Thinking

Question 1

THINKING

Answer 1

INDICTIVE REASONING
- predicting future via past data/making judgements
- stat generalisations/probabilistic judgements/predictions
- hypothesis testing/rule induction
DEDUCTIVE REASONING
- solving logical/mathematical problems w/right answers
- based on grounds/given facts/generating valid conclusions/evaluating ones validity
PROBLEM SOLVING
- how to get from A-B
- numerous solutions/varying constraint degrees
JUDGEMENT/DECISION MAKING
- choosing among options
CREATIVE THINKING
- daydreaming/imagining

Question 2

THINKING RESEARCH

Answer 2

• focused primarily where there’s:
• right answer/way of evaluating answer rationality/efficiency of getting to answer
• asks HOW people think (processes/representations)
• human imperfection emphasis (why irrationality/efficiency limits relative to ideal)
• practical focus motivation:
• practical false medical/legal/military importance
• improvements via training/IT support
• attempts changing behaviour

Question 3

GENERAL DUAL-PROCESS THEORY: SYSTEM 1

Answer 3

• intuitive/automatic/unconscious/quick & dirty/approximate BUT domain-specific
• procedures/schemas/heuristics are:
• adaptive/effective when applied in appropriate domain (otherwise error)
• only approximate w/some built in biases

Question 4

GENERAL DUAL-PROCESS THEORY: SYSTEM 2

Answer 4

• slow/sequential/effortful BUT…
• logical/rational/conscious reasoning system
• allocates attention to demanding effortful mental activities
• constrained via limited WM capacity/basic cognitive machinery limits

Question 5

SYSTEMS 1 + 2 COMBINED

Answer 5

• effortful S2 = depleted; self-control/cognitive effort = mental work
• if cognitively busy, S1 = ^ beh influence/^ temptation following (ie. selfish choices/superficial social judgements)

Question 6

INDUCTIVE REASONING

Answer 6

• illustrate basic cognitive machinery properties
• limit optimal reasoning strategies
• introduce biases:
• difficulty attending relevant info w/salient/irrelevant info available
• limited WM capacity
• LTM retrieval properties
• shifting mental set/perspective difficulties

Question 7

DEDUCTIVE REASONING

Answer 7

• logical reasoning w/quantifiers (some/not/all) = reasoning via imagining concrete examples (mental models) rather than abstract/general logical operations
• illustrated WM limit impacts
• Wason’s 4 card deductive reasoning test w/if-then propositions = explains performance effects on problem content/characteristic error nature in domain-specific heuristics

Question 8

PROBABILITY/FREQUENCY JUDGEMENTS

Answer 8

• some frequency facts = told to us/searchable (ie. lifetime morbid schizophrenia risk = 0.7%)
• BUT many based on experience (ie. will it rain today?)

Question 9

MEMORY AVAILABILITY

Answer 9

TVERSKY & KAHNEMAN (1973)

• availability heuristic = judge as ^ probable/frequent events/objects ^ readily available in memory/environment
• works as generally easier to retrieve event/object memory that’s frequent
• retrievability also affected via: recency/salience/current case similarity
• over-estimate event probability where examples = easily retrievable AKA…
• availability bias = recent/personally salient/presently similar examples

Question 10

AVAILABILITY BIAS EXAMPLES

Answer 10

• Cape Cod “Jaws” screening = drop in California coast swimming
• seeing accident/police = drivers slow (for a while)
  SLOVIC et al (1980)
• people overestimate dying of rare BUT dramatic/reported causes (ie. floods/tornadoes/measles)
• underestimate dying of common causes (ie. strokes/cancers/diabetes)
• aka. fear of British kids being run over > ran over

Question 11

REPRESENTATIVENESS BIAS/BASE RATE NEGLECT

Answer 11

• when evaluating particular cases:
• tend to ignore important info source aka. base knowledge rate = particular event classes overall frequencies (ie. is someone = X features, we think they have X’s standard properties)
• possible best guess basis for category member w/o other info BUT biases prototypical property attribution even w/other info

Question 12

REPRESENTATIVENESS BIAS EXAMPLE

Answer 12

TVERSKY & KAHNEMAN (1973)

• 100 descriptions (70 = lawyers; 30 = engineers; ie. kids/hobbies/age/motivations); pp asked engineer prob
• > 90% went w/older/conservatism/maths hobbies etc.

Question 13

REPRESENTATIVENESS BIAS X SEQUENTIAL EXENTS

Answer 13

• head prob x11 in row = same as any as coin = no memory; expecting non-representativeness redressing = gambler’s fallacy
• difficulty ignoring sequence representativeness/unusualness or focusing on known individual event probs

Question 14

FUNCTIONAL FIXEDNESS

Answer 14

DUNCKER et al (1945)

• classic Gestalt psychologists exps
• pps asked to support lighted candle on vertical wooden wall w/nails/matches etc
• < successful than pps w/same prob BUT pins tipped out of box

Question 15

CONSERVATISM X CONFIRMATION BIAS IN INDUCTIVE REASONING

Answer 15

• IRL/research we want rules/principles describing experienced instances/testing hypothesised rules VS further observations
  WASON (1960)
• 2-4-6 seq generated rule; pp tries to guess via new 3n sequences w/positive/negative feedback (ie. fits rule/doesn’t then declares rule hypothesis)
• tend to offer over-specific hypotheses (ie. n+2)/conservatively reluctant to abandon them/seek confirmatory VS disconfirmatory evidence
• scientists equally prone

Question 16

PROBLEM SOLVING

Answer 16

• studies = situations w/start/goal states; fast goal achievement via available operators subject to certain constraints (ie. missionaries/cannibals/Hanoi’s tower)
  LUCHIN’S WATER JUG
• start w/full 8 pint jug/empty 5 pint jug/empty 3 pint jug
• end w/4 pints of water in largest jug

Question 17

THE “PROBLEM SPACE”

Answer 17

NEWELL & SIMON (1972)

• soluble problem = at least 1 path through state space between start/goal
• problem solver must search w/o knowing advanced optimal path/traversable intermediate states for operators that:
• move them through intermediate states towards goal
• avoid dead-end backing up/going in circles
• minimise path

Question 18

WM CAPACITY/HEURISTICS X PROBLEM SOLVING

Answer 18

• huge workplace + time = exhaustively enumerate all possible legal moves/pick shortest path (ie. Big Blue playing chess) BUT illegitimate WM capacity SO…
• recognise familiar patterns/retrieve previous effective moves via LTM (ie. Kasparov’s chess)
• hunt through initial/goal states in small steps via heuristics (ie. mean-end analysis/don’t repeat moves)
  MEAN-END ANALYSIS
• pick general means of goal reaching; unavailable = create sub-goal of achieving availability means till 1 generates satisfiable sub-goal via available operator
• requires WM goal stack maintenance

Question 19

DESIGN LIMITS INTRINSIC TO COGNITIVE MACHINERY

Answer 19

• design limits (in cog caps (memory retrieval properties/limited WM/relevant info attendance difficulty/cognitive set shift difficulty/sequential reasoning effortfulness difficulty) =
• heuristic reliance (approx rules of thumb)
• intrinsic biases when heuristics applied

Question 20

MENTAL MODELS X SYLLOGISTIC REASONING

Answer 20

JOHNSON-LAIRD et al

• we DON’T reason w/formal logic mental version
• given premises we imagine +1 possible concrete worlds where premise = true (mental models)
• then generate conclusion/determine if offered conclusion = valid via mental model examination
• errors arise via: failure of generating all possible premise mental models/WM capacity lack for multiple model maintenance
• only 1st model construction matches conclusion = think inference valid BUT untrue
• 2nd model also describes affairs consistent w/premises where conclusion = false

Question 21

ILLUSORY CORRELATION

Answer 21

IMAGINE…

• doc investigates disease presenting certain symptoms BUT present w/other diseases
• particular S in 80%; concludes S = good D predictor
• WRONG! must compare D frequency w/S VS w/o S
• 40/50 w/o S = D = confirmation?
• WRONG! must consider all cases w/S+D VS w/o (aka S+D/S-D/D-S/neither)
• 4/5 patients develop D regardless of S or w/o
• wrong conclusion = availability bias; patients w/o S/D = < striking than those who do

Question 22

ABSTRACT VS CONCRETE REASONING

Answer 22

• concrete scenarios imagined assisting reasoning/mental models BUT…
• ability limited via WM representational capacity
• may fail all possible scenario consideration
• more available instances = easier to access

Question 23

TROUBLE W/CONDITIONAL PROPOSITIONS

Answer 23

WASON’S 4-CARD PROBLEM

• all cards w/letter on 1 side; number on other
• RULE = if card had vowel, then odd number on other side; which cards flipped to check if true?
• pps choose A (as should) BUT 1 VS 2 too
• A+2 correct why? logical = if P then Q, only combo inconsistent w/rule = P+ NOT!Q so those (A+2) cards; BUT this says nothing about “if Q…” so no point checking 1

Question 24

ARE PEOPLE JUST ILLOGICAL?

Answer 24

JOHNSON-LAIRD (1972)
- NO; changing problem content/context w/o changing formal structure = dramatic performance improvements
CHENG & HOLYOAK (1985)
- formally identical prob w/form w/transit/entering on 1 side then disease list on other; check rule observance: “if the form has “entering” on 1 side, then the other includes cholera amongst diseases”
- half pps given rationale (mere transit passengers don’t need cholera inoculation; visitors do); other told to check for tropical diseases
- w/o = poor 60% perf; w/ = well 90%
- SO if given/familiar w/social rules/permissions rule context, performance = good

Question 25

DOMANI-SPECIFIC "IF-THEN" DEONTIC REASONING

Answer 25

- questions why performance ^ w/concrete contexts CHENG & HOLYOAK - successful conditions engage familiar permission schema for social rules (what ought to happen aka. IF YOU WANT P, THEN YOU MUST Q) - deontic "if-then" = same truth conditions as logical

Question 26

DOMAIN-SPECIFIC "IF-THEN" CAUSAL REASONING

Answer 26

OAKSFORD & CHATER (1994) - why characteristic error made (ie. Q alternative in abstract problem versions)? - pps choices = rational under causal/probabilistic "if-then"; NOT same truth conditions as logical IT (ie. clouds cause rain = IF ran, THEN clouds) - if proposition interpreted as causal/correlational claim = clouds imply rain prob - SO IF clouds, reasonable to check for rain to collect info about relationship strength

Question 27

SUMMARY: WHY IS THINKING ERROR-PRONE?

Answer 27

- "design limits" in cognitive caps (memory retrieval properties/limited WM/difficulty attending relevant info/difficulty shifting cognitive sets) = - heuristic reliance (approx rules of thumb) - intrinsic biases applying heuristics - habit of reasoning w/concrete mental models coupled w/failure/inability to generate/represent all possible mental models - "capture" of reasoning via relatively automatic domain-specific heuristics = - adaptive in right contexts BUT... - inappropriate for cases at hand