The
impact
of
clinical
anxiety
on
learning
and
decision-making
is
well-established.
However,
the
influence
temporary
states
optimal
belief
updating
in
healthy
individuals
remains
less
explored.
In
this
study,
we
investigated
how
anxious
affect
process
forming
revising
sensorimotor
predictions.
Study
participants
engaged
a
virtual
reality
interceptive
task
while
manipulated
performance
incentives
to
induce
situational
pressure.
We
then
assessed
changes
physiological
arousal,
self-reported
levels,
performance,
eye
movement
patterns.
Employing
Bayesian
computational
models
perception,
analysed
quickly
predictive
movements
were
adjusted
across
multiple
trials.
results
revealed
that
heightened
led
slower
rate
movements,
accompanied
by
an
increase
visual
exploration
environment.
These
findings
deepen
our
understanding
emotional
states,
like
anxiety,
interact
with
active
inference
behaviours.
Specifically,
they
highlight
limitation
imposed
behaviours
during
conditions.
discuss
implications
these
within
context
theoretical
frameworks
such
as
free
energy
principle,
which
conceptualises
state
internal
entropy
organisms
seek
alleviate.
Neuroscience of Consciousness,
Год журнала:
2024,
Номер
2024(1)
Опубликована: Янв. 1, 2024
Here
we
build
on
recent
findings
which
show
that
greater
alignment
between
our
subjective
experiences
(how
feel)
and
physiological
states
(measurable
changes
in
body)
plays
a
pivotal
role
the
overall
psychological
well-being.
Specifically,
propose
or
'coherence'
affective
arousal
(e.g.
how
excited
'feel')
autonomic
heart
rate
pupil
dilation)
may
be
key
for
maintaining
up-to-date
uncertainty
representations
dynamic
environments.
Drawing
advances
interoceptive
inference,
also
coherence
reflects
integration,
facilitates
adaptive
belief
updating,
impacts
capacity
to
adapt
uncertainty,
with
downstream
consequences
We
highlight
of
meta-awareness
arousal,
third
level
permit
conscious
awareness,
learning
about,
intentional
regulation
lower-order
sources
arousal.
Practices
emphasizing
(like
meditation)
therefore
elicit
some
their
known
benefits
via
improved
coherence.
suggest
is
likely
associated
markers
functioning
emotional
awareness
self-regulatory
capacities)
discuss
mind-body
practices
increase
Psychedelics
(hallucinogenic
5-HT2A
agonists
such
as
psilocybin)
are
gaining
recognition
for
their
potential
to
treat
a
range
of
conditions,
including
anxiety-related
psychopathology.
Despite
early
promising
results,
the
mechanisms
by
which
psychedelic
therapy
alleviates
anxiety
not
well
understood.
Here,
we
review
neural
and
cognitive
underlying
psychopathology
impact
psychedelics
on
these
mechanisms.
This
culminates
in
novel
neurocognitive
model
how
promote
long-term
anxiolysis.
We
conceptualize
case
contextual
information
provided
hippocampus
entrains
amygdala
salience
network
bias
processing
toward
that
“refills”
perpetuates
this
cycle,
due
expression
excitatory
inhibitory
neurons
cortex
hippocampus,
respectively.
acutely
free
cortical
networks
from
hippocampal-dependent
constraints
part
through
respectively,
while
intrinsic
plasticity
and/or
psychedelic-mediated
allows
“resetting
hippocampal
buffer.”
As
acute
effects
wane,
increased
may
enable
adaptively
integrate
into
frame
is
less
biased
or
constrained
prior
aversive
conditioning,
thus
promoting
an
overall
reduction
anxious
thoughts
appraisals.
end
discussing
challenges
anxiety,
can
increase
suggest
directions
future
research
determine
optimal
treatment
paths
informed
neuroscience.
Frontiers in Psychology,
Год журнала:
2024,
Номер
15
Опубликована: Июнь 13, 2024
Hierarchical
predictive
processing
provides
a
framework
outlining
how
prior
expectations
shape
perception
and
cognition.
Here,
we
highlight
hierarchical
as
for
explaining
social
context
group-based
knowledge
can
directly
intergroup
perception.
More
specifically,
argue
that
confers
uniquely
valuable
toolset
to
explain
extant
findings
generate
novel
hypotheses
We
first
provide
an
overview
of
processing,
specifying
its
primary
theoretical
assumptions.
then
review
evidence
showing
influences
Next,
outline
account
well
in
the
literature.
underscore
strengths
compared
other
frameworks
this
space.
finish
by
future
directions
laying
out
test
implications
cognition
more
broadly.
Taken
together,
explanatory
value
capacity
hypothesis
generation
Systems,
Год журнала:
2022,
Номер
10(6), С. 254 - 254
Опубликована: Дек. 13, 2022
Living
systems
are
complex
dynamic
information
processing
energy
consuming
entities
with
properties
of
consciousness,
intelligence,
sapience,
and
sentience.
Sapience
sentience
autonomous
attributes
consciousness.
While
sapience
has
been
well
studied
over
the
years,
that
is
relatively
rare.
The
nature
will
be
considered,
a
metacybernetic
framework
using
structural
adopted
to
explore
metaphysics
Metacybernetics
delivers
cyberintrinsic
model
cybernetic
in
nature,
but
also
uses
theory
arising
from
Frieden’s
work
Fisher
information.
This
used
their
relationship.
Since
living
energy-consuming
entities,
it
natural
for
thermodynamic
metaphysical
models
arise,
most
theoretical
studies
have
set
within
framework.
Hence,
approach
introduced
connected
theory.
In
contexts,
thermodynamics
free-energy,
which
plays
same
role
modelling
as
intrinsic
exist
at
dynamical
interface
thermodynamics,
overall
purpose
this
paper
alternative
perspective
metacybernetics.
Bringing
precision
to
the
understanding
and
treatment
of
mental
disorders
requires
instruments
for
studying
clinically
relevant
individual
differences.
One
promising
approach
is
development
computational
assays:
integrating
models
with
cognitive
tasks
infer
latent
patient-specific
disease
processes
in
brain
computations.
While
recent
years
have
seen
many
methodological
advancements
modelling
cross-sectional
patient
studies,
much
less
attention
has
been
paid
basic
psychometric
properties
(reliability
construct
validity)
measures
provided
by
assays.
In
this
review,
we
assess
extent
issue
examining
emerging
empirical
evidence.
To
contextualize
this,
also
provide
a
more
general
perspective
on
key
developments
that
are
needed
translating
assays
clinical
practice.
Emerging
evidence
suggests
most
show
poor-to-moderate
reliability
often
little
improvement
over
simple
behavioral
measures.
Furthermore,
used
test
accounts
lack
convergent
validity,
which
compromises
their
interpretability.
Taken
together,
these
issues
pose
risk
invalidating
previous
findings
undermining
ongoing
research
efforts
using
study
(and
even
group)
We
suggest
single-task
designs,
currently
dominate
landscape,
partly
blame
problems
therefore
not
suitable
solving
them.
Instead,
validity
need
be
studied
systematically
longitudinal
designs
batteries
tasks.
Finally,
enable
applications,
it
will
necessary
establish
predictive
make
efficient
burdensome.
The
notion
of
burden
features
as
a
central
aspect
research
into
the
challenges
faced
by
patients
and
their
carers,
especially
in
regard
to
long-term
health
conditions
multimorbidity.
Research
this
area
has
considered
burdens
that
stem
from
presence
disease
(e.g.,
symptom
burden)
well
associated
with
healthcare
interventions
treatment
burden).
While
there
have
been
number
attempts
theorize
burden,
is,
at
present,
little
consensus
on
how
ought
be
understood.
It
particular,
unclear
what
makes
something
why
certain
things
are
perceived
burdensome,
forces
factors
moderate
experience
burdensome
experiences
relate
other
experiential
constructs,
such
wellbeing,
despair,
suffering.
present
paper
seeks
advance
our
understanding
drawing
predictive
processing
accounts
brain
function.
All
burdens,
it
is
suggested,
origins
reduced
capacity
fulfill
neurally-realized
expectations
(or
predictions).
This
marked
hypothesized
increase
particular
form
prediction
error,
dubbed
expected
error.
In
addition
providing
unitary
theoretical
approach
account
supports
effort
apply
wider
array
clinical
health-related
phenomena.
Entropy,
Год журнала:
2024,
Номер
26(11), С. 953 - 953
Опубликована: Ноя. 6, 2024
Major
Depressive
Disorder
(MDD)
is
a
complex,
heterogeneous
condition
affecting
millions
worldwide.
Computational
neuropsychiatry
offers
potential
breakthroughs
through
the
mechanistic
modeling
of
this
disorder.
Using
Kolmogorov
theory
(KT)
consciousness,
we
developed
foundational
model
where
algorithmic
agents
interact
with
world
to
maximize
an
Objective
Function
evaluating
affective
valence.
Depression,
defined
in
context
by
state
persistently
low
valence,
may
arise
from
various
factors-including
inaccurate
models
(cognitive
biases),
dysfunctional
(anhedonia,
anxiety),
deficient
planning
(executive
deficits),
or
unfavorable
environments.
Integrating
algorithmic,
dynamical
systems,
and
neurobiological
concepts,
map
agent
brain
circuits
functional
networks,
framing
etiological
routes
linking
depression
biotypes.
Finally,
explore
how
stimulation,
psychotherapy,
plasticity-enhancing
compounds
such
as
psychedelics
can
synergistically
repair
neural
optimize
therapies
using
personalized
computational
models.
The
impact
of
clinical
anxiety
on
learning
and
decision-making
is
well-established.
However,
the
influence
temporary
states
optimal
belief
updating
in
healthy
individuals
remains
less
explored.
In
this
study,
we
investigated
how
anxious
affect
process
forming
revising
sensorimotor
predictions.
Study
participants
engaged
a
virtual
reality
interceptive
task
while
manipulated
performance
incentives
to
induce
situational
pressure.
We
then
assessed
changes
physiological
arousal,
self-reported
levels,
performance,
eye
movement
patterns.
Employing
Bayesian
computational
models
perception,
analysed
quickly
predictive
movements
were
adjusted
across
multiple
trials.
results
revealed
that
heightened
led
slower
rate
movements,
accompanied
by
an
increase
visual
exploration
environment.
These
findings
deepen
our
understanding
emotional
states,
like
anxiety,
interact
with
active
inference
behaviours.
Specifically,
they
highlight
limitation
imposed
behaviours
during
conditions.
discuss
implications
these
within
context
theoretical
frameworks
such
as
free
energy
principle,
which
conceptualises
state
internal
entropy
organisms
seek
alleviate.