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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_27_библиотеки_им_акад_М_И_Перельмана
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9. Sunsets. Medium link.
10. Apples. Short link.
The Fire node links to the following 4 elements.
1. Fire Engine. Short link.
2. House. Short link.
3. Red. Short link.
4. Apples. Short link.
The House node links to the following 2 elements.
1. Fire. Short link.
2. Fire Engine. Medium link.
The Orange node links to the following 3 elements.
1. Red. Short link.
2. Green. Short link.
3. Yellow. Short link.
The Yellow node links to the following 3 elements.
1. Orange. Short link.
2. Green. Short link.
3. Red. Medium link.
The Green node links to the following 3 elements.
1. Yellow. Short link.
2. Orange. Short link.
3. Red. Short link.
The Roses node links to the following 3 elements.
1. Red. Short link.
2. Violets. Short link.
3. Flowers. Short link.
The Violets node links to the following 2 elements.
1. Roses. Short link.
2. Flowers. Short link.
The Flowers node links to the following 2 elements.
1. Roses. Short link.
2. Violets. Short link.
The Apples node links to the following 4 elements.
1. Fire. Short link.
2. Red. Short link.
3. Cherries. Short link.
4. Pears. Short link.
The Cherries node links to the following 3 elements.
1. Apples. Short link.
2. Red. Short link.
3. Pears. Short link.
The Pears node links to the following 2 elements.
1. Apples. Short link.
2. Cherries. Short link.
The Sunsets node links to the following 3 elements.
1. Red. Medium link.
2. Sunrises. Short link.
The Sunrises node links to the following 3 elements.
1. Sunrises. Short link.
2. Red. Medium link.
The Clouds node links to the following 2 elements.
1. Sunrises. Short link.
2. Sunsets. Short link.
Back to Figure
The limbic system is a complex set of structures that lies on both sides of the
thalamus, just under the temporal lobe of the cerebral cortex. It includes the
hypothalamus, the hippocampus, the amygdala, and several other nearby areas. The
limbic system is primarily responsible for our emotional life, and the formation of
memories. In the diagram the limbic system is shown with the hippocampus and
amygdala, along the left side of the thalamus and the hypothalamus, just beneath the
front of the thalamus.
The diagram is annotated to highlight the hippocampus and the amygdala.
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The hippocampus consists of two “horns” that curve back from the amygdala to
the thalamus.
The amygdalas are two almond-shaped masses of neurons on either side of the
thalamus at the lower end of the hippocampus.
Back to Figure
An author introduction reads as follows. The most notable aspect of Henry
Molaison’s pattern of memory impairment was that his performance was intact on
tasks that required only memories retrieved without conscious awareness. For
example, the mirror-tracing task has participants trace a star by looking only at their
drawing hand in a mirror. This might seem easy, but it isn’t. While looking at your
drawing hand in the mirror, your brain has to translate a traced line toward the left into
the motion of moving your hand to the right, and vice versa. With practice,
participants can learn how to do this task well. H.M. also became good at this task
after a few rounds of practice. However, when asked if he remembered completing
the mirror-tracing task before, he never remembered having practiced it.
Section A of Figure 6.15 features an illustration of the brain of Henry Molaison and
an illustration of a normal brain. Each brain is shown in the inferior horizontal plane,
from underneath, and in the coronal plane, from the front.
The normal brain shows the hippocampus and parahippocampal region in the
coronal plane, and the amygdala, piriform cortex, and the temporal pole in the
temporal lobe in the inferior view. The brain of H, M is missing all of these elements
as they were removed as part of a brain surgery to solve his epileptic fits. The
removal of these parts of the brain resulted in H M suffering from anterograde
amnesia, an inability to form new long-term memories.
The illustration shows a person sitting at a table in front of a mirror. A piece of
paper is positioned on the table. They are holding up a piece of card so that they
cannot directly see their hand. They are focused on looking at their hand in the mirror
and drawing a 5 pointed star.
The 3 line charts record the performance of H, M over a period of 3 days. The
task was attempted 10 times each day and the number of errors was noted as
follows.
The number of trial attempts are plotted against the X-axis, with a range of 1 to
10. The number of errors are recorded on the Y-axis, with a range from zero to 30, at
intervals of 10.
The estimated data points for the 3 days are consolidated and presented in the
following table.
Back to Figure
An author introduction reads as follows. Amnesic patients and normal control
subjects were tested for their memory of words previously learned. The amnesics
performed poorly on the measures of explicit memory (recall and recognition) but not
on indirect measures of implicit memory (word-fragment and word-stem completion
tasks). The amnesics retained the information but didn’t know it.
The series of 4 bar charts is divided into 2 sections, Explicit Tests including a free
recall test and a recognition test, and Implicit Tests including a word-fragment
identification and word-stem completion. The estimated data points from the 4 charts
are presented as 4 tables as follows.
Chart 1. Explicit Test, Recall test.
Chart 2. Explicit Test, Recognition test.
Chart 3. Implicit Test. Word Fragment Identification test.
Chart 4. Implicit Test. Word-stem completion test.
Back to Figure
An author introduction reads as follows. Hermann Ebbinghaus’s forgetting curve
indicates the rate at which nonsense syllables were forgotten. You can see that there
was a steep decline in performance within the first day and that the rate of forgetting
leveled off over time.
The time in days is plotted on the X-axis, with a range from 1 to 30 days. The
percentage of list retained is plotted on the Y-axis, with a range from zero to 60 per
cent.
The estimated data points are presented in the following table.
Back to Figure
An author introduction reads as follows. This forgetting curve indicates the rate at
which adults forgot the Spanish they took in high school. Compared to new
graduates, those tested two to three years later forgot much of what they learned.
After that, however, test scores stabilized.
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The time in years is plotted on the X-axis, with a range from 1 to 49.5 years. The
percentage of Spanish vocabulary retained is plotted on the Y-axis, with a range from
zero to 100 per cent.
The estimated data points are presented in the following table.
Back to Figure
An author introduction reads as follows. Which of these pennies is the real thing?
The answer appears later in the chapter.
There are 15 different examples of a 1 cent penny. Each of them has slight
differences. There are 5 possible changes for each coin. These are as follows.
1. The position of the image of Abraham Lincoln. Left facing or right facing.
2. The text curving around the top edge of the coin.
3. The text positioned on the right-hand side of the coin, to the right of Lincoln.
4. The text positioned to the left-hand side of the coin, to the left of Lincoln.
5. The text positioned at the lower edge of the coin.
The 15 options are presented in the following table. The changes included in each
of the columns.
Back to Figure
An author introduction reads as follows. Filling out forms can reveal real-world
interference issues. As shown in this student loan application example, proactive
interference occurs when older information, such as your previous address, inhibits
memory for newer information, such as your new address. You might forget your new
address where you live now but remember your old address with ease. Retroactive
interference occurs when newer information inhibits memory for older information.
This would prevent you from remembering your past address of five years, but
remembering your new address is effortless. The more similar the two sets of items
are, the greater is the interference.
The diagram contains 2 examples of trying to complete a short-term student loan
application form.
Example 1. Retroactive Interference.
When trying to complete the section, Permanent or Parent’s address, you can
remember your new address, but not the address of where you lived the past 5 years.
Example 2. Proactive Interference.
When trying to complete the section, Address for Semester, you can remember
your old address, but not the address of where you just moved.
Back to Figure
An author introduction reads as follows. College graduates of varying ages were
asked to recount their most memorable experiences while in college. You can see
that among the memories that could be pinpointed in time, there was a large number
from the first two months of their first year and a large number from the other major
transitional period, the last month of their senior year.
The months of the academic year are plotted against the X-axis, with a range from
September to May. The memory incidence as a percentage is plotted on the Y-axis,
with a range from zero to 12, at intervals of 2. Data is recorded for First year,
Sophomore year, Junior year, and Senior year.
The estimated data points are presented in the following table.
Back to Figure
An author introduction reads as follows. Do you recognize, or have fondness for,
classic songs from your caregivers’ era that bring back thoughts of home?
Interestingly, for college-age listeners, there are two eras, the late 1960s to the early
1980s, for songs that are recognizable, liked, and autobiographical.
The years are plotted against the X-axis, with a range from 1950 to 2010, at
intervals of 10 years. The rating of songs are plotted against the Y-axis, with a range
from zero to 10, at intervals of 1. There are 3 data lines plotted on the graph as
follows.
1. Personal memories, autobiographical songs. Blue line with diamond markers.
2. Recognize the song. Red line with square markers.
3. Like the song. Green line with triangle markers.
The graph is annotated to indicate the following information.
The parents were born in 1962.
The parents are aged 20 in 1982.
The children or listeners are born in 1992.
The listeners are 20 years old in 2012.
The estimated data points for each line are presented in the following table. The
chart rises for all 3 variables for songs during the late 1960s and early 1980s.
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7 THOUGHT, LANGUAGE, AND INTELLIGENCE
iStock.com/baona
Learning Objectives
Distinguish the difference between a prototype and a concept.
Recognize the methods humans use to solve problems and how
humans are also hindered by those same problem-solving methods.
Define language, and recognize its properties.
Explain how the words we use influence our ideas about others,
ourselves, and the world.
Critique the theoretical types of intelligence for accuracy and bias.
Appreciate the range of intelligences proposed by the field of
psychology.
Apply the nature versus nurture debate to group variations in
intelligence.
WHAT’S YOUR PREDICTION: ARE PEOPLE
GETTING SMARTER?
The Situation
Just about everyone is curious about intelligence and the tests that are used to
measure it. Most of the tests were created early in the 20th century, and since then IQ
(intelligence quotient) scores have been used to determine academic potential in
schools throughout the world. Hmm. The fact that people have been taking IQ tests
for many years—and that you may have taken a test very similar to one taken by your
parents and grandparents—raises a fascinating question: Have scores changed over
time? Do people today have a lower or higher IQ than a few years ago, or is IQ a
fairly stable trait that undergoes little change over time?
Being trained in psychology, you’re accustomed to conducting experiments, often
in the laboratory. To answer the question about IQ trends across generations,
however, you’ll need to use different methods. You’ll need to gather old scores from
IQ tests that were taken at different times by comparable groups of people. So, you
contact researchers all over the world and ask if they would send you test scores that
have been compiled over the years. In particular, you want scores from tests that
were never altered over time and were given to large groups of adults of different
generations. You receive the data you need from a number of developed countries,
including Australia, Austria, Belgium, Brazil, Canada, China, France, Germany, Great
Britain, Israel, Japan, the Netherlands, New Zealand, Norway, Switzerland, and the
United States. Now it’s time to analyze the results.
Make a Prediction
As we’ll learn shortly, IQ tests are set so that the average score in the population
is always 100. This means that if raw scores were to rise or fall over time, the scale
would have to be readjusted like a thermostat in order to keep that average. The
question is, what tends to happen to raw scores over the passage of time? Based on
1920 standards, which set the average IQ at 100, what do you think the raw,
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nonadjusted scores were in 1930, 1940, and other decades up to 1990? Did IQ
steadily increase or decrease over time, fluctuate in response to historical events, or
stay essentially the same? Think carefully about the problem. Then, modeling Figure
7.1, plot your predicted trend for each decade before looking at the actual results.
Figure 7.1 Your Predictions
The Results
When James Flynn (1987) first compiled the IQ scores in 14 developed nations
(he then added 6 more), the worldwide trend was unmistakable. Figure 7.2 shows
that, from one generation to the next, steady and massive gains in IQ scores were
observed—so much so that today’s average adult scores 24 points higher than in
1920. Named after its discoverer, this phenomenon is now known as the Flynn effect.
Description
Figure 7.2 The Actual Results
What Does It All Mean?
For years, psychologists have hotly debated the nature of intelligence, the validity
of standard IQ tests, and the extent to which being smart is the product of nature,
nurture, or both (Rindermann, Becker, & Coyle, 2017; Weber, Dekhtyar, & Herlitz,
2017). Flynn’s discovery that IQ scores have risen steadily provoked new discussion
of, and research into, these core issues. Is it possible that while IQ has risen,
“intelligence” has not? Just what is intelligence, and how is it related to our ability to
shape thoughts and express them? As we’ll learn in this chapter, thought, language,
and intelligence are interconnected.
We humans are a funny species. As a civilization, we have invented the wheel,
kept historical records to guide present and future generations, landed space ships
on the moon, unlocked the atom, cracked the genetic code, and revolutionized all we
do using computers that connect us to the global Internet. When you stop to think
about it, our list of triumphs is long and impressive. Yet at the same time, we
massacre each other in war, wreak havoc on the environment, discriminate against
racial and ethnic groups different from our own, mistreat our partners, throw hard-
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earned money away in games of chance, take drugs that make us sick, and deceive
ourselves into believing in conspiracy theories. After all that, how can we refer to
ourselves as “intelligent” life?
What is it about the way we humans think that leads us to be both rational and
irrational? How do we use our intelligence to solve difficult problems and then
evaluate the solutions, and what kinds of errors are we prone to make along the way?
Are we logical in our reasoning, or are the judgments and decisions we make infected
with bias? And what role does language have to play in the way we think? What is
language, and is it this capacity that most clearly defines humans as more intelligent
than other animal species? In the coming sections, we will examine some of the basic
processes of thought, language, and intelligence. But first, let’s examine concepts—
the basic building blocks of abstract thought and language.
CONCEPTS
LEARNING OBJECTIVES
Distinguish the difference between a prototype and a concept.
Explain how concepts are stored in memory.
Create an argument for why a robin is considered “birdier” than a chicken.
Freedom. Sports. Cancer. Animals. Education. Furniture. Sex. War. Peace. Music.
Heroes. Triangles. Happiness. Each of these words represents a distinct concept—a
mental grouping of persons, places, ideas, events, or objects that share common
properties (Markman, 1999; Van Loocke, 1999). As discussed in the chapter on
memory, our long-term store of knowledge can be pictured as a complex but orderly
network of semantic concepts. So, when one concept in the network is activated,
other closely related concepts pop to mind, or are primed. Look at the semantic
network depicted in Figure 7.3. Note that a robin being a type of bird is illustrated by
its linkage, and this linkage in itself is a concept that is stored in memory. What’s
interesting about semantic networks is that one concept can be used to bring others
to mind. Thus, hearing the word bird makes it easier to pull robin, chicken, and animal
from memory (McNamara, 1994).
concept. A mental grouping of persons, ideas, events, or objects that share
common properties.
Description
Figure 7.3 A Semantic Network
Some members of a category are perceived to be more typical than others, as
illustrated in Figure 7.4. Thus, to most people, a robin is a “birdier” bird than a
chicken, an ostrich, or a penguin—all of which have wings and feathers and hatch
from eggs but do not fly. What makes a category member more or less typical?
Review Figure 7.3 and notice the lists of characteristics that are linked to the
concepts bird, robin, and chicken. When people are asked to list properties of
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different concepts, the most typical members, called prototypes, have more of these
properties (Smith, Shoben, & Ripps, 1974; Rosch, 1975).
prototype. A “typical” member of a category, one that has most of the defining
features of that category.
Figure 7.4 Pet Prototypes iStock.com/Capuski; iStock.com/David-Prado;
iStock.com/fotografixx
Consider the categories listed in Table 7.1. The more prototypical an item is, the
more easily we recognize it as a member of the group and use it to make judgments
about the group as a whole (Whitney, 1986).
Table 7.1
The use of prototypes is illustrated in many studies. For example, Lance Rips
(1975) had subjects read a story about an island that was inhabited by sparrows,
robins, eagles, hawks, ducks, geese, and ostriches. Some subjects were informed
that a disease had infected the robins, whereas others were told that the disease had
infected the ducks. Subjects were then asked, “What other species would be
infected?” Remembering what you just read about prototypes, can you anticipate the
result? Subjects in the robin-infected group predicted that the disease would spread
to all other bird species on the island. In contrast, the duck group predicted that only
the geese, a “related” species, would be infected. Evidently, robins serve as a
prototype for birds, but ducks do not. It’s also interesting that the first words children
use to describe objects within various categories usually pertain to prototypic
members of those categories—apples rather than lemons, chairs rather than lamps,
and so on (Poulin-DuBois, 1995).
Although many human concepts consist of taxonomies that are based on
similarities among members such as dogs, foods, furniture, or rock bands, others
bring items together according to what we know about their “thematic relations.” In
other words, you might sort a list of foods into such taxonomic categories as meats,
fruits, vegetables, and dairy products; or you might sort them according to how or
when they are eaten—such as breakfast foods, main dishes, fast foods, and
desserts. In a series of studies, Emilie Lin and Gregory Murphy (2001) presented
people with triads of words. Each triad contained a target word and two related words
—one taxonomically related, the other thematically related. The subject’s task was to
pick the related word that goes best with the target. Table 7.2 lists 10 triads. How
would you pair each one to form categories? What goes best with French fries: baked
potato or ketchup? What about movie theater: opera house or popcorn? Across five
studies, subjects selected the thematic choice 61 percent of the time. This result
suggests that there is more than one way to conceptualize the world, and that people
often construct categories according to thematic relations, not taxonomic similarity.
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Table 7.2
SOLVING PROBLEMS
LEARNING OBJECTIVES
Recognize the methods humans use to solve problems and how humans are also
hindered by those same problem-solving methods.
Determine if heuristics are more efficient problem-solving methods than trial
and error.
Examine why some psychologists believe in problem solving by insight—and
why others think insight is just an illusion.
Identify some of the “blind spots” that impair our ability to solve problems.
When you lock your keys in the car, play Candy Crush Saga, mediate a dispute
between friends, or struggle to learn a new app, the solution you’re looking for
requires that you combine and manipulate concepts, often in new ways, to solve the
problem or to make the necessary judgment. When a solution cannot simply be
pulled from memory, it takes effort to obtain. As we’ll learn, it helps to view problem
solving as a process that involves defining the problem, representing it in some way,
and then generating and evaluating possible solutions. These steps are not a fixed
series of stages but, rather, are mental activities that we use in cycles. So, if you’re
stuck on a problem and realize that you have not represented it correctly in the first
place, you might start the process over again.
Representing the Problem
Many problems we encounter come to us in the form of words and concepts
activated from semantic networks. Playing the TV game Jeopardy!, trying to recite the
lyrics of an old song, and working on a crossword puzzle are some examples. But
there are other ways as well to depict problems.
Mental Images
Often, people represent information through images, or mental pictures. To run
cold water from the faucet in your kitchen sink, which way do you move the handle?
Which way do you twist a screw to tighten it? And if you can picture a map of the
world, which city is farther north, London or New York? To answer these questions,
people generate visual images.
image. A mental representation of visual information.
In the past, psychologists had to take people at their word when they said they
had formed mental pictures. Today, there are more objective ways to study the
“mind’s eye”—and these methods have confirmed that imagery is a pervasive aspect
of human thought. Consider some specific examples. In one study, Margaret IntonsPeterson (1993) gave people verbal descriptions of simple line drawings, like the one
in Figure 7.5, and found that the more rotations that were involved, the longer it took
subjects to generate the image. This result suggests that people solve this problem
by manipulating mental pictures of the described forms. Other research also suggests
that if mental rotation is needed to solve a spatial problem, people take longer to
make the judgment (Shepard & Cooper, 1982). Some people are better at mental
rotation than others.
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Description
Figure 7.5 Mental-Rotation Tasks
Mental Models
Do you understand how a virus spreads from one computer to another? Can you
describe how a car engine works? What about the economy: Do you know how the
inflation and unemployment rates interact? At times, the problems that confront us
can be best represented in the form of mental models, which are intuitive theories of
the way things work. When accurate, these theories can be powerful tools for
reasoning. By having specific mental models of how human beings, organizations,
machines, and other things work, we can diagnose problems and adapt accordingly
(Gentner & Stevens 1983; Johnson-Laird, 1983, 2001).
mental models. Intuitive theories about the way things work.
Unfortunately, our mental models are often in error. Before reading on, try the
problems in Figure 7.6. These problems are used to study intuitive physics—the
mental models people have about the laws of motion. Research shows that people
are poor intuitive physicists (Kubricht, Holyoak, & Lu, 2017). Consider three common
errors. First, many people wrongly believe in the “impetus principle” that an object set
in motion acquires its own internal force, which keeps it in motion. So when asked to
predict the path of a metal ball rolling through a spiral tube, a majority of subjects
predicted that the ball would follow a curved path even after it exits the tube
(McCloskey & Kuhl, 1983). A second error is the “straight-down belief” that something
dropped from a moving object will fall in a straight vertical line. So when asked to
predict the path of a ball dropped at shoulder height by a walking adult, most subjects
wrongly assumed that the ball would fall straight down rather than in a forward
trajectory (McCloskey & Kuhl, 1983). A third error is made in the “water-level task”
shown in Figure 7.6. When shown a tilted glass or a container filled with liquid, some
subjects—including many bartenders and waitresses—harbor the belief that the
water surface tilts as well rather than remains parallel to the ground (Hecht & Proffitt,
1995). It’s interesting that physics students don’t always perform better than others on
these types of problems, which suggests that mental models can be difficult to
change (Donley & Ashcraft, 1992; Kozhevnikov & Hegarty, 2001).
Description
Figure 7.6 Intuitive Physics Solution to Figure 7.5: The answer is drawing number
3.
Source: Hecht, H., & Proffitt, D. R. (1995). The Price of Expertise:
Effects of Experience on the Water-Level Task. Psychological Science,
6(2), 90–95. https://doi.org/10.1111/j.1467-9280.1995.tb00312.x
Generating Solutions
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Once a problem is represented through words, static images, or mental models,
we try out possible solutions and test to determine if they work. If the problem is
solved, life goes on. If not, we return to the proverbial drawing board to come up with
new ideas. There are many different ways to find solutions, but there are four basic
problem-solving processes: trial and error, algorithms, heuristics, and insight.
Trial and Error
Trial and error is the simplest problem-solving strategy there is, and it’s often
effective. As discussed in the chapter on learning, Edward Thorndike, in 1898,
studied animal intelligence by putting cats in a “puzzle box,” placing food outside a
door, and timing how long it took for them to figure out how to escape. At first, the
cats tried various ineffective behaviors. They tried reaching with their paws, but the
food was too far away. They scratched at the bars, but that did not work. They
pushed at the ceiling, but that did not work either. Then they would literally stumble
upon the solution (which was to step on a lever that opened the door) and repeat that
solution whenever they were in the box. The cats solved the problem by trial and
error.
trial and error. A problem-solving strategy in which several solutions are
attempted until one is found that works.
As you can imagine, this aimless, hit-or-miss approach is not the most efficient
way to proceed. Think about the last time your smartphone crashed. Did you start
furiously tapping various areas of the screen, get no result, and resort to shutting it
down in the hopes that a restart would correct the problem? Sometimes, strategies
such as this prove enlightening. For example, Thomas Edison—the most prolific
inventor in American history—tested thousands of light bulb filaments before
stumbling on the one that worked. The problem is that this strategy often takes too
long or fails completely. If possible, it’s better to take a more systematic, planned
approach.
To study how cats learn, Edward Thorndike created puzzle boxes. In these puzzle
boxes, cats would perform a series of behaviors until a behavior was successful. This
demonstrated that cats understood trial and error.
iStock.com/iunderhill
Algorithms and Heuristics
An algorithm is a step-by-step procedure that is guaranteed, eventually, to
produce a solution. When you were taught in school how to solve two-digit addition
problems or long division, you learned an algorithm. An alternative is to use
heuristics, mental shortcuts, or rules of thumb, which may or may not lead to the
correct solution. The “I before E” heuristic for spelling I–E words is a good example.
To appreciate the difference between algorithms and heuristics, consider the following
anagram problem: Unscramble the letters L K C C O to make a word. One strategy is
to use an algorithm—to try all possible combinations by systematically varying the
letters in each position. Eventually, you will form the correct word. An alternative is to
use a heuristic. For example, you could try the most familiar letter combinations. A
common ending for English words is CK, so you might start with this combination and
arrive quickly at the solution: CLOCK.
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