Unit IV Web AssignmentThis assignment allows you to demonstrate .docx

Unit IV Web Assignment
This assignment allows you to demonstrate the following
outcomes:
4. Identify the basic principles of marketing.
4.1 Identify both the potential target market and the potential
market segment that a business intends to reach.
4.2 Identify the connection between a commercial’s message
and the specific market segment.
4.3 Identify how marketing research contributes to reaching a
specific audience.
Instructions: The purpose of this assignment is for you to
identify the basic principles of marketing. First, locate two
commercials by Coca-Cola that are available on your favorite
video-hosting website (i.e., YouTube). Insert the links to each
video in the table below. Then, view each commercial while
considering the questions in the table, and complete the table by
answering the questions as they pertain to each commercial.
Finally, save all of your work to this template and submit it in
Blackboard for grading.
Items to Identify
Commercial One
Commercial Two
1. Provide a link to each commercial.
2. What product/service is offered in the commercial?
3. What message does the commercial send to the audience?

4. How would you describe the general target market for the
company? Be sure to include the following as you describe the
target market:
· Age range
· Likes/dislikes
· Interests
· Life goals
5. How would you describe the specific market segment that the
commercial is designed to reach? Be sure to include the
following as you describe the market segment:
· Age range
· Likes/dislikes
· Interests
· Life goals
6. What is the connection between the commercial’s message
and the specific market segment?
7. Prior to producing the commercial, how would utilizing a
marketing research method(s) contribute to reaching the
intended audience?

of 2
Measuring Intelligence
INTRODUCTION
There is a good chance that you have had at least some
experience being interviewed—perhaps a job interview or a
college admittance interview. What do interviewers want to find
out about you? You might have heard questions similar to these:
“What experience do you have waiting tables?”
“Why does studying literature interest you?”
“How would you react if you saw a kid struggling in the
pool while you are the lifeguard?”
“So you’re Bill Hanson’s kid? What’s that old rascal up to
these days?”
Except for the last question (and let’s face it; sometimes it’s
more about who you know than whatyou know), these are
questions meant to assess your skills as well as your
intelligence—which is related to skill. But what is intelligence?
Is it just one thing or a combination of things? Intelligence is
defined as your ability to use knowledge to do many things:
• reason
• make decisions
• make sense of events
• solve problems
• understand complex ideas
• learn quickly
• adapt to environmental challenges
Interviews are used to gauge intelligence, but increasingly
standardized psychometric tests—tests that examine what
people know and how they solve problems—are used to measure
intelligence as well. Standardized psychometric tests tend to fall
under two types: achievement tests assess people’s current level

of skill and of knowledge, while aptitude tests try to predict
what skills and jobs people will be good at in the future.
In this ZAPS lab, you will have a chance to experience some
specific types of questions that appear on many aptitude tests—
including tests used to determine suitably to graduate school
programs and jobs.
Instructions:
In this section you can try your hand at answering 20 reasoning
problems. There are 4 different question types—all of which are
sometimes used in intelligence tests. For each category there are
5 questions.
The first thing you should do is relax. This is not an
intelligence test. Among the many differences between this and
an actual intelligence test is the fact that you aren't being timed,
and actual intelligence tests are usually timed in some manner.
However, if you find it is taking too long to answer a question,
just guess. Most intelligence tests are also fairly long, and they
get progressively tougher.
After you click an answer choice, press 'Submit Answer' to see
if your answer was correct or incorrect and to see some
feedback.
The first task involves Mathematical Sequences. For each
sequence, indicate from the choices which number comes next
in the sequence. Note that there is only one correct answer.
You'll see five mathematical sequence questions.
Your aim in this ZAPS lab is to:
Click or tap a choice to answer the question.
a) Choose one topic area from the given four, and answer
questions relevant to that topic.
b) Answer questions in topic areas of your choice, as quickly as
possible.
c) Participate in a timed intelligence test, and gain information
about your overall intelligence.
d) Answer questions in the different topic areas, without
focusing on speed.

EXPERIENCE
Instructions
The next task involves Analogies (“________ is to x as y is to
_________.”) For each analogy, choose an option from each of
the two columns. Note that there is only one correct
combination. You will see five analogy questions.
Instructions:
The next task involves Dynamic Images. For each question, you
will see a series of 5 images. These images are related to each
other in a some way. Because of this relation, only one of the
images on the bottom can succeed the sequence (in the space
indicated with a question mark). Select that image by clicking
on it. Note that there is only one correct option. You will see
five dynamic image questions.
Instructions :
The final task tests Spatial Ability. For each question, you will
see an "unfolded image" on top. Below that, folded images are
presented. Your task is to click on the folded image that is
identical to the unfolded image. Note that there is only one
correct option. An example of this type of problem is given
below.
(The correct answer for this sample problem is #1—the left-
most choice.) You will answer five spatial ability questions.

Data Introduction:
In this ZAPS lab there were three distinct types of tasks, and
four categories. Your data will show the number of correct
answers (out of five) in each of the four categories shown in
parentheses:
1. Numerical tasks test your mathematical skill (numerical
insight)
2. Word tasks test your verbal skills (analogies)
3. Picture tasks test your abstract ability (dynamic images) and
spatial insight (spatial insight)
Would you want your intelligence to judged solely on the basis
of the results of intelligence tests? Why or why not?
You will initially receive full credit for any answer, but your
instructor may review your response later.
Submit Answer
YOUR DATA
DISCUSSION
Standardized psychometric tests are used (sometimes
controversially) at the elementary and high school levels to
assess both individual student achievement and—in some
cases—to judge the performance of teachers and schools. In
some states they are used at the higher education level as well.
Less controversially, intelligence tests are also widely used to
detect learning disabilities in children.
Intelligence tests are used to measure the skill levels and
aptitudes of potential employees. In combination with
interviews, employers may use the scores on the different
subtypes of intelligence tests to assess an applicant's aptitude to
perform a specific job. For example, it may be more important
for an architect to have better spatial insight, and a lawyer to
have strong verbal and analytical skills.

Intelligence tests have a long history. The Tang Dynasty (618-
907 A.D.) of China, for example, used difficult civil service
examinations to select scholar-officials for government posts.
Modern Western intelligence tests date back over a hundred
years to Alfred Binet, a psychologist commissioned by the
French Ministry of Education to devise a way to identify
children with learning problems. As part of this effort, Binet
conducted a measurment of individuals called their mental
age—determined by comparing a child's test score with the
average score for children for each chronological age.
Another psychologist, Wilhelm Stern, created a better known
measurement of intelligence called intelligence quotient
(IQ) partly based on mental age. A child's IQ is determined by
dividing mental age by chronological age and multiplying the
result by 100. Adult IQs are measured in comparison with the
average adult, and not with adults at different ages. Named for
psychologist David Wechsler, the WAIS (Wechsler Adult
Intelligence Scale) is another widely used intelligence test.
WAIS tests are divided between verbal and performance parts—
in a similar but much more sophisticated way to the Experience
in this ZAPS lab. The WAIS verbal part measures
comprehension, vocabulary, and general knowledge and
includes tests of working memory. The performance part
involves nonverbal tasks and measures reaction times.
One question that has plagued intelligence testing is whether
there is a kind of general, overarching intelligence, or more
discrete and independent kinds of intelligence. Many
intelligence tests include several subtests. Appreciating a
person’s intelligence, therefore, requires an understanding of
how the results of the subtests relate to one another. Charles
Spearman invented a statistical technique known as factor
analysis to measure the relationships among different skills and
intelligence test tasks. The essence of factor analysis is to
identify traits that correlate, and those that can be significantly
differentiated from other traits. For example, there is usually a
moderate-to-high positive correlation between scores on verbal

portions and math portions of IQ tests. Given the strength of
correlations among different skills, Spearman claimed that
performance on individual sections of intelligence tests was due
to the existence of a g factor (for "general intelligence"). In
other words, what makes someone score high (or low) on both
the verbal and math sections of a test is general intelligence.
Even if there are moderate correlations between someone’s
verbal and math scores, we can all think of people (perhaps
ourselves) who are good at reading comprehension and
vocabulary, but can't calculate a 20 percent tip. Or maybe one
of your friends is a mathematical genius, but has no social skills
at all. A prominent view in contemporary psychology is that
of modularity, which claims that cognitive skills are mutually
independent of each other. Robert Sternberg popularized this
notion in the 1980s by claiming that intelligence is made up by
three components: an analytical, creative and practical
component. The analytical component refers to the capacity for
using learned strategies while solving problems. The creative
component involves the ability to solve problems in more novel
and creative ways (e.g., "thinking outside the box"). The
practical component of intelligence refers to "real world"
problem solving skills ("How can I stop my neighbor’s dog
from barking while I study for my exam?"). The concept of
modularity has also been supported by research in
neuropsychology and language.
Based on the research in neuropsychology and psychology,
Howard Gardner proposed that there are actually eight distinct
kinds of intelligence:
1. verbal
2. visual-spatial
3. logical-mathematical
4. musical
5. intrapersonal (the ability to understand oneself)
6. interpersonal (the ability to understand other people)
7. naturalistic (the ability to understand patterns in nature)
8. bodily-kinesthetic (the ability to learn and create complex

patterns of movement)
Gardner based these divisions on studies conducted on savants,
who are mentally challenged in most areas, but extremely gifted
in at least one. For instance, some savants demonstrate
remarkable mathematical or musical abilities, but are otherwise
profoundly disabled. Gardner’s multiple intelligences theory is
popular with many educators because it is much more flexible
than standard measures of intelligence (verbal and logical-
mathematical).
Based on what you know and have seen, do you think either of
the two broad theories of intelligence—"general intelligence" or
"modularity"—is more plausible than the other? Why?
Submit Answer
LEARNING CHECK
1. Maya enjoys reading, writing and public speaking. Words are
her strength overall, and she excels in Literature and Drama at
school. She despises Mathematics, and always does badly on
quizzes and exams. According to contemporary views on
intelligence, Maya’s advanced verbal skill and relatively poor
mathematical skill can be attributed to:
a) Modularity
b) the “g” factor
c) she is a savant
2. Of the four areas of intelligence that appeared in the
Experience (numerical insight, analogies, dynamic images and
spatial insight), a writer would most likely excel in analogies,
and a sculptor would probably excel in spatial insight.
a) True

b) False
3. According to the multiple intelligences theory proposed by
Howard Gardner, a professional ballerina and a professional
football player would likely have _________ intelligence in
common.
a) bodily-kinesthetic
b) musical
c) intrapersonal
d) logical-mathematical
Decision Making
INTRODUCTION
Think about your class schedule for this semester. Chances are
that it came together as a result of you engaging in decision
making—the selecting of the best alternative from among
several options. For instance, why are you in this psych course?
Perhaps it’s because you decided to major in psychology instead
of in business or English or to remain undeclared in your major.
Maybe this is an elective course, and you choose it over another
course in economics or geology. You may be taking this course
because it is a distributional requirement, and perhaps you had
to decide among sections offered in the morning, afternoon, and
evening.
In theory, the mental process for making any decision is simple:
You determine the relative costs and benefits of each choice,
then rationally choose the option with the most favorable
outcomes. For instance, if you chose psychology for an elective

course, perhaps you weighed the value of what you would learn
in psychology against that of some other course, such as
geology, and then rationally choose psychology because you
expect it will be more applicable to your life now or in the
future.
Until the late 20th century, this highly rational process for
decision making is how most psychologists and economists
assumed people acted. Expected utility theory, the dominant
theory of decision making, held that people simply calculate the
“expected utility,” or value, of the possible choices for any
decision they need to make and choose the option that
maximizes the desired outcome.
In the 1970s, psychologists Daniel Kahneman and Amos
Tversky realized that they themselves made less-than-rational
decisions that were not consistent with expected utility theory.
They wondered why and engaged in research in which they
asked people what they would do in different situations and
under different contexts to evaluate whether people always
rationally maximize expected value.
In this ZAPS lab, you will participate in a series of “mini-
experiments” from Kahneman and Tversey’s classic research on
decision making.
Instructions
You will answer a series of ten questions. Many of the
questions describe a hypothetical scenario and ask what you
would do in that scenario. Other questions simply ask you to
make a judgment or estimate some value. Your responses to
these questions will not be graded; just try to answer each one
with the choice, judgment, or estimate you would honestly make
if you were notparticipating in a psychology experiment.
EXPERIENCE
Question 1: You have a chance to participate in a guessing
game. A coin is tossed, and you must guess whether heads or

tails will come up.
· If you guess heads and heads comes up you will win $1.
· If you guess tails and tails comes up you will win $2.
· If you’re wrong, you won’t lose anything.
Would you participate in the game?
Yes, I would participate.
No, I would not participate.
EXPERIENCE
Question 1: You have a chance to participate in a guessing
game. A coin is tossed, and you must guess whether heads or
tails will come up.
· If you guess heads and heads comes up you will win $1.
· If you guess tails and tails comes up you will win $2.
· If you’re wrong, you won’t lose anything.
Would you participate in the game?
Yes, I would participate.
No, I would not participate.
Experimenters often ask questions like this one to make sure
participants are answering honestly. You were asked whether
you would participate in a “bet” where you always win and can
never lose. Obviously, everyone should participate. So if a
participant answers “no” to such a question, it is a red flag to
the experimenter that the participant is not paying attention or
is not taking the experiment seriously.
Question 2: You have a chance to gamble on the toss of a coin.
· If the coin shows heads, you will win $125.
· If the coin shows tails, you will lose $100.
Would you accept the gamble?
Yes, I would accept the gamble.
No, I would not accept the gamble.

In the case of Question 2, you would win $125 if heads comes
up but lose only $100 if tails comes up, so the expected value is
125 - 100 = +25, and expected utility theory predicts everyone
should take the bet.
However, as this graph indicates, 80% of respondents in fact
say that they would not take the bet. Kahneman and
Tversky’s prospect theoryholds that people evaluate
the psychological prospects of the choices, rather than the
objective expected values. In this case, you must weigh the fear
of losing $100 against the hope of gaining $125, and for most
people, the former is more salient than the latter. Research has
shown that people are generally more eager to avoid costs than
potentially gain benefits, a principle known as loss aversion.
Question 3: Would you accept a gamble that offers:
· a 10% chance to win $95
· a 90% chance to lose $5?
Yes, I would accept the gamble.
No, I would not accept the gamble.
Question 4: Would you pay $5 to participate in a lottery that
offers:
· a 10% chance to win $100
· a 90% chance to win nothing?
Yes, I would participate in the lottery.
No, I would not participate in the lottery.
The expected values are identical in this question and the
previous one:
· In Question 3’s gamble, there is a 10% chance you will win
$95 (expected value = 0.1 × $95 = +$9.50), and a 90% chance to
lose $5 (expected value = -$4.50).
· In Question 4’s lottery, you also have a 10% chance to win
$95 ($100 minus the $5 you paid for the ticket) and a 90%
chance to lose $5 (the price you paid for the ticket).

However, as the graph shows, most people would not accept the
gamble, but would participate in the lottery.
This illustrates another principle demonstrated and described by
Tversky and Kahneman: framing. The way a choice is worded
(framed) changes the psychology of how people perceive the
choice, and prospect theory says it is the value of the choices’
prospects that determines what people do, as opposed to the
objective expected values.
Note that the actual expected value is (0.1 × $95) − (0.9 × $5) =
+$5 for both the gamble and the lottery, so according to
expected utility theory, everyone should both take the gamble
and buy the lottery ticket, since doing nothing has an expected
value of $0.
Question 5: You have decided to see a play where admission is
$10 a ticket. As you enter the theater, you discover that you
have lost a $10 bill. Would you still pay $10 for a ticket to the
play?
Yes, I would still pay for a ticket.
No, I would not pay for a ticket.
Question 6: You have decided to see a play where admission is
$10 a ticket. As you enter the theater, you discover that you
have lost the ticket. Would you pay $10 for another ticket to the
play?
Yes, I would pay for another ticket.
No, I would not pay for another ticket.
Questions 5 and 6 are another pair of questions that illustrate
framing effects.
You should recognize that, as with the previous two questions,
the objective situation is identical in both cases: you just lost
something worth $10 (a ten dollar bill in Question 5 and your
first ticket in Question 6), and you must now choose whether to
spend another $10 to see the play.
But, as with Questions 3 and 4, people do not respond

identically to the two situations. As shown in the graph, a
majority of people say they would still pay for a ticket after
discovering they’ve lost a $10 bill, but a majority of people say
they would not pay for a second ticket after losing their first
one.
Question 7: The coast redwood (Sequoia sempervirens) is the
tallest species of tree on Earth. Do you think the height of the
tallest redwood tree in the world is more or less than 1,200
feet? (No Googling, please.)
more than 1,200 feet
less than 1,200 feet
Question 8: Do you think the height of the tallest redwood tree
in the world is more or less than 400 feet? (Again, please
refrain from Googling.)
more than 400 feet
less than 400 feet
Questions 7 and 8 illustrate anchoring, which is another type of
framing effect described by Kahneman and Tversky.
As shown in the first set of bars (“High Anchor”) in the graph at
right, when people are first asked whether the tallest redwood is
more or less than 1,200 feet (as you were), most respond in the
second question that the tallest redwood is more than 400 feet
(green bar).
However, when people are first asked whether the tallest
redwood is more or less than 100 feet, most respond to the
second question by estimating that the tallest redwood
is less than 400 feet (red bar in the “Low Anchor” condition).
This shows that when making their estimates in the second
question, people start from the “anchor” established in the first
question. (In fact, the actual height of the tallest measured
redwood tree is 379 feet.)
Question 9: Kristen is 31 years old, single, outspoken, and very

bright. She majored in English literature. As a student, she was
deeply concerned with issues of discrimination and social
justice, and later she participated in the “Occupy Wall Street”
demonstrations of 2011.
Which of the following statements is more probable?
Kristen is a bank teller.
Kristen is a bank teller and a liberal Democrat.
This question illustrates yet another discovery by Tversky and
Kahneman. If you think about the choices carefully, you will
realize that the first choice must be more likely than the second
choice, because every liberal Democrat bank teller is also a
bank teller, while not all bank tellers are liberal Democrats. But
Kristen sounds so much like a liberal Democrat (and so unlike a
bank teller) that most people mistakenly choose the second
option, as shown in the graph at right.
Tversky and Kahneman postulated that when making decisions
under conditions of uncertainty, we automatically
apply heuristics (aka “rules of thumb”) to try to reduce the
uncertainty. In this case, we apply the representativeness
heuristic: Kristen’s description is more representative of a
stereotypical liberal Democrat than of a stereotypical banker, so
our intuitive heuristic tells us that the second option seems
more plausible. Working out which option is
more probable takes more mental work, so we may not bother to
do it—and even when we do, the pull of the more plausible
choice is difficult to overcome.
Question 10: If a random word is taken from an English
dictionary, is it more likely that the word starts with a K, or that
K is the third letter? (Again, no Googling please.)
It is more likely that the word starts with a K.
It is more likely that K is the third letter of the word.
Here, you probably applied another of the Kahneman and

Tversky-discovered heuristics: availability. Lacking the time to
go through every word in the dictionary and answer the actual
question, you substituted a different question (“is it easier to
think of words that start with K or have K as the third letter?”),
which is answerable: words that start with K (e.g. kangaroo,
kick, ketchup) come to mind much more readily than words with
K as the third letter (e.g. ask, bike, cake). As the graph at right
shows, 90% of people go with this intuition.
As is the case with most heuristics, availability works, on
average, better than no decision rule at all, but it can fail us at
times, including this one: in fact, there are about three times as
many English words whose third letter is K than there are words
that start with K.
Data Introduction
The ten questions you answered in this ZAPS lab represent
several different “mini-experiments” conducted by Tversky and
Kahneman. The results table you will see in this section is
designed to summarize all the data together. There is one row
for each question, with the following columns:
· A summary of the question you were asked.
· The “Correct” response to the question. In some cases there is
a factually correct response (e.g., the height of the tallest
redwood tree); in other cases, we list as the correct response the
response predicted by expected utility theory—the one that an
entirely rational respondent would choose to maximize his or
her expected value. After the correct response we give the
percentage of people in a neutral reference group (a group of
people who are not currently taking a psychology class) who
chose this option.
· The “Incorrect” response to the question, and percentage of
people in the reference group who chose this response.
· Your response.
· The percentages of other students in your class who chose the
“Correct” and “Incorrect” responses.
The response cells in the table are color-coded to help highlight
the most important results:

· The responses chosen by the majority of people in the
reference group are shown on a blue background, while the
responses chosen by the minority are shown on an orange
background.
· Questions where people generally make a systematic “error”
(compared to expected utility theory) can thus be easily seen:
they are the rows where the “Correct” answer cell is orange and
the “Incorrect” answer cell is blue.
· Your response is shown in blue if it matches the majority
response, or orange if it matches the minority response.
Your answer for each question will be shown in blue type if
which of the following applies?
Your answer matches the answer that the majority of people
typically give.
Your answer matches the answer that the minority of people
typically give.
Your answer does not match the one predicted by expected
utility theory.
Your answer matches the one predicted by expected utility
theory.
YOUR DATA
DISCUSSION
By taking the time to ask people what decisions they would
make and carefully phrasing their questions in interesting ways,
Kahneman and Tversky revolutionized the study of decision
making, showing that people evaluate the psychological
prospects of the choices, rather than the objective expected
values. Put another way, expected utility theory predicts what
individuals would do if they were entirely rational; Tversky and
Kahneman’s prospect theory research revealed what most

people actually do.
For his work on decision making, Kahneman won the Nobel
Prize for Economics in 2002; Tversky would have shared the
prize had he not passed away in 1996.
As you saw and read as you worked through the examples,
Tversky and Kahneman revealed some of the specific ways in
which psychological prospects can substantially differ from
expected values. Here is a review:
· The principle of framing states that the way a choice is
worded can substantially change its psychological prospects.
One of the most powerful framing effects involves whether a
choice is framed in terms of gains or losses. For example,
people prefer lotteries, which frame the prospect in terms of
potential gains, to straight-up bets which frame the prospect in
terms of how much one might lose.
· Context can have profound effects on people’s judgments
when making estimates, as when anchoring causes one judgment
to be affected by the results of a previous estimate, even though
both judgments were intended to have been made independently.
· When making decisions under conditions of uncertainty, we
automatically apply heuristics (aka “rules of thumb” or mental
shortcuts) to try to reduce the uncertainty. For example,
the representativeness heuristic directs us to choose the
most plausible conclusion when we are unsure of which
conclusion is most probable, and the availability heuristic tells
us to replace a question we cannot realistically answer with a
similar question whose answer is more readily available. Like
other heuristics, representativeness and availability usually
provide us with a “better than nothing” answer, but sometimes
the pull of a heuristic is so strong that we ignore more
definitive information that really should form the foundation of
our judgment.
Why might people sometimes have difficulty reasoning
scientifically? Do you think that if people had more advanced
training in areas such as statistics, economics, and psychology,
they would make decisions that are more economically

rewarding?
Submit Answer
LEARNING CHECK
Answer the questions below to complete this ZAPS activity.
Your performance in this section accounts for 10% of your
grade.
Consider the following gamble. A standard pack of playing
cards, including 26 red cards (diamonds and hearts) and 26
black cards (spades and clubs), is shuffled, and the card on top
of the deck is turned face up.
· If the face-up card is red, the gambler wins $11
· If the face-up card is black, the gambler loses $9.
Expected value theory predicts which of the following?
a) Most people will accept the gamble.
b) Most people will reject the gamble.
c) People will be equally likely to accept or reject the gamble.
Now consider the same gamble again:
· If the face-up card is red, the gambler wins $11
· If the face-up card is black, the gambler loses $9.
Prospect theory predicts which of the following?
a) Most people will accept the gamble.
b) Most people will reject the gamble.
c) People will be equally likely to accept or reject the gamble.
In a 1997 experiment, participants were first asked whether the
Indian leader Mahatma Gandhi died before or after a certain
age, then were asked to guess the precise age at which Gandhi
died. People who were first asked whether or not Gandhi died at

age 9 gave an estimate (50 years) much lower on average than
those who were first asked whether or not he died at age 140
(67 years). This experiment is a perfect example of which of the
following?
a) loss aversion
b) the anchoring effect
c) the representativeness heuristic
d) the availability heuristic
chapter 13
Problem Solving
and Intelligence
General Problem-Solving Methods
People solve problems all the time. Some problems are
pragmatic ("I want to Tom borrowed my car. How can I get
there?"). Others are social ("I really want Amy to notice me;
how should I arrange it?"). Others are academic ("Im trying to
prove this theorem. How can I do it, starting from these
axioms?"). What these situations share, though, is the desire to
figure out how to reach some goal-a configuration that defines
what we call problem solving. How do people solve go to the
store, but problems?
Problem Solving as Search
Researchers compare problem solving to a process of search, as
though you were navigating through a maze, seeking a path
toward your goal (see Newe & Simon, 1972; also Bassok &
Novick, 2012 Mayer, 2012). To make this point concrete,
consider the Hobbits and Orcs problem in Figure 13.1. For this

problem, you have choices for the various moves you can make
(transporting creatures back and forth), but you're limited by the
size of the boat and the requirement that Hobbits can never be
outnumbered (lest they be eaten). This situation leaves you with
a set of options shown graphically in Figure 13.2. The figure
shows the moves available early in the solution and depicts the
options as a tree. with each step leading to more branches. All
the branches together form the problem space- that is, the set of
all states that can be reached in solving the problem.
To solve this problem, one strategy would be to trace through
the entire problem space, exploring each branch in turn. This
would be like exploring every possible corridor in a maze, an
approach that would guarantee that you'd eventually find the
solution. For most problems, however, this approach would be
hopeless. Consider the game of chess. In chess, which move is
best at any point in the game depends on what your opponent
will be able to do in response to your move, and then what
you'll do next. To make sure you're choosing the best move,
therefore, you need to think ahead through a few cycles of play,
so that you can select as your current move the one that will
lead to the best sequence. Let's imagine, therefore, that you
decide to look ahead just three cycles of play-three of your
moves and three of your opponent's. Some calculation, however,
tells us that for three cycles of chess play there are roughly 700
million possibilities for how the game could go; this number
immediately rules out the option of considering every
possibility. If you could evaluate 10 sequences per second,
you'd still need more than 2 years, on a 24/7 schedule, to
evaluate the full set of options for each move. And, of course,
there's nothing special here about chess, because most real-life
problems offer so many options that you couldn't possibly
explore every one. Plainly, then, you somehow need to narrow
your search through a problem space, and specifically, what you

need is a problem-solving heuristic. As we've discussed in other
chapters. heuristics are strategies that are efficient but at the
cost of occasional errors. In the domain of problem solving, a
heuristic is a strategy that narrows your search through the
problem space-but (you hope) in a way that still leads to the
problem's solution.
General Problem-Solving Heuristics
One commonly used heuristic is called the hill-climbing
strategy. To understand this term, imagine that you're hiking
through the woods and trying to figure out which trail leads to
the mountaintop. You obviously need to climb uphill to reach
the top, so whenever you come to a fork in the trail, you select
the path that's going uphill. The problem-solving strategy works
the same way: At each point you choose the option that moves
you in the direction of your goal. This strategy is of limited use,
however, because many problems require that you briefly move
away from your goal; only then, from this new position, can the
problem be solved. For instance, if you want Mingus to notice
you more, it might help if you go away for a while; that way,
he'll be more likely to notice you when you come back. You
would never discover this ploy, though, if you relied on the hill-
climbing strategy. Even so, people often rely on this heuristic.
As a result, they have difficulties whenever a problem requires
them to "move backward in order to go forward." Often, at these
points, people drop their current plan and seek some other
solution to the problem: "This must be the wrong strategy; I'm
going the wrong way" (See, e.g., Jeffries, Polson, Razran, &
Atwood, 1977; Thomas, 1974.) Fortunately, people have other
heuristics available to them. For example, people often rely on
means-end analysis. In this strategy, you compare your current
state to the goal state and you ask "What means do I have to
make these more alike?" Figure 13.3 offers a commonsense
example.

Pictures and Diagrams
People have other options in their mental toolkit. For example,
it's often helpful to translate a problem into concrete terms,
relying on a mental image or a picture. As an illustration,
consider the problem in Figure 13.4. Most people try an
algebraic solution to this problem (width of each volume
multiplied by the number of volumes, divided by the worm's
eating rate) and end up with the wrong People generally get this
problem right, though, if they start by visualizing the
arrangement. Now, they can see the actual positions of the
worm's starting point and end point, and this usually answer.
takes them to the correct answer. (See Figure 13.5; also see
Anderson, 1993; Anderson & Helstrup, 1993; Reed, 1993;
Verstijnen, Hennessey, van Leeuwen, Hamel, & Goldschmidt,
1998.)
Drawing on Experience
Where do these points leave us with regard to the questions with
which we began-and, in particular, the ways in which people
differ from one another in their mental abilities? There's
actually little difference from one person to the next in the use
of strategies like hill climbing or means-end analysis-most
people can and do use these strategies. People do differ, of
course, in their drawing ability and in their imagery prowess
(see Chapter 11), but these points are relevant only for some
problems. Where, then, do the broader differences in problem-
solving skill arise?
Problem Solving via Analogy
Often, a problem reminds you of other problems you've solved
in the past, and so you can rely on your past experience in
tackling the current challenge. In other words, you solve the
current problem by means of an analogy with other, already

solved, problems. It's easy to show that analogies are helpful
(Chan, Paletz, & Schunn, 2012; Donnelly & McDaniel, 1993;
Gentner & Smith, 2012; Holyoak, 2012), but it's also plain that
people under-use analogies. Consider the tumor problem (see
Figure 13.6A). This problem is difficult, but people generally
solve it if they use an analogy. Gick and Holyoak (1980) first
had their participants read about a related situation (see Figure
13.6B) and then presented them with the tumor problem. When
participants were encouraged to use this hint, 75% were able to
solve the tumor problem. Without the hint, only 10% solved the
problem.
Note, though, that Gick and Holyoak had another group of
participants read the "general and fortress" story, but these
participants weren't told that this story was relevant to the
tumor problem. problem (see Figure 13.7). (Also see Kubricht,
Lu, & Holyoak, Only 30% of this group solved the tumor 2017.)
Apparently, then, uninstructed use of analogies is rare, and one
reason lies in how people search through memory when seeking
an analogy. In solving the tumor problem, people seem to ask
themselves: "What else do I know about tumors?" This search
will help them remember other situations in which they thought
about tumors, but it won't lead them to the "general and
fortress" problem. This (potential) analogue will therefore lie
dormant in memory and provide no help. (See e.g., Bassok,
1996; Cummins, 1992; Hahn, Prat-Sala, Pothos, & Brumby,
2010; Wharton, Holyoak, Downing, & Lange, 1994.) To locate
helpful analogies in memory, you generally need to look beyond
the superficial features of the problem and think instead about
the principles governing the problem-focusing on what's
sometimes called the problem's "deep structure." As a related
point, you'll be able to use an analogy only if you figure out
how to map the prior case onto the problem now being solved-
only if you realize, for example, that converging groups of
soldiers correspond to converging rays and that a fortress-to-be-

captured corresponds to a tumor-to-be-destroyed. This mapping
process can be difficult (Holyoak, 2012; Reed, 2017), and
failures to figure out the mapping are another reason people
regularly fail to find and use analogies.
Strategies to Make Analogy Use More Likely
Perhaps, then, we have our first suggestion about why people
differ in their problem-solving ability Perhaps the people who
are better problem solvers are those who make better use of
analogies- plausibly, because they pay attention to a problem's
deep structure rather than its superficial traits. Consistent with
these claims, it turns out that we can improve problem solving
by encouraging people to pay attention to the problems'
underlying dynamic. For example, Cummins (1992) instructed
participants in one group to analyze a series of algebra
problems one by one. Participants in a second group were asked
to compare the problems to one another, describing what the
problems had in common. The latter instruction forced
participants to think about the problems underlying structure;
guided by this perspective, the participants were more likely,
later on, to use the training problems as a basis for forming and
using analogies. (Also see Catrambone, Craig, & Nersessian,
2006; Kurtz & Loewenstein, 2007; Lane & Schooler, 2004;
Pedrone, Hummel, & Holyoak 2001.)
Expert Problem Solvers
How far can we go with these points? Can we use these simple
ideas to explain the difference between ordinary problem
solvers and genuine experts? To some extent, we can. We just
suggested, for example, that it's helpful to think about problems
in terms of their deep structure, and this is, it seems, the way
experts think about problems. In one study, participants were
asked to categorize simple physics problems (Chi, Feltovich, &
Glaser, 1981). Novices tended to place together all the problems

involving river currents., all the problems involving springs,
and so on, in each case focusing on the surface form of the
problem. In contrast, experts (Ph.D. students in physics)
ignored these details of the problems and, instead, sorted
according to the physical principles relevant to the problems'
solution. (For more on expertise, see Ericsson & Towne, 2012.)
We've also claimed that attention to a problem's deep structure
promotes analogy use, so if experts are more attentive to this
structure, they should be more likely to use analogies-and they
are (e.g., Bassok & Novick, 2012). Experts' reliance on
analogies is evident both in the laboratory (e.g., Novick and
Holyoak, 1991) and in real-world settings. Christensen and
Schunn (2005) recorded work meetings of a group of engineers
trying to create new products for the medical world. As the
engineers discussed their options, analogy use was frequent-
with an analogy being offered in the discussion every 5
minutes!
Setting Subgoals
Experts also have other advantages. For example, for many
problems, it's helpful to break a problem into subproblems so
that the overall problem can be solved part by part rather than
all at once. This, too, is a technique that experts often use.
Classic evidence on this point comes from studies of chess
experts (de Groot, 1965, 1966; also see Chase & Simon, 1973).
The data show that these experts are particularly skilled in
organizing a chess game-in seeing the structure of the game,
understanding its parts, and perceiving how the parts are related
to one another. This skill can be revealed in many ways,
including how chess masters remember board positions. In one
procedure, chess masters were able to remember the positions of
20 pieces after viewing the board for just 5 seconds; novices
remembered many fewer (see Figure 13.8). In addition, there
was a clear pattern to the experts' recollection: In recalling the
layout of the board, the experts would place four or five pieces

in their proper positions, then pause, then recall another group,
then pause, and so on. In each case, the group of pieces was one
that made "tactical sense"-for example, the pieces involved in a
"forked" attack, a chain of mutually defending pieces, and the
like. (For similar data with other forms of expertise, see
Tuffiash, Roring, & Ericsson, 2007 also see Sala & Gobet,
2017.)
It seems, then, that the masters-experts in chess-memorize the
board in terms of higher-order units, defined by their strategic
function within the game. This perception of higher-order units
helps to organize the experts' thinking. By focusing on the units
and how they're related to one another, the experts keep track of
broad strategies without getting bogged down in the details.
Likewise, these units set subgoals for the experts. Having
perceived a group of pieces as a coordinated attack, an expert
sets the subgoal of preparing for the attack. Having perceived
another group of pieces as the early deevelopment of a pin (a
situation in which a player cannot move without exposing a
more valuable piece to an attack), the expert creates the subgoal
of avoiding the pin. It turns out, though, that experts also have
other advantages, including the simple fact that they know much
more about their domains of expertise than novices do. Experts
also organize their knowledge more effectively than novices. In
particular, studies indicate that experts' knowledge is heavily
cross-referenced, so that each bit of information has
associations to many other bits (e.g., Bédard & Chi, 1992;
Bransford, Brown & Cocking, 1999; Reed, 2017). As a result,
experts have better access to what they know. It's clear,
therefore, that there are multiple factors separating novices
from experts, but these factors all hinge on the processes we've
already discussed-with an emphasis subproblems, and memory
search. Apparently, then, we can use our theorizing so far to
describe on analogies, how people (in particular, novices and
experts) differ from one another.

e. Demonstration 13.1: Analogies
Analogies are a powerful help in solving problems, and they are
also an excellent way to convey new information. Imagine that
you're a teacher, trying to explain some points about astronomy.
Which of the following explanations do you think would be
more effective?
Literal Version
Collapsing stars spin faster and faster as they fold in on
themselves and their size decreases. This principle called
phenomenon of spinning faster as the star's size shrinks occurs
because of a "conservation of angular momentum."
Analogy Version
Collapsing stars spin faster as their size shrinks. Stars are thus
like ice skaters, who pirouette faster as they pull in their arms.
Both stars and skaters operate by a principle called
"conservation of angular momentum"
Which version of the explanations would make it easier for
students to answer a question like the following one?
What would happen if a star "expanded" instead of collapsing?
a) Its rate of rotation would increase.
b) Its rate of rotation would decrease.
c) Its orbital speed would increase.
d) Its orbital speed would decrease.
Does your intuition tell you that the analogy version would be
better as a teaching tool? If so, then your intuition is in line
with the data! Participants in one study analogy version. Later,
they were asked questions about these were presented with
materials just like these, in either a literal or an materials, and

those instructed via analogy reliably did better. Do you think
your teachers make effective use of analogy? Can you think of
ways they can improve their use of analogy?
Defining the Problem
Experts, we've said, define problems in their area of expertise in
terms of the problems' underlying dynamic. As a result, the
experts are more likely to break a problem into meaningful
parts, more likely to realize what other problems are analogous
to the current problem, and so more likely to benefit from
analogies. Clearly, then, there are better and worse ways to
define a problem-ways that will lead to a problem? And what
solution and ways that will obstruct it. But what does it mean to
"define" a determines how people define the problems they
encounter?
III-Defined and Well-Defined Problems
For many problems, the goal and the options for solving the
problems are clearly stated at the start: Get all the Hobbits to
the other side of the river, using the boat. Solve the math
problem, using the axioms stated. Many problems, though, are
rather different. For example, we all hope for peace in the
world, but what will this goal involve? There will be no
fighting, of course, but what other traits will the goal have?
Will the nations currently on the map still be in place? How will
disputes be settled? It's also unclear what steps should be tried
in an effort toward reaching this goal. Would diplomatic
negotiations work? Or would economic measures be more
effective?
Problems like this one are said to be ill-defined, with no clear
statement at the outset of how the goal should be characterized
or what operations might serve to reach that goal. Other
examples of ill-defined problems include "having a good time

while on vacation" and "saving money for college (Halpern,
1984; Kahney, 1986; Schraw, Dunkle, &Bendixen, 1995) When
confronting ill-defined problems, your best bet is often to create
subgoals, because many ill-defined problems have reasonably
well-defined parts, and by solving each of these you can move
toward solving the overall problem. A different strategy is to
add some structure to the problem by including extra constraints
or extra assumptions. In this way, the problem becomes well-
defined instead of ill-defined-perhaps with a narrower set of
options in how you might approach it, but with a clearly
specified goal state and, eventually, a manageable set of
operations to try.
Functional Fixedness
Even for well-defined problems, there's usually more than one
way to understand the problem. Consider the problem in Figure
13.9. To solve it, you need to cease thinking of the box as a
container and instead think of it as a potential platform. Thus,
your chances of solving the problem depend on how you
represent the box in your thoughts, and we can show this by
encouraging one representation or another. In a classic study,
participants were given the equipment shown in Figure 13.9A:
some matches, a box of tacks, and a candle. This configuration
(implicitly) underscored the box's conventional function. As a
result, the configuration increased functional fixedness-the
tendency to be rigid in how one thinks about an object's
function. With fixedness in place, the problem was rarely solved
(Duncker, 1945; Fleck & Weisberg, 2004).
Other participants were given the same tools, but configured
differently. They were given some matches, a pile of tacks, the
box (now empty), and a candle. In this setting, the participants
were less they to solve the problem (Duncker, 1945). (Also see
were less likely to think of the box likely to think of the box as

a container for the tacks, and so likely as a container. As a
result, they were more Figure 13.10; for more on fixedness, see
McCaffrey, 2012.)
"Thinking outside the Box"
A related obstacle derives from someone's problem-solving set-
the collection of beliefs and assumptions a person makes about
a problem. One often-discussed example involves the nine-dot
problem (see Figure 13.11). People routinely fail to solve this
problem, because-according to some interpretations-they
(mistakenly) defined by the dots. In fact, this problem is
probably the source of the cliché "You need to think assume
that the lines they draw need to stay inside the "square" outside
the box."
Ironically, though, this cliché may be misleading. In one study,
participants were told explicitly that to solve the problem their
lines would need to go outside the square. The hint provided
little participants still failed to find the solution (Weisberg &
Alba, 1981). Apparently, benefit, and most beliefs about "the
box" aren't the obstacle. Even when we eliminate these beliefs,
performance remains poor. Nonetheless, the expression "think
outside the box" does get the broad idea right, because to most
people assume solve this problem people do need to jettison
their initial approach. Specifically, begin and end on dots.
People also have the idea that they'll need to that the lines they
draw must maximize the number of dots "canceled" with each
move; as a result, they seek solutions in which each line cancels
a full row or column of dots. It turns out, though, that these
assumptions are guided by these mistaken beliefs, people find
this problem quite hard. (See Kershaw & Ohlsson, 2004;
MacGregor, Ormerod, & Chronicle, 2001; also Öllinger, Jones,
Faber, & Knoblich, wrong; and so, 2013.)

In the nine-dot problem, people seem to be victims of their own
problem-solving set; to find the solution, they need change that
set. This phrasing, however, makes it sound like a set is a bad
thing, blocking the discovery of a solution. Let's emphasize,
though, that sets also provide a benefit. as you seek most
problems offer a huge number of options This is because (as we
mentioned earlier) the solution-an enormous number of moves
you might try or approaches you might consider. A problem-
solving set helps you, therefore, by narrowing your options,
which in turn eases the search for a solution. Thus, in solving
the nine-dot problem, you didn't waste any time wondering
whether e holding the pencil between your toes or whether the
problem you should try drawing the lines sitting down while
you worked on it instead of standing up. These are was hard
because you were foolish ideas, so you brushed past them. But
what identifies them as foolish? It's your problem- plausible,
which ones are solving set, which tells you, among other things,
which options physically possible, and so on. are
In this way, a set can blind you to important options and thus be
an obstacle. But a set can also blind you to a wide range of
futile strategies, and this is a good thing: It enables you to
focus, much more productively, on options that are likely to
work out. Indeed, without a set, you might be so distracted by
silly notions that even the simplest problem would become
insoluble.
e. Demonstration 13.2: Verbalization and Problem Solving
Research on problem solving has attempted to determine what
factors help problem solving (making it faster or more
effective) and what factors hinder problem solving. Some of
these factors are surprising. You'll need a clock or a timer for
this one. Read the first problem below, and give yourself 2
minutes to find the solution. If you haven't found the solution in
this time, take a break for 90 seconds; during that break, turn

away from the problem and try to say out loud, in as much
detail as possible, everything you can remember about how
you've been trying to solve the problem. Provide information
about your approach, your strategies, any solutions you tried,
and so on. Then, go back and try working on the problem for
another 2 minutes.
Problem 1: The drawing below shows 10 pennies. Can you make
the triangle point downward by moving only 3 of the pennies?
Now, do the same for the next problem-2 minutes of trying a
solution, 90 seconds of describing out loud everything you've
thought of so far in working on the problem, and then another 2
minutes of working on the problem.
Problem 2: Nine sheep are kept together in a square pen. Build
two more square enclosures that will isolate each sheep, so that
each is in a pen all by itself.
Do you think the time spent describing your efforts so far
helped you, perhaps by allowing you to organize your thinking,
or hurt you? In fact, in several studies, this sort of "time off, to
describe your efforts so far" makes it less likely that people will
solve these problems. In one study, participants solved 36% of
the problems if they had verbalized their efforts so far, but 46%
of the problems if they didn't go through the verbalization step.
Does this fit with your experience? Why might verbalization
interfere with this form of problem solving? One likely
explanation is that verbalization focuses your attention on steps
you've already tried, and this may make it more difficult to
abandon those steps and try new approaches. The verbalization
will also focus your attention on the sorts of strategies that are
conscious, deliberate, and easily described in words; this focus

might interfere with strategies that are unconscious, not
deliberate, and not easily articulated. In all cases, though, the
pattern makes it clear that sometimes "thinking out loud" and
trying to communicate your ideas to others can actually be
counterproductive! Exactly why this is, and what this implies
about problem solving, is a topic in need of further research.
Demonstration adapted from Schooler, J., Ohlsson, S., &
Brooks, K. (1993). Thoughts beyond words: When language
overshadows insight. Journal of Experimental Psychology:
General, 122, 166-183. Expand the bar below for the solutions
to Demonstration 13.2.
Creativity
There is no question, though, that efforts toward a problem
solution are sometimes hindered by someone's set, and this
observation points us toward another way in which people
differ. Some people are remarkably flexible in their approaches
to problems; they seem easily able to "think outside the box."
Other people, in contrast, seem far too ready to rely on routine,
so they're more vulnerable to the obstacles we've just described.
How should we think about these differences? Why do some
people reliably produce novel and unexpected solutions, while
other people offer only familiar solutions? This is, in effect, a
question of why some people are creative and others aren't-a
question that forces us to ask: What is creativity?
Case Studies of Creativity
One approach to this issue focuses on individuals who've been
enormously creative-artists like Pablo Picasso and Johann
Sebastian Bach, or scientists like Charles Darwin and Marie
Curie. By studying these giants, perhaps we can draw hints
about the nature of creativity when it arises, on a much smaller
scale, in day-to-day life-when, for example, you find a creative
way to begin a conversation or to repair a damaged friendship.

As some researchers put it, we may be able to learn about
"little-c creativity" (the everyday sort) by studying "Big-C
Creativity" (the sort shown by people we count as scientific or
artistic geniuses-Simonton & Damian, 2012).
Research suggests, in fact, that highly creative people like Bach
and Curie tend to have certain things in common, and we can
think of these elements as "prerequisites" for creativity (e.g.,
Hennessey & Amabile, 2010). These individuals, first of all,
generally have great knowledge and skills in their domain. (This
point can't be surprising: If you don't know a lot of chemistry,
you can't be a creative chemist. If you're not a skilled
storyteller, you can't be a great novelist.) Second, to be
creative, you need certain personality traits: a willingness to
take risks, a willingness to ignore criticism, an ability to
tolerate ambiguous findings or situations, and an inclination not
to "foliow the crowd" Third, highly creative people tend to be
motivated by the pleasure of their work rather than by the
promise of external rewards. With this, highly creative people
tend to work extremely hard on their endeavors and to produce a
lot of their product, whether these products are poems,
paintings, or scientific papers. Fourth, these highly creative
people have generally been "in the right place at the right time"-
that is, in environments that allowed them freedom, provided
them with the appropriate supports, and offered them problems
"ripe" for solution with the resources available. Notice that
these observations highlight the contribution of factors outside
the person, as well as the person's own capacities and skills.
The external environment, for example, is the source of crucial
knowledge and resources, and it often defines the problem
itself. This is why many authors have suggested that we need a
systematic "sociocultural approach" to creativity-one that
considers the social and historical context, as well as the
processes unfolding inside the creative individual's mind (e.g.,
Sawyer, 2006).

We still need to ask, however: What does go on in a creative
mind? If a person has all the prerequisites just listed, what
happens next to produce the creative step forward? Actually,
there is wide disagreement on these points (see Hennessey &
Amabile, 2010; Mumford & Antes, 2007). It will be instructive,
though, to examine a proposal offered years ago by Wallas
(1926). His notion fits well with some commonsense ideas about
creativity, and this is one of the reasons his framework
continues to guide modern research. Nonetheless, the evidence
forces us to question several of Wallas's claims.
The Moment of Illumination
According to Wallas, creative thought proceeds through four
stages. In the first stage, preparation the problem solver gathers
information and does some work on the problem, but with little
progress. In the second stage, incubation, the problem solver
sets the problem aside and seems not to be working on it.
Wallas argued, though, that the problem solver continues to
work on the problem unconsciously during this stage, so
actually the problem's solution is continuing to develop, unseen.
This development leads to the third stage, illumination, in
which a key insight or new idea emerges, paving the way for the
fourth stage, verification, in which the person confirms that the
new idea really does lead to a solution and works out the
details. Historical evidence suggests, however, that many
creative discoveries don't include the steps Wallas described-
or, if they do, they include these steps in a complex, back-and-
forth sequence (Weisberg, 1986). Likewise, the moment of
illumination celebrated in Wallas's proposal may be more myth
than reality. When we examine creative discoveries in science
or art, we usually find that the new ideas emerged, not from
some glorious and abrupt leap forward, but instead from a
succession of "mini-insights," each moving the process forward
in some small way (Klein, 2013; Sawyer, 2006).

And when people do have the "Aha!" experience that, for
Wallas, signified illumination, what does this involve? Metcalfe
(1986; Metcalfe & Weibe, 1987) gave her participants a series
of "insight problems" like those shown in Figure 13.12A. As
participants worked on each problem, they rated their progress
by using a judgment of "warmth" ("Im getting and these ratings
did capture the "moment of insight." Initially, the participants
didn't have a clue how to proceed and gave warmth ratings of 1
or 2; then, abruptly, they saw a way forward, and at that instant
their warmth ratings shot up to the top of the scale. warmer...,
I'm getting warmer
To understand this pattern, though, we need to look separately
at those participants who subsequently announced the correct
solution to the problem and those who announced an incorrect
solution. Remarkably, the pattern is the same for both groups
(see Figure 13.12B). In other words, some participants abruptly
announced that they were getting "hot" and, moments later,
solved the problem. Other participants made the same
announcement and, moments later, slammed into a dead end. It
seems, therefore, that when you say "Aha!" it means only that
you've discovered a new approach, one that you've not yet
considered. This is important, because often a new approach is
just what you need. But there's nothing magical about the
"moment of illumination." This moment doesn't signal that
you've at last discovered a path leading to the solution. Instead,
it means only that you've discovered something new to try, with
no guarantee that this "something new" will be helpful. (For
more on procedures that can promote "Aha!" moments, see
Patrick, Ahmed, Smy, Seeby & Sambrooks, 2015; also Patrick &
Ahmed, 2014. For more on the insight process overall, see
Bassok & Novick, 2012; Smith & Ward, 2012; van Steenburgh,
Fleck, Beeman, & Kounios, 2012. For discussion of neural
mechanisms underlying insight, see Kounios & Beeman, 2014,
2015, and also Figure 13.13. For an alternative view, though,
see Chuderski & Jastrze bski, 2018.)

Incubation
What about Wallas's second stage, incubation? His claim here
fits well with a common experience: You're working on a
problem but getting nowhere with it. You give up and turn to
other matters. Sometime later, when you're thinking about
something altogether different, the problem's solution pops into
your thoughts. What has happened? According to Wallas, your
time away from the problem allowed incubation to proceed-
unconscious work on the problem that allowed considerable
progress. Systematic studies, however, tell us that this pattern is
(at best) unreliable. In these studies participants are given a
problem to solve. Some participants work on the problem
continuously some are interrupted for a while. The prediction,
based on Wallas's proposal, is that we'll observe better
performance in the latter group-the group that can benefit from
incubation. The data, however, are mixed. Some studies do
show that time away from a problem helps in finding the
problem's solution, but many studies find no effect at all. (See
Baird et al., 2012; Dodds Ward, & Smith, 2007, 2012; Gilhooly,
Georgiou, Garrison, Reston, & Sirota, 2012; Hélie & Sun, 2010;
Sio, Kotovsky, & Cagan, 2017.) The explanation for this mixed
pattern isn't clear. Some researchers argue that incubation is
disrupted if you're under pressure to solve the problem. Other
authors focus on how you spend your time during the incubation
period, with the suggestion that incubation takes place only if
the circumstances allow your thoughts to "wander" during this
period. (For reviews of this literature, see Baird et al., 2012;
Gilhooly et al., 2012; Kounios & Beeman, 2015.)
Why should "mind wandering" be relevant here? Recall that in
Chapter 9 we described the process of spreading activation
through which one memory can activate related memories. It
seems likely that when you're carefully working on a problem,
you try to direct this flow of activation-and perhaps end up

directing it in unproductive ways. When you simply allow your
thoughts to wander, though, the activation can flow wherever
the memory connections take it, and this may lead to new ideas
being activated. (See Ash & Wiley, 2006; Bowden, Jung-
Beeman, Fleck, & Kounios, 2005; Kounios & Beeman, 2015;
Radel, Davaranche, Fournier, & Dietrick, 2015; Smith & Ward,
2012.) This process provides no guarantee that helpful (and
perhaps unanticipated) ideas will be activated. In this way,
incubation is like illumination-a or productive ideas will come
to mind, only that more source of new possibilities that may or
may not pay off Other authors, however, offer a more mundane
explanation of incubation effects. They note that your early
efforts with a problem may have been tiring or frustrating, and
if so, the interruption simply provides an opportunity for the
frustration or fatigue to dissipate. Likewise, your early efforts
with a problem may have been dominated by a particular
approach, a particular set. If you put the problem aside for a
while, it's possible you'll forget about these earlier tactics,
freeing you to explore other, more productive avenues. (See
Smith & Blankenship, 1989, 1991; Storm, Angello, & Bjork,
2011: Storm & Patel, 2014; Vul & Pashler, 2007.)
For the moment, there is no consensus about which of these
proposals is best, so it's unclear why time away from a problem
sometimes helps and sometimes doesn't. To put the matter
bluntly sometimes when you're stymied by a problem, it does
help to walk away from it for a while. But sometimes your best
bet is just to keep plugging away, doggedly trying to move
forward. For now research provides less guidance than we'd like
in choosing which of these is the better plan.
The Nature of Creativity
Where does this discussion leave us with regard to our
overarching question-the question of how people differ in their
cognitive abilities? In discussing expertise, have a stack of

advantages: a tendency to think about problems in terms of their
structure rather than their surface form, a broad knowledge base
deriving from their experience in a field, heavily cross-
referenced memories, and so on. Now, in discussing creativity,
we've seen a similar pattern: Highly creative people tend to
have certain personality traits (e.g., a willingness to take risks),
a lot of knowledge, intense motivation, and some amount of
luck. But what mental processes do they rely on? When we
examine Darwin's notebooks or Picasso's early sketches, we
discover that these creative giants relied on analogies, hints,
heuristics, and a lot of hard work, just as the rest of us do
(Gruber, 1981; Sawyer, 2006; Weisberg, 1986). In some cases,
creativity may even depend on blind trial and error (e.g., Klein,
2013; Simonton, 2011); with this sort of process, the great artist
or great we saw that experts in a domain inventor is simply
someone who's highly discriminating-and thus able to discern
which of the randomly produced products actually have value.
Research also suggests that creative people may have
advantages in how they search through memory. Some authors
emphasize the skill of convergent thinking-an ability to spot
ways in which seemingly distinct ideas might be interconnected.
This ability is sometimes measured through the Remote
Associates Test (Mednick, 1962; Mednick & Mednick, 1967;
also Smith, Huber, & Vul, 2013; Figure 13.14). In this test,
you're given a trio of words, and you need to find one more
word that fits with each of the three. For example, you might be
given the trio cross, rain, and tie; the correct answer is bow (as
in crossbow, rainbow, and bowtie)
Other authors emphasize the skill of divergent thinking -an
ability to move one's thoughts novel, unanticipated directions.
(See Guilford, 1967, 1979; also see Beaty, Silvia, Nusbaum,
Jauk, & Benedek, 2014; Hass, 2017; Vartanian, Martindale, &
Matthews, 2009; see Figure 13.15. For a related in idea, see
Zabelina, Saporta, & Beeman, 2016.) Here, there's no "right

answer" Instead, success in divergent thinking is reflected in an
ability to come up with a large number of new ideas-ideas that
can then be evaluated to see if they're of any value.
Overall, though, what is it that allowed Darwin or Picasso,
Rachel Carson or achieve their monumental creativity?
Research provides no reason to think these giants possessed
Georgia O'Keeffe, to some special "creativity mechanism." (See
DeHaan, 2011; Goldenberg, Mazursky, & Solomon, 1999; Klahr
& Simon, 2001; Simonton, 2003, 2009; Simonton & Damian,
2012; Weisberg, 2006.) However, there is one way in which
these individuals surely were distinctive. Many of us are smart
or particularly skillful in memory search; many of us are willing
to take risks and to ignore criticism; many of us live in a
cultural setting that might support a new discovery. What
distinguishes creative geniuses, though, is that they are the
special people who have all of these ingredients-the right
intellectual tools, the right personality characteristics, the good
fortune to be living in the right context, and so on. It's a rare
individual who has all of these elements, and it's probably the
combination of all these elements that provides the recipe for
extraordinary creativity.
e. Demonstration 13.3: Incubation
Many people believe that an "incubation" period aids problem
solving. In other words, spending time away from the problem
helps you to find the solution, because during the time away,
it's claimed, you are unconsciously continuing to work on the
problem. Is this belief warranted? Is it an accurate reflection of
how problem solving truly proceeds? To find out, psychologists
study problem solving in the lab, using problems like these.
(Similar problems are presented in the chapter.)

Each of the figures shown here represents a familiar word or
phrase. Can you decipher thein? (To get you started, the first
figure represents the common phrase "Forgive and forget."
We'll give you the other solutions in a moment.) In one study,
participants were more likely to solve these puzzles if they
worked on them for a while, then took a break, then returned to
the puzzles. This result seems to indicate an incubation benefit,
but the explanation for the result actually may not involve any
sort of unconscious problem solving. Instead, the break may
have helped simply because it allowed the to lose track of the
bad strategies they'd been trying so far, and this in turn allowed
participants them to get a fresh start on the problem when they
returned to it. Support for this interpretation comes from the
fact that in this study, a helpful clue was provided for some of
the figures (e.g., the sixth picture shown here might be
accompanied by the word "first"). For other figures, the clue
was actually misleading (e.g., the first picture here might be
accompanied by the clue "opposites"). The participants were
helped much more by the break if they'd been misled at the
start. This pattern is just what we'd expect if the break allowed
the participants to set aside (and maybe even to forget) the
misleading clue, so that they were no longer derailed by it. On
this basis, the break didn't allow unconscious problem solving;
it instead allowed something simpler: a bit of rest, a bit of
forgetting, and hence a fresh start. The remaining solutions, by
the way, are "you fell out of touch," "just between you and me
"backward glance," "three degrees below zero," and "first aid."
e. Demonstration 13.4: Remote Associates
Can creativity be measured? Some researchers think it can, and
they design tests that (they believe) tap into the processes that
are essential for creativity. For example, the chapter mentions
that some researchers believe creativity depends on the ability
to detect unanticipated, perhaps distant, relationships among
ideas. One test designed to measure this ability is the Remote

Associates Test. In this test, you're given three words, and you
need to come up with a word that can be combined with each of
the three. Thus, you might be given "cottage, Swiss, cake," and
you need to come up with "cheese" (as in "cottage cheese,"
"Swiss cheese," and "cheesecake"). The chapter provides some
examples of remote associates. Below you'll find a number of
additional test items. If you wish, time yourself, allowing 2
seconds to work on each item; how many can you solve?
Expand the table below to see the answers and the percentage of
research participants who, in one study, were able to solve each
problem within 2 seconds of trying.
Intelligence
We all admire people who seem wonderfully intelligent, and we
express concerns about (and try to help) those we consider
intellectually dull. But what is "intelligence"? Is there a way to
measure it? And should we be focused on intelligence (singular)
or intelligences (plural)? In other words, is there such a thing as
"intelligence-in-general," so that someone who has this resource
will be better off in all mental endeavors? Or should we instead
talk about different types of intelligence, so that someone might
be "smart" in some domains but less so in others?
Measuring Intelligence
Scholars disagree about how the word "intelligence" should be
defined. Remarkably, though, early efforts toward measuring
intelligence proceeded without a clear definition. Instead,
Alfred Binet (1857-1911) and his colleagues began with a
simple idea-namely, that intelligence is a capacity that matters
for many aspects of cognitive functioning. They therefore
created a test that included a range of tasks: copying a drawing,

repeating arithmetic, and so on. Performance was then assessed
with a composite score, summing across string of digits,
understanding a story, doing these various tasks. In its original
form, the test score was computed as a ratio between someone's
"mental age" (the level of development reflected in the test
performance) and his or her chronological age. (The ratio was
then multiplied by 100 to get the final score.) This ratio-or
quotient-was the source of the test's name: The test evaluated a
person's "intelligence quotient," or IQ..
Modern forms of the test no longer calculate this ratio, but
they're still called "IQ tests." One commonly used test is the
Wechsler Intelligence Scale for Children, or WISC (Wechsler,
2003). Similarly, adult intelligence is often evaluated with the
Wechsler Adult Intelligence Scale, or WAIS. Like Binet's
original test, though, these modern tests rely on numerous
subtests. In the WAIS, for example, there are tests to assess
general knowledge, vocabulary, and comprehension (see Figure
13.16A), and a perceptual-reasoning scale includes visual
puzzles like the one shown in Figure 13.16B. Separate subtests
assess working memory and speed of intellectual processing.
Other intelligence tests have different formats. The Raven's
Progressive Matrices Test (Figure 13.16C; Raven & Raven,
2008) hinges entirely on a person's ability to analyze figures
and detect patterns. This test presents the test taker with a series
of grids (these are the "matrices"), and he or she must select an
option that completes the pattern in each grid. This test is
designed to minimize influence from verbal skills or
background knowledge.
Reliability and Validity
Whenever we design a test-to assess intelligence, personality, or

anything else-we need to determine whether the test is reliable
and valid. Reliability refers to how consistent a measure is, and
one aspect of reliability is consistency from one occasion to
another: If we give you a test, wait a while, and then give it
again, do we get the same outcome? The issue here is test-retest
reliability. Intelligence tests have strong test-retest reliability.
There is, for example, a high correlation between measurements
of someone's IQ at age 6 and measurements of IQ when she's
18. For that matter, if we know someone's IQ at age 11, we can
accurately predict what her IQ will be at age 80. (See, e.g.,
Deary, 2014; Deary, Pattie, & Starr, 2013; Plomin & Spinath,
2004.) It's important, though, that IQ scores can change-
especially if there's a substantial change in the person's
environment. (We'll return to this point later in the chapter; also
see Ramsden et al., 2011.) Nonetheless, if the environment is
reasonably stable, the data show remarkable constancy in IQ
scores, even over spans as long as 70 or 80 years.
What about the validity of the IQ test? In general, the term
validity refers to whether a test actually measures what it is
intended to measure, and one way to approach this issue is via
an assessment of predictive validity. The logic behind this
assessment is straightforward. If intelligence tests truly measure
what they're supposed to, then someone's score on the test
should enable us to predict how well that person will do in
settings that require intelligence. And here, too, the results are
impressive. For example, there's at least a .50 correlation
between a person's IQ and measures of academic performance,
such as grade-point average. (See Arneson, Sackett, & Beatty,
2011; Deary 2012; Kuncel, Hezlett, & Ones, 2004; Strenze,
2007.) IQ scores are also correlated with performance outside of
the academic world. For example, an IQ score is a strong
predictor of how someone will perform on the job, although-
sensibly-the data indicate that IQ matters more for some jobs
than for others (Sackett, Borneman, & Connelly, 2008; Schmidt
& Hunter, 1998, 2004). Jobs of low complexity require

relatively little intelligence, correlation between IQ and job
performance is small (although still positive) for such jobs-for
example, a correlation of .20 between IQ and performance on an
assembly line. As jobs become more so the complex,
intelligence matters more, so the correlation between IQ and
performance gets stronger (Gottfredson, 1997). For example, we
find correlations between .50 and .60 when we look at IO scores
and people's success as accountants or shop managers.
IQ scores are also correlated with other life outcomes. People
with higher IQ's tend to end up with higher-prestige careers and
are less likely to suffer various life problems (and so are less
likely to end up in jail, less likely to become pregnant as teens,
and more). Higher-IQ people even live longer-with various
mechanisms contributing to this longevity. Among other
considerations, higher-IQ individuals are less likely to die in
automobile accidents (see Table 13.1) and less likely to have
difficulty following doctors' instructions. (See Deary, Weiss, &
Batty, 2010; Gottfredson, 2004; Kuncel et al., 2004; Lubinski,
2004; Murray, Pattie, Starr, & Deary, 2012.)
Let's emphasize, though, that no matter what the setting, IQ
scores are never a perfect predictor of life outcomes. (Notice
that all of these correlations are appreciably less than +1.00.)
This cannot be a surprise, however, because of course other
factors beyond intelligence matter in all of these domains. For
example, how well you'll do in school also depends on your
motivation, whether you stay healthy, whether your friends
encourage you to study or instead go to parties, and dozens of
other factors. These points make it inevitable that intelligence
levels won't be a perfect predictor of your academic
achievement. More bluntly, these points make it inevitable that
some high-IQ people will perform poorly in school, while some
low-IQ people will excel. (Clearly, therefore, an IQ score
doesn't define your destiny.) Even so, there's no question that

there's a strong correlation between IQ and many important life
outcomes-academic or job performance, longevity, and more. In
fact, in many domains IQ is a better predictor of performance
than any other factor. It seems plain, then, that IQ tests do have
impressive predictive validity-with the clear implication that
these tests are measuring something interesting, important, and
consequential.
General versus Specialized Intelligence
If-as it seems-IQ tests are measuring something important, what
is this "something"? This question is often framed in terms of
two options. One proposal is that the tests measure a singular
ability that can apply to any content. The idea is that your score
on an IQ test reveals your general intelligence, a capacity that
provides an advantage on virtually any mental task. (See
Spearman, 1904, 1927; for more recent discussion, see
Kaufman, Kaufman, & Plucker, 2012.)
In contrast, some authors argue that there's no such thing as
being intelligent in a general way. Instead, they claim, each
person has a collection of more specific talents-and so you
might be "math smart" but not strong with language, or "highly
verbal" but not strong with tasks requiring visualization. From
this perspective, if we represent your capacities with a single
number-an IQ score-this is only a crude summary of what you
can do, because it averages together the things you're good at
and the things you're not. Which proposal is correct? One way
to find out relies on the fact that, as we've said, many
intelligence tests include numerous subtests. It's therefore
instructive to compare a person's score on each subtest with his
or her scores on other subtests. In this way, we can ask: If
someone does well on one portion of the test, is she likely to do
well across the board? If someone does poorly on one subtest,
will he do poorly on other subtests as well? If we observe these
patterns, this would indicate that there is such a thing as

intelligence-in-general, a cognitive capacity that shapes how
well someone does no matter what the specific task might be.
More than a century ago, Charles Spearman developed a
statistical procedure that enables us to pursue this issue in a
precise, quantitative manner. The procedure is called factor
analysis, and, as the name implies, this procedure looks for
common factors-elements that contribute to multiple subtests
and therefore link those subtests. Factor analysis confirms that
there is a common element shared by all the components of the
IQ test. (See Carroll, 1993; Deary, 2012; Johnson, Carothers, &
Deary, 2008; Watkins, Wilson, Kotz, Carbone, & Babula, 2006.)
Some subtests (e.g., comprehension of a simple story) depend
heavily on this general factor; others (e.g., the ability to recall a
string of digits) depend less on the factor. Nonetheless, this
general factor matters across the board, and that's why scores on
all the subtests end up correlated with one another.
Spearman named this common element general intelligence,
usually abbreviated with the letter g. Spearman (1927) argued
that people with a high level of g will have an advantage in
virtually every intellectual endeavor. Conversely, if someone
has a low level of g, that person will do poorly on a wide range
of tasks.
A Hierarchical Model of Intelligence
The data tell us, though, that g isn't the whole story, because
people also have more specialized skills. One of these skills
involves aptitude for verbal tasks, so that performance on (say)
a reading comprehension test depends on how much g a person
has and also on the strength of these verbal skills. A second
specialized ability involves quantitative or numerical aptitude,
so performance on an arithmetic test depends on how much g a
person has and also the strength of these skills. Putting these
pieces together, we can think of intellectual performance as
having a hierarchical structure as shown in Figure 13.17. At the

top of the hierarchy is g, contributing to all tasks. At the next
level down are the abilities we just mentioned-linguistic and
numerical-and several more including spatial skill, an ability to
handle fast-paced mental tasks, and an ability to learn new and
unfamiliar materials. Then. at the next level are more specific
capacities, each useful for a narrow and specialized set of tasks.
(See Carroll, 1993; Flanagan, McGrew, & Ortiz, 2000; Johnson,
Nijenhuis, & Bouchard, 2007: McGrew, 2009; Snow, 1994,
1996.)
This hierarchical conception leads to a prediction. If we choose
tasks from two different categories-say, a verbal task and one
requiring arithmetic-we should still find a correlation in
performance, because no matter how different these tasks seem,
they do have something in common: They both draw on g. If we
choose tasks from the same category, though-say, two verbal
tasks or two quantitative tasks-we should find a higher
correlation because these tasks have two things in common:
They both draw on g, and they both draw on the more
specialized capacity needed for just that category. The data
confirm both of these predictions-moderately strong
correlations among all of the IQ test's subtests, and even
stronger correlations among subtests in the same category It
seems, then, that both of the broad hypotheses we introduced
earlier are correct. There is some sort of general capacity that is
useful for all mental endeavors, but there are also various forms
of more specialized intelligence. Each person has some amount
of the general capacity and draws on it in all tasks; this is why
there's an overall consistency in each person's performance. At
the same time, the consistency isn't perfect, because each task
also requires more specialized abilities. Each of us has our own
profile of strengths and weaknesses for these skills, with the
result that there are things we do relatively well and things we
do less well.

Fluid and Crystallized Intelligence
It's also important to distinguish between fluid intelligence and
crystallized intelligence (Carroll, 2005; Horn, 1985; Horn &
Blankson, 2005). Fluid intelligence involves the ability to deal
with novel problems. It's the form of intelligence you need
when you have no well-practiced routines you can bring to bear
on a problem. Crystallized intelligence, in contrast, involves
your acquired knowledge, including your verbal knowledge and
your repertoire of skills-skills useful for dealing with problems
similar to those already encountered. Fluid and crystallized
intelligence are highly correlated (e.g., if you have a lot of one,
you're likely to have a lot of the other). Nonetheless, these two
aspects of intelligence differ in important ways. Crystallized
intelligence usually increases with age, but fluid intelligence
reaches its peak in early adulthood and then declines across the
lifespan (see Figure 13.18; Horn, 1985; Horn & Noll, 1994;
Salthouse, 2004, 2012). Similarly, many factors-including
alcohol consumption, fatigue, and depression-cause more
impairment in tasks requiring fluid intelligence than in those
dependent on crystallized intelligence (Duncan, 1994; Hunt,
1995). Therefore, someone who is tired will probably perform
adequately on tests involving familiar routines and familiar
facts. The same individual, however, may be markedly impaired
if the test requires quick thinking or a novel approach- earmarks
of fluid intelligence.
The Building Blocks of Intelligence
It's clear, then, that intelligence has many components.
However, one component-g-is crucial, because this aspect of
intelligence is relevant to virtually all mental activities. But
what is g? What gives a person more g or less? One proposal is
simple. Mental processes are quick but do take time, and
perhaps the people we consider intelligent are those who are
especially fast in these processes. This speed would enable
quickly; it also would give them time for more steps in them to
perform intellectual tasks more comparison with those of us

who aren't so quick. (See Coyle, Pillow, Snyder, & Kochunov,
2011; Deary, 2012; Nettelbeck, 2003; Sheppard, 2008; Vernon,
1987.) As a variant of this proposal, some researchers argue that
intelligence is created by faster processing, not throughout the
brain, but in particular neural pathways-for example, the
pathways linking temporal and parietal areas. (See Schubert,
Hagemann & Frischkom, 2017; for a related idea, see Figure
13.19.)
Support for these ideas comes from measures of inspection
time-the time a person needs to decide which of two lines is
longer or which of two tones is higher. These measures
correlate .30 with intelligence scores. (See Bates & Shieles,
2003; Danthiir, Roberts, Schulze, & around Wilhelm, 2005;
Deary & Derr, 2005; Ravenzwaaij, Brown, & Wagenmakers,
2011.) The correlation is negative because lower response times
go with higher scores on intelligence tests. A different proposal
about g centers on the notion of working-memory capacity
(WMC). We first met this notion in Chapter 6, where we saw
that WMC is actually a measure of executive control-and so it is
a measure of how well people can monitor and direct their own
thought processes. We mentioned in the earlier chapter that
people with a larger WMC do better on many intellectual tasks,
specifically designed to measure g. (See Burgess, Braver,
Conway, & including, we now add, tests Gray, 2011; Engel &
Kane, 2004; Fukuda, Vogel, Mayr, & Awh, 2011; Jewsbury,
Bowden, & Strauss, 2016; Redick et al., 2016.) Perhaps,
therefore, the people we consider intelligent are those who
literally so they can coordinate their priorities in an appropriate
have better control of their own thoughts, way, override errant
impulses, and in general proceed in a deliberate manner when
making judgments or solving problems. (For debate on how
exactly WMC contributes to intelligence, see Bleckley, Foster &
Engle, 2015; Gonthier & Thomassin, 2015; Harrison, Shipstead,
& Engle, 2015; Kanerva & Kalakoski, 2016; Redick et al.,
2013.)

e. Demonstration 13.5: Defining Intelligence
More than a century ago, Alfred Binet and his colleagues
developed a measure of intelligence. They were guided,
however, largely by their intuitions about intelligence, rather
than by any sort of well- developed theory. That invites us to
ask: What are your intuitions about intelligence? First, take a
piece of paper and list 8 to 10 activities that can be performed
especially well by someone who is intelligent. (If you prefer,
you can turn this around: Name 8 to 10 activities that, if
someone does them well, indicate that the person is intelligent.)
Second, now do the reverse: List 8 to 10 activities that, in your
view, have little to do with intelligence-so that a highly
intelligent person will have no advantage on these activities in
comparison to a less intelligent person. Third, our language
includes many terms used to describe people with high levels of
intellectual ability. The terms include "smart," "clever," and
many others. Can you list 8 to 10 words that describe either a
higher level of intellectual ability or perhaps a lower level
("slow," "dull," etc.)? Fourth, can we combine your first list
(activities that involve intelligence) and your third list (words
used to describe intellectual ability)? Do some of the terms
apply to some activities but not to others? Do some of the
activities reflect one type of "being smart" but not others?
Finally, in light of what you've said in the first four steps here,
can you offer a possible definition of intelligence? How do you
think this term should be defined?
Intelligence beyond the IQ Test
We've now seen that IQ tests do measure something important,
and we've gained important insights into what this "something"
is. But, stepping away from what the IQ test does measure, we
can also ask what it doesn't measure. Are there types of
intelligence not included in conventional intelligence tests?

Practical Intelligence
Some people seem to be "street-smart"-that is, capable of
sophisticated reasoning in day-to-day settings-even though they
seem to lack the sort of analytical skill needed in a classroom.
Psychologist Robert Sternberg has studied this sort of practical
intelligence, with some of his research focused on whether
teaching is more effective when instruction is matched to
students abilities (with different forms of instruction for
students high in practical ability, students high in analytical
ability, and students high in creative ability). Evidence suggests
that "tuning" the curriculum in this way can be helpful. (See
Grigorenko, Jarvin, & Sternberg, 2002: Sternberg &
Grigorenko, 2004; also see Sternberg, Kaufman, & Grigorenko,
2008; Wagner, 2000. For concerns though, see Gottfredson,
2003.)
Measures of Rationality
A different sort of complexity has been highlighted by Keith
Stanovich. He reminds us that we all know people who are
smart according to their test scores, but who nonetheless ignore
facts, are overconfident in their judgment, are insensitive to
inconsistencies in their views, and more. It's people like these
who lead us to ask: "How could someone that smart be so
stupid?" In light of such cases (and much other evidence),
Stanovich argues that we need separate measures of intelligence
and rationality (Stanovich, 2009; Stanovich, West, & Toplak,
2016). He defines the latter term as the capacity for critically
assessing information as it is gathered in the natural
environment.
Other Types of Intelligence
Still other authors highlight a capacity they call emotional

intelligence-the ability to understand one's own emotions and
others, as well as the ability to control one's emotions when
appropriate (Mayer, Roberts, & Barsade, 2008; Salovey &
Mayer, 1990). Tests have been constructed to measure this
capacity, and people who score well on these tests are judged to
create a more positive atmosphere in the workplace and to have
more leadership potential (Grewal & Salovey, 2005; Lopes
Salovey, Côté, & Beers, 2005). Likewise, college students who
score well on these tests are rated by their friends as being more
caring and more supportive; they're also less likely to
experience conflict with peers (Brackett & Mayer, 2003; Mayer
et al., 2008).
Perhaps the best-known challenge to IQ testing, however, comes
from Howard Gardrner's theory of multiple intelligences.
Gardner (1983, 2006) argues for eight types of intelligence.
Three of these linguistic intelligence, logical-mathematical
intelligence, and are assessed in standard IQ tests: spatial
intelligence. But Gardner also argues that we should
acknowledge musical intelligence, bodily-kinesthetic
intelligence (the ability to learn and create complex patterns of
movement), interpersonal intelligence (the ability to understand
ourselves), and naturalistic intelligence (the ability to
understand other people), intrapersonal intelligence (the to
understand patterns in ability nature). Some of Gardner's
evidence comes from the study of people with so-called savant
syndrome- including people like Stephen Wiltshire, mentioned
at the start of this chapter. These individuals are profoundly
disabled, with IQ scores as low as 40 or 50, but each of them
has a stunning level of specialized talent. Some are incredible
artists (see Figure 13.20). Others are "calendar calculators."
immediately when asked questions like "What day of the week
was March 19 in the able to answer vear 1642?" Still others
have remarkable musical skills and can effortlessly memorize
lengthy musical works (Hill. 1978: Miller. 1999).

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Unit IV Web AssignmentThis assignment allows you to demonstrate .docx