Transcript Introduction to Psychology

Module 28
Thinking
Can you get out?
Thinking
 Cognition
 mental activities associated with thinking,
knowing, remembering, and communicating
 Cognitive Psychologists
 study these mental activities
 concept formation
 problem solving
 decision making
 judgment formation
Thinking
 Concept
 mental grouping of similar objects, events,
ideas, or people
 set of ideas and properties which can be used to
group things together (abstract – justice, concrete –
furniture).
 Prototype
 mental image or best example of a category
 matching new items to the prototype provides a
quick and easy method for including items in a
category (as when comparing feathered creatures
to a prototypical bird, such as a robin)
Problem Solving
Nine Dot
Tower of Hanoi
Cognition Demos
Problem Solving
Petals around a Rose Game
Riddles
Problem Solving
Petals around a Rose Game –
 How did you solve the problem?
 What information did you attend to in
attempting to solve the problem? Why?
 Do obstacles in problem solving reflect problems
in isolating relevant information or are they also
the result of frustration or performance anxiety?
Problem Solving
Levine’s Theory of Hypothesis Testing –
 We begin a concept-formation task with a “pool”
of hypotheses.
 From this “pool” we select a “working hypothesis”
that determines our initial responses.
 As long as feedback is consistent with our working
hypothesis we retain it.
 If feedback contradicts our hypothesis, we shift and
choose a new working hypothesis that is consistent
with the current feedback, and also consistent with
as much of the feedback as we can remember.
Thinking
Write out the specific steps you would take to
solve the following problem –
2876948 ÷ 4
Answer - 719237
Thinking
 Algorithm
 methodical, logical rule or procedure
that guarantees solving a particular
problem
 contrasts with the usually speedier–but
also more error-prone--use of heuristics
Thinking
 Heuristic
 simple thinking strategy that often
allows us to make judgments and
solve problems efficiently
 usually speedier than algorithms
 more error-prone than algorithms
Thinking
Unscramble
SPLOYOCHYG
 Algorithm
 all 907,208 combinations
 Heuristic
 throw out all YY combinations
 other heuristics?
Thinking
 Insight
 sudden and often novel realization of the solution to
a problem
 contrasts with strategy-based solutions
 Confirmation Bias
 tendency to search for information that confirms
one’s preconceptions
 Fixation
 inability to see a problem from a new perspective
 impediment to problem solving
The Matchstick
Problem
 How would you
arrange six
matches to form
four equilateral
triangles?
The Candle-Mounting
Problem
 Using these
materials, how
would you
mount the
candle on a
bulletin board?
Thinking
 Mental Set
 tendency to approach a problem in
a particular way
 especially a way that has been
successful in the past but may or
may not be helpful in solving a new
problem
 Train Example
Thinking
 Functional Fixedness
 tendency to think of things
only in terms of their usual
functions
 impediment to problem solving
The Matchstick
Problem
 Solution to the
matchstick
problem
The Candle-Mounting
Problem
 Solving this
problem
requires
recognizing that
a box need not
always serve as
a container
Heuristics
 Representativeness Heuristic
 judging the likelihood of things in
terms of how well they seem to
represent, or match, particular
prototypes
 may lead one to ignore other relevant
information
REPRESENTATIVENESS: Base predictions on similarity to
other events or situations (but we may ignore other relevant
information such as the actual frequency of events)
Assume that all families with exactly six children are surveyed in a city.
In 100 of these families the exact order of births of boys (B) and girls (G)
was G-B-G-B-B-G. What is your guess as to the number of families in
which the exact order of birth was each of the following? Estimate a
number for each of the following
following:(adapted from Kahneman & Tversky, 1973):
1. G-G-B-G-B-B
For each of these
2. B-B-B-B-B-B
possibilities, the
3. G-B-B-G-B-G
expected number
4. B-B-B-G-G-G
of families is 100.
Statistically, all four alternatives are equally likely (50% B, 50% G)
Sex of previous births doesn’t affect sex of next birth.
REPRESENTATIVENESS: Which birth orders “look” random?
Most people misunderstand how randomness works.
They expect things to “even out” in the short run.
REPRESENTATIVENESS: Base predictions on similarity to
other events or situations (but we may ignore other relevant
information such as the actual frequency of events)
Imagine that you just met a man named Steve. Steve is very shy and
withdrawn, invariably helpful, but with little interest in people or in
the world of reality. A meek and tidy soul, he has a need for order
and structure and a passion for detail. Which statement about Steve
is more likely
likely:(adapted from Kahneman & Tversky, 1973):
a. Steve is a retail salesperson (3,964,680 in the United States)
b. Steve is a librarian (139,460 in the United States)
c. Both “a” and “b” are equally likely (within 5% of each other)
Approximately 28.4 retail salespersons for every librarian.
Steve is much more likely to be a retail salesperson.
But Steve’s description fits our stereotype of librarians.
Data from the Bureau of Labor Statistics (2000) survey
At a party there are 30% engineers and 70% lawyers.
You meet a person at this party and find out he
is a 45 year old man. He is married and has 4
children. He is generally conservative, careful
and ambitious. He shows no interest in politics
and social issues and spends most of his free
time on hobbies, that include home carpentry,
sailing and mathematical puzzles.
How likely is it that this person is an engineer?
Linda is thirty-one 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 antinuclear demonstrations.”
A.
B.
C.
D.
E.
F.
G.
H.
Linda is a teacher in an elementary school.
Linda works in a bookstore and takes yoga classes.
Linda is active in the feminist movement.
Linda is a psychiatric social worker.
Linda is a member of the League of Women Voters.
Linda is a bank teller.
Linda is an insurance salesperson.
Linda is a bank teller and is active in the feminist
movement.
Representativeness Heuristic:
 We make judgment of category in a simple
way: how representative is this person of the
prototypical member of the category?
 Linda problem
 Lawyer problem
 Misconceptions of chance
 HHHHHHHH
 THTHHTTH
 HHHHTTTT
Heuristics
 Availability Heuristic
 estimating the likelihood of events
based on their availability in memory
 if instances come readily to mind
(perhaps because of their vividness),
we presume such events are common
 Example: airplane crash
Availability Heuristic
 How we judge the frequency or likelihood of
an event. That is to say, we ask how likely is
“event X” to occur?
 If we can easily think of an example, we think
it is more likely to happen.
 But, vivid events are remembered easier
AVAILABILITY: Base predictions on information that is easy
to think about or recall (but it may not mean it is more likely)
Are there more words in the English language that begin with K or
have K as their third letter? (adapted from Tversky & Kahneman, 1973)
a. There are more words that begin with K (easier to think of examples)
b. There are more words that have K as their third letter
c. Both “a” and “b” are about the same (within 5% of each other).
In Slovic, Fischhoff, & Lichtenstein’s (1976) study, few participants
For each correctly
guessed
of the following
on these
pairs,
pairs
indicate
(see the
which
% correct
causeat
ofleft)
death
Thewas
more
more
frequent in
common
cause
the United
of death
States
is identified
during the
in green
1970’s:
along with its ratio
1.
compared
A. Stroke,
to the less
or common cause B.
of All
death.
accidents
2.
20%
A. Stroke
Diabetes,
(1.85
or to 1)
B. All
Breast
accidents
cancer
3.
23%
A. Diabetes
Lung cancer,
(1.25orto 1)
B. Breast
Stomach
cancer
cancer
4.
25%
A. Lung
Appendicitis,
cancer, or
B. Stomach
Pregnancy
cancer (1.25 to 1)
5.
17%
A. Appendicitis
Tornado, or (2.00 to 1)
B. Pregnancy
Asthma
42%
A. Tornado, or
B. Asthma (20.90 to 1)
Thinking
 Overconfidence
 tendency to be more confident than
correct
 tendency to overestimate the
accuracy of one’s beliefs and
judgments
 Example – Parents asking how you did
on a test.
Thinking
Are We Scaring Ourselves to Death?
Thinking
 Framing
 the way an issue is posed
 how an issue is framed can
significantly affect decisions and
judgments
 Example: What is the best way to
market ground beef--as 25% fat or
75% lean?
 General Example
Thinking
 People are “risk averse” – the first dollar
that one acquires is worth slightly more
than the second, the second slightly more
than the third, and so on, until those with a
lot of money valued each additional dollar
very little.
 Others would say that people are “loss
averse” – losses loom larger than gains.
Thus, people avoid fair bets because the
prospect of gain isn’t worth the pain of
loss.
Thinking
 Belief Bias
 the tendency for one’s preexisting beliefs to
distort logical reasoning
 sometimes by making invalid conclusions
seem valid or valid conclusions seem invalid
 Belief Perseverance
 clinging to one’s initial conceptions after the
basis on which they were formed has been
discredited
Thinking
Is the conclusion in the following valid?
No cars run when they’re out of fuel.
My car is out of fuel.
Therefore my car does not now run.
YES! The conclusion of the statements is valid.
Thinking
Is the conclusion in the following valid?
Some A are B.
Some B are C.
Therefore some A are C.
At first glance this may appear logical, however it
is not.
Thinking
Some women are Democrats.
Some Democrats are men.
Therefore some women are men.
Thinking
Some of the beekeepers are artists.
None of the chemists are beekeepers.
Therefore some of the artists are not chemists.
Valid – Yes!
Some birds can swim.
No fish are birds.
Therefore some animals that swim are not fish.
Artificial Intelligence
 Artificial Intelligence
 designing and programming
computer systems
 to do intelligent things
 to simulate human thought processes
 intuitive reasoning
 learning
 understanding language
Artificial Intelligence
 Computer Neural Networks
 computer circuits that mimic the
brain’s interconnected neural cells
 performing tasks
 learning to recognize visual patterns
 learning to recognize smells