Summary Summaries of lecture content for the module of emotion
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Vak
Emotions (PS2023)
Instelling
Cardiff University (CF)
The notes I created when revising for my emotion module, including topics of evidence and judgement, consciousness , mechanisms of emotion, mental chronometry, emotion in context.
disease avoidance
main:mechanisms emotion
of ingroup outgroup
+
sub:
recognition
deficits in ener
feat conditioning pathways
+
·
Amygdala
the insula
·
Anger+aggression
·face perception
eye gaze
·
,Magement
es
Evidence Evidence:information
* abt world
t
↳ judgements made
using evidence, t
lead decisions.
to
Judgementis an
automatic process as we are constantly
it
asong
-
Evidence is probibaustic and nothing isabsolutley certain important
to
world.
make distinctions between EVIDENCE +
hypothetical state of
↳ do this
by making a judgementofthe evidence.
utility: now
&
consequentia
are the
ofdecision
outcomes rateegneigeymanan
emeremee
mus
↑
En
A
pS
Judging given enfo via a story askedthe probability of
+
Smith being true un
meteation
to aside more
-
-> Reliability for
I
startsimple, then same
S detail
Relevance
~
Base Rates: is seem to know
is
I
it t he base rate
-
on event
is what
more less
d
or
likely than others
unnen
Einfe Per thebeen
despite
a
evidence not
being
diagnostic.
↓Psraconsumaty ee
it Its impossible. This is
Posterior
-n
probabluty:A
nun run
judgement
mu
due to the representationess after values of o-1 depending
selung dotal evidence we
nevrstcontheunpossesare
on the
-
evidence have.
expectations more than first Based
3 an
frequency
of Examples ofjudgements as
judgements
*
availability probabluties:probability that
based on the
I'll like the new iPhone based
heuristic -
how quickly south
it already
comes to mind. on a poor rating gOt know
↓
prone to bias due to probabltiel
Prior eve n
e m e re
-
we
Imaginability
* don't
have data to and the
Retrievability
# Judgement
agre
Negative events are over-estimate is the
Example:What
like
+ dwelled on.
probably thatill
e the new phone:
evidence
we done
weigh up evidence data
Liklehood of
heuristics-read mumm
3areneare
We use
to biases + fallacies. How likely would I
be to see this evidence
can lead to criational
under scenarios?
different
behaviour distorted
+
judgements as they Example:how often do I like
*
aren'tbased on sound produces with poor reviews
appraisal of
evidence
, mmm
i
·mun
remoteonee
probability terms
Pr: probablity we
·
H:hypothesis
D:Data
1:given
Allows us to see data when
*
hyp is
TH:untrue
hyp true+ when hyp is fake.
then elminate
we
were
things
impossible
thatare
usinginto you have
said
userwondering
#
an
Thereances
me
E
unto
e
between
distinction
hypothetical ofwould
state
+ actual evidence about ↳ is
mee
it -
Being rational not
we sum totals of being right
possible outcomes *We cannotbe rational
to for true
text alle to involvementof
positives heuristics -
explains why
S
mestables are mode
When async
* probabluties, the format of info matters. In the world,
We encounter FREQUENCIES, hence they are usually preferred (Dunes, 2020
umun
means3
Probability Types: are Comside) Tooby (1996) presented ps
+
Frequentist:Based on well with frequentistprobs with additional
definedsampling procedures probes -
data is provided
* "Whatis the probability that
a two dice rous sum to 6: found thatbase
*
rate is ignored
UNTILsampling is mentioned
Bayesian: single event
I
↳ probablity will
probability Associated
-
is a
vague term. ps
with subjective bellefs respond despite
not
understanding.
- "Whatis the probability it b framing is important
rain tomorrow?"
will
r
with decisions framed
Tyversy bahenman (1981) +
presented ps
-
different ways.
M
1
condition 2
condition
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