The g factor (also known as general intelligence, general mental ability or general intelligence factor) is a construct developed in psychometric investigations of cognitive abilities and human intelligence. It is a variable that summarizes positive correlations among different cognitive tasks, reflecting the fact that an individual's performance on one type of cognitive task tends to be comparable to that person's performance on other kinds of cognitive tasks. The g factor typically accounts for 40 to 50 percent of the between-individual performance differences on a given cognitive test, and composite scores ("IQ scores") based on many tests are frequently regarded as estimates of individuals' standing on the g factor.^[1] The terms IQ, general intelligence, general cognitive ability, general mental ability, and simply intelligence are often used interchangeably to refer to this common core shared by cognitive tests.^[2] However, the g factor itself is a mathematical construct indicating the level of observed correlation between cognitive tasks.^[3] The measured value of this construct depends on the cognitive tasks that are used, and little is known about the underlying causes of the observed correlations.

The existence of the g factor was originally proposed by the English psychologist Charles Spearman in the early years of the 20th century. He observed that children's performance ratings, across seemingly unrelated school subjects, were positively correlated, and reasoned that these correlations reflected the influence of an underlying general mental ability that entered into performance on all kinds of mental tests. Spearman suggested that all mental performance could be conceptualized in terms of a single general ability factor, which he labeled g, and many narrow task-specific ability factors. Soon after Spearman proposed the existence of g, it was challenged by Godfrey Thomson, who presented evidence that such intercorrelations among test results could arise even if no g-factor existed.^[4] Today's factor models of intelligence typically represent cognitive abilities as a three-level hierarchy, where there are many narrow factors at the bottom of the hierarchy, a handful of broad, more general factors at the intermediate level, and at the apex a single factor, referred to as the g factor, which represents the variance common to all cognitive tasks.

Traditionally, research on g has concentrated on psychometric investigations of test data, with a special emphasis on factor analytic approaches. However, empirical research on the nature of g has also drawn upon experimental cognitive psychology and mental chronometry, brain anatomy and physiology, quantitative and molecular genetics, and primate evolution.^[5] Scientists consider g to be a statistical regularity and uncontroversial, and a general cognitive factor appears in data collected from people in nearly every human culture.^[6] Yet, there is no consensus as to what causes the positive correlations between tests.

Research in the field of behavioral genetics has shown that the construct of g is highly heritable in measured populations. It has a number of other biological correlates, including brain size. It is also a significant predictor of individual differences in many social outcomes, particularly in education and employment. However, critics of g have contended that an emphasis on g is misplaced and entails a devaluation of other important abilities. Stephen J. Gould famously denounced the concept of g as supporting an unrealistic reified view of human intelligence.

Cognitive ability testing

Correlations between mental tests

Cognitive ability tests are designed to measure different aspects of cognition. Specific domains assessed by tests include mathematical skill, verbal fluency, spatial visualization, and memory, among others. However, individuals who excel at one type of test tend to excel at other kinds of tests, too, while those who do poorly on one test tend to do so on all tests, regardless of the tests' contents.^[9] The English psychologist Charles Spearman was the first to describe this phenomenon.^[10] In a famous research paper published in 1904,^[11] he observed that children's performance measures across seemingly unrelated school subjects were positively correlated. This finding has since been replicated numerous times. The consistent finding of universally positive correlation matrices of mental test results (or the "positive manifold"), despite large differences in tests' contents, has been described as "arguably the most replicated result in all psychology".^[12] Zero or negative correlations between tests suggest the presence of sampling error or restriction of the range of ability in the sample studied.^[13]

Using factor analysis or related statistical methods, it is possible to identify a single common factor that can be regarded as a summary variable characterizing the correlations between all the different tests in a test battery. Spearman referred to this common factor as the general factor, or simply g. (By convention, g is always printed as a lower case italic.) Mathematically, the g factor is a source of variance among individuals, which means that one cannot meaningfully speak of any one individual's mental abilities consisting of g or other factors to any specified degree. One can only speak of an individual's standing on g (or other factors) compared to other individuals in a relevant population.^[13][14][15]

Different tests in a test battery may correlate with (or "load onto") the g factor of the battery to different degrees. These correlations are known as g loadings. An individual test taker's g factor score, representing their relative standing on the g factor in the total group of individuals, can be estimated using the g loadings. Full-scale IQ scores from a test battery will usually be highly correlated with g factor scores, and they are often regarded as estimates of g. For example, the correlations between g factor scores and full-scale IQ scores from David Wechsler's tests have been found to be greater than .95.^[1][13][16] The terms IQ, general intelligence, general cognitive ability, general mental ability, or simply intelligence are frequently used interchangeably to refer to the common core shared by cognitive tests.^[2]

The g loadings of mental tests are always positive and usually range between .10 and .90, with a mean of about .60 and a standard deviation of about .15. Raven's Progressive Matrices is among the tests with the highest g loadings, around .80. Tests of vocabulary and general information are also typically found to have high g loadings.^[17][18] However, the g loading of the same test may vary somewhat depending on the composition of the test battery.^[19]

The complexity of tests and the demands they place on mental manipulation are related to the tests' g loadings. For example, in the forward digit span test the subject is asked to repeat a sequence of digits in the order of their presentation after hearing them once at a rate of one digit per second. The backward digit span test is otherwise the same except that the subject is asked to repeat the digits in the reverse order to that in which they were presented. The backward digit span test is more complex than the forward digit span test, and it has a significantly higher g loading. Similarly, the g loadings of arithmetic computation, spelling, and word reading tests are lower than those of arithmetic problem solving, text composition, and reading comprehension tests, respectively.^[13][20]

Test difficulty and g loadings are distinct concepts that may or may not be empirically related in any specific situation. Tests that have the same difficulty level, as indexed by the proportion of test items that are failed by test takers, may exhibit a wide range of g loadings. For example, tests of rote memory have been shown to have the same level of difficulty but considerably lower g loadings than many tests that involve reasoning.^[20][21]

Theories

While the existence of g as a statistical regularity is well-established and uncontroversial among experts, there is no consensus as to what causes the positive intercorrelations. Several explanations have been proposed.^[22]

Factor structure of cognitive abilities

An illustration of Spearman's two-factor intelligence theory. Each small oval is a hypothetical mental test. The blue areas correspond to test-specific variance (s), while the purple areas represent the variance attributed to g.

Factor analysis is a family of mathematical techniques that can be used to represent correlations between intelligence tests in terms of a smaller number of variables known as factors. The purpose is to simplify the correlation matrix by using hypothetical underlying factors to explain the patterns in it. When all correlations in a matrix are positive, as they are in the case of IQ, factor analysis will yield a general factor common to all tests. The general factor of IQ tests is referred to as the g factor, and it typically accounts for 40 to 50 percent of the variance in IQ test batteries.^[34] The presence of correlations between many widely varying cognitive tests has often been taken as evidence for the existence of g, but McFarland (2012) showed that such correlations do not provide any more or less support for the existence of g than for the existence of multiple factors of intelligence.^[35]

Charles Spearman developed factor analysis in order to study correlations between tests. Initially, he developed a model of intelligence in which variations in all intelligence test scores are explained by only two kinds of variables: first, factors that are specific to each test (denoted s); and second, a g factor that accounts for the positive correlations across tests. This is known as Spearman's two-factor theory. Later research based on more diverse test batteries than those used by Spearman demonstrated that g alone could not account for all correlations between tests. Specifically, it was found that even after controlling for g, some tests were still correlated with each other. This led to the postulation of group factors that represent variance that groups of tests with similar task demands (e.g., verbal, spatial, or numerical) have in common in addition to the shared g variance.^[36]

An illustration of John B. Carroll's three stratum theory, an influential contemporary model of cognitive abilities. The broad abilities recognized by the model are fluid intelligence (Gf), crystallized intelligence (Gc), general memory and learning (Gy), broad visual perception (Gv), broad auditory perception (Gu), broad retrieval ability (Gr), broad cognitive speediness (Gs), and processing speed (Gt). Carroll regarded the broad abilities as different "flavors" of g.

Through factor rotation, it is, in principle, possible to produce an infinite number of different factor solutions that are mathematically equivalent in their ability to account for the intercorrelations among cognitive tests. These include solutions that do not contain a g factor. Thus factor analysis alone cannot establish what the underlying structure of intelligence is. In choosing between different factor solutions, researchers have to examine the results of factor analysis together with other information about the structure of cognitive abilities.^[37]

There are many psychologically relevant reasons for preferring factor solutions that contain a g factor. These include the existence of the positive manifold, the fact that certain kinds of tests (generally the more complex ones) have consistently larger g loadings, the substantial invariance of g factors across different test batteries, the impossibility of constructing test batteries that do not yield a g factor, and the widespread practical validity of g as a predictor of individual outcomes. The g factor, together with group factors, best represents the empirically established fact that, on average, overall ability differences between individuals are greater than differences among abilities within individuals, while a factor solution with orthogonal factors without g obscures this fact. Moreover, g appears to be the most heritable component of intelligence.^[38] Research utilizing the techniques of confirmatory factor analysis has also provided support for the existence of g.^[37]

A g factor can be computed from a correlation matrix of test results using several different methods. These include exploratory factor analysis, principal components analysis (PCA), and confirmatory factor analysis. Different factor-extraction methods produce highly consistent results, although PCA has sometimes been found to produce inflated estimates of the influence of g on test scores.^[19][39]

There is a broad contemporary consensus that cognitive variance between people can be conceptualized at three hierarchical levels, distinguished by their degree of generality. At the lowest, least general level there are many narrow first-order factors; at a higher level, there are a relatively small number – somewhere between five and ten – of broad (i.e., more general) second-order factors (or group factors); and at the apex, there is a single third-order factor, g, the general factor common to all tests.^[40][41][42] The g factor usually accounts for the majority of the total common factor variance of IQ test batteries.^[43] Contemporary hierarchical models of intelligence include the three stratum theory and the Cattell–Horn–Carroll theory.^[44]

"Indifference of the indicator"

Spearman proposed the principle of the indifference of the indicator, according to which the precise content of intelligence tests is unimportant for the purposes of identifying g, because g enters into performance on all kinds of tests. Any test can therefore be used as an indicator of g.^[6] Following Spearman, Arthur Jensen more recently argued that a g factor extracted from one test battery will always be the same, within the limits of measurement error, as that extracted from another battery, provided that the batteries are large and diverse.^[45] According to this view, every mental test, no matter how distinctive, calls on g to some extent. Thus a composite score of a number of different tests will load onto g more strongly than any of the individual test scores, because the g components cumulate into the composite score, while the uncorrelated non-g components will cancel each other out. Theoretically, the composite score of an infinitely large, diverse test battery would, then, be a perfect measure of g.^[46]

In contrast, L. L. Thurstone argued that a g factor extracted from a test battery reflects the average of all the abilities called for by the particular battery, and that g therefore varies from one battery to another and "has no fundamental psychological significance."^[47] Along similar lines, John Horn argued that g factors are meaningless because they are not invariant across test batteries, maintaining that correlations between different ability measures arise because it is difficult to define a human action that depends on just one ability.^[48][49]

To show that different batteries reflect the same g, one must administer several test batteries to the same individuals, extract g factors from each battery, and show that the factors are highly correlated. This can be done within a confirmatory factor analysis framework.^[22] Wendy Johnson and colleagues have published two such studies.^[50][51] The first found that the correlations between g factors extracted from three different batteries were .99, .99, and 1.00, supporting the hypothesis that g factors from different batteries are the same and that the identification of g is not dependent on the specific abilities assessed. The second study found that g factors derived from four of five test batteries correlated at between .95–1.00, while the correlations ranged from .79 to .96 for the fifth battery, the Cattell Culture Fair Intelligence Test (the CFIT). They attributed the somewhat lower correlations with the CFIT battery to its lack of content diversity for it contains only matrix-type items, and interpreted the findings as supporting the contention that g factors derived from different test batteries are the same provided that the batteries are diverse enough. The results suggest that the same g can be consistently identified from different test batteries.^[40][52] This approach has been criticized by psychologist Lazar Stankov in the Handbook of Understanding and Measuring Intelligence, who councluded "Correlations between the g factors from different test batteries are not unity."^[53]

A study authored by Scott Barry Kaufman and colleagues showed that the general factor extracted from the Woodjock-Johnson cognitive abilities test, and the general factor extracted from the Achievement test batteries are highly correlated, but not isomorphic. ^[54]

Population distribution

The form of the population distribution of g is unknown, because g cannot be measured on a ratio scale. (The distributions of scores on typical IQ tests are roughly normal, but this is achieved by construction, i.e., by normalizing the raw scores.) It has been argued that there are nevertheless good reasons for supposing that g is normally distributed in the general population, at least within a range of ±2 standard deviations from the mean. In particular, g can be thought of as a composite variable that reflects the additive effects of many independent genetic and environmental influences, and such a variable should, according to the central limit theorem, follow a normal distribution.^[55]

Spearman's law of diminishing returns

A number of researchers have suggested that the proportion of variation accounted for by g may not be uniform across all subgroups within a population. Spearman's law of diminishing returns (SLODR), also termed the cognitive ability differentiation hypothesis, predicts that the positive correlations among different cognitive abilities are weaker among more intelligent subgroups of individuals. More specifically, SLODR predicts that the g factor will account for a smaller proportion of individual differences in cognitive tests scores at higher scores on the g factor.

SLODR was originally proposed by Charles Spearman,^[56] who reported that the average correlation between 12 cognitive ability tests was .466 in 78 normal children, and .782 in 22 "defective" children. Detterman and Daniel rediscovered this phenomenon in 1989.^[57] They reported that for subtests of both the WAIS and the WISC, subtest intercorrelations decreased monotonically with ability group, ranging from approximately an average intercorrelation of .7 among individuals with IQs less than 78 to .4 among individuals with IQs greater than 122.^[58]

SLODR has been replicated in a variety of child and adult samples who have been measured using broad arrays of cognitive tests. The most common approach has been to divide individuals into multiple ability groups using an observable proxy for their general intellectual ability, and then to either compare the average interrelation among the subtests across the different groups, or to compare the proportion of variation accounted for by a single common factor, in the different groups.^[59] However, as both Deary et al. (1996).^[59] and Tucker-Drob (2009)^[60] have pointed out, dividing the continuous distribution of intelligence into an arbitrary number of discrete ability groups is less than ideal for examining SLODR. Tucker-Drob (2009)^[60] extensively reviewed the literature on SLODR and the various methods by which it had been previously tested, and proposed that SLODR could be most appropriately captured by fitting a common factor model that allows the relations between the factor and its indicators to be nonlinear in nature. He applied such a factor model to a nationally representative data of children and adults in the United States and found consistent evidence for SLODR. For example, Tucker-Drob (2009) found that a general factor accounted for approximately 75% of the variation in seven different cognitive abilities among very low IQ adults, but only accounted for approximately 30% of the variation in the abilities among very high IQ adults.

A recent meta-analytic study by Blum and Holling^[61] also provided support for the differentiation hypothesis. As opposed to most research on the topic, this work made it possible to study ability and age variables as continuous predictors of the g saturation, and not just to compare lower- vs. higher-skilled or younger vs. older groups of testees. Results demonstrate that the mean correlation and g loadings of cognitive ability tests decrease with increasing ability, yet increase with respondent age. SLODR, as described by Charles Spearman, could be confirmed by a g-saturation decrease as a function of IQ as well as a g-saturation increase from middle age to senescence. Specifically speaking, for samples with a mean intelligence that is two standard deviations (i.e., 30 IQ-points) higher, the mean correlation to be expected is decreased by approximately .15 points. The question remains whether a difference of this magnitude could result in a greater apparent factorial complexity when cognitive data are factored for the higher-ability sample, as opposed to the lower-ability sample. It seems likely that greater factor dimensionality should tend to be observed for the case of higher ability, but the magnitude of this effect (i.e., how much more likely and how many more factors) remains uncertain.

Practical validity

The extent of the practical validity of g as a predictor of educational, economic, and social outcomes is the subject of ongoing debate.^[62] Some researchers have argued that it is more far-ranging and universal than any other known psychological variable,^[63] and that the validity of g increases as the complexity of the measured task increases.^[64][65] Others have argued that tests of specific abilities outperform g factor in analyses fitted to certain real-world situations.^[66][67][68]

A test's practical validity is measured by its correlation with performance on some criterion external to the test, such as college grade-point average, or a rating of job performance. The correlation between test scores and a measure of some criterion is called the validity coefficient. One way to interpret a validity coefficient is to square it to obtain the variance accounted by the test. For example, a validity coefficient of .30 corresponds to 9 percent of variance explained. This approach has, however, been criticized as misleading and uninformative, and several alternatives have been proposed. One arguably more interpretable approach is to look at the percentage of test takers in each test score quintile who meet some agreed-upon standard of success. For example, if the correlation between test scores and performance is .30, the expectation is that 67 percent of those in the top quintile will be above-average performers, compared to 33 percent of those in the bottom quintile.^[69][70]

Genetic and environmental determinants

Heritability is the proportion of phenotypic variance in a trait in a population that can be attributed to genetic factors. The heritability of g has been estimated to fall between 40 and 80 percent using twin, adoption, and other family study designs as well as molecular genetic methods. Estimates based on the totality of evidence place the heritability of g at about 50%.^[102] It has been found to increase linearly with age. For example, a large study involving more than 11,000 pairs of twins from four countries reported the heritability of g to be 41 percent at age nine, 55 percent at age twelve, and 66 percent at age seventeen. Other studies have estimated that the heritability is as high as 80 percent in adulthood, although it may decline in old age. Most of the research on the heritability of g has been conducted in the United States and Western Europe, but studies in Russia (Moscow), the former East Germany, Japan, and rural India have yielded similar estimates of heritability as Western studies.^{[40][103][104][105]}

As with heritability in general, the heritability of g can be understood in reference to a specific population at a specific place and time, and findings for one population do not apply to a different population that is exposed to different environmental factors.^[106] A population that is exposed to strong environmental factors can be expected to have a lower level of heritability than a population that is exposed to only weak environmental factors. For example, one twin study found that genotype differences almost completely explain the variance in IQ scores within affluent families, but make close to zero contribution towards explaining IQ score differences in impoverished families.^[107] Notably, heritability findings also only refer to total variation within a population and do not support a genetic explanation for differences between groups.^[108] It is theoretically possible for the differences between the average g of two groups to be 100% due to environmental factors even if the variance within each group is 100% heritable.

Behavioral genetic research has also established that the shared (or between-family) environmental effects on g are strong in childhood, but decline thereafter and are negligible in adulthood. This indicates that the environmental effects that are important to the development of g are unique and not shared between members of the same family.^[104]

The genetic correlation is a statistic that indicates the extent to which the same genetic effects influence two different traits. If the genetic correlation between two traits is zero, the genetic effects on them are independent, whereas a correlation of 1.0 means that the same set of genes explains the heritability of both traits (regardless of how high or low the heritability of each is). Genetic correlations between specific mental abilities (such as verbal ability and spatial ability) have been consistently found to be very high, close to 1.0. This indicates that genetic variation in cognitive abilities is almost entirely due to genetic variation in whatever g is. It also suggests that what is common among cognitive abilities is largely caused by genes, and that independence among abilities is largely due to environmental effects. Thus it has been argued that when genes for intelligence are identified, they will be "generalist genes", each affecting many different cognitive abilities.^{[104][109][110]}

Much research points to g being a highly polygenic trait influenced by many common genetic variants, each having only small effects. Another possibility is that heritable differences in g are due to individuals having different "loads" of rare, deleterious mutations, with genetic variation among individuals persisting due to mutation–selection balance.^[110][111]

A number of candidate genes have been reported to be associated with intelligence differences, but the effect sizes have been small and almost none of the findings have been replicated. No individual genetic variants have been conclusively linked to intelligence in the normal range so far. Many researchers believe that very large samples will be needed to reliably detect individual genetic polymorphisms associated with g.^[40][111] However, while genes influencing variation in g in the normal range have proven difficult to find, many single-gene disorders with intellectual disability among their symptoms have been discovered.^[112]

It has been suggested that the g loading of mental tests have been found to correlate with heritability,^[33] but both the empirical data and statistical methodology bearing on this question are matters of active controversy.^{[113][114][115]} Several studies suggest that tests with larger g loadings are more affected by inbreeding depression lowering test scores. There is also evidence that tests with larger g loadings are associated with larger positive heterotic effects on test scores, which has been suggested to indicate the presence of genetic dominance effects for g.^[116]

Neuroscientific findings

g has a number of correlates in the brain. Studies using magnetic resonance imaging (MRI) have established that g and total brain volume are moderately correlated (r~.3–.4). External head size has a correlation of ~.2 with g. MRI research on brain regions indicates that the volumes of frontal, parietal and temporal cortices, and the hippocampus are also correlated with g, generally at .25 or more, while the correlations, averaged over many studies, with overall grey matter and overall white matter have been found to be .31 and .27, respectively. Some but not all studies have also found positive correlations between g and cortical thickness. However, the underlying reasons for these associations between the quantity of brain tissue and differences in cognitive abilities remain largely unknown.^[2]

Most researchers believe that intelligence cannot be localized to a single brain region, such as the frontal lobe. Brain lesion studies have found small but consistent associations indicating that people with more white matter lesions tend to have lower cognitive ability. Research utilizing NMR spectroscopy has discovered somewhat inconsistent but generally positive correlations between intelligence and white matter integrity, supporting the notion that white matter is important for intelligence.^[2]

Some research suggests that aside from the integrity of white matter, also its organizational efficiency is related to intelligence. The hypothesis that brain efficiency has a role in intelligence is supported by functional MRI research showing that more intelligent people generally process information more efficiently, i.e., they use fewer brain resources for the same task than less intelligent people.^[2]

Small but relatively consistent associations with intelligence test scores include also brain activity, as measured by EEG records or event-related potentials, and nerve conduction velocity.^[117][118]

g in non-humans

Evidence of a general factor of intelligence has also been observed in non-human animals. Studies have shown that g is responsible for 47% of the variance at the species level in primates^[119] and around 55% of the individual variance observed in mice.^[120][121] A review and meta-analysis of general intelligence, however, found that the average correlation among cognitive abilities was 0.18 and suggested that overall support for g is weak in non-human animals.^[122]

While not able to be assessed using the same intelligence measures used in humans, cognitive ability can be measured with a variety of interactive and observational tools focusing on innovation, habit reversal, social learning, and responses to novelty. Non-human models of g such as mice are used to study genetic influences on intelligence and neurological developmental research into the mechanisms behind and biological correlates of g.^[123]

g (or c) in human groups

Similar to g for individuals, a new research path aims to extract a general collective intelligence factor c for groups displaying a group's general ability to perform a wide range of tasks.^[124] Definition, operationalization and statistical approach for this c factor are derived from and similar to g. Causes, predictive validity as well as additional parallels to g are investigated.^[125]

Other biological associations

Height is correlated with intelligence (r~.2), but this correlation has not generally been found within families (i.e., among siblings), suggesting that it results from cross-assortative mating for height and intelligence, or from another factor that correlates with both (e.g. nutrition). Myopia is known to be associated with intelligence, with a correlation of around .2 to .25, and this association has been found within families, too.^[126]

Group similarities and differences

Cross-cultural studies indicate that the g factor can be observed whenever a battery of diverse, complex cognitive tests is administered to a human sample. The factor structure of IQ tests has also been found to be consistent across sexes and ethnic groups in the U.S. and elsewhere.^[118] The g factor has been found to be the most invariant of all factors in cross-cultural comparisons. For example, when the g factors computed from an American standardization sample of Wechsler's IQ battery and from large samples who completed the Japanese translation of the same battery were compared, the congruence coefficient was .99, indicating virtual identity. Similarly, the congruence coefficient between the g factors obtained from white and black standardization samples of the WISC battery in the U.S. was .995, and the variance in test scores accounted for by g was highly similar for both groups.^[127]

Most studies suggest that there are negligible differences in the mean level of g between the sexes, but that sex differences in cognitive abilities are to be found in more narrow domains. For example, males generally outperform females in spatial tasks, while females generally outperform males in verbal tasks.^[128] Another difference that has been found in many studies is that males show more variability in both general and specific abilities than females, with proportionately more males at both the low end and the high end of the test score distribution.^[129]

Differences in g between racial and ethnic groups have been found, particularly in the U.S. between black- and white-identifying test takers, though these differences appear to have diminished significantly over time,^[114] and to be attributable to environmental (rather than genetic) causes.^[114][130] Some researchers have suggested that the magnitude of the black-white gap in cognitive test results is dependent on the magnitude of the test's g loading, with tests showing higher g loading producing larger gaps (see Spearman's hypothesis),^[131] while others have criticized this view as methodologically unfounded.^[132][133] Still others have noted that despite the increasing g loading of IQ test batteries over time, the performance gap between racial groups continues to diminish.^[114] Comparative analysis has shown that while a gap of approximately 1.1 standard deviation in mean IQ (around 16 points) between white and black Americans existed in the late 1960s, between 1972 and 2002 black Americans gained between 4 and 7 IQ points relative to non-Hispanic Whites, and that "the g gap between Blacks and Whites declined virtually in tandem with the IQ gap."^[114] In contrast, Americans of East Asian descent generally slightly outscore white Americans.^[134] It has been claimed that racial and ethnic differences similar to those found in the U.S. can be observed globally,^[135] but the significance, methodological grounding, and truth of such claims have all been disputed.^{[136][137][138][139][140][141]}

Relation to other psychological constructs

Elementary cognitive tasks

Main articles: Elementary cognitive task and Mental chronometry

An illustration of the Jensen box, an apparatus for measuring choice reaction time.

Elementary cognitive tasks (ECTs) also correlate strongly with g. ECTs are, as the name suggests, simple tasks that apparently require very little intelligence, but still correlate strongly with more exhaustive intelligence tests. Determining whether a light is red or blue and determining whether there are four or five squares drawn on a computer screen are two examples of ECTs. The answers to such questions are usually provided by quickly pressing buttons. Often, in addition to buttons for the two options provided, a third button is held down from the start of the test. When the stimulus is given to the subject, they remove their hand from the starting button to the button of the correct answer. This allows the examiner to determine how much time was spent thinking about the answer to the question (reaction time, usually measured in small fractions of second), and how much time was spent on physical hand movement to the correct button (movement time). Reaction time correlates strongly with g, while movement time correlates less strongly.^[142] ECT testing has allowed quantitative examination of hypotheses concerning test bias, subject motivation, and group differences. By virtue of their simplicity, ECTs provide a link between classical IQ testing and biological inquiries such as fMRI studies.

Criticism

See also

• Charles Spearman – English psychologist (1863–1945)
• Factor analysis in psychometrics – Statistical method
• Fluid and crystallized intelligence – Factors of general intelligence
• Flynn effect – 20th-century rise in intelligence test scores
• Intelligence – Ability to perceive, infer, acquire, retain and apply information.
• Intelligence quotient – Score from a test designed to assess intelligence
• Malleability of intelligence – Processes by which intelligence can change over time
• Spearman's hypothesis
• Eugenics – Aim to improve perceived human genetic quality

References

Bundled references

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