bioRxiv (Cold Spring Harbor Laboratory),
Journal Year:
2023,
Volume and Issue:
unknown
Published: June 30, 2023
ABSTRACT
Humans
have
the
ability
to
craft
abstract,
temporally
extended
and
hierarchically
organized
plans.
For
instance,
when
considering
how
make
spaghetti
for
dinner,
we
typically
concern
ourselves
with
useful
“subgoals”
in
task,
such
as
cutting
onions,
boiling
pasta,
cooking
a
sauce,
rather
than
particulars
many
cuts
onion,
or
exactly
which
muscles
contract.
A
core
question
is
decomposition
of
more
abstract
task
into
logical
subtasks
happens
first
place.
Previous
research
has
shown
that
humans
are
sensitive
form
higher-order
statistical
learning
named
“community
structure”.
Community
structure
common
feature
tasks
characterized
by
ordering
subtasks.
This
can
be
captured
model
where
learn
predictions
upcoming
events
multiple
steps
future,
discounting
further
away
time.
One
“successor
representation”,
been
argued
hierarchical
abstraction.
As
yet,
no
study
convincingly
this
abstraction
put
use
goal-directed
behavior.
Here,
investigate
whether
participants
utilize
learned
community
informed
action
plans
Participants
were
asked
search
paintings
virtual
museum,
grouped
together
“wings”
representing
museum.
We
find
participants’
choices
accord
museum
their
response
times
best
predicted
successor
representation.
The
degree
reflect
correlates
several
measures
performance,
including
These
results
suggest
representation
subserves
abstractions
relevant
AUTHOR
SUMMARY
achieve
diverse
range
goals
highly
complex
world.
Classic
theories
decision
making
focus
on
simple
involving
single
goals.
In
current
study,
test
recent
theoretical
proposal
aims
address
flexibility
human
making.
By
predict
events,
acquire
‘model’
world
they
then
leverage
plan
However,
given
complexity
world,
planning
directly
over
all
possible
overwhelming.
show
that,
leveraging
predictive
model,
group
similar
simpler
“hierarchical”
representations,
makes
these
representations
markedly
efficient.
Interestingly,
seem
remember
both
simplified
using
them
distinct
purposes.
Journal of Neuroscience,
Journal Year:
2024,
Volume and Issue:
44(14), P. e1369232024 - e1369232024
Published: Feb. 26, 2024
Networks
are
a
useful
mathematical
tool
for
capturing
the
complexity
of
world.
In
previous
behavioral
study,
we
showed
that
human
adults
were
sensitive
to
high-level
network
structure
underlying
auditory
sequences,
even
when
presented
with
incomplete
information.
Their
performance
was
best
explained
by
model
compatible
associative
learning
principles,
based
on
integration
transition
probabilities
between
adjacent
and
nonadjacent
elements
memory
decay.
present
explored
neural
correlates
this
hypothesis
via
magnetoencephalography
(MEG).
Participants
(
Successive
auditory
inputs
are
rarely
independent,
their
relationships
ranging
from
local
transitions
between
elements
to
hierarchical
and
nested
representations.
In
many
situations,
humans
retrieve
these
dependencies
even
limited
datasets.
However,
this
learning
at
multiple
scale
levels
is
poorly
understood.
Here,
we
used
the
formalism
proposed
by
network
science
study
representation
of
higher-order
structures
interaction
in
sequences.
We
show
that
human
adults
exhibited
biases
perception
elements,
which
made
them
sensitive
high-order
such
as
communities.
This
behavior
consistent
with
creation
a
parsimonious
simplified
model
evidence
they
receive,
achieved
pruning
completing
elements.
observation
suggests
brain
does
not
rely
on
exact
memories
but
world.
Moreover,
bias
can
be
analytically
modeled
memory/efficiency
trade-off.
correctly
accounts
for
previous
findings,
including
transition
probabilities
well
structures,
unifying
sequence
across
scales.
finally
propose
putative
implementations
bias.
Developmental Science,
Journal Year:
2024,
Volume and Issue:
27(4)
Published: Feb. 19, 2024
In
many
domains,
learners
extract
recurring
units
from
continuous
sequences.
For
example,
in
unknown
languages,
fluent
speech
is
perceived
as
a
signal.
Learners
need
to
the
underlying
words
this
signal
and
then
memorize
them.
One
prominent
candidate
mechanism
statistical
learning,
whereby
track
how
predictive
syllables
(or
other
items)
are
of
one
another.
Syllables
within
same
word
predict
each
better
than
straddling
boundaries.
But
does
learning
lead
memories
words-or
just
pairwise
associations
among
syllables?
Electrophysiological
results
provide
strongest
evidence
for
memory
view.
responses
can
be
time-locked
boundaries
(e.g.,
N400s)
show
rhythmic
activity
with
periodicity
durations.
Here,
I
reproduce
such
simple
Hebbian
network.
When
exposed
statistically
structured
syllable
sequences
(and
when
not
excessively
long),
network
activation
duration
maxima
on
word-final
syllables.
This
because
receive
more
excitation
earlier
which
they
associated
less
predictable
that
occur
words.
The
also
sensitive
information
whose
electrophysiological
correlates
were
used
support
encoding
ordinal
positions
thus
explain
neural
tasks
without
any
representations
might
rely
cues
beyond
learn
their
native
language.
RESEARCH
HIGHLIGHTS:
Statistical
may
utilized
identify
speech)
but
generate
explicit
Exposure
leads
period
words).
memory-less
model
well
putative
encodings
observed
research.
Direct
tests
needed
establish
whether
declarative
bioRxiv (Cold Spring Harbor Laboratory),
Journal Year:
2024,
Volume and Issue:
unknown
Published: Jan. 8, 2024
Abstract
The
debate
over
whether
conscious
attention
is
necessary
for
statistical
learning
has
produced
mixed
and
conflicting
results.
Testing
individuals
with
impaired
consciousness
may
provide
some
insight,
but
very
few
studies
have
been
conducted
due
to
the
difficulties
associated
testing
such
patients.
In
this
study,
we
examined
ability
of
patients
varying
levels
disorders
(DOC),
including
coma,
unresponsive
wakefulness
syndrome,
minimally
patients,
emergence
from
state
extract
regularities
an
artificial
language
composed
four
randomly
concatenated
pseudowords.
We
used
a
methodology
based
on
frequency
tagging
in
EEG,
which
was
developed
our
previous
speech
segmentation
sleeping
neonates.
Our
study
had
two
main
objectives:
firstly,
assess
automaticity
process
explore
correlations
between
level
covert
abilities
regularities,
second,
potential
new
diagnostic
indicator
aid
patient
management
by
examining
correlation
successful
markers
level.
observed
that
were
preserved
suggesting
inherently
automatic
low-level
process.
Due
significant
inter-individual
variability,
word
not
be
sufficiently
robust
candidate
clinical
use,
unlike
temporal
accuracy
auditory
syllable
responses,
correlates
strongly
coma
severity.
Therefore,
propose
stimulus
train,
simple
measure,
should
further
investigated
as
possible
metric
DOC
diagnosis.
PLoS Computational Biology,
Journal Year:
2024,
Volume and Issue:
20(2), P. e1011312 - e1011312
Published: Feb. 20, 2024
Humans
have
the
ability
to
craft
abstract,
temporally
extended
and
hierarchically
organized
plans.
For
instance,
when
considering
how
make
spaghetti
for
dinner,
we
typically
concern
ourselves
with
useful
“subgoals”
in
task,
such
as
cutting
onions,
boiling
pasta,
cooking
a
sauce,
rather
than
particulars
many
cuts
onion,
or
exactly
which
muscles
contract.
A
core
question
is
decomposition
of
more
abstract
task
into
logical
subtasks
happens
first
place.
Previous
research
has
shown
that
humans
are
sensitive
form
higher-order
statistical
learning
named
“community
structure”.
Community
structure
common
feature
tasks
characterized
by
ordering
subtasks.
This
can
be
captured
model
where
learn
predictions
upcoming
events
multiple
steps
future,
discounting
further
away
time.
One
“successor
representation”,
been
argued
hierarchical
abstraction.
As
yet,
no
study
convincingly
this
abstraction
put
use
goal-directed
behavior.
Here,
investigate
whether
participants
utilize
learned
community
informed
action
plans
Participants
were
asked
search
paintings
virtual
museum,
grouped
together
“wings”
representing
museum.
We
find
participants’
choices
accord
museum
their
response
times
best
predicted
successor
representation.
The
degree
reflect
correlates
several
measures
performance,
including
These
results
suggest
representation
subserves
abstractions
relevant
bioRxiv (Cold Spring Harbor Laboratory),
Journal Year:
2024,
Volume and Issue:
unknown
Published: April 23, 2024
Abstract
Statistical
structures
and
our
ability
to
exploit
them
are
a
ubiquitous
component
of
daily
life.
Yet,
we
still
do
not
fully
understand
how
track
these
sophisticated
statistics
the
role
they
play
in
sensory
processing.
Predictive
coding
frameworks
hypothesize
that
for
stimuli
can
be
accurately
anticipated
based
on
prior
experience,
rely
more
strongly
internal
model
world
“surprised”
when
expectation
is
unmet.
The
current
study
used
this
phenomenon
probe
listeners’
sensitivity
probabilistic
generated
using
rapid
50
milli-second
tone-pip
sequences
precluded
conscious
prediction
upcoming
stimuli.
Over
three
experiments
measured
response
time
deviants
frequency
outside
expected
range.
Predictable
were
either
triplet-based
or
network-style
structure
deviant
detection
contrasted
against
same
set
tones
but
random,
unpredictable
order.
All
found
structured
enhanced
relative
random
sequences.
Additionally,
Experiment
2
different
instantiations
community
demonstrate
level
uncertainty
modulated
saliency.
Finally,
3
placed
within
an
established
immediately
after
transition
between
communities,
where
perceptual
boundary
should
generate
momentary
uncertainty.
However,
manipulation
did
impact
performance.
Together
results
contexts
from
statistical
modulate
processing
ongoing
auditory
signal,
leading
improved
detect
unexpected
stimuli,
consistent
with
predictive
framework.
bioRxiv (Cold Spring Harbor Laboratory),
Journal Year:
2024,
Volume and Issue:
unknown
Published: Jan. 16, 2024
Abstract
Networks
are
a
useful
mathematical
tool
for
capturing
the
complexity
of
world.
In
previous
behavioral
study,
we
showed
that
human
adults
were
sensitive
to
high-level
network
structure
underlying
auditory
sequences,
even
when
presented
with
incomplete
information.
Their
performance
was
best
explained
by
model
compatible
associative
learning
principles,
based
on
integration
transition
probabilities
between
adjacent
and
non-adjacent
elements
memory
decay.
present
explored
neural
correlates
this
hypothesis
via
magnetoencephalography
(MEG).
Participants
passively
listened
sequences
tones
organized
in
sparse
community
comprising
two
communities.
An
early
difference
(~150
ms)
observed
brain
responses
tone
transitions
similar
probability
but
occurring
either
within
or
This
result
implies
rapid
automatic
encoding
sequence
structure.
Using
time-resolved
decoding,
estimated
duration
overlap
representation
each
tone.
The
decoding
exhibited
exponential
decay,
resulting
significant
representations
successive
tones.
Based
extended
decay
profile,
long-horizon
novelty
index
found
correlation
measure
MEG
signal.
Overall,
our
study
sheds
light
mechanisms
sensitivity
structures
highlights
potential
role
Hebbian-like
supporting
at
various
temporal
scales.
bioRxiv (Cold Spring Harbor Laboratory),
Journal Year:
2024,
Volume and Issue:
unknown
Published: March 14, 2024
Humans
can
spontaneously
detect
complex
algebraic
structures.
Historically,
two
opposing
views
explain
this
ability,
at
the
root
of
language
and
music
acquisition.
Some
argue
for
existence
an
innate
specific
mechanism,
like
“merge”
(Chomsky)
or
“neural
recursion”
(Dehaene).
Others
that
ability
emerges
from
experience
(e.g.
Bates):
i.e.
when
generic
learning
principles
continuously
process
sensory
inputs.
These
views,
however,
remain
difficult
to
test
experimentally.
Here,
we
use
deep
models
evaluate
factors
lead
spontaneous
detection
structures
in
auditory
modality.
Specifically,
train
multiple
with
a
variable
amount
natural
sounds
self-supervised
objective.
We
then
expose
these
experimental
paradigms
classically
used
processing
Like
humans,
repeated
sequences,
probabilistic
chunks
Also
diminishes
structure
complexity.
Importantly,
emerge
alone:
more
are
exposed
sounds,
they
increasingly
Finally,
does
not
pretrained
only
on
speech,
rapidly
than
environmental
sounds.
Overall,
our
study
provides
operational
framework
clarify
sufficient
built-in
acquired
model
human’s
advanced
capacity
Significance
Statement
Experimentalists
have
repeatedly
observed
human
advantage
structures,
notably
through
paradigms.
This
is
thought
be
key
emergence
cognitive
operations.
Yet,
it
remains
debated
if
discovered
form
mechanism.
In
article,
authors
show
how
progressively
learns
structure.
The
replicate
several
findings
but
under
certain
developmental
conditions.
Notably,
exposition
detection.
As
result,
work
proposes
as
abstract
abilities.
bioRxiv (Cold Spring Harbor Laboratory),
Journal Year:
2024,
Volume and Issue:
unknown
Published: June 16, 2024
Abstract
Recent
studies
showed
that
humans,
regardless
of
age,
education,
and
culture,
can
extract
the
linear
trend
a
noisy
graph.
Here,
we
examined
whether
such
skills
for
intuitive
statistics
are
confined
to
humans
or
may
also
exist
in
non-human
primates.
We
trained
Guinea
baboons
(
Papio
papio
)
associate
arbitrary
geometrical
shapes
with
increasing
decreasing
trends
noiseless
scatterplots,
while
varying
number
points,
noise
level,
regression
slope.
Many
successfully
learned
this
conditional
match-to-sample
task
both
plots.
Crucially,
successful
baboons,
accuracy
varied
as
sigmoid
function
t-value
regression,
same
statistical
index
upon
which
base
their
answers,
even
after
controlling
other
variables.
These
results
compatible
hypothesis
human
perception
data
graphics
is
based
on
pre-emption
recycling
phylogenetically
older
competence
primate
visual
system
extracting
principal
axes
displays.