PSY 323 – Cognition Chapter 13: Judgment, Decisions & Reasoning

2 Specific observations Broad conclusion Crows in Washington are black All crows are black Inductive conclusions Are very broad They are probably true based on evidence Not definitely true, though

You do not judge inductive conclusions based on validity, but on strength (strength is subjective) For example: 1.Representativeness of observations 2.Number of observations 3.Quality of the evidence 3

We use our memory of actual instances for our judgment. So, when we make a judgment, things that are available in our mind determine our judgment. Tversky & Kahneman (1973) Asked participants:  Think of words that begin with r.  Think of words that have r in the third position?  Which is easier to think of?

Results  70% said more words started with r  This despite the fact that there are three times as many words with r in the third position (car, park, barren, march) Interpretation  Words that begin with r are more available to our memory 5 Tversky & Kahneman (1973)

Which cause of death do you consider to be more likely for people in the US? That is, if you randomly picked someone would they be more likely to die from cause A or cause B? AB  Homicide orAppendicitis  Auto-train collision orDrowning  Botulism orAsthma  Asthma orTornado  Appendicitis orPregnancy 6 Lichtenstein et al. (1978)

7 Results 

Chapman & Chapman (1969) Illusory Correlations  We think things are correlated, but they are not  A stereotype about the characteristics of a particular group may lead people to pay particular attention to behaviors associated with that stereotype, and this attention creates an illusory correlation that reinforces the stereotype  This phenomenon is related to the availability heuristic because selective attention to the stereotypical behaviors make these behaviors more “available” 8

Making judgments based on resemblance  The probability that A is a member of class B can be determined by how well the properties of A resembles the properties we usually associate with class B 9

Tversky & Kahneman (1974) These researchers presented this example to participants:  We randomly pick one male from the population of the US. He wears glasses, speaks quietly, and reads a lot. Is it more likely that this male is a librarian or a construction worker? 10 Amos TverskyDaniel Kahneman

Results  Most selected male librarian Interpretation  Representativeness heuristic: Properties of an object determine its class with little regard for probability in the population (base rate) 11 Tversky & Kahneman (1974)

 Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with the issues of discrimination and social justice. Which of the following is more probable?  Linda is a bank teller.  Linda is a bank teller and is active in the feminist movement. 12 Tversky & Kahneman (1983)

Most participants choose the second one, even though they’re more likely right by choosing the first one 13 People ignore the conjunction rule: P(two events) < P(one of the two events) Conjunction Rule 

14 A certain town has two hospitals. The large hospital has ~45 babies born a day, and the small hospital has ~15 births a day. About 50% of all babies are boys. However, the exact percentage varies by day. For a period of 1 year, each hospital recorded the days on which more than 60% of babies born were boys.  Which hospital recorded more of these days? Tversky & Kahneman (1974)

The small hospital  Why? Law of large numbers - The more samples you take, the more representative the resulting group will be 15

Wason (1960)  A tendency to selectively look for information that conforms to our hypothesis and to overlook information that argues against it  Very strong effect  Can cause us to ignore relevant information 16

How do we choose? Utility  Outcomes that are desirable are in the person’s best interest; outcomes that achieve a person’s goals Expected utility theory  Assumes people are rational when making decisions  However, this doesn’t always hold true 17 See next slide 

Denes-Raj & Epstein (1994)  Researchers offered participants the opportunity to earn up to $7 by receiving $1 every time they drew a red jelly bean  Many participants chose the larger bowl with the less favorable probability 18

People are not good at predicting their emotional utility  Predicting the utility (how much satisfaction you would get) of one choice over the other isn’t that easy  Many psychological factors influence your perception of utility

Immediate emotions Integral immediate emotions  Emotions that are associated with the act of decision-making Incidental immediate emotions  Emotions unrelated to the decision yet can still affect the decision 20

Slovic et al. (2000)  Wording of a problem can affect the decision  See Mr. Jones example on p. 384

Framing Effect Tversky & Kahneman (1981)  Presented participants with the following scenario:  Imagine that the US is preparing for the outbreak of an unusual disease that is expected to kill 600 people. Two alternatives programs to combat the disease have been proposed. Assume that the exact scientific estimates of the consequences of the programs are as follows. ◦ If Program A is adopted, 200 people will be saved. ◦ If Program B is adopted, there is a 1/3 probability that 600 people will be saved, and a 2/3 probability that no people will be saved. Which program would you choose?

Presented participants with the following scenario: Two alternatives programs to combat the disease have been proposed. Assume that the exact scientific estimates of the consequences of the programs are as follows. ◦ If Program C is adopted, 400 people will die. ◦ If Program D is adopted, there is a 1/3 probability that nobody will die, and a 2/3 probability that 600 people will die.  Which program would you choose? Tversky & Kahneman (1981)

Framing Effect Tversky & Kahneman (1981) Results 

Risk-aversion strategy  Program A: The idea of saving 200 lives with certainty is more attractive than the risk that no one will be saved Risk-taking strategy  Program D: The idea of losing 400 lives with certainty is less attractive than the risk that a 2 in 3 chance that 600 people will die Tversky & Kahneman (1981)

 Example: ◦ If you are lucky, you have a chance to win $1000. Which game do you choose?  Game A. a sure gain of $250  Game B. 25% chance to gain $1000 and 75% chance to gain nothing  Game A  84%  Game B  1 6% Tversky & Kahneman (1981)

 Example: ◦ You are given $1000, provided that you will play either one of the following games. Which game do you choose? ◦ Game C. a sure loss of $750 ◦ Game D. 75% chance to lose $1000 and 25% chance to lose nothing. ◦ Game C.  13% ◦ Game D.  87% Tversky & Kahneman (1981)

Deductive conclusions  Are very specific from a sequence of statements called syllogisms  Logical conclusions 28 Broad statements - premises Specific conclusion All birds are animals All animals eat food All birds eat food

Categorical Syllogisms  The relationship between two categories is described by using statements that begin with all, no, or some. 29 All birds are animals. (All A are B) All animals have four legs. (All B are C) All birds have four legs. (All A are C)

Premise  Two statements that precede the conclusion Conclusion  A third statement that follows the premises Validity  A syllogism is valid when its conclusions follows logically from its two premises (deductive reasoning) 30

CONDITIONAL SYLLOGISMS Conditional reasoning is reasoning about propositions using the logical relation known as implication Formally, it is based on propositional logic ‘if….then’, ‘if…& only if’ (also not, and, and or are used in propositional logic) Example: If I study, I’ll get a good grade. I studied Therefore, I’ll get a good grade.

CONDITIONAL REASONING: WASON SELECTION TASK Rule: If there is a vowel on one side of a card, then there is an even number on the other. Question: Which card(s) do you need to turn over to test that this rule is true for these four cards? Approximately 4% of people (university students) get this right on their first try. E K 4 7 Wason (1966)

CONDITIONAL REASONING Griggs & Cox (1982) Found that real-life concrete information helps your reasoning See next slide 

Each card has an age on one side and the name of a beverage on the other side. Indicate the minimum number of cards you need to turn over to test that the following rule is true for all four cards: If a person is drinking beer, then he or she must be over 19 years old. Griggs & Cox (1982)

Answer You have to turn beer and 16 years old Results 73% of the participants were correct with concrete version; none with abstract Conditional Reasoning

Interpretation Why is there such a big difference in the two problems? People use a pragmatic (real-life) reasoning schema to solve a these problems A way of thinking about cause and effect in the world that is learned as part of experiencing everyday life Permission schema is an example Only if something is satisfied, you can do something else Conditional Reasoning

Some of the slides in this presentation prepared with the assistance of the following web sites:  archlab.gmu.edu/people/jthompsz/11- ReasoningDecisionMaking_2.ppt   ftp:// /pub/fred/teaching/Cog_Psych_8.ppt ftp:// /pub/fred/teaching/Cog_Psych_8.ppt