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Defining Critical Thinking

The Importance of Critical Thinking in Intelligence Analysis

By James Hess, Ph.D.   |  04/13/2021

Intelligence analysts are charged with a difficult and challenging mission – to analyze current threats and to predict future threats. The good thing is that there is help in this mission, a discipline known as critical thinking.

Critical thinking is defined as  “disciplined thinking that is clear, rational, open-minded, and informed by evidence.”  One might think that given their position and training, intelligence analysts are natural critical thinkers. To a certain extent, that is correct, but critical thinking is difficult and requires a lot of practice to do it well.

The Two Schools of Thought About Critical Thinking

While critical thinking has developed considerably over the past decade, there are still two predominate schools of thought about this discipline. The first is  The Foundation for Critical Thinking , founded by Drs. Richard Paul and Linda Elder. They have been studying and evaluating critical thinking processes for many years.

They represent the first school of thought: that critical thinking is a  “process for taking charge of and responsibility for one’s thinking.”  This process provides methods for how to think through problems by using techniques such as flexible and agile thinking, questioning assumptions, and criterion-based judgment.  

Paul and Elder represent a holistic approach to applying critical thinking to our thought processes. In turn, these techniques inform the thought process by enabling improved analysis through their critical thinking framework:  elements of thought, intellectual standards, and intellectual traits .

The other predominate school of thought for critical thinking is represented by former dean of the College of Arts and Sciences at Santa Clara University and provost of Loyola University Chicago, Dr. Peter Facione. His seminal work, “ Critical Thinking: What It Is and Why It Counts ” is the result of three decades of research.

Facione argues that domain-specific application of cognitive skills produces more effective critical thinking. This means that critical thinking can provide significant improvement of one’s thinking when it is applied to processes and procedures within a discipline; that is, specificity of thought rather than improving general thought.

Facione identified six cognitive skills that can be applied to any discipline to improve critical thinking. They are:

• Interpretation
• Explanation
• Self-regulation

During my Ph.D. work, I developed a process that applied Facione’s six cognitive skills to intelligence analysis. I called this process Critical Thinking Applied to Intelligence Analysis Process (CTIAP). The overall concept of CTIAP is that by developing a critical thinking framework and applying it to intelligence analysis, intelligence reporting can be evaluated more efficiently and effectively. In return, this could result in improved analysis and assessments.

After developing this process, I tested its effectiveness in my dissertation,  “Improving intelligence in a counterinsurgency or counterterrorism environment through the application of a critical thinking-based framework.”   My findings concluded that, indeed, analysis and assessments can be improved through the application of CTIAP.

American Military University currently offers two classes at both the  undergraduate  and  graduate  levels in intelligence analysis – INTL 402 and INTL 508. In both classes, students are taught critical thinking approaches. They can also pursue their own evaluation of critical thinking frameworks applied to intelligence analysis in the Bachelor’s Senior Seminar in Intelligence Studies, the Master’s Capstone or the  Doctorate of Strategic Intelligence (DSI) .

Critical Thinking is Useful in All Aspects of Life

Critical thinking can be useful in all aspects of life. Regardless if one defers to Paul and Elder’s critical thinking model or Facione’s domain-specific applications of critical thinking, studying and applying these cognitive skills can be useful for any process. Intelligence analysis is challenging, albeit critically important, and leveraging critical thinking skills can improve the most seasoned of analysts.

Dr. James Hess is a professor at American Public University. Dr. Hess received his Ph.D. from Louisiana State University, where he studied improving analytical methodologies in counterinsurgency and counter-terrorism environments.

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What Is Intelligence In Psychology

Charlotte Ruhl

Research Assistant & Psychology Graduate

BA (Hons) Psychology, Harvard University

Charlotte Ruhl, a psychology graduate from Harvard College, boasts over six years of research experience in clinical and social psychology. During her tenure at Harvard, she contributed to the Decision Science Lab, administering numerous studies in behavioral economics and social psychology.

Learn about our Editorial Process

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Intelligence in psychology refers to the mental capacity to learn from experiences, adapt to new situations, understand and handle abstract concepts, and use knowledge to manipulate one’s environment. It includes skills such as problem-solving, critical thinking, learning quickly, and understanding complex ideas.

Key Takeaways

• Defining and classifying intelligence is extremely complicated. Theories of intelligence range from having one general intelligence (g) to certain primary mental abilities and multiple category-specific intelligences.
• Following the creation of the Binet-Simon scale in the early 1900s, intelligence tests, now referred to as intelligence quotient (IQ) tests, are the most widely-known and used measure for determining an individual’s intelligence.
• Although these tests are generally reliable and valid tools, they have flaws as they lack cultural specificity and can evoke stereotype threats and self-fulfilling prophecies.
• IQ scores are normally distributed , meaning that 95% of the population has IQ scores between 70 and 130. However, some extreme examples exist of people with scores far exceeding 130 or far below 70.

What Is Intelligence?

It might seem useless to define such a simple word. After all, we have all heard this word hundreds of times and probably have a general understanding of its meaning.

However, the concept of intelligence has been a widely debated topic among members of the psychology community for decades.

Intelligence has been defined in many ways: higher level abilities (such as abstract reasoning, mental representation, problem solving, and decision making), the ability to learn, emotional knowledge, creativity, and adaptation to meet the demands of the environment effectively.

Psychologist Robert Sternberg defined intelligence as “the mental abilities necessary for adaptation to, as well as shaping and selection of, any environmental context (1997, p. 1).

History of Intelligence

The study of human intelligence dates back to the late 1800s when Sir Francis Galton (the cousin of Charles Darwin) became one of the first to study intelligence.

Galton was interested in the concept of a gifted individual, so he created a lab to measure reaction times and other physical characteristics to test his hypothesis that intelligence is a general mental ability producing biological evolution (hello, Darwin!).

Galton theorized that because quickness and other physical attributes were evolutionarily advantageous, they would also provide a good indication of general mental ability (Jensen, 1982).

Thus, Galton operationalized intelligence as reaction time.

Operationalization is an important process in research that involves defining an unmeasurable phenomenon (such as intelligence) in measurable terms (such as reaction time), allowing the concept to be studied empirically (Crowthre-Heyck, 2005).

Galton’s study of intelligence in the laboratory setting and his theorization of the heritability of intelligence paved the way for decades of future research and debate in this field.

Theories of Intelligence

Some researchers argue that intelligence is a general ability, whereas others make the assertion that intelligence comprises specific skills and talents. Psychologists contend that intelligence is genetic, or inherited, and others claim that it is largely influenced by the surrounding environment.

As a result, psychologists have developed several contrasting theories of intelligence as well as individual tests that attempt to measure this very concept.

Spearman’s General Intelligence (g)

General intelligence, also known as g factor, refers to a general mental ability that, according to Spearman, underlies multiple specific skills, including verbal, spatial, numerical, and mechanical.

Charles Spearman, an English psychologist, established the two-factor theory of intelligence back in 1904 (Spearman, 1904). To arrive at this theory, Spearman used a technique known as factor analysis.

Factor analysis is a procedure through which the correlation of related variables is evaluated to find an underlying factor that explains this correlation.

In the case of intelligence, Spearman noticed that those who did well in one area of intelligence tests (for example, mathematics) also did well in other areas (such as distinguishing pitch; Kalat, 2014).

In other words, there was a strong correlation between performing well in math and music, and Spearman then attributed this relationship to a central factor, that of general intelligence (g).

Spearman concluded that there is a single g-factor that represents an individual’s general intelligence across multiple abilities and that a second factor, s, refers to an individual’s specific ability in one particular area (Spearman, as cited in Thomson, 1947).

Together, these two main factors compose Spearman’s two-factor theory.

Thurstone’s Primary Mental Abilities

Thurstone (1938) challenged the concept of a g-factor. After analyzing data from 56 different tests of mental abilities, he identified a number of primary mental abilities that comprise intelligence as opposed to one general factor.

The seven primary mental abilities in Thurstone’s model are verbal comprehension, verbal fluency, number facility, spatial visualization, perceptual speed, memory, and inductive reasoning (Thurstone, as cited in Sternberg, 2003).

Although Thurstone did not reject Spearman’s idea of general intelligence altogether, he instead theorized that intelligence consists of both general ability and a number of specific abilities, paving the way for future research that examined the different forms of intelligence.

Gardner’s Multiple Intelligences

Following the work of Thurstone, American psychologist Howard Gardner built off the idea that there are multiple forms of intelligence.

He proposed that there is no single intelligence, but rather distinct, independent multiple intelligences exist, each representing unique skills and talents relevant to a certain category.

Gardner (1983, 1987) initially proposed seven multiple intelligences : linguistic, logical-mathematical, spatial, musical, bodily-kinesthetic, interpersonal, and intrapersonal, and he has since added naturalist intelligence.

Gardner holds that most activities (such as dancing) will involve a combination of these multiple intelligences (such as spatial and bodily-kinesthetic intelligences). He also suggests that these multiple intelligences can help us understand concepts beyond intelligence, such as creativity and leadership .

And although this theory has widely captured the attention of the psychology community and the greater public, it does have its faults.

There have been few empirical studies that actually test this theory, and this theory does not account for other types of intelligence beyond the ones Gardner lists (Sternberg, 2003).

Triarchic Theory of Intelligence

Just two years later, in 1985, Robert Sternberg proposed a three-category theory of intelligence, integrating components that were lacking in Gardner’s theory. This theory is based on the definition of intelligence as the ability to achieve success based on your personal standards and your sociocultural context.

According to the triarchic theory, intelligence has three aspects: analytical, creative, and practical (Sternberg, 1985).

Analytical intelligence , also referred to as componential intelligence, refers to intelligence that is applied to analyze or evaluate problems and arrive at solutions. This is what a traditional IQ test measures.

Creative intelligence is the ability to go beyond what is given to create novel and interesting ideas. This type of intelligence involves imagination, innovation, and problem-solving.

Practical intelligence is the ability that individuals use to solve problems faced in daily life when a person finds the best fit between themselves and the demands of the environment.

Adapting to the demands of the environment involves either utilizing knowledge gained from experience to purposefully change oneself to suit the environment (adaptation), changing the environment to suit oneself (shaping), or finding a new environment in which to work (selection).

Other Types of Intelligence

After examining the popular competing theories of intelligence, it becomes clear that there are many different forms of this seemingly simple concept.

On the one hand, Spearman claims that intelligence is generalizable across many different areas of life, and on the other hand, psychologists such as Thurstone, Gardener, and Sternberg hold that intelligence is like a tree with many different branches, each representing a specific form of intelligence.

To make matters even more interesting, let’s throw a few more types of intelligence into the mix!

Emotional Intelligence

Emotional Intelligence is the “ability to monitor one’s own and other people’s emotions, to discriminate between different emotions and label them appropriately, and to use emotional information to guide thinking and behavior” (Salovey and Mayer, 1990).

Emotional intelligence is important in our everyday lives, seeing as we experience one emotion or another nearly every second of our lives. You may not associate emotions and intelligence with one another, but in reality, they are very related.

Emotional intelligence refers to the ability to recognize the meanings of emotions and to reason and problem-solve on the basis of them (Mayer, Caruso, & Salovey, 1999). The four key components of emotional Intelligence are (i) self-awareness, (ii) self-management, (iii) social awareness, and (iv) relationship management.

In other words, if you are high in emotional intelligence, you can accurately perceive emotions in yourself and others (such as reading facial expressions), use emotions to help facilitate thinking, understand the meaning behind your emotions (why are you feeling this way?), and know how to manage your emotions (Salovey & Mayer, 1990).

Fluid vs. Crystallized Intelligence

Raymond Cattell (1963) first proposed the concepts of fluid and crystallized intelligence and further developed the theory with John Horn.

Fluid intelligence is the ability to problem solve in novel situations without referencing prior knowledge, but rather through the use of logic and abstract thinking. Fluid intelligence can be applied to any novel problem because no specific prior knowledge is required (Cattell, 1963). As you grow older fluid increases and then starts to decrease in the late 20s.

Crystallized intelligence refers to the use of previously-acquired knowledge, such as specific facts learned in school or specific motor skills or muscle memory (Cattell, 1963). As you grow older and accumulate knowledge, crystallized intelligence increases.

The Cattell-Horn (1966) theory of fluid and crystallized intelligence suggests that intelligence is composed of a number of different abilities that interact and work together to produce overall individual intelligence.

For example, if you are taking a hard math test, you rely on your crystallized intelligence to process the numbers and meaning of the questions, but you may use fluid intelligence to work through the novel problem and arrive at the correct solution. It is also possible that fluid intelligence can become crystallized intelligence.

The novel solutions you create when relying on fluid intelligence can, over time, develop into crystallized intelligence after they are incorporated into long-term memory.

This illustrates some of the ways in which different forms of intelligence overlap and interact with one another, revealing its dynamic nature.

Intelligence Testing

Binet-simon scale.

During the early 1900s, the French government enlisted the help of psychologist Alfred Binet to understand which children were going to be slower learners and thus required more assistance in the classroom (Binet et al., 1912).

As a result, he and his colleague, Theodore Simon, began to develop a specific set of questions that focused on areas such as memory and problem-solving skills.

They tested these questions on groups of students aged three to twelve to help standardize the measure (Binet et al., 1912). Binet realized that some children were able to answer advanced questions that their older peers were able to answer.

As a result, he created the concept of mental age, or how well an individual performs intellectually relative to the average performance at that age (Cherry, 2020).

Ultimately, Binet finalized the scale, known as the Binet-Simon scale, that became the basis for the intelligence tests still used today.

The Binet-Simon scale of 1905 comprised 30 items designed to measure judgment, comprehension, and reasoning, which Binet deemed the key characteristics of intelligence.

Stanford-Binet Intelligence Scale

When the Binet-Simon scale made its way over to the United States, Stanford psychologist Lewis Terman adapted the test for American students and published the Stanford-Binet Intelligence Scale in 1916 (Cherry, 2020).

The Stanford-Binet Scale is a contemporary assessment that measures intelligence according to five features of cognitive ability,

including fluid reasoning, knowledge, quantitative reasoning, visual-spatial processing, and working memory. Both verbal and nonverbal responses are measured.

This test used a single number, referred to as the intelligence quotient (IQ), to indicate an individual’s score.

The average score for the test is 100, and any score from 90 to 109 is considered to be in the average intelligence range. Scores from 110 to 119 are considered to be High Average. Superior scores range from 120 to 129 and anything over 130 is considered Very Superior.

To calculate IQ, the student’s mental age is divided by his or her actual (or chronological) age, and this result is multiplied by 100. If your mental age is equal to your chronological age, you will have an IQ of 100, or average. If your mental age is 12, but your chronological age is only 10, you will have an above-average IQ of 120.

WISC and WAIS

Just as theories of intelligence build off one another, intelligence tests do too. After Terman created Stanford-Binet test, American psychologist David Wechsler developed a new tool due to his dissatisfaction with the limitations of the Stanford-Binet test (Cherry, 2020).

Like Thurstone, Gardner, and Sternberg, Wechsler believed intelligence involved many different mental abilities and felt that the Stanford-Binet scale too closely reflected the idea of one general intelligence.

Because of this, Wechsler created the Wechsler Intelligence Scale for Children (WISC) and the Wechsler Adult Intelligence Scale (WAIS) in 1955, with the most up-to-date version being the WAIS-IV (Cherry, 2020).

The Wechsler Intelligence Scale for Children (WISC), developed by David Wechsler, is an IQ test designed to measure intelligence and cognitive ability in children between the ages of 6 and 16. It is currently in its fourth edition (WISC-V) released in 2014 by Pearson.

Above Image: WISC-IV Sample Test Question

The Wechsler Adult Intelligence Scale (WAIS) is an IQ test designed to measure cognitive ability in adults and older adolescents, including

verbal comprehension, perceptual reasoning, working memory, and processing speed.

The latest version of the Wechsler Adult Intelligence Scale (WAIS-IV) was standardized on 2,200 healthy people between the ages of 16 and 90 years (Brooks et al., 2011).

The standardization of a test involves giving it to a large number of people of different ages to compute the average score on the test at each age level.

The overall IQ score combines the test takers’ performance in all four categories (Cherry, 2020). And rather than calculating this number based on mental and chronological age, the WAIS compares the individual’s score to the average score at that level, as calculated by the standardization process.

The Flynn Effect

It is important to regularly standardize an intelligence test because the overall level of intelligence in a population may change over time.

This phenomenon is known as the Flynn effect (named after its discoverer, New Zealand researcher James Flynn) which refers to the observation that scores on intelligence tests worldwide increase from decade to decade (Flynn, 1984).

Aptitude vs. Achievement Tests

Other tests, such as aptitude and achievement tests, are designed to measure intellectual capability. Achievement tests measure what content a student has already learned (such as a unit test in history or a final math exam), whereas an aptitude test measures a student’s potential or ability to learn (Anastasi, 1984).

Although this may sound similar to an IQ test, aptitude tests typically measure abilities in very specific areas.

Criticism of Intelligence Testing

Criticisms have ranged from the claim that IQ tests are biased in favor of white, middle-class people. Negative stereotypes about a person’s ethnicity, gender, or age may cause the person to suffer stereotype threat, a burden of doubt about his or her own abilities, which can create anxiety that result in lower scores.

Reliability and Construct Validity

Although you may be wondering if you take an intelligence test multiple times will you improve your score and whether these tests even measure intelligence in the first place, research provides reassurance that these tests are both very reliable and have high construct validity.

Reliability simply means that they are consistent over time. In other words, if you take a test at two different points in time, there will be very little change in performance or, in the case of intelligence tests, IQ scores.

Although this isn’t a perfect science, and your score might slightly fluctuate when taking the same test on different occasions or different tests at the same age, IQ tests demonstrate relatively high reliability (Tuma & Appelbaum, 1980).

Additionally, intelligence tests also reveal strong construct validity , meaning that they are, in fact, measuring intelligence rather than something else.

Researchers have spent hours on end developing, standardizing, and adapting these tests to best fit the current times. But that is also not to say that these tests are completely flawless.

Research documents errors with the specific scoring of tests and interpretation of the multiple scores (since typically, an individual will receive an overall IQ score accompanied by several category-specific scores), and some studies question the actual validity, reliability, and utility for individual clinical use of these tests (Canivez, 2013).

Additionally, intelligence scores are created to reflect different theories of intelligence, so the interpretations may be heavily based on the theory upon which the test is based (Canivez, 2013).

Cultural Specificity

There are issues with intelligence tests beyond looking at them in a vacuum.  These tests were created by Western psychologists who created such tools to measure euro-centric values.

However, it is important to recognize that the majority of the world’s population does not reside in Europe or North America, and as a result, the cultural specificity of these tests is crucial.

Different cultures hold different values and even have different perceptions of intelligence, so is it fair to have one universal marker of this increasingly complex concept?

For example, a 1992 study found that Kenyan parents defined intelligence as the ability to do without being told what needed to be done around the homestead (Harkness et al., 1992), and, given the American and European emphasis on speed, some Ugandans define intelligent people as being slow in thought and action (Wober, 1974).

Together, these examples illustrate the flexibility of defining intelligence, making capturing this concept in a single test, let alone a single number even more challenging.  And even within the U.S., do perceptions of intelligence differ?

An example is in San Jose, California, where Latino, Asian, and Anglo parents had varying definitions of intelligence.  The teachers’ understanding of intelligence was more similar to that of the Asian and Anglo communities, and this similarity predicted the child’s performance in school (Okagaki & Sternberg, 1993).

That is, students whose families had more similar understandings of intelligence were doing better in the classroom.

Intelligence takes many forms, ranging from country to country and culture to culture.  Although IQ tests might have high reliability and validity, understanding the role of culture is as, if not more, important in forming the bigger picture of an individual’s intelligence.

IQ tests may accurately measure academic intelligence, but more research must be done to discern whether they truly measure practical intelligence or even just general intelligence in all cultures.

Social and Environmental Factors

Another important part of the puzzle to consider is the social and environmental context in which an individual lives and the IQ test-related biases that develop as a result.

These might help explain why some individuals have lower scores than others. For example, the threat of social exclusion can greatly decrease the expression of intelligence.

A 2002 study gave participants an IQ test and a personality inventory, and some were randomly chosen to receive feedback from the inventory indicating that they were “the sort of people who would end up alone in life” (Baumeister et al., 2002).

After a second test, those who were told they would be loveless and friendless in the future answered significantly fewer questions than they did on the earlier test.

These findings can translate into the real world where not only the threat of social exclusion can decrease the expression of intelligence but also a perceived threat to physical safety.

In other words, a child’s poor academic performance can be attributed to the disadvantaged, potentially unsafe communities in which they grow up.

Stereotype Threat

Stereotype threat is a phenomenon in which people feel at risk of conforming to stereotypes about their social group. Negative stereotypes can also create anxiety that results in lower scores.

In one study, Black and White college students were given part of the verbal section from the Graduate Record Exam (GRE), but in the stereotype threat condition, they told students the test diagnosed intellectual ability, thus potentially making the stereotype that Blacks are less intelligent than Whites salient.

The results of this study revealed that in the stereotype threat condition, Blacks performed worse than Whites, but in the no stereotype threat condition, Blacks and Whites performed equally well (Steele & Aronson, 1995).

And even just recording your race can also result in worsened performance. Stereotype threat is a real threat and can be detrimental to an individual’s performance on these tests.

Self-Fulfilling Prophecy

Stereotype threat is closely related to the concept of a self-fulfilling prophecy in which an individual’s expectations about another person can result in the other person acting in ways that conform to that very expectation.

In one experiment, students in a California elementary school were given an IQ test, after which their teachers were given the names of students who would become “intellectual bloomers” that year based on the results of the test (Rosenthal & Jacobson, 1968).

At the end of the study, the students were tested again with the same IQ test, and those labeled as “intellectual bloomers” significantly increased their scores.

This illustrates that teachers may subconsciously behave in ways that encourage the success of certain students, thus influencing their achievement (Rosenthal & Jacobson, 1968), and provides another example of small variables that can play a role in an individual’s intelligence score and the development of their intelligence.

This is all to say that it is important to consider the less visible factors that play a role in determining someone’s intelligence. While an IQ score has many benefits in measuring intelligence, it is critical to consider that just because someone has a lower score does not necessarily mean they are lower in intelligence.

There are many factors that can worsen performance on these tests, and the tests themselves might not even be accurately measuring the very concept they are intended to.

Extremes of Intelligence

IQ scores are generally normally distributed (Moore et al., 2013). That is, roughly 95% of the population has IQ scores between 70 and 130. But what about the other 5%?

Individuals who fall outside this range represent the extremes of intelligence.

Those who have an IQ above 130 are considered to be gifted (Lally & French, 2018), such as Christopher Langan, an American horse rancher, who has an IQ score around 200 (Gladwell, 2008).

Those individuals who have scores below 70 do so because of an intellectual disability marked by substantial developmental delays, including motor, cognitive, and speech delays (De Light, 2012).

Some of the time, these disabilities are the product of genetic mutations.

Down syndrome, for example, resulting from extra genetic material from or a complete extra copy of the 21st chromosome, is a common genetic cause of an intellectual disability (Breslin, 2014). As such, many individuals with Down Syndrome have below-average IQ scores (Breslin, 2014).

Savant syndrome is another example of extreme intelligence. Despite having significant mental disabilities, these individuals demonstrate certain abilities in some fields that are far above average, such as incredible memorization, rapid mathematical or calendar calculation ability, or advanced musical talent (Treffert, 2009).

The fact that these individuals who may be lacking in certain areas such as social interaction and communication make up for it in other remarkable areas further illustrates the complexity of intelligence and what this concept means today, as well as how we must consider all individuals when determining how to perceive, measure, and recognize intelligence in our society.

Intelligence Today

Today, intelligence is generally understood as the ability to understand and adapt to the environment by using inherited abilities and learned knowledge.

Many new intelligence tests have arisen, such as the University of California Matrix Reasoning Task (Pahor et al., 2019), that can be taken online and in very little time, and new methods of scoring these tests have been developed too (Sansone et al., 2014).

Admission into university and graduate schools relies on specific aptitude and achievement tests, such as the SAT, ACT, and the LSAT – these tests have become a huge part of our lives.

Humans are incredibly intelligent beings and rely on our intellectual abilities daily. Although intelligence can be defined and measured in countless ways, our overall intelligence as a species makes us incredibly unique and has allowed us to thrive for generations on end.

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6.5: Introduction to Thinking and Intelligence

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Why is it so difficult to break habits—like reaching for your ringing phone even when you shouldn’t, such as when you’re driving? How does a person who has never seen or touched snow in real life develop an understanding of the concept of snow? How do young children acquire the ability to learn language with no formal instruction? Psychologists who study thinking explore questions like these.

Cognitive psychologists also study intelligence. What is intelligence, and how does it vary from person to person? Are “street smarts” a kind of intelligence, and if so, how do they relate to other types of intelligence? What does an IQ test really measure? These questions and more will be explored in this chapter as you study thinking and intelligence.

In other chapters, we discussed the cognitive processes of perception, learning, and memory. In this chapter, we will focus on high-level cognitive processes. As a part of this discussion, we will consider thinking and briefly explore the development and use of language. We will also discuss problem solving and creativity before ending with a discussion of how intelligence is measured and how our biology and environments interact to affect intelligence. After finishing this chapter, you will have a greater appreciation of the higher-level cognitive processes that contribute to our distinctiveness as a species.

What Is Cognition?

Learning outcomes.

By the end of this section, you will be able to:

• Describe cognition
• Distinguish concepts and prototypes
• Explain the difference between natural and artificial concepts

Imagine all of your thoughts as if they were physical entities, swirling rapidly inside your mind. How is it possible that the brain is able to move from one thought to the next in an organized, orderly fashion? The brain is endlessly perceiving, processing, planning, organizing, and remembering—it is always active. Yet, you don’t notice most of your brain’s activity as you move throughout your daily routine. This is only one facet of the complex processes involved in cognition. Simply put, cognition is thinking, and it encompasses the processes associated with perception, knowledge, problem solving, judgment, language, and memory. Scientists who study cognition are searching for ways to understand how we integrate, organize, and utilize our conscious cognitive experiences without being aware of all of the unconscious work that our brains are doing (for example, Kahneman, 2011).

Upon waking each morning, you begin thinking—contemplating the tasks that you must complete that day. In what order should you run your errands? Should you go to the bank, the cleaners, or the grocery store first? Can you get these things done before you head to class or will they need to wait until school is done? These thoughts are one example of cognition at work. Exceptionally complex, cognition is an essential feature of human consciousness, yet not all aspects of cognition are consciously experienced.

Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem solving, in addition to other cognitive processes. Cognitive psychologists strive to determine and measure different types of intelligence, why some people are better at problem solving than others, and how emotional intelligence affects success in the workplace, among countless other topics. They also sometimes focus on how we organize thoughts and information gathered from our environments into meaningful categories of thought, which will be discussed later.

CONCEPTS AND PROTOTYPES

The human nervous system is capable of handling endless streams of information. The senses serve as the interface between the mind and the external environment, receiving stimuli and translating it into nervous impulses that are transmitted to the brain. The brain then processes this information and uses the relevant pieces to create thoughts, which can then be expressed through language or stored in memory for future use. To make this process more complex, the brain does not gather information from external environments only. When thoughts are formed, the brain also pulls information from emotions and memories ( Figure ). Emotion and memory are powerful influences on both our thoughts and behaviors.

In order to organize this staggering amount of information, the brain has developed a file cabinet of sorts in the mind. The different files stored in the file cabinet are called concepts. Concepts are categories or groupings of linguistic information, images, ideas, or memories, such as life experiences. Concepts are, in many ways, big ideas that are generated by observing details, and categorizing and combining these details into cognitive structures. You use concepts to see the relationships among the different elements of your experiences and to keep the information in your mind organized and accessible.

Concepts are informed by our semantic memory (you learned about this concept when you studied memory) and are present in every aspect of our lives; however, one of the easiest places to notice concepts is inside a classroom, where they are discussed explicitly. When you study United States history, for example, you learn about more than just individual events that have happened in America’s past. You absorb a large quantity of information by listening to and participating in discussions, examining maps, and reading first-hand accounts of people’s lives. Your brain analyzes these details and develops an overall understanding of American history. In the process, your brain gathers details that inform and refine your understanding of related concepts like democracy, power, and freedom.

Concepts can be complex and abstract, like justice, or more concrete, like types of birds. In psychology, for example, Piaget’s stages of development are abstract concepts. Some concepts, like tolerance, are agreed upon by many people, because they have been used in various ways over many years. Other concepts, like the characteristics of your ideal friend or your family’s birthday traditions, are personal and individualized. In this way, concepts touch every aspect of our lives, from our many daily routines to the guiding principles behind the way governments function.

Another technique used by your brain to organize information is the identification of prototypes for the concepts you have developed. A prototype is the best example or representation of a concept. For example, for the category of civil disobedience, your prototype could be Rosa Parks. Her peaceful resistance to segregation on a city bus in Montgomery, Alabama, is a recognizable example of civil disobedience. Or your prototype could be Mohandas Gandhi, sometimes called Mahatma Gandhi (“Mahatma” is an honorific title) ( Figure ).

Mohandas Gandhi served as a nonviolent force for independence for India while simultaneously demanding that Buddhist, Hindu, Muslim, and Christian leaders—both Indian and British—collaborate peacefully. Although he was not always successful in preventing violence around him, his life provides a steadfast example of the civil disobedience prototype (Constitutional Rights Foundation, 2013). Just as concepts can be abstract or concrete, we can make a distinction between concepts that are functions of our direct experience with the world and those that are more artificial in nature.

NATURAL AND ARTIFICIAL CONCEPTS

In psychology, concepts can be divided into two categories, natural and artificial. Natural concepts are created “naturally” through your experiences and can be developed from either direct or indirect experiences. For example, if you live in Essex Junction, Vermont, you have probably had a lot of direct experience with snow. You’ve watched it fall from the sky, you’ve seen lightly falling snow that barely covers the windshield of your car, and you’ve shoveled out 18 inches of fluffy white snow as you’ve thought, “This is perfect for skiing.” You’ve thrown snowballs at your best friend and gone sledding down the steepest hill in town. In short, you know snow. You know what it looks like, smells like, tastes like, and feels like. If, however, you’ve lived your whole life on the island of Saint Vincent in the Caribbean, you may never have actually seen snow, much less tasted, smelled, or touched it. You know snow from the indirect experience of seeing pictures of falling snow—or from watching films that feature snow as part of the setting. Either way, snow is a natural concept because you can construct an understanding of it through direct observations or experiences of snow ( Figure ).

An artificial concept, on the other hand, is a concept that is defined by a specific set of characteristics. Various properties of geometric shapes, like squares and triangles, serve as useful examples of artificial concepts. A triangle always has three angles and three sides. A square always has four equal sides and four right angles. Mathematical formulas, like the equation for area (length × width) are artificial concepts defined by specific sets of characteristics that are always the same. Artificial concepts can enhance the understanding of a topic by building on one another. For example, before learning the concept of “area of a square” (and the formula to find it), you must understand what a square is. Once the concept of “area of a square” is understood, an understanding of area for other geometric shapes can be built upon the original understanding of area. The use of artificial concepts to define an idea is crucial to communicating with others and engaging in complex thought. According to Goldstone and Kersten (2003), concepts act as building blocks and can be connected in countless combinations to create complex thoughts.

A schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.

There are several types of schemata. A role schema makes assumptions about how individuals in certain roles will behave (Callero, 1994). For example, imagine you meet someone who introduces himself as a firefighter. When this happens, your brain automatically activates the “firefighter schema” and begins making assumptions that this person is brave, selfless, and community-oriented. Despite not knowing this person, already you have unknowingly made judgments about him. Schemata also help you fill in gaps in the information you receive from the world around you. While schemata allow for more efficient information processing, there can be problems with schemata, regardless of whether they are accurate: Perhaps this particular firefighter is not brave, he just works as a firefighter to pay the bills while studying to become a children’s librarian.

An event schema, also known as a cognitive script, is a set of behaviors that can feel like a routine. Think about what you do when you walk into an elevator ( Figure ). First, the doors open and you wait to let exiting passengers leave the elevator car. Then, you step into the elevator and turn around to face the doors, looking for the correct button to push. You never face the back of the elevator, do you? And when you’re riding in a crowded elevator and you can’t face the front, it feels uncomfortable, doesn’t it? Interestingly, event schemata can vary widely among different cultures and countries. For example, while it is quite common for people to greet one another with a handshake in the United States, in Tibet, you greet someone by sticking your tongue out at them, and in Belize, you bump fists (Cairns Regional Council, n.d.)

Because event schemata are automatic, they can be difficult to change. Imagine that you are driving home from work or school. This event schema involves getting in the car, shutting the door, and buckling your seatbelt before putting the key in the ignition. You might perform this script two or three times each day. As you drive home, you hear your phone’s ring tone. Typically, the event schema that occurs when you hear your phone ringing involves locating the phone and answering it or responding to your latest text message. So without thinking, you reach for your phone, which could be in your pocket, in your bag, or on the passenger seat of the car. This powerful event schema is informed by your pattern of behavior and the pleasurable stimulation that a phone call or text message gives your brain. Because it is a schema, it is extremely challenging for us to stop reaching for the phone, even though we know that we endanger our own lives and the lives of others while we do it (Neyfakh, 2013) ( Figure ).

Remember the elevator? It feels almost impossible to walk in and  not  face the door. Our powerful event schema dictates our behavior in the elevator, and it is no different with our phones. Current research suggests that it is the habit, or event schema, of checking our phones in many different situations that makes refraining from checking them while driving especially difficult (Bayer & Campbell, 2012). Because texting and driving has become a dangerous epidemic in recent years, psychologists are looking at ways to help people interrupt the “phone schema” while driving. Event schemata like these are the reason why many habits are difficult to break once they have been acquired. As we continue to examine thinking, keep in mind how powerful the forces of concepts and schemata are to our understanding of the world.

In this section, you were introduced to cognitive psychology, which is the study of cognition, or the brain’s ability to think, perceive, plan, analyze, and remember. Concepts and their corresponding prototypes help us quickly organize our thinking by creating categories into which we can sort new information. We also develop schemata, which are clusters of related concepts. Some schemata involve routines of thought and behavior, and these help us function properly in various situations without having to “think twice” about them. Schemata show up in social situations and routines of daily behavior.

Review Questions

Cognitive psychology is the branch of psychology that focuses on the study of ________.

• human development
• human thinking
• human behavior
• human society

Which of the following is an example of a prototype for the concept of leadership on an athletic team?

• the equipment manager
• the star player
• the head coach
• the scorekeeper

Which of the following is an example of an artificial concept?

• a triangle’s area

An event schema is also known as a cognitive ________.

Critical Thinking Questions

Describe a social schema that you would notice at a sporting event.

Explain why event schemata have so much power over human behavior.

Personal Application Question

Describe a natural concept that you know fully but that would be difficult for someone else to understand and explain why it would be difficult.

[glossary-page] [glossary-term]artificial concept:[/glossary-term] [glossary-definition]concept that is defined by a very specific set of characteristics[/glossary-definition]

[glossary-term]cognition:[/glossary-term] [glossary-definition]thinking, including perception, learning, problem solving, judgment, and memory[/glossary-definition]

[glossary-term]cognitive psychology:[/glossary-term] [glossary-definition]field of psychology dedicated to studying every aspect of how people think[/glossary-definition]

[glossary-term]concept:[/glossary-term] [glossary-definition]category or grouping of linguistic information, objects, ideas, or life experiences[/glossary-definition]

[glossary-term]cognitive script:[/glossary-term] [glossary-definition]set of behaviors that are performed the same way each time; also referred to as an event schema[/glossary-definition]

[glossary-term]event schema:[/glossary-term] [glossary-definition]set of behaviors that are performed the same way each time; also referred to as a cognitive script[/glossary-definition]

[glossary-term]natural concept:[/glossary-term] [glossary-definition]mental groupings that are created “naturally” through your experiences[/glossary-definition]

[glossary-term]prototype:[/glossary-term] [glossary-definition]best representation of a concept[/glossary-definition]

[glossary-term]role schema:[/glossary-term] [glossary-definition]set of expectations that define the behaviors of a person occupying a particular role[/glossary-definition]

[glossary-term]schema:[/glossary-term] [glossary-definition](plural = schemata) mental construct consisting of a cluster or collection of related concepts[/glossary-definition] [/glossary-page]

• Introduction. Provided by : OpenStax CNX. Located at : https://cnx.org/contents/[email protected]:3DT0XBfK@3/Introduction . License : CC BY-SA: Attribution-ShareAlike
• What is Cognition?. Provided by : OpenStax CNX. Located at : https://cnx.org/contents/[email protected]:u8MlFxBQ@3/What-Is-Cognition . License : CC BY-SA: Attribution-ShareAlike

6 Thinking and Intelligence

What is the best way to solve a problem? How does a person who has never seen or touched snow in real life develop an understanding of the concept of snow? How do young children acquire the ability to learn language with no formal instruction? Psychologists who study thinking explore questions like these and are called cognitive psychologists.

Cognitive psychologists also study intelligence. What is intelligence, and how does it vary from person to person? Are “street smarts” a kind of intelligence, and if so, how do they relate to other types of intelligence? What does an IQ test really measure? These questions and more will be explored in this chapter as you study thinking and intelligence.

In other chapters, we discussed the cognitive processes of perception, learning, and memory. In this chapter, we will focus on high-level cognitive processes. As a part of this discussion, we will consider thinking and briefly explore the development and use of language. We will also discuss problem solving and creativity before ending with a discussion of how intelligence is measured and how our biology and environments interact to affect intelligence. After finishing this chapter, you will have a greater appreciation of the higher-level cognitive processes that contribute to our distinctiveness as a species.

Learning Objectives

By the end of this section, you will be able to:

• Describe cognition
• Distinguish concepts and prototypes
• Explain the difference between natural and artificial concepts
• Describe how schemata are organized and constructed

Imagine all of your thoughts as if they were physical entities, swirling rapidly inside your mind. How is it possible that the brain is able to move from one thought to the next in an organized, orderly fashion? The brain is endlessly perceiving, processing, planning, organizing, and remembering—it is always active. Yet, you don’t notice most of your brain’s activity as you move throughout your daily routine. This is only one facet of the complex processes involved in cognition. Simply put,  cognition  is thinking, and it encompasses the processes associated with perception, knowledge, problem solving, judgment, language, and memory. Scientists who study cognition are searching for ways to understand how we integrate, organize, and utilize our conscious cognitive experiences without being aware of all of the unconscious work that our brains are doing (for example, Kahneman, 2011).

Upon waking each morning, you begin thinking—contemplating the tasks that you must complete that day. In what order should you run your errands? Should you go to the bank, the cleaners, or the grocery store first? Can you get these things done before you head to class or will they need to wait until school is done? These thoughts are one example of cognition at work. Exceptionally complex, cognition is an essential feature of human consciousness, yet not all aspects of cognition are consciously experienced.

Cognitive psychology  is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem solving, in addition to other cognitive processes. Cognitive psychologists strive to determine and measure different types of intelligence, why some people are better at problem solving than others, and how emotional intelligence affects success in the workplace, among countless other topics. They also sometimes focus on how we organize thoughts and information gathered from our environments into meaningful categories of thought, which will be discussed later.

Concepts and Prototypes

The human nervous system is capable of handling endless streams of information. The senses serve as the interface between the mind and the external environment, receiving stimuli and translating it into nerve impulses that are transmitted to the brain. The brain then processes this information and uses the relevant pieces to create thoughts, which can then be expressed through language or stored in memory for future use. To make this process more complex, the brain does not gather information from external environments only. When thoughts are formed, the mind synthesizes information from emotions and memories ( Figure 7.2 ). Emotion and memory are powerful influences on both our thoughts and behaviors.

In order to organize this staggering amount of information, the mind has developed a “file cabinet” of sorts in the mind. The different files stored in the file cabinet are called concepts.  Concepts  are categories or groupings of linguistic information, images, ideas, or memories, such as life experiences. Concepts are, in many ways, big ideas that are generated by observing details, and categorizing and combining these details into cognitive structures. You use concepts to see the relationships among the different elements of your experiences and to keep the information in your mind organized and accessible.

Concepts are informed by our semantic memory (you will learn more about semantic memory in a later chapter) and are present in every aspect of our lives; however, one of the easiest places to notice concepts is inside a classroom, where they are discussed explicitly. When you study United States history, for example, you learn about more than just individual events that have happened in America’s past. You absorb a large quantity of information by listening to and participating in discussions, examining maps, and reading first-hand accounts of people’s lives. Your brain analyzes these details and develops an overall understanding of American history. In the process, your brain gathers details that inform and refine your understanding of related concepts like democracy, power, and freedom.

Concepts can be complex and abstract, like justice, or more concrete, like types of birds. In psychology, for example, Piaget’s stages of development are abstract concepts. Some concepts, like tolerance, are agreed upon by many people because they have been used in various ways over many years. Other concepts, like the characteristics of your ideal friend or your family’s birthday traditions, are personal and individualized. In this way, concepts touch every aspect of our lives, from our many daily routines to the guiding principles behind the way governments function.

Another technique used by your brain to organize information is the identification of prototypes for the concepts you have developed. A  prototype  is the best example or representation of a concept. For example, what comes to your mind when you think of a dog? Most likely your early experiences with dogs will shape what you imagine. If your first pet was a Golden Retriever, there is a good chance that this would be your prototype for the category of dogs.

Natural and Artificial Concepts

In psychology, concepts can be divided into two categories, natural and artificial.  Natural concepts  are created “naturally” through your experiences and can be developed from either direct or indirect experiences. For example, if you live in Essex Junction, Vermont, you have probably had a lot of direct experience with snow. You’ve watched it fall from the sky, you’ve seen lightly falling snow that barely covers the windshield of your car, and you’ve shoveled out 18 inches of fluffy white snow as you’ve thought, “This is perfect for skiing.” You’ve thrown snowballs at your best friend and gone sledding down the steepest hill in town. In short, you know snow. You know what it looks like, smells like, tastes like, and feels like. If, however, you’ve lived your whole life on the island of Saint Vincent in the Caribbean, you may never have actually seen snow, much less tasted, smelled, or touched it. You know snow from the indirect experience of seeing pictures of falling snow—or from watching films that feature snow as part of the setting. Either way, snow is a natural concept because you can construct an understanding of it through direct observations, experiences with snow, or indirect knowledge (such as from films or books) ( Figure 7.3 ).

An  artificial concept , on the other hand, is a concept that is defined by a specific set of characteristics. Various properties of geometric shapes, like squares and triangles, serve as useful examples of artificial concepts. A triangle always has three angles and three sides. A square always has four equal sides and four right angles. Mathematical formulas, like the equation for area (length × width), are artificial concepts defined by specific sets of characteristics that are always the same. Artificial concepts can enhance the understanding of a topic by building on one another. For example, before learning the concept of “area of a square” (and the formula to find it), you must understand what a square is. Once the concept of “area of a square” is understood, an understanding of area for other geometric shapes can be built upon the original understanding of area. The use of artificial concepts to define an idea is crucial to communicating with others and engaging in complex thought. According to Goldstone and Kersten (2003), concepts act as building blocks and can be connected in countless combinations to create complex thoughts.

A  schema  is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.

There are several types of schemata. A  role schema  makes assumptions about how individuals in certain roles will behave (Callero, 1994). For example, imagine you meet someone who introduces himself as a firefighter. When this happens, your brain automatically activates the “firefighter schema” and begins making assumptions that this person is brave, selfless, and community-oriented. Despite not knowing this person, already you have unknowingly made judgments about him. Schemata also help you fill in gaps in the information you receive from the world around you. While schemata allow for more efficient information processing, there can be problems with schemata, regardless of whether they are accurate: Perhaps this particular firefighter is not brave, he just works as a firefighter to pay the bills while studying to become a children’s librarian.

An  event schema , also known as a  cognitive script , is a set of behaviors that can feel like a routine. Think about what you do when you walk into an elevator ( Figure 7.4 ). First, the doors open and you wait to let exiting passengers leave the elevator car. Then, you step into the elevator and turn around to face the doors, looking for the correct button to push. You never face the back of the elevator, do you? And when you’re riding in a crowded elevator and you can’t face the front, it feels uncomfortable, doesn’t it? Interestingly, event schemata can vary widely among different cultures and countries. For example, while it is quite common for people to greet one another with a handshake in the United States, in Tibet, you greet someone by sticking your tongue out at them, and in Belize, you bump fists (Cairns Regional Council, n.d.)

Because event schemata are automatic, they can be difficult to change. Imagine that you are driving home from work or school. This event schema involves getting in the car, shutting the door, and buckling your seatbelt before putting the key in the ignition. You might perform this script two or three times each day. As you drive home, you hear your phone’s ring tone. Typically, the event schema that occurs when you hear your phone ringing involves locating the phone and answering it or responding to your latest text message. So without thinking, you reach for your phone, which could be in your pocket, in your bag, or on the passenger seat of the car. This powerful event schema is informed by your pattern of behavior and the pleasurable stimulation that a phone call or text message gives your brain. Because it is a schema, it is extremely challenging for us to stop reaching for the phone, even though we know that we endanger our own lives and the lives of others while we do it (Neyfakh, 2013) ( Figure 7.5 ).

Remember the elevator? It feels almost impossible to walk in and  not  face the door. Our powerful event schema dictates our behavior in the elevator, and it is no different with our phones. Current research suggests that it is the habit, or event schema, of checking our phones in many different situations that make refraining from checking them while driving especially difficult (Bayer & Campbell, 2012). Because texting and driving has become a dangerous epidemic in recent years, psychologists are looking at ways to help people interrupt the “phone schema” while driving. Event schemata like these are the reason why many habits are difficult to break once they have been acquired. As we continue to examine thinking, keep in mind how powerful the forces of concepts and schemata are to our understanding of the world.

• Define language and demonstrate familiarity with the components of language
• Understand the development of language
• Explain the relationship between language and thinking

Language  is a communication system that involves using words and systematic rules to organize those words to transmit information from one individual to another. While language is a form of communication, not all communication is language. Many species communicate with one another through their postures, movements, odors, or vocalizations. This communication is crucial for species that need to interact and develop social relationships with their conspecifics. However, many people have asserted that it is language that makes humans unique among all of the animal species (Corballis & Suddendorf, 2007; Tomasello & Rakoczy, 2003). This section will focus on what distinguishes language as a special form of communication, how the use of language develops, and how language affects the way we think.

Components of Language

Language, be it spoken, signed, or written, has specific components: a lexicon and grammar.  Lexicon  refers to the words of a given language. Thus, lexicon is a language’s vocabulary.  Grammar  refers to the set of rules that are used to convey meaning through the use of the lexicon (Fernández & Cairns, 2011). For instance, English grammar dictates that most verbs receive an “-ed” at the end to indicate past tense.

Words are formed by combining the various phonemes that make up the language. A  phoneme  (e.g., the sounds “ah” vs. “eh”) is a basic sound unit of a given language, and different languages have different sets of phonemes. Phonemes are combined to form  morphemes , which are the smallest units of language that convey some type of meaning (e.g., “I” is both a phoneme and a morpheme). We use semantics and syntax to construct language. Semantics and syntax are part of a language’s grammar.  Semantics  refers to the process by which we derive meaning from morphemes and words.  Syntax  refers to the way words are organized into sentences (Chomsky, 1965; Fernández & Cairns, 2011).

We apply the rules of grammar to organize the lexicon in novel and creative ways, which allow us to communicate information about both concrete and abstract concepts. We can talk about our immediate and observable surroundings as well as the surface of unseen planets. We can share our innermost thoughts, our plans for the future, and debate the value of a college education. We can provide detailed instructions for cooking a meal, fixing a car, or building a fire. Through our use of words and language, we are able to form, organize, and express ideas, schema, and artificial concepts.

Language Development

Given the remarkable complexity of a language, one might expect that mastering a language would be an especially arduous task; indeed, for those of us trying to learn a second language as adults, this might seem to be true. However, young children master language very quickly with relative ease. B. F.  Skinner  (1957) proposed that language is learned through reinforcement. Noam  Chomsky  (1965) criticized this behaviorist approach, asserting instead that the mechanisms underlying language acquisition are biologically determined. The use of language develops in the absence of formal instruction and appears to follow a very similar pattern in children from vastly different cultures and backgrounds. It would seem, therefore, that we are born with a biological predisposition to acquire a language (Chomsky, 1965; Fernández & Cairns, 2011). Moreover, it appears that there is a critical period for language acquisition, such that this proficiency at acquiring language is maximal early in life; generally, as people age, the ease with which they acquire and master new languages diminishes (Johnson & Newport, 1989; Lenneberg, 1967; Singleton, 1995).

Children begin to learn about language from a very early age ( Table 7.1 ). In fact, it appears that this is occurring even before we are born. Newborns show a preference for their mother’s voice and appear to be able to discriminate between the language spoken by their mother and other languages. Babies are also attuned to the languages being used around them and show preferences for videos of faces that are moving in synchrony with the audio of spoken language versus videos that do not synchronize with the audio (Blossom & Morgan, 2006; Pickens, 1994; Spelke & Cortelyou, 1981).

DIG DEEPER: The Case of Genie

In the fall of 1970, a social worker in the Los Angeles area found a 13-year-old girl who was being raised in extremely neglectful and abusive conditions. The girl, who came to be known as Genie, had lived most of her life tied to a potty chair or confined to a crib in a small room that was kept closed with the curtains drawn. For a little over a decade, Genie had virtually no social interaction and no access to the outside world. As a result of these conditions, Genie was unable to stand up, chew solid food, or speak (Fromkin, Krashen, Curtiss, Rigler, & Rigler, 1974; Rymer, 1993). The police took Genie into protective custody.

Genie’s abilities improved dramatically following her removal from her abusive environment, and early on, it appeared she was acquiring language—much later than would be predicted by critical period hypotheses that had been posited at the time (Fromkin et al., 1974). Genie managed to amass an impressive vocabulary in a relatively short amount of time. However, she never developed a mastery of the grammatical aspects of language (Curtiss, 1981). Perhaps being deprived of the opportunity to learn language during a critical period impeded Genie’s ability to fully acquire and use language.

You may recall that each language has its own set of phonemes that are used to generate morphemes, words, and so on. Babies can discriminate among the sounds that make up a language (for example, they can tell the difference between the “s” in vision and the “ss” in fission); early on, they can differentiate between the sounds of all human languages, even those that do not occur in the languages that are used in their environments. However, by the time that they are about 1 year old, they can only discriminate among those phonemes that are used in the language or languages in their environments (Jensen, 2011; Werker & Lalonde, 1988; Werker & Tees, 1984).

After the first few months of life, babies enter what is known as the babbling stage, during which time they tend to produce single syllables that are repeated over and over. As time passes, more variations appear in the syllables that they produce. During this time, it is unlikely that the babies are trying to communicate; they are just as likely to babble when they are alone as when they are with their caregivers (Fernández & Cairns, 2011). Interestingly, babies who are raised in environments in which sign language is used will also begin to show babbling in the gestures of their hands during this stage (Petitto, Holowka, Sergio, Levy, & Ostry, 2004).

Generally, a child’s first word is uttered sometime between the ages of 1 year to 18 months, and for the next few months, the child will remain in the “one word” stage of language development. During this time, children know a number of words, but they only produce one-word utterances. The child’s early vocabulary is limited to familiar objects or events, often nouns. Although children in this stage only make one-word utterances, these words often carry larger meaning (Fernández & Cairns, 2011). So, for example, a child saying “cookie” could be identifying a cookie or asking for a cookie.

As a child’s lexicon grows, she begins to utter simple sentences and to acquire new vocabulary at a very rapid pace. In addition, children begin to demonstrate a clear understanding of the specific rules that apply to their language(s). Even the mistakes that children sometimes make provide evidence of just how much they understand about those rules. This is sometimes seen in the form of  overgeneralization . In this context, overgeneralization refers to an extension of a language rule to an exception to the rule. For example, in English, it is usually the case that an “s” is added to the end of a word to indicate plurality. For example, we speak of one dog versus two dogs. Young children will overgeneralize this rule to cases that are exceptions to the “add an s to the end of the word” rule and say things like “those two gooses” or “three mouses.” Clearly, the rules of the language are understood, even if the exceptions to the rules are still being learned (Moskowitz, 1978).

Language and Thought

When we speak one language, we agree that words are representations of ideas, people, places, and events. The given language that children learn is connected to their culture and surroundings. But can words themselves shape the way we think about things? Psychologists have long investigated the question of whether language shapes thoughts and actions, or whether our thoughts and beliefs shape our language. Two researchers, Edward Sapir and Benjamin Lee Whorf began this investigation in the 1940s. They wanted to understand how the language habits of a community encourage members of that community to interpret language in a particular manner (Sapir, 1941/1964). Sapir and Whorf proposed that language determines thought. For example, in some languages, there are many different words for love. However, in English, we use the word love for all types of love. Does this affect how we think about love depending on the language that we speak (Whorf, 1956)? Researchers have since identified this view as too absolute, pointing out a lack of empiricism behind what Sapir and Whorf proposed (Abler, 2013; Boroditsky, 2011; van Troyer, 1994). Today, psychologists continue to study and debate the relationship between language and thought.

• Describe problem solving strategies
• Define algorithm and heuristic
• Explain some common roadblocks to effective problem solving and decision making

People face problems every day—usually, multiple problems throughout the day. Sometimes these problems are straightforward: To double a recipe for pizza dough, for example, all that is required is that each ingredient in the recipe is doubled. Sometimes, however, the problems we encounter are more complex. For example, say you have a work deadline, and you must mail a printed copy of a report to your supervisor by the end of the business day. The report is time-sensitive and must be sent overnight. You finished the report last night, but your printer will not work today. What should you do? First, you need to identify the problem and then apply a strategy for solving the problem.

Problem-Solving Strategies

When you are presented with a problem—whether it is a complex mathematical problem or a broken printer, how do you solve it? Before finding a solution to the problem, the problem must first be clearly identified. After that, one of many problem solving strategies can be applied, hopefully resulting in a solution.

A  problem-solving strategy  is a plan of action used to find a solution. Different strategies have different action plans associated with them ( Table 7.2 ). For example, a well-known strategy is  trial and error . The old adage, “If at first, you don’t succeed, try, try again” describes trial and error. In terms of your broken printer, you could try checking the ink levels, and if that doesn’t work, you could check to make sure the paper tray isn’t jammed. Or maybe the printer isn’t actually connected to your laptop. When using trial and error, you would continue to try different solutions until you solved your problem. Although trial and error is not typically one of the most time-efficient strategies, it is a commonly used one.

Another type of strategy is an algorithm. An  algorithm  is a problem-solving formula that provides you with step-by-step instructions used to achieve a desired outcome (Kahneman, 2011). You can think of an algorithm as a recipe with highly detailed instructions that produce the same result every time they are performed. Algorithms are used frequently in our everyday lives, especially in computer science. When you run a search on the Internet, search engines like Google use algorithms to decide which entries will appear first in your list of results. Facebook also uses algorithms to decide which posts to display on your newsfeed. Can you identify other situations in which algorithms are used?

A heuristic is another type of problem solving strategy. While an algorithm must be followed exactly to produce a correct result, a  heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. A “rule of thumb” is an example of a heuristic. Such a rule saves the person time and energy when making a decision, but despite its time-saving characteristics, it is not always the best method for making a rational decision. Different types of heuristics are used in different types of situations, but the impulse to use a heuristic occurs when one of the five conditions is met (Pratkanis, 1989):

• When one is faced with too much information
• When the time to make a decision is limited
• When the decision to be made is unimportant
• When there is access to very little information to use in making the decision
• When an appropriate heuristic happens to come to mind in the same moment

Working backward is a useful heuristic in which you begin solving the problem by focusing on the end result. Consider this example: You live in Washington, D.C., and have been invited to a wedding at 4 PM on Saturday in Philadelphia. Knowing that Interstate 95 tends to back up any day of the week, you need to plan your route and time your departure accordingly. If you want to be at the wedding service by 3:30 PM, and it takes 2.5 hours to get to Philadelphia without traffic, what time should you leave your house? You use the working backward heuristic to plan the events of your day on a regular basis, probably without even thinking about it.

Another useful heuristic is the practice of accomplishing a large goal or task by breaking it into a series of smaller steps. Students often use this common method to complete a large research project or a long essay for school. For example, students typically brainstorm, develop a thesis or main topic, research the chosen topic, organize their information into an outline, write a rough draft, revise and edit the rough draft, develop a final draft, organize the references list, and proofread their work before turning in the project. The large task becomes less overwhelming when it is broken down into a series of small steps.

EVERYDAY CONNECTION: Solving Puzzles

Problem-solving abilities can improve with practice. Many people challenge themselves every day with puzzles and other mental exercises to sharpen their problem-solving skills. Sudoku puzzles appear daily in most newspapers. Typically, a sudoku puzzle is a 9×9 grid. The simple sudoku below ( Figure 7.7 ) is a 4×4 grid. To solve the puzzle, fill in the empty boxes with a single digit: 1, 2, 3, or 4. Here are the rules: The numbers must total 10 in each bolded box, each row, and each column; however, each digit can only appear once in a bolded box, row, and column. Time yourself as you solve this puzzle and compare your time with a classmate.

Here is another popular type of puzzle ( Figure 7.8 ) that challenges your spatial reasoning skills. Connect all nine dots with four connecting straight lines without lifting your pencil from the paper:

Take a look at the “Puzzling Scales” logic puzzle below ( Figure 7.9 ). Sam Loyd, a well-known puzzle master, created and refined countless puzzles throughout his lifetime (Cyclopedia of Puzzles, n.d.).

Not all problems are successfully solved, however. What challenges stop us from successfully solving a problem? Albert Einstein once said, “Insanity is doing the same thing over and over again and expecting a different result.” Imagine a person in a room that has four doorways. One doorway that has always been open in the past is now locked. The person, accustomed to exiting the room by that particular doorway, keeps trying to get out through the same doorway even though the other three doorways are open. The person is stuck—but she just needs to go to another doorway, instead of trying to get out through the locked doorway. A  mental set  is where you persist in approaching a problem in a way that has worked in the past but is clearly not working now.

Functional fixedness  is a type of mental set where you cannot perceive an object being used for something other than what it was designed for. Duncker (1945) conducted foundational research on functional fixedness. He created an experiment in which participants were given a candle, a book of matches, and a box of thumbtacks. They were instructed to use those items to attach the candle to the wall so that it did not drip wax onto the table below. Participants had to use functional fixedness to solve the problem ( Figure 7.10 ). During the  Apollo 13  mission to the moon, NASA engineers at Mission Control had to overcome functional fixedness to save the lives of the astronauts aboard the spacecraft. An explosion in a module of the spacecraft damaged multiple systems. The astronauts were in danger of being poisoned by rising levels of carbon dioxide because of problems with the carbon dioxide filters. The engineers found a way for the astronauts to use spare plastic bags, tape, and air hoses to create a makeshift air filter, which saved the lives of the astronauts.

Researchers have investigated whether functional fixedness is affected by culture. In one experiment, individuals from the Shuar group in Ecuador were asked to use an object for a purpose other than that for which the object was originally intended. For example, the participants were told a story about a bear and a rabbit that were separated by a river and asked to select among various objects, including a spoon, a cup, erasers, and so on, to help the animals. The spoon was the only object long enough to span the imaginary river, but if the spoon was presented in a way that reflected its normal usage, it took participants longer to choose the spoon to solve the problem. (German & Barrett, 2005). The researchers wanted to know if exposure to highly specialized tools, as occurs with individuals in industrialized nations, affects their ability to transcend functional fixedness. It was determined that functional fixedness is experienced in both industrialized and nonindustrialized cultures (German & Barrett, 2005).

In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. Sometimes, however, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the $2,000 home? Why would the realtor show you the run-down houses and the nice house? The realtor may be challenging your anchoring bias. An  anchoring bias  occurs when you focus on one piece of information when making a decision or solving a problem. In this case, you’re so focused on the amount of money you are willing to spend that you may not recognize what kinds of houses are available at that price point.

The  confirmation bias  is the tendency to focus on information that confirms your existing beliefs. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis.  Hindsight bias  leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did.  Representative bias describes a faulty way of thinking, in which you unintentionally stereotype someone or something; for example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.

Finally, the  availability heuristic  is a heuristic in which you make a decision based on an example, information, or recent experience that is that readily available to you, even though it may not be the best example to inform your decision .  Biases tend to “preserve that which is already established—to maintain our preexisting knowledge, beliefs, attitudes, and hypotheses” (Aronson, 1995; Kahneman, 2011). These biases are summarized in  Table 7.3 .

Were you able to determine how many marbles are needed to balance the scales in  Figure 7.9 ? You need nine. Were you able to solve the problems in  Figure 7.7  and  Figure 7.8 ? Here are the answers ( Figure 7.11 ).

• Define intelligence
• Explain the triarchic theory of intelligence
• Identify the difference between intelligence theories
• Explain emotional intelligence
• Define creativity

Classifying Intelligence

What exactly is intelligence? The way that researchers have defined the concept of intelligence has been modified many times since the birth of psychology. British psychologist Charles Spearman believed intelligence consisted of one general factor, called  g , which could be measured and compared among individuals. Spearman focused on the commonalities among various intellectual abilities and de-emphasized what made each unique. Long before modern psychology developed, however, ancient philosophers, such as Aristotle, held a similar view (Cianciolo & Sternberg, 2004).

Other psychologists believe that instead of a single factor, intelligence is a collection of distinct abilities. In the 1940s, Raymond Cattell proposed a theory of intelligence that divided general intelligence into two components: crystallized intelligence and fluid intelligence (Cattell, 1963). Crystallized intelligence  is characterized as acquired knowledge and the ability to retrieve it. When you learn, remember, and recall information, you are using crystallized intelligence. You use crystallized intelligence all the time in your coursework by demonstrating that you have mastered the information covered in the course.  Fluid intelligence  encompasses the ability to see complex relationships and solve problems. Navigating your way home after being detoured onto an unfamiliar route because of road construction would draw upon your fluid intelligence. Fluid intelligence helps you tackle complex, abstract challenges in your daily life, whereas crystallized intelligence helps you overcome concrete, straightforward problems (Cattell, 1963).

Other theorists and psychologists believe that intelligence should be defined in more practical terms. For example, what types of behaviors help you get ahead in life? Which skills promote success? Think about this for a moment. Being able to recite all 45 presidents of the United States in order is an excellent party trick, but will knowing this make you a better person?

Robert Sternberg developed another theory of intelligence, which he titled the  triarchic theory of intelligence  because it sees intelligence as comprised of three parts (Sternberg, 1988): practical, creative, and analytical intelligence ( Figure 7.12 ).

Practical intelligence , as proposed by Sternberg, is sometimes compared to “street smarts.” Being practical means you find solutions that work in your everyday life by applying knowledge based on your experiences. This type of intelligence appears to be separate from the traditional understanding of IQ; individuals who score high in practical intelligence may or may not have comparable scores in creative and analytical intelligence (Sternberg, 1988).

Analytical intelligence is closely aligned with academic problem solving and computations. Sternberg says that analytical intelligence is demonstrated by an ability to analyze, evaluate, judge, compare, and contrast. When reading a classic novel for a literature class, for example, it is usually necessary to compare the motives of the main characters of the book or analyze the historical context of the story. In a science course such as anatomy, you must study the processes by which the body uses various minerals in different human systems. In developing an understanding of this topic, you are using analytical intelligence. When solving a challenging math problem, you would apply analytical intelligence to analyze different aspects of the problem and then solve it section by section.

Creative intelligence  is marked by inventing or imagining a solution to a problem or situation. Creativity in this realm can include finding a novel solution to an unexpected problem or producing a beautiful work of art or a well-developed short story. Imagine for a moment that you are camping in the woods with some friends and realize that you’ve forgotten your camp coffee pot. The person in your group who figures out a way to successfully brew coffee for everyone would be credited as having higher creative intelligence.

Multiple Intelligences Theory  was developed by Howard Gardner, a Harvard psychologist and former student of Erik Erikson. Gardner’s theory, which has been refined for more than 30 years, is a more recent development among theories of intelligence. In Gardner’s theory, each person possesses at least eight intelligences. Among these eight intelligences, a person typically excels in some and falters in others (Gardner, 1983).  Table 7.4  describes each type of intelligence.

Gardner’s theory is relatively new and needs additional research to better establish empirical support. At the same time, his ideas challenge the traditional idea of intelligence to include a wider variety of abilities, although it has been suggested that Gardner simply relabeled what other theorists called “cognitive styles” as “intelligences” (Morgan, 1996). Furthermore, developing traditional measures of Gardner’s intelligences is extremely difficult (Furnham, 2009; Gardner & Moran, 2006; Klein, 1997).

Gardner’s inter- and intrapersonal intelligences are often combined into a single type: emotional intelligence.  Emotional intelligence  encompasses the ability to understand the emotions of yourself and others, show empathy, understand social relationships and cues, and regulate your own emotions and respond in culturally appropriate ways (Parker, Saklofske, & Stough, 2009). People with high emotional intelligence typically have well-developed social skills. Some researchers, including Daniel Goleman, the author of  Emotional Intelligence: Why It Can Matter More than IQ , argue that emotional intelligence is a better predictor of success than traditional intelligence (Goleman, 1995). However, emotional intelligence has been widely debated, with researchers pointing out inconsistencies in how it is defined and described, as well as questioning results of studies on a subject that is difficult to measure and study empirically (Locke, 2005; Mayer, Salovey, & Caruso, 2004)

The most comprehensive theory of intelligence to date is the Cattell-Horn-Carroll (CHC) theory of cognitive abilities (Schneider & McGrew, 2018). In this theory, abilities are related and arranged in a hierarchy with general abilities at the top, broad abilities in the middle, and narrow (specific) abilities at the bottom. The narrow abilities are the only ones that can be directly measured; however, they are integrated within the other abilities. At the general level is general intelligence. Next, the broad level consists of general abilities such as fluid reasoning, short-term memory, and processing speed. Finally, as the hierarchy continues, the narrow level includes specific forms of cognitive abilities. For example, short-term memory would further break down into memory span and working memory capacity.

Intelligence can also have different meanings and values in different cultures. If you live on a small island, where most people get their food by fishing from boats, it would be important to know how to fish and how to repair a boat. If you were an exceptional angler, your peers would probably consider you intelligent. If you were also skilled at repairing boats, your intelligence might be known across the whole island. Think about your own family’s culture. What values are important for Latinx families? Italian families? In Irish families, hospitality and telling an entertaining story are marks of the culture. If you are a skilled storyteller, other members of Irish culture are likely to consider you intelligent.

Some cultures place a high value on working together as a collective. In these cultures, the importance of the group supersedes the importance of individual achievement. When you visit such a culture, how well you relate to the values of that culture exemplifies your  cultural intelligence , sometimes referred to as cultural competence.

Creativity  is the ability to generate, create, or discover new ideas, solutions, and possibilities. Very creative people often have intense knowledge about something, work on it for years, look at novel solutions, seek out the advice and help of other experts, and take risks. Although creativity is often associated with the arts, it is actually a vital form of intelligence that drives people in many disciplines to discover something new. Creativity can be found in every area of life, from the way you decorate your residence to a new way of understanding how a cell works.

Creativity is often assessed as a function of one’s ability to engage in  divergent thinking . Divergent thinking can be described as thinking “outside the box;” it allows an individual to arrive at unique, multiple solutions to a given problem. In contrast,  convergent thinking describes the ability to provide a correct or well-established answer or solution to a problem (Cropley, 2006; Gilford, 1967)

• Explain how intelligence tests are developed
• Describe the history of the use of IQ tests
• Describe the purposes and benefits of intelligence testing

While you’re likely familiar with the term “IQ” and associate it with the idea of intelligence, what does IQ really mean? IQ stands for  intelligence quotient  and describes a score earned on a test designed to measure intelligence. You’ve already learned that there are many ways psychologists describe intelligence (or more aptly, intelligences). Similarly, IQ tests—the tools designed to measure intelligence—have been the subject of debate throughout their development and use.

When might an IQ test be used? What do we learn from the results, and how might people use this information? While there are certainly many benefits to intelligence testing, it is important to also note the limitations and controversies surrounding these tests. For example, IQ tests have sometimes been used as arguments in support of insidious purposes, such as the eugenics movement (Severson, 2011). The infamous Supreme Court Case,  Buck v. Bell , legalized the forced sterilization of some people deemed “feeble-minded” through this type of testing, resulting in about 65,000 sterilizations ( Buck v. Bell , 274 U.S. 200; Ko, 2016). Today, only professionals trained in psychology can administer IQ tests, and the purchase of most tests requires an advanced degree in psychology. Other professionals in the field, such as social workers and psychiatrists, cannot administer IQ tests. In this section, we will explore what intelligence tests measure, how they are scored, and how they were developed.

Measuring Intelligence

It seems that the human understanding of intelligence is somewhat limited when we focus on traditional or academic-type intelligence. How then, can intelligence be measured? And when we measure intelligence, how do we ensure that we capture what we’re really trying to measure (in other words, that IQ tests function as valid measures of intelligence)? In the following paragraphs, we will explore the how intelligence tests were developed and the history of their use.

The IQ test has been synonymous with intelligence for over a century. In the late 1800s, Sir Francis Galton developed the first broad test of intelligence (Flanagan & Kaufman, 2004). Although he was not a psychologist, his contributions to the concepts of intelligence testing are still felt today (Gordon, 1995). Reliable intelligence testing (you may recall from earlier chapters that reliability refers to a test’s ability to produce consistent results) began in earnest during the early 1900s with a researcher named Alfred Binet ( Figure 7.13 ). Binet was asked by the French government to develop an intelligence test to use on children to determine which ones might have difficulty in school; it included many verbally based tasks. American researchers soon realized the value of such testing. Louis Terman, a Stanford professor, modified Binet’s work by standardizing the administration of the test and tested thousands of different-aged children to establish an average score for each age. As a result, the test was normed and standardized, which means that the test was administered consistently to a large enough representative sample of the population that the range of scores resulted in a bell curve (bell curves will be discussed later).  Standardization  means that the manner of administration, scoring, and interpretation of results is consistent.  Norming  involves giving a test to a large population so data can be collected comparing groups, such as age groups. The resulting data provide norms, or referential scores, by which to interpret future scores. Norms are not expectations of what a given group  should  know but a demonstration of what that group  does  know. Norming and standardizing the test ensures that new scores are reliable. This new version of the test was called the Stanford-Binet Intelligence Scale (Terman, 1916). Remarkably, an updated version of this test is still widely used today.

In 1939, David Wechsler, a psychologist who spent part of his career working with World War I veterans, developed a new IQ test in the United States. Wechsler combined several subtests from other intelligence tests used between 1880 and World War I. These subtests tapped into a variety of verbal and nonverbal skills because Wechsler believed that intelligence encompassed “the global capacity of a person to act purposefully, to think rationally, and to deal effectively with his environment” (Wechsler, 1958, p. 7). He named the test the Wechsler-Bellevue Intelligence Scale (Wechsler, 1981). This combination of subtests became one of the most extensively used intelligence tests in the history of psychology. Although its name was later changed to the Wechsler Adult Intelligence Scale (WAIS) and has been revised several times, the aims of the test remain virtually unchanged since its inception (Boake, 2002). Today, there are three intelligence tests credited to Wechsler, the Wechsler Adult Intelligence Scale-fourth edition (WAIS-IV), the Wechsler Intelligence Scale for Children (WISC-V), and the Wechsler Preschool and Primary Scale of Intelligence—IV (WPPSI-IV) (Wechsler, 2012). These tests are used widely in schools and communities throughout the United States, and they are periodically normed and standardized as a means of recalibration. As a part of the recalibration process, the WISC-V was given to thousands of children across the country, and children taking the test today are compared with their same-age peers ( Figure 7.13 ).

The WISC-V is composed of 14 subtests, which comprise five indices, which then render an IQ score. The five indices are Verbal Comprehension, Visual Spatial, Fluid Reasoning, Working Memory, and Processing Speed. When the test is complete, individuals receive a score for each of the five indices and a Full Scale IQ score. The method of scoring reflects the understanding that intelligence is comprised of multiple abilities in several cognitive realms and focuses on the mental processes that the child used to arrive at his or her answers to each test item.

Interestingly, the periodic recalibrations have led to an interesting observation known as the Flynn effect. Named after James Flynn, who was among the first to describe this trend, the  Flynn effect  refers to the observation that each generation has a significantly higher IQ than the last. Flynn himself argues, however, that increased IQ scores do not necessarily mean that younger generations are more intelligent per se (Flynn, Shaughnessy, & Fulgham, 2012).

Ultimately, we are still left with the question of how valid intelligence tests are. Certainly, the most modern versions of these tests tap into more than verbal competencies, yet the specific skills that should be assessed in IQ testing, the degree to which any test can truly measure an individual’s intelligence, and the use of the results of IQ tests are still issues of debate (Gresham & Witt, 1997; Flynn, Shaughnessy, & Fulgham, 2012; Richardson, 2002; Schlinger, 2003).

The Bell Curve

The results of intelligence tests follow the bell curve, a graph in the general shape of a bell. When the bell curve is used in psychological testing, the graph demonstrates a normal distribution of a trait, in this case, intelligence, in the human population. Many human traits naturally follow the bell curve. For example, if you lined up all your female schoolmates according to height, it is likely that a large cluster of them would be the average height for an American woman: 5’4”–5’6”. This cluster would fall in the center of the bell curve, representing the average height for American women ( Figure 7.14 ). There would be fewer women who stand closer to 4’11”. The same would be true for women of above-average height: those who stand closer to 5’11”. The trick to finding a bell curve in nature is to use a large sample size. Without a large sample size, it is less likely that the bell curve will represent the wider population. A  representative sample  is a subset of the population that accurately represents the general population. If, for example, you measured the height of the women in your classroom only, you might not actually have a representative sample. Perhaps the women’s basketball team wanted to take this course together, and they are all in your class. Because basketball players tend to be taller than average, the women in your class may not be a good representative sample of the population of American women. But if your sample included all the women at your school, it is likely that their heights would form a natural bell curve.

The same principles apply to intelligence test scores. Individuals earn a score called an intelligence quotient (IQ). Over the years, different types of IQ tests have evolved, but the way scores are interpreted remains the same. The average IQ score on an IQ test is 100. Standard deviations  describe how data are dispersed in a population and give context to large data sets. The bell curve uses the standard deviation to show how all scores are dispersed from the average score ( Figure 7.15 ). In modern IQ testing, one standard deviation is 15 points. So a score of 85 would be described as “one standard deviation below the mean.” How would you describe a score of 115 and a score of 70? Any IQ score that falls within one standard deviation above and below the mean (between 85 and 115) is considered average, and 68% of the population has IQ scores in this range. An IQ score of 130 or above is considered a superior level.

Only 2.2% of the population has an IQ score below 70 (American Psychological Association [APA], 2013). A score of 70 or below indicates significant cognitive delays. When these are combined with major deficits in adaptive functioning, a person is diagnosed with having an intellectual disability (American Association on Intellectual and Developmental Disabilities, 2013). Formerly known as mental retardation, the accepted term now is intellectual disability, and it has four subtypes: mild, moderate, severe, and profound ( Table 7.5 ).  The Diagnostic and Statistical Manual of Psychological Disorders  lists criteria for each subgroup (APA, 2013).

On the other end of the intelligence spectrum are those individuals whose IQs fall into the highest ranges. Consistent with the bell curve, about 2% of the population falls into this category. People are considered gifted if they have an IQ score of 130 or higher, or superior intelligence in a particular area. Long ago, popular belief suggested that people of high intelligence were maladjusted. This idea was disproven through a groundbreaking study of gifted children. In 1921, Lewis Terman began a longitudinal study of over 1500 children with IQs over 135 (Terman, 1925). His findings showed that these children became well-educated, successful adults who were, in fact, well-adjusted (Terman & Oden, 1947). Additionally, Terman’s study showed that the subjects were above average in physical build and attractiveness, dispelling an earlier popular notion that highly intelligent people were “weaklings.” Some people with very high IQs elect to join Mensa, an organization dedicated to identifying, researching, and fostering intelligence. Members must have an IQ score in the top 2% of the population, and they may be required to pass other exams in their application to join the group.

DIG DEEPER: What’s in a Name? 

In the past, individuals with IQ scores below 70 and significant adaptive and social functioning delays were diagnosed with mental retardation. When this diagnosis was first named, the title held no social stigma. In time, however, the degrading word “retard” sprang from this diagnostic term. “Retard” was frequently used as a taunt, especially among young people, until the words “mentally retarded” and “retard” became an insult. As such, the DSM-5 now labels this diagnosis as “intellectual disability.” Many states once had a Department of Mental Retardation to serve those diagnosed with such cognitive delays, but most have changed their name to the Department of Developmental Disabilities or something similar in language.

Erin Johnson’s younger brother Matthew has Down syndrome. She wrote this piece about what her brother taught her about the meaning of intelligence:

His whole life, learning has been hard. Entirely possible – just different. He has always excelled with technology – typing his thoughts was more effective than writing them or speaking them. Nothing says “leave me alone” quite like a text that reads, “Do Not Call Me Right Now.” He is fully capable of reading books up to about a third-grade level, but he didn’t love it and used to always ask others to read to him. That all changed when his nephew came along, because he willingly reads to him, and it is the most heart-swelling, smile-inducing experience I have ever had the pleasure of witnessing.

When it comes down to it, Matt can learn. He does learn. It just takes longer, and he has to work harder for it, which if we’re being honest, is not a lot of fun. He is extremely gifted in learning things he takes an interest in, and those things often seem a bit “strange” to others. But no matter. It just proves my point – he  can  learn. That does not mean he will learn at the same pace, or even to the same level. It also, unfortunately, does not mean he will be allotted the same opportunities to learn as many others.

Here’s the scoop. We are all wired with innate abilities to retain and apply our learning and natural curiosities and passions that fuel our desire to learn. But our abilities and curiosities may not be the same.

The world doesn’t work this way though, especially not for my brother and his counterparts. Have him read aloud a book about skunks, and you may not get a whole lot from him. But have him tell you about skunks straight out of his memory, and hold onto your hats. He can hack the school’s iPad system, but he can’t tell you how he did it. He can write out every direction for a drive to our grandparents’ home in Florida, but he can’t drive.

Society is quick to deem him disabled and use demeaning language like the r-word to describe him, but in reality, we haven’t necessarily given him opportunities to showcase the learning he can do. In my case, I can escape the need to memorize how to change the oil in my car without anyone assuming I can’t do it, or calling me names when they find out I can’t. But Matthew can’t get through a day at his job without someone assuming he needs help. He is bright. Brighter than most anyone would assume. Maybe we need to redefine what is smart.

My brother doesn’t fit in the narrow schema of intelligence that is accepted in our society. But intelligence is far more than being able to solve 525 x 62 or properly introduce yourself to another. Why can’t we assume the intelligence of someone who can recite all of a character’s lines in a movie or remember my birthday a year after I told him/her a single time? Why is it we allow a person’s diagnosis or appearance to make us not just wonder if, but entirely doubt that they are capable? Maybe we need to cut away the sides of the box we have created for people so everyone can fit.

My brother can learn. It may not be what you know. It may be knowledge you would deem unimportant. It may not follow a traditional learning trajectory. But the fact remains – he can learn. Everyone can learn. And even though it is harder for him and harder for others still, he is not a “retard.” Nobody is.

When you use the r-word, you are insinuating that an individual, whether someone with a disability or not, is unintelligent, foolish, and purposeless. This in turn tells a person with a disability that they too are unintelligent, foolish, and purposeless. Because the word was historically used to describe individuals with disabilities and twisted from its original meaning to fit a cruel new context, it is forevermore associated with people like my brother. No matter how a person looks or learns or behaves, the r-word is never a fitting term. It’s time we waved it goodbye.

Why Measure Intelligence?

The value of IQ testing is most evident in educational or clinical settings. Children who seem to be experiencing learning difficulties or severe behavioral problems can be tested to ascertain whether the child’s difficulties can be partly attributed to an IQ score that is significantly different from the mean for her age group. Without IQ testing—or another measure of intelligence—children and adults needing extra support might not be identified effectively. In addition, IQ testing is used in courts to determine whether a defendant has special or extenuating circumstances that preclude him from participating in some way in a trial. People also use IQ testing results to seek disability benefits from the Social Security Administration.

• Describe how genetics and environment affect intelligence
• Explain the relationship between IQ scores and socioeconomic status
• Describe the difference between a learning disability and a developmental disorder

High Intelligence: Nature or Nurture?

Where does high intelligence come from? Some researchers believe that intelligence is a trait inherited from a person’s parents. Scientists who research this topic typically use twin studies to determine the  heritability  of intelligence. The Minnesota Study of Twins Reared Apart is one of the most well-known twin studies. In this investigation, researchers found that identical twins raised together and identical twins raised apart exhibit a higher correlation between their IQ scores than siblings or fraternal twins raised together (Bouchard, Lykken, McGue, Segal, & Tellegen, 1990). The findings from this study reveal a genetic component to intelligence ( Figure 7.15 ). At the same time, other psychologists believe that intelligence is shaped by a child’s developmental environment. If parents were to provide their children with intellectual stimuli from before they are born, it is likely that they would absorb the benefits of that stimulation, and it would be reflected in intelligence levels.

The reality is that aspects of each idea are probably correct. In fact, one study suggests that although genetics seem to be in control of the level of intelligence, the environmental influences provide both stability and change to trigger manifestation of cognitive abilities (Bartels, Rietveld, Van Baal, & Boomsma, 2002). Certainly, there are behaviors that support the development of intelligence, but the genetic component of high intelligence should not be ignored. As with all heritable traits, however, it is not always possible to isolate how and when high intelligence is passed on to the next generation.

Range of Reaction  is the theory that each person responds to the environment in a unique way based on his or her genetic makeup. According to this idea, your genetic potential is a fixed quantity, but whether you reach your full intellectual potential is dependent upon the environmental stimulation you experience, especially in childhood. Think about this scenario: A couple adopts a child who has average genetic intellectual potential. They raise her in an extremely stimulating environment. What will happen to the couple’s new daughter? It is likely that the stimulating environment will improve her intellectual outcomes over the course of her life. But what happens if this experiment is reversed? If a child with an extremely strong genetic background is placed in an environment that does not stimulate him: What happens? Interestingly, according to a longitudinal study of highly gifted individuals, it was found that “the two extremes of optimal and pathological experience are both represented disproportionately in the backgrounds of creative individuals”; however, those who experienced supportive family environments were more likely to report being happy (Csikszentmihalyi & Csikszentmihalyi, 1993, p. 187).

Another challenge to determining the origins of high intelligence is the confounding nature of our human social structures. It is troubling to note that some ethnic groups perform better on IQ tests than others—and it is likely that the results do not have much to do with the quality of each ethnic group’s intellect. The same is true for socioeconomic status. Children who live in poverty experience more pervasive, daily stress than children who do not worry about the basic needs of safety, shelter, and food. These worries can negatively affect how the brain functions and develops, causing a dip in IQ scores. Mark Kishiyama and his colleagues determined that children living in poverty demonstrated reduced prefrontal brain functioning comparable to children with damage to the lateral prefrontal cortex (Kishyama, Boyce, Jimenez, Perry, & Knight, 2009).

The debate around the foundations and influences on intelligence exploded in 1969 when an educational psychologist named Arthur Jensen published the article “How Much Can We Boost I.Q. and Achievement” in the Harvard Educational Review . Jensen had administered IQ tests to diverse groups of students, and his results led him to the conclusion that IQ is determined by genetics. He also posited that intelligence was made up of two types of abilities: Level I and Level II. In his theory, Level I is responsible for rote memorization, whereas Level II is responsible for conceptual and analytical abilities. According to his findings, Level I remained consistent among the human race. Level II, however, exhibited differences among ethnic groups (Modgil & Routledge, 1987). Jensen’s most controversial conclusion was that Level II intelligence is prevalent among Asians, then Caucasians, then African Americans. Robert Williams was among those who called out racial bias in Jensen’s results (Williams, 1970).

Obviously, Jensen’s interpretation of his own data caused an intense response in a nation that continued to grapple with the effects of racism (Fox, 2012). However, Jensen’s ideas were not solitary or unique; rather, they represented one of many examples of psychologists asserting racial differences in IQ and cognitive ability. In fact, Rushton and Jensen (2005) reviewed three decades worth of research on the relationship between race and cognitive ability. Jensen’s belief in the inherited nature of intelligence and the validity of the IQ test to be the truest measure of intelligence are at the core of his conclusions. If, however, you believe that intelligence is more than Levels I and II, or that IQ tests do not control for socioeconomic and cultural differences among people, then perhaps you can dismiss Jensen’s conclusions as a single window that looks out on the complicated and varied landscape of human intelligence.

In a related story, parents of African American students filed a case against the State of California in 1979, because they believed that the testing method used to identify students with learning disabilities was culturally unfair as the tests were normed and standardized using white children ( Larry P. v. Riles ). The testing method used by the state disproportionately identified African American children as mentally retarded. This resulted in many students being incorrectly classified as “mentally retarded.”

What are Learning Disabilities?

Learning disabilities are cognitive disorders that affect different areas of cognition, particularly language or reading. It should be pointed out that learning disabilities are not the same thing as intellectual disabilities. Learning disabilities are considered specific neurological impairments rather than global intellectual or developmental disabilities. A person with a language disability has difficulty understanding or using spoken language, whereas someone with a reading disability, such as dyslexia, has difficulty processing what he or she is reading.

Often, learning disabilities are not recognized until a child reaches school age. One confounding aspect of learning disabilities is that they most often affect children with average to above-average intelligence. In other words, the disability is specific to a particular area and not a measure of overall intellectual ability. At the same time, learning disabilities tend to exhibit comorbidity with other disorders, like attention-deficit hyperactivity disorder (ADHD). Anywhere between 30–70% of individuals with diagnosed cases of ADHD also have some sort of learning disability (Riccio, Gonzales, & Hynd, 1994). Let’s take a look at three examples of common learning disabilities: dysgraphia, dyslexia, and dyscalculia.

Children with  dysgraphia  have a learning disability that results in a struggle to write legibly. The physical task of writing with a pen and paper is extremely challenging for the person. These children often have extreme difficulty putting their thoughts down on paper (Smits-Engelsman & Van Galen, 1997). This difficulty is inconsistent with a person’s IQ. That is, based on the child’s IQ and/or abilities in other areas, a child with dysgraphia should be able to write, but can’t. Children with dysgraphia may also have problems with spatial abilities.

Students with dysgraphia need academic accommodations to help them succeed in school. These accommodations can provide students with alternative assessment opportunities to demonstrate what they know (Barton, 2003). For example, a student with dysgraphia might be permitted to take an oral exam rather than a traditional paper-and-pencil test. Treatment is usually provided by an occupational therapist, although there is some question as to how effective such treatment is (Zwicker, 2005).

Dyslexia is the most common learning disability in children. An individual with  dyslexia  exhibits an inability to correctly process letters. The neurological mechanism for sound processing does not work properly in someone with dyslexia. As a result, dyslexic children may not understand sound-letter correspondence. A child with dyslexia may mix up letters within words and sentences—letter reversals, such as those shown in  Figure 7.17 , are a hallmark of this learning disability—or skip whole words while reading. A dyslexic child may have difficulty spelling words correctly while writing. Because of the disordered way that the brain processes letters and sounds, learning to read is a frustrating experience. Some dyslexic individuals cope by memorizing the shapes of most words, but they never actually learn to read (Berninger, 2008).

Dyscalculia

Dyscalculia  is difficulty in learning or comprehending arithmetic. This learning disability is often first evident when children exhibit difficulty discerning how many objects are in a small group without counting them. Other symptoms may include struggling to memorize math facts, organize numbers, or fully differentiate between numerals, math symbols, and written numbers (such as “3” and “three”).

Additional Supplemental Resources

• Use Google’s QuickDraw web app on your phone to quickly draw 5 things for Google’s artificially intelligent neural net. When you are done, the app will show you what it thought each of the drawings was. How does this relate to the psychological idea of concepts, prototypes, and schemas? Check out here.  Works best in Chrome if used in a web browser
• This article lists information about a variety of different topics relating to speech development, including how speech develops and what research is currently being done regarding speech development.
• The Human intelligence site includes biographical profiles of people who have influenced the development of intelligence theory and testing, in-depth articles exploring current controversies related to human intelligence, and resources for teachers.

• In 2000, psychologists Sheena Iyengar and Mark Lepper from Columbia and Stanford University published a study about the paradox of choice.  This is the original journal article.
• Mensa , the high IQ society, provides a forum for intellectual exchange among its members. There are members in more than 100 countries around the world.  Anyone with an IQ in the top 2% of the population can join.
• This test developed in the 1950s is used to refer to some kinds of behavioral tests for the presence of mind, or thought, or intelligence in putatively minded entities such as machines.
• Your central “Hub” of information and products created for the network of Parent Centers serving families of children with disabilities.
• How have average IQ levels changed over time? Hear James Flynn discuss the “Flynn Effect” in this Ted Talk. Closed captioning available.
• We all want customized experiences and products — but when faced with 700 options, consumers freeze up. With fascinating new research, Sheena Iyengar demonstrates how businesses (and others) can improve the experience of choosing. This is the same researcher that is featured in your midterm exam.
• What does an IQ Score distribution look like?  Where do most people fall on an IQ Score distribution?  Find out more in this video. Closed captioning available.
• How do we solve problems?  How can data help us to do this?  Follow Amy Webb’s story of how she used algorithms to help her find her way to true love. Closed captioning available.
• In this Ted-Ed video, explore some of the ways in which animals communicate, and determine whether or not this communication qualifies as language.  A variety of discussion and assessment questions are included with the video (free registration is required to access the questions). Closed captioning available.
• Watch this Ted-Ed video to learn more about the benefits of speaking multiple languages, including how bilingualism helps the brain to process information, strengthens the brain, and keeps the speaker more engaged in their world.  A variety of discussion and assessment questions are included with the video (free registration is required to access the questions). Closed captioning available.
• This video is on how your mind can amaze and betray you includes information on topics such as concepts, prototypes, problem-solving and mistakes in thinking. Closed captioning available.
• This video on language includes information on topics such as the development of language, language theories, and brain areas involved in language, as well as language disorders. Closed captioning available.
• This video on the controversy of intelligence includes information on topics such as theories of intelligence, emotional intelligence, and measuring intelligence. Closed captioning available.
• This video on the brains vs. bias includes information on topics such as intelligence testing, testing bias, and stereotype threat. Closed captioning available.

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Introduction to Psychology Copyright © 2020 by Julie Lazzara is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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Intelligence

IQ, Giftedness

Reviewed by Psychology Today Staff

Reading a road map upside-down, excelling at chess, and generating synonyms for "brilliant" may seem like three different skills. But each is thought to be a measurable indicator of general intelligence or "g," a construct that includes problem-solving ability, spatial manipulation, and language acquisition that is relatively stable across a person's lifetime.

IQ—or intelligence quotient—is the standard most widely used to assess general intelligence. IQ tests seek to measures a variety of intellectual skills that include verbal, non-verbal and spatial. Any person from any walk of life can be highly intelligent, and scoring high on one sub-test tends to correlate with high scores in other tests, though this is not always the case. IQ tests compare a person's performance with that of other people who are the same age—what’s known as a normative sample.

Research has shown that IQ is generally strongly correlated with positive life outcomes, including health and longevity, job performance, and adult income. It is also protective in ways that are not fully understood: People with high IQs seem to be at an advantage in coping with traumatic events—they are less likely to develop full-blown PTSD and more capable of overcoming it when they do—and may experience less rapid decline during the course of Alzheimer's Disease.

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There may be. Research suggest that people who are high in the personality trait of openness tended to be more mentally flexible and verbally fluent and more likely to take creative, unconventional approaches to solving problems. Extraverted people were also more likely to score higher on test of verbal fluency because they tended to talk more, and be less concerned about mistakes. And people higher in the trait of conscientiousness tend to perform better on memory tasks because they’re generally better organized and willing to work harder.

No, not even close. This pervasive pop-culture myth—one survey found that 50 percent of science teachers believed it was true—has no basis in reality. We use 100 percent of our brains every day, as is clearly shown by functional magnetic resonance imaging scans. Neurons only make up 10 percent of the cells in our brains but the other 90 percent work full-time, maintaining homeostasis, providing structural support, and removing pathogens. The source of the famous notion is pioneering psychologist William James, who once write that “we are making use of only a small part of our possible mental and physical resources,” and he was right—but our untapped potential has little to do with our brain cells.

No, a larger brain does not make a person more intelligent . Some studies have suggested, for example, that a larger brain may contribute as much as 6 percent boost to one’s intelligence, but this research has come into question, and some experts doubt that a larger brain would bring any advantages because it would necessarily demand greater energy consumption, potentially contributing a drag on a person’s resources. Considering all animals, including humans, there is a theory that the size of a creature’s brain relative the size of their body may confer a higher level of intelligence, though—and human brains constitute up a higher ratio of our body size than do the brains of many other animals.

The theory known as “the Flynn effect” maintains that average IQ scores have and will continue to rise over time, primarily due to changes in our environment—better diet and greater access to education and information, for example. But in recent years, IQ scores appear to declining —one-half to two points per decade—possibly a reflection of a decline in those same environmental factors.

In the early 1980s, Harvard researcher Howard Gardner proposed that, along with IQ, there may be multiple kinds of intelligence that people possess in varying quantities, including visual-spatial, logical-mathematical, and interpersonal (emotional) intelligence. According to this theory, someone high in interpersonal intelligence would likely excel at cooperating within a group, while someone with high levels of logical-mathematical intelligence would have a heightened capacity to understand numbers, patterns, and logic. But while the concept has gained much public attention — and is often used as part of personality or employment tests—many researchers dispute the idea of different intelligences and have criticized Gardner's theory, criteria, and research designs. For example, emotional intelligence cannot be reliably measured through testing as general intelligence can, the critics argue, and so it lacks the power to explain differences between people.

For more, see Emotional Intelligence .

A critical prerequisite for intellectual growth is the idea that one can gain mastery and improve on native ability. While one can indeed improve memory and problem-solving abilities over time via practice or environmental pressure, this does not mean that one is becoming "more" intelligent. IQ scores do not fluctuate markedly over the course of a person's lifetime, and they tend to consistently correlate with other tests, such as the SAT. Many supplements and computer programs are marketed as brain boosters, but there is little long-term evidence to support those claims.

One reason people attend, and stay in, school through high school, college, and beyond, is to become more intelligent. And while additional years of schooling should increase one’s store of general knowledge and career prospects, until recently research had not concluded that formal education also increased one’s IQ. But then a meta-analysis determined that each additional year of schooling appeared to raise IQ by one to five points. Exactly how schooling boosts IQ is not clear, though, nor is whether or how the effect accumulates over many years of education. But experts point to the study as a sign of a more crucial truth: that an individual’s intelligence can change over time.

The right ones seem to be able to. Successful players of games requiring strategy, creativity , and teamwork , research finds, tend to have a higher IQ than others. A similar connection between IQ and gaming success was not found in studies of first-person shooter-type games that rely on hand-eye coordination. But other studies find that playing certain games can actually help boost IQ. Studies that involved popular puzzle-based strategy games, particularly those involving complex, changing environments, led to gains in problem solving, spatial skills, and persistence. Significantly, such results were not found in studies of so-called “brain-training” games marketed as cognitive boosters.

A growing body of research supports the idea that exercise can help boost cognition, especially moderate-to-vigorous aerobic exercise. In one example, researchers found that, for older people, time spent in moderate-to-vigorous cardiovascular exercise was positively correlated with increases in “fluid” intelligence—processing speed, memory, and reasoning. In the same study, sedentary time was correlated with boosts in “crystallized” intelligence, such as vocabulary development. Light physical activity, however, provided little cognitive benefit.

Stimulants like methylphenidate ( Ritalin ) and mixed amphetamine salts ( Adderall ) deliver proven benefits for many people with ADHD. But the question of whether such stimulants could improve cognitive ability is highly controversial. Recent research, however, suggests that the drugs do not deliver any cognitive enhancement—aside from an increase in confidence , interest, and energy in people’s tasks. A boost in optimism when tackling a difficult assignment is not the same as a boost in intelligence, but it can help deliver better results by motivating people to deploy their existing cognitive resources more vigorously.

While most research finds very little difference in the mean IQ between men and women,  men are overrepresented at the tails of the distribution. This means that more men than women have scores that reflect severe retardation, and more men than women score in the profoundly gifted or "genius" range. Research shows that men are a lot more likely than women to overstate their intelligence. In one example, 71 percent of men claimed to be smarter than the average person, compared to just 59 percent of women.

There’s a persistent stereotype that people high in the trait of psychopathy are smarter than most others because they are skilled at both presenting a false façade to potential victims and at manipulating targets into doing what they want those people to do. But research shows that this is not the case. In fact, some studies find, psychopaths are generally less intelligent than others, particularly so when it comes to capabilities like recognizing emotions in others. So why do they seem so intelligent and devious? Researchers suggest that it’s because they constantly target people with schemes, to the point that even if their percentage of success is quite low, they do occasionally rope in a target.

No, but many become obsessed with the idea that they could be. Studies of narcissism have found that a belief in their intellectual superiority is often crucial to their identity . Narcissists of the type known as grandiose are highly likely to believe they are smarter than other people; some place an especially high value on IQ testing. Vulnerable narcissists, on the other hand, who tend to be more introverted, insecure, and neurotic , are not as likely to believe that they are smarter than others, but they are more likely than others to find taking intelligence tests to be highly stressful .

It has long been believed that left-handed people are smarter than right-handers, but research does not support the notion. In fact, a meta-analysis of studies including more than 20,000 people found that right-handers had a slightly higher IQ, on average, than left-handers, but the difference was not significant.

This is emerging as a core philosophical question as AI systems increase in power and humans become more concerned about how many aspects of work, decision-making , and even creative production could eventually be turned over to computer intelligence. But there are some tasks humans perform far better, such as image recognition, and humans can also be seen as more flexible and adaptive learners. Some argue that the human propensity to ask original questions sets us apart from machine intelligence, along with the ability to leverage others people’s intelligence while solving problems together.

The vast majority of people claim that they find intelligence to be among the most desirable traits in a potential romantic partner. As with other favorable traits, though, this appeal most strongly influences initial interest in a new partner. Once people begin dating, other factors like personality and conflict style play important roles in determining whether a couple will stay together. But for a certain group, intelligence is their primary erotic turn-on . Some research suggests that these individuals, known as sapiosexuals , may represent a distinct sexual orientation . Interestingly, whether one finds intelligence to be a turn-on does not seem to be determined by one’s own level of intelligence. But for sapiosexuals, looks and even gender may not be as vital a factor in sexual attraction as intelligence.

Generally, yes. Studies of adolescents found that more intelligent individuals were more well-liked by peers than others—although other research finds that more intelligent people tend to like fewer people than others, and to prefer being with other intelligent people. In the dating pool, smarter people may be at an advantage because others’ preference for being with smart people is strongest at the beginning of relationships.

Generally, it’s an advantage, although some research suggests that the most intelligent people may be at a disadvantage . When people were asked to consider whether they would want to date people in different percentiles of intelligence, the favorability rankings increased steadily from the 50th percentile to the 90th, at which point interest declined. This research is consistent with other findings that even the most appealing traits tend not be desired in the extreme.

In surveys, men and women both claim that they are at least as attracted to intelligence as they are to good looks. In practice, especially for men, that is not always the case. The idea that highly intelligent women may be at a disadvantage in the dating pool , research suggests, is no myth: Men tend to shy away from women who are clearly more intelligent than they are. (Women are less likely to have the same reaction to intelligent men.) Experts suggest that intelligent women avoid dumbing themselves down to attract a partner or going out of their way to support a partner’s ego, as in the end those strategies are likely to lead to unfulfilling relationships.

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What Is Cognition?

• Describe cognition
• Distinguish concepts and prototypes
• Explain the difference between natural and artificial concepts

Imagine all of your thoughts as if they were physical entities, swirling rapidly inside your mind. How is it possible that the brain is able to move from one thought to the next in an organized, orderly fashion? The brain is endlessly perceiving, processing, planning, organizing, and remembering—it is always active. Yet, you don’t notice most of your brain’s activity as you move throughout your daily routine. This is only one facet of the complex processes involved in cognition. Simply put, cognition is thinking, and it encompasses the processes associated with perception, knowledge, problem solving, judgment, language, and memory. Scientists who study cognition are searching for ways to understand how we integrate, organize, and utilize our conscious cognitive experiences without being aware of all of the unconscious work that our brains are doing (for example, Kahneman, 2011).

Upon waking each morning, you begin thinking—contemplating the tasks that you must complete that day. In what order should you run your errands? Should you go to the bank, the cleaners, or the grocery store first? Can you get these things done before you head to class or will they need to wait until school is done? These thoughts are one example of cognition at work. Exceptionally complex, cognition is an essential feature of human consciousness, yet not all aspects of cognition are consciously experienced.

Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem solving, in addition to other cognitive processes. Cognitive psychologists strive to determine and measure different types of intelligence, why some people are better at problem solving than others, and how emotional intelligence affects success in the workplace, among countless other topics. They also sometimes focus on how we organize thoughts and information gathered from our environments into meaningful categories of thought, which will be discussed later.

CONCEPTS AND PROTOTYPES

The human nervous system is capable of handling endless streams of information. The senses serve as the interface between the mind and the external environment, receiving stimuli and translating it into nervous impulses that are transmitted to the brain. The brain then processes this information and uses the relevant pieces to create thoughts, which can then be expressed through language or stored in memory for future use. To make this process more complex, the brain does not gather information from external environments only. When thoughts are formed, the brain also pulls information from emotions and memories ( Figure ). Emotion and memory are powerful influences on both our thoughts and behaviors.

In order to organize this staggering amount of information, the brain has developed a file cabinet of sorts in the mind. The different files stored in the file cabinet are called concepts. Concepts are categories or groupings of linguistic information, images, ideas, or memories, such as life experiences. Concepts are, in many ways, big ideas that are generated by observing details, and categorizing and combining these details into cognitive structures. You use concepts to see the relationships among the different elements of your experiences and to keep the information in your mind organized and accessible.

Concepts are informed by our semantic memory (you learned about this concept when you studied memory) and are present in every aspect of our lives; however, one of the easiest places to notice concepts is inside a classroom, where they are discussed explicitly. When you study United States history, for example, you learn about more than just individual events that have happened in America’s past. You absorb a large quantity of information by listening to and participating in discussions, examining maps, and reading first-hand accounts of people’s lives. Your brain analyzes these details and develops an overall understanding of American history. In the process, your brain gathers details that inform and refine your understanding of related concepts like democracy, power, and freedom.

Concepts can be complex and abstract, like justice, or more concrete, like types of birds. In psychology, for example, Piaget’s stages of development are abstract concepts. Some concepts, like tolerance, are agreed upon by many people, because they have been used in various ways over many years. Other concepts, like the characteristics of your ideal friend or your family’s birthday traditions, are personal and individualized. In this way, concepts touch every aspect of our lives, from our many daily routines to the guiding principles behind the way governments function.

Another technique used by your brain to organize information is the identification of prototypes for the concepts you have developed. A prototype is the best example or representation of a concept. For example, for the category of civil disobedience, your prototype could be Rosa Parks. Her peaceful resistance to segregation on a city bus in Montgomery, Alabama, is a recognizable example of civil disobedience. Or your prototype could be Mohandas Gandhi, sometimes called Mahatma Gandhi (“Mahatma” is an honorific title) ( Figure ).

Mohandas Gandhi served as a nonviolent force for independence for India while simultaneously demanding that Buddhist, Hindu, Muslim, and Christian leaders—both Indian and British—collaborate peacefully. Although he was not always successful in preventing violence around him, his life provides a steadfast example of the civil disobedience prototype (Constitutional Rights Foundation, 2013). Just as concepts can be abstract or concrete, we can make a distinction between concepts that are functions of our direct experience with the world and those that are more artificial in nature.

NATURAL AND ARTIFICIAL CONCEPTS

In psychology, concepts can be divided into two categories, natural and artificial. Natural concepts are created “naturally” through your experiences and can be developed from either direct or indirect experiences. For example, if you live in Essex Junction, Vermont, you have probably had a lot of direct experience with snow. You’ve watched it fall from the sky, you’ve seen lightly falling snow that barely covers the windshield of your car, and you’ve shoveled out 18 inches of fluffy white snow as you’ve thought, “This is perfect for skiing.” You’ve thrown snowballs at your best friend and gone sledding down the steepest hill in town. In short, you know snow. You know what it looks like, smells like, tastes like, and feels like. If, however, you’ve lived your whole life on the island of Saint Vincent in the Caribbean, you may never have actually seen snow, much less tasted, smelled, or touched it. You know snow from the indirect experience of seeing pictures of falling snow—or from watching films that feature snow as part of the setting. Either way, snow is a natural concept because you can construct an understanding of it through direct observations or experiences of snow ( Figure ).

An artificial concept , on the other hand, is a concept that is defined by a specific set of characteristics. Various properties of geometric shapes, like squares and triangles, serve as useful examples of artificial concepts. A triangle always has three angles and three sides. A square always has four equal sides and four right angles. Mathematical formulas, like the equation for area (length × width) are artificial concepts defined by specific sets of characteristics that are always the same. Artificial concepts can enhance the understanding of a topic by building on one another. For example, before learning the concept of “area of a square” (and the formula to find it), you must understand what a square is. Once the concept of “area of a square” is understood, an understanding of area for other geometric shapes can be built upon the original understanding of area. The use of artificial concepts to define an idea is crucial to communicating with others and engaging in complex thought. According to Goldstone and Kersten (2003), concepts act as building blocks and can be connected in countless combinations to create complex thoughts.

A schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.

There are several types of schemata. A role schema makes assumptions about how individuals in certain roles will behave (Callero, 1994). For example, imagine you meet someone who introduces himself as a firefighter. When this happens, your brain automatically activates the “firefighter schema” and begins making assumptions that this person is brave, selfless, and community-oriented. Despite not knowing this person, already you have unknowingly made judgments about him. Schemata also help you fill in gaps in the information you receive from the world around you. While schemata allow for more efficient information processing, there can be problems with schemata, regardless of whether they are accurate: Perhaps this particular firefighter is not brave, he just works as a firefighter to pay the bills while studying to become a children’s librarian.

An event schema , also known as a cognitive script , is a set of behaviors that can feel like a routine. Think about what you do when you walk into an elevator ( Figure ). First, the doors open and you wait to let exiting passengers leave the elevator car. Then, you step into the elevator and turn around to face the doors, looking for the correct button to push. You never face the back of the elevator, do you? And when you’re riding in a crowded elevator and you can’t face the front, it feels uncomfortable, doesn’t it? Interestingly, event schemata can vary widely among different cultures and countries. For example, while it is quite common for people to greet one another with a handshake in the United States, in Tibet, you greet someone by sticking your tongue out at them, and in Belize, you bump fists (Cairns Regional Council, n.d.)

Because event schemata are automatic, they can be difficult to change. Imagine that you are driving home from work or school. This event schema involves getting in the car, shutting the door, and buckling your seatbelt before putting the key in the ignition. You might perform this script two or three times each day. As you drive home, you hear your phone’s ring tone. Typically, the event schema that occurs when you hear your phone ringing involves locating the phone and answering it or responding to your latest text message. So without thinking, you reach for your phone, which could be in your pocket, in your bag, or on the passenger seat of the car. This powerful event schema is informed by your pattern of behavior and the pleasurable stimulation that a phone call or text message gives your brain. Because it is a schema, it is extremely challenging for us to stop reaching for the phone, even though we know that we endanger our own lives and the lives of others while we do it (Neyfakh, 2013) ( Figure ).

Remember the elevator? It feels almost impossible to walk in and not face the door. Our powerful event schema dictates our behavior in the elevator, and it is no different with our phones. Current research suggests that it is the habit, or event schema, of checking our phones in many different situations that makes refraining from checking them while driving especially difficult (Bayer & Campbell, 2012). Because texting and driving has become a dangerous epidemic in recent years, psychologists are looking at ways to help people interrupt the “phone schema” while driving. Event schemata like these are the reason why many habits are difficult to break once they have been acquired. As we continue to examine thinking, keep in mind how powerful the forces of concepts and schemata are to our understanding of the world.

In this section, you were introduced to cognitive psychology, which is the study of cognition, or the brain’s ability to think, perceive, plan, analyze, and remember. Concepts and their corresponding prototypes help us quickly organize our thinking by creating categories into which we can sort new information. We also develop schemata, which are clusters of related concepts. Some schemata involve routines of thought and behavior, and these help us function properly in various situations without having to “think twice” about them. Schemata show up in social situations and routines of daily behavior.

Review Questions

Cognitive psychology is the branch of psychology that focuses on the study of ________.

• human development
• human thinking
• human behavior
• human society

Which of the following is an example of a prototype for the concept of leadership on an athletic team?

• the equipment manager
• the scorekeeper
• the team captain
• the quietest member of the team

Which of the following is an example of an artificial concept?

• a triangle’s area

An event schema is also known as a cognitive ________.

Critical Thinking Questions

Describe an event schema that you would notice at a sporting event.

Answers will vary. When attending a basketball game, it is typical to support your team by wearing the team colors and sitting behind their bench.

Explain why event schemata have so much power over human behavior.

Event schemata are rooted in the social fabric of our communities. We expect people to behave in certain ways in certain types of situations, and we hold ourselves to the same social standards. It is uncomfortable to go against an event schema—it feels almost like we are breaking the rules.

Personal Application Question

Describe a natural concept that you know fully but that would be difficult for someone else to understand and explain why it would be difficult.

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9.1 Defining and Measuring Intelligence

Learning objectives.

• Define intelligence and list the different types of intelligences psychologists study.
• Summarize the characteristics of a scientifically valid intelligence test.
• Outline the biological and environmental determinants of intelligence.

Psychologists have long debated how to best conceptualize and measure intelligence (Sternberg, 2003). These questions include how many types of intelligence there are, the role of nature versus nurture in intelligence, how intelligence is represented in the brain, and the meaning of group differences in intelligence.

General (g) Versus Specific (s) Intelligences

In the early 1900s, the French psychologist Alfred Binet (1857–1914) and his colleague Henri Simon (1872–1961) began working in Paris to develop a measure that would differentiate students who were expected to be better learners from students who were expected to be slower learners. The goal was to help teachers better educate these two groups of students. Binet and Simon developed what most psychologists today regard as the first intelligence test, which consisted of a wide variety of questions that included the ability to name objects, define words, draw pictures, complete sentences, compare items, and construct sentences.

Binet and Simon (Binet, Simon, & Town, 1915; Siegler, 1992) believed that the questions they asked their students, even though they were on the surface dissimilar, all assessed the basic abilities to understand, reason, and make judgments. And it turned out that the correlations among these different types of measures were in fact all positive; students who got one item correct were more likely to also get other items correct, even though the questions themselves were very different.

On the basis of these results, the psychologist Charles Spearman (1863–1945) hypothesized that there must be a single underlying construct that all of these items measure. He called the construct that the different abilities and skills measured on intelligence tests have in common the general intelligence factor (g) . Virtually all psychologists now believe that there is a generalized intelligence factor, g, that relates to abstract thinking and that includes the abilities to acquire knowledge, to reason abstractly, to adapt to novel situations, and to benefit from instruction and experience (Gottfredson, 1997; Sternberg, 2003). People with higher general intelligence learn faster.

Soon after Binet and Simon introduced their test, the American psychologist Lewis Terman (1877–1956) developed an American version of Binet’s test that became known as the Stanford-Binet Intelligence Test . The Stanford-Binet is a measure of general intelligence made up of a wide variety of tasks including vocabulary, memory for pictures, naming of familiar objects, repeating sentences, and following commands.

Although there is general agreement among psychologists that g exists, there is also evidence for specific intelligence (s) , a measure of specific skills in narrow domains . One empirical result in support of the idea of s comes from intelligence tests themselves. Although the different types of questions do correlate with each other, some items correlate more highly with each other than do other items; they form clusters or clumps of intelligences.

One distinction is between fluid intelligence , which refers to the capacity to learn new ways of solving problems and performing activities, and crystallized intelligence , which refers to the accumulated knowledge of the world we have acquired throughout our lives (Salthouse, 2004). These intelligences must be different because crystallized intelligence increases with age—older adults are as good as or better than young people in solving crossword puzzles—whereas fluid intelligence tends to decrease with age (Horn, Donaldson, & Engstrom, 1981; Salthouse, 2004).

Other researchers have proposed even more types of intelligences. L. L. Thurstone (1938) proposed that there were seven clusters of primary mental abilities , made up of word fluency, verbal comprehension, spatial ability, perceptual speed, numerical ability, inductive reasoning, and memory. But even these dimensions tend to be at least somewhat correlated, showing again the importance of g.

One advocate of the idea of multiple intelligences is the psychologist Robert Sternberg. Sternberg has proposed a triarchic (three-part) theory of intelligence that proposes that people may display more or less analytical intelligence, creative intelligence, and practical intelligence . Sternberg (1985, 2003) argued that traditional intelligence tests assess analytical intelligence, the ability to answer problems with a single right answer, but that they do not well assess creativity (the ability to adapt to new situations and create new ideas) or practicality (e.g., the ability to write good memos or to effectively delegate responsibility).

As Sternberg proposed, research has found that creativity is not highly correlated with analytical intelligence (Furnham & Bachtiar, 2008), and exceptionally creative scientists, artists, mathematicians, and engineers do not score higher on intelligence than do their less creative peers (Simonton, 2000). Furthermore, the brain areas that are associated with convergent thinking , thinking that is directed toward finding the correct answer to a given problem, are different from those associated with divergent thinking , the ability to generate many different ideas for or solutions to a single problem (Tarasova, Volf, & Razoumnikova, 2010). On the other hand, being creative often takes some of the basic abilities measured by g, including the abilities to learn from experience, to remember information, and to think abstractly (Bink & Marsh, 2000).

Test your divergent thinking. How many uses for a paper clip can you think of?

Dead Hochman – paper clips – CC BY 2.0.

Studies of creative people suggest at least five components that are likely to be important for creativity:

• Expertise . Creative people have carefully studied and know a lot about the topic that they are working in. Creativity comes with a lot of hard work (Ericsson, 1998; Weisberg, 2006).
• Imaginative thinking . Creative people often view a problem in a visual way, allowing them to see it from a new and different point of view.
• Risk taking . Creative people are willing to take on new but potentially risky approaches.
• Intrinsic interest . Creative people tend to work on projects because they love doing them, not because they are paid for them. In fact, research has found that people who are paid to be creative are often less creative than those who are not (Hennessey & Amabile, 2010).
• Working in a creative environment . Creativity is in part a social phenomenon. Simonton (1992) found that the most creative people were supported, aided, and challenged by other people working on similar projects.

The last aspect of the triarchic model, practical intelligence, refers primarily to intelligence that cannot be gained from books or formal learning. Practical intelligence represents a type of “street smarts” or “common sense” that is learned from life experiences. Although a number of tests have been devised to measure practical intelligence (Sternberg, Wagner, & Okagaki, 1993; Wagner & Sternberg, 1985), research has not found much evidence that practical intelligence is distinct from g or that it is predictive of success at any particular tasks (Gottfredson, 2003). Practical intelligence may include, at least in part, certain abilities that help people perform well at specific jobs, and these abilities may not always be highly correlated with general intelligence (Sternberg, Wagner, & Okagaki, 1993). On the other hand, these abilities or skills are very specific to particular occupations and thus do not seem to represent the broader idea of intelligence.

Another champion of the idea of multiple intelligences is the psychologist Howard Gardner (1983, 1999). Gardner argued that it would be evolutionarily functional for different people to have different talents and skills, and proposed that there are eight intelligences that can be differentiated from each other ( Table 9.1 “Howard Gardner’s Eight Specific Intelligences” ). Gardner noted that some evidence for multiple intelligences comes from the abilities of autistic savants , people who score low on intelligence tests overall but who nevertheless may have exceptional skills in a given domain, such as math, music, art, or in being able to recite statistics in a given sport (Treffert & Wallace, 2004).

Table 9.1 Howard Gardner’s Eight Specific Intelligences

Source: Adapted from Gardner, H. (1999). Intelligence reframed: Multiple intelligences for the 21st century . New York, NY: Basic Books.

Although intelligence is often conceptualized in a general way (as the g factor), there is a variety of specific skills that can be useful for particular tasks.

Nayu Kim – Playing piano – CC BY 2.0; Helgi Halldórsson – Run faster, Jump higher – CC BY-SA 2.0; Thomas Hawk – Bahamian Clown – CC BY-NC 2.0; Sudipta Mallick – painter – CC BY 2.0; Blondinrikard Fröberg – Torsten, math teacher – CC BY 2.0.

The idea of multiple intelligences has been influential in the field of education, and teachers have used these ideas to try to teach differently to different students. For instance, to teach math problems to students who have particularly good kinesthetic intelligence, a teacher might encourage the students to move their bodies or hands according to the numbers. On the other hand, some have argued that these “intelligences” sometimes seem more like “abilities” or “talents” rather than real intelligence. And there is no clear conclusion about how many intelligences there are. Are sense of humor, artistic skills, dramatic skills, and so forth also separate intelligences? Furthermore, and again demonstrating the underlying power of a single intelligence, the many different intelligences are in fact correlated and thus represent, in part, g (Brody, 2003).

Measuring Intelligence: Standardization and the Intelligence Quotient

The goal of most intelligence tests is to measure g, the general intelligence factor. Good intelligence tests are reliable , meaning that they are consistent over time, and also demonstrate construct validity , meaning that they actually measure intelligence rather than something else. Because intelligence is such an important individual difference dimension, psychologists have invested substantial effort in creating and improving measures of intelligence, and these tests are now the most accurate of all psychological tests. In fact, the ability to accurately assess intelligence is one of the most important contributions of psychology to everyday public life.

Intelligence changes with age. A 3-year-old who could accurately multiply 183 by 39 would certainly be intelligent, but a 25-year-old who could not do so would be seen as unintelligent. Thus understanding intelligence requires that we know the norms or standards in a given population of people at a given age. The standardization of a test involves giving it to a large number of people at different ages and computing the average score on the test at each age level .

It is important that intelligence tests be standardized on a regular basis, because the overall level of intelligence in a population may change over time. The Flynn effect refers to the observation that scores on intelligence tests worldwide have increased substantially over the past decades (Flynn, 1999). Although the increase varies somewhat from country to country, the average increase is about 3 IQ points every 10 years. There are many explanations for the Flynn effect, including better nutrition, increased access to information, and more familiarity with multiple-choice tests (Neisser, 1998). But whether people are actually getting smarter is debatable (Neisser, 1997).

Once the standardization has been accomplished, we have a picture of the average abilities of people at different ages and can calculate a person’s mental age , which is the age at which a person is performing intellectually . If we compare the mental age of a person to the person’s chronological age, the result is the intelligence quotient (IQ) , a measure of intelligence that is adjusted for age . A simple way to calculate IQ is by using the following formula:

IQ = mental age ÷ chronological age × 100.

Thus a 10-year-old child who does as well as the average 10-year-old child has an IQ of 100 (10 ÷ 10 × 100), whereas an 8-year-old child who does as well as the average 10-year-old child would have an IQ of 125 (10 ÷ 8 × 100). Most modern intelligence tests are based the relative position of a person’s score among people of the same age, rather than on the basis of this formula, but the idea of an intelligence “ratio” or “quotient” provides a good description of the score’s meaning.

A number of scales are based on the IQ. The Wechsler Adult lntelligence Scale (WAIS) is the most widely used intelligence test for adults (Watkins, Campbell, Nieberding, & Hallmark, 1995). The current version of the WAIS, the WAIS-IV, was standardized on 2,200 people ranging from 16 to 90 years of age. It consists of 15 different tasks, each designed to assess intelligence, including working memory, arithmetic ability, spatial ability, and general knowledge about the world (see Figure 9.4 “Sample Items From the Wechsler Adult Intelligence Scale (WAIS)” ). The WAIS-IV yields scores on four domains: verbal, perceptual, working memory, and processing speed. The reliability of the test is high (more than 0.95), and it shows substantial construct validity. The WAIS-IV is correlated highly with other IQ tests such as the Stanford-Binet, as well as with criteria of academic and life success, including college grades, measures of work performance, and occupational level. It also shows significant correlations with measures of everyday functioning among the mentally retarded.

The Wechsler scale has also been adapted for preschool children in the form of the Wechsler Primary and Preschool Scale of Intelligence (WPPSI-III) and for older children and adolescents in the form of the Wechsler Intelligence Scale for Children (WISC-IV) .

Figure 9.4 Sample Items From the Wechsler Adult Intelligence Scale (WAIS)

Source: Adapted from Thorndike, R. L., & Hagen, E. P. (1997). Cognitive Abilities Test (Form 5): Research handbook . Chicago, IL: Riverside Publishing.

The intelligence tests that you may be most familiar with are aptitude tests , which are designed to measure one’s ability to perform a given task, for instance, to do well in college or in postgraduate training. Most U.S. colleges and universities require students to take the Scholastic Assessment Test (SAT) or the American College Test (ACT), and postgraduate schools require the Graduate Record Examination (GRE), Medical College Admissions Test (MCAT), or the Law School Admission Test (LSAT). These tests are useful for selecting students because they predict success in the programs that they are designed for, particularly in the first year of the program (Kuncel, Hezlett, & Ones, 2010). These aptitude tests also measure, in part, intelligence. Frey and Detterman (2004) found that the SAT correlated highly (between about r = .7 and r = .8) with standard measures of intelligence.

Intelligence tests are also used by industrial and organizational psychologists in the process of personnel selection . Personnel selection is the use of structured tests to select people who are likely to perform well at given jobs (Schmidt & Hunter, 1998). The psychologists begin by conducting a job analysis in which they determine what knowledge, skills, abilities, and personal characteristics ( KSAPs ) are required for a given job. This is normally accomplished by surveying and/or interviewing current workers and their supervisors. Based on the results of the job analysis, the psychologists choose selection methods that are most likely to be predictive of job performance. Measures include tests of cognitive and physical ability and job knowledge tests, as well as measures of IQ and personality.

The Biology of Intelligence

The brain processes underlying intelligence are not completely understood, but current research has focused on four potential factors: brain size, sensory ability, speed and efficience of neural transmission, and working memory capacity.

There is at least some truth to the idea that smarter people have bigger brains. Studies that have measured brain volume using neuroimaging techniques find that larger brain size is correlated with intelligence (McDaniel, 2005), and intelligence has also been found to be correlated with the number of neurons in the brain and with the thickness of the cortex (Haier, 2004; Shaw et al., 2006). It is important to remember that these correlational findings do not mean that having more brain volume causes higher intelligence. It is possible that growing up in a stimulating environment that rewards thinking and learning may lead to greater brain growth (Garlick, 2003), and it is also possible that a third variable, such as better nutrition, causes both brain volume and intelligence.

Another possibility is that the brains of more intelligent people operate faster or more efficiently than the brains of the less intelligent. Some evidence supporting this idea comes from data showing that people who are more intelligent frequently show less brain activity (suggesting that they need to use less capacity) than those with lower intelligence when they work on a task (Haier, Siegel, Tang, & Abel, 1992). And the brains of more intelligent people also seem to run faster than the brains of the less intelligent. Research has found that the speed with which people can perform simple tasks—such as determining which of two lines is longer or pressing, as quickly as possible, one of eight buttons that is lighted—is predictive of intelligence (Deary, Der, & Ford, 2001). Intelligence scores also correlate at about r = .5 with measures of working memory (Ackerman, Beier, & Boyle, 2005), and working memory is now used as a measure of intelligence on many tests.

Although intelligence is not located in a specific part of the brain, it is more prevalent in some brain areas than others. Duncan et al. (2000) administered a variety of intelligence tasks and observed the places in the cortex that were most active. Although different tests created different patterns of activation, as you can see in Figure 9.5 “Where Is Intelligence?” , these activated areas were primarily in the outer parts of the cortex, the area of the brain most involved in planning, executive control, and short-term memory.

Figure 9.5 Where Is Intelligence?

fMRI studies have found that the areas of the brain most related to intelligence are in the outer parts of the cortex.

Source: Adapted from Duncan, J., Seitz, R. J., Kolodny, J., Bor, D., Herzog, H., Ahmed, A.,…Emslie, H. (2000). A neural basis for general intelligence. Science, 289 (5478), 457–460.

Is Intelligence Nature or Nurture?

Intelligence has both genetic and environmental causes, and these have been systematically studied through a large number of twin and adoption studies (Neisser et al., 1996; Plomin, DeFries, Craig, & McGuffin, 2003). These studies have found that between 40% and 80% of the variability in IQ is due to genetics, meaning that overall genetics plays a bigger role than does environment in creating IQ differences among individuals (Plomin & Spinath, 2004). The IQs of identical twins correlate very highly ( r = .86), much higher than do the scores of fraternal twins who are less genetically similar ( r = .60). And the correlations between the IQs of parents and their biological children ( r = .42) is significantly greater than the correlation between parents and adopted children ( r = .19). The role of genetics gets stronger as children get older. The intelligence of very young children (less than 3 years old) does not predict adult intelligence, but by age 7 it does, and IQ scores remain very stable in adulthood (Deary, Whiteman, Starr, Whalley, & Fox, 2004).

But there is also evidence for the role of nurture, indicating that individuals are not born with fixed, unchangeable levels of intelligence. Twins raised together in the same home have more similar IQs than do twins who are raised in different homes, and fraternal twins have more similar IQs than do nontwin siblings, which is likely due to the fact that they are treated more similarly than are siblings.

The fact that intelligence becomes more stable as we get older provides evidence that early environmental experiences matter more than later ones. Environmental factors also explain a greater proportion of the variance in intelligence for children from lower-class households than they do for children from upper-class households (Turkheimer, Haley, Waldron, D’Onofrio, & Gottesman, 2003). This is because most upper-class households tend to provide a safe, nutritious, and supporting environment for children, whereas these factors are more variable in lower-class households.

Social and economic deprivation can adversely affect IQ. Children from households in poverty have lower IQs than do children from households with more resources even when other factors such as education, race, and parenting are controlled (Brooks-Gunn & Duncan, 1997). Poverty may lead to diets that are undernourishing or lacking in appropriate vitamins, and poor children may also be more likely to be exposed to toxins such as lead in drinking water, dust, or paint chips (Bellinger & Needleman, 2003). Both of these factors can slow brain development and reduce intelligence.

If impoverished environments can harm intelligence, we might wonder whether enriched environments can improve it. Government-funded after-school programs such as Head Start are designed to help children learn. Research has found that attending such programs may increase intelligence for a short time, but these increases rarely last after the programs end (McLoyd, 1998; Perkins & Grotzer, 1997). But other studies suggest that Head Start and similar programs may improve emotional intelligence and reduce the likelihood that children will drop out of school or be held back a grade (Reynolds, Temple, Robertson, & Mann 2001).

Intelligence is improved by education; the number of years a person has spent in school correlates at about r = .6 with IQ (Ceci, 1991). In part this correlation may be due to the fact that people with higher IQ scores enjoy taking classes more than people with low IQ scores, and they thus are more likely to stay in school. But education also has a causal effect on IQ. Comparisons between children who are almost exactly the same age but who just do or just do not make a deadline for entering school in a given school year show that those who enter school a year earlier have higher IQ than those who have to wait until the next year to begin school (Baltes & Reinert, 1969; Ceci & Williams, 1997). Children’s IQs tend to drop significantly during summer vacations (Huttenlocher, Levine, & Vevea, 1998), a finding that suggests that a longer school year, as is used in Europe and East Asia, is beneficial.

It is important to remember that the relative roles of nature and nurture can never be completely separated. A child who has higher than average intelligence will be treated differently than a child who has lower than average intelligence, and these differences in behaviors will likely amplify initial differences. This means that modest genetic differences can be multiplied into big differences over time.

Psychology in Everyday Life: Emotional Intelligence

Although most psychologists have considered intelligence a cognitive ability, people also use their emotions to help them solve problems and relate effectively to others. Emotional intelligence refers to the ability to accurately identify, assess, and understand emotions, as well as to effectively control one’s own emotions (Feldman-Barrett & Salovey, 2002; Mayer, Salovey, & Caruso, 2000).

The idea of emotional intelligence is seen in Howard Gardner’s interpersonal intelligence (the capacity to understand the emotions, intentions, motivations, and desires of other people) and intrapersonal intelligence (the capacity to understand oneself, including one’s emotions). Public interest in, and research on, emotional intellgence became widely prevalent following the publication of Daniel Goleman’s best-selling book, Emotional Intelligence: Why It Can Matter More Than IQ (Goleman, 1998).

There are a variety of measures of emotional intelligence (Mayer, Salovey, & Caruso, 2008; Petrides & Furnham, 2000). One popular measure, the Mayer-Salovey-Caruso Emotional Intelligence Test ( http://www.emotionaliq.org ), includes items about the ability to understand, experience, and manage emotions, such as these:

• What mood(s) might be helpful to feel when meeting in-laws for the very first time?
• Tom felt anxious and became a bit stressed when he thought about all the work he needed to do. When his supervisor brought him an additional project, he felt ____ (fill in the blank).

Contempt most closely combines which two emotions?

• anger and fear
• fear and surprise
• disgust and anger
• surprise and disgust

Debbie just came back from vacation. She was feeling peaceful and content. How well would each of the following actions help her preserve her good mood?

• Action 1: She started to make a list of things at home that she needed to do.
• Action 2: She began thinking about where and when she would go on her next vacation.
• Action 3: She decided it was best to ignore the feeling since it wouldn’t last anyway.

One problem with emotional intelligence tests is that they often do not show a great deal of reliability or construct validity (Føllesdal & Hagtvet, 2009).Although it has been found that people with higher emotional intelligence are also healthier (Martins, Ramalho, & Morin, 2010), findings are mixed about whether emotional intelligence predicts life success—for instance, job performance (Harms & Credé, 2010). Furthermore, other researchers have questioned the construct validity of the measures, arguing that emotional intelligence really measures knowledge about what emotions are, but not necessarily how to use those emotions (Brody, 2004), and that emotional intelligence is actually a personality trait, a part of g, or a skill that can be applied in some specific work situations—for instance, academic and work situations (Landy, 2005).

Although measures of the ability to understand, experience, and manage emotions may not predict effective behaviors, another important aspect of emotional intelligence— emotion regulation —does. Emotion regulation refers to the ability to control and productively use one’s emotions. Research has found that people who are better able to override their impulses to seek immediate gratification and who are less impulsive also have higher cognitive and social intelligence. They have better SAT scores, are rated by their friends as more socially adept, and cope with frustration and stress better than those with less skill at emotion regulation (Ayduk et al., 2000; Eigsti et al., 2006; Mischel & Ayduk, 2004).

Because emotional intelligence seems so important, many school systems have designed programs to teach it to their students. However, the effectiveness of these programs has not been rigorously tested, and we do not yet know whether emotional intelligence can be taught, or if learning it would improve the quality of people’s lives (Mayer & Cobb, 2000).

Key Takeaways

• Intelligence is the ability to think, to learn from experience, to solve problems, and to adapt to new situations. Intelligence is important because it has an impact on many human behaviors.
• Psychologists believe that there is a construct that accounts for the overall differences in intelligence among people, known as general intelligence (g).
• There is also evidence for specific intelligences (s), measures of specific skills in narrow domains, including creativity and practical intelligence.
• The intelligence quotient (IQ) is a measure of intelligence that is adjusted for age. The Wechsler Adult lntelligence Scale (WAIS) is the most widely used IQ test for adults.
• Brain volume, speed of neural transmission, and working memory capacity are related to IQ.
• Between 40% and 80% of the variability in IQ is due to genetics, meaning that overall genetics plays a bigger role than does environment in creating IQ differences among individuals.
• Intelligence is improved by education and may be hindered by environmental factors such as poverty.
• Emotional intelligence refers to the ability to identify, assess, manage, and control one’s emotions. People who are better able to regulate their behaviors and emotions are also more successful in their personal and social encounters.

Exercises and Critical Thinking

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• Did your parents try to improve your intelligence? Do you think their efforts were successful?
• Consider the meaning of the Flynn effect. Do you think people are really getting smarter?
• Give some examples of how emotional intelligence (or the lack of it) influences your everyday life and the lives of other people you know.

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Critical Thinking: Creating Job-Proof Skills for the Future of Work

Daniela dumitru.

1 Teacher Training Department, Bucharest University of Economic Studies, 010374 Bucharest, Romania

2 Doctoral School of Psychology and Educational Sciences, University of Bucharest, 050663 Bucharest, Romania

Diane F. Halpern

3 Department of Psychology, Claremont McKenna College, Claremont, CA 91711, USA; moc.liamg@nreplahfenaid

In this study, we explore the transformative impact of artificial intelligence (AI) on the job market and argue for the growing importance of critical thinking skills in the face of job automation and changing work dynamics. Advancements in AI have the potential to disrupt various professions, including, for example, programming, legal work, and radiology. However, solely relying on AI systems can lead to errors and misjudgments, emphasizing the need for human oversight. The concept of “job-proof skills” is introduced, highlighting the importance of critical thinking, problem-solving, empathy, ethics, and other human attributes that machines cannot replicate with the same standards and agility. We maintain that critical thinking can be taught and learned through appropriate classroom instruction and transfer-focused approaches. The need for critical thinking skills is further reinforced by the influx of information and the spread of misinformation in the age of social media. Moreover, employers increasingly value critical thinking skills in their workforce, yet there exists a gap between the demand for these skills and the preparedness of college graduates. Critical thinking is not only essential for the future of work, but also for informed citizenship in an increasingly complex world. The potential impact of AI on job disruption, wages, and employment polarization is discussed, highlighting the correlation between jobs requiring critical thinking skills and their resistance to automation. We conclude by discussing collaborative efforts between universities and labor market organizations to adapt curricula and promote the development of critical thinking skills, drawing on examples from European initiatives. The need to prioritize critical thinking skills in education and address the evolving demands of the labor market is emphasized as a crucial step for navigating the future of work and opportunities for workers.

1. Introduction: Critical Thinking: Creating Job-Proof Skills for the Future of Work

The rapid evolution of online technologies has ushered in a paradigm shift in employment, redefining the nature of work and the skills required to succeed in the digital age. This transformative landscape, characterized by the ubiquitous presence of the Internet, social media platforms, and advanced artificial intelligence systems, has created a plethora of new opportunities and challenges in the labor market. As we navigate this digital frontier, it is becoming increasingly clear that traditional employment paradigms are undergoing a profound transformation. The convergence of online technologies with the demands of a networked world has not only created new job opportunities, but it has also disrupted established industries, rendering some job roles obsolete while creating demand for previously unforeseen skills. In this era of unprecedented connectivity and innovation, examining the intricate interplay between online technologies and jobs is paramount as it holds the key to understanding the dynamics of our rapidly evolving workforce.

Artificial intelligence (AI) is disrupting many jobs and promises “to change the way the world works” ( adminGPT 2023, para. 13 ). The number and range of AI programs are increasing at a rapid pace, and they are likely to continually improve to meet user demands. Consider, for example, ChatGPT, which can respond to questions and requests in a way that seems to come from a human rather than a computer program. GPT stands for “generative pretrained transformer”. It is generative in that it can provide responses that it never “learned”; it is pretrained with a large language model ( Bushwick et al. 2023 ). Newer versions can describe visual images, although thus far, they cannot create visual images. Its uses are seemingly endless. It is easy to imagine how such programs can change the lives of blind individuals. In fact, it can and will change the lives of all of us.

In this paper, we argue that these advances in online technologies will make critical thinking (CT) more important than ever before. Many who are preparing to enter the job market, and many who are already employed, will need to adapt to new forms of job automation and different ways of working.

Consider, for example, that an early achievement of ChatGPT was its generation of Python code (a computer language) to compute various tasks, such as data analysis. Apparently, getting ChatGPT to generate code is so easy that several YouTube videos have popped up claiming that they can teach novice users to use ChatGPT to generate code in 90 s. ( Data Professor 2023 ). The benefits are obvious, but so are the potential job losses for people who work in Python. Python coders will need to upgrade their skills, perhaps first becoming experts in the use of ChatGPT and similar programs, but this also has a positive side--they can spend more time working on larger questions such as which analyses are needed, and, of course, carefully reviewing the work produced by AI to ensure that it is accurate and understandable. Early versions of ChatGPT responses often contained errors. A New York lawyer learned the hard way: Steven A. Schwartz, a lawyer for 30 years, used ChatGPT to create a legal document ( Weiser and Schweber 2023 ). It was filled with fake citations and bogus judicial opinions. Sadly, Mr. Schwartz never checked the accuracy of the document he filed in court. The judge was not amused. This highly public and embarrassing event should be a lesson for all of us. Current AI programs cannot be trusted to take over our work, though they may be able to aid or supplement it. However, other AI programs can “read” radiographs more accurately than human radiologists, which provides a benefit to both radiologists and patients. There is an immediate positive effect for this advancement: Radiologists will have more time to directly work with patients, and yes, they must also check the accuracy of the outputs from their programs when presenting diagnoses.

For the rest of us, whether we are students or early or late in our careers, we need to focus on the development of “job-proof skills” in the face of AI advances. A report from the United Nations defines job-proof skills as “conceptual and strategic thinking, problem-solving, empathy, optimism, ethics, emotional intelligence, and judgments are the future-proof skills and attributes that machines will not be able to replicate with the same standards and agility as qualified human beings” ( Elkeiy 2022, para. 5 ). In other words, critical thinking skills will always be needed.

2. What Is Critical Thinking?

Although some scholars in the field of critical thinking have emphasized differences among various definitions, we believe that the commonalities are evident (c.f., Dwyer 2017 ; Nisbett 2015 ; Lipman 1991 ; Fisher 2001 ). There are some differences in the use of terms and several skills might be more important, but all of the definitions (more or less) conform to our preferred definition: “Critical thinking is the use of those cognitive skills and abilities that increase the probability of a desirable outcome. It is purposeful, reasoned, and goal directed. It is the kind of thinking involved in solving problems, formulating inferences, calculating likelihoods, and making decisions. Critical thinkers use these skills appropriately, without prompting, and usually with conscious intent, in a variety of settings. That is, they are predisposed to think critically. When we think critically, we are evaluating the outcomes of our thought processes--how good a decision is or how well a problem is solved. Critical thinking also involves evaluating the thinking process--the reasoning that went into the conclusion we’ve arrived at, or the kinds of factors considered in making a decision” ( Halpern and Dunn 2023, pp. 6–7 ). The reason we need a common definition of critical thinking is that, without it, instructors can and have passed almost anything off as instruction in critical thinking. However, common ground is to be found concerning CT definitions. In a European project, which we shall refer to in Section 4.3 , the critical thinking definition is based on the works of Halpern and Dunn ( 2023 ), Facione ( 1990 ), Paul and Elder ( 2008 ), and Kuhn ( 1999 ). During two debate sessions, 33 international participants from higher education and the labor market defined critical thinking as a deliberate cognitive process guided by conscious, dynamic, self-directed, self-monitored, and self-correcting thought ( Rebelo et al. 2023 ). It relies on both disciplinary and procedural knowledge, along with metacognitive aspects (including metacognitive, meta-strategic, and epistemological dimensions). Critical thinking can be cultivated and enhanced through the development of competencies, and it is facilitated by various attitudes, such as systematic thinking, open-mindedness, empathy, flexibility, and cognitive maturity. Additionally, it encompasses intellectual skills such as reflection, self-regulation, analysis, inference, explanation, synthesis, and systematic thought. Critical thinking not only stimulates problem-solving capabilities but also facilitates effective communication, fosters independent and holistic thinking, and bolsters decision-making and active citizenship ( Pnevmatikos et al. 2021 ).

2.1. Can Critical Thinking Be Learned?

We teach writing, oral communication, and mathematics with the (often implicit) belief that these skills will be learned and transferred to multiple settings both inside and outside of the classroom. There is a large and growing research literature showing that, with appropriate classroom instruction in critical thinking, including specific instruction designed for transfer, the skills will spontaneously transfer and in uncued (i.e., there are no reminders to use the critical thinking skill that was learned in class) situations ( Dumitru 2012 ; Heijltjes et al. 2014 ; Tiruneh 2019 ). Several such studies were presented by Dwyer ( 2017 ) and Halpern and Dunn ( 2023 ). For the sake of brevity, we review just one recent study. The study was designed to counteract the effects of conspiracy theories. When people believe conspiracy theories, they often act in harmful ways–such as refusing to get the COVID-19 vaccine, which resulted in the death of large numbers of people around the world, or attacking the United State Capitol Building on 6 January 2021 in the belief that there was a conspiracy afoot designed to steal the United States 2020 presidential election from Donald Trump. In a review of the research literature on the efficacy of interventions, the researchers found “there was one intervention which was characteristically different to the rest” ( O’Mahony et al. 2023, para. 23 ). It was a semester-long university course in critical thinking that was designed to teach students the difference between good scientific practices and pseudoscience. These courses require effort and commitment, but they are effective. The same conclusion applies to all interventions designed to enhance critical thinking. There are no fast and easy “once and done” strategies that work. This is why we recommend continuous and pervasive coursework to make sure that the learning of CT skills “sticks.”

2.2. The Need for Critical Thinking Skills

Online technologies-related (including AI) job loss and redesign are not the only reasons why we need to concentrate on teaching and learning the skills of critical thinking. COVID-19 left 140 million people out of work, and many of their jobs will never return ( Roslansky 2021 ). We are drowning in a tsunami of information, confronted with advertisements online, in news reports, social media, podcasts, and more. The need to be able to distinguish good information from bad is critical. In addition, employers want to hire people with critical thinking skills. In a recent report by Hart Research Associated ( 2018 ), they found that in an employer survey of 501 business executives, 78% said that critical thinking/analytic reasoning is the most important skill they want in their employees, but they also added that only 34% of college graduates arrive well prepared in critical thinking. This gap between what employers want and their perception of the preparedness of the workforce was larger for critical thinking than for any other area. In fact, every report on the future of work made this same point. Consider this quote from The World Economic Forum ( 2020 ) on the future of jobs: “Skills gaps continue to be high as in-demand skills across jobs change in the next five years. The top skills and skill groups which employers see as rising in prominence in the lead up to 2025 include groups such as critical thinking and analysis as well as problem-solving.” (p. 5). In a report from the Office of the European Union: Key Competences for Lifelong Learning, the commissioner wrote “Critical thinking, media literacy, and communication skills are some of the requirements to navigate our increasingly complex world” ( Navracsics 2019, p. 3 ). Of course, critical thinking is not just needed in the world of work. A true democracy requires an educated citizenry with citizens who can think critically about world social issues, such as the use/threat of AI, war, poverty, climate change, and so much more. Irrational voters are a threat to all of us—and to themselves.

The need to think critically is not new, but it has taken on a new urgency as social media and other forms of communication have made the deliberate spread of misinformation move at the speed of light. There is nothing new about the use of lies, half-truths, and innuendos to get people to believe something that is not true. Anyone can post anything on popular media sites, and this “fake news” is often copied and shared thousands of times. Sometimes the information is spread with a deliberate attempt to mislead; other times, it is copied and spread by people who believe it is true. These messages are often used to discredit political adversaries, create social unrest, and incite fear. It can be a difficult task to determine what to believe and what to discard. Vosoughi et al. ( 2018 ) analyzed data from 126,000 tweets that were spread by approximately 3 million people. How did the researchers discriminate true data from false data? The same way we all should. They used several different fact-checking sites and found 95% to 98% agreement regarding the truth or falsehood of information. They found that false data spread more quickly and more widely than true data because the false data tended to be novel and sensational, rendering it salient and seductive.

In today’s landscape, the imperative to foster critical thinking skills is becoming increasingly apparent as we grapple with the rapid rise of social media and artificial intelligence technologies and their profound impact on the future of work. The confluence of these transformative forces has ushered in a new era characterized by the potential for significant job disruption. As online technologies advance and automation becomes more widespread, certain traditional job roles may become obsolete, requiring the development of innovative skills and adaptability in the workforce. In this context, critical thinking emerges as a central element in preparing individuals to navigate the evolving job market. It equips individuals with the ability to analyze complex information, discern credible sources from the proliferation of social media information, and make informed decisions in an era of blurring boundaries between human and machine contributions to the workforce. Cultivating critical thinking skills will be essential to ensuring that individuals can take advantage of the opportunities presented by new technologies while mitigating the challenges of job disruption in this AI-driven future.

3. Critical Thinking Skills and Job Disruption and Replacement

Eloundou et al. in 2023 estimated that about 15% of all U.S. workers’ jobs could be accomplished much faster and at the same level of quality with currently available AI. There are large differences in the extent to which various occupations and industries will be affected by advancements in AI. For example, tasks that require a high degree of human interaction, highly specialized domain knowledge, or creating innovative technologies will be minimally affected; whereas, other occupations such as providing captions for images or answering questions about a text or document are more likely to be affected. Routine-based jobs in general are more likely to be dislodged by advanced technologies ( Acemoglu 2002 ). Using the basic definitions of skills that are standard in O*Net, Eloundou et al. ( 2023 ) found a clear negative correlation between jobs requiring knowledge of science and critical thinking skills and the likelihood that AI will “take over” the job. These findings reinforce our main point—the best way to gain job-proof skills is with critical thinking.

The effect of online technologies on wages is complicated because of the large number of factors that come together to determine earnings. Acemoglu and Autor ( 2011 ) advocated for a model that simultaneously considers the level of the tasks required for any job (low, medium, and high), where a high level of skill is defined as one that allows employees to perform a variety of tasks, the demand for the tasks, and technological changes that can complement a task or replace it. They assert that employment has become increasingly polarized with the growth in both high education, high wage occupations and low education, and low wage occupations in the United States and the European Union. To understand and predict which occupations will be most disrupted by AI (and other developing technologies), an investigator will need to simultaneously consider all of these variables. Technological advancements can generate shifts in demand, favoring either high- or low-skilled workers. According to Acemoglu and Autor ( 2011 ), we can expect some of the largest disruptive effects at the middle level of skills, where some of the tasks performed at this level can be more easily replaced by new technologies, with widespread employment growth in high- and low-skilled occupations.

4. Business-University Collaborations

The pursuit of promoting high standards of critical thinking in university students across various academic disciplines is a challenging endeavor that should be leveraged through collaboration with stakeholders. In such collaborations, stakeholders can contribute to refining the skills required by learners and bring their own perspectives to academic instruction. This close partnership between universities and stakeholders helps minimize gaps and mismatches in the transition to the labor market, facilitates research collaboration, and increases student motivation.

Collaborations between businesses and universities have gained increasing importance in today’s rapidly evolving educational and economic landscape. These partnerships are instrumental in bridging the gap between academic learning and the real-world skills demanded by the job market. One key aspect of business-university collaboration (BUC) is the alignment of curricula with the dynamic needs of industries. This entails the joint effort of higher education institutions (HEIs) and industry experts to design, develop, and deliver educational programs that equip students with practical, job-ready skills. The curriculum design phase involves tailoring study programs, courses, and modules to address skills gaps and align with the specific requirements of employers.

Moreover, BUC extends beyond the classroom. Collaborations often involve business engagement in educational activities, including guest lectures, internships, co-op programs, and research projects. These interactions provide students with invaluable exposure to real-world scenarios, allowing them to apply theoretical knowledge in practical settings.

In essence, BUC is a multifaceted partnership that benefits both students and businesses. It ensures that educational programs remain relevant, fostering a seamless transition from academia to the workforce. This collaborative approach not only enhances students’ employability but also contributes to the overall growth and innovation of industries.

Operationalizing the collaboration implicates a particular focus on curriculum design, development, and delivery. These involve the collaboration between higher education institutions and labor market partners to create or enhance undergraduate or postgraduate study programs, courses, or modules. This collaborative effort aims to address skills gaps, align curricula with employers’ needs, integrate training initiatives, and improve graduates’ employability. Additionally, curriculum delivery includes various forms of business involvement, such as guest lectures, placements, supervision, mentoring, and work-based learning activities.

While the existing literature often discusses the barriers and motivations for university-business collaboration ( Healy et al. 2014 ; Orazbayeva et al. 2020 ), there is a need for more empirical insights into the roles and responsibilities of each party engaged in joint curriculum design, development, and delivery, as well as lessons learned from these collaborations ( Rebelo et al. 2023 ).

4.1. Why Do We Need Higher Education’s Help?

In the preceding sections of this paper, we delved into the disruptive forces of artificial intelligence (AI) on the job market and the critical need for individuals to adapt to these changes by developing “job-proof skills”. The rise of online technologies such as ChatGPT presents both opportunities and challenges, particularly in fields where middle-level skills are required. To effectively tackle these challenges, we must turn our attention to the pivotal role of education and the cultivation of essential skills such as critical thinking.

We highlighted how AI is rapidly transforming various industries and the need for individuals to adapt to these changes. Moreover, we explored the question of whether critical thinking can be learned, showcasing research evidence that supports the teachability of this skill. Now, we shall explore practical strategies for fostering critical thinking skills through collaborations between universities and businesses. The idea here is to create an educational framework that equips students with the capabilities needed to thrive in the evolving workforce.

Building upon the success of two European projects, “Critical thinking across higher education curricula—CRITHINKEDU” and “Critical thinking for successful jobs—THINK4JOBS”, we argue that incorporating practical experience and CT development through apprenticeships is a possible action for better higher education classes. This collaborative approach between HEI and LMO designed to address the differing perspectives and terminologies used by these two entities regarding critical thinking could be an important curriculum design for the better adaptation of job market technology disruptions.

Research conducted by Eloundou et al. ( 2023 ), which shows that critical thinking skills and science skills are less likely to be taken by AI, compels us to sustain the THINK4JOBS apprenticeship curricula as a possible teaching protocol for critical thinking enhancement to face challenges posed by AI at work.

The results from these projects demonstrate significant progress in students’ critical thinking skills and dispositions. These improvements, as highlighted below in Section 4.3 , underscore the effectiveness of embedding critical thinking in the curriculum. The guidelines formulated for implementing Critical Thinking Blended Apprenticeship Curricula provide a roadmap for educators to follow when effectively integrating critical thinking into their courses.

As we ponder the possibility of a world where critical thinking is widespread, we can envision a future where individuals are equipped to confront the ideological fanaticism that threatens global stability. Critical thinking, as both a cognitive skill and a disposition, has the potential to shape a workforce capable of adapting to the ever-changing landscape of work, making informed decisions, and contributing to a more rational and democratic world. The THINK4JOBS project emphasizes the practical steps taken to prepare students for the future job market and sets the stage for further exploration of the role of critical thinking in addressing global challenges, including AI presence in the job market.

4.2. CRITHINKEDU Proctocol for Critical Thinking Education across Curricula

Given that the best education for the future of work is the acquisition of critical thinking skills, how can we facilitate this sort of education? One way to obtain a job-proof education is to create classes with the help of labor market organizations. Two projects funded by the European Union were designed to bring to life the idea that better communication and collaboration between universities and employers result in a better adaptation of the curriculum, especially a curriculum involving critical thinking skill development.

Between 2016 and 2019, the project “Critical thinking across the European higher education curriculum—CRITHINKEDU” focused on how CT is taught in various academic domains. The CRITHINKEDU project, involving universities across Europe, exemplifies how academia and industry can join forces to bridge the gap between classroom learning and real-world job demands. This initiative aimed to enhance the curriculum by explicitly emphasizing critical thinking skill development. It revealed that employers across various fields value critical thinking, and they perceive it as essential for recent graduates entering the workforce.

The participants were eleven universities from nine European countries (Belgium, Czech Republic, Greece, Italy, Spain, Portugal, Romania, Lithuania, and Ireland; Dominguez 2018). Qualitative research was conducted with 32 focus groups comprised of professionals from various European countries and fields. The findings align with previous studies: “CT is a set of interconnected skills (interpretation, inference, analysis, explanation, evaluation, self-regulation”, see Payan-Carreira et al. ( 2023, p. 16 ), and dispositions (open-mindedness, refection, attentiveness, organization, perseverance, intrinsic goal motivation ( Payan-Carreira et al. 2023 ), essential for recent graduates in response to labor market demands. However, an important consideration is that the practical application of CT varies across professional fields. The participants in this study defined the ideal critical thinker as someone with a cultivated mindset, motivated to learn and improve, and equipped with cognitive and behavioral tools to anticipate, regulate, and monitor their thinking. CT is associated with problem-solving and decision-making and is intertwined with other skills such as proactivity, adaptability, creativity, emotional intelligence, communication, and teamwork. The report from this project also introduced “a European collection of the Critical Thinking skills and dispositions needed in different professional fields for the 21st century” ( Dominguez 2018 ), which categorizes CT skills and dispositions based on professional fields and offers a basis for defining learning objectives and adapting university curricula. This study provides valuable insights from 189 European employers into CT needs in the labor market for new graduates. The interviewed professionals had an obvious preference for CT skills in STEM fields and an obvious preference for dispositions in the Humanities. Social Sciences and bio-medical sciences professionals were equally interested in CT skills and dispositions, with a slight preference for dispositions ( Dominguez 2018, p. 28 ).

4.3. Next Steps: THINK4JOBS Blended Appreticeship Curricula

After the termination of the CRITHINKEDU project, partners from Romania, Greece, Lithuania, and Portugal, with the addition of a new partner from Germany, proposed a new research application: “Critical Thinking for Successful Jobs—THINK4JOBS” ( www.think4jobs.uowm.gr ). The idea was to utilize the results from the previous project and, together with labor market organizations, create new courses that are more adapted to the reality of the future of work. The core element of the classes was explicit teaching of critical thinking, using real-life cases and methods. In an apprenticeship model, critical thinking skills are embedded in a relevant context. The value of realistic contexts is that students can see the need for the skills being taught in a workplace scenario. Relevant contexts enhance student engagement and motivation to learn. Dumitru et al. ( 2021 ) focused on improving students’ critical thinking skills and dispositions through collaboration between Higher Education Institutions (HEIs) and Labor Market Organizations (LMOs). The aim was to bridge the gap between HEI curricula and the expectations of the labor market by incorporating apprenticeships that provide practical experience and CT development.

The process of mapping responses from those in the labor market organizations onto college curricula involved the use of research methods such as observation, focus groups, and documentary analysis, with stakeholders from HEIs and LMOs participating. The findings indicated that while there were no definitive “gaps” between HEIs and LMOs, there were contextual differences in the approach to CT. HEIs focus on long-term career preparation, while LMOs emphasize short-term learning strategies. The terminology and expression of CT also differed between the two contexts. Based on the findings, ten work-based scenarios were created, with one from each discipline involved in the project. Overall, the report ( Dumitru et al. 2021 ) highlighted the different goals and perspectives of HEIs and LMOs regarding CT, emphasizing the need for collaboration and a common understanding of which skills should be included in the college curriculum.

There is a different context in the approach to CT, since HEIs usually use different learning activities, focusing more on career preparation with long-term goals, while LMOs follow compact and short-term learning and teaching strategies. Furthermore, the findings suggest that CT is a new workplace requirement and that HEIs and LMOs do not choose the same terminology when referring to the concept, with HEIs usually choosing scientific terms. Another element that emerged is that CT is generally expressed in a declarative way in higher education institutions, while in LMOs the application to specific cases follows a more procedural approach. Put another way, LMOs are focused on making a profit, while HEI is focused on being socially responsible.

In the second phase of the project, partners ( Pnevmatikos et al. 2021 ) focused on the development of a collaborative training curriculum for Higher Education Instructors and LMO tutors. The purpose of the training was to enhance comprehension and knowledge of critical thinking for both sides of this collaboration, since previous research indicated a potential lack of conceptual and procedural understanding between these two entities. Additionally, the training aimed to facilitate the promotion, support, and evaluation of students’ CT skills within apprenticeship curricula, as well as the creation of blended curricula utilizing an open-source learning platform. The training course encompassed workshops that delved into various aspects of CT, including analyzing and reassembling ideas about CT, formulating a working definition of CT, instructional methodologies, blended learning techniques, usage of a learning platform, CT assessment, and the development of a Memorandum of Understanding (MoU) between higher education institutions and LMOs. The participants’ knowledge about these topics was assessed through pre- and post-training online questionnaires. Although data analysis showed various predicted trends, only perceived self-confidence in the topics covered during the training obtained statistical significance ( Pnevmatikos et al. 2021 ).

In the final report from this project, Payan-Carreira et al. ( 2023 ) presented the results of the implementation of the critical thinking Blended Apprenticeships Curricula (CTBAC) and discussed the improvements in critical thinking skills and dispositions observed in students. The study involved cross-disciplinary analysis and assessed changes before and after the piloting activities. A total of 609 students participated, and their critical thinking skills and dispositions were evaluated.

The consortium chose the Critical Thinking Self-Assessment Scale (CTSAS) developed by Nair ( 2011 ) as an instrument to assess CT skills based on an earlier conceptualization ( Facione 1990 ). The questionnaire has been tested in various geographic and cultural contexts, demonstrating good reliability, internal consistency, and confirmatory factor analysis results. However, the original CTSAS was considered too long to complete, consisting of 115 items, so a shorter version was specifically developed for this project. The short form of the questionnaire (CTSAS-SF) was created through a two-step process. Items with loading weights below .500 were eliminated, resulting in 84 remaining items. Redundant and non-cognitive-focused items were marked for elimination, leaving 60 items. The short form maintained the original scale’s framework and utilized a seven-point Likert scale ranging from 0 (Never) to 6 (Always) for students to respond to items assessing various dimensions and subdimensions of CT skills.

The CTSAS-SF validation process, with confirmatory factor analysis, resulted in two models with equivalent satisfactory goodness-of-fit indices. Model 4, the second-order factor model (RMSEA = .051; TLI = .924; CFI = .927), had a chi-square/df ratio of 2.33. The Cronbach alpha of the overall instrument was excellent (α = .969). Sample items are shown in Table 1 .

Sample items forming Critical Thinking Self-Assessment Scale (CTSAS), Nair ( 2011 ).

Compared to instruments for assessing CT skills, the availability of instruments for measuring critical thinking (CT) dispositions is limited. However, one of the instruments adopted by the consortium to assess CT dispositions is the Student-Educator Negotiated Critical Thinking Dispositions Scale (SENCTDS), which was developed by Quinn et al. ( 2020 ). The scale was validated with a mixed population of Irish and American undergraduate students. The scale considers a variety of CT dispositions that the authors consider important for the labor market and real-world decision-making. Some of the items in the scale combine Facione ’s ( 1990 ) original CT dispositions into new dimensions that are relevant to academic and labor market success, such as organization, perseverance, and intrinsic goal motivation. The scale consists of six dimensions (Reflection, Attentiveness, Open-mindedness, Organization, Perseverance, and Intrinsic Goal Motivation) and presents statements for students to respond to using a 7-point Likert scale. The Likert scale ranges from 1 (strongly disagree) to 7 (strongly agree). The original version of the SENCTDS contains 21 items. The validation process, with confirmatory factor analysis, identified only one model presenting a satisfactory goodness-of-fit index—model 3, comprised of six correlated factors (RMSEA = .054; TLI = .974; CFI = .969) with a chi-square/df ratio of 2.57. The instrument presented a high Cronbach alpha (α = .842), suggesting a strong internal consistency of the instrument. Sample items are presented in Table 2 .

Sample items from Student-Educator Negotiated Critical Thinking Dispositions Scale (SENCTDS), developed by Quinn et al. ( 2020 ).

The analysis showed gains in critical thinking skills and indicated that changes were more prominent in skills than dispositions. All skills (interpretation, analysis, inference, explanation, self-regulation, and evaluation) obtained significant differences between the pretest and posttest, with p ≤ .0001 to all skills, plus the integrated critical thinking skills score was t = 9.705 and p ≤ .0001, which demonstrates strong significant difference between pre- and the posttest. Dispositions displayed no significant differences regarding the integrated score, but showed significant differences in reflection (t = 1.766, p = .079), open-mindedness (t = 2.636, p = .009), organization (t = 2.568, p = .011), and intrinsic goal motivation (t = 1.712, p = .088).

Based on the findings from the implementation of the blended apprenticeship curricula, the following guidelines were formulated for implementing Critical Thinking Blended Apprenticeship Curricula ( Payan-Carreira et al. 2023 ):

• Provide an explanation of the importance of critical thinking—Clearly communicate to students why critical thinking is a vital skill in today’s workforce and how it is valued in specific professions. Explicitly incorporate the development of critical thinking as an outcome of the course.
• Emphasize continuous and pervasive CT training—To achieve success, there should be a concerted effort across disciplinary curricula to foster students’ critical thinking skills and dispositions. Skills require training, and dispositions necessitate the internalization of desired attitudes. Therefore, sufficient time and a collaborative approach at the disciplinary level are necessary for consistent and significant progress.
• Allocate dedicated time—Building on the previous point, it is essential to allocate specific time within the course to work on the proposed critical thinking goals. Students and educators need to schedule activities and create opportunities for preparation, development, and feedback exchange. This ensures that the intervention leads to meaningful, lasting learning.
• Establish connections with real-world scenarios—Foster student engagement and improve their perception of learning experiences by incorporating case studies that reflect situations professionals encounter in their daily work. By grounding the learning content in reality, students are more likely to be motivated and actively participate in the educational process.

Foster reflection on CT skills and dispositions—Offer students the chance to reflect on their reasoning processes and the attitudes they have developed throughout their learning experiences. Encouraging reflective thinking enhances the effectiveness of learning interventions and helps cultivate a deeper understanding of one’s experiences.

These steps aim to guide educators in effectively implementing the critical thinking blended apprenticeship curricula while also maximizing the impact of critical thinking development in students.

The two European projects made a great start in integrating the skills that employers want employees to learn from university curricula, but the results are nonetheless provisional. There is not a clear agreement among participating universities regarding how best to teach critical thinking, nor any regarding its importance for future jobs. We urge that more work should be done to nurture critical thinking within university curricula in order to provide our current students—who represent the future of the workforce—the much-wanted job-proof skills they need.

5. European Recommendations and Good Practices

Critical thinking stands as a pivotal goal for European Higher Education Institutions. To facilitate the attainment of this objective, we present an educational protocol that draws from comprehensive research and practical experiences, including insights from the CRITHINKEDU project. This protocol amalgamates insights from both theoretical and empirical studies on critical thinking with practical strategies for its cultivation.

Recommendations go toward signing memorandums of understanding between universities and labor market organizations to cultivate strong partnerships ( Rebelo et al. 2023 ). Effective collaboration between universities and businesses is crucial in fostering critical thinking. This partnership thrives on the synergy that results when academic institutions and businesses combine their expertise, resources, and perspectives. Strategies such as aligning goals, fostering long-term commitment, and promoting a culture of collaboration can strengthen these partnerships and ensure that academic research is harmoniously aligned with real-world needs.

Another recommendation relates to the formulation of compelling goals . Accurate and transparent goals are fundamental to the successful implementation of university-industry collaborations to promote critical thinking. These goals must be clearly defined and easily understood at multiple levels, from the institutional to the program and course levels. Recognition of critical thinking as an overarching goal implies its integration into assessment and evaluation processes.

Another recommendation is to develop flexible curricula . To effectively foster critical thinking, curricula must demonstrate adaptability and responsiveness to emerging trends and market demands. The use of agile curriculum design methodologies and the involvement of business partners in curriculum development is of great value. Approaches such as problem-based and case-based learning facilitate rapid adaptation to evolving market needs, such as the use of AI-powered software to solve work tasks better and faster. Regular feedback mechanisms and ongoing collaboration with business partners ensure that curricula remain relevant and flexible.

Incorporating real-world challenges and case studies into curricula bridges the gap between academia and the business world, creating an environment that encourages experiential learning. The active involvement of business stakeholders in providing relevant challenges plays a key role. Students’ problem-solving skills are enhanced by shifting from traditional teaching methods to project-based, problem-based, or case-based learning. Engaging students through apprenticeships, internships, guest lectures, and seminars immerses them in authentic work environments and fosters their professional development.

Ongoing, multi-faceted evaluation is a cornerstone of the collaboration between higher education and the business community to cultivate critical thinking. Assessment includes measuring learners’ progress in critical thinking, the effectiveness of curricula, and the impact of partnerships through the use of key performance indicators.

Regarding how to implement a critical thinking curriculum, pedagogical research ( Elen et al. 2019 ) suggests that in the development of critical thinking, whether it is regarded as a skill, disposition, or a combination of both, three categories of supportive measures can be identified: modeling, induction, and declaration.

Modeling: Support the development of critical thinking skills by demonstrating what it means to think critically at the institutional, programmatic, and course levels, considering multiple perspectives and alternative viewpoints.

Induction: Support critical thinking development by provoking critical thinking through the presentation of open-ended questions, unstructured tasks, complex problems, and real-world issues. The exact nature of “induction” and how it is implemented may vary across fields and disciplines. Induction can be carried out in a variety of ways; for example, presenting unstructured problems, providing authentic tasks, encouraging constructive controversy, asking “why” questions, or encouraging student autonomy.

Explanation: Promote the development of critical thinking by articulating or explicitly stating what is at stake, what strategies can be used, and what criteria must be met. This explanation can take the form of oral or written communication and should always be explicit and specific. Declaring and making things explicit can be accomplished in a variety of ways, including using critical thinking rubrics, developing elaborate concept maps, providing feedback on critical thinking, and engaging in discussion and reflection on critical issues.

This integrated approach, encompassing university-business collaboration and an educational protocol, underscores the significance of critical thinking in higher education. It provides a structured framework for nurturing this essential skill by aligning objectives, fostering partnerships, adapting curricula, and implementing ongoing evaluation practices. In doing so, educational institutions are better poised to equip students with the critical thinking skills needed to thrive in a rapidly evolving world.

6. Concluding Remarks or Can Critical THINKING Save the World?

In summary, the dynamic interaction between universities, businesses, and the evolving technology landscape, including the rise of artificial intelligence (AI) and online technologies, underscore the critical need to nurture and develop students’ critical thinking skills. As we navigate the challenges posed by AI and the ever-expanding digital realm, collaborative efforts between academia and industry have proven to be instrumental in preparing students for the future job market.

Incorporating real-world experiences, such as apprenticeships, into the curriculum is an important step toward improving students’ critical thinking skills in real-world contexts. Projects such as “Critical thinking across higher education curricula—CRITHINKEDU” and “Critical thinking for successful jobs—THINK4JOBS” have demonstrated the potential of these collaborations to bridge the gap between classroom learning and industry needs. In addition, the development of flexible curricula that can adapt to the evolving needs of the job market, especially considering online technologies, is essential. By integrating real-world challenges and case studies into the curriculum, students gain valuable problem-solving skills and are better prepared to navigate the complexities of the digital age.

Ongoing assessment and evaluation are critical components of this collaborative effort, ensuring that critical thinking remains a central focus and that students are making meaningful progress in acquiring this essential skill.

With the disruption of AI and the ubiquity of online technologies, the integration of critical thinking into higher education curricula is more important than ever. It enables students not only to thrive in a technology-driven world, but also to contribute to a rational, democratic, and globally interconnected society. The partnerships forged between universities and businesses, along with a well-defined educational protocol, provide a roadmap for cultivating these essential skills and preparing students for the challenges and opportunities of the future job market. The imperative to foster critical thinking in university curricula remains a fundamental step in equipping tomorrow’s workforce to navigate the complexities of an AI-influenced job market and a rapidly changing world.

Lilienfeld ( 2007, para. 3 ) said it well: “The greatest threat to the world is ideological fanaticism, by ideological fanaticism I mean the unshakeable conviction that one’s belief system and that of other in-group members is always right and righteous and that others’ belief systems are always wrong and wrong-headed”. Imagine a world where (most or even many) people use the skills of critical thinking. Just maybe, CT could save the world.

The job market will require a psychologically adaptable toolkit, and we propose that critical thinking is an essential component therein. The disruptions imposed by new technological advances such as AI will require students to learn new employable skills because we will need not just an engineer, but a critical thinking engineer; not just a programmer, but a critical thinking programmer; and not just a journalist, but a critical thinking journalist. The dignity of workers—their humanity and our collective survival—may well depend on CT, a very human creation.

Acknowledgments

We sincerely thank Dana Dunn, Moravian University, for comments on an earlier version of this manuscript.

Funding Statement

Daniela Dumitru received funding from European Commission/EACEA, through the ERASMUS+ Programme, “Critical Thinking for Successful Jobs—Think4Jobs” Project, with the reference number 2020-1-EL01-KA203-078797.

Author Contributions

Conceptualization, D.F.H. and D.D.; investigation, D.F.H. and D.D.; resources, D.F.H. and D.D.; writing—original draft preparation, D.F.H. and D.D.; writing—review and editing, D.F.H. and D.D. All authors have read and agreed to the published version of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

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