ACM Journal on Computing and Sustainable Societies,
Journal Year:
2023,
Volume and Issue:
1(2), P. 1 - 27
Published: Sept. 16, 2023
Towards
providing
personalized
care,
digital
mental-wellness
apps
today
ask
questions
to
learn
about
subjects.
However,
not
all
subjects
using
these
will
have
mood
problems;
thus,
they
do
need
follow-up
questions.
In
this
study,
we
investigate
an
alternate
mechanism
handle
such
non-sensitive
posts
(i.e.,
those
indicating
problems)
in
college
settings.
To
so,
generate
and
use
training
data
provided
by
a
cohort
of
peer
students
so
that
responses
are
contextual,
emotionally
aware,
empathetic
while
also
being
terminal
(not
asking
questions).
Using
from
real
app
used
students,
identify
AI
models
trained
with
our
peer-provided
dataset
desirable
posts,
state-of-the-art
(Facebook’s)
Empathetic
Dataset
yields
many
questions,
hence
giving
perception
intrusive.
We
believe
mental
wellness
must
assume
any
subject
has
problems.
Perceptions
intrusiveness
questions)
be
factor
design.
can
provide
rich
reliable
datasets
for
apps,
topic
is
yet
explored.
Psychonomic Bulletin & Review,
Journal Year:
2024,
Volume and Issue:
31(5), P. 1981 - 2004
Published: March 4, 2024
Abstract
The
mental
lexicon
is
a
complex
cognitive
system
representing
information
about
the
words/concepts
that
one
knows.
Over
decades
psychological
experiments
have
shown
conceptual
associations
across
multiple,
interactive
levels
can
greatly
influence
word
acquisition,
storage,
and
processing.
How
semantic,
phonological,
syntactic,
other
types
of
be
mapped
within
coherent
mathematical
framework
to
study
how
works?
Here
we
review
multilayer
networks
as
promising
quantitative
interpretative
for
investigating
lexicon.
Cognitive
map
multiple
at
once,
thus
capturing
different
layers
might
co-exist
This
starts
with
gentle
introduction
structure
formalism
networks.
We
then
discuss
mechanisms
phenomena
could
not
observed
in
single-layer
were
only
unveiled
by
combining
lexicon:
(i)
multiplex
viability
highlights
language
kernels
facilitative
effects
knowledge
processing
healthy
clinical
populations;
(ii)
community
detection
enables
contextual
meaning
reconstruction
depending
on
psycholinguistic
features;
(iii)
layer
analysis
mediate
latent
interactions
mediation,
suppression,
facilitation
lexical
access.
By
outlining
novel
perspectives
where
shed
light
representations,
including
next-generation
brain/mind
models,
key
limitations
directions
cutting-edge
future
research.
Computers in Human Behavior,
Journal Year:
2024,
Volume and Issue:
158, P. 108266 - 108266
Published: April 17, 2024
Past
studies
of
sexual
assault
have
found
that
passive
voice
descriptions
rape
elicit
an
increased
perception
victim
responsibility
compared
to
active
narratives
(Bohner,
2001),
contributing
blaming
and
the
perpetuation
myths.
Building
on
this,
we
investigate
relationship
between
passive/active
usage
perception,
but
from
perspective
survivors
as
disclosed
in
their
online
narratives.
We
collect
Reddit's
r/sexualassault
board
group
them
into
a
group.
detect
differences
two
groups
text
using
cognitive
network
science
approach
creates
representations
such
nodes
represent
words/concepts
while
links
syntactic
semantic
relationships
them.
systematically
identify
are
significantly
more
central
one
other,
thus
identifying
characteristic
concepts
semantically
differentiate
then
contexts
these
applying
frame
analysis.
find
related
psychological
distress
(e.g.
PTSD,
flashback)
narratives,
providing
quantitative
evidence
link
focus
distress.
also
family
members
parent,
brother)
suggesting
connection
others'
roles
survivors'
experiences.
Our
results
reveal
important
language
mental
health
has
valuable
implications
for
therapeutic
interventions.
Communications Psychology,
Journal Year:
2024,
Volume and Issue:
2(1)
Published: May 23, 2024
Psychological
constructs
are
commonly
quantified
with
closed-ended
rating
scales.
However,
recent
advancements
in
natural
language
processing
(NLP)
enable
the
quantification
of
open-ended
responses.
Here
we
demonstrate
that
descriptive
word
responses
analyzed
using
NLP
show
higher
accuracy
categorizing
emotional
states
compared
to
traditional
One
group
participants
(N
=
297)
generated
narratives
related
depression,
anxiety,
satisfaction,
or
harmony,
summarized
them
five
words,
and
rated
Another
434)
evaluated
these
(with
words
scales)
from
author's
perspective.
The
were
NLP,
machine
learning
was
used
categorize
into
corresponding
states.
results
showed
a
significantly
number
accurate
categorizations
based
on
(64%)
than
scales
(44%),
questioning
notion
more
precise
measuring
language-based
measures.
We
introduce
CounseLLMe
as
a
multilingual,
multimodal
dataset
of
400
simulated
mental
health
counselling
dialogues
between
two
state-of-the-art
Large
Language
Models
(LLMs).
These
conversations
-
20
quips
each
were
generated
either
in
English
(using
OpenAI's
GPT
3.5
and
Claude-3's
Haiku)
or
Italian
(with
Haiku
LLaMAntino)
with
prompts
tuned
also
the
help
professional
psychotherapy.
investigate
resulting
through
comparison
against
human
on
same
topic
depression.
To
compare
linguistic
features,
knowledge
structure
emotional
content
LLMs
humans,
we
employed
textual
forma
mentis
networks,
i.e.
cognitive
networks
where
nodes
represent
concepts
links
indicate
syntactic
semantic
relationships
dialogues'
quips.
find
that
LLM-LLM
matches
one
humans
terms
patient-therapist
trust
exchanges,
1
5
contain
along
10
conversational
turns
versus
$24\%$
rate
found
humans.
ChatGPT
Haiku's
patients
can
reproduce
feelings
conflict
pessimism.
However,
display
non-negligible
levels
anger/frustration
is
missing
LLMs.
LLMs'
are
worse
reproducing
patterns.
All
reproduced
patterns
increased
absolutist
pronoun
usage
second-person,
trust-inducing,
therapists.
Our
results
realistically
several
aspects
thusly
release
public
for
novel
data-informed
opportunities
machine
psychology.
