Cognitive Exam 4 Decision making and reasoning

elements of a decision

• judgment: to judge or form an opinion

• reasoning: the process of drawing conclusions

• decision: the process of choosing between alternatives

inductive reason

The process of drawing a general conclusion based on specific observations

• ex: The temp in Riverside reaches 100 every summer. Therefore, it will reach 100 in Riverside this summer

• The premise (set up) is stated as observations of specific cases

• The temp in Riverside reaches 100 every summer

• conclusion is generalized from premise

• Therefore, it will reach 100 in Riverside this summer

• specific cases →broad principles

• every summer, this summer

• if the premise is true, the conclusion is probably true

= Therefore, it will reach 100 in Riverside this summer

****bottom up processing:

• facts/observation→hypothesis→theory

• if the premise is true, the conclusion is probably true

Inductive reasoning strengthening: Representativeness of observations

Question: How well will I do in Psych 134?

Who’s observations would lead to a more certain conclusion

• Psych friends or business friends

• Psych :)

how well observations about a particular category represent all members of that category

Inductive reasoning strengthening: number of observations

Question: How well will I do in Psych 134?

Who’s observations would lead to a more certain conclusion

• three friends or hundreds of peers?

• hundreds :)

how many observations are made

Inductive reasoning strengthening: quality of evidence

Question: How well will I do in Psych 134?

Who’s observations would lead to a more certain conclusion

• observing friends or previous grade distributions?

• previous grade distributions :)

observations can be supported by scientific evidence

Inductive reasoning weakening: confirmation bias

we look for information that supports our opinions and ignore information that refutes it

• ex: Which question would help you determine if someone is an introvert?

  • what things do you like to do when you are alone? (SUPPORTS)

  • what do you like most about being with other people? (DISCONFIRMS)

• ex: which set of numbers would help you determine the rule used to generate the sequence “2, 4, 6”?

  • 8,10,12 (CONFIRMS)

  • 1,2,3 (DISCONFIRMS)

****inductive arguments are weakened by a bias to confirm (or support) our opinions

Inductive reasoning weakening: myside bias

we evaluate evidence in a way that is biased toward our own opinions and attitudes

• ex: capital punishment

  • group FOR capital punishment

    “evidence of the deterring effect of capital punishment was convincing”

  • group AGAINST capital punishment

    “evidence of the deterring effect of capital punishment was NOT convincing”

Inductive reasoning weakening: backfire effect

our support for a given opinion can be stronger when faced with facts that oppose it (DOUBLING DOWN)

• ex: discussion with dad about truth/beliefs

• ex: Climate change denial: Despite the overwhelming scientific evidence supporting the reality of climate change, some individuals continue to reject this evidence and deny the existence of climate change

Inductive reasoning and heuristics: availability heuristics

events that come to mind more easily are judged as being more probable

• ex: are there more 4-letter English words with L as the 1st letter or 3rd letter?

• ex: whats more likely to kill you in kansas: drowning or tornado?

• ex: which is more likely to happen: death by shark attack or falling coconut?

undue weight is given to anecdotal evidence that comes to mind more easily

• ex: I wanna buy a volvo but my mom said hers sucks

• ex: will you vaccinate your kids

• ex: after watching scary movie u think noise outside is zombie

Availability heuristics: illusory correlations

when a relationship between two events appears to exist, but, in reality, there is little or no relationship

• ex: it always rains… …on the weekend

• ex: it always rains… …after I wash my car

• ex: stereotypes

Inductive reasoning and heuristics: representativeness heuristics

events that are more similar to a given category are more likely to be judged as being part of that category

• base-rate

• conjunction rule

• law of large numbers

Representativeness heuristic: base rate

the relative proportion of different classes in the population

• ex: What does Cousin Joe do for a living?—COMEDIAN??

  there are more doctors in the world than comedians but the description makes us think he is

Representativeness heuristic: conjunction rule

the probability of a conjunction of two events cannot be higher than the probability of the events alone

• ex: which best describes my buddy Brent?

  CEO of Chase

  CEO of Chase and annual salary is at least 1000

****Then the conjunction A and B (Brent is CEO and makes at least $1000) is more specific, so it must be less likely or equally likely than either A or B alone.

Representativeness heuristic: law of large numbers

the more individuals that are randomly drawn from a population, the more representative the group will be of the entire population

You’re asked:

If you only bet on red, are you more likely to win if you play for 5 spins or 50 spins?

✅ Correct Answer: 50 Spins

Why? Because of the law of large numbers:

• Over many trials (like 50 spins), the outcomes are more likely to reflect the actual probabilities — in roulette, red is close to 50%, so you’d expect around 25 reds.

• This means your results will be more representative of the true odds.

Deductive reasoning

the process of determining whether a specific conclusion logically follows from general statements

• ex: All men are mortal. Socrates is a man. Therefore, Socrates is mortal.

• premise is stated as facts or general principles

• All men are mortal. Socrates is a man

• conclusion is drawn from logical rules applied to the premise

• Therefore, Socrates is mortal.

• broad principles → specific cases

• All men, Socrates

• if the premise is true, the conclusion is definitely true

= Therefore, Socrates is mortal

****top down processing

• hypothesis→facts→conclusion

Deductive reasoning: Syllogism

consist of two broad statements (premises) and a conclusion

Deductive reasoning: Categorical Syllogism

statements being with “all”, “no”, or “some”

All teachers are inspiring. Prf. DZ is a teacher. ∴ Prf. DZ is inspiring.

Categorical Syllogism: Valid

the conclusion follows logically from the premises

****not all valid syllogisms are true

• valid syllogism … and true

  All dogs are animals. All animals eat food. ∴ all dogs eat food.

• valid syllogism … but not true

  All dogs are animals. All animals have wings. ∴ all dogs have wings

Categorical Syllogism: Invalid

the conclusion does not follow logically from the premises

• invalid syllogism … though possibly true

  All dogs are animals. Some animals are small. ∴ some of the dogs are small.

• invalid syllogism … and not true

  All dogs are animals. Some animals are in space. ∴ some of the dogs are in space

Deductive Reasoning: Belief bias

the tendency to think a syllogism is valid if its conclusions are believable

Deductive reasoning: Conditional Syllogism

the first premise has an “if…then” format

If I study, then I will get a good grade. I studied. ∴ I will get a good grade.

• ex: If I give my dog double dinner every day, then they will gain weight I gave Izzie double dinner every day ∴ she gained weight

• ex: If I give my dog double dinner every day, then they will gain weight Izzie did not gain weight ∴ I did not give her double dinner

• ex: If I give my dog double dinner every day, then they will gain weight Izzie gained weight ∴ Izzie got double dinner every night

• ex: If you are a doctor, then you finished college. You finished college. ∴ you are a doctor.

• ex: If I give my dog double dinner every day, then they will gain weight I did not give Izzie double dinner every day ∴ she will not gain weight

• ex: If you are a doctor, then you finished college. You are not a doctor ∴ you did not finish college

**** it is easier to see the the logic is invalid when using statements that also make it inaccurate

first two are valid

last two are invalid

Conditional syllogism: falsification principle

to test a rule, it is necessary to look for a situation that would falsify it

• ex: which cards do you need to turn over to test the rule?

  Which do you turn over to test the rule?

  • ✅ A (vowel) — must have an even number on the other side to confirm the rule.

  • ✅ 7 (odd number) — if there’s a vowel on the other side, it falsifies the rule.

  • 🚫 4 — doesn’t matter what’s on the back. The rule doesn’t say only even numbers must have vowels.

  • 🚫 D — not a vowel, so irrelevant.

  ✔ Answer: A and 7

• ex: which cards do you need to turn over to test the rule?

  You are a police officer. Each card represents a person in a car. One side shows whether the person is the driver and the other side shows their age. You must enforce the rule that if a person is driving, then they must be over 16 years old.

  Rule: If someone is driving, then they must be over 16 years old.
  Cards shown: driver, passenger, 26, 13

  • Driving

  • Not driving

  • 25 years old

  • 13 years old

  Which do you turn over?

  • ✅ Driving — you need to check if they’re 16+

  • ✅ 13 years old — you need to check if this person is driving

  • 🚫 Not driving — irrelevant

  • 🚫 25 years old — they meet the age requirement, no need to check

  ✔ Answer: Driving and 15 years old

****real life problems are easier to solve than abstract problems

decision making: expected utility theory

assumes if people have all relevant information, they will make a decision that results in outcomes that help to achieve their goals

framing effect

decisions are influenced by how the choices are stated

status quo bias

the tendency to do nothing when faced with making a decisios

• ex: organ donor

  • OPT-IN (in red): You are not an organ donor by default — you have to choose to become one.

  • OPT-OUT (in green): You are an organ donor by default — you have to take action to decline.

risk aversion

the tendency to avoid taking risks

dual systems approach

idea that we may have different systems for decision makin