1. Psychology 100
Chapter 8
Part III
Thinking
&
Intelligence

2. Outline Cognition, C’ont Intelligence Study Question:
Inductive reasoning
Limits in reasoning
Intelligence
Study Question:
• What is the availability heuristic? Give an example of a reasoning error that might be attributed to availability. 2

3. Cognition Inductive Reasoning Algorithms and Heuristics
Reasoning under uncertainty: Inductive reasoning
Algorithms versus heuristics
Kahneman and Tversky’s work
Behavioural decision work
Ups and downs of heuristics
Cf. Visual illusions

4. Cognition Inductive Reasoning Algorithms and Heuristics
The representiveness heuristic
E.g., Flip a coin 6 times, which is more likely
HHHHHH or HHTHTT
Which lottery ticket is most likely to win the next 6-49? or
The representativeness heuristic - samples are like the populations that they are pulled from.
The representativeness heuristic leads to a number of decision biases

5. Cognition Inductive Reasoning The representiveness heuristic
The law of small numbers
Who is more likely to have days where more than 60% of the births are male? St. Martha’s or the IWK?
Ignoring base rates
Cancer Screening example
1% of women at age forty who participate in routine screening have breast cancer. 80% of women with breast cancer will get positive results. 9.6% of women without breast cancer will also get positive results. A woman in this age group had a positive mammography in a routine screening. What is the probability that she actually has breast cancer?
The Gambler’s fallacy
The hot hand in basketball

6. Cognition Inductive Reasoning The Availability Heuristic
Our estimates of how often things occurs or are influenced by the ease with which relevant examples can be remembered
This leads to a number of biases
1) Which is a more likely cause of death in the United States: being killed by falling airplane parts or being killed by a shark?
Airplane parts! 30 X more likely than shark attacks.
2) Do more Americans die from a) homicide and car accidents, or b) diabetes and stomach cancer?
Diabetes and stomach cancer by a ratio of nearly 2:1.
3) Which claims more lives in the US: lightning or tornadoes?
Lightning

7. Cognition Inductive reasoning The Availability Heuristic
Important factors
Vividness and Saliency
E.g., the full moon
Repetition effects
Anything that makes recollection easier
Role of the media

8. Cognition Inductive reasoning
Government cutbacks are about take a hit on students. It is expected that 600 people will lose their bursaries. The student union has proposed two alternative programs to fight the cutbacks:
If Program A is adopted, 200 students will have their bursaries saved.
If Program B is adopted (a legal option), there is a one-third probability that 600 students will have their bursaries saved, and a two-thirds probability that no students will have their bursaries saved.
Which program would you favour?

9. Cognition Inductive Reasoning The framing effect (Kahneman & Tversky)
The wording of question in conjunction with the background context can influence the decision.
Both of the previous plans were rejected by the N.S. federation of students, who are now consideing the following:
If Plan C is adopted, 400 students will lose their bursaries.
If Plan D is adopted (another legal option), there is one-third probability that nobody will lose their bursaries, and a two-thirds probability that 600 students will lose their bursaries.
Kahneman & Tversky’s results
Plan A
1/3 Saved
Plan B
P=1/3 Saved
Plan C
2/3 Die
Plan D
P=2/3 Die
72%
28 %
22%
78 %

10. Cognition Inductive reasoning The framing effect (Kahneman & Tversky)
Risk seeking and avoidance
When questions are framed in terms of gains we avoid risk (Prefer A over B)
When framed in terms of losses we are risk-seekers (Prefer D over C)
Other findings relating to the Framing Effect
It is unrelated to statistical sophistication
It is not eliminated when the contradiction is pointed out

11. Cognition Limitations in reasoning Limited domain knowledge
Our cognitive representation of the situation (AKA mental model) often has incomplete information.
Thermostats do not work like water faucets
Hitting the elevator button 5 times is not faster than hitting it once
20° C is not twice as warm as 10 °C
Quasi-magical behaviour

12. Cognition
Limitations in reasoning

13. Cognition
Limitations in reasoning

14. Cognition Limitations in reasoning
Naïve Physics and Mental Models (McCloskey et al.)

15. Cognition
Limitations in reasoning
Results (A & B)

16. Cognition
Limitations in reasoning
Results (C)

17. Cognition Limitations in reasoning Domain of knowledge
Our domain of knowledge concerning physics is poor.
Impetus theory: a pre-Newtonian and incorrect concept concerning “curvature momentum”
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, and also participated in anti-globalization demonstrations.
Rank the following in terms of their likelihood of describing Linda
Linda is a teacher at a local elementary school
Linda is a bank teller and is active in the feminist movement
Linda is an insurance agent
Linda is psychiatric social worker
Linda is a bank teller

18. Problem Solving Cognition Limitations in reasoning
Conjunction fallacy: Judging the probability of a conjunction to be greater than the probability of a constituent event.
Very Unlikely 6 4
Very Likely
Likelihood ratio 5 3
Statiscally
Naive
Intermediate
Statistically
Sophisticated

19. Intelligence Alfred Binet (1857-1911)
Role of environment
Higher-order abilities
Focus on children
Binet/ simon test
Stanford-Binet scale: Mental age revision
IQ (intelligence Quotient) = (Mental/Chronological)X100
e.g, 15/12 X 100 = 125
Weschler Adult Intelligence Scale-Rev. (WAIS-R)
Verbal and Performance components 3

20. Intelligence Intelligence Decisions Francis Galton (1822-1911)
Role of heredity
Speed of processing
Correlational statistics
Charles Spearman ( )
Intelligence: solo entity or many abilities?
Invented factor analysis to look at multiple correlation
Example: Six tests
Vocabulary
Picture completion
Reading comprehension
Object assembly (puzzle)
General information
Block design
Charles Spearman 3

21. Intelligence Hypothetical Correlations among scores Tests 0.32 0.60
Picture
Completion
Reading
Comprehension
Object
Assembly
General
Information
Block
Designs
Tests
Vocabulary
0.32
0.60
0.39
0.58
0.44
Picture
Completion
0.40
0.54
0.38
0.64
Reading
Comprehension
0.29
0.64
0.31
Object
Assembly
0.33
0.60
General
Information
0.37

22. Intelligence g Spearman’s theory
All the tests are positively correlated
“g”: General ability, which effects all tests
The tests are not perfectly correlated
“s”: Specific abilities, which effect each test g S1 S2 S3 S4 S5 S6
Test 1
Test 2
Test 3
Test 4
Test 5
Test 6 3

23. Intelligence Hypothetical Correlations among scores Tests 0.32 0.60
Picture
Completion
Reading
Comprehension
Object
Assembly
General
Information
Block
Designs
Tests
Vocabulary
0.32
0.60
0.39
0.58
0.44
Picture
Completion
0.40
0.54
0.38
0.64
Reading
Comprehension
0.29
0.64
0.31
Object
Assembly
0.33
0.60
General
Information
0.37

24. Intelligence Raymond Cattell (1905-1998) Discovered two underlying g’s
Fluid Intelligence
Raw ability to manipulate information
Crystallized Intelligence
Ability acquired through experience
Test 1
Test 3
Test 5
Test 2
Test 4
Test 6
Fluid
Crystal 3