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PNB 2XA3 > Week 11+12+13 (M4) > Flashcards

Week 11+12+13 (M4) Flashcards

1
Q

Is judgement solely dependent on formal logic?

A

No! That is idealized reasoning

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2
Q

Normative vs descriptive

A

Normative = ideal

Descriptive = reality

What people actually do is FAR from ideal reasoning

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3
Q

Inductive reasoning

+ top-down or bottom-up

A

Observations to general conclusion/theory

Bottom-up

specific to general

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4
Q

Deductive reasoning

A

General premise to conclusion about a specific case

Top down

General to specific

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5
Q

Availability heuristic

A

Judge frequencies of events (probabilities) using availability

  • relies on memory

ex. more n or ing words

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6
Q

What impacts availability heuristic

A

Imageable things
- increase likelihood estimates
- we fear what we perceive as uncontrollable

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7
Q

The Von Restorff effect + what heuristic

A

aka isolation effect or distinctiveness principle

  • memory is better for things that stand out; things that are distinctive, isolated, humorous, bizarre, etc.

availability bias

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8
Q

Anchoring bias + issue

A

Estimates of frequency - start from a point (ANCHOR) and move up or down

People even use a completely unreliable number as an anchor and go from there

Problem - hard to put aside original estimates - whether right or wrong, and even if subjects
know it is not reliable, will serve as an anchor

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9
Q

Heuristic

A

quick and easy way to judge something (not always accurate)

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10
Q

Representativeness heuristic + relates to…

A
  • assume homogeneity (that all members of a category are the same) and that each member of a category is representative of that category

stereotypes

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11
Q

Conjunction fallacy + what heuristic

bank teller

A

aka support theory

People use similarity to a prototypical example, rather than probability, as a basis for judgment

Representativeness

ex. bank teller fem mvmt lady

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12
Q

Sunk cost fallacy

A

“Throwing good money after bad”

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13
Q

The gambler’s fallacy

+ what heuristic

A

the belief that prior outcomes can
influence the outcomes of probabilistic
events

“due for a crash”

representativeness heuristic

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14
Q

Law of large numbers

A

things do tend to even out in the end, proportionally, over a LOT of trials. BUT, this does not extend to small samples

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15
Q

Reasoning from a single case to an entire population

A

AKA “The man who” argument
My grandma smoked every day but died in her sleep…

  • representativeness heuristic issue
  • assumption of homogeneity
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16
Q

Hindsight bias

A

People think after the fact that they would have known something before the fact when they really wouldn’t have. Turning vague statements into solid predictions after the fact.
- thought you were certain in the past

I knew it all along; I told you so; hindsight is 20/20

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17
Q

Hindsight bias - doctor example

A

The doctors in Group 2, who already knew the diagnosis, were told to IGNORE it, and to just do the task on the basis of the evidence at hand.

Results - Doctors who already knew the diagnosis assigned probabilities to that diagnosis THREE TIMES HIGHER than did the doctors who didn’t know the diagnosis.

Even though the actual diagnosis was the LEAST likely disease, given the symptoms.

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18
Q

The Better-than-average Effect
AKA: Illusory Superiority

A

People, on average, think that they are better
than average.

– Of course, it is impossible for everyone to be
better than average

– Exception is the clinically depressed

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19
Q

Covariation

A
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20
Q

Illusions of covariation

A

People project their own prior theories onto the data - see only the patterns you expect to see.

e.g. interpreting Rorschach tests

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21
Q

Data vs. theory driven covariation interpretations

A

Data-driven
- when participants
have NO prior
expectations or beliefs
about the things being
judged
- systematic
- conservative
(low estimates
unless correlation is
strong)

Theory-driven
- when participants DO
have prior expectations
or beliefs about the
things being judged
- inaccurate,
variable
- over-estimate

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22
Q

Confirmation bias - when detecting covariation

A

– People don’t look at all the evidence, just a
subset
– More attentive to evidence that CONFIRMS our
beliefs than to evidence that might falsify them

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23
Q

When detecting covariation issues with consulting your memory schemata

A

– helps to remember examples that fit your prior belief, don’t remember counter examples – leads
to availability effect for further reasoning

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24
Q

Base rate + diagnositc info

A

Edgar is a quiet man, who likes to read poetry. Is he more likely to be an English professor or a truck driver?

how DIAGNOSTIC is this? (what proportion
of English professors fit this description, and
what proportion of truck drivers fit this
description)
AND
what is the BASE RATE for English professors
and truck drivers

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25
Q

Base rate neglect

+ what heuristic do we use instead

A

we tend to use representativeness and ignore the base rate

You need to ask: Out of how many?

ex. the bank teller feminist thing

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26
Q

Bayes theorem base rates

A

…

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27
Q

Base rates + diagnostic info = which do we pick

A

If given JUST the base rates, people use them effectively.

If given JUST the diagnostic information, people use it (following categories, stereotypes, heuristics…)

BUT give BOTH base rates and diagnostic information, people tend to ignore the base rates

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28
Q

Dual system hypothesis

A

System 1 = heuristics
System 2 = less biased but more effortful

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29
Q

Data format

A
  • Framing effects
  • Triggering statistical knowledge

Training in statistical reasoning works

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30
Q

What is reasoning

A

Testing and adjusting our beliefs

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31
Q

Logical vs. realistic reasoning

A

Logic (ideal)
– Logical reasoning
– Rational thinking
– Utility theory

What people do
– Biases
– Heuristics
– Errors
– Irrational thinking
– Pragmatic reasoning

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32
Q

Issues with adjusting our beliefs

A
  • overconfidence
  • belief perseverance
  • confirmation bias
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33
Q

Overconfidence

A

Both experts and novices are more confident in their judgments than is justifiable

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34
Q

Belief perseverance

A

We don’t want to let go, even in the face of strong evidence

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35
Q

Confirmation bias

A

people test their hypotheses by choosing tests that confirm them, rather than tests that would disconfirm them

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36
Q

Categorical syllogism

A

A form of deductive
reasoning consisting of a major premise,
a minor premise, and a conclusion

either valid or invalid

ex. human mortality thing

37
Q

Conditional syllogisms

A
  • These begin with an “IF – THEN” structure
  • If P, then Q

ex. Modus Ponens and Modus Tollens are two
examples of conditional syllogisms

38
Q

Modus Ponens - valid

A

affirming the antecedent

Major premise: If antecedent, then consequent

Minor premise: antecedent

Conclusion: consequent

39
Q

Modus Tollens - valid

A

denying the consequent

Major premise: If antecedent, then consequent

Minor premise: NOT consequent

Conclusion: NOT antecedent

40
Q

Are people better at modus ponens or tollens

A

People are usually pretty good at modus ponens

Much worse at modus tollens – people find it harder to see why the conclusion follows

41
Q

Inverse error / denying the antecedent

A

invalid

If P, then Q
NOT P
= NOT Q

“If P” does not say anything about “If NOT P”

42
Q

Converse error / affirming the consequent

A

invalid:
If P, then Q
Q
= P

P may not be the only condition for Q

valid:
If AND ONLY IF P, then Q
Q
= P

bidirectional: P implies Q, and Q implies P

43
Q

Wason’s four card task

A

Each card has a letter on one side and a number on the
other side. Which cards must be turned over
to check the following rule?…

“If a card has a vowel on one side,
it must have an even number on the other side.”

flip A and 7

44
Q

Pragmatic reasoning schemata

A

General schemata rather than “if-then” rules

  • Permission
  • Cause and effect
45
Q

Are people good at syllogisms

A

No they’re terrible

46
Q

Belief bias

A
  • If people believe the conclusion, they tend to think that the argument is valid.
  • If people don’t believe the conclusion, they tend to think that the argument is invalid.
47
Q

Atmosphere errors

A
  • Some conclusions “seem” more appropriate, given the context.
  • If the premises use the word “all”, people are more likely to accept a conclusion that also uses the word “all.”
48
Q

Conversion errors

A

People often treat:
All A’s are B as equivalent to…
All B’s are A

People often treat: Some A’s are not B as equivalent to…
Some B’s are not A

49
Q

Improving logical thinking

A
  • Formal training in logic does not help much
  • Training in the use of reasoning schemata does help – triggers a pragmatic reasoning schema
50
Q

Utility Theory

A
  • What do you value?
  • How does each option
    move you towards what you want?
  • Every choice has costs and benefits

Maybe just pick the option that minimizes costs and maximizes benefits

51
Q

Utility theory - issues

A

Sometimes costs and benefits are really difficult to compare

ex. good porch vs. closer to bus line

Uncertainty
ex. possible that housemates might add another roommate next year

52
Q

Subjective utility theory

A

Figure out the SUBJECTIVE UTILITY of each factor
- What value does it have for YOU
- Subjective utility can also be negative (“disutility”)

To make a decision, sum the subjective utilities of each choice. Higher utility score wins.

53
Q

Utility theory calculation

A

Expected utility = (probability of outcome) x (utility of outcome)

probability times value

54
Q

The certainty effect

UT

A

issue with “ideal” utility theory

“A bird in the hand is worth two in the bush”

People prefer sure gains
aren’t “utility maximizers”

55
Q

Non-transitive relationships

A

non-transitive relationships can be worked on even though they are irrational according to UT

56
Q

Mental accounting

A

Would you drive 30 minutes across town to save $20 on a $10,000 car?

Would you drive 30 minutes across town to save $20 on a $100 vacuum cleaner?

57
Q

Psychological utility

A

isn’t the same as dollar value

e.g. the psychological difference between $0 and $10,000 is much larger than between $1,000,000 and $1,010,000

58
Q

Framing effects

A

If dealing with possible losses, decision makers are RISK SEEKING - maybe you could avoid the loss

If dealing with possible gains, decision makers are RISK AVERSE - hold on to what you have (c.f. certainty effect)

59
Q

Prospect theory

A

It’s more about value assigned to gains and losses
We are less willing to gamble with profits than losses
Choice varies by framing the problem as a gain or loss

60
Q

Loss aversion

A

Given a choice between avoiding loss and acquiring gains: Strongly prefer to avoid losses

61
Q

Sunk cost reasoning + UT

A

Don’t want to give up something you already own

According to Utility theory, it makes no sense to pay attention to sunk costs

62
Q

Rational vs. justifiable

A

Rational = max. utility

Justifiable = reason-based choice

63
Q

Satisficing rather than optimizing

A

make a choice that is good enough rather than the optimal decision

optimal decision may require more time or more effort than the decision is actually worth

64
Q

Regret theory

A

avoiding regret is a big motivation (might contribute to the certainty effect)

65
Q

Improvement over time

A

We like things to get better over time

ex. hand in cold water thing

66
Q

Prisoner’s dilemma + game theory vs. reality

A

Game theory (Nash Equilibrium): defecting is dominant
strategy because it has a slightly higher payoff.

Overall, the best outcome is mutual cooperation.
People show a systematic bias toward cooperation

67
Q

Tragedy of the commons

A

There is a community of shepherds whose sheep graze on a common ground. If each herder restrains the number of sheep feeding on the commons, then the commons will not become exhausted from overgrazing.

Rational choice: compete for the resource

Better than rational choice: cooperation to conserve the resource

68
Q

Problem solving

A
  • You are not always presented with choice between options
  • You frequently have to CREATE those options yourself
69
Q

Problem solving as a search

A
  • general problem solving method
  • search through a “problem space”

ex. like a maze

70
Q

Tower of Hanoi

A

number of steps 2^n - 1

n = number of disks

71
Q

Operations vs. problem space

A

operations: the relevant things you can do

problem space: the SET of all the relevant things you can do

72
Q

4 aspects of general problem solving methods

A
  1. Initial state:
    – knowledge and resources you already have
    – The “givens”
  2. Goal state:
    – Where do you want to end up?
  3. Operators:
    – tools and actions that can change your current state
    – Means of transforming conditions
  4. Path constraints:
    – limitations on what moves you can make
    – obstacles
73
Q

Brute force solutions

A

Lay out the entire problem space

  • go through each possible solution

takes way too much time

74
Q

Problem solving strategies (6)

A
  1. hill-climbing
  2. means-end analysis
  3. working backwards
  4. building mental models
  5. representation
  6. analogy
75
Q

Hill climbing heuristic + problem

A

always go uphill
- take one step closer to your goal

problem: sometimes solving problems requires that you take steps away from your goal

76
Q

Means-end analysis

A

What is the difference between my current state and my goal?

how to reduce that distance

breaks the problem into more easily solvable sub-problems

involves both forward and backward search

77
Q

Working backwards

A

start at a goal, work backwards from there

ex. water lillies covering lake

Generally, working backwards is helpful when the number of directions backward from the goal state is small, and the number of possible directions from the initial state forward is very large.

It can also provide a “new” perspective when you are stuck.

78
Q

Mental models and imagery

A

Sometimes, solving a problem requires finding a different REPRESENTATION of the problem

  • can use mental models to help reason out categorical syllogisms
    (picture it)
79
Q

Representation - the problem space

A
  • Solving a problem requires problem representation and problem execution

The wrong representation can interfere with analysis:
- Incomplete information
- Computational complexity
- Somethings easy to solve in one format are difficult to solve in another

80
Q

Analogy + issues

A
  • Ability to draw analogies is a central intellectual tool

Challenges:
- Spontaneous, uninstructed use of analogies seems to be quite rare
- A suitable analogy isn’t always obvious
- Sometimes you need to find one yourself = difficult
- The analogy has to connect with the deep structure of the problem, not just surface properties

ex. tumor

81
Q

Expertise

A
  • Memory advantage leads to problem solving advantage
  • Being able to see the larger structure also helps problem solving
  • Experts tend to think about the deep structure of problems more than novices do
  • Experts are better at knowing what information is irrelevant - not following so many blind alleys
  • Experts are also good at CHUNKING into meaningful bits
82
Q

Experts have…

A

MORE information
DIFFERENT information
- Knowledge of patterns (ex. Symptoms all connected to one disease), etc.
CROSS-REFERENCED information
- More connections linking information in memory
AUTOMATIZED procedures
- Autopilot frees up other mental resources

83
Q

Limits of expertise

A
  • Experts sometimes don’t remember details of problems very well
  • Understood for gist, forgot the rest
  • More likely to make INTRUSION errors (assumptions based on prior knowledge)
  • Experts are no better than novices outside of their domain
84
Q

Functional fixedness

A

Stuck or rigid in one way of thinking about an object, and what its function is

Ex. tack, match, candle task

85
Q

Problem solving set

A
  • Most problem spaces are huge - larger than we even realise
  • Don’t realise how huge they are because of the heuristics and problem solving sets we use
  • There are costs and benefits to problem solving sets
86
Q

Einstellung

A

“attitude” = general term for rigidity in problem solving

based on starting assumptions about a problem (helpful or harmful)

87
Q

Four elements of creative performance

A
  1. Preparation
  2. Incubation
  3. Illumination/insight
  4. Verification
88
Q

AHA moment

A
  • Might still be wrong
  • Even though you think you’re getting closer and closer
89
Q

What makes an idea or a person “creative”?

A

Divergent thinking
- Think of ideas that are not normally associated

Finding new connections between ideas
- Similar to divergent thinking
- Remote associations task …

Underlying idea - creativity is based on coming up
with new, unanticipated approaches to a problem

PNB 2XA3 (8 decks)

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