Transcript Psychology 100 Chapter 8 Part III Thinking & Intelligence

Psychology
100
Chapter 8
Part III
Thinking
&
Intelligence
Outline
o Cognition, C’ont
o Inductive reasoning
o Limits in reasoning
o Intelligence
Study Question:
• What is the availability heuristic? Give an example of a
reasoning error that might be attributed to availability.
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
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?
04-11-19-29-33-39 or 01-02-03-04-05-06
 The representativeness heuristic - samples are like
the populations that they are pulled from.
• The representativeness heuristic leads to a
number of decision biases
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
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
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
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 onethird 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?
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 %
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
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
Cognition
• Limitations in reasoning
Cognition
• Limitations in reasoning
Cognition
• Limitations in reasoning
– Naïve Physics and Mental Models (McCloskey et al.)
Cognition
• Limitations in reasoning
– Results (A & B)
Cognition
• Limitations in reasoning
– Results (C)
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
Problem
Cognition
Solving
• Limitations in reasoning
– Conjunction fallacy: Judging the probability of a
conjunction to be greater than the probability of a constituent
event.
Likelihood ratio
Very Unlikely 6
5
4
3
Very Likely
Statiscally
Naive
Intermediate Statistically
Sophisticated
Intelligence
• Alfred Binet (1857-1911)
– Role of environment
– Higher-order abilities
– Focus on children
– Binet/ simon test
Alfred Binet
• Stanford-Binet scale: Mental age revision
– IQ (intelligence Quotient) =
(Mental/Chronological)X100
e.g, 15/12 X 100 = 125
• Weschler Adult Intelligence Scale-Rev. (WAISR)
– Verbal and Performance components
•
•
Intelligence
Intelligence
Decisions
Francis Galton (1822-1911)
– Role of heredity
> Speed of processing
> Correlational statistics
Francis Galton
Charles Spearman (1863-1945)
– Intelligence: solo entity or many abilities?
– Invented factor analysis to look at multiple
correlation
Example: Six tests
1.
2.
3.
4.
5.
6.
Vocabulary
Picture completion
Reading comprehension
Object assembly (puzzle)
General information
Block design
Charles Spearman
Intelligence
Hypothetical Correlations among scores
Tests
Vocabulary
Picture
Completion
Reading
Comprehension
Object
Assembly
General
Information
Picture
Completion
0.32
Reading
Comprehension
Object
Assembly
General
Information
Block
Designs
0.60
0.39
0.58
0.44
0.40
0.54
0.38
0.64
0.29
0.64
0.31
0.33
0.60
0.37
Intelligence
• 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
Intelligence
Hypothetical Correlations among scores
Tests
Vocabulary
Picture
Completion
Reading
Comprehension
Object
Assembly
General
Information
Picture
Completion
0.32
Reading
Comprehension
Object
Assembly
General
Information
Block
Designs
0.60
0.39
0.58
0.44
0.40
0.54
0.38
0.64
0.29
0.64
0.31
0.33
0.60
0.37
Intelligence
• Raymond Cattell (1905-1998)
– Discovered two underlying g’s
> Fluid Intelligence
Raymond Cattell
 Raw ability to manipulate information
> Crystallized Intelligence
 Ability acquired through experience
Crystal
Fluid
Test 1
Test 3
Test 5
Test 2
Test 4
Test 6