Week 11+12+13 (M4) Flashcards

(89 cards)

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
Base rate neglect | + what heuristic do we use instead
we tend to use representativeness and ignore the base rate You need to ask: Out of how many? ex. the bank teller feminist thing
26
Bayes theorem base rates
...
27
Base rates + diagnostic info = which do we pick
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
28
Dual system hypothesis
System 1 = heuristics System 2 = less biased but more effortful
29
Data format
- Framing effects - Triggering statistical knowledge Training in statistical reasoning works
30
What is reasoning
Testing and adjusting our beliefs
31
Logical vs. realistic reasoning
Logic (ideal) – Logical reasoning – Rational thinking – Utility theory What people do – Biases – Heuristics – Errors – Irrational thinking – Pragmatic reasoning
32
Issues with adjusting our beliefs
- overconfidence - belief perseverance - confirmation bias
33
Overconfidence
Both experts and novices are more confident in their judgments than is justifiable
34
Belief perseverance
We don’t want to let go, even in the face of strong evidence
35
Confirmation bias
people test their hypotheses by choosing tests that confirm them, rather than tests that would disconfirm them
36
Categorical syllogism
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
Conditional syllogisms
* These begin with an “IF – THEN” structure * If P, then Q ex. Modus Ponens and Modus Tollens are two examples of conditional syllogisms
38
Modus Ponens - valid
affirming the antecedent Major premise: If antecedent, then consequent Minor premise: antecedent Conclusion: consequent
39
Modus Tollens - valid
denying the consequent Major premise: If antecedent, then consequent Minor premise: NOT consequent Conclusion: NOT antecedent
40
Are people better at modus ponens or tollens
People are usually pretty good at modus ponens Much worse at modus tollens – people find it harder to see why the conclusion follows
41
Inverse error / denying the antecedent
invalid If P, then Q NOT P = NOT Q “If P” does not say anything about “If NOT P”
42
Converse error / affirming the consequent
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
Wason's four card task
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
Pragmatic reasoning schemata
General schemata rather than "if-then" rules - Permission - Cause and effect
45
Are people good at syllogisms
No they're terrible
46
Belief bias
- 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
Atmosphere errors
- 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
Conversion errors
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
Improving logical thinking
* Formal training in logic does not help much * Training in the use of reasoning schemata does help – triggers a pragmatic reasoning schema
50
Utility Theory
- 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
Utility theory - issues
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
Subjective utility theory
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
Utility theory calculation
Expected utility = (probability of outcome) x (utility of outcome) probability times value
54
The certainty effect | UT
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
Non-transitive relationships
non-transitive relationships can be worked on even though they are irrational according to UT
56
Mental accounting
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
Psychological utility
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
Framing effects
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
Prospect theory
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
Loss aversion
Given a choice between avoiding loss and acquiring gains: Strongly prefer to avoid losses
61
Sunk cost reasoning + UT
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
Rational vs. justifiable
Rational = max. utility Justifiable = reason-based choice
63
Satisficing rather than optimizing
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
Regret theory
avoiding regret is a big motivation (might contribute to the certainty effect)
65
Improvement over time
We like things to get better over time ex. hand in cold water thing
66
Prisoner's dilemma + game theory vs. reality
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
Tragedy of the commons
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
Problem solving
- You are not always presented with choice between options - You frequently have to CREATE those options yourself
69
Problem solving as a search
- general problem solving method - search through a "problem space" ex. like a maze
70
Tower of Hanoi
number of steps 2^n - 1 n = number of disks
71
Operations vs. problem space
operations: the relevant things you can do problem space: the SET of all the relevant things you can do
72
4 aspects of general problem solving methods
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
Brute force solutions
Lay out the entire problem space - go through each possible solution takes way too much time
74
Problem solving strategies (6)
1. hill-climbing 2. means-end analysis 3. working backwards 4. building mental models 5. representation 6. analogy
75
Hill climbing heuristic + problem
always go uphill - take one step closer to your goal problem: sometimes solving problems requires that you take steps away from your goal
76
Means-end analysis
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
Working backwards
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
Mental models and imagery
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
Representation - the problem space
- 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
Analogy + issues
- 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
Expertise
- 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
Experts have...
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
Limits of expertise
- 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
Functional fixedness
Stuck or rigid in one way of thinking about an object, and what its function is Ex. tack, match, candle task
85
Problem solving set
- 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
Einstellung
“attitude” = general term for rigidity in problem solving based on starting assumptions about a problem (helpful or harmful)
87
Four elements of creative performance
1. Preparation 2. Incubation 3. Illumination/insight 4. Verification
88
AHA moment
- Might still be wrong - Even though you think you’re getting closer and closer
89
What makes an idea or a person “creative”?
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