influential
cognitive
neuroscience,
they
also
criticized
for
lacking
empirical
support
to
justify
their
status.
This
limited
evidence
base
partly
reflects
considerable
methodological
challenges
presented
when
trying
test
unique
models.
confluence
technological
advances
prompted
recent
surge
human
nonhuman
neurophysiological
seeking
fill
this
gap.
Here,
we
will
review
new
evaluate
degree
its
findings
key
claims
PP.
Proceedings of the National Academy of Sciences,
Journal Year:
2022,
Volume and Issue:
119(32)
Published: Aug. 3, 2022
Understanding
spoken
language
requires
transforming
ambiguous
acoustic
streams
into
a
hierarchy
of
representations,
from
phonemes
to
meaning.
It
has
been
suggested
that
the
brain
uses
prediction
guide
interpretation
incoming
input.
However,
role
in
processing
remains
disputed,
with
disagreement
about
both
ubiquity
and
representational
nature
predictions.
Here,
we
address
issues
by
analyzing
recordings
participants
listening
audiobooks,
using
deep
neural
network
(GPT-2)
precisely
quantify
contextual
First,
establish
responses
words
are
modulated
ubiquitous
Next,
disentangle
model-based
predictions
distinct
dimensions,
revealing
dissociable
signatures
syntactic
category
(parts
speech),
phonemes,
semantics.
Finally,
show
high-level
(word)
inform
low-level
(phoneme)
predictions,
supporting
hierarchical
predictive
processing.
Together,
these
results
underscore
processing,
showing
spontaneously
predicts
upcoming
at
multiple
levels
abstraction.
Communications Biology,
Journal Year:
2022,
Volume and Issue:
5(1)
Published: Feb. 16, 2022
Deep
learning
algorithms
trained
to
predict
masked
words
from
large
amount
of
text
have
recently
been
shown
generate
activations
similar
those
the
human
brain.
However,
what
drives
this
similarity
remains
currently
unknown.
Here,
we
systematically
compare
a
variety
deep
language
models
identify
computational
principles
that
lead
them
brain-like
representations
sentences.
Specifically,
analyze
brain
responses
400
isolated
sentences
in
cohort
102
subjects,
each
recorded
for
two
hours
with
functional
magnetic
resonance
imaging
(fMRI)
and
magnetoencephalography
(MEG).
We
then
test
where
when
these
maps
onto
responses.
Finally,
estimate
how
architecture,
training,
performance
independently
account
generation
representations.
Our
analyses
reveal
main
findings.
First,
between
primarily
depends
on
their
ability
context.
Second,
reveals
rise
maintenance
perceptual,
lexical,
compositional
within
cortical
region.
Overall,
study
shows
modern
partially
converge
towards
solutions,
thus
delineates
promising
path
unravel
foundations
natural
processing.
Current Directions in Psychological Science,
Journal Year:
2019,
Volume and Issue:
28(3), P. 280 - 291
Published: April 16, 2019
The
last
two
decades
of
neuroscience
research
has
produced
a
growing
number
studies
that
suggest
the
various
psychological
phenomena
are
by
predictive
processes
in
brain.
When
considered
together,
these
form
coherent,
neurobiologically-inspired
program
for
guiding
about
mind
and
behavior.
In
this
paper,
we
briefly
consider
common
assumptions
hypotheses
unify
an
emerging
framework
discuss
its
ramifications,
both
improving
replicability
robustness
innovating
theory
suggesting
alternative
ontology
human
mind.
