Week 10: Reasoning Flashcards

1
Q

Reasoning and Decision Making

A

Judgement:
estimating magnitudes/probabilities of some characteristic

Reasoning: drawing conclusions

Decisions: making choices between alternatives

Forming an opinion on a particular thing

Finding magnitude of certain characteristics

Taking available evidence that we have and concluding

Looking at available alternatives and choosing among them and acting in that certain way

Conclusions help us make decisions

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

Deductive Reasoning

A

Starts broad principles to make logical predictions about specific cases

Determining whether a conclusion logically follows from premises

Syllogistic reasoning

Two statements called premises

Third statement called conclusion

Categorical syllogism

Describe relation between two categories using. all, no, or some

Do not confuse “validity” with “truth”

Syllogism is valid if conclusion follows logically from its two premises

Truth refers to a relationship that holds in reality

Syllogisms can have validity but not the truth, and can have truth but not validity

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

Deductive

A

Situations where we start with broad principles and try to make a logical prediction about specific case

General to specific

Remember, don’t confuse validity with truth

Trying to determine if a syllogism is valid

Truth is what we usually think about

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

Categorical Syllogism

A

All men are mortal. (major premise)

Socrates is a man. (minor premise)
(therefore)

Socrates is mortal. (conclusion)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

Logic and Eular

A

Assess syllogistic logic

Have category A represented by a circle, show a relationship with A and B by circle overlap

All A are B

No A are B

Some A are not B, Some A are B

Goal isn’t to find a way that sylllogism is true, but false

False makes it invalid

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

Abstract

A

All A are B (major)

All C are B (minor) therefore, All A are C (conclusion)

All A are not C in the diagram

True or False (valid)?

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

Concrete

A

All Liberals are human. (major premise)

All Conservatives are human. (minor premise)
(therefore)

All Liberals are Conservatives. (conclusion)

True or False (valid)?

Misinterpretation: All A are B = All B are A

Biggest error people make is that they feel like they can rearrange the equation

You can’t just flip it around

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

Form vs. Content

A

All dogs are animals. (major premise)

Some animals are pets. (minor premise)
(therefore)

Some dogs are pets. (conclusion)

True or False (valid)?

Dog category is independent from pet circle so syllogen is false

Don’t focus on content, focus on form of diagram

Using sharks make it easier to conceptualize

Belief bias: if content pushes you towards one interpretation, makes it hard to evaluate it logically

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

Reasoning Error

A

Belief bias (Henle, 1962): The tendency to think that a syllogism is valid if its conclusions are believable

Therefore, beware of syllogism’s conclusions that are true or agree with your beliefs

All of the students are tired

Some tired people are irritable

Some of the students are irritable

Atmosphere effect (Woodsworth & Sells, 1935): global impression or feel of the premises (e.g., same form)

All + All = All ?

All birds are mammals
All mammals sleep
All mammals are birds

Confirmation bias: the tendency to selectively look for information that conforms to our hypothesis and overlook information that argues against it

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

Reasoning Error

A

Some of the students are irritable is incorrect based on diagram

Atmosphere effect: idea that if the premises give a general feeling of particular form so that you have all As or Bs or Cs, that sort of gives the impression that it must be true

All + all = all

Since all mammals are not birds

Tendency that we have to prove that something is true due to expectation

What we want to do in logic and generally approaching things and we try to find ways that something is untrue

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

Conditional Syllogisms

A

A logical determination of whether the evidence supports, refutes, or is irrelevant to the stated relationship

Conditional clause (premise 1) – antecedent (if clause)

consequent (then clause)

Evidence (premise 2)

Conclusion?
Psyc 221
Psyc 221

First premise is the (if then) part

Second premise is the evidence support, refutes or is irrelevant to the first premise

Then you have to determine if you can reach some type of conclusion

Modus Ponens (97%)
Modus Tollens (60%)

The only time that you can come up with a valid conclusions is if you are affirming antiselent or denying the consequent

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

Example

A

Raining (antecedent)

Domique gets wet (consequent)

Evidence: it is raining

Conclusion: therefore, dominque gets wet (modus ponens)

Therefore its not raining (modus tollens)

Tricky since someone could just dump a bucket of water on her

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

Example 2

A

If i live in vancity

Then i live in BC

Evidence: i live in vancity
Conclusion: therefore, i live in BC

Or i do not live in BC, therefore, i do not live in vancouver

Or i live in BC, therefore, i do not live in vancouver

I dont live in vancouver, but i do live in BC

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

Wason Four Card Problem (1966)

A

To determine which cards needed to be flipped over to test the rule

One side had number and other had letter

If vowel then it was an even number

7 would go against the rule

E and 7 would need to be flipped

People have the most difficulty with this

Becomes easier when stated in terms that you can actually think of

Concrete terms will help

Agrees with the schema, a permission schema which agrees with the rules of society

Drinking is allowed to certain individuals in society

16 yr old would invalidate the question, drinking beer would also

Falsification principle:to test a rule, you must look for situations that falsify the rule
* Most participants fail to do this
* When problem is stated in concrete everyday terms, correct responses greatly increase

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

Wason Four Card Problem (1966)

A

To determine which cards needed to be flipped over to test the rule

One side had number and other had letter

If vowel then it was an even number

7 would go against the rule

E and 7 would need to be flipped

People have the most difficulty with this

Becomes easier when stated in terms that you can actually think of

Concrete terms will help

Agrees with the schema, a permission schema which agrees with the rules of society

Drinking is allowed to certain individuals in society

16 yr old would invalidate the question, drinking beer would also

Falsification principle:to test a rule, you must look for situations that falsify the rule
* Most participants fail to do this
* When problem is stated in concrete everyday terms, correct responses greatly increase

Permission schema: if A is satisfied, B can be carried out
* Used in the concrete versions * People are familiar with rules

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

Inductive Reasoning

A

Reasoning that is based on observation
* Generalizing from observations of specific cases to more general conclusion
* Reaching conclusions from evidence
* Conclusions are suggested only, with varying
degrees of certainty.
* Strength of argument
* Representativeness of observations * Number of observations
* Quality of evidence

Used to make scientific discoveries
* Hypotheses and general conclusions
* Usedineverydaylife
* Make a prediction about what will happen based on observation about what has happened in the past

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

Inductive Reasoning

A

This is the opposite of deductive
Particular observation and determining the general conclusion
Based on evidence
We are trying to figure out what is being suggested
We still want as much certainty as possible and there are things that will strengthen the argument
You want a representatives sample
E.g., all crows are black in particular city
But if you only have the crows in one city, you cannot generalize all crows to the ones you have seen
Need higher amount of observations, the stronger the conclusion will be
Is there multiple sources that point to your conclusion
Such as articles with similar observations

Essentially what is used in science
Constantly looking at certain situations, taking these data points and taking hypothesis and determining general conclusions
Everyday, you have these expectations in the world that things will behave the way you expect, these are the inductions

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

Accounts of Reasoning/Decision Making

A

Normative: how things ought to go; what people should do

Descriptive: how things are; what people actually do (do)

Heuristic: a short cut; a strategy that risks error to gain efficiency (speed)

Algorithm: a guaranteed route to an outcome, which may be more tedious and effortful

Normative is how things should work in society

Descriptive is what people actually do

Heuristic is a short cut that allows us to reach a decision faster

Algorithm is sitting down and actually thinking about what we actually need but is very tedious

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
19
Q

Kahneman and Tversky

A

pioneered the research on judgment under uncertainty
* emphasized the heuristics we use to process evidence and make judgments
* early work done at Hebrew University of Jerusalem

This work started in the 70s at University of Jearusalm
Proposed theory about decision making called prospect theory
Largely done in collab with Tversky
We typically rely on heuristics since we can’t get all the information and even if we could, it would take forever

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
20
Q

Representative Heuristic

A

assume that each member of a category is representative of that category (like the prototype?), and has all its traits
* also assume the reverse—if something has a lot of the traits of a category, it probably belongs to that category
* be willing to draw conclusions from a quite small sample: “seen one, seen ‘em all”

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
21
Q

Representative Heuristic

A

Argued that when we think about members of categories, we make the assumption that every member is like everyone else in the group
We also make the opposite as if something has alot of the traits of the category, it may be apart of it
If you have a situation where you are seeing something, you have a lot of info of that object since you are assuming that objects category therefore you know all the information about that item
Coin flip example: coin is random, assuming that this is random event and there are 2 possibilities, when things are random, we expect them to change
If you flip a coin 8 times, any of these 3 possibilities are equal due to chance
Each throw is independent

Gamblers Fallacy: feel like random events, the next thing should be a particular outcome
E.g., flipping a coin 4 times and 3 times it landed on heads, bet on last one, most will bet for a tails
People will think tails is more likely even though this isn’t true

22
Q

Kahneman and Tversky (1973)

A

Talked to a bunch of students as samples in different groups
1 group: given a bunch of majors that they may be pursuing, what probability will students be in each category
Were given a prompt about a person
Asked questions about his similarity to other grads, likely a grad student, and a personality sketch of highschool and likelihood of being a student
More likely to be seen as comp sci or engineering student, likelihood highest as that as well
This proved that people were judging, people assumed his characteristics to people who were probably inclined with those majors
Even happened in the final group, even though things may have changed, people still said that he was more likely to be a comp sci/engineering student since he was similar to them
His test carried a lot of weight into the future
E.g., linda being judged as a feminist accountant doesn’t make sense since all accountants have the same category

This is the conjunction fallacy: 60% of being accountant 20% feminist, 12% of being feminist accountant
Probaility of conjoinment of 2 events is the multiplication of the two probabilities
Hospital birthrates: probabilities of which gender is born more
Large vs. small
10 births vs. 100 births
Any given day, fluctuations can happen so 10 is better and much more likely

23
Q

Kahneman and Tversky (1973)

A

Likelihood judgments closely mirrored the similarity judgments rather than base rate
* How likely is it that Tom is a comp. sci. student versus humanities student?
* 95% pick comp. sci.
* base rate estimations by other subjects had humanities as 3 times more likely
* Even when based on projective tests that are unreliable, and things change from high school to grad. school

24
Q

English

A

More r words or words with r as third letter?
Twice as many words with third r
Why do people think first one is right?
Fairly easy to generate words with r and harder to generate words with third r
Hence why we use availability heuristic

25
Availability Heuristic
scan quickly through memory seeking relevant instances * if instances come quickly to mind, they are likely to be frequent in experience * availability or accessibility in memory serves as a proxy for frequency in experience * but the organization of memory can create biases Scan through memory to see how often this particular event has happened Use as proxy to say it must be more like;y The more likely something is rehearsed, better chance of being in LTM Salience, emotionality of an event Organize in particular way
26
Slovic, Fischhoff & Lichtenstein (1976)
Subjects asked to estimate frequency of various causes of death. Homicide Drowning Asthma Asthma Appendicitis Appendicitis Auto-train crash Botulism Tornado Pregnancy Students in the US Gave people a bunch of potential causes of death and asked to estimate of how many people die Per 200 million US First 2: hugely overestimated from tornado Last 2: underestimated probability Due to not hearing it as often Tornado being on news, boy dying from asthma is not on news These come to mind more, so we overestimate them
27
Hindsight Bias
hindsight is 20/20” * the “knew it all along” effect * looking at a situation retrospectively (after the fact), we saw all the signs leading up to this particular outcome – going on 3rd (or 4th) and 1 in football – knowing the outcome of an experiment before it is conducted – knowing a relationship would fall apart 20/20 Idea that you know the outcome of the situation, it is easy to see the outcome occurring E.g., football fan, team going 3rd and 1 in football Before the play, it would be 50/50 If they mess it up, they were stupid When people tell you the results of the experiment, not uncommon that people will say “obviously” Bystander effect: diffusion of responsibility Knowing that a relationship will fall apart, as saying “i could see that coming”
28
Anchoring
people are influenced toward a possible anchor value, even if they should know better * Prelec & Ariely (2006) – MIT students bid on auction items (told price was last 2 digits of Soc. Sec. #) – 80-99 paid $26, 00-19 paid $9 for trackball * Annoying sounds – paid either 90 or 10 cents – Would take 73 or 33 cents to hear it again People get influenced into believing something Prelec and Areily: Bunch of items for sale and said that cost was the last 2 digits of SS number Could be $1 or $90 Asked what the max price would be for item Willing to pay 3x more for same item Only difference was where they started Paid users either 90 or 10 cents Asked if they were willing to do it again 10 cent group was willing to do it 33 cents
29
Class Example
If given 65, say 45 If given 10, say 25 Actual answer was 54/56 so 96% 1 - 8 multiplication -> 512 8 - 1 multiplication -> 2250 This is the anchoring effect: actual answer is over 40k
30
Decision Making
Reasoning/choosing among alternatives * What clothes to wear * Who to date/marry * What school to attend * Even relatively simple decisions are cognitively demanding * Donders – ~100ms to decide left of right * Posner & Boies(1971) We do it all the time When you have these decisions, a lot of them feel really difficult You have to think about them to make them
31
Posner & Boies (1971)
* Used a dual task technique * secondary task measures resource demands orsparecapacity(likeathermometer)ofthe primary task at various points * primary task = letter matching (Aa, AA, AB) * secondary task = turn off tone on some trials; short RT = low resources in primary task Duel task technique Two things at the same time Idea that the secondary task will basically let you know how resource demanding the first source was How quickly they could do secondary task If quickly, means they had a lot of resources If resources were being allocated to resource task, it would take longer and cause errors Measured how long it took to turn off tone
32
Capacity Decision
When first letter came up and tone came up same time, people were able to turn it off faster If it was inbetweem, then rt went up At second letter, got slower At key, rt was high but not as high
33
Process Account
Task 1. wait 2. warning 3. first letter 4. delay 5. second letter Process 1. none? 2. alerting 3. LTM retrieval 4. WM rehearsal 5. a) LTM retrieval b) trace matching c) decision (response choice) d) motor output Prior to trial, we don’t know whats going on Triggers system to be ready to respond LTM retrieval is automatic The delay is due to WM rehearsal Even if you find a match, you still keep on going because it would take longer to make a decision to every letter rather than making one decision at the end
34
Decision Making
each decision has costs (taking us farther from our goals) and benefits (moving us toward our goals; providing our values) * we must weight the costs against the benefits * goal of making decision to maximize utility Every particular situation, making one decision will have a cost associated with it This decision will also have benefits though People might do a cost-benefit analysis
35
Expected Utility
expected utility = (Probability of an outcome) X (Utility of that outcome) * Assumption: People are rational * If they have all relevant information, they will make a decision that results in the maximum expected utility Evaluation of risks What you will get out of a situation is going to be the product of how likely a particular outcome is X how much that outcome means to you Requires us to have good estimates of these concepts so you have the relevant information about probability of outcome and how valuable that outcome is Advertising (likelood) for lottery tickets Office pools for lottery tickets, talkin to others Serotonin release when thinking about if you won (these are the utility), you think about how good the situation is Expected utility goes up, likelihood goes down, makes seem like a better deal than it is
36
Lottery Example
lottery ticket = $2 * likelihood of winning = 1 out of 14,000,000 * prize = $4,000,000 * expected utility = (1/14M) X ($4M) = $0.29 * therefore, you are giving away 7 times what you can expect to receive each time!
37
Decision Making
Decisions depend on how choices are presented * Opt-in procedure * Active step to be organ donor * Opt-out procedure * Organ donor unless request not to be * Status quo bias * The tendency to do nothing when faced with making a decision * Framing of alternatives Depend on how choices are presented Organ donation - various ways that this can be put towards people Filling out form to opt in for donation Some countries are fixed organ donors unless requested not to Status quo bias: we have a bias to do nothing Companies keep giving deals as you won’t leave since you are already apart of the company and you have to do work to switch Other companies will give other deals to entice you to join them
38
Framing
Assume yourself richer by $300 than you are today. You must choose between – a sure gain of $100 – a 50% chance to gain $200 or gain nothing * Assume yourself richer by $500 than you are today. You must choose between – a sure loss of $100 – a 50% chance to lose nothing or lose $200 People choose different things according to how they are phrased If you are going to be loosing something, you will throw the dice and hope that you will not loose much As you are gaining something, people don’t wanna loose what they are gaining
39
More Framing
Imagine that Canada is preparing for the outbreak of a new disease, which is expected to kill 600 people * Two alternatives have been suggested in labwork A: 200 people saved B: 1/3 probability that all 600 people will be saved, but a 2/3 probability that none of the 600 will be saved Contrast this with these alternatives A: 400 will die B: 1/3 probability that no one will die but a 2/3 that all of the 600 will die
40
Framing
phrasing of the decision affects our choice * framing in terms of losses tends to make us risk-seeking * framing in terms of gains makes us risk- averse
41
Decision Making
Emotions affect decisions * Expected emotions * Emotions that people predict that they will feel concerning an outcome * Explain risk aversion * People inaccurately predict their emotions * Overestimate negative emotions Emotions affect decisions People are not good at predicting on how they will feel about a situation Kermer and colleagues Gave people $5 and flipped coin, 50% you will gain 5 and 50% you will loose 3 First tell how you will feel if you win or lose Lose, down by 4 points, win, go up 2 points (happiness) Prediction is twice is bad of negative feeling if i lose After filler test, people who lost were down by a point and people who won went up a point So winning does make you happy but the difference seems to be about the same Not what people estimated
42
Emotions
Incidental emotions: Emotions that are not specifically related to decision-making * May be related to one’s general disposition or personality, some recent experience, or one’s general environment or surroundings * Can affect one’s overall decision making processes Emotions that are from other factors can effect DM Study done and simple task was given A highlighter, how much are you willing to sell it for Sad, disgust and neutral How much are you willing to buy it for Neutral: buy low, sell high Disgust: buy low, sell low (just wanna stay constant) Sad: buy high, sell low (just want change)
43
Unconscious Bias
Prejudice * Implicit Association Test (IAT) Response to pictures White or black person Or bad or good Face and words Idea is that there is an unconscious assocaitation of the words and face
44
Green, Carney, Pallin, Ngo, Raymond, Iezzoni & Banaji (2007)
Measured explicit attitudes * Measure IAT * Read a vignette about Mr. Thompson a 50- year-old male presenting to the emergency department with chest pain and an EKG suggestive of anterior myocardial infarction. It is stated that primary angioplasty is not an option and no absolute contraindications to thrombolysis are evident. Study in hospitals in Boston, went to medical residents Questionaire on their explicit attitudes on black and white people Given a prompt
45
Results
No explicit bias reported * Diagnosis of Coronary Artery Disease: Very likely - 29.8% white vs. 40.1% black * Were not anymore likely to treat blacks with Thrombolysis No difference in explicit bias Low on IAT, they were more likely prescribe thrombolysis to black people High on IAT, more likely to give to white people Predominant patients were black but still showed bias
46
Context
Number of response options * Redelmeirer & Shafir (1995) – found that physicians were less likely (53%) to prescribe arthritis meds when there were two medication options than when there was only one (72%) Simonson & Tversky (1992) – more likely to buy $240 camera when more expensive option added ($170, $240, $470 instead of just $170 and $240) Past History: Shen et al. (2010) – physician cesarean section decision affected by seriousness of preceding cases
47
Context
Number of options Had to decide which medication to give so did not give any 50% of people bought more expensive camera at 2 options People chose $240 with 3 options More likely to buy expensive option when more expensive options are present Probability that physician ordered a c-section if beforehand cases were easy, opposite caused c section went down 1600 more people died after 9/11 from driving more often
48
Rationalization (Tversky & Shafir, 1992)
You can buy a highly desired vacation package after passing an exam. Do you do it? *Most of us (~60%) will because we’re celebrating *But most of us (~60%) will also buy it after failing —consolation *However,if we don’t know how we did on the exam, only about 33% of us will buy it *Pass/fail information isn’t relevant,but having some information so that we can justify it is obviously important Passing or failing exam would result in 60% of taking vacation for different reasons though (pass/fail) Only 33% if exam results were unknown People were able to justify themselves when more info was given
49
Sanfey et al. (2003)
UltimatumGame * Proposer decides how to split money and responder decides whether to accept *From a utility stand point should always accept *Responder often rejects low offers because they became angry that offers were unfair *Does not happen when dealing with a computer
50
Sandy
Game where situation is essentially one person is given 10$ and decide how they want to split it up between others Participants were the receiver so they were asked to chose money or reject As long as the person is offering something, you should take it Found when they thought they were playing with another person, wouldn’t accept the deal unless it was 3 dollars or more 35% or less accepted 1 dollar offer when they were dealing with another person If it was computer, then it was nearly 70% They thought it was unfair Willing to sacrifice own benefit to punish others, but computer cannot be punished so there is no effect fMRI was used Prefrontal cortex was also involved with decision making process Activation in right anterior insula correlated with likelihood of rejecting offer Need to justify is just as important as utility If we are put in situation, we would consider the best solution People wouldn’t accept a bribe as it goes against their morals/nature When others ask for your decisions, you should be able to justify them
51
How Do We Decide?
utility is not the only goal, or even necessarily the most important one Social and emotional factors are also important considerations need for reasonable choices, with integrity justification is critical—need to make sensible and defensible decisions no accepted normative theory
52
Two System (Processes)?
System 1: – Intuitive – Fast – Unconscious – Automatic System 2: – Reflective – Slow – Conscious – Controlled