Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

Social Psychology II > The Cognitive Heuristics > Flashcards

The Cognitive Heuristics Flashcards

You may prefer our related Brainscape-certified flashcards:
AP® Psychology flashcards Psychology 101 flashcards GRE® Psychology flashcards
1
Q

FAMILIARITY OF HEURISTICS

A
  • present throughout whole module
  • include: categorisation/attribution/stereotyping/attitudes
  • a lot suggests we use rules for inferences
  • idea of inferential beh conceptualised as choices from alternatives each w/designated value/occurrence probability
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

ARTIFICIAL INTELLIGENCE ARCHIVES

A
  • social psych borrowed heuristic term from AI
  • computers/algorithms/futile search for optimal solution (we don’t always find them)
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

MOSKOWITZ (2005)

A
  • human rules = fairly rational BUT only useful if uncertainty exists/if too much effort required to arrive at complete/accurate judgement
  • heuristic reliance when option of ^ accurate analysis exists/when uncertainty reduced by useful data presence -> heuristics = bias source
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

HEURISTIC EXAMPLE

A

Q: SHOULD I HAVE A RISKY FLING?
1. I could stay w/current partner.
2. I could abandon them for fling w/someone I met at work.
1 = safe/comfortable/easy BUT dull/predictable/tedious
2 = exciting/different/new BUT reckless/uncertain/potentially disastrous

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

WHAT WE SHOULD DO WHEN MAKING A DECISION

A
  • assess available alternatives for likelihood/worth of promised outcomes (probability/value)
  • calculate each outcome utility (value product/outcome probability)
  • choose option maximising utility
  • AKA. we make decisions most likely to deliver desired benefits
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

OPTIMAL DECISION PREVENTION

A
  • we aren’t computers
  • may be too much info to rationally sift through
  • we rarely have time
  • can’t be sure of outcome (may still be unhappy)
  • life decisions do not come via crystal ball/guarentee
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

MARCH & SIMON (1958)

A
  • mostly we are satisfiers > optimisers
  • satisfiers = making adequate inferences/decisions
  • optimisers = drawing best possible inferences -> reaching best possible decisions
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

KAHNEMAN & TVERSKY

A
  • look at ways we satisfice relying on heuristics
    using economic theory
  • looked at slow/fast thinking via 2 systems
  • we can be blind to the obvious/our own blindness
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

THE 2 SYSTEMS

A

SYSTEM 1
- allows to orient to sudden sound source
- complete phrase “bread and…”
- answer to automatic 2+2
SYSTEM 2
- allows to brace for starter gun in race/look for woman w/white hair/fill out tax form
- requires attention/effort

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

2 SYSTEMS COGNITIVE HEURISTIC APPLICATION

A
  • samples w/smaller numbers -> extreme outcomes ^ likelihood (ie. VERY high/low kidney cancer rates in rural USA areas)
  • yes it’s really that simple
  • larger samples = ^ reliable > small samples BUT…
  • sparse populations stand out more/yield ^ extreme results = grab more attention
    EXAMPLES
  • London Blitz bombings believed as targeted BUT statistical analysis -> random process
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

AVUGOS ET AL (2013)

A
  • meta-analysis of GLIOVICH ET AL (1985) aka. multiple basketball shots in row = random
  • little documented evidence (ie. lit reviews) BUT meta-analysis = scientific/robust tool
  • no “hot-hand” evidence found
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

SAMPLE SIZES TODAY

A
  • sample size matters BUT we often fail to take account of it
  • statistics produce many observations that seem to beg for causal explanations BUT are simple chance
  • System 1 = thinking mode leaping on causal connections aka. runs ahead of facts/jumps to conclusions
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

SYSTEM 1 HEURISTICS

A

REPRESENTATIVENESS
AVAILABILITY
ADJUSTMENT/ANCHORING

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

REPRESENTATIVENESS: EXAMPLE

A

TVERSKY & KAHNEMAN (1974)
- eg. Tom = graduate at main state uni. Rank graduate specialisations in likelihood order (computer science/engineering/business administration/physical sciences/library science/law/medicine/humanities/social science)
- thinking about/deciding based on how popular course are = base rate info usage
- when given personal sketch of Tom, the results change based; stereotypes = System 1; disciplined/systematic consideration = System 2
- but what if info = based on uncertain validity? what about base rate info? does remembering this prevent System 1?

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

REPRESENTATIVENESS

A
  • representativeness heuristic = mental shortcut whereby instances = assigned to categories based on how similar they are to category in general
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

REPRESENTATIVENESS: EMPIRICAL EXAMPLE

A

TVERSKY & KAHNEMAN (1974)
- estimate prob man = engineer/lawyer
- man sampled randomly from group of BOTH engineers/lawyers
- 2 (70% engineer/30% lawyer OR 30% engineer/70% lawyer) x 2 (personality profile/no profile) conditions
- no profile = estimate reflected base rates (ie. ^ engineers = engineer answer)
- profile = less rationale/ignored base rate/basically guessed (50/50 judgements)

17
Q

WHY DO WE OVERLOOK THE BASE RATE INFO?

A
  • sometimes there is truth to stereotypes BUT they can mislead us (ie. does a woman falling asleep on someone in a train have a college degree?)
  • we use:
    1. base rate info
    2. predictive value (source credibility)
    3. sample sizing (small = less reliable)
18
Q

CAN WE OVERCOME TENDENCY TO OVERLOOK BASE RATE INFO?

A

SCHWARZ ET AL (1991)
- difficult BUT possible
- instructing people to think “like statistician” enhanced base-rate info use BUT think “like clinician” = opposite effect
- Q: doing task while puffing cheeks out VS frowning–what happens?
- frowning -> ^ vigilance = enhanced System 2 activation -> ^ base info rate use

19
Q

AVAILABILITY: EXAMPLE

A
  • Q: how many celebrities succumbed to plastic surgery?
  • would you be systematic (conservative figure)?
  • OR would you recall well-known celebrity instances that you know of?
  • latter = availability heuristic
20
Q

AVAILABILTY

A
  • availability heuristic = cognitive shortcut allowing to draw upon info about how quickly info comes into mind about particular event to deduce frequency/likelihood in the future
  • associative bonds = strengthened via repetition
  • AH exploits inverse law form aka. uses association strength as frequency judgement basis
  • BUT… not always this simple
21
Q

BIASED ESTIMATES REASONS

A
  1. Not always about frequency ie. familiarity/salience
  2. Personal experiences w/things occurring frequently may be idiosyncratic
22
Q

AVAILABILITY: EMPIRICAL EXAMPLE

A

TVERSKY & KAHNEMAN (1973)
- pps memories famous names list
- conditions = either men/women are more famous
- some asked to judge how many men/women in each list (equal numbers); others asked to recall names
RESULTS: gender w/^ famous names = ^ frequent (pps recalled 50% ^ of it)
- fame made names salient -> easier recall -> frequency overestimation of the group

23
Q

WHY DO WE SUMBIT TO AVAILABILITY HEURISTIC?

A
  • assumption of exemplar volume (content) correlates w/retrieval ease
  • OR we feel that if info = easy in mind -> it must say how frequent it is
    SCHWARZ ET AL (1991)
  • pps recall 6/12 assertive behs
  • judge own assertiveness
  • pps recalling 6 assertive examples = ^ assertive rating > 12 examples
  • AKA. feeling of difficulty/ease of retrieval (SYSTEM 1) matters = numbers/content (SYSTEM 2)
24
Q

ANCHROING/ADJUSTEMENT

A
  • when making judgements under uncertainty you can reduce ambiguity by starting w/anchor
  • EG: how many handouts should I print out?
  • last time = 75% attendance BUT next tutorial = exam tips… 100% this time?
  • we do this w/people too (correspondence bias)
25
Q

WHEEL OF FORTUNE

A
  • pps stood in front of wheel of fortune marked 1-100; asked to spin
  • wheel rigged to stop at 10/65
  • pps asked: what is best guess at percentage of African Nations in UN?
  • 10 mean estimates = 25%
  • 65 mean estimates = 45%
  • aka. anchoring heuristic = most robust/reliable results in experimental psychology
26
Q

ANCHORING/ADJUSTEMENT HEURISTIC

A
  • anchoring/adjustment heuristic = cognitive heuristic making us place weight upon initial standards/schemas (anchors) -> we may not always adjust sufficiently far from anchors to reach accurate judgements
27
Q

ADJUSTEMENTT HEURISTIC: EMPIRIAL EXAMPLE

A

ENGLICH ET AL (2006)
- participating legal experts shown realistic case materials involving alleged rape case; asked to provide sentencing decision
- pps received 1/3 anchors:
1. irrelevant source (journalist)
2. randomly chosen anchor
3. pps randomly decided on anchor themselves via dice toss
- anchor high (3y)/low (1y) for each case
- all 3 conditions = anchor constrained sentencing decisions

28
Q

ADJUSTEMENT HEURISTIC: WHY?

A
  • TVERSKY & KAHNEMAN didn’t agree here:
    TVERSKY
  • traditional view
  • deliberate BUT insufficient attempt to adjust from irrelevant value providing anchor
  • System 2
    KAHNEMAN
  • anchoring occurs via priming
  • System 1
  • OVERALL = both probably right in some ways
29
Q

ADJUSTEMENT = EFFORTFUL

A
  • AKA. System 2
    EPLEY & GILOVICH (2006)
  • link w/correspondence bias
  • cognitive load -> people adjust less
  • insufficient adjustment = failure of weak/insufficient System 2
30
Q

ANCHORING = PRIMING EFFECT

A
  • AKA. System 1
    MUSSWEILER & STRACK (2000)
  • anchor primes associated concept in memory
  • “is annual mean temp in Germany ^/lower than 20C/5C?”
  • 20C = easier recognition of sun/beach
  • 5C = easier recognition of frost/ski
31
Q

! SUMMARY !

A
  • OVERALL: when faced w/difficult qs we oft substitute easier qs to answer
  • we fall for System 1 thinking
    LAW OF NUMBERS
  • fail to take into account sample size
  • seek causality for random event
    REPRESENTATIVENESS
  • probability judgement based on appearance
    AVAILABILITY
  • judgement based on how easily it comes to mind
    ADJUSTMENT/ANCHORING
  • estimate judgement via amending initial base valiue

Social Psychology II (18 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2024 Bold Learning Solutions. Terms and Conditions