Representativeness Heuristic Then: Framing Effects Psychology 355: Cognitive Psychology Instructor: John Miyamoto 6/2 /2015: Lecture 10-2 This Powerpoint

Representativeness Heuristic

Then: Framing Effects

Psychology 355: Cognitive Psychology

Instructor: John Miyamoto

6/2/2015: Lecture 10-2

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Outline

• Finish discussion of the availability heuristic

• Representativeness heuristic

• Preferential choiceFraming effects

Psych 355, Miyamoto, Spr '15 2

Lecture probably ends here

Main Claims of the Heuristics & Biases Movement

3Psych 355, Miyamoto, Spr '15

Main Claims of the Heuristics & Biases (H&B) Movement

• Human cognitive processes do not follow the pattern

of a rational model. o (Rational model = expected utility theory & Bayesian decision model)

• Human decision making uses heuristic reasoning strategies –

reasoning strategies that are useful because they are easy and

generally effective, even though they can lead to errors.

Heuristic reasoning strategies ....o .... are often fast and effective, o .... place low demands on cognitive resources.o .... but they can lead to errors in particular situations.

List of Many Heuristics that are Studied in the Heuristics & Biases Program


Some Heuristics in Inductive Reasoning

• Availability

• Representativeness

• Anchoring & Adjustment

• Confirmation bias

• Focusing illusion

• Framing effects

• Mental accounting

• More heuristics have been proposed than are listed here.

Availability Heuristic


“Other Factors” that Influence the Availability of Events

Continuation of this Slide: List Examples of Other Factors


“Other Factors” that Influence the Availability of Events

Examples of

“Other Factors”

• Egocentric bias.

• Dramatic events seem more common than non-dramatic events.

• Recent events seem more common than earlier events.

• Biases in the media create biases in the availability of stereotypes.

Effects of Information Sampling Bias in the Media


Information Sampling Bias in Everyday Media

Things we all know:o TV ads do not give an accurate picture of the value of products.o Political spin doctors are trying to manipulate our beliefs.o TV news is emphasizes dramatic events; it ignores undramatic events.o The portrayal of men/women, black/whites, rich/poor, gay/straight,

on TV is not a representative presentation of these groups.o Our own experiences are not typical of everybody’s experience.o Etc.

• We all know that these information sources are biased, but can

we really correct for these biases when forming beliefs? Doubtful.

Summary re Availability Heuristic - Skip Over

{Biases∈¿ InformationSources } { Biases∈¿ Availability } {Biases∈¿ Perceived Likelihood

of Events }

Summary re the Availability Heuristic

• Judging probability in terms of availability is a heuristic.♦ I.e., it is generally a reasonable way to estimate likelihood, but it can lead

to certain systematic errors.

• Factors that are not related to experienced frequency can makemake particular events very available.

♦ E.g., the perceived probability of being killed by a random crazy person will tend to be exaggerated.

Psych 355, Miyamoto, Spr '15 8Introduction to the Representativeness Heuristic



Representativeness Heuristic:

Events that are more representative are regarded as more probable.

"more representative"

means

"more similar to a stereotype

or to a typical member of a class."

Event A is more representative

than Event B

Event A is more probable

than Event B

Example of Jim: An Athletic, Muscular & Competitive Guy


Question: Jim is tall and very muscular. He's also very competitive.

He drives an expensive car and wears flashy clothing.

Which is more probable?

a) Jim is a professional athlete.

b) Jim is a lawyer or financial analyst.

• People predict that Jim is a professional athlete because

Jim is similar to a stereotype of a professional athlete.

• It is a better bet that Jim is a lawyer or financial analyst

because there are many more lawyers and financial analysts than

professional athletes.

This response is predicted by the Representativeness Heuristic

Representativeness Heuristic – An Example

Return to Slide with Diagram of Representativeness Heuristic

This is the better bet.

11

Representativeness Heuristic:

Events that are more representative are regarded as more probable.

o Example: Jim is muscular/athletic/competitive.

Psych 355, Miyamoto, Spr '15

Jim is more similar to the stereotype.

Jim is less similar to the stereotype.


Event A is more representative

than Event B

Event A is more probable

than Event B

a professional athlete?

a lawyer or financial analyst?Is he .....

Intro to the Lawyer/Engineer Problem


Lawyer/Engineer Problem (K&T, 1973)

DESCRIPTION OF JACK: Jack is a 45-year-old man. He is married and has four children. He is generally conservative,

careful, and ambitious. He shows no interest in political and social issues.

• 30:70 Condition: High Base Rate for Engineer

If Jack's description were drawn at random from a set of

30 descriptions of lawyers and 70 descriptions of engineers,

what would be the probability that Jack is one of the engineers?

• 70:30 Condition: Low Base Rate for Engineer

If Jack's description were drawn at random from a set of

70 descriptions of lawyers and 30 descriptions of engineers,

what would be the probability that Jack is one of the engineers?

Findings re Lawyer/Engineer Problem


Results re Lawyer/Engineer Problem

• Probability of "engineer" was rated to be about the same

in the low and high base rate conditions.

(Insensitivity to Base Rate)o High base rate condition = 30:70 Condition

Low base rate condition = 70:30 Conditiono Probability theory implies that Jack is much more likely to be

an engineer in the high base rate condition than in the low base rate condition.

• Why do people ignore base rates? See next slide

Why Do People Ignore Base Rates? The Representativeness Explanation


Why Do People Often Ignore Base Rates?

The Representativeness Heuristic: People judge probability based

on the similarity of the current case to a stereotype.

(a) Jack is equally similar to a typical engineer in the

low and high base rate conditions.

(b) People ignore the base rate because the base rate is irrelevant to

the judgment of how similar Jack is to a typical engineer.

• Probability theory shows that the base rate is very relevant

to judging the probability that Jack is an engineer.

• Cognitive theory shows that the base rate is often not

psychologically relevant to judging the probability that Jack is an

engineer.

When Does It Matter Whether People Ignore Base Rates?


When Does It Matter Whether People Ignore Base Rates?

• Evidence shows that physicians sometimes overlook base rates

when attempting to diagnose a disease.

• Evidence suggests that investors are overly influenced by

short-term information regarding the value of stocks.

• Business decisions tend to be overly influenced by short-term

trends.

Criticism of Goldstein’s Description of the Lawyer/Engineer Problem


Criticism of Goldstein’s Description of the Lawyer/Engineer Problem

• The Goldstein description of this study is inadequate because it does

not contrast the 30:70 condition with the 70:30 condition. It only

mentions the 70:30 condition.

• The important finding is that subjects in the 30:70 and 70:30

conditions are equally confident that Jack is an engineer (subjects in

the two conditions overlook the difference in the base rate). o Knowing only the result for the 70:30 condition does not establish that subjects

ignore base rates.o See Goldstein p. 374.

Ignorance of Regression Effects

Statistical Theory Implies that Regression Effects Will Occur

• Very tall fathers tend to have sons who are not quite as tall.

• Very short fathers tend to have sons who are somewhat taller.

• Why?


very tall father

fairly tall son

very short father

fairly short son

Statistical Theory Implies that Regression Effects Will Occur

• Statistical reason for regression effects: A predicted value will be closer to the mean than is the variable on which the prediction is based.

Zpredicted Y = ZX

Zpredicted Y = predicted z-score for Y

= the population correlation between X and Y

ZX = z-score for the predictor X

• Implication: If X and Y are not perfectly correlated, then the predicted value of Y is always closer to its mean than the value of X.


very tall father

fairly tall son

very short father

fairly short son


People's Tendency to Ignore Regression Effects

• Sophomore Slump: A baseball player who does exceptionally well

during his rookie season often does noticeably worse during his

sophomore (second) season. Why does this happen?

• Reason: A player’s batting average during the rookie season is not

perfectly correlated with his batting average during the sophomore

season. Therefore, on the average, a regression effect is inevitable.o Same holds for any other statistic, like number of home runs, stolen bases,

earned run average (for a pitcher), etc.

Misconceptions of Regressions – Other Examples


Misconceptions of Regression – Other Examples

• Israeli flight instructors and the effects of praise and punishment.

• Evaluating medical treatments or psychotherapies that select

patients who are already in extreme difficulty.

Why Does the Representativeness Heuristic Cause People to Overlook Regression Effects?

Why Does the Representativeness Heuristic Cause People to Overlook Regression Effects?

• Suppose a pilot just made a terrible landing. What should you predict for the next landing?

♦ Another terrible landing (most similar outcome)

A bad landing that is not as bad as the first landing (less similar to the previous landing, but it is more probable because of regression to the mean).

• People predict another terrible landing because it is the most similar outcome, while ignoring a factor, regression to the mean, that is statistically relevant, but not related to similarity.

Psych 355, Miyamoto, Spr '15 21Conjunction Fallacies


Conjunction Fallacies – The Famous Linda Problem

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.

F: Judge the probability that Linda is a feminist.

T: Judge the probability that Linda is a bank teller.

F & T: Judge the probability that Linda is a feminist

and a bank teller.

• Probability Theory: P(F) ≥ P(F & T), P(T) ≥ P(F & T)

• Typical Judgment: P(F) > P(F & T) > P(T)

Why Are Conjunction Fallacies Psychologically Interesting?


Why Conjunction Fallacies Are Psychologically Interesting?

• Conjunction fallacies strongly support the claim:

Human reasoning with uncertainty is different from

probability theory.o Human reasoning with uncertainty is based on a various heuristics – the

conjunction fallacy is caused by the use of a representativeness heuristic.

Two Question Regarding Conjunction Fallacies:

• What is wrong with the judgment pattern:

P(F) > P(F & T) > P(T)?

• Why do people's judgments have this pattern?

Probability & the Set Inclusion Principle


Probability and the Set Inclusion Principle

• If set B is a subset of set A, then

the probability of B must be

equal or less than the probability

of A.

B A P(B) < P(A)

Rationale: When B occurs,

A also occurs, so the probability

of B cannot exceed the

probability of A.

A B

Sample Space (set of all possibilities)

Interpretation of Linda Problem in terms of Set Inclusion


F: Judge the probability that Linda is a feminist.

T: Judge the probability that Linda is a bank teller.

F & T:Judge the probability that Linda is a feminist and a bank teller.

• Probability Theory: P(F) ≥ P(F & T), P(T) ≥ P(F & T)

• Typical Judgment: P(F) > P(F & T) > P(T)

Why Do People Make Conjunction Errors?

Conjunction Fallacy

Sample Space

F F & T T

Linda Problem: 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.


Tuesday, June 02, 2015: The Lecture Ended Here


Why Do People Make Conjunction Errors?

• Remember: The representativeness heuristic predicts that people judge the

probability based on how similar the individual case is to a typical member

(stereotype) of a group.

• The description of Linda sounds more similar to someone who is a feminist

and a bank teller, than to someone who is only a bank teller.

Criticisms of the Representativeness Explanation of Conjunction Fallacies

stronger similarity

Descriptionof Linda

Bank TellerPrototype

Feminist Bank TellerPrototype

weaker similarity


Criticisms of This Interpretation

• Criticism: The Linda problem is just one problem.

Reply: Same pattern is found with many similar problems.

• Criticism: Maybe people think “bank teller” means someone who is

a bank teller and not a feminist.

• Criticism: Conjunction errors can be eliminated by stating

the question in terms of frequencies instead of probabilities.

Summary re Representativeness Heuristic


Summary re Representativeness Heuristic

• There is nothing wrong with using similarity as a relevant factor

in judging a probability. o The problem is that attention to similarity causes people to ignore

other factors, like base rates, regression effects and set inclusion, that are also relevant to judging probability.

Consequences of the Use of the Representativeness Heuristic

• Base rate neglect

• Conjunction errors

• Overlooking the importance of sample size

• Overlooking regression effects

Two Major Issues in Psych of Decision Making - Probability & Preference

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Representativeness Heuristic Then: Framing Effects Psychology 355: Cognitive Psychology Instructor: John Miyamoto 6/2 /2015: Lecture 10-2 This Powerpoint