1 Decision Making On the Reality of Cognitive Illusions (Kahneman & Tversky, 1996) On Narrow Norms...

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Decision MakingOn the Reality of Cognitive Illusions (Kahneman &

Tversky, 1996)

On Narrow Norms and Vague Heuristics: A Reply to Kahneman and Tversky (Gigerenzer, 1996)

A reexamination of the evidence for the somatic marker hypothesis: What participants really know in the Iowa gambling task (Maia & McClelland, 2004)

Expectations and outcomes: Decision-making in the primate brain (McCoy & Platt, 2005)

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Kahneman & Tversky (1996)

VS.

Gigerenzer (1996)

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On the Reality of Cognitive Illusions

(Kahneman & Tversky, 1996)

• Judgmental heuristics– Useful BUT sometimes lead to errors

or biases

• Main goal of studying heuristics and biases : understand the cognitive processes that produce valid/invalid judgments.

• Criticism : portrays human mind in an overly negative light

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Base-Rate Neglect

• Base rate that is known to subject, at least approximately, is ignored or significantly underweighted

– Due to use of representativeness heuristics.

– Representativeness: “Fit” or similiarityHe must be a business man

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Base-Rate Neglect

• Diagnosing whether a patient has rare (25%) or common (75%) disease (Bluck & Bower, 1988)

– Judgments of relative frequency of the two diseases were determined entirely by diagnosticity of symptom, with no regard for base-rate frequencies of the diseases

– K & T : Our mind is not a frequency monitoring device!

– G : Misinterpretation of the original study (“base-rate info is not ignored, only underused”)

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Base-Rate Neglect

• Outcome Ranking paradigm

• Give case data about person, ask subject to rank outcomes (e.g., occupations) by different criteria (e.g., representativeness, probability, base rate)

– Rankings by representativeness and by probability were nearly identical

– K & T : The role of representativeness in prediction is critical (underusing base-rate).

– G : Use of random sampling makes this disappear.

• → K & T: No!

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Conjunction Errors

• If A includes B then the probability of B cannot exceed the probability of A; A B implies P(A) ≥ P(B)

– Conjunction Rule: P(A & B) ≤ P(A)

– Because representativeness and availability are not constrained by this rule, violations are expected in situations where a conjunction is more representative or available than one of its components

∩

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Conjunction Errors

• “Linda” Experiment

• Imagine a young woman, named Linda, who resembles a feminist, but not a bank teller

• Which is more probable?

– (a) Linda is a bank teller

– (b) Linda is a bank teller who is active in the feminist movement

– K & T : Although (a) is more likely than (b), judgment based on representativeness results in conjunction error.

– G : Content-blind!

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Conjunction Errors

• “Linda” Experiment

– K & T : Although (a) is more likely than (b), judgment based on representativeness results in conjunction error.

– G : Content-blind!

– Sound reasoning starts w/ investigating content of problem

• What is 'probable' ??• The meaning of AND in this context

– According to K&T, content of problem is irrelevant and only terms probable and and is important.

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Conjunction Errors

How detection of inclusion and appreciation of its significance affect conjunction errors

• Estimating frequency of each category facilitates detection of inclusion– e.g., estimate # of bank tellers & # of feminist bank tellers

• → According to K & T : There are conditions under which the correct answer is made transparent, but the phenomenon is still very interesting!

• → According to G : Why is this?? What is the underlying process that makes the correct answer transparent? (critique about vague heuristics)

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OverconfidenceMean confidence exceeds overall accuracy• Which city has the larger population?

• (a) Tokyo (b) New York

• How confident are you? (e.g., 0% – 100%)

Illusion of validity• Average confidence for single items VS.

estimates of the percentage of items they answered correctly– Inside view (single-case approach) VS Outside

view (frequentistic approach) • Does not contradict K&T's theoretical

position: Diff perspectives yield diff estimates

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Critiques of heuristics & biases

Ex) Representativeness remain vague, undefined, and unspecified with respect to conditions that elicit them and to the cognitive processes that underlie them.

<Narrow Norms>• Problems for “Linda” example: (1) prob. theory

is imposed as a norm for single event (e.g., whether Linda is a bank teller) (2) norm is applied in content-blind way

<Vague Heuristics> (1) One-word-labels traded as explanations

(2) explanation by redescription

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A reexamination of the evidence for the somatic marker hypothesis (Maia & McClelland,

2004)

Somatic Marker Hypothesis (Bechara, Damasio, & Damasio, 2000)

• Affective somatic states associated with prior decision outcomes are used to guide future decisions

• Optimal decision making is not simply result of rational, cognitive calculation of gains & losses but based on good/bad emotional reactions to prior outcomes of choices (Hinson, Jameson, Whitney, 2002)

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2004)

Iowa Gambling Task (IGT)• Task: select a card from 4 decks on each trial

with a goal to have the best possible net outcome

• Two $100 decks and two $50 decks

– $100 decks: longterm net loss (avg of $25/trial)

– $50 decks: longterm net gain (avg of $25/trial)

• Provide evidence for somatic marker hypothesis

– e.g., skin-conductance response

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2004)

Problems w/ previous studies using IGT• Prior studies (Bechera et al, 1997) asked

subjects about explicit knowledge w/ questions such as “tell me all you know about what is going on in this game”

– Open-ended questions often fail to identify all of conscious knoweldge. (MAYBE THEY KNOW BUT THEIR KNOWLEDGE WASN'T ASSESSED PROPERLY)

• “Good Deck” VS “Bad Deck”

– Good and bad decks should be determined on each trial based on mean net outcomes a subject has had w/ each deck up until that trial

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2004)

• Defining levels of conscious knowledge

– Level 0. No conscious knowledge for preference

– Level 1. Conscious knowledge specifying pref. for one of two best decks but no conscious knowledge about outcomes of decks

– Level 2. Conscious knowledge specifying a pref. & about outcomes

• Question: Do individuals behave advantageously even when they are at Level 0 or 1?

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Knowledge at

Level 1→ Conscious knowledge

for preference

Knowledge at Level 2

→ Conscious knowledge of outcomes

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2004)

• All of the verbal report measures demonstrate knowledge of the advantageous strategy for the majority of participants

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2004)

• Participants were at least at Level 1 of knowledge when they behaved advantageously• Vast majority of cases, participants have both level 1 and level 2 knowledge.

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2004)

• Seems that people know what they're doing in IGT.

– Knowledge may be sufficient to explain behavior in IGT

• Possibility that SCRs reflect emotional responses elicited by knowledge that is consciously accessible

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Expectations and outcomes: decision-making in the primate brain (McCoy & Plan,

2005)

Expected Value Theory

→ choose option w/ highest expected value (probability x expected reward)

• Humans/animals are sensitive to expected value of available options

– → suggests that nervous systems somehow represent info about estimated costs and benefits of potential behav. to link sensation to action

• Stages of Oculomotor decision-making

Sensation → reward expectation → action → outcome evaluation

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2005)

Expected value and primate parietal cortex

• The role of Lateral Intraparietal area (LIT)

– When monkeys were permitted to freely choose between two eye movements (looking up VS down)

• modulated by both the size of reward and the probability of reinforcement.


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Pattern of target choices was

sensitive to the amount of fruit juice associated w/ each

target

LIP neuronal activity was also sensitive to expected value

High Value Trials

Low Value Trials

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2005)

Expected value and primate cortex

• Dorsolateral prefrontal cortex, supplementary eye fields, substantia nigra pars reticulata, superior colluculus

– Expected value systematically biases neuronal activity throughout the cortical and subcortical oculomotor afferents to the superior colliculus

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2005)

Learning from mistakes: dopamine and prediction error

• Expected value of eye movements is rapidly updated in primate brain when reward contingencies change

• Reward prediction error

– comparison of expected and actual reward– dopamine neurons in substantia nigra &

ventral tegmental area: elevated by delivery of unpredicted rewards, unchanged following predicted reward, depressed when expected rewards are withheld. → these neurons encode reward prediction error, thus determining the direction and rate of learning


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Monkeys rapidly learn to choose the high

value target

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2005)

Updating expectations: posterior cingulate cortex

• Neurons in posterior cingulate cortex (CGp)

– carries info about the predicted and experienced reward value of eye movement

– reward modulation of neuronal activity in CGp: When reward differs from expectation, learning occurs (useful for instructing “when” & “how rapidly” learning should occur)

• Role of cingulate cortex in linking motivational outcomes to action


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unrewarded trials (when reward was expected)

rewarded trials (when reward was expected)

Monkeys change the strategy of their behavioral response in order to maximize their receipt of reward- Cingulate cortex and supplementary eye field plays an important role.

1 Decision Making On the Reality of Cognitive Illusions (Kahneman & Tversky, 1996) On Narrow Norms...

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