What Is Intelligence In Psychology

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.

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.

Three broad approaches drive most of the disagreement. Biological definitions treat intelligence as successful adaptation to the environment.

The psychometric approach instead measures individual differences through IQ tests, focusing on the structure of ability. The information-processing approach asks not how much ability someone has, but what mental steps they take to solve a problem.

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. His hypothesis: intelligence is a general mental ability shaped by 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.

Galton’s programme failed on its own terms: his reaction-time measures correlated poorly with real-world accomplishment. The idea was revived later, though, once researchers had better instruments and started linking reaction time to psychometric ability.

What survived was modest, but real (Sheppard & Vernon, 2008).

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

Galton’s study of intelligence in the laboratory setting paved the way for decades of future research and debate. So did his theorizing about the heritability of intelligence: the share of the differences in people’s IQ scores that traces back to genetic differences, rather than to upbringing.

Nature and Nurture: The Genetics of Intelligence

Galton’s hunch that intelligence runs in families set off a debate lasting well over a century. How much of a person’s intelligence comes from genes, and how much from upbringing?

Psychologists test this by studying adopted children. An adopted child shares genes with their biological parents, but not an upbringing. They share an upbringing with their adoptive parents, but not genes.

Adoption Studies: Genes vs. Environment

The logic is simple.

Comparing an adopted child’s IQ with both sets of parents lets researchers estimate how much each ingredient matters. The Colorado Adoption Project did exactly this, tracking hundreds of adopted children from infancy.

What did the data show?

Birth mothers and the children they had placed for adoption turned out to be “just as similar” on verbal and spatial ability as parents who raised their own children. The same adopted children’s scores did not resemble their adoptive parents at all (Plomin & DeFries, 1998).

Environment still matters. Just not for individual differences.

In the Minnesota Transracial Adoption Study, Black children raised in their own, less advantaged homes averaged an IQ of around 90. Black children adopted into advantaged White families averaged 106, a 16-point gain (Scarr & Weinberg, 1976).

A follow-up found something stranger. Unrelated adopted siblings who closely resembled each other as young children saw that resemblance fade to almost nothing by adolescence. Biologically related siblings, meanwhile, stayed just as similar as before.

As children grow older, they increasingly choose their own friends and activities, and only genetically related family members tend to choose similar ones (Scarr & Weinberg, 1983).

Modern Genetic Evidence

A 2015 study revisited this question at a scale the classic adoption studies could not match.

Aim: Kendler, Turkheimer, Ohlsson, Sundquist, and Sundquist (2015) tested whether the rearing environment causally affects cognitive ability, using Sweden’s complete national population register instead of a small volunteer sample.

Method: The researchers compared IQ, drawn from Swedish military conscription records, between siblings from the same family: one reared by their biological parents, the other adopted away. The sample included 436 full-sibling pairs and 2,341 half-sibling pairs.

The results were striking.

Results: Adopted siblings scored 4.41 IQ points higher than their non-adopted, home-reared full siblings. Each extra unit of the adoptive parent’s education added a further 1.71-point gain, and the larger half-sibling sample replicated the pattern.

Conclusion: Rearing environment genuinely raises IQ in late adolescence, and adoptive parents’ education explained much of that gain (Kendler et al., 2015).

Does that boost last? A 30-year follow-up of Minnesota families found that it largely does not.

By early adulthood, environmental factors accounted for only about 8% of the variation in IQ, while heritability was estimated at .42 (Willoughby, McGue, Iacono, & Lee, 2021).

One criticism cuts across all of this evidence. Adoption agencies do not place children at random.

They screen and match families, which could artificially shrink the very correlations these studies measure (Kamin, 1981). A 2026 review revisited this concern with genetic data the original researchers lacked, and found the correction did not meaningfully change the classic findings (Willoughby, Edwards, & Lee, 2026).

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 underlies multiple specific skills, including verbal, spatial, numerical, and mechanical ability. Charles Spearman proposed it in 1904.

The English psychologist used a statistical technique called factor analysis to arrive at his theory (Spearman, 1904). Factor analysis looks for patterns in how different test scores rise and fall together, to find a hidden factor shared across them.

Spearman applied this method to intelligence test scores.

People who did well in one area, such as mathematics, also tended to do well in other areas, such as distinguishing pitch (Kalat, 2014). That pattern held across many different skills.

Spearman attributed this correlation to a single underlying factor: general intelligence, or g. He also proposed a second factor.

This factor, s, covers an individual’s specific ability in one particular area (Spearman, as cited in Thomson, 1947). Together, these two factors, g and s, make up Spearman’s two-factor theory.

Spearman’s claim invited a direct test.

Aim: Spearman (1904) set out to determine whether the various things people called intelligence reflected one underlying capacity or many independent ones.

Method: He tested schoolchildren on several school subjects and separate measures like pitch discrimination, then examined how the scores correlated with each other.

Results: Every test correlated positively with every other. Spearman called this the positive manifold.

Conclusion: He concluded that every intellectual activity draws on a general factor (g) plus a specific factor (s) unique to that task.

The positive manifold has proven remarkably robust. But Spearman’s jump from a shared statistic to an inherited “mental energy” went beyond his data, an error now called reification (Gould, 1981). Sharing variance does not explain why tests correlate.

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, word fluency, number facility, spatial visualization, perceptual speed, memory, and inductive reasoning (Thurstone, as cited in Sternberg, 2003).

Mental Ability	Description
Word Fluency	Ability to use words quickly and fluency in performing such tasks as rhyming, solving anagrams, and doing crossword puzzles.
Verbal Comprehension	Ability to understand the meaning of words, concepts, and ideas.
Numerical Ability	Ability to use numbers to quickly compute answers to problems.
Spatial Visualization	Ability to visualize and manipulate patterns and forms in space.
Perceptual Speed	Ability to grasp perceptual details quickly and accurately and to determine similarities and differences between stimuli.
Memory	Ability to recall information such as lists or words, mathematical formulas, and definitions.
Inductive Reasoning	Ability to derive general rules and principles from the presented information.

Thurstone conceded the point himself. When he tested a broader, more typical sample of schoolchildren instead of narrow, high-achieving college students, the primary abilities turned out to be closely linked.

A second analysis of those links recovered a general factor after all (Thurstone, 1947). The narrow first sample had hidden the very correlations that produce g.

Gardner’s Multiple Intelligences

Following the work of Thurstone, American psychologist Howard Gardner built on the idea that intelligence takes multiple forms. He rejected a single intelligence. Instead, he argued that distinct, independent intelligences exist, each representing skills relevant to a different category.

Gardner (1983, 1987) initially proposed seven multiple intelligences, separate mental abilities that each work fairly independently of the others: linguistic, logical-mathematical, spatial, musical, bodily-kinesthetic, interpersonal, and intrapersonal. He has since added naturalist intelligence.

Most activities involve a combination of these intelligences. Dancing, for example, draws on both spatial and bodily-kinesthetic intelligence.

Gardner also suggests the theory helps explain concepts beyond intelligence itself, such as creativity and leadership.

The theory has widely captured the psychology community’s attention, especially in education. But is that claim true?

Aim: Visser, Ashton, and Vernon (2006) tested whether Gardner’s eight intelligences are truly independent of one another.

Method: 200 adults completed two tests for each of the eight intelligences, and all sixteen scores were analyzed together.

Results: A large general factor ran through the results. Purely cognitive intelligences, such as linguistic and logical-mathematical, loaded heavily onto it. Bodily-kinesthetic and other physical intelligences barely did.

Conclusion: The eight intelligences are not independent. The cognitive ones behave like ordinary sub-factors within general intelligence, not separate intelligences (Visser, Ashton, & Vernon, 2006).

Gardner and Moran (2006) countered that paper-and-pencil tests unfairly force his theory into the psychometric mould it was built to reject. It also does not account for forms 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).

1. 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.
2. 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.
3. 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 three strategies. You can use knowledge gained from experience to change yourself to suit the environment (adaptation). You can also change the environment to suit yourself (shaping), or find a new environment altogether (selection).

Practical intelligence carries the theory’s boldest claim: that it is genuinely separate from academic ability. That claim needed a real test.

Aim: Sternberg and colleagues (2001) tested whether practical intelligence is truly separate from academic intelligence.

Method: 85 children in a rural Kenyan village completed a test of tacit knowledge about local herbal medicines, alongside standard tests of reasoning, vocabulary, and school achievement.

Results: Tacit knowledge scores showed no positive link to the academic measures. Several correlations were even negative.

Conclusion: Practical intelligence is a distinct skill that ordinary tests fail to capture, and in some communities it may even trade off against academic performance (Sternberg et al., 2001).

The finding is not as clean as it looks. Children who spend more time learning practical skills at home may simply spend less time in school, which alone could explain the negative link.

Gottfredson (2003) went further, arguing that practical intelligence has never been shown to add real predictive power over general intelligence (g).

Taken together, these competing theories show that intelligence is not a simple concept. Views differ sharply.

Spearman argued that intelligence is generalizable across many areas of life. Thurstone, Gardner, and Sternberg instead described it as a tree with many branches, each representing a specific form of intelligence.

A few more types of intelligence add further branches to that tree.

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 everyday life. We experience one emotion or another in nearly every moment.

You may not associate emotions with intelligence, but the two are closely related. Emotional intelligence refers to the ability to recognize the meaning of emotions and to reason and problem-solve using them (Mayer, Caruso, & Salovey, 1999).

The four key components of emotional intelligence are:

• Self-awareness: recognizing your own emotions as they happen.
• Self-management: regulating those emotions effectively.
• Social awareness: reading other people’s emotions accurately.
• Relationship management: using that awareness to guide interactions with others.

In short, someone high in emotional intelligence can accurately read emotions in themselves and others, such as facial expressions. They can also use emotions to help facilitate thinking and understand why they feel a certain way, and know how to manage their 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. These abilities interact and work together to produce overall individual intelligence.

Take a hard math test as an example. You rely on 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.

Fluid intelligence can even become crystallized intelligence. Novel solutions you create using fluid intelligence can, over time, develop into crystallized intelligence once they are incorporated into long-term memory.

This shows how different forms of intelligence overlap and interact, revealing its dynamic nature.

Intelligence Testing

The Flynn Effect

Test norms need regular updating. Measured intelligence in a population can drift over time.

James Flynn discovered this drift. It is now called the Flynn effect. Scores on intelligence tests have risen from decade to decade almost everywhere they have been tracked (Flynn, 1984).

The rise is too large to be real. Flynn (1987) found increases of up to roughly 20 points per generation on the most abstract, supposedly culture-free tests, far more than on tests of school-taught knowledge.

Something else was changing fast. Intelligence itself was not.

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. That’s about mastery. An aptitude test instead 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

You may wonder whether retaking an intelligence test improves your score, or whether these tests even measure intelligence in the first place. Research offers reassurance: these tests are both reliable and have high construct validity.

Reliability means they are consistent over time.

If you take a test at two different points in time, there will be very little change in performance or, for intelligence tests, in IQ scores.

This isn’t a perfect science. Your score might fluctuate slightly across occasions or across different tests at the same age, but IQ tests still demonstrate relatively high reliability (Tuma & Appelbaum, 1980).

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

That claim deserves scrutiny. Borsboom, Mellenbergh, and van Heerden (2004) set a stricter test for validity. A test is valid for a trait only if that trait genuinely exists and causes the scores, not merely if the scores predict something useful.

On this view, showing that IQ tests predict school grades proves only that. It does not prove the test measures intelligence itself.

Researchers have spent hours developing, standardizing, and adapting these tests to fit the current times. That is not to say the tests are flawless.

Research documents errors in the specific scoring of tests and the interpretation of the multiple scores an individual receives alongside their overall IQ score. Some studies question the actual validity, reliability, and utility of these tests for individual clinical use (Canivez, 2013).

Intelligence scores are also created to reflect different theories of intelligence, so interpretations may depend heavily on the theory a given test is based on (Canivez, 2013).

Cultural Specificity

There are issues with intelligence tests beyond looking at them in a vacuum.

These tests were created by Western psychologists. They were built to measure Eurocentric values. Yet most of the world’s population does not live in Europe or North America.

So the cultural specificity of these tests matters. Different cultures hold different values and even different perceptions of intelligence, so is it fair to have one universal marker of this complex concept?

A 1992 study found that Kenyan parents defined intelligence as the ability to do what needed to be done around the homestead without being told (Harkness et al., 1992).

Some Ugandans see it differently. Given the American and European emphasis on speed, they instead define intelligent people as being slow in thought and action (Wober, 1974).

These examples show how flexible intelligence is as a concept. Capturing it in a single test, let alone a single number, becomes even more challenging.

Do perceptions differ even within the U.S.? In San Jose, California, Latino, Asian, and Anglo parents held varying definitions of intelligence.

Teachers’ understanding of intelligence was more similar to that of the Asian and Anglo communities, and this similarity predicted a child’s school performance (Okagaki & Sternberg, 1993). Students whose families shared that understanding did better in the classroom.

Intelligence takes many forms. It ranges from country to country and culture to culture. Although IQ tests might have high reliability and validity, culture matters just as much, if not more, in the bigger picture of a person’s intelligence.

IQ tests may accurately measure academic intelligence. But more research is needed to discern whether they truly measure practical intelligence, or even just general intelligence, across all cultures.

Social and Environmental Factors

Another important part of the puzzle is social and environmental context. It shapes the IQ-test biases a person experiences. These might help explain why some people score lower 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. The setup was simple.

Some were randomly chosen to receive feedback claiming they were “the sort of people who would end up alone in life” (Baumeister et al., 2002).

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

This was a single small laboratory study, and its findings have not been replicated. That gap matters. Whether social or physical threats truly lower performance outside the lab remains untested, and one experiment cannot settle a claim this broad.

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 create anxiety that results in lower scores.

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

In that condition, Black students performed worse than White students. In the no-stereotype-threat condition, Black and White students performed equally well (Steele & Aronson, 1995).

Even just recording your race can worsen performance. Stereotype threat is real, and it can hurt an individual’s performance on these tests.

More recent, larger reviews have shrunk this picture considerably. Shewach, Sackett, and Quint (2019) found the effect was much smaller under realistic testing conditions, and close to zero in some of them.

Self-Fulfilling Prophecy

Stereotype threat is closely related to a self-fulfilling prophecy. In this pattern, an individual’s expectations about another person can cause that person to act in ways that confirm the expectation.

In one experiment, students at a California elementary school were given an IQ test. Teachers then received the names of students who would supposedly become “intellectual bloomers” that year, based on the results (Rosenthal & Jacobson, 1968). The test was identical.

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

This suggests teachers may subconsciously behave in ways that encourage certain students’ success, influencing their achievement (Rosenthal & Jacobson, 1968). It is another example of a small variable shaping both an individual’s intelligence score and the development of their intelligence.

That finding has been debated ever since. Critics have questioned both the study’s methods and how large its real effect was.

It is worth considering the less visible factors that shape someone’s intelligence.

An IQ score has real benefits for measuring intelligence. But a lower score does not necessarily mean someone is lower in intelligence.

Many factors can worsen performance on these tests, and the tests themselves might not even accurately measure the very concept they are meant to.

Extremes of Intelligence

IQ scores are generally normally distributed (Moore et al., 2013). Roughly 95% of the population has IQ scores between 70 and 130.

What about the other 5 percent? Individuals who fall outside this range represent the extremes of intelligence. These are life’s outliers.

Those with an IQ above 130 are considered gifted (Lally & French, 2018). Christopher Langan, an American horse rancher, is one example: his IQ score is around 200 (Gladwell, 2008).

Those with scores below 70, by contrast, have an intellectual disability. It is marked by substantial developmental delays.

These affect motor skills, cognition, and speech (De Ligt, 2012). Some of these disabilities stem from genetic mutations.

Down syndrome, for example, results from an extra copy of the 21st chromosome and is a common genetic cause of intellectual disability (Breslin, 2014). Many individuals with Down syndrome have below-average IQ scores as a result (Breslin, 2014).

Savant syndrome is another example of extreme intelligence.

Despite having significant mental disabilities, these individuals show certain abilities far above average in specific fields, such as incredible memorization, rapid calculation, or advanced musical talent (Treffert, 2009).

Some may lack skills in areas such as social interaction and communication, yet excel remarkably elsewhere. This further illustrates the complexity of intelligence, and why we must consider all individuals when we measure and define it in society.

Intelligence Today

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

New tests keep appearing. One example is the University of California Matrix Reasoning Task, which can be taken online in very little time (Pahor et al., 2019). New methods of scoring these tests have been developed too (Sansone et al., 2014).

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

Humans are incredibly intelligent beings. We rely on our intellectual abilities daily.

Intelligence can be defined and measured in countless ways. Yet our overall intelligence as a species makes us unique, and has allowed us to thrive for generations.

Critical Evaluation

Two problems run through every theory covered so far. Showing that test scores correlate does not explain why they correlate. A shared statistical factor is not automatically a single thing inside the head. Gould (1981) called this mistake reification, treating a number as if it were a real, physical capacity.

Several rival explanations exist. Separate pools of mental resources might just happen to overlap. Abilities might reinforce each other’s growth over time.

Or the same general processes might run through many different tests. All of them reproduce the same pattern of correlations equally well, and the data cannot yet tell them apart.

Prediction is real, but it is often oversold. IQ scores do predict school grades, job performance, and health outcomes.

But test performance is not a pure readout of ability. Effort matters too.

Segal (2012) found that scores on a low-stakes test, one where nothing depended on doing well, still predicted people’s later earnings once measured ability was held constant. Motivation, not just capacity, was doing some of the work.

Contemporary Research

Genetics has now entered the picture directly.

Aim: Savage and colleagues (2018) searched for the specific genes linked to differences in general cognitive ability.

Method: A genome-wide meta-analysis pooled data from 14 cohorts and 269,867 people, then checked which genes the associated variants pointed to.

Results: Over 200 gene regions were linked to intelligence. Most sat in genes active in brain tissue involved in learning and memory.

Conclusion: Cognitive ability has a highly polygenic basis, and the implicated genes converge on real neurobiology, not random noise (Savage et al., 2018).

That specific skill resists training. Melby-Lervåg, Redick, and Hulme (2016) pooled 145 comparisons of working-memory training programmes. Trainees improved on the trained tasks themselves. Their gains did not transfer to reading, arithmetic, or general reasoning.

Education tells a different story. Ritchie and Tucker-Drob (2018) combined three separate kinds of natural experiment, including school-entry cut-off dates and compulsory-schooling law changes, across more than 600,000 people.

An extra year of schooling raised measured intelligence by roughly one to five IQ points, and the gain held up across every method they tried.

Training the skill directly does little. Simply staying in school longer does more.

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