Griggs Chapter 6: Thinking and Intelligence

Report
Thinking and
Intelligence
Psychology:
A Concise Introduction
2nd Edition
Richard Griggs
Chapter 6
Prepared by
J. W. Taylor V
Thinking

The processing of information to solve
problems and make judgments and
decisions
The Journey…
 Problem
Solving
 Thinking
Under Uncertainty
 Intelligent
Thinking
Problem Solving
Blocks to Problem Solving
Solution Strategies
A Problem

A situation in which there is a goal, but it is
not clear how to reach the goal


A well-defined problem is one with clear
specifications of the start state (where you are),
goal state (where you want to be) and the
processes for reaching the goal state (how to get
there)
An ill-defined problem is a problem lacking clear
specification of the start state, goal state, or the
processes for reaching the goal state
Problem Solving
Involves two steps...
Interpreting
the problem
Trying to solve
the problem
Blocks to Problem Solving
Interpretation blocks

Fixation is the inability
to create a new
interpretation of a
problem

For instance, in the 9-dot
problem, the directions
do not say one cannot
go “outside” the mental
square formed by the
9 dots
Blocks to Problem Solving
Interpretation blocks

Functional fixedness is the inability to
see that an object can have a function
other than its typical one

For example, if you need a screwdriver but
don’t have one, a dime could be used to
serve the purpose of a screwdriver

Limits our ability to solve problems that
require using an object in a novel way

To combat functional fixedness, you should
systematically think about the possible
novel uses of all the various objects in the
problem environment
Blocks to Problem Solving
Strategy blocks

Our past experience with problem solving can lead us to
mental set, the tendency to use previously successful
solution strategies without considering others that are
more appropriate for the current problem


In the two-letter series problems, mental set likely hindered you
because you viewed the letters in the series as single entities
and looked for relationships between them, not each of the
letters as part of some larger entity
Sometimes when searching for new approaches to a
problem, we may experience insight, a new way of
interpreting a problem that immediately gives you the
solution
The Matchstick Problem
Overcoming Blocks

To combat the blocks in problems solving,
ask yourself questions such as:



Is my interpretations of the problem unnecessarily
constraining possible solutions?
Can I use any of the objects in the problem in
novel ways to solve the problem?
Do I need a new type of solution strategy?
Solution Strategies
Algorithm
Heuristic
Algorithm


A step-by-step procedure
that guarantees a correct
answer to a problem
For example, using
multiplication correctly
guarantees you the
correct solution to
a multiplication
problem
Heuristic


A solution strategy that seems reasonable
given your past experiences with solving
problems, especially similar problems
May pay off with a quick correct answer, but
it may lead to no answer or an incorrect one
Types of Heuristics

The anchoring and
adjustment heuristic uses an
initial estimate as an anchor
and then this anchor is
adjusted up or down

For instance, when meeting a
new person, your first impression
forms an anchor of that person,
and you may not process
subsequent information about
that person as fully as it should
be processed
Types of Heuristics

The working backward heuristic is attempting to
solve a problem by working from the goal state
backward to the start state

For instance, consider the following situation: Water lilies
growing in a pond double in area every 24 hours. On the first
day of spring, only one lily pad is on the surface of the pond.
Sixty days later, the entire pond
is covered. On what day is the
pond half covered?” If you work
backward with the fact the pond
is completely covered on the 60th
day, you can solve this question
easily…half of the pond must be
covered on the 59th day.
Types of Heuristics

The means-ends analysis heuristic is
breaking down the problem into subgoals
and working toward decreasing the
distance to the goal state by achieving
these subgoals

For example, when trying to write a major term
paper, students should be encouraged (and
perhaps shown) how to break down this big
task into smaller tasks that, when completed,
will result in a final, large term paper
The Tower of Hanoi Problem
Algorithms vs. Heuristics

When going through a new grocery store looking
for pickles, you could go up and down every
aisle, examining each product until
you found the pickles


This would be using an algorithm
Or, you could look at the signs
above the aisles and look for
the word “Condiments” and
assume that pickles will be on
that aisle

This would be using a heuristic
Thinking Under Uncertainty
Judging Probability
Hypothesis Testing
Probability and Uncertainty

The probability of an event is the likelihood
that it will happen



Probabilities range from 0 (never happen) to 1 (always
happens)
An event with 0.5 probability of occurring is maximally
uncertain because it is equally likely to occur and not
to occur
In addition to judging the uncertainty of events
in our environment, we attempt to reduce our
uncertainty about the world by trying to find out
how various events are related to each other
Judging Probability
Two main heuristics we use to make
judgments about probabilities...
The
Representativeness
Heuristic
The
Availability
Heuristic
The Representativeness Heuristic

A rule of thumb for judging the probability of
membership in a category by how well an object
resembles (i.e., is representative of) that category


You hear about a person who likes to write, read, and
interpret poetry. Is it more likely that this person is:



The more representative the object is, the more probable
A hockey fan? OR
An English professor who likes hockey?
We tend to use the representativeness heuristic
because the mind categorizes information
automatically
The Over-lapping
Set Diagram for
the Linda
Problem
The Conjunction Fallacy


The conjunction rule states that the
likelihood of the overlap of two uncertain
events cannot be greater than the likelihood
of either of the two events because the
overlap is only part of each event
The conjunction fallacy, which can occur
when we use the representativeness
heuristic, is incorrectly judging the overlap of
two uncertain events to be more likely than
either of the two events
The Gambler’s Fallacy

The erroneous belief that a chance process is
self-correcting in that an event that has not
occurred for a while is more likely to occur


People believe that short sequences (e.g., a
series of 9 coin tosses) should reflect the
long-run probabilities
If a coin lands heads 8
times in a row, people
think there is a greater
chance of it being tails
on the 9th toss
The Availability Heuristic

Is the rule of thumb that the
more available an event is in
our memory, the more
probable it is

For instance, we can think of
more words beginning with the
letter “r” than with “r” in the third
position of a word because we
organized words in our
memories by how they begin,
not by their third letters (actually,
words with “r” in the third
position are more frequent)
The Availability Heuristic

An event may be prominent
in our memories because it
happened recently or
because it is particularly
striking or vivid

For instance, deaths from
shark attacks are highly
publicized, creating greater
fear of this mode of death than
of diabetes, which is a far more
likely cause of death
Overcoming Heuristics

To overcome the representativeness and
availability heuristics make sure you have not
overlooked relevant
probability information
and plausible reasons
for differential
availability
Hypothesis Testing
Confirmation
Bias
Belief
Perseverance
Illusory
Correlation
Person-Who
Reasoning
Confirmation Bias

The tendency to seek evidence that confirms one’s beliefs

That is, people do not test their beliefs about the world by trying to
disconfirm them, but rather, by trying to confirm them
The four cards below have information on both sides. On one side of
a card is a letter, and on the other side is a number. Consider this rule:
If a card has a vowel on one side, then it has an even number on the
other side. Select the card or cards that you definitely must turn over
to determine whether the rule is true or false for these four cards.
Illusory Correlation


The erroneous belief that two variables
are related when they actually are not
We tend to focus on instances in which
there seems to be a relationship
between the variables in question,
ignoring all disconfirming instances

If we believe a relationship exists between
two things (e.g., wearing a certain color
shirt and getting a good grade on a test),
then we will tend to notice and remember
instances that confirm this relationship
Belief Perseverance

The tendency to cling to one’s beliefs in the face of
contradictory evidence

Personal-who reasoning is questioning a wellestablished finding because you know a person
(one instance) who violates the established finding

For example, a student may insist
that eating a steak, baked potato
loaded with butter, sour cream,
cheese, and salt for dinner is
healthy because his grandfather
did so every night for 50 years and
lived to be 90 years old
Intelligent Thinking
Intelligence Tests
Controversies
about Intelligence
A Bit of History…


First attempts to develop intelligence tests
took place in late 19th century England and
in early 20th century France
Embedded in the nature-nurture controversy
Francis Galton


Sir Francis Galton was trying to
develop an intelligence test for the
purpose of eugenics, selective
reproduction to enhance the capacities of the human race.
Believed in the genetic determination of intelligence and
thought he could measure intelligence by measuring
various aspects of the human brain and nervous system (a
strong nature emphasis)


Developed tests of sensory abilities and reaction time and tested
thousands of people (found, however, that these were not good
predictors of intelligence)
Nevertheless, invented the basic mathematics behind
correlational statistics
Binet & Simon

In France in the early part of the
20th century, Binet and Simon
were working on the problem of
mental retardation when France
switched to mass public
education


Developed a test to diagnose
children who were subnormal
Published in 1905, this test was the
first accepted test of intelligence
Binet & Simon

Based on the concept of mental
age – the age typically
associated with a child’s level of
performance


If a child’s mental age was less than
their chronological/actual age, they
would need remedial work
Demonstrates a nurture emphasis
on intelligence
Terman


Lewis Terman at Stanford University used Binet and
Simon’s test, after revising it for American school children
In 1916, Terman’s revision became known as the StanfordBinet, and Terman used the classic intelligence quotient
formula by William Stern, a German psychologist




IQ = (mental age/chronological age) X 100
Consequently, when a child’s mental age as
assessed by the test was greater than the child’s
chronological age, the child’s IQ was greater
than 100
When a child’s mental age as assessed by the
test was less than the child’s chronological age,
the child’s IQ was less than 100
Note that the IQ formula itself is no longer used
Weschler



David Wechsler was Chief
Psychologist at Bellevue Hospital in
New York City in the 1930s and
was in charge of adult patients of diverse backgrounds
 The Stanford-Binet was not designed to assess adult
intelligence, and the IQ was particularly problematic for
adults because at some point the mental age levels off but
the chronological age keeps increasing (so a person’s IQ
declines simply because of natural aging)
Developed his own tests, the Wechsler Bellevue Scale, in 1939
(later called the Wechsler Adult Intelligence Scale – WAIS)
Provides test scores for a battery of both verbal tests (such as
vocabulary and comprehension) and performance (nonverbal) tests (such as block design and picture arrangement)
Psychometric Properties
Standardization
Reliability
Validity
Standardization


A process that allows test scores to be
interpreted by providing test norms
The test must be given to a large representative
sample of the relevant population, and the
scores of this sample serve as norms for
interpretation


For example, Terman standardized his Stanford-Binet
on American children of various ages – any child’s raw
score could be compared to the standardization norms
to calculate the child’s mental age
Wechsler collected standardization data for various
adult age groups, and the data for each age group form
a normal distribution
Deviation IQ Scores


To calculate a person’s deviation IQ, Wechsler
compared how far the person’s raw score was
from the mean raw score in terms of standard
deviation units from the mean
To make the deviation scores resemble the IQ
formula, he set the mean to 100 and the
standard deviation to 15

Deviation IQ score = 100 plus or minus (15x the
number of standard deviation units a person’s raw test
score is from the mean for the relevant age group
norms)
Deviation IQ Scores on the WAIS
Reliability
The extent to which the scores for a test are consistent

In the test-retest method, the test is given twice to the
same sample, and the correlation coefficient for the two sets
of scores is computed
A reliable test yields a strong positive correlation
Alternate form reliability can be assessed if multiple forms
of the test are available
 Here, a researcher gives different forms of the test to
the same sample at different times and computes the
correlation coefficient for performance on the two forms
Split-half reliability is determined by correlating
performance of two halves of one given test
 For example, the odd and even number items



Validity

The extent to which a test measures what it is
supposed to measure or predict what it is supposed
to predict



Content validity means that the test covers the content
that it is supposed to cover
Predictive validity means that the test predicts behavior
that is related to what is being measured by the test
It is important to note that if a test is valid, it will also
be reliable

However, a test can be reliable, but not valid (e.g., using
wrist size to measure intelligence; wrist size is quite
reliable, but does not contain validity given the interest in
measuring intelligence)
Controversies About Intelligence
General
vs.
Specific
Nature
vs.
Nurture
Theories of Intelligence

Charles Spearman argued that intelligence test
performance is a function of two types of factors



A g factor (general intelligence)
Some s factor (specific intellectual abilities such as
reasoning)
Believed that the g factor was more important
because people who did well on one subtest
usually did well on most of the subtests, and
people who did poorly on one subtest usually
did poorly on most of the subtests
Theories of Intelligence


L. L. Thurstone argued for the importance of
several mental abilities –
verbal comprehension,
number facility, spatial
relations, perceptual
speed, word fluency,
associative memory, and reasoning
Identified these abilities via factor analysis,
which is a statistical technique that identifies
cluster of test item that measure the same
ability (factor)
Theories of Intelligence

Cattell and Horn proposed two types of
intelligence, which have been of interest to
researchers in aging


Fluid intelligence refers to abstract reasoning,
memory, and the speed of information
processing
Crystallized intelligence refers to accumulated
knowledge and verbal and numerical skills
Theories of Intelligence

Howard Gardner’s theory of multiple intelligences
includes 8 independent types of intelligence
Linguistic
Language ability (e.g., reading, writing, speaking)
Logical-Mathematical Mathematical problem solving & scientific analysis
Spatial
Reasoning about visual spatial relationships
Musical
Musical skills (e.g., the ability to compose and
understand music)
Bodily-Kinesthetic
Skill in body movement and handling objects
Intrapersonal
Understanding oneself
Interpersonal
Understand other people
Naturalist
Ability to discern patterns in nature
Theories of Intelligence

Robert Sternberg’s triarchic theory of
intelligence proposes three types of
intelligence
1. Analytical intelligence is essentially what is
measured by standard intelligence tests, the
necessary skills for good academic performance
2. Practical intelligence could be equated with
good common sense or “street smarts”
3. Creative intelligence is concerned with the
ability to solve novel problems and deal with
unusual situations
Nature vs. Nurture


Most contemporary psychologists believe
that both heredity (nature) and
environmental experiences (nurture) are
important in determining intelligence
The disagreement is over the relative
contribution of each part to intelligence
The Case for Nature

Genetic similarity studies are important in
determining the relative contribution of nature
and nurture to intelligence




Identical twins have 100% genetic similarity
Fraternal twins and siblings have 50% similarity
Two unrelated people have 0% similarity
If intelligence were due to heredity, the
average correlations between intelligence
scores should decrease as genetic similarity
decreases, and researchers have found this to
be the case
The Case for Nurture


However, there are also results that support
environmental influences on intelligence
For example, if identical twins are raised
together, the correlation between their
intelligence test scores is +0.86, but if the
identical twins are raised apart, the
correlation falls to +0.72
Both Nature and Nurture


The average correlation between fraternal
twins raised together (+0.60) is less than that
for identical twins reared apart (+0.72),
indicating the influence of heredity
The average correlation is greater than that for
ordinary siblings reared together (+0.47),
indicating environmental influences because
the environment influences of fraternal twins is
more similar than for ordinary siblings at
different ages
Both Nature and Nurture



There is a modest correlation between the
intelligence test scores of adopted children with
their parents, and this correlation disappears as
the children age
The correlation between the scores for adopted
children and their biological parents, however,
increases as the children age
This stronger relationship between a person’s
intelligence and that of their biological parents
means that nature plays a larger role in
determining a person’s intelligence than
environmental experiences
Heritability

An index of the degree of variation of a trait within a
given population that is due to heredity



For intelligence, most research suggests 50% to 70% of
the variation in intelligence test scores is estimated to be
due to heredity
Because it is not 100%, this means that heredity and
environment interact to determine intelligence
In essence, heredity determines a reaction range,
genetically determined limits for an individual’s
intelligence, but the quality of the person’s
environmental experiences determine where the
individual falls within this range
Caveats


Heritability is a group statistic and not
relevant to individual people
Heritability has nothing to do with the
differences that have been observed
between populations, such as the difference
in scores for Asian versus American
schoolchildren
The Flynn Effect


Refers to the fact that in the United States
and other Western industrialized nations,
average intelligence scores have improved
steadily over the past century
Proposed explanations involve many
environmental factors such as better
nutrition and more education

similar documents