Reasoning and decision-making Flashcards
Heuristics and biases (state)
- Availability
- how easy it is to bring instance to mind
- Representativeness
- based on similarity
- Anchoring
- assimilation of numeric estimate towards another value
Availability (heuristic)
Can be biased if experience doesn’t = true frequencies OR if ease to recall based on something other than frequency
Lichtenstein et al (1978) - estimate frequencies of causes of death
- overestimating low frequency events, underestimate high frequency events (e.g. heart disease)
- rare events receive disproportionate attention –> greater availability
Tversky & Kahneman (1973) –> recalled more famous names (more available)
- 12.3 famous recall vs 8.4 not famous /20
- conjunction fallacy: ease of recall bias: belief that conjunction of 2 events can have higher probability than either event individually
Tversky & Kahneman (1983) –> 4 page novel - number of -ing words vs number of -n- words
- 13.4 for -ing
- 4.7 for -n-
- even though -ing is a subset of -n-
Representativeness (heuristic)
Base rate neglect = similarity-based judgements are insensitive to prior probabilities
Kahneman & Tversky (1973)
- told profile pulled out of 70% engineer pool OR 70% lawyer pool
- probability of people saying he’s an engineer doesn’t change relative to pool
- based on prototype (similarity) instead
- given profile, list of 9 academic subjects
- told to judge:
- rank subjects by likelihood they specialise in it
- rank how typical they are of each group
- estimate proportion of all people that study it
- negative correlation for base rate estimation
- % of all people AND likelihood they specialise
- positive correlation for representativeness
- how typical + how likely
Anchoring (heuristic)
DEMONSTRATING
- Tversky & Kahneman (1974) –> wheel of fortune (10 vs 65)
- Chapman & Johnson (1999) - last 2 digits of SSN = anchor %
EXPLAINING
- Anchor + adjust –> start estimation from anchor + adjust (effortful cognitive work)
- BUT: you’d expect incentives would help people do better –> not the case
- Epley & Gilovich (2005)
- BUT: you’d expect incentives would help people do better –> not the case
- Anchor value changes our perception of magnitude of other candidate values
- Frederick & Mochon (2011)
- Externally presented anchors seen as a hint/suggestion even if uninformative
- participation bias?
Ecological rationality (state)
- mistakes made
Evolved or based on experience interacting with world
- Natural frequency formats = better
- Gambler’s fallacy
- Hot hand fallacy
- Representativeness
- Memory constraints
National frequency formats (ecological rationality)
Eddy (1982)
- medical professionals given probabilities of breast cancer given mammograms with FB rates
- 95/100 gave the wrong answer
- inverse fallacy: probability of cancer given a positive test = the probability of a positive test given cancer
- only 8% got it right
- people evolved to register counts not abstract percentages
- NB: bayes theorem would get right answer
- alter estimates based on new evidence + background information
- 95/100 gave the wrong answer
- Hoffrage & Gigerenzer (1998): eg above in natural frequency formats
- out of Z number of people, X have it and Y don’t
- 46% get it right
- we are better at understanding natural frequencies - makes more sense to us
- evolved to keep track of event frequencies by natural sampling (in this case, no need to take into account base-rate information)
Fallacies:
- gambler’s fallacy
- hot-hand fallacy
- memory constraints
Gambler’s fallacy = when random process throws up a streak of the same outcome, there will be a correction for it
- Croson & Sundali (2005) - witnessed in Nevada casino when streak of 4+ the same, people likely to bet against it
- Tversky & Kahneman (1971) - people expect local sequences to have properties of large sequences:
- in which case HTHTHT would be more likely than HHHHTTTT
Hot hand fallacy = infer streak is not representative of randomness - so streak will continue
- Gilovich et al (1985) - number of baskets scored in a row - believe if on a streak, they’ll make the next one
- because streak is not representative of randomness, they think it is not random + that it will continue
Ayton & Fischer (2004) - roulette style game:
- probability of predicting same as last time decreases as streak increases (Gambler’s)
- confidence in predicting skill increases as streak of successful predictions increases (Hot hand)
- SO: people take into account previous experience, intentional human performance + positive recency
- people view random mechanical outcomes as sampling without replacement (where odds DO decrease - for Gambler’s)
A&F (2004) –> 2 possible outcomes with different alteration rates
- if low AR - more likely to say it was basketball shots
- if high AR - more likely to say it was coin toss
- consistent with sampling without replacement + skill improvement
BUT: memory constraints
- Hahn + Warren (2009) - looked at mathematical properties of fair coin under realistic conditions
- there is a higher likelihood of high AR sequence for short/finite sequences
- these are the ones people see + remember
- so it is rational for people to expect T after long streak of H
- there is a higher likelihood of high AR sequence for short/finite sequences
Why do people make mistakes on reasoning (about syllogisms OR propositional reasoning)
- Heuristics
- Comprehension
- Mental models
- Framing and experience
Syllogistic + propositional resoning
- what are they
- common mistakes
syllogisms + propositional reasoning –> 2 kinds of deductive reasoning
Syllogism = 2 premises then conclusion involving quantifiers: all, no, some, some…not
- All A are B, All B are C –> so All A are C
- 88% correct (Robert & Sykes, 2005)
- All B are A, all B are C –> so some A are C
- 8% correct (Robert & Sykes, 2005)
Propositional reasoning: about propositions containing conditionals (if, and, not, or):
- Schroyens et al (2001): ‘if A then B’ rule
- modus ponens (97% correct) = it’s A..so it’s B
- modus tollens (74%) = it’s not B..so not A
- affirmation of the consequent (64% commit it) = if B then A
- denial of the antecedent (56% commit it) = if not A then not B
Wason (1968) - 4 card selection task (D, K, 3, 7) –> rule = if D then 3 on other side
- only 1/34 turned over right cards to test rule (D+7)
Oaksford & Chater (1994) - rule = if p then q
- p=89%; not-p=62%; q=62%; not-q=25%
initial theory = confirmation bias - to show rule is true
Heuristics
- syllogisms
- propositional reasoning
SYLLOGISMS
- Atmosphere theory = people match ‘mood’ of premises to nood of conclusion
- quantity - can be universal (all/no) or particular (some, some not)
- quality - can be affirmative (all,some) or negative (no, some…not)
- Begg + Denny (1969) - 64 syllogisms, 4 possible conclusions (45/65 have no valid conclusion - how did people respond to them)
- when both premises were positive - 79% conclusions endorsed were positive
- when at least one premise was negative - 73% chosen conclusions were negative
- when both premises universal - 77% conclusions chosen were universal
- when at least one premise was particular - 90% chosen conclusions were particular
- BUT: doesn’t explain why sometimes people correctly endorse that there is no valid conclusion
PROPOSITIONAL REASONING
- Evans & Lynch (1973) 4 card task –> S, 9, G, 4: ‘if S then not-9’
- if confirmation bias - turn s and 4
- people turn S and 9
- Matching heuristic - turn cards mensioned in rule
Comprehension:
- syllogisms
- propositional reasoning
SYLLOGISMS:
- errors in syllogistic reasoning partly reflect use of language in formal logic vs every day use
- all A are B –> A=B
- some –> not all (but in logic can be all too)
- Ceraso & Provitera (1971) –> giving explanations/clarifying premises reduces error rates
- 58% with traditional format
- 94% correct with modified version
PROPOSITIONAL:
- Wagner-Egger (2007): bidirectional interpretation of ‘if’ (if A then B = if B then A) - common pattern of misunderstanding
- not an error or bias in selection task - just don’t understand rules
- Gebauer & Laming (1997) - the illogicality is only apparent, if people understand it they do fine
- BUT: doesn’t explain why people do worse on modus tollens than modus ponens
Mental models
- syllogisms
- propositional reasoning
SYLLOGISMS:
- Laird (2005):
- construct mental model of world implied by premises (comprehension)
- make a composite model + draw conclusion (description)
- validate by searching for alternative models and checking they don’t contradict conclusion (validation)
- more models = more likely conclusion right
- Copeland & Radvansky (2004):
- more possible models = less accurate + slower
- better working memory = more accurate + faster
- Newstead et al (1999) - no correlation between no. of models considered + accuracy
- people usually just construct 1 - multiple models are harder
PROPOSITIONAL
- initial model created relates to items mentioned in the rule
- explains why modus ponena easier than modus tollens
- can avoid affirmation of the consequent + denial of the antecedent if put in cognitive effort to flesh out models
Framing and experience
- syllogisms
- propositional reasoning
SYLLOGISMS:
- Evans et al (1983) –> if valid more likely to endorse it; if believable more likely to accept it
- belief bias = people don’t reason independently of experience or framing of problem
- Klaver et al (2000) –> base rates kick in when uncertain of conclusion - pushes reasoning after
- dual process framework
- if conclusion believable = only construct 1 model (consistent - verification
- if conclusion unbelievable = look for inconsistent models (tto falsify)
PROPOSITIONAL
- Giggs & Cox (1982) - beer, coke, 16, 25
- ‘if beer then over 19’
- if abstract - 0% correct
- if in context - 73% correct
- due to prior experience with rule
- BUT: Cosmides (1989) - even if no experience, context helps (due to social contract violation sensitivity)
- “If a man eats cassava root, then he must have a tattoo on his face” – still chance
- If give extra information – that cassava root eating is privilege of married men (married men have the tattoo) then performance increases to 75%
- BUT: Manktelow & Over (1990)
- “If you clear up spilt blood, then you must wear rubber gloves” (75% correct)
- but this is not a cost-benefit situation or social contract
- due to relevance/expected utility
- Girotto et al (2001) –> if going to X, must be vaccinated
- 62% correct –> going to X, not immunised (rule-violation sensitivity)
- if boss worried they made a mistake - don’t actually need it
- 71% check going to X and immunised
- information relevant to problem
Kinds of decision making
- Riskless multi-attribute choice
- 2+ options differing on 2+ attributes, no probability
- Intertemporal choice
- choosing between options available at different points in time
- Decisions under uncertainty
- don’t know in advance what the outcomes are (don’t know the probabilities)
- Decisions under risk
- 1+ of the possible outcomes are probabilistic (know the probability)
- THESE ARE THE ONES WE LOOK AT
Rational choice theory:
- expected value
- BUT issues
- expected utility theory
- BUT issues
EXPECTED VALUE
The EV of an option = sum of each possible outcome weighted by its probability
BUT: K+T (1979) - people risk averse for gains
- £3000 for sure over 80% chance of £4000
EXPECTED UTILITY
Transform actual value to subjective value
- explains risk aversion –> decreasing utility for higher rewards, less sensitive to increasingly large gains
BUT:
- people make decisions with respect to a starting point
- preference reversal –> risky when losses not gains –> EU only looks at end states (K+T, 1979)
- people weigh losses + gains differnetly - framing of the question alters choice (T+K, 1981)
Prospect theory
- explaining actual (seemingly irrational) behaviour of people
- the value function
- the decision weights function

K+T (1979):
- reference-dependence –> outcomes considered as gains/losses with respect to reference point
- risk attitudes –> risk averse for gains (decreasing sensitivity - concave); risk-seeking for losses (decreasing sensitivity - convex)
- Loss aversion - loss curve steeper than gain curve
- ‘losses loom larger than gains’
- EVIDENCE:
- Endowment effect (Knetsch, 1989): give mug or hot chocolate (reference point) - keep or swap
- loss feels worse than gains even if equally desirable at the start
- Endowment effect (Knetsch, 1989): give mug or hot chocolate (reference point) - keep or swap
- Decision weights function –> maps objective probabilities onto decision weights (W x V)
- small probabilities are overweightted
- large probabilities are underweighted
- steeper around 0+1 (near certainty) - more significant –> certainty effect
- Editing - when considering options, people round small amounts + combine probabilities with the same outcomes

Probability
- when people don’t act rationally
- against expected utility theory
Allais’ paradox (K+T, 1979)
- people treat 0%–> 1% chance differently from 66%–>67%
- starting point makes a difference –> violates rationality + EU
Certainty effect (K+T, 1981)
- change in probability has a greater effect if going from point of certainty than midrange
- making one option certain distorts probability judgement
Non-linear probabilities (Gonzalez & Wu, 1999)
- 5–>10% more significant than 30–>35%
- 65–>70% less significant than 90–>95%
- probabilities treated differently even if just near certainty
Problems with prospect theory
Limited scope
- valuation vs choice (Lichtenstein & Slovic, 1971) - p-bet (high prob.), $-bet (high value)
- 93% of people who chose the p-bet sell $-bet for a higher amound (want p but $ more valuable)
- only 17% showed reversal in other direction
Attraction effect (asymmetrical dominance
- Ariely (2009) - online ($59.99), online + print ($125), print ($125)
- 84% chose O+P
- if P removed, 68% chose O
- violates core assuption of rational choice theory –> independence from irrelevant alternatives
- Wedell (1991) - preference reversal if decoy more similar - attracted to option that dominates decoy
Empirical problem –> differing probabilities change risk-taking behaviour
- Weber & Chapman (2005) - high probability - people risk averse, low probability - different vanishes - more risky
- prospect theory would expect same difference (between % choosing low/high stakes) no matter the probability
Purely descriptive –> lacks mechanism to explain how people arrive at decision
- POSSIBLE RESPONSE: decision by sampling
Decision by sampling (mechanism of prospect theory)
Stewart et al (2006)
- when people given a value/probability, they turn it into subjective value by comparing it to other values available in memory
- cognitive mechanism of decision making
- Looked at bank credits –> put small amounts in more frequently (easy to map objective –> subjective value based on freq. of encounter)
- Looked at bank debits –> spend small amounts more frequently (spending small amounts = more frequent than saving small amounts)
- put numerical value to everyday english phrases used for probabilities - what people thought they were objectively
- overweighting of small probabilities
- underweighting of large probabilitites
- steep around 1 + ) –> same as decision weight function

Basic emotions
- what are they?
- views
- yes/no?
Universal emotions - culturally-ubiquitous
- Darwin (1872) - categorical idea:
- anger/fear/sadness/disgust/enjoyment (across species)
- Ekman (1992) –> basic emotions have rapid onset, brief duration, unbidden occurrence, distinctive signs, physiological correlates
Dimensional view:
- Russel & Barrett (1998) : core affect –> arousal + valence (pleasant/unpleasant) dimensions (high to low)
NOT universal (Gendron et al., 2018)
- depends on culture + definition of emotion
- 1975-2008 –> moderate-strong evidence for universal expression of emotions
- 2008-2018 –> no strong, 2 moderate, 9 weak evidence
Physiology of emotions
- for and against specific physiology
James-Lange view (James 1884; Lange 1885):
- stimulus –> percept –> physiology –> emotion
- emotion = product of somatic/physiological change
NO:
- Cannon (1927) - people without peripheral inputs still experience emotion
- peripheral arousal doesn’t create emotion
- peripheral states no sufficiently differentiated per emotion
- Siegel et al (2018) –> metaanalysis - prediction of emotion from physiology = 31-32% correct
- almost the same as if angry were guessed for all
Role of cognition in emotion
Schachter (1964) the effect of somatic arousal depends on its attribution - how it’s interpreted given the social context
Schachter & Singer (1962) –> physiological arousal provides raw ingredients, cognition defines the emotion
- given ‘suproxin’ (adrenaline) + told side effects –> attribute arousal to the pill
- stooge not that effective (euphoric or angry)
- given suproxin + not told side effects –> attribute arousal to mood as result of stooge (euphoric –> positive mood; angry –> negative mood)
Zajonc et al (1980): rejects role of congition in emotion
- previously-encountered stimuli elicit more positive affect than novel do, even if no conscious awareness of having seen it
Scherer (1984) –> appraisal theorist –> cognitive appraisals underlying emotion need not be conscious
- various appraisal dimension shape emotion: certainty, control, responsibility of others, attention
Common ground of contemporary views of emotion
Scherer & Moors (2019) - review:
- multi-level appraisals –> cognitive components - evaluation of memories/event/stimuli
- physiology - physical responses in body
- action tendencies - propensity to behave in certain ways
- motor expression - facial expression, voice tone, body language, gestures
- component integration-experienced feeling –> subjectively what it’s like to feel an emotion
- no one-to-one mapping - all integrated

Amygdala lesions and emotion
- Blanchard & Blanchard (1972)
- reduced fear conditioning - decreased capacity to learn to be afraid of something
- Calder et al (1996)
- failure to recognise fear from face photos (mean = 4/10 vs control = 8.6/10)
- Adolphs et al (1997)
- decreased memory of emotional components of narrative (usually strongest)
- Hamann et al (1999)
- better encoding of emotional info = more excitation in amygdala
ventromedial prefrontal cortex damage and emotion
- Damasio et al (1990)
- no increased SCR for emotional stimuli with social significance
- Koenigs et al (2007)
- more likely to overcome an emotional response in moral dilemma - easy util.
- Anderson et al (2006)
- increased irritability + frustration, decreased emotionality - blunted affect
- Bechara et al (1994) - EVR
- normal reasoning ability but poor real world decision making
Amygdala + vmPFC damage and decision making
- IGT
Bechara et al (1994) - Iowa Gambing Task
- A + B - good decks (in long run), C + D - bad decks (in long run)
- controls learn to avoid C+D, patients do not –> controls ‘feel’ risky decks
Bechara et al (1996) - IGT + SCR
- when punished SCR increases a lot, when reward it increases a bit
- controls - high SCR before turning cards (anticipatory SCR), patients didn’t
Bechara et al (1999) - SCR before + after card
- controls - SCR after card (loss+reward), SCR before card (higher for risky)
- amygdala –> not much SCR before OR after card
- associates reward-punishment outcomes with stimulis
- vmPFC –> SCR after card, not much before
- summons association at point of decision-making
Bechara et al (1997) - what are people conscious of?
- pre-hunch stage:
- controls start generating SCR for bad decks (but not explicitly aware); patients don’t
- hunch stage
- controls have sense that C+D = risky, no knowledge or SCR for patients
- conceptual period
- controls could explicitly say what was going on (70% reached this), 50% patients did but still didn’t choose risky less often
- concluded
- nonconscious biases guide behaviour in controls before conscious knowledge does
- physiological arousal guides behaviour
explanation = somatic marker hypothesis
Somatic marker hypothesis
situation –> elicits emotion –> amygdala learns association –> vmPFC retrieves association –> vmPFC activates automic nervous system –> causes physiological arousal –> has biasing effect on reasoning –> affects decision making

Problems with somatic marker hypothesis
- We may not need somatic cues
- Heims et al (2005) - patients with pure autonomic failure
- physiology doesn’t change in respons to stress
- perform better than controls on IGT
- so lack of somatic signal doesn’t stop decision making
- Somatic cues may not signal outcomes
- Tomb et al (2002) - in IGT, bad decks had higher variability (big loss outcomes)
- if A+B are better AND have higher variability, the SCR increases more than for the bad decks
- so maybe physiological response encodes the variability of outcomes (not goodness)
- REPLY: Damasio et al (2002)- in Tomb’s version the anticipatory SCR of good decks does indicate goodness
- BUT then same signals for goodness AND badness
- no need to posit unconscious knowledge
- Maia & McClelland (2004) - B (1997):
- questions too vague, good/bad decks badly defined
- explicit knowledge is better than expected (18/20 knew from q1)
- behavioural data falls behind explicit knowledge
- still explore other decks even if they know
- they base knowledge on the cards thyey’ve experienced, not experimenters knowledge of good/bad decks
- alternative explanations of patient data
- Fellows & Farah (2005) - vmPFC have problems with reversal learning
- in IGT first few trials with no loss (bad decks)
- vmPFC learn association between risky decks + winning - this is hard to reverse
- so..shuffled trials (started from 9)
- vmPFC performed much better
- Rolls et al (1994) - vmPFC patients struggle with reversal learning
- Dunn, Dalgleish & Lawrence (2006)
- reversal learning could underpin poor IGT performance
- vmPFC patients may not care enough about negative eoutcomes to actively avoid them (apathy –> see Barrash et al 2000)
Intuitive Reasoning Task
- to replace IGT
Dunn et al (2010): choose deck, predict colour when turned over (50/50)
- decks are rigged - A+B have 60% chance of winning, C+D have 40% (not about experimenter knowledge or order)
- amount at stake same for A+C and B+D (so not about variability)
- addresses: reversal learning, deconfounds magnitude + goodness
- re-ran M+M’s 2004 conscious knowledge probing
- found they were NOT consciously aware
- supports somatic marker hypothesis
- found they were NOT consciously aware
- results:
- people choose better decks (A+B) more often
- SCR increases for C+D prior to choice (anticipatory SCR)
- HR decreases before choosing good decks (A+B)
- physiological response strongly predicts patient data
Why cost-benefit analysis is wrong
- behavioural economics
- heuristics
- emotion
Behavioural economics
- humans are fallible - don’t necessarily think rationally (Thaler & Sunstein, 2008)
Heuristic decision makinb
- in risky situations - use rules of thumb - instinctive (Tversky & Kahneman, 1974)
Emotional impulses
- overwhelm rational cognitions
- especially if short time-frame
- (Bensons & Sams, 2013)