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U1 Psychology of Cognition 213 > Final Lecture 19: April 1 > Flashcards

Final Lecture 19: April 1 Flashcards

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1
Q

summarize last lecture

A

The GPS is a computer simulation to model well-defined problem solving. Problems with a defined problem space with a goal, constraints and operators

¤ The GPS uses the means-end analysis heuristic
¤ Considers the ultimate goal and follow production rules to get closer to it ¤ These rules can create sub-goals
¤ Verified by think-aloud protocols

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2
Q

What can stop creativity

A

‘Strong but wrong’ effect

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3
Q

what is the ‘Strong but wrong’ effect

A

The tendency to default to ‘pre-programmed’ sequences because they
don’t require a lot of conscious effort
¤ This can stop you from seeing things in a new way
¤ Example: After moving to a new apartment, when walking home from work the next day, you go to your old apartment

¤ A mental slip which can lead to faulty decisions ….

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4
Q

what are the Types of Reasoning

A

deductive and inductive

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5
Q

what is Deductive reasoning

A

¤ The mental act of testing a general statement to draws conclusions (top down)
¤ The scientific method of testing general theories
¤ The complete truth

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6
Q

what is Inductive reasoning

A

Making general conclusions from specific observations (bottom up)
¤ The scientific method of forming hypotheses from data
¤ Probably but not definitely true reasoning

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7
Q

Deductive or Inductive Reasoning? Person X went a park everyday and saw dogs wearing top-hats. They conclude that all dogs that visit this park wear top-hats

A

inductive (bottom up)

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8
Q

Deductive or Inductive Reasoning? All dogs have a good sense of smell. The Cog-Dog is a dog. Therefore, the Cog-Dog has a good sense of smell.

A

deductive (top down)

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9
Q

Aristotle studied logic how

A

by examining how people made judgments (deductive reasoning)

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10
Q

Logical rules that represent expressions of truth are obtained how

A

from

our experiences and used for reasoning (logicism)

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11
Q

are we born logical reasoners

A

yes

Infants watched animations that included two objects with similar tops
The two objects were placed behind a screen; one was ‘scooped’ up and placed in a bucket with only the top visible

The screen was lowered to show the other object
¤ Infant should use logic to infer that the non-shown object was in the
bucket
To test this, the object in the bucket was shown
¤ Infants looked longer at the object during this test phase ONLY when it was inconsistent with logical deduction

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12
Q

why aren’t we aways logical

A

We act in a conflicting manner
¤ We’re often will stand in a line that’s crowded, but not a short line ¤ Ordering diet coke with a Big Mac
¤ We can make faulty links between events
boil things down to two choices when there are more than two ¤ Do you like dogs

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13
Q

what is Syllogisms

A

Statements with premises and conclusion that you use deductive reasoning to determine if the conclusion is correct

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14
Q

what are Premises

A

are the parts of a reason that work together to support a conclusion

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15
Q

are premises presume to be true

A

yes

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16
Q

what are the Categoric syllogisms

A
  • Major premise

* Minor premise • Conclusion

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17
Q

what is Validity

A

depends on whether the conclusion follows those premises (only on the logical form and not the content)

¤ Different than how we use this term in everyday conversation in which validity = truth

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18
Q

what are some Valid syllogisms

A
  • AllAareB
  • All C are A
  • Therefore all C are B

Coffee is a beverage
Lattes are coffee
Therefore lattes are beverages

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19
Q

what are some Invalid syllogisms

A
  • AllAareB
  • All C are B
  • Therefore all C are A

Coffee is a beverage
A milkshake is a beverage Therefore milkshakes are coffee

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20
Q

what are the 4 Types of premise rules

A

Universal affirmative:
Universal negative:
particular affirmative:
particular negative:

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21
Q

what are ¤ Universal affirmative:

A

All A are B

¤ All rocks are hard things [Does not mean all hard things are rocks]

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22
Q

what is ¤ Universal negative:

A

No A are B

¤ No rocks are soft things [Also means no soft things are rocks]

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23
Q

what is ¤ The particular affirmative:

A

Some A are B

¤ Some rocks are hard things [many ways to to interpret these]

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24
Q

what is ¤ The particular negative:

A

Some A are not B

¤ Some rocks are not hard things [many ways to interpret]

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25
Q

People tend to have more trouble reasoning with negative or positive information

A

People tend to have more trouble reasoning with negative than positive information

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26
Q

what is Omission bias

A

People tend to react more to strongly to harmful actions than to harmful inactions
¤ According to this bias, withholding the truth is not as bad as lying
¤ This is because inaction (the lack of something, negative premise) is harder to classify as wrong

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27
Q

what are Logical errors made with syllogisms

A

Logical reasoning doesn’t have to represent what we expect, but the content of a syllogism can lead to errors
Hidden premises
If we are simply logical rule followers, these assumptions should not affect reasoning

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28
Q

wha are Hidden premises

A

Assumptions brought to syllogistic reasoning

29
Q

what is the Believability effect:

A

If a syllogisms conclusion is believable, people will accept it as true despite logic

30
Q

it is more difficult to reason with ‘particular’ statements (some C’s are A; some C’s are not A) than what

A

universal statements (All A are B; No A are B)

31
Q

We tend reason according to the specific way we interpret the premises
explain

A

¤ We often interpret “some” as “some but not all.”
¤ Logically, “some” means “at least one and possibly all.”
¤ But this doesn’t follow logic of the premises

All men are mortal
Socrates is a man
Therefore Socrates is a mortal

All malfunctioning machines are annoying
Some things that are annoying are computers
Therefore, some computers are not malfunctioning machines

32
Q

give A summary of syllogisms

A

This is a type of deductive reasoning when a conclusion is made from two true statements (premises) that share terms with the conclusion

There are some situations in deducing if the premises logically explain a given conclusions are very difficult
¤ Premises with negative (not) information are hard to reason with
¤ Premises that reflect content we know change the difficulty level of the
syllogism (Believability)
¤ Premises that are some statements are hard to reason with

33
Q

what is Another measure of deductive reasoning

A

Conditional reasoning

34
Q

what is Conditional reasoning

A

¤ Judge if this statement is true: “If P then Q” : If it is raining, I will get wet ¤ P is the antecedent and Q is the consequence

This statement can be tested many ways
¤ What happens if Q is true? If I am wet, is it raining?
¤ What happens if P is false? If it isn’t raining, am I wet? ¤ What happens if Q is false? If I am not wet, is it raining?

35
Q

what is Wason’s four card task

A

If a card has a vowel on one side, then it has an even number on the other side, which cards should you flip?

Conditional statement: If ‘vowel’ then ‘even’

People are terrible at following this logic
¤ 33% only turn over only card ‘E’
¤ 46% turn over card ‘E’ and ‘2’
¤ Very few turned over card ‘E’ and ‘5’ (the correct method)
Test if vowel then even; Test if not even, then not vowel
¤ The inability to solve this correctly is because people tend to confirm what they are testing

36
Q

what is Wason’s general rule

A

General logical rule to solve: “ If P then Q ”.

37
Q

what is The logically correct solution for Watson’s general rule

A

¤ Choose the P card (to see if there is a not-Q on the back) and the not-Q
card (to see if there is a P on the back).
¤ Need to test if the statement is false; eliminate false statements

38
Q

Wason card task – illustrates what

A

the ‘traps’ that can come from reasoning

39
Q

what is the Social contract theory

A

A social contract specifies the costs and benefits of an arrangement that are used to make deductions.

¤ This ‘contract’ includes a cheat-detection module that is hard- wired (universal)
¤ detect potential violations of the contract
40
Q

what are the Theories of deductive reasoning

A 
Mental logic (natural deduction hypothesis)
Mental models
41
Q

what is Mental logic (natural deduction hypothesis)

A

Reasoners rely on formal rules to reason and make conclusions

Using if-then rules to guide behavior.
¤ IF I am hungry AND I have money, THEN ¤ EITHER go to the store OR go to a restaurant

¤ Following these rules leads to logic
¤ But context/content can affect our reasoning

42
Q

what are Mental models

A

¤ Reasoners construct models of the premises to help with deductive reasoning ¤ A mental model is a representations in the mind of real or imaginary situations ¤ These models are always true and guide reasoning by principles not rules

Evidence
¤ Context affects reasoning and why we reason differently with familiar content ¤ Explains why we have problems reasoning with negative examples

43
Q

what are the characteristics of Inductive reasoning

A

Making general conclusions from specific observations
¤ Bottom-up reasoning
¤ The scientific method of forming hypotheses from data
¤ Probably but not definitely true reasoning

44
Q

explain Biases in ‘everyday’ thinking

A

Factors can affect how strongly we believe in the certainty of our conclusions made by inductive reasoning

45
Q

our biases com from where

A

These are represented by biases that comes from statistical heuristics created from specific observations
¤ a focus of behavioral economics research

46
Q

what are the Three ‘categories’ of bias in every day thinking

A

¤ Biases affect how we interpret information

¤ Biases that affect how we judge frequency (how often something happens) ¤ Biases that affect how we make predictions

47
Q

what is Interpretation: Representativeness bias

A

Using categorical information to make judgments
¤ Matching what we perceive to our existing representations
¤ Similarity- or prototype-based evaluations
¤ Related to stereotypes, schemas, and other preexisting knowledge structures

48
Q

what is The Alan example

A

¤You randomly select one male from the Canadian population and that male, Alan, wears glasses, speaks quietly and reads a lot. It is more likely Alan is a farmer or a librarian?
¤Most people say librarian
¤But this ignores base rate information – there are more farmers than librarians in Canada

49
Q

Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations.
¤ Which is the most likely alternative:
1. Linda is a bank teller
2. Linda is a bank teller and is active in the feminist movement

A
  1. Linda is a bank teller and is active in the feminist movement

because of the Conjunction fallacy

50
Q

what is the Conjunction fallacy

A

A false assumption that more specific facts are more likely than a general facts.

51
Q

what is Frequency: The availability bias

A

We confuse the frequency with which we can remember something with how frequently it actually occurs
¤ The easier it is to remember something, the more likely you’ll think it is to happen in the future
¤ Related to memory

52
Q

¤ American’s think the world is more violence occurs that does occur
¤ News and media violence makes thinking of examples of crime very easy

what is this an example of

A

frequency bias

53
Q

can we remember challenges we have to overcome better than other people’s challenges

A

We can

¤ Our challenges are more available from memory
¤ We perceive things as harder for us compared to others
¤ Some examples:
¤ Both Democrats and Republicans think the electoral maps works against their party ¤ Siblings think parents were harder on them than their sister/brother

54
Q

what is A related effect: Illusory correlations

A

Link two co-occurring events in mind and assuming a relationship
Sometimes this is correct and leads to good outcomes
¤ There are clouds, it will rain
Sometimes this leads to illusory correlations
¤ Play-off beards and other lucky items/practices people take that they think are linked to winning

55
Q

what is Recognition heuristic

A

An easily accessed cue – something that is well recognized – will guide decision making when there are two options to choose between

¤ If one object is recognized, but not the other, then go with the recognized object

56
Q

does Recognition heuristic = availability heuristic

A

no!!! Recognition heuristic ≠ availability heuristic

57
Q

Name recognition of cities can be used to predict population size ¤ Which one is most populated city, Montreal or Moosejaw?

A

Most say Montreal because it is more recognizable, and this leads to the correct answer (recognition heuristic)

58
Q

what is Frequency: Small-sample fallacy

A

Assumption that a small sample resembles the population from which they are drawn

Experiment: Participants asked to consider two hospitals
15 babies/ day or 45 babies/day
¤ “On a given day, is one hospital more likely to report more than 60% of the babies are boys?”
¤ Most participants say that both hospital are equally likely to report this but … This is wrong!
¤ Smaller samples (15 babies/day) are more prone to extreme proportions and larger samples (45 babies/day) better reflect true proportions

59
Q

what is Anchoring & adjustment heuristic

A

people’s judgements of the magnitude of something is biased (ie
adjusted) by some initial value they are exposed (i.e., the anchor).

We anchor estimates to unrelated information

60
Q

Which of the following produces

8×7×6×5×4×3×2×1 or 1×2×3×4×5×6×7×8

A

most will say first because of Anchoring & adjustment heuristic

61
Q

Participants were given a random number between 0 and 100 and asked:
v “Is this number higher or lower than the percentage of African nations in the United Nations?”
v Now, estimate the actual percentage

what was the conclussion

A

¤ Those who were given a HIGH random number gave greater percent
estimates than those given a LOW random number
¤ We anchor estimates to unrelated information

62
Q

what is Prediction: Gambler’s fallacy

A

The mistake that a predicted outcome of an independent event depends on past outcomes
¤ We like to find patterns in sequences and don’t like randomness

63
Q

A coin flip lands heads three times in a row. What are the odds that it
will be heads on the next toss?

A

¤ Answer is 50-50

¤ Misperception that a ’tails’ must be coming (Prediction: Gambler’s fallacy)

64
Q

The misguided belief that one is due for a ‘win’ after a run of ‘losses’ this is an example of what

A

Prediction: Gambler’s fallacy

65
Q

what is Hot hand (Gambler’s fallacy)

A

person who experiences success will keep having success ¤ ‘On a roll’ or ‘A winning steak’
¤ A basketball player who has just made
a basket is more likely to make the next basket

Just because something feels true, doesn’t mean it is true

66
Q

Heuristics and biases

Why do we have them?

A

Heuristic processing is central for making intuitive and rapid judgments, yet …
¤ The over-application of heuristics can lead to serious errors in our judgments and reasoning
¤ E.g., stereotyping; gambling addictions

67
Q

How does the availability heuristic influence our judgments?

a. It makes judgments more accurate when information is available
b. We confuse the frequency of what we can remember with actual occurrences
c. It facilitates ambiguous decisions
d. We always recall things more easily when they occur frequently

A

b. We confuse the frequency of what we can remember with actual occurrences

68
Q

give a summary

A

¤ Theories of reasoning
¤ Mental logic
¤ Human reasoning depended on formal rules

¤ Mental models
¤ We construct internal models or simulations to reason

¤ We have biases in decision making
¤ When we make decisions or judgments, we use heuristics guided by the principles of what we expect

U1 Psychology of Cognition 213 (21 decks)

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