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7
Thinking,
Intelligence,
and Language
The Value of a Really Good Idea
rom the alarm clock to the computer, we are surrounded by evidence of people’s really great ideas. So many of the
everyday things we take for granted started as someone else’s good idea. These inventions happened because some-
body noticed a problem and came up with an ingenious solution.
Having a really good idea is the beginning of a long journey—one that can be diffi cult and costly. A relatively
new resource to assist individuals in that journey is crowdfunding, the raising of money, often via the Internet, to
support the creative initiatives of people and groups. An innovative crowdfunding website, Kickstarter.com, is a place
where creative people can apply to receive startup funds to put their really good ideas into action (Pogue, 2012).
Would-be inventors post descriptions of their projects, which can range from artistic (cutting a CD) to technological
(building a new gadget), and propose a budget for their venture. If visitors to the site pledge enough funds to cover
the anticipated costs, the posters get the money to bring their big dreams to reality. Interestingly, those who pledge
funds to the projects are not investors. They receive nothing (except perhaps a T-shirt) in return for their pledge
money; they are lending nancial support to the inventors based solely on their own enthusiasm for a really good
idea. Forty-four percent of the projects get funded, and some are surprisingly successful. The inventor of a wristband
for an iPod nano requested $15,000, and Kickstarter donors contributed $700,000.
Why would everyday people give their hard-earned cash to an idea ? Some value the opportunity to get to know
the creative force behind what just might be tomorrow’s great new thing. Others cannot resist the appeal of the
human capacity to recognize a problem and to devise a creative solution. This chapter explores the thinking and
intelligence that underpin such endeavors.
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The Cognitive Revolution in Psychology // 243
Cognitive psychology is the study of mental processes. This chapter investigates the
basic cognitive processes of thinking, problem solving, reasoning, and decision making.
We rst defi ne cognition and look at the cognitive revolution that led to new thinking
about the workings of the human mind. We then review capacities associated with
superior problem solving: critical thinking, creativity, and, perhaps most important,
intelligence. We conclude by surveying the unique contributions of language to mental
processes.
Cognitive psychologists study cognition —the way in which information is processed and
manipulated in remembering, thinking, and knowing. Cognitive psychology is a relatively
young eld, scarcely more than a half-century old. Let’s begin by tracing its history.
After the rst decade of the twentieth century, behaviorism dominated the thinking of
experimental psychologists. Behaviorists such as B. F. Skinner argued that the human
mind is a black box best left to philosophers, and they considered observable behavior
to be psychologists’ proper focus. The behaviorist perspective had little use for the men-
tal processes occurring in that dark place between your ears.
I n t h e 1 9 5 0 s p s y c h o l o g i s t s v i e w s b e g a n t o c h a n g e . T h e a d v e n t o f c o m p u t e r s p r o v i d e d
a new way to think about the workings of the human mind. If we could “see” what
computers were doing internally, maybe we could use our observations to study human
mental processes, scientists reasoned. Indeed, computer science was a key motivator in
the birth of the study of human cogni-
tion. The first modern computer,
developed by mathematician John von
Neumann in the late 1940s, showed
that machines could perform logical
operations. In the 1950s, researchers
speculated that computers might model
some mental operations, and they
believed that such modeling might
shed light on how the human mind
works (Marcus, 2001).
Cognitive psychologists often use
the computer as an analogy to help
explain the relationship between cog-
nition and the brain (Forsythe, Bernard,
& Goldsmith, 2006). They describe
the physical brain as the computer’s
hardware and cognition as its soft-
ware. Herbert Simon (1969) was
among the pioneers in comparing the
human mind to computer processing
systems. In this analogy, the sensory
and perceptual systems provide an
“input channel, similar to the way
data are entered into the computer
cognition
The way in which information
is processed and manipu-
lated in remembering, think-
ing, and knowing.
1
The Cognitive Revolution
in Psychology
Mathematician John von Neumann (1903–1957) pioneered in the early development
of computers. The fact that his computer could perform logical operations led
researchers to imagine that computers might model some mental processes and that
such modeling might shed light on how the human mind functions.
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244 // CHAPTER 7 // Thinking, Intelligence, and Language
Computers
Output
Input
Human
Brain, mind,
cognition
(memory, problem
solving, reasoning,
consciousness)
Output
Input
Hardware
and software
(memory,
operations)
(Figure 7.1). As input (information) comes into the mind, mental
processes, or operations, act on it, just as the computer’s soft-
ware acts on the data. The
transformed input generates
information that remains in
memory much in the way a
computer stores what it has
worked on. Finally, the infor-
mation is retrieved from mem-
ory and “printed out” or
“displayed” (so to speak) as an
observable response.
Computers provide a logi-
cal and concrete, but over-
simplified, model of the
mind’s processing of informa-
tion. Inanimate computers and
human brains function quite dif-
ferently in some respects. For example, most
computers receive information from a human
who has already
coded the information
and removed much of
its ambiguity. In con-
trast, each brain neuron
can respond to ambiguous information transmitted
through sensory receptors such as the eyes and ears.
Computers can do some things better than humans.
Computers can perform complex numerical calculations
much faster and more accurately than humans could ever
hope to (Liu & others, 2012). Computers can also apply
and follow rules more consistently and with fewer errors
than humans and can represent complex mathematical
patterns better than humans.
S t i l l , t h e b r a i n s e x t r a o r d i n a r y c a p a b i l i t i e s w i l l p r o b -
ably not be mimicked completely by computers at any
time in the near future. Attempts to use computers to
process visual information or spoken language have
achieved only limited success in speci c situations.
The human brain also has an incredible ability to learn
new rules, relationships, concepts, and patterns that it
can generalize to novel situations. In comparison, com-
puters are limited in their ability to learn and general-
ize. Although a computer can improve its ability to
recognize patterns or use rules of thumb to make deci-
sions, it does not have the means to develop new learn-
ing goals.
Furthermore, the human mind is aware of itself; the
computer is not. Indeed, no computer is likely to
approach the richness of human consciousness (Agnati
& others, 2012; Nunez, 2012).
N o n e t h e l e s s , t h e c o m p u t e r s r o l e i n c o g n i t i v e p s y -
chology continues to increase. An entire scienti c eld
called arti cial intelligence (AI) f o c u s e s o n c r e a t i n g
machines capable of performing activities that require
intelligence when they are done by people. AI is
artifi cial intelligence (AI)
A scientifi c fi eld that focuses
on creating machines capa-
ble of performing activities
that require intelligence
when they are done by
people.
FIGURE 7.1
Computers and
Human Cognition
An analogy is commonly drawn
between human cognition and
the way computers work. The
physical brain is analogous to
a computer’s hardware, and
cognition is analogous to a
computer’s software.
Artifi cial intelligence (AI) researchers are exploring
frontiers that were once the context for sci-fi movie
plots. Cog is a human-form robot built by the
Humanoid Robotics Group at the Massachusetts
Institute of Technology. The sensors and structures in
Cog’s AI system model human sensory and motor
activity as well as perception. Cog’s creators have
sought to achieve humanlike functioning and
interactions with people—both of which, they hope,
will lead to new, humanlike learning experiences
forthe robot. Think about it: How might research
ndings from experiments such as Cog be applied to
real-world situations?
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Thinking // 245
1. Behaviorists thought that psychology
should properly focus on
A. mental processes.
B. the subconscious mind.
C. private behavior.
D. observable behavior.
2. The name for the scientific field that is
concerned with making machines that
mimic human information processing is
A. cognitive science.
B. cognitive neuroscience.
C. artifi cial intelligence.
D. computer science.
3. Cognition involves
A. manipulating information.
B. processing information.
C. thinking.
D. all of the above
A P P L Y I T ! 4. When Demarre plays
chess against his friend, he almost always
wins, but when he plays against his com-
puter, he typically loses. What is the most
likely explanation for Demarre’s experience?
A. Computers are smarter than human
beings.
B. When playing against his friend,
Demarre always cheats.
C. When playing against his friend,
Demarre is able to use his human cogni-
tive skills to “read” his opponent’s facial
expressions and predict what the latter
will do. These cues are missing when he
plays against his computer.
D. Demarre’s computer is better than his
friend at picking up on Demarre’s cues
and predicting what he will do next.
e s p e c i a l l y h e l p f u l i n t a s k s r e q u i r i n g s p e e d , p e r s i s t e n c e , a n d a v a s t m e m o r y ( G o e l &
Davies, 2011; Hermundstad & others, 2011). AI systems also assist in diagnosing med-
ical illnesses and prescribing treatment, examining equipment failures, evaluating loan
applicants, and advising students about which courses to take (Chang, 2012). Com-
puter scientists continue to develop computers that more closely approximate
human thinking (Fleuret & others, 2011).
By the late 1950s the cognitive revolution was in full swing. The term cog-
nitive psychology became a label for approaches that sought to explain observ-
able behavior by investigating mental processes and structures that could not
be directly observed (Robinson-Riegler & Robinson-Riegler, 2012; Sternberg
& Sternberg, 2012). In Chapter 6, we examined the operations involved in mem-
ory. We now build on that knowledge by exploring thinking, problem solving, and
decision making.
It would be pretty
funny if we could display a little
hour gl ass on our f or ehead s t o l et
ot her s know we ar e t hi nki ng.
Hav e y ou n ot i c ed t hat
int elligent comput ers on TV and
in t he movies almost always t urn
out t o be evi l ? Why do you t hi nk
fictional treatments of AI
of t en por t r ay smar t
compu t er s as scar y ?
When you save a computer le, you might hear a sound from inside or see an hour-
glass icon, and you know the computer is processing the work you have just
done. Unlike a computer, the brain does not make noise to let us know it is
working. Rather, the brain’s processing is the silent operation of thinking.
Thinking i n v o l v e s m a n i p u l a t i n g i n f o r m a t i o n m e n t a l l y b y f o r m i n g c o n c e p t s ,
solving problems, making decisions, and re ecting in a critical or creative man-
ner (Holyoak & Morrison, 2012). Let’s explore the nature of concepts—the com-
ponents of thinking—and investigate the cognitive processes of problem solving,
reasoning, and decision making.
Concepts
A fundamental aspect of thinking is the notion of concepts. Concepts are mental catego-
ries that are used to group objects, events, and characteristics. Humans have a special
ability for creating categories to help us make sense of information in our world (Rips,
Smith, & Medin, 2012). We know that apples and oranges are both fruits. We know that
poodles and collies are both dogs and that cockroaches and ladybugs are both insects.
These items differ from one another in various ways, and yet we recognize that they
belong together because we have concepts for fruits, dogs, and insects.
thinking
The process of
manipulating in-
formation men-
tally by forming
concepts, solving
problems, mak-
ing decisions,
and refl ecting
critically or
creatively.
concept
A mental category that is
used to group objects,
events, and characteristics.
2
Thinking
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246 // CHAPTER 7 // Thinking, Intelligence, and Language
Concepts are important for four reasons. First, concepts allow us to generalize. If we
did not have concepts, each object and event in our world would be unique and brand
new to us each time we encountered it. Second, concepts allow us to associate
experiences and objects. Basketball, ice hockey, and track are sports. The con-
cept sport g i v e s u s a w a y t o c o m p a r e t h e s e a c t i v i t i e s . T h i r d , c o n c e p t s a i d
memory by making it more ef cient so that we do not have to reinvent the
wheel each time we come across a piece of information. Imagine having to
think about how to sit in a chair every time we nd ourselves in front of one.
Fourth, concepts provide clues about how to react to a particular object or experience.
Perhaps you have had the experience of trying an exotic new cuisine and feeling puzzled
as you consider the contents of your plate. If a friend tells you reassuringly, “That’s
food!” you know that given the concept food, it is okay to dig in.
One way that psychologists explain the structure
of concepts is the prototype model. The prototype
model emphasizes that when people evaluate whether
a given item re ects a certain concept, they compare
the item with the most typical item(s) in that category
and look for a “family resemblance” with that item’s
properties. Birds generally y and sing, so we know
that robins and sparrows are both birds. We recognize
exceptions to these properties, however—we know
that a penguin is still a bird even though it does not
y or sing. The prototype model maintains that peo-
ple use characteristic properties to create a represen-
tation of the average or ideal member—the
prototype—for each concept. Comparing individual
cases to our mental prototypes may be a good way
to decide quickly whether something (or someone)
ts a particular category.
Problem Solving
Concepts tell us what we think about but not why we
think (Patalano, Wengrovitz, & Sharpes, 2009). Why
do we bother to engage in the mental effort of think-
ing? Consider Levi Hutchins, an ambitious young
clockmaker who in 1787 invented the alarm clock.
Why d i d h e g o t o t h e t r o u b l e ? H e h a d a s p e c i c
goal—he wanted to get up before sunrise every morn-
ing—yet he faced a dilemma in accomplishing that
goal. Problem solving means nding an appropriate
way to attain a goal when the goal is not readily
available (Bassok & Novick, 2012). Problem solving
entails following several steps, overcoming mental
obstacles, and developing expertise.
FOLLOWING THE STEPS IN PROBLEM
SOLVING Psychological research points to four
steps in the problem-solving process.
1. Find and Frame Problems Recognizing a
problem is the rst step toward a solution (Mayer,
2000). Finding and framing problems involves asking
questions in creative ways and “seeing” what others
do not.
prototype model
A model empha-
sizing that when
people evaluate
whether a given
item refl ects a
certain concept,
they compare the
item with the
most typical
item(s) in that
category and
look for a “family
resemblance”
with that item’s
properties.
problem solving
The mental pro-
cess of fi nding an
appropriate way
to attain a goal
when the goal
is not readily
available.
Although it has a ducklike bill and lays eggs, the platypus is
nevertheless a mammal like the tiger, as platypus females produce
milk with which they feed their young. The prototypical birdlike
characteristics of the platypus can lead us to think mistakenly that
the platypus is a bird. Its atypical properties place the platypus on
the extreme of the concept mammal.
Pr o t o t y p e s c o me i n h a n d y ,
but t hey can be mi sl eadi ng w hen
appl i ed t o peopl e, as we wi l l s ee.
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Thinking // 247
The ability to recognize and frame a problem is dif cult to learn. Furthermore, many
real-world problems are ill de ned or vague and have no clear-cut solutions (Schunk,
2011). Inventors are visionaries who see problems that everyone else is content to live
with. Recognizing problems involves being aware of and open to experiences (two men-
tal habits we will examine later). It also means listening carefully to that voice in your
head that occasionally sighs, “There must be a better way.
2. Develop Good Problem-Solving Strategies Once we nd a problem and
clearly de ne it, we need to develop strategies for solving it. Among the effective strat-
egies are subgoals, algorithms, and heuristics.
Subgoaling involves setting intermediate goals or de ning intermediate problems that
put us in a better position for reaching the nal goal or solution. Imagine that you are
writing a paper for a psychology class. What are some subgoaling strategies for approach-
ing this task? One might be locating the right books and research journals on your
chosen topic. At the same time that you are searching for the right publications, you will
likely bene t from establishing some subgoals within your time frame for completing
the project. If the paper is due in two months, you might set a subgoal of a rst draft
of the paper two weeks before it is due, another subgoal of completing your reading for
the paper one month before it is due, and still another subgoal of starting your library
research tomorrow. Notice that in establishing the subgoals for meeting
the deadline, we worked backward. Working backward in
establishing subgoals is a good strategy. You rst create the
subgoal that is closest to the nal goal and then work back-
ward to the subgoal that is closest to the beginning of the
problem-solving effort.
Algorithms are strategies that guarantee a solution to a
problem. Algorithms come in different forms, such as formulas, instructions, and the
testing of all possible solutions (Liu & Er, 2012; Mandal & Sairam, 2012). We use
algorithms in cooking (by following a recipe) and driving (by following directions to an
address).
An algorithmic strategy might take a long time. Staring at a rack of letters during a
game of Scrabble, for example, you might nd yourself moving the tiles around and
trying all possible combinations to make a high-scoring word. Instead of using an algo-
rithm to solve your Scrabble problem, however, you might rely on some rules of thumb
about words and language.
Heuristics are such shortcut strategies or guidelines that suggest a solution to a
problem but do not guarantee an answer (Bednark & others, 2012; Marewsky &
Schooler, 2011). In your Scrabble game, you know that if you have a Q, you
are going to need a U. If you have an X and a T, the T is probably not going
to come right before the X. In this situation, heuristics allow you to be more
ef cient than algorithms would. In the real world, we are more likely to solve
the types of problems we face by heuristics than by algorithms. Heuristics help us
to narrow down the possible solutions and to nd one that works.
3. Evaluate Solutions Once we think we have solved a problem, we will not know
how effective our solution is until we nd out if it works. It helps to have in mind a
clear criterion for the effectiveness of the solution. For example, what will your criterion
be for judging the effectiveness of your solution to the psychology assignment, your
psychology paper? Will you judge your solution to be effective if you simply complete
the paper? If you get an A ? If the instructor says that it is one of the best papers a stu-
dent ever turned in on the topic?
4. Rethink and Rede ne Problems and Solutions over Time An important
nal step in problem solving is to rethink and rede ne problems continually (Bereiter &
Scardamalia, 1993). Good problem solvers tend to be more motivated than the average
subgoaling
Setting intermediate goals
or defi ning intermediate
problems in order to be in a
better position for reaching
a fi nal goal or solution.
algorithms
Strategies—including for-
mulas, instructions, and
the testing of all possible
solutions—that guarantee a
solution to a problem.
heuristics
Shortcut strate-
gies or guide-
lines that suggest
a solution to a
problem but do
not guarantee an
answer.
al
o
f
start
i
ng your
lib
rary
r
meet
i
ng
We w i l l c o me b a c k t o
heur i st i c s. K eep i n mi n d t hat
they are cognitive shortcuts.
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248 // CHAPTER 7 // Thinking, Intelligence, and Language
person to improve on their past performances and to make original contributions. Can
we make the computer faster and more powerful? Can we make the iPod shuf e even
smaller?
A N O B S T A C L E T O P R O B L E M S O L V I N G : B E C O M I N G F I X A T E D A key
ingredient of being a good problem solver is to acknowledge that one does not know
everything—that one’s strategies and conclusions are always open to revision. Optimal
problem solving may require a certain amount of humility, or the ability to admit that
one is not perfect and that there may be better ways than one’s tried and true methods
to solve life’s problems. It is easy to fall into the trap of becoming xated on a particu-
lar strategy for solving a problem.
Fixation involves using a prior strategy and failing to look at a problem from a fresh,
new perspective. Functional xedness occurs when individuals fail to solve a problem
because they are xated on a thing’s usual functions. If you have ever used a shoe to
hammer a nail, you have overcome functional xedness to solve a problem.
An example of a problem that requires overcoming functional xedness is the Maier
string problem, depicted in Figure 7.2 (Maier, 1931). The problem is to gure out how
to tie two strings together when you must stand in one spot and cannot reach both at the
same time. It seems as though you are stuck. However, there is a pair of pliers on a
table. Can you solve the problem?
The solution is to use the pliers as a weight, tying them to the end of one string
(Figure 7.3). Swing this string back and forth like a pendulum and grasp the stationary
string. Your past experience with pliers and your xation on their usual function makes
this a dif cult problem to solve. To do so, you need to nd an unusual use for the
pliers—in this case, as a weight to create a pendulum.
Effective problem solving often necessitates trying something new, or thinking outside
the box —that is, exploring novel ways of approaching tasks and challenges and nding
solutions.
This might require admitting that one’s past strategies were not ideal or do
not readily translate to a particular situation. Students who are used to succeeding
in high school by cramming for tests and relying on parental pressure to get
homework done may nd that in college these strategies are no longer viable
ways to succeed. To explore how xation might play a role in your own problem
solving, see Figure 7.4.
xation
Using a prior strategy
and failing to look at a
problem from a fresh,
new perspective.
functional
xedness
Failing to solve a
problem as a
r esult of fi xation
on a thing’s usual
functions.
FIGURE 7.2 Maier String Problem How can you tie the
two strings together if you cannot reach them both at the same time?
FIGURE 7.3 Solution to the Maier String
Problem Use the pliers as a weight to create a pendulum motion
that brings the second string closer.
Recal l f r om Cha pt er 5
that multicultural experiences
ar e one way t o i ncr eas e i ns i ght
and cr eat i vi t y.
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Thinking // 249
Reasoning and Decision Making
In addition to forming concepts and solving problems, thinking includes the higher-order
mental processes of reasoning and decision making. These activities require rich connec-
tions among neurons and the ability to apply judgment. The end result of this type of
thinking is an evaluation, a conclusion, or a decision.
R E A S O N I N G Reasoning is the mental activity of transforming information to
reach conclusions. Reasoning is involved in problem solving and decision making.
It is also a skill closely tied to critical thinking (Hahn & Oaksford, 2012). Reason-
ing can be either inductive or deductive (Figure 7.5).
Inductive reasoning involves reasoning from speci c observations to make
generalizations (Schiefele & Raabe, 2011). Inductive reasoning is an important
way that we form beliefs about the world. For instance, having turned on your
cell phone many times without having it explode, you have every reason to believe
that it will not explode the next time you turn it on. From your prior experiences
with the phone, you form the general belief that it is not
likely to become a dangerous object.
A g r e a t d e a l o f s c i e n t i c knowledge is the product of
inductive reasoning. We know, for instance, that men and
women are genetically different, with women having two
X chromosomes and men having an X and a Y chromo-
some, although no one has actually tested every single
human being’s chromosomes to verify this generaliza-
tion. Psychological research is often inductive as well,
studying a sample of participants in order to yield con-
clusions about the population from which the sample is
drawn.
I n c o n t r a s t , deductive reasoning is reasoning from a
general case that we know to be true to a speci c instance
(Markovits, Forgues, & Brunet, 2012). Using deductive rea-
soning, we draw conclusions based on facts. For example, we
might start with the general premise that all Texans love the
Dallas Cowboys. Thus, if John is a Texan, we logically might
surmise that John loves the Dallas Cowboys. Notice, however,
reasoning
The mental activ-
ity of transform-
ing information
to reach
conclusions.
inductive
reasoning
Reasoning from
specifi c observa-
tions to make
generalizations.
deductive reasoning
Reasoning from a general
case that is known to be
true to a specifi c instance.
Indu ctive
Reasoning
Deductive
Reasoning
General
GeneralSpecific
Specific
FIGURE 7.5 Inductive and Deductive
Reasoning (Left) The upside-down pyramid
represents inductive reasoning—going from speci c
to general. (Right) The right-side-up pyramid
represents deductive reasoning—going from
general to speci c.
Imagine taking a sip of
mi l k f r o m a c o n t a i n e r a n d f i n d i n g
that it tastes sour. Inductive
reasoning is at work when you
throw out the whole container
even t hough you haven t
tasted every drop.
Sour mi l k not wi t h-
st anding, st r ong i nduct i ve
reasoning is generally based
on many obs er vat i ons . A
woman who dec i d es t hat
al l men ar e r at s af t er
two bad boyfriends is
over g e ner al i zi ng.
The Candle Problem
How would you mount a candle on a wall so
that it won’t drip wax on a table or a floor
while it is burning?
The Nine-Dot Problem
Take out a piece of paper and copy the
arrangement of dots shown below. Without
lifting your pencil, connect the dots using
only four straight lines.
The Six-Matchstick Problem
Arrange six matchsticks of equal length to
make four equilateral triangles, the sides of
which are one matchstick long.
FIGURE 7.4 Examples of How Fixation Impedes Problem Solving These tasks help psychologists measure creative problem solving.
Solutions to the problems are presented at the end of the chapter on page 275.
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250 // CHAPTER 7 // Thinking, Intelligence, and Language
that the logic of this deductive reasoning requires that the rst
statement be true; if all Texans do not love the Cowboys, John
just might be a Philadelphia Eagles fan.
When psychologists and other scientists use theories to make
predictions and then evaluate their predictions by making further
observations, deductive reasoning is at work. When psycholo-
gists develop a hypothesis from a theory, they are using a form
of deductive reasoning, because the hypothesis is a speci c,
logical extension of the general theory. If the theory is true, then
the hypothesis will be true as well.
D E C I S I O N M A K I N G Think of all the decisions, large
and small, that you have to make in life. Should you major in
biology, psychology, or business? Should you go to graduate
school right after college or get a job rst? Should you establish
yourself in a career before settling down to have a family? Do
you want fries with that? Decision making involves evaluating
alternatives and choosing among them (LeBoeuf & Sha r, 2012).
Reasoning uses established rules to draw conclusions. In contrast, in decision making,
such rules are not established, and we may not know the consequences of the decisions
(Knox & others, 2011; Molet & others, 2012). Some of the information might be miss-
ing, and we might not trust all of the information we have. Making decisions means
weighing information and coming to some conclusion that we feel will maximize our
outcome: Yes, we will be able to see the movie from this row in the theater; no, we will
not run that red light to get to class on time.
T W O S Y S T E M S O F R E A S O N I N G A N D D E C I S I O N M A K I N G Recall
from Chapter 4 the idea of automatic and controlled processes in consciousness. Many
psychologists similarly divide reasoning and decision making into two levels—one that
is automatic (often referred to as system 1 ) and one that is controlled (often called
s ystem 2 ) (Evans, 2012; Stanovich, 2012). The automatic system involves processing that
is rapid, heuristic, and intuitive; it entails following one’s hunches or gut feelings about
a particular decision or problem (Halberstadt, 2010; Kahneman & Klein, 2009). Intuitive
judgment means knowing that something feels right even if the reason why is unknown
(Topolinski & Strack, 2009). In contrast, the controlled system is slower, effortful, and
analytical. It involves conscious re ection about an issue. This is the kind of thinking
that might be required to solve a dif cult math problem, for example.
Although conscious effortful thinking is invaluable for solving many problems,
research has shown that intuitive processing may also have an important role to play
in decision making (Dijksterhuis & Nordgren, 2006; Halberstadt, 2010; Hicks & oth-
ers, 2010; Morewedge & Kahneman, 2010). No doubt you have had the experience
of consciously grappling with a problem and spending a good deal of time trying to
solve it, with no success. Then you take a break to listen to music or go running, and
suddenly the solution pops into your head. Research by Ap Dijksterhuis and his col-
leagues (2006) might ring a bell for you. In a series of laboratory studies, the exper-
imenters presented participants with a number of pieces of information relevant to a
decision, such as which apartment to pick out of a variety of possibilities. After seeing
the information, half of the participants were distracted while the other half had time
to think consciously about the decision. The results showed that the distracted
participants were more likely to pick the best option among the many choices. Simi-
lar results have been found for the accuracy of predictions about sporting events
(Dijksterhuis & others, 2009; Halberstadt, 2010). Sometimes, following your gut
feelings can be a good way to reach an optimal choice (Nordgren & Dijksterhuis,
2009; Topolinski & Strack, 2008).
The popular media sometimes portray intuitive hunches as sort of magical. However,
these gut feelings do not emerge out of thin air. Rather, they are the product of automatic
decision making
The mental activ-
ity of evaluating
alternatives and
choosing among
them.
“Don’t spread it around, but on the really tough ones,
I just go with ‘eenie, meenie, minie, moe.
Used by permission of CartoonStock, www.CartoonStock.com.
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Thinking // 251
processes like those explored in Chapter 4 (Halberstadt, 2010); of learned associations
such as those described in Chapter 5 (Kahneman & Klein, 2009; Unkelbach, 2007); and
of implicit memory, as described in Chapter 6 (Cheng & Huang, 2011). Your gut feelings
about the right answer on a test are certainly more likely to be accurate if you have put
in the requisite hours of conscious effortful study.
What determines which system of reasoning we use? The type of problem to be solved
may dictate the type of processing used. If a problem is dif cult, we might harness
system 2 processing. For less taxing decisions, we might go with system 1, following
our immediate gut feeling. One factor that in uences processing style is mood. A nega-
tive mood (feeling sad or worried) fosters more analytical, effortful cognitive processing
(Moberly & Watkins, 2009). In contrast, a positive mood (feeling happy or cheerful)
facilitates more rapid processing, promoting the use of heuristics and viewing the big
picture. A positive mood may provide a signal that all is well and that we can safely go
with our intuitive hunches for the task at hand (Clore & Palmer, 2009).
It is important to keep in mind that system 1 processes are as rapid as they are because
they often rely on heuristics. The use of heuristics can lead to mistakes when these rules
of thumb are applied inappropriately (Kahneman & Klein, 2009), as we now consider.
Biases and Heuristics Another fruitful subject of decision-making research is the
biases and heuristics (rules of thumb) that affect the quality of decisions (Bednark &
others, 2012; Grif n, 2012; Kahneman, Lovallo, & Sibony, 2011). In many cases, our
decision-making strategies are well adapted to deal with a variety of problems (Nisbett &
Ross, 1980). Heuristics, for example, are intuitive and ef cient ways of solving problems
and making decisions; they are often at work when we make a decision by following a
gut feeling. However, heuristics and gut feelings can lead to mistakes. Here we look at
a few biases and heuristic errors, summarized in Figure 7.6.
Confirmation
Bias
Description
Tendency to search for
and use information that
supports rather than
refutes one’s ideas
Example: A politician
accepts news that supports
his views and dismisses
evidence that runs counter
to these views.
Hindsight Bias
Description
Tendency to report falsely,
after the fact, that one
accurately predicted an
outcome
Example: You read about
the results of a particular
psychological study and
say, “I always knew that,”
though in fact you have
little knowledge about the
issues examined in the
study.
Description
Tendency to ignore infor-
mation about general
principles in favor of
very specific but vivid
information
Example: You read a
favorable expert report on
a TV you are intending to
buy, but you decide not to
buy it when a friend tells
you about a bad experience
with that model.
Description
Tendency to make
judgments about group
membership based on
physical appearances or
one’s stereotype of a
group rather than available
base rate information
Example: The victim of a
holdup, you view police
photos of possible perpe-
trators. The suspects look
very similar to you, but you
choose the individual
whose hair and clothing
look dirtiest and most
disheveled.
Availability
Heuristic
Description
Prediction about the
probability of an event
based on the ease of
recalling or imagining
similar events
Example: A girl from an
extended family in which
no family member ever
attended college tells her
mother that she wants to
be a doctor. Her mother
cannot imagine her
daughter in such a career
and suggests that she
become a home health-
care aide.
Base Rate Fallacy
Representativeness
Heuristic
FIGURE 7.6 Decision-Making Problems: Biases and Heuristics Biases and heuristics (rules of thumb) affect the quality of many of the
decisions we make.
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252 // CHAPTER 7 // Thinking, Intelligence, and Language
Con rmation bias is the tendency to search for and use information that supports
our ideas rather than refutes them (N. W. Jackson, 2012; Mendel & others, 2011). Our
decisions can also become further biased because we tend to seek out and listen to
people whose views con rm our own while we avoid those with dissenting views. It is
easy to detect the con rmation bias in the way that many people think. Consider politi-
cians. They often accept news that supports their views and dismiss evidence that runs
counter to those views. Avoiding con rmation bias means applying the same rigorous
analysis to both sides of an argument.
Hindsight bias is our tendency to report falsely, after the fact, that we accurately
predicted an outcome (Yopchick & Kim, 2012). It is sometimes referred to as the “I knew
it all along effect.” With this type of bias, people tend to view events that have happened
as more predictable than they were, and to represent themselves as being more accurate
in their predictions than they actually were (Nestler, Blank, & von Collani, 2008).
Although the hindsight bias might sound self-serving in the sense that it means remem-
bering ourselves as having known more than we really did know, cognitive psychologists
recognize that this bias may be based on new learning and on updating our knowledge
about the world (Nestler, Blank, & Egloff, 2010; Pezzo, 2011). One reason for hindsight
bias is that actual events are more vivid in our minds than all those things that failed to
happen, an effect called the availability heuristic.
T h e availability heuristic r e f e r s t o a p r e d i c t i o n a b o u t t h e p r o b a b i l i t y o f a n e v e n t
based on the ease of recalling or imagining similar events (McDermott, 2009). For exam-
ple, have you ever experienced a sudden fear of ying after hearing about an airplane
crash? Shocking events such as plane crashes stick in our minds, making such disasters
seem common. The chance of dying in a plane crash in a given year, however, is tiny
(1 in 400,000) compared to the chance of dying in a car accident (1 in 6,500). Because
car accidents are less newsworthy, they are less likely to catch our attention and remain
in our awareness. The availability heuristic can reinforce generalizations about others in
daily life (Chou & Edge, 2012). Imagine, for instance, that Elvedina, a Mexican American
girl, tells her mother that she wants to be a doctor. Her mother, who has never seen a
Latina doctor, nds it hard to conceive of her daughter’s pursuing such a career and
might suggest that she try nursing instead.
A l s o r e ective of the impact of vivid cases on decision making is the base rate
f a l l a c y , the tendency to ignore information about general principles in favor of very
speci c but vivid information. Let’s say that as a prospective car buyer, you read Con-
sumer Reports and nd that a panel of experts rates a particular vehicle exceptionally
well. You might still be swayed in your purchasing decision, however, if a friend tells
you about her bad experiences with that car. Similarly, imagine being told that the aver-
age exam score for a test in your psychology class was 75 percent. If you were asked
to guess a random student’s score, 75 percent would be a good answer—the mean tells
us the central tendency of any distribution. Yet if the student provided just a little bit of
information, such as how many hours he or she studied, you might give too much weight
to that speci c information, losing sight of the valuable base rate information you have
in the class mean.
To experience another heuristic in action, consider the following example. Your psy-
chology professor tells you she has assembled 100 men in the hallway outside your
classroom. The group consists of 5 librarians and 95 members of the Hells Angels motor-
cycle club. She is going to randomly select one man to enter the room, and you can win
$100 if you accurately guess whether he is a librarian or a Hells Angel. The man stands
before you. He is in his 50s, with short graying hair, and he wears thick glasses, a
button-down white shirt, a bow tie, neatly pressed slacks, and loafers. Is he a librarian
or a Hells Angel? If you guessed librarian, you have fallen victim to the representative-
ness heuristic.
T h e representativeness heuristic i s t h e t e n d e n c y t o m a k e j u d g m e n t s a b o u t g r o u p
membership based on physical appearances or the match between a person and one’s
stereotype of a group rather than on available base rate information (Nilsson, Juslin, &
Olsson, 2008). Essentially, a stereotype is the use of concepts to make generalizations
confi rmation bias
The tendency to search for
and use information that
supports one’s ideas rather
than refutes them.
hindsight bias
The tendency to report
falsely, after the fact, that
one has accurately
predicted an outcome.
availability heuristic
A prediction about the
probability of an event
based on the ease of
recalling or imagining
similar events.
base rate fallacy
The tendency to ignore
information about general
principles in favor of very
specifi c but vivid information.
representativeness heuristic
The tendency to make judg-
ments about group mem-
bership based on physical
appearances or the match
between a person and one’s
stereotype of a group rather
than on available base rate
information.
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Thinking // 253
about a group of people. We will examine stereotypes in some detail in Chapter 11.
In the example just described, the base rate information tells you that 95 times
out of 100, the man in your class is likely to be a Hells Angel. The best approach
to winning the $100 might be simply to shut your eyes and guess Hells Angel,
no matter what the man looks like.
The representativeness heuristic can be particularly damaging in the context
of social judgments. Consider a scenario where a particular engineering corpora-
tion seeks to hire a new chief executive of cer (CEO). Lori, a top-notch candidate
with an undergraduate engineering degree and an MBA from an outstanding business
school, applies. If there are few women in upper management at the rm, the company’s
board of directors might inaccurately view Lori as not tting their view of the
prototypical CEO—and miss the chance to hire an exceptional candidate.
Thus, heuristics help us make decisions rapidly. To solve problems accu-
rately and make the best decisions, however, we must sometimes override these
shortcuts and think more deeply, critically, and creatively .
Thinking Critically and Creatively
Problem solving and decision making are basic cognitive processes that we use multiple
times each day. Certain strategies lead to better solutions and choices than others, and
some people are especially good at these cognitive exercises. In this section we examine
two skills associated with superior problem solving: critical thinking and creativity.
C R I T I C A L T H I N K I N G Critical thinking means thinking
re ectively and productively and evaluating the evidence. Recall
from Chapter 1 that scientists are critical thinkers. Critical thinkers
grasp the deeper meaning of ideas, question assumptions, and
decide for themselves what to believe or do (Bonney & Sternberg,
2011; Fairweather & Cramond, 2011). Critical thinking requires
maintaining a sense of humility about what we know (and what
we do not know). It means being motivated to see past the obvious.
Critical thinking is vital to effective problem solving. However,
few schools teach students to think critically and to develop a deep
understanding of concepts (Brooks & Brooks, 2001). Instead, espe-
cially in light of pressures to maximize students’ scores on stan-
dardized tests, teachers concentrate on getting students to give a
single correct answer in an imitative way rather than on encourag-
ing new ideas (Bransford & others, 2006). Further, many people
are inclined to stay on the surface of problems rather than to stretch
their minds. The cultivation of two mental habits is essential to
critical thinking: mindfulness and open-mindedness.
Mindfulness means being alert and mentally present for one’s everyday activities.
The mindful person maintains an active awareness of the circumstances of his or her life.
According to Ellen Langer (1997, 2000, 2005), mindfulness is a key to critical thinking.
Langer distinguishes mindful behavior from mindless behaviors—automatic activities we
perform without thought.
In a classic study, Langer found that people (as many as 90 percent) would mindlessly
give up their place in line for a copy machine when someone asked, “Can I go rst? I
need to make copies” as compared to when the same person simply said, “Can I go
rst?” ( just 60 percent) (Langer, Blank, & Chanowitz, 1978). For the mindless persons
in the study, even a completely meaningless justi cation—after all, everyone in line was
there to make copies—was reason enough to step aside. A mindless person engages in
automatic behavior without careful thinking. In contrast, a mindful person is engaged
with the environment, responding in a thoughtful way to various experiences.
mindfulness
The state of
being alert and
mentally present
for one’s every-
day activities.
Gut i ns t i nct s can be
power f ul . I mag i ne i gnor i ng
your hunch t hat t he man was a
librarian, and yet it t urned out
that he
was
a l i br ar ian ( despi t e
the odds). How hard would
you ki ck your s el f t hen?
Not e t h at t he c ompany s
di r ect or s ar e usi ng a
pr ot ot ype
mo d e l
for t he cat egor y CEOand
ma k i n g a mi s t a k e .
“I never heard of anyone pulling a
muscle while thinking.”
Used by permission of CartoonStock, www.CartoonStock.com.
EXPERIENCE IT!
Critical Thinking and
Metacognition
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254 // CHAPTER 7 // Thinking, Intelligence, and Language
To get a sense of the roles of
divergent and convergent thinking
in creativity, try the following
exercise. First take 10 minutes and
jot down all of the uses that you can
think of for a cardboard box. Don’t
hold back—include every possibility
that comes to mind. That list
represents divergent thinking. Now
look the list over. Which of the
possible uses are most unusual or
most likely to be worthwhile?
That’s convergent thinking.
Open-mindedness means being receptive to other ways of looking at things.
People often do not even know that there is another side to an issue or evidence
contrary to what they believe. Simple openness to other viewpoints can help to
keep individuals from jumping to conclusions. As Socrates once said, knowing
what it is you do not know is the rst step to true wisdom.
Being mindful and maintaining an open mind may be more dif cult than
the alternative of going through life on automatic pilot. Critical thinking is
valuable, however, because it allows us to make better predictions about the
future, to evaluate situations objectively, and to effect appropriate changes.
In some sense, critical thinking requires courage. When we expose our-
selves to a broad range of perspectives, we risk nding out that our assump-
tions might be wrong. When we engage our critical minds, we may discover
problems, but we are also more likely to have opportunities to make positive
changes.
C R E A T I V E T H I N K I N G In addition to thinking critically, coming up
with the best solution to a problem may involve thinking creatively. The word
creative can apply to an activity or a person, and creativity as a process may
be open even to people who do not think of themselves as creative. When we
talk about creativity as a characteristic of a person, we are referring to the ability
to think about something in novel and unusual ways and to devise unconventional
solutions to problems (Baer & Kaufman, 2012; Gregerson, Kaufman, & Snyder, 2012;
Kaufman & Sternberg, 2010).
We can look at the thinking of creative people in terms of divergent and convergent
thinking. Divergent thinking produces many solutions to the same problem. Conver-
gent thinking produces the single best solution to a problem. Creative thinkers do
both types of thinking. Divergent thinking occurs during brainstorming, which occurs
when a group of people openly throw out a range of possible solutions to a problem,
even some that might seem crazy. Having a lot of possible solutions, however, still
requires that they come up with the solution that is best. That is where convergent
thinking comes in. Convergent thinking means taking all of those possibilities and
nding the right one for the job. Convergent thinking is best when a problem has only
one right answer.
Individuals who think creatively also show the following characteristics (Perkins,
1994).
Flexibility and playful thinking: Creative thinkers are exible and play with problems.
This trait gives rise to the paradox that although creativity takes hard work, the work
goes more smoothly if it is taken lightly. In a way, humor greases the wheels of
creativity (Goleman, Kaufman, & Ray, 1993). When you are joking around, you are
more likely to consider any possibility and to ignore the inner censor who can con-
demn your ideas as off base.
Inner motivation: Creative people often are motivated by the joy of creating. They
tend to be less inspired than less creative people by grades, money, or favorable feed-
back from others. Thus, creative people are motivated more internally than externally
(Hennessey, 2011).
Willingness to face risk: C r e a t i v e p e o p l e m a k e m o r e m i s t a k e s t h a n t h e i r l e s s i m a g i -
native counterparts because they come up with more ideas and more possibilities.
They win some; they lose some. Creative thinkers know that being wrong is not a
failure—it simply means that they have discovered that one possible solution does
not work.
Objective evaluation of work: M o s t c r e a t i v e t h i n k e r s s t r i v e t o e v a l u a t e t h e i r w o r k
objectively. They may use established criteria to make judgments or rely on the judg-
ments of respected, trusted others. In this manner, they can determine whether further
creative thinking will improve their work.
open-mindedness
The state of be-
ing receptive to
other ways of
looking at things.
creativity
The ability to
think about
something
innovel and
unusual ways
andto devise
unconventional
solutions to
problems.
divergent thinking
Thinking that produces
many solutions to the same
problem.
convergent thinking
Thinking that produces the
single best solution to a
problem.
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Intelligence // 255
1. An example of a concept is
A. a basketball.
B. a daisy.
C. a vegetable.
D. an eagle.
2. Deductive reasoning starts at
_________ and goes to _________.
A. the general; the specifi c
B. the specifi c; the general
C. xation; function
D. function; fi xation
3. All of the following are characteristic of
creative thinkers except
A. inner motivation.
B. functional fi xedness.
C. objectivity.
D. risk taking.
APPLY IT! 4. The students in an archi-
tecture class are given the assignment to
design a new student center for their cam-
pus. They have one week to submit their
first drafts. Jenny and David, two students
in the class, spend the first day very differ-
ently. Jenny quickly decides on what her
building will look like and starts designing
it. David spends the first day doodling and
devises 20 different styles he might use,
including a Gothic version, a spaceship
design, a blueprint with a garden growing
on top, and another plan that looks like a
giant elephant (the school’s mascot).
When Jenny sees David’s sketches, she
scoffs, “You’re wasting your time. We have
only a week for the first draft!” On the sec-
ond day, David selects his best effort and
works on that one until he finishes it.
Which of the following is true of these
strategies?
A. Jenny is effectively using divergent
thinking in her strategy.
B. David should be using heuristics given
the limited time for the project.
C. Jenny is criticizing David for engaging
in deductive reasoning.
D. David is using divergent thinking fi rst
and will get to convergent thinking
later, while Jenny has engaged only
in convergent thinking.
PSYCHOLOGY IN OUR WORLD
Help Wanted: Critical and Creative Thinkers
I
t is hard to imagine any occupation where critical thinking and creativity would not be useful
abilities. Each year, the Great Place to Work Institute conducts a survey to identify the best
workplaces in the United States, which are then announced in Fortune magazine.
Today, the best places to work are characterized as exible, diverse, learning-
oriented, and open in communication.
In 2012, Google, the Internet search engine company, was voted the
number 1 best place to work in the United States (“100 best companies to
work for,” 2012). Google gets 472,771 job applicants a year. What do
Google managers look for in potential employees? They seek to hire creative,
brainy, and hardworking people who work well in a context that is relatively
free of structure.
In both hiring decisions and the day-to-day workfl ow, many organizations seek
creativity and thinking outside the box. Some organizations even hire consultants to
help promote creative and critical thinking in managers and employees. Being a
great problem solver involves not only know-how and motivation but also the
capacity to meet every challenge with an open mind.
Like the term creative, the word intelligent can apply to a behavior or a person. We might
say that someone who decides to quit smoking has made an intelligent choice. When we
apply the word to a person, however, de ning intelligent can be trickier.
C u l t u r e s v a r y i n t h e w a y s t h e y d e ne intelligence (Ang, van Dyne, & Tan, 2011;
Sternberg, 2012c; Zhang & Sternberg, 2012). Most European Americans think of
3
Intelligence
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256 // CHAPTER 7 // Thinking, Intelligence, and Language
i n t e l l i g e n c e i n t e r m s o f r e a s o n i n g a n d t h i n k i n g s k i l l s , b u t p e o p l e i n K e n y a c o n s i d e r r e s p o n -
sible participation in family and social life an integral part of intelligence. An intelligent
person in Uganda is someone who knows what to do and follows through with appropri-
ate action. Intelligence to the Iatmul people of Papua New Guinea involves the ability to
remember the names of 10,000 to 20,000 clans. The residents of the widely dispersed
Caroline Islands incorporate the talent of navigating by the stars into their de nition of
intelligence (Figure 7.7).
In the United States, we generally de ne intelligence as an all-purpose ability to do
well on cognitive tasks, to solve problems, and to learn from experience. The idea that
intelligence captures a common general ability that is re ected in performance on various
cognitive tests was introduced by Charles Spearman (1904). Spearman noted that school-
children who did well in math also did well in reading, and he came up with the idea
that intelligence is a general ability, which he called g. This view of intelligence suggests
that general intelligence underlies performance in a variety of areas, whether it is math-
ematics, verbal ability, or abstract reasoning. Spearman’s g essentially assumes that the
intelligent person is a jack-of-all-cognitive trades.
Measuring Intelligence
Psychologists measure intelligence using tests that produce a score known as the person’s
intelligence quotient ( IQ ). To understand how IQ is derived and what it means, let’s rst
examine the criteria for a good intelligence test: validity, reliability, and standardization.
In the realm of testing, validity refers to the extent to which a test measures what
it is intended to measure. If a test is supposed to measure intelligence, then it should
measure intelligence, not some other characteristic, such as anxiety. One of the
most important indicators of validity is the degree to which it predicts an indi-
vidual’s performance when that performance is assessed by other measures, or
crit e ria, of the attribute (Neukrug & Fawcett, 2010). For example, a psychologist
might validate an intelligence test by asking employers of the individuals who
took the test how intelligent they are at work. The employers’ perceptions would
be a criterion for measuring intelligence. When the scores on a measure relate to
important outcomes (such as employers’ evaluations), we say the test has high
criterion vali d ity.
intelligence
All-purpose ability to do
well on cognitive tasks, to
solve problems, and to learn
from experience.
validity
The extent to
which a test
measures what it
is intended to
measure.
FIGURE 7.7 Iatmul and Caroline Island Intelligence The intelligence of the Iatmul people of Papua New Guinea involves the ability to remember
the names of many clans. On the 680 Caroline Islands in the Paci c Ocean east of the Philippines, the intelligence of the inhabitants includes the ability to navigate
by the stars.
Test validit y means
that a scale measures what
it says it measures. Recall from
Chapt er 1 t hat i n ex per i m ent s ,
val i di t y r ef er s t o t he s oundnes s
of t he concl usi ons t hat a
researcher draws from
an ex per i ment .
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Intelligence // 257
Reliability is the extent to which a test yields a consistent, reproducible measure of
performance. That is, a reliable test is one that produces the same score over time and
repeated testing. Reliability and validity are related. If a test is valid, then it must be
reliable, but a reliable test need not be valid. People can respond consistently on a test,
but the test might not be measuring what it purports to measure.
Good intelligence tests are not only reliable and valid but also standardized (Salvia,
Ysseldyke, & Bolt, 2010). Standardization involves developing uniform procedures for
administering and scoring a test, as well as creating norms, or performance standards,
for the test. Uniform testing procedures require that the testing environment be as simi-
lar as possible for all individuals. Norms are created by giving the test to a large group
of individuals representative of the population for whom the test is intended. Norms tell
us which scores are considered high, low, or average. Many tests of intelligence are
designed for individuals from diverse groups. So that the tests are applicable to such
different groups, many of them have norms for individuals of different ages, socioeco-
nomic statuses, and ethnic groups (Urbina, 2011). Figure 7.8 summarizes the criteria for
test construction and evaluation.
I Q T E S T S In 1904, the French Ministry of Education asked psy-
chologist Alfred Binet to devise a method that would determine
which students did not learn effectively from regular classroom
instruction. School of cials wanted to reduce overcrowding by plac-
ing such students in special schools. Binet and his student Theophile
Simon developed an intelligence test to meet this request. The test
consisted of 30 items ranging from the ability to touch one’s nose
or ear on command to the ability to draw designs from memory and
to de ne abstract concepts. To measure intelligence, Binet came up
with the idea of comparing a person’s mental abilities to the mental
abilities that are typical for a particular age group.
Binet developed the concept of mental age (MA) , which is an
individual’s level of mental development relative to that of others.
Binet reasoned that a child of very low mental ability would perform
like a normal child of a younger age. To think about a person’s level
of intelligence, then, we might compare the person’s mental age
(MA) to his or her chronolog i cal age (CA), or age from birth. A
very bright child has an MA considerably above CA; a less bright
child has an MA considerably below CA.
The German psychologist William Stern devised the term intel-
ligence quotient (IQ) in 1912. IQ consists of an individual’s men-
tal age divided by chronological age multiplied by 100:
IQ ! (MA/CA) " 100
If mental age is the same as chronological age, then the individual’s IQ is 100 (aver-
age); if mental age is above chronological age, the IQ is more than 100 (above average);
if mental age is below chronological age, the IQ is less than 100 (below average). For
example, a 6-year-old child with a mental age of 8 has an IQ of 133, whereas a 6-year-
old child with a mental age of 5 has an IQ of 83.
In childhood, mental age increases as the child ages, but once he or she reaches about
age 16, the concept of mental age loses its meaning. That is why many experts today
prefer to examine IQ scores in terms of how unusual a person’s score is when compared
to the scores of other adults. For this purpose, researchers and testers use standardized
norms that they have identi ed in the many people who have been tested.
In fact, over the years, the Binet test has been given to thousands of children and
adults of different ages selected at random from different parts of the United States.
Administering the test to large numbers of individuals and recording the results have
revealed that intelligence measured by the Binet test approximates a normal distribution
(Figure 7.9). A normal distribution is a symmetrical, bell-shaped curve, with a majority
reliability
The extent to
which a test
yields a consis-
tent, reproduc-
ible measure of
performance.
standardization
The development
of uniform proce-
dures for adminis-
tering and scoring
a test and the cre-
ation of norms
(performance
standards) for the
test.
mental age (MA)
An individual’s level of men-
tal development relative to
that of others.
intelligence quotient (IQ)
An individual’s mental age
divided by chronological
age multiplied by 100.
normal distribution
A symmetrical, bell-shaped
curve, with a majority of the
scores falling in the middle
of the possible range and
few scores appearing
toward the extremes of
therange.
Is test performance
consistent?
Does the test measure
what it purports to
measure?
Validit y
Reliab ilit y
Are uniform procedures
for administering and
scoring the test used?
St and ard izat ion
FIGURE 7.8 Test
Construction and
Evaluation Tests are a
tool for measuring important
a b i l i t i e s s u c h a s i n t e l l i g e n c e .
Good tests show high
reliability and validity and are
standardized so that people’s
scores can be compared.
Alfred Binet (1857–1911)
Binet constructed the fi rst intelligence
test after being asked to create a
measure to determine which children
would benefi t from instruction in
France’s schools.
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258 // CHAPTER 7 // Thinking, Intelligence, and Language
of the scores falling in the middle of the possible range and few scores appearing toward
the extremes of the range. The Stanford-Binet continues to be one of the most widely
used individual tests of intelligence (Musso & others, 2011).
Individuals from the age of 2 through adulthood take the current Stanford-Binet test
(the name re ects the fact that the revisions were completed at Stanford University). It
includes a wide variety of items, some requiring verbal responses, others nonverbal
responses. For example, items that characterize a 6-year-old’s performance on the test
include the verbal ability to de ne at least six words, such as orange and envelope, and
the nonverbal ability to trace a path through a maze. Items that re ect the average adult’s
intelligence include de ning such words as disproportionate and regard, explaining a
proverb, and comparing idleness and laziness.
C U L T U R A L B I A S I N T E S T I N G M a n y e a r l y i n t e l l i g e n c e t e s t s
were culturally biased, favoring people who were from urban rather than
rural environments, of middle rather than low socioeconomic status, and
non-Latino White rather than African American (Provenzo, 2002). For
example, a question on an early test asked what one should do if one
nds a 3-year-old child in the street. The correct answer was “call the
police. However, children from inner-city families who perceive the
police as scary are unlikely to choose this answer. Similarly, children
from rural areas might not choose this answer if there is no police force
nearby. Such questions clearly do not measure the knowledge necessary
to adapt to one’s environment or to be “intelligent” in an inner-city or
a rural neighborhood (Scarr, 1984). In addition, members of minority
groups may not speak English or may speak nonstandard English. Con-
sequently, they may be at a disadvantage in trying to understand verbal
questions that are framed in standard English, even if the content of the
test is appropriate (Cathers-Shiffman & Thompson, 2007).
R e s e a r c h e r s h a v e s o u g h t t o d e v e l o p t e s t s t h a t a c c u r a t e l y r e ect a
person’s intelligence, regardless of cultural background. Culture-fair
tests a r e i n t e l l i g e n c e t e s t s t h a t a r e i n t e n d e d t o b e c u l t u r a l l y u n b i a s e d .
One type of culture-fair test includes questions that are familiar to peo-
ple from all socioeconomic and ethnic backgrounds. A second type
contains no verbal questions. Figure 7.10 shows a sample question from
the Raven Progressive Matrices Test. Even though tests such as the
Raven Progressive Matrices are designed to be culture-fair, people with
more education still score higher than do those with less education.
culture-fair tests
Intelligence tests
that are intended
to be culturally
unbiased.
Cumulative percentages
Percentage of cases
under the normal curve
2% 98%
2.14%13.59%34.13%34.13%13.59%2.14% 0.13%0.13%
84%50%16%
Stanford-Binet
IQs
68 14813211610052 84
FIGURE 7.9 The Normal Curve
and Stanford-Binet IQ Scores The
distribution of IQ scores approximates a normal
curve. Remember that the area under the curve
represents the number of people who obtain a
given score. > Does most of the population fall
in the low, middle, or high range of scores?
How do you know? > If someone scored 132
on the test, how many people scored below
that person’s score? > Intelligence follows a
normal distribution, meaning it produces this
bell curve. What other human characteristics
might have the same distribution?
PSYCHOLOGICAL INQUIRY
1
5
2
6
3
7
4
8
FIGURE 7.10 Sample Item from the
Raven Progressive Matrices Test For
this item, the respondent must choose which of the
numbered gures would come next in the order.
Why is number 6 the right answer?
Simulated item similar to those found in the Raven’s Progressive Matrices
(Advanced Progressive Matrices). Copyright © 1998 NCS Pearson, Inc.
Reproduced with permission. All rights reserved. “Raven’s Progressive
Matrices” is a trademark, in the US and/or other countries, of Pearson
Education, Inc. or its affiliates.
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Intelligence // 259
W h y i s i t s o h a r d t o c r e a t e c u l t u r e - f a i r t e s t s ? J u s t a s t h e d e nition of intelligence may
vary by culture, most tests of intelligence re ect what is important to the dominant cul-
ture. If tests have time limits, the test will be biased against groups not concerned with
time. If languages differ, the same words might have different meanings for different
language groups. Even pictures can produce bias, because some cultures have
less experience with drawings and photographs (Anastasi & Urbina, 1996).
Because of such dif culties, Robert Sternberg (2012c; Zhang & Sternberg, 2012)
concludes that there are no culture-fair tests, only culture-reduced tests.
M o r e o v e r , w i t h i n t h e s a m e c u l t u r e , d i f f e r e n t g r o u p s c a n h a v e d i f f e r e n t
attitudes, values, and motivation, and these variations can affect their perfor-
mance on intelligence tests (Ang & van Dyne, 2009; Sternberg, 2012c).
Questions about railroads, furnaces, seasons of the year, distances between
cities, and so on can be biased against groups who have less experience than
others with these contexts. One explanation for the effects of education on
IQ test scores is that education (and other environmental factors) may in u-
ence intelligence, a possibility to which we now turn.
Genetic and Environmental
Influences on Intelligence
T h e r e i s n o d o u b t t h a t g e n e s i n uence intelligence (Esposito, Grigorenko, & Sternberg, 2012;
Hanscombe & others, 2012). A recent research review concluded that there may be more
than 1,000 genes that affect intelligence, each possibly having a small in uence on an indi-
vidual’s intelligence (Davies & others, 2011). However, researchers have not been able to
identify the speci c genes that contribute to intelligence and its components (Deary, 2012).
Some researchers use a statistic called heritability to describe the extent to which the
observable differences among people in a group can be explained by the genetic differ-
ences of the group’s members. Heritability is the proportion of observable differences
in a group that can be explained by differences in the genes of the group’s members.
For intelligence, that means that heritability tells us how much of the differences we
observe in intelligence is attributable to differences in genes. Because heritability is a
proportion, the highest degree of heritability is 100 percent. It has commonly been
reported that the heritability of intelligence is approximately 50 percent, which clearly
allows for a considerable in uence of the environment.
As you consider this discussion of genetic in uences on intelligence, you might nd
yourself re ecting on some of the less than brilliant things your parents have done over
the years, and you may be feeling discouraged. If IQ is heritable, is there any hope? The
heritability statistic has certainly been one way that some researchers have tried to gauge
the in uence of genetics on psychological characteristics including intelligence. How-
ever, it is important to understand this statistic for what it can and cannot tell us about
intelligence or any characteristic. First and most important, heritability is a statistic
that provides information about a group, not a single individual. That means that nd-
ing out that heritability for intelligence is 50 percent tells us nothing at all about the
source of an individual person’s intelligence. We cannot dissect your intelligence and
determine that you got 50 percent of it from your parents’ genes and 50 percent from
environmental experiences that you have had with your parents, teachers, friends, and
others. Heritability has no meaning when applied to a single case.
Also, heritability estimates can change over time and across different groups (Nisbett &
others, 2012; Turkheimer & others, 2003). If a group of individuals lives in the same
advantageous setting (with good nutrition, supportive parents, excellent schools, stable
neighborhoods, and plenty of opportunities), heritability estimates for intelligence might
be quite high, as this optimal environment allows genetic characteristics to ourish to their
highest potential. However, if a group of individuals lives in a highly variable environment
(with some individuals experiencing rich, nurturing environments full of opportunity and
heritability
The proportion of observ-
able differences in a group
that can be explained by
differences in the genes of
the group’s members.
Try out a culture-fair IQ test
foryourself. Go to http://
psychologytoday.tests.psychtests.
com/take_test.php?idRegTest!3202.
Once you have checked it out, do a
web search for intelligence tests and
see if you get the same results when
you take different tests. Do the
websites provide information about
the reliability and validity of the
tests?
n
t
t
t
t
EXPERIENCE IT!
Genes and Intelligence
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260 // CHAPTER 7 // Thinking, Intelligence, and Language
others experiencing less supportive contexts), genetic characteristics may be less predictive
of differences in intelligence in that group, relative to environmental factors.
Even if the heritability of a characteristic is very high, the environment still matters.
Take height, for example. More than 90 percent of the variation in height is explained
by genetic variation. Humans continue to get taller and taller, however, and this trend
demonstrates that environmental factors such as nutrition have an impact. Similarly, in
the case of intelligence, researchers widely agree that for most people, modi cations in
environment can change their IQ scores considerably (Esposito, Grigorenko, & Sternberg,
2012; Nisbett & others, 2012). Enriching an environment can improve children’s achieve-
ment in school and support their development of crucial workplace skills. Children from
impoverished socioeconomic backgrounds who are adopted into more economically
advantaged families often have IQs that are higher than those of their biological parents
(Sternberg, Grigorenko, & Kidd, 2005). A recent research review by leading experts on
intelligence concluded that the importance of environmental in uences has been estab-
lished by the 12- to 18-point increase in IQ that is observed when children from low-
income backgrounds are adopted by parents from middle-income backgrounds (Nisbett
& others, 2012). Although heredity in uences intellectual ability, environmental factors
and opportunities also make a difference.
Researchers are increasingly interested in manipulating the early environment of chil-
dren who are at risk for impoverished intelligence (Phillips & Lowenstein, 2011). Pro-
grams that educate parents to be more sensitive caregivers and that train them to be
better teachers can make a difference in a child’s intellectual development, as can support
services such as high-quality child-care programs (Morrison, 2012).
One effect of education on intelligence is evident in rapidly increasing IQ test scores
around the world, a phenomenon called the Flynn effect ( F l y n n , 1 9 9 9 , 2 0 0 6 , 2 0 1 1 ) .
Scores on these tests have been rising so fast that a high percentage of people regarded
as having average intelligence at the turn of the twentieth century would be regarded as
having below average intelligence today (Figure 7.11). Because the increase has taken
place in a relatively short period of time, it cannot be due to heredity but rather may be
due to rising levels of education attained by a much greater percentage of the world’s
population or to other environmental factors, such as the explosion of information to
which people are now exposed.
PSYCHOLOGICAL INQUIRY
FIGURE 7.11 The Increase in IQ Scores from 1932 to 1997 As measured by the
Stanford-Binet intelligence test, American children seem to be getting smarter. Scores of a group tested in
1932 fell along a bell-shaped curve, with half below 100 and half above. Note that if children’s scores from
1997 were plotted on this same scale, the average would be 120. > How would average children from
1932 compare to children from the more recent sample? Would they still be “average”? > Note the far
ends (or tails) of the 1932 curve. How would these children be considered by the standards of the more
recent scores? > What do you think is responsible for the Flynn effect?
55 115 120 130 14570 85 100
19971932
160
Intellectually
deficient
Intellectually
very superior
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Intelligence // 261
Many different intelligence tests are
available online, such as at www.
iqtest.com/. Give this one a try and
then do a web search for intelligence
tests and see if you get the same
results when you take a different
test. Do the websites tell you how
reliable the tests are? Do they
provide information on
standardization or validity? If your
scores on the two tests are very
different, what might account for
this variation?
E n v i r o n m e n t a l i n uences are complex (Grusec, 2011; Wright & others, 2012).
Growing up with all the advantages does not guarantee success. Children from
wealthy families may have easy access to excellent schools, books, tutors, and
travel, but they may take such opportunities for granted and not be motivated
to learn and to achieve. Alternatively, poor or disadvantaged children may be
highly motivated and successful. Caregivers who themselves lacked educa-
tional opportunities may instill a strong sense of the value of learning and
achievement in their children. Oprah Winfrey, the offspring of an unwed
teenage couple, was reared in the inner city by her grandmother, who
instilled in her a love of reading and a strong belief that she had the abil-
ity to do great things.
L e t s r e t u r n t o t h e i d e a t h a t t h e w o r d intelligent d e s c r i b e s n o t o n l y
people but also behaviors. Mastering skills, thinking about life actively,
and making life decisions thoughtfully are intelligent behaviors that peo-
ple can engage in regardless of the numerical intelligence quotient on their
permanent record. Intelligent behavior is always an option, no matter one’s
IQ score. As we saw in Chapter 5, our beliefs about cognitive ability,
speci cally whether it is xed or changeable, have important implications
for the goals we set for learning new skills (Dweck, 2006, 2012). We never
know what we might accomplish if we try, and no one is doomed because of
a number, no matter how powerful that number may seem.
Cognitive abilities are clearly important to academic accomplishments, but
they are also involved in the mastery of social and interpersonal skills. Cognitive
abilities, such as abstract reasoning, are fundamental to skills such as taking another
person’s perspective and understanding the behaviors and intentions of others (Murphy
& Hall, 2011). Recent research has found a provocative connection between intelligence
and the social problem of prejudice. To read about this controversial research, see
Challenge Your Thinking.
Extremes of Intelligence
I n t e l l i g e n c e , t h e n , a p p e a r s t o e m e r g e f r o m a c o m b i n a t i o n o f g e n e t i c h e r i t a g e a n d e n v i r o n -
mental factors. As we have seen, scores on IQ tests generally conform to the bell-shaped
normal curve. We now examine the implications of falling
on either tail of that curve.
G I F T E D N E S S T h e r e a r e p e o p l e w h o s e a b i l i t i e s a n d
accomplishments outshine those of others—the A # s t u -
dent, the star athlete, the natural musician. People who are
gifted have high intelligence (an IQ of 130 or higher) and/
or superior talent in a particular area. Lewis Terman (1925)
conducted a study of 1,500 children whose Stanford-Binet
IQs averaged 150, a score that placed them in the top
1 percent. A popular myth is that gifted children are
m a l a d j u s t e d , b u t T e r m a n f o u n d t h a t h i s p a r t i c i p a n t s ( t h e
“Termites”) were not only academically gifted but also
socially well adjusted. Many of them later became success-
ful doctors, lawyers, professors, and scientists. Do gifted
children grow into gifted and highly creative adults? In
T e r m a n s r e s e a r c h , g i f t e d c h i l d r e n t y p i c a l l y d i d b e c o m e
experts in a well-established domain, such as medicine,
law, or business; but the Termites did not become major
creators or innovators (Winner, 2000, 2006).
In light of the sweeping social and economic changes of
the digital age, are today’s gifted children perhaps better
gifted
Possessing high
intelligence (an
IQ of 130 or
higher) and/or
superior talent in
a particular area.
Olympic speed-skating gold medalist Joey Cheek well
illustrates giftedness. Beyond his accomplishments on the ice,
he is studying economics and Chinese at Princeton University
and is cofounder of Team Darfur, an organization dedicated to
raising awareness of human rights abuses in Sudan.
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262 // CHAPTER 7 // Thinking, Intelligence, and Language
able than the Termites to use their gifts in innovative and important ways in adulthood?
The results from a longitudinal study of profoundly gifted children begun by Julian
Stanley at Johns Hopkins University in 1971 seem to indicate just that. The Study of
Mathematically Precocious Youth includes 320 participants whom researchers recruited
before age 13 based on IQ scores, with the group’s average IQ estimated at 180. This
group is said to represent the top 1 in 10,000 IQ scores (Lubinski & others, 2001). Fol-
lowing up on these individuals in their 20s, David Lubinski and his colleagues (2006)
found that these strikingly gifted young people were doing remarkable things. At age 23,
they were pursuing doctoral degrees at a rate 50 times higher than the average. Some
reported achievements such as receiving creative writing awards, creating original art and
music, publishing in scholarly journals, and developing commercially viable software
and video games. Thus, unlike the Termites, this group has been extraordinarily creative
and innovative (Wai, Lubinski, & Benbow, 2005).
Like intelligence itself, giftedness is likely a product of both heredity and environment.
Experts who study giftedness point out that gifted individuals recall showing signs of high
ability in a particular area at a very young age, prior to or at the beginning of formal train-
ing (Howe & others, 1995). This result suggests the importance of innate ability in gifted-
ness. However, researchers also have found that the individuals who enjoy world-class
status in the arts, mathematics, science, and sports all report strong family support and years
of training and practice (Bloom, 1985). Deliberate practice is an important characteristic of
individuals who become experts in a particular domain (Grigorenko & others, 2009).
An increasing number of experts argue that typical U.S. classrooms often do not meet
the educational needs of gifted children (Reis & Renzulli, 2011; Sternberg, 2012d). Some
Challenge
YOUR THINKING
Is Intelligence Related to Prejudice and Political Beliefs?
A
n important aspect of our in-
terpersonal world is our atti-
tudes toward individuals who are
different from us. Prejudice, which we will
examine in Chapter 11, means having a
negative attitude about someone based
on the person’s membership in a par-
ticular group. A speci c type of preju-
dice is racism, a negative attitude
toward members of a particular ra-
cial group. Most research on racism
has focused on the motivational or
emotional side of this problematic
attitude (Jost & others, 2003). From
this perspective, prejudice can be understood as emerging from
competition for resources between different groups or as arising
from the need to feel good about one’s own group memberships.
Researchers have explored the link between cognitive ability
and prejudicial attitudes. This work has generally shown that intel-
ligence is negatively correlated with prejudice (Piber-Dabrowska,
Sedek, & Kofta, 2010). In other words, the more intelligent people
are, the less likely they are to be prejudiced, and vice versa.
For example, in a recent study, Gordon Hodson and Michael
Busseri (2012) examined two large representative samples in the
United Kingdom that included nearly 16,000 people. Intelligence
was measured when the participants were 10 or
11 years old, and racial attitudes were as-
sessed when they were in their early 30s.
Prejudicial attitudes were assessed with
items like “I wouldn’t mind working with
people from other races” (for such an item, a
high rating would indicate low racism). Results for both sam-
ples showed that lower intelligence in childhood predicted greater
racism in adulthood. The negative relationship between intelligence
and prejudice remained even when differences in education levels
and socioeconomic status were taken into account. These results
suggest the not terribly controversial conclusion that holding preju-
dicial attitudes is not especially intelligent.
The researchers then boldly took these questions one step
further. Beyond measuring intelligence and racism, Hodson and
Busseri measured intelligence and social conservatism. They
assessed social conservatism by asking individuals about their
positions on such matters as valuing traditional roles for men and
women and supporting harsh sentences for criminals. The results
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Intelligence // 263
educators conclude that the problem of inadequate education of gifted adolescents has
been compounded by the federal government’s No Child Left Behind policy, which
seeks to raise the achievement level of students who are not doing well in school
at the expense of enriching the education of gifted children (Clark, 2008; Cloud,
2008). Ellen Winner (1996, 2006) recommends that when children and adoles-
cents are underchallenged, they be allowed to attend advanced classes in their
domain of exceptional ability, as did Bill Gates, Microsoft’s founder, who took
college math classes at 13, and famed cellist Yo-Yo Ma, who graduated from
high school at 15 and then attended the Juilliard School of Music.
I N T E L L E C T U A L D I S A B I L I T Y Just as some individuals are at the high
extreme of intelligence, others are at the lower end. Intellectual disability (for-
merly termed mental retardation ) is a condition of limited mental ability in which
an individual has a low IQ, usually below 70 on a traditional intelligence test, and
has dif culty adapting to everyday life; he or she would have exhibited these character-
istics by age 18. In the United States, about 5 million people t this de nition of intel-
lectual disability. Note that for a person to be described as intellectually disabled, low
IQ and low adaptiveness are evident in childhood. We do not usually think of a college
student who suffers massive brain damage in a car accident, resulting in an IQ of 60, as
intellectually disabled.
Intellectual disability may have an organic cause, or it may be cultural and social in
origin (Hallahan, Kauffman, & Pullen, 2012). Organic intellectual disability is caused
by a genetic disorder or brain damage; organic r e f e r s t o t h e t i s s u e s o r o r g a n s o f t h e b o d y ,
intellectual
disability
A condition of
limited mental
ability in which
an individual has
a low IQ, usually
below 70 on a
traditional intel-
ligence test, and
has diffi culty
adapting to
everyday life.
indicated that individuals who were lower in cognitive ability were
more likely to endorse traditional views on these matters. The
researchers argued that this endorsement explained the respon-
dents’ tendency to report more prejudicial attitudes.
When these results spread across the Internet, blog headlines
clearly revealed the biases of bloggers in responding to the con-
troversial ndings. “Conservatives scienti cally proven as stupid
and racist” declared a liberal blog. “Low IQ and liberal beliefs
linked to poor research?” posed a conservative blog. As observed
by social psychologist Brian Nosek, who was not involved in the
study, Hodson and Busseri “pulled off the trifecta of controversial
topics. When one selects intelligence, political ideology, and rac-
ism and looks at any of the relationships between those three
variables, it’s bound to upset somebody” (quoted in Pappas,
2012).
Before trying to sort out what these controversial ndings can
and cannot—tell us about potential associations between cognitive
ability, prejudice, and political orientation, let’s consider the re-
search methods used in the study. First, the researchers’ measure
of political orientation was very limited: Social conservatism was
de ned in narrow terms, using items that omitted many aspects of
this factor. Further, economic conservatism or political conserva-
tism was not addressed more generally. Second, the measure of
racism relied on self-reports. Consider that possibly brighter individ-
uals know better than to admit to any undesirable negative atti-
tudes. Third, this research was correlational, meaning that
causation cannot be assumed between the factors studied (for
example, we cannot conclude that being less intelligent caused
individuals to be more socially
conservative). Fourth, group-level
results cannot be generalized to
speci c individuals. As Hodson
and Busseri themselves noted,
there are certainly very intelli-
gent conservatives in the world,
and not-so-bright liberals as
well. Further, all social conser-
vatives are not prejudiced, and
all prejudiced individuals are
not social conservatives.
Holding either extreme right-
wing or extreme left-wing views
may be a way for those with
lower cognitive ability to nd
easy answers in a dif cult
world. Thinking critically and
mindfully about the social
world may be a more dif cult
but worthwhile alternative.
Thinking critically and mindfully
about provocative research is
similarly worthwhile.
What Do You Think?
According to the Flynn effect
(see p. 260), intelligence has
been rising around the world.
This increase in intelligence
should mean, based on the
research about which you just
read, that prejudice is likely
decreasing. Do you think that
is the case? Explain.
The researchers assessed
the intelligence of children
when they were 11 years
old in an effort to predict
prejudice in adulthood. What
other childhood factors
might likely be stronger
predictors of prejudice in
adulthood than intelligence?
A serious public policy
ques t i on i n t he Uni t ed St at es
is, should resources be dedicat ed
to enhancing the ex per i ence of
gi f t ed s t udent s or t o br i ngi ng
ever yone up t o a s t andar d?
Wh a t s y o u r o p i n i o n ? H o w
mu c h mo n e y w o u l d y o u b e
wi l l i ng t o pay i n t axes t o
accompl i s h bot h goal s?
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264 // CHAPTER 7 // Thinking, Intelligence, and Language
so there is some physical damage in organic retardation. Down syn-
drome, one form of organic intellectual disability, occurs when an extra
chromosome is present in the individual’s genetic makeup. Most people
who suffer from organic retardation have an IQ between 0 and 50.
Cultural-familial intellectual disability i s a m e n t a l d e cit with no
evidence of organic brain damage. Individuals with this type of disability
have an IQ between 55 and 70. Psychologists suspect that such mental
de cits result at least in part from growing up in a below-average intel-
lectual environment. As children, individuals with this disability can be
identi ed in school, where they often fail, need tangible rewards (candy
rather than grades, for example), and are highly sensitive to what peers
and adults expect of them (Vaughn, Bos, & Schumm, 2003). As adults,
however, these individuals usually go unnoticed, perhaps because adult
settings do not tax their cognitive skills as much. It may also be that
the intelligence of such individuals increases as they move toward
adulthood.
T h e r e a r e s e v e r a l c l a s s i cations of intellectual disability (Hodapp &
others, 2011). In one classi cation system, disability ranges from mild,
to moderate, to severe or profound, according to the persons IQ (Heward,
2013). The large majority of individuals diagnosed with intellectual dis-
ability fall in the mild category. Most school systems still use this system.
However, these categories, based on IQ ranges, are not perfect predictors
of functioning. Indeed, it is not unusual to nd clear functional d i f f e r -
ences between two people who have the same low IQ. For example, looking at two indi-
viduals with a similarly low IQ, we might nd that one of them is married, employed, and
involved in the community while the other requires constant supervision in an institution.
Such differences in social competence have led psychologists to include de cits in adaptive
behavior in their de nition of intellectual disability (Turnbull & others, 2013).
The American Association on Intellectual and Developmental Disabilities (2010) has
developed an assessment that examines a person’s level of adaptive behavior in three life
domains:
Conceptual skills: For example, literacy and understanding of numbers, money, and time.
Social skills: For example, interpersonal skills, responsibility, self-esteem, and ability
to follow rules and obey.
Practical skills: For example, activities of daily living such as personal care, occupa-
tional skills, health care, travel/transportation, and use of the telephone.
Assessment of capacities in these areas can be used to determine the amount of care the
person requires for daily living, not as a function of IQ but of the person’s ability to
negotiate life’s challenges.
Individuals with Down syndrome may never accomplish the academic feats of those
who are gifted. However, they may be capable of building close, warm relations with
others; inspiring loved ones; and bringing smiles into an otherwise gloomy day (Van
Riper, 2007). Individuals with Down syndrome moreover might possess different kinds
of intelligence, even if they are low on general cognitive ability. The possibility that other
intelligences exist alongside cognitive ability (or disability) has inspired some psycholo-
gists to suggest that we need more than one concept of intelligence.
Theories of Multiple Intelligences
I s i t m o r e a p p r o p r i a t e t o t h i n k o f a n i n d i v i d u a l s i n t e l l i g e n c e a s a g e n e r a l a b i l i t y o r , r a t h e r ,
as a number of speci c abilities? Traditionally, most psychologists have viewed intelli-
gence as a general, all-purpose problem-solving ability that, as we have seen, is sometimes
referred to as g, O t h e r s h a v e p r o p o s e d t h a t w e t h i n k a b o u t d i f f e r e n t k i n d s o f i n t e l l i g e n c e ,
Individuals with Down syndrome may excel in
sensitivity toward others. The possibility that
other strengths or intelligences coexist with
cognitive ability (or disability) has led some
psychologists to propose the need for
expanding the concept of intelligence.
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Intelligence // 265
such as emotional intelligence, t h e a b i l i t y t o p e r c e i v e e m o t i o n s i n o u r s e l v e s
and others accurately (Brackett, Rivers, & Salovey, 2011; Mayer & others,
2011). Robert Sternberg and Howard Gardner have developed in uential
theories presenting the viewpoint that there are multiple intellige n ces.
S T E R N B E R G S T R I A R C H I C T H E O R Y A N D G A R D N E R S M U L -
TIPLE INTELLIGENCES Robert J. Sternberg (1986, 2004, 2008, 2011,
2012a, 2012b) developed the triarchic theory of intelligence , which says that
intelligence comes in multiple (speci cally, three) forms:
Analytical intelligence: The ability to analyze, judge, evaluate, compare, and
contrast.
Creative intelligence: The ability to create, design, invent, origi-
nate, and imagine.
Practical intelligence: The ability to use, apply, imple-
ment, and put ideas into practice.
Howard Gardner (1983, 1993, 2002) suggests there are
nine types of intelligence, or “frames of mind.These are
described here, with examples of the types of vocations in
which they are re ected as strengths (Campbell, Campbell,
& Dickinson, 2004):
Verbal: The ability to think in words and use language
to express meaning. Occupations: author, journalist,
speaker.
Mathematical: The ability to carry out mathematical
operations. Occupations: scientist, engineer, accountant.
Spatial: The ability to think three-dimensionally. Occupations: architect, artist, sailor.
Bodily-kinesthetic: T h e a b i l i t y t o m a n i p u l a t e o b j e c t s a n d t o b e p h y s i c a l l y a d e p t .
Occupations: surgeon, craftsperson, dancer, athlete.
Musical: The ability to be sensitive to pitch, melody, rhythm, and tone. Occupa-
tions: composer, musician.
Interpersonal: The ability to understand and interact effectively with others. Occu-
pations: teacher, mental health professional.
Intrapersonal: The ability to understand oneself. Occupations: theologian, psychologist.
Naturalist: The ability to observe patterns in nature and understand natural and
human-made systems. Occupations: farmer, botanist, ecologist, landscaper.
Existentialist: The ability to grapple with the big questions of human exis-
tence, such as the meaning of life and death, with special sensitivity to
issues of spirituality. Gardner has not identi ed an occupation for existen-
tial intelligence, but one career path would likely be philosopher.
According to Gardner, everyone has all of these intelligences to varying degrees. As
a result, we prefer to learn and process information in different ways. We learn best
when we can do so in a way that uses our stronger intelligences.
EVALUATING THE APPROACHES OF MULTIPLE INTELLIGENCES
Sternberg’s and Gardner’s approaches have stimulated teachers to think broadly about
what makes up children’s competencies. They have motivated educators to develop pro-
grams that instruct students in multiple domains. These theories have also contributed to
interest in assessing intelligence and classroom learning in innovative ways, such as by
evaluating student portfolios (Woolfolk, 2013).
Doubts about multiple intelligences persist, however. A number of psychologists think
that the proponents of multiple intelligences have taken the concept of speci c intelligences
triarchic theory
of intelligence
Sternberg’s
theory that intel-
ligence comes in
three forms: ana-
lytical, creative,
and practical.
s
i
n ourse
l
ves
e
r & others,
d
i
n
uent
i
a
l
l
i
ge
n
ce
s
.
N
ERS MUL-
2004, 2008, 2011,
n
ce
,
w
hi
c
h
says t
h
at
l
uate, compare, and
, origi
-
Do y o u k n o w p e o p l e s o me
mi g h t c a l l b o o k s ma r t a n d
ot her s who ar e peopl e s mar t ?
Wh a t k i n d s o f i n t e l l i g e n c e d o
they show?
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266 // CHAPTER 7 // Thinking, Intelligence, and Language
too far (Reeve & Charles, 2008). Some critics argue that a research base to support the
three intelligences of Sternberg or the nine intelligences of Gardner has not yet emerged.
One expert on intelligence, Nathan Brody (2007), observes that people who excel at one
type of intellectual task are likely to excel in others. Thus, individuals who do well at
memorizing lists of digits are also likely to be good at solving verbal problems and
spatial layout problems. Other critics ask, if musical skill, for example, re ects a distinct
type of intelligence, why not also label the skills of outstanding chess players, prize ght-
ers, painters, and poets as types of intelligence? In sum, controversy still characterizes
whether it is more accurate to conceptualize intelligence as a general ability, speci c
abilities, or both (Brody, 2007; Nisbett & others, 2012; Sternberg, 2012a, 2012b).
Our examination of cognitive abilities has highlighted how individuals differ in the
quality of their thinking and how thoughts may differ from one another. Some thoughts
re ect critical thinking, creativity, or intelligence. Other thoughts are perhaps less
inspired. One thing thoughts have in common is that they often involve language. Even
when we talk to ourselves, we do so with words. The central role of language in cogni-
tive activity is the topic to which we now turn.
1. The reproducibility of a test’s result is
known as
A. criterion validity.
B. validity.
C. standardization.
D. reliability.
2. A 10-year-old child has a mental age
of8. The child’s IQ is
A. 60.
B. 80.
C. 100.
D. 125.
3. The heritability index for intelligence is
approximately
A. 35 percent.
B. 50 percent.
C. 75 percent.
D. 90 percent.
A P P L Y I T ! 4. Shortly after Joshua’s
birth, his parents found that he had a ge-
netic condition that causes mental retarda-
tion. They worried about their son’s future.
Which of the following most accurately
relates to Joshua’s future?
A. Genes are not the sole cause of intelli-
gence, and providing Joshua with a rich
and stimulating environment will make
the most of his genetic endowment. Fur-
thermore, although Joshua may never
excel in cognitive ability, there are likely
other realms of life in which he can be
successful.
B. Because intelligence is strongly heri-
table, providing a rich environment to
Joshua is unlikely to have any impact
on his eventual intelligence and
ability.
C. Genes cause 75 percent of Joshua’s in-
telligence, so environmental factors can
infl uence only 25 percent of his cogni-
tive abilities.
D. Genes have nothing to do with intelli-
gence, so Joshua’s parents have no need
to worry.
Language is a form of communication, whether spoken, written, or signed, that is based
on a system of symbols. We need language to speak with others, listen to others, read,
and write (Berko Gleason, 2009). Language is not just how we speak to others but how
we talk to ourselves. Consider an occasion, for example, when you have experienced the
feeling of a guilty conscience, of having done something you should not have. The little
voice in your head that clamors, “You shouldn’t have done that! Why did you do it?”
speaks to you in your mother tongue. In this section we rst examine the fundamental
characteristics of language and then trace the links between language and cognition.
The Basic Properties of Language
All human languages have in nite generativity , the ability to produce an endless num-
ber of meaningful sentences. This superb exibility comes from ve basic rule systems:
Phonology : a language’s sound system. Language is made up of basic sounds, or
phonemes. Phonological rules ensure that certain sound sequences occur (for example,
sp, ba, and ar ) and others do not (for example, zx and qp ) (Menn & Stoel-Gammon,
language
A form of
communication—
whether spoken,
written, or
signed—that is
based on a sys-
tem of symbols.
infi nite generativity
The ability of language to
produce an endless number
of meaningful sentences. phonology
A language’s
sound system.
4
Language
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Language // 267
2009). A good example of a phoneme in the English language is /k/ , the sound rep-
resented by the letter k in the word ski and by the letter c in the word cat . Although
the /k/ sound is slightly different in these two words, the /k/ sound is described as a
single phoneme in English.
Morphology : a language’s rules for word formation. Every word in the English lan-
guage is made up of one or more morphemes. A morpheme is the smallest unit of
language that carries meaning. Some words consist of a single morpheme—for exam-
ple, help. Others are made up of more than one; for example, helper has two mor-
phemes, help ! er . The morpheme -er means “one who”—in this case, “one who
helps.As you can see, not all morphemes are words; for example, pre-, -tion, and
-ing are morphemes. Just as the rules that govern phonemes ensure that certain sound
sequences occur, the rules that govern morphemes ensure that certain strings of sounds
occur in particular sequences (Croft, 2012).
Syntax : a language’s rules for combining words to form acceptable phrases and sen-
tences (Dixon, 2012). If someone says, “John kissed Emily” or “Emily was kissed by
John,you know who did the kissing and who was kissed in each case because you
share that person’s understanding of sentence structure. You also understand that the
sentence “You didn’t stay, did you?” is a grammatical sentence but that “You didn’t
stay, didn’t you?” is unacceptable.
Semantics : the meaning of words and sentences in a particular language. Every word
has a unique set of semantic features (Pan & Uccelli, 2009). Girl and woman, for
example, share many semantic features (for instance, both signify female human
beings), but they differ semantically in regard to age. Words have semantic restrictions
on how they can be used in sentences. The sentence “The bicycle talked the boy into
buying a candy bar” is syntactically correct but semantically incorrect. The sentence
violates our semantic knowledge that bicycles do not talk.
Pragmatics : the useful character of language and the ability of language to commu-
nicate even more meaning than is said (Al-Wer, 2012). The pragmatic aspect of lan-
guage allows us to use words to get the things we want. If you ever nd yourself in
a country in which you know only a little of the language, you will certainly take
advantage of pragmatics. Wandering the streets of, say, Madrid, you might approach
a stranger and ask, simply, Autobus?” (the Spanish word for bus ). You know that
given your in ection and perhaps your desperate facial expression, the person will
understand that you are looking for the bus stop.
With this basic understanding of language in place, we can examine the connections
between language and cognition.
Language and Cognition
Language is a vast system of symbols capable of expressing most thoughts. Language
is the vehicle for communicating most of our thoughts to one another. Although we do
not always think in words, our thinking would be greatly impoverished without words.
The connection between language and thought has been of considerable interest to
psychologists. Some have even argued that we cannot think without language. This
proposition has produced heated controversy. Is thought dependent on language, or is
language dependent on thought?
T H E R O L E O F L A N G U A G E I N C O G N I T I O N Recall from Chapter 6 that
memory is stored not only in the form of sounds and images but also in words. Language
helps us think, make inferences, tackle dif cult decisions, and solve problems (Gleitman
& Papafragou, 2012; Goldin-Meadow & Cook, 2012). Language is a tool for represent-
ing ideas (Kovacs, 2009).
morphology
A language’s rules for word
formation.
syntax
A language’s rules for
combining words to form
acceptable phrases and
sentences.
semantics
The meaning of words and
sentences in a particular
language.
pragmatics
The useful character of
language and the ability of
language to communicate
even more meaning than is
verbalized.
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268 // CHAPTER 7 // Thinking, Intelligence, and Language
T o d a y , m o s t p s y c h o l o g i s t s w o u l d a c c e p t t h e s e p o i n t s . H o w e v e r , l i n g u i s t B e n j a m i n
Whorf (1956) went a step further: He argued that language determines the way we think,
a view that has been called the linguistic relativity hypothesis . Whorf and his student
Edward Sapir were specialists in Native American languages, and they were fasci-
nated by the possibility that people might perceive the world differently as the
result of the different languages they speak. The Inuit people in Alaska, for instance,
have a dozen or more words to describe the various textures, colors, and physical
states of snow. In contrast, English has relatively few words to describe snow, and
thus, according to Whorfs view, English speakers cannot see the different kinds of
snow because they have no words for them.
Whorfs bold claim appealed to many scholars. Some even tried to apply Whorfs
view to sex differences in color perception. Asked to describe the colors of two sweaters,
a woman might say, “One is mauve and the other is magenta,while a man might say,
“They’re both pink. Whorfs view of the in uence of language on perceptual ability
might suggest that women are able to see more colors than men simply because they
have a richer color vocabulary (Hepting & Solle, 1973). It turns out, however, that men
can learn to discriminate among the various hues that women use, and this outcome
suggests that Whorfs view is not quite accurate.
Indeed, critics of Whorfs ideas say that words merely re ect, rather than cause, the
way we think. The Inuits’ adaptability and livelihood in Alaska depend on their capacity
to recognize various conditions of snow and ice. A skier or snowboarder who is not Inuit
might also know numerous words for snow, far more than the average person, and a
person who does not know the words for the different types of snow might still be able
to perceive these differences.
I n t e r e s t i n g l y , r e s e a r c h h a s s h o w n t h a t W h o r f m i g h t h a v e b e e n a c c u r a t e f o r i n f o r m a t i o n
that is presented to the left hemisphere of the brain. That is, when colors were presented
in the right visual eld (and therefore went to the left brain), having names for the colors
enhanced perception of and discrimination between those colors (Gilbert & others, 2006).
Language is a key feature of culture, and one of the ways that psychologists study
the link between language and cognition is by comparing cognitive reasoning across
differing cultures. To read about this research, see the Intersection.
Whorf’s view is that our cultural experiences with a particular concept shape a catalog of names that can be either rich or poor. Consider how rich your
mental library of names for a camel might be if you had extensive experience with camels in a desert world, and how poor your mental library of names
for snow might be if you lived in a tropical world of palm trees and parrots. Despite its intriguing appeal, Whorf’s view is controversial, and many
psychologists do not believe it plays a pivotal role in shaping thought.
Thi nk of al l t he
wor d s we have f or c of f ee
dr i nk s. What mi ght t hi s say
about our s oci et y?
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Language // 269
Language, Culture, and Cognition: How Does
Language Shape Answers to the Question “Where”?
INTERSECTION
Children in the studies were asked to
memorize the arrangement of a set of ani-
mal toys—for instance, a cow, a sheep, and
a pig—lined up on a table. At this rst table,
children stood looking at the objects from
the south end. Then they were asked to re-
create the arrangement on another table,
while standing at the west end of the table
(requiring a 90-degree rotation of the ob-
jects). The researchers found that all chil-
dren were generally very good at rearranging
the toys to re-create the previous pattern,
but they did so in different ways. The Dutch
children placed the toys so that they re-
mained in the same positions from their
perspective. The Namibian children, on the
other hand, rearranged the toys so that the
animals conformed to their placement in
geocentric terms. Confronted with more com-
plex tasks, the children showed these same
differences—and when they were instructed
to use the other way of thinking, they were
not successful. Speci cally, when Dutch chil-
dren were told to remember which toy was
on the western side of the table, and when Hai||om children were
told to remember which toy was on the right, they both had dif culty
(even though both languages have terms for these locations). The
researchers concluded that spatial reasoning preferences are
strongly related to linguistic conventions, even by the age of 8.
Clearly, the human brain can han-
dle both relativistic and geocentric
spatial descriptions, but cultures and
languages vary in terms of how much
they use these and for what contexts.
The way we talk can in uence the way
we think in ways we might never no-
tice. Many of us are confounded if
our GPS tells us to “head east,” but
small children in Namibia would nd
that a lot easier than trying to re-
member what “turn right” means.
I
magine the following scenario. Sitting
at breakfast with friends, you ask,
“Where’s the salt?” One of your com-
panions answers, “It’s to the west of
the eggs.” Where would you look? (You might
be tempted to pull out the compass app on
your smartphone to locate your desired sea-
soning on the table.) Although it may be
surprising, in many languages, spatial
relationships between objects are described
in just such terms.
How we think about spatial relationships
is one aspect of cognitive reasoning. This
kind of reasoning is at work when we note
that a book is on top of a desk, the dog is un-
der the table, and the salt is just to your left.
Spatial reasoning in uences our judgments
as well. While driving, for instance, we gauge
the timing and amount of pressure we should
apply to the brake to stop our car to avoid hit-
ting the vehicle in front of us. And when play-
ing a video game, spatial reasoning allows us
to shoot a moving target accurately.
An interesting question is, does the way
we talk about spatial relationships in uence our capacities for
spatial reasoning? A recent series of studies by Daniel Haun and
colleagues (2011) examined this question using schoolchildren
from the Netherlands and Namibia. Like other European lan-
guages (including English), Dutch includes relativistic terms to de-
scribe spatial relationships. This means that the self serves as a
landmark in spatial statements, so that a person might say, “The
salt is to the left of your plate (from my perspective).” Namibians
speak a language called "‚ Akhoe Hai||om (Hai||om, for short)
which relies on absolute or geocentric terms to describe spatial
relations. This means that the placement of objects in the world
is the key, not where those objects are relative to the self. In
Hai||om, then, someone might well say, “The salt is to the west
(literally, where the sun sets) of your plate.” Most European lan-
guages reserve geocentric terms for geographic contexts (“The
lake is on the east side of town”) and never use them for other
objects.
\\
How might these
differences affect
individuals moving from one
culture to another?
\\
What might using the
self as a landmark in spatial
reasoning say about the
Western sense of self?
Although the strongest form of Whorf’s hypothesis—that language determines
p e r c e p tion—seems doubtful, research has continued to demonstrate the in uence of
language on how we think, even about something as fundamental as our own personali-
ties. For example, in a series of studies, researchers interviewed bilingual individuals
(that is, people who uently speak two languages, in this case Spanish and English)
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270 // CHAPTER 7 // Thinking, Intelligence, and Language
(Ramirez-Esparza & others, 2006). Each person rated his or her own personality charac-
teristics, once in Spanish and once in English. Across all studies, and regardless of
whether the individuals lived in a Spanish-speaking or an English-speaking country,
respondents reported themselves as more outgoing, nicer, and more responsible when
responding to the survey in English.
T H E R O L E O F C O G N I T I O N I N L A N G U A G E Clearly, then, language can
in uence cognition (Siegel & Surian, 2012). Researchers also study the possibility
that cognition is an important foundation for language (Jackendoff, 2012).
One feature of human language that separates it from animal communication
is the capacity to talk about objects that are not currently present (Hockett,
1960). A study comparing 12-month-old infants (who had not yet begun to talk)
to chimpanzees suggests that this cognitive skill may underlie eventual language
(Liszkowski & others, 2009). In this study, infants were more likely to com-
municate their desire for a toy by pointing to the place where the toy used to
be . For many infants, this was the rst thing they did to get their point across to
another person who was present. In contrast, chimpanzees rarely pointed to where
their desired object (food) had been, except as they desperately started pointing all
over the place.
So, even before they can talk, humans are communicating with oth-
ers about what they want. Sometimes that communication demonstrates an apprecia-
tion of shared knowledge even about objects that are no longer present.
If language is a re ection of cognition in general, we would expect to nd a close
link between language ability and general intellectual ability. In particular, we would
expect to nd that problems in cognition are paralleled by problems in language. We
would anticipate, for example, that general intellectual disability is accompanied by low-
ered language abilities. It is often but not always the case that individuals with intel-
lectual disability have reduced language pro ciency. For instance, individuals with
Williams syndrome, a genetic disorder that affects about 1 in 20,000 births, tend to show
extraordinary verbal, social, and musical abilities while having an extremely low IQ and
dif culty with motor tasks and numbers. Williams syndrome demonstrates that intellec-
tual disability is not always accompanied by poor language skills.
In summary, although thought in uences language and language in uences thought,
there is increasing evidence that language and thought are not part of a single system.
Instead, they seem to have evolved as separate but related components of the mind.
Biological and Environmental
Influences on Language
Everyone who uses language in some way “knows” its rules and has
the ability to create an in nite number of words and sentences. Is
this knowledge the product of biology, or is language learned and
in uenced by experiences in the environment?
B I O L O G I C A L I N F L U E N C E S Scientists believe that humans
acquired language about 100,000 years ago. In evolutionary time,
then, language is a very recent human ability. However, a number
of experts believe that biological evolution that occurred long before
language emerged undeniably shaped humans into linguistic crea-
tures (Chomsky, 1975). The brain, nervous system, and vocal appa-
ratus of our predecessors changed over hundreds of thousands of
years. Physically equipped to do so, Homo sapiens w e n t b e y o n d
grunting and shrieking to develop abstract speech. This sophisticated
language ability gave humans an enormous edge over other animals
and increased their chances of survival (Pinker, 1994).
Cons i der how t hi s
research connects to theory
of mi nd, descr i bed i n Chapt er 4 .
Wh y w o u l d i n f a n t s p o i n t t o a
pl ace wher e an obj ect used t o be
if t hey didn’t know t hat
somet hing was goi ng on in t he
head of a per son wi t h t hem?
Wh y w o u l d w e t a l k t o o n e
anot her at al l i f we di dn t
know t hat ot her peopl e
have a subj ect i ve
awar enes s ?
Used by permission of CartoonStock, www.CartoonStock.com.
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Language // 271
Language Universals American linguist Noam Chomsky (1975) has
argued that humans come into the world biologically prewired to learn lan-
guage at a certain time and in a certain way. According to Chomsky and
many other language experts, the strongest evidence for language’s biological
basis is the fact that children all over the world reach language milestones
at about the same time and in about the same order, despite vast variations
in the language input they receive from their environments. For example, in
some cultures adults never talk to infants under 1 year of age, yet these
infants still acquire language.
In Chomsky’s view, children cannot possibly learn the full rules and
structure of languages by only imitating what they hear. Rather, nature must
provide children with a biological, prewired, universal grammar, allowing
them to understand the basic rules of all languages and to apply these rules
to the speech they hear. They learn language without an awareness of its
underlying logic. Think about it: The terms we used above to de ne the
characteristics of language— phonology, morphology, semantics, and so
forth—may be new to you, but on some level you have mastered these
principles. This mastery is demonstrated by your reading of this book, writ-
ing a paper for class, and talking with a friend. Like all other humans, you
are engaged in the use of a rule-based language system even without know-
ing that you know those rules.
Language and the Brain There is strong evidence to back up experts
who believe language has a biological foundation. Neuroscience research has
shown that the brain contains particular regions that are predisposed to lan-
guage use (Tremblay, Monetta, & Joanette, 2009). As we saw in Chapter 2,
accumulating evidence suggests that language processing, such as speech and grammar,
mainly occurs in the brain’s left hemisphere (Harpaz, Levkovitz, & Lavidor, 2009;
Hornickel, Skoe, & Kraus, 2009). Recall the importance of Broca’s area, which contrib-
utes to speech production, and Wernicke’s area, which is involved in language compre-
hension. Using brain-imaging techniques such as PET scans, researchers have found that
when an infant is about 9 months old, the part of the brain that stores and indexes many
kinds of memory becomes fully functional (Bauer, 2009). This is also the time at which
infants appear to be able to attach meaning to words, for instance to look at the ball if
someone says “ball”—a development suggesting links among language, cognition, and
the development of the brain.
E N V I R O N M E N T A L I N F L U E N C E S Decades ago, behaviorists opposed Chomskys
hypothesis and argued that language represents nothing more than chains of responses
acquired through reinforcement (Skinner, 1957). A baby happens to babble “ma-ma”;
mama rewards the baby with hugs and smiles; the baby says mama more and
more.Bit by bit, said the behaviorists, the baby’s language is built up. According to
behaviorists, language is a complex learned skill, much like playing the piano or
dancing.
Such a view of language development is simply not tenable, however, given the rapid
way children learn language, as well as the lack of evidence that social environments
carefully reinforce language skills (R. Brown, 1973). This is not to say the environment
has no role in language development. Many language experts argue that a child’s expe-
riences, the particular language to be learned, and the context in which learning takes
place can strongly in uence language acquisition (Berko Gleason, 2009).
Cases of children who have lacked exposure to language provide evidence for the
important role of the environment in language development. In 1970, a California social
worker made a routine visit to the home of a partially blind woman who had applied for
public assistance. The social worker discovered that the woman and her husband had
kept their 13-year-old daughter, Genie, locked away in almost total isolation during her
childhood. Genie could not speak or stand erect. She had spent every day bound naked
Noam Chomsky (b. 1928)
MIT linguist Noam Chomsky was one of
the early architects of the view that
children’s language development cannot
be explained by environmental input. In
Chomsky’s view, language has strong
biological underpinnings, with children
biologically prewired to learn language
at a certain time and in a certain way.
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272 // CHAPTER 7 // Thinking, Intelligence, and Language
FIGURE 7.12 The Power of Smile and Touch Research has
shown that when mothers immediately smiled and touched their 8-month-old
infants after they had babbled, the infants subsequently made more complex
speechlike sounds than when mothers responded randomly to their infants.
to a child’s potty seat. She could move only her hands and feet.
At night, she had been placed in a kind of straightjacket and
caged in a crib with wire mesh sides and a cover. Whenever
Genie had made a noise, her father had beaten her. He had
never communicated with her in words; he had growled
and barked at her instead (Rymer, 1993).
After she was rescued from her parents, Genie
spent a number of years in extensive rehabilitation
programs, including speech and physical therapy
(Curtiss, 1977). She eventually learned to walk,
although with a jerky motion, and to use the
toilet. Genie also learned to recognize many
words and to speak in rudimentary sentences.
Gradually, she was able to string together two-
word combinations such as “Big teeth, “Little
marble,” and “Two hand” and then three-word
combinations such as “Small two cup.As far as
we know, unlike normal children, Genie did not
learn to ask questions and did not develop a
language system that allowed her to understand
English grammar. As an adult, she speaks in
short, mangled sentences such as “Father hit
leg,“Big wood,and “Genie hurt.
C h i l d r e n w h o , l i k e G e n i e , a r e a b u s e d a n d
lack exposure to language for many years
rarely speak normally. Some language experts
have argued that these cases support the idea that there is a “critical period” for language
development, a special time in a child’s life (usually the preschool years) during which
language must develop or it never will. Because these children also suffer severe emo-
tional trauma and possible neurological de cits, however, the issue is still far from clear.
Whether or not these cases suggest such a critical period, they certainly support the idea
that the environment is crucial for the development of language.
C l e a r l y , m o s t h u m a n s d o n o t l e a r n l a n g u a g e i n a s o c i a l v a c u u m . M o s t c h i l d r e n a r e
bathed in language from a very early age (Berko Gleason, 2009). The support and involve-
ment of caregivers and teachers greatly facilitate a child’s language learning (Gold eld &
Snow, 2009; Pan & Uccelli, 2009). For example, one study showed that when mothers
immediately smiled and touched their 8-month-old infants after they had babbled, the
infants subsequently made more complex speechlike sounds than when mothers responded
to their infants in a random manner (Goldstein, King, & West, 2003) (Figure 7.12).
R e s e a r c h ndings about environmental in uences on language learning complicate
the understanding of its foundations. In the real world of language learning, children
appear to be neither exclusively biologically programmed linguists nor exclusively
socially driven language experts (Ratner, 1993). We have to look at how biology and
environment interact when children learn language. That is, children are biologically
prepared to learn language but bene t enormously from being immersed in a competent
language environment from an early age ( Gold eld & Snow, 2009).
Language Development over the Life Span
Most individuals develop a clear understanding of their language’s structure, as well as a
large vocabulary, during childhood. Most adults in the United States have acquired a vocab-
ulary of nearly 50,000 words. Researchers have taken a great interest in the process by which
these aspects of language develop (Pan, 2011). Their many studies have provided an under-
standing of the milestones of language development (Figure 7.13).
EXPERIENCE IT!
Baby Sign Language
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Language // 273
Language researchers are fascinated by babies’ speech even before the little ones
say their rst words (Cartmill, Demir, & Goldin-Meadow, 2011). Ba b bling endlessly
repeating sounds and syllables, such as bababa a n d dadada —begins at the age of
about 4 to 6 months and is determined by biological readiness, not by the amount
of reinforcement or the ability to hear (Menn & Stoel-Gammon, 2009). Even deaf
babies babble for a time (Lenneberg, Rebelsky, & Nichols, 1965). Babbling probably
allows babies to exercise their vocal cords and helps develop the ability to articulate
different sounds.
Patricia Kuhl’s research reveals that long before they begin to learn words, infants
can sort through a number of spoken sounds in search of the ones that have meaning
(1993, 2000, 2007, 2009, 2011a, 2011b). Kuhl argues that from birth to about 6 months
of age, children are “universal linguists” who are capable of distinguishing each of the
sounds that make up human speech. By about 6 months of age, they have started to
specialize in the speech sounds (or phonology) of their native language (Figure 7.14).
Understands adult literary works15–20 Years
Vocabulary increases with addition of more abstract words
Understanding of complex grammar forms
Increased understanding of function a word plays in a
sentence
Understands metaphor and satire
11–14 Years
Word definitions include synonyms
Conversational strategies continue to improve
911 Years
Vocabulary continues to increase rapidly
More skilled use of syntactical rules
Conversational skills improve
6 –8 Years
Vocabulary reaches an average of about 10,000 words
Coordination of simple sentences
56 Years
Mean length of utterances increases to 34 morphemes
in a sentence
Use of yes and no questions, wh- questions
Use of negatives and imperatives
Increased awareness of pragmatics
3 –4 Years
Vocabulary rapidly increases
Correct use of plurals
Use of past tense
Use of some prepositions
2 Years
Vocabulary increases to an average of 200 words
Two-word combinations
1824 Months
Understands 50+ words on average
1218 Months
Babbling expands to include sounds of spoken language
Gestures used to communicate about objects
First words spoken 1013 months
6–12 Months
Cooing
Discrimination of vowels
Babbling present by 6 months
0–6 Months
Note: This list is meant not to be exhaustive but rather to highlight some of the
main language milestones. Also keep in mind that there is a great deal of
variation in the ages at which children can reach these milestones and still be
considered within the normal range of language development.
FIGURE 7.13 Language Milestones All children are
different and acquire language at varying rates, but these milestones
provide a general sense of how language emerges in human life.
FIGURE 7.14 From Universal Linguist to
Language-Specific Listener A baby is shown in
Patricia Kuhl’s research laboratory. In this research, babies
listen to recorded voices that repeat syllables. When
the sounds of the syllables change, the babies quickly
learn to look at the bear. Using this technique, Kuhl has
demonstrated that babies are universal linguists until
about 6 months of age but in the next 6 months become
language-speci c listeners.
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274 // CHAPTER 7 // Thinking, Intelligence, and Language
Around the world, young children learn to speak in two-word
utterances at 18 to 24 months of age.
A childs rst words, uttered at the age of 10 to 13
months, name important people (“dada”), familiar animal s
(“kitty”), vehicles (“car”), toys (“ball”), food (“milk”),
body parts (“eye”), clothes (“hat”), household items
(“clock”), and greetings (“bye”). These were babies’ rst
words a century ago, and they are babies rst words still
(Bloom, 2004).
By the time children reach the age of 18 to 24 months,
they usually utter two-word statements. They quickly grasp
the importance of expressing concepts and the role that
language plays in communicating with others (Sachs,
2009). To convey meaning in two-word statements, the
child relies heavily on gesture, tone, and context.
Although these two-word sentences omit many parts
of speech, they are remarkably effective in conveying
many messages. When a toddler demands, “Pet
doggie!” parents know he means, “May I please pet the doggie?” Very young children
learn that language is a good way to get what they want, suggesting that they grasp
another aspect of language—its pragmatics.
Although childhood is an important time for language learning, we continue to
learn language (new words, new skills) throughout life (Obler, 2009). For many years,
it was claimed that if individuals did not learn a second language prior to puberty,
they would never reach native-language learners’ levels in the second language (Johnson
& Newport, 1991). However, recent research indicates a more complex conclusion:
Sensitive periods likely vary across different language systems (Thomas & Johnson,
2008). Thus, for late second-language learners, such as adolescents and adults, new
vocabulary is easier to learn than new sounds and new grammar (Neville, 2006). For
example, childrens ability to pronounce words with a native-like accent in a second
language typically decreases with age, with an especially sharp drop occurring after
about 10 to 12 years of age.
For adults, learning a new language requires a special kind of cognitive exercise.
As we have seen, a great deal of language learning in infancy and childhood involves
recognizing the sounds that are part of one’s native tongue. This process also entails
learning to ignore sounds that are not important to one’s rst language. For instance,
in Japanese, the phonemes / l / a n d / r / are not distinguished from each other, so that,
for a Japanese adult, the word lion i s n o t d i s t i n g u i s h a b l e f r o m t h e n a m e Ryan .
Research suggests that mastering a new language in adulthood may involve overriding
such learned habits and learning to listen to sounds that one previously ignored.
Indeed, adults can learn to hear and discriminate sounds that are part of a new lan-
guage, and this learning can contribute to speech uency and language skill (Evans
& Iverson, 2007).
Thus, learning a new language in adulthood involves cognitively stretching ourselves
away from our assumptions. Such a process might play a role in enhancing not only our
language skills but also our cognitive ability generally. Susanne Jaeggi and colleagues
(2008) found that undertaking complex memory tasks led to enhanced reasoning ability.
In this work, the participants engaged in a complicated memory game similar to the
card game Concentration, in which all the cards are placed face down and players
have to remember where each one is in order to nd matches. After training for a
half hour a day for several days, participants increased their scores on reasoning
ability, compared to a control group who did not complete the training. The more
the participants trained, the smarter they got. One aspect of the study is especially
interesting. The researchers designed the memory game so that as participants mastered
it, it became harder and harder. In short, getting smarter is not just a matter of mastering
a skill and then resting on our laurels. Reasoning ability can increase, but for that to
happen, we have to keep