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Is there a basic mathematical relationship between IQ and learning speed?

Is there a basic mathematical relationship between IQ and learning speed?


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Is there a basic mathematical relationship between IQ and learning speed?… such as linear, exponential, etc?

For example, if some number of people with an approximate IQ of 100 (95-105) take X minutes to complete a task (lets also say plus-minus 5%), is there an expected average Y time people with an average 120 would need?

Can you cite the research?


How Is Math Used in Psychology?

Psychological researchers use statistical methods to determine if certain treatments are effective, and clinical psychologists must be able to interpret statistical data to interpret diagnostic material and studies. Psychologists working with groups also rely on statistics for measurements.

As with all sciences, psychology is partially based on a mathematical foundation. Hypotheses need to be tested, and statistical analysis provides a means of determining whether treatments appear to be effective or not. Psychologists working with patients must read studies to determine what scientific literature shows to be most effective, and they must have a strong understanding of statistics to do so.

Diagnosing problems is an essential part of clinical psychology, but the results rarely show up on brain scans or other tools. As a result, psychologists rely on surveys and information provided by patients. Psychologists must be able to interpret these results to make the best diagnosis possible. Understanding the prevalence of various problems also helps.

Psychologists often work with groups of people in schools, offices and other organizations, and much of their work requires them to locate and measure various trends. However, they must be able to determine whether trends are real effects or just statistical noise. Again, statistics play a key role, and using p-values and Bayesian statistical methods allows psychologists to make important observations.


Howard Gardner’s 9 Types of Intelligence

Many of us are familiar with three general categories in which people learn: visual learners, auditory learners, and kinesthetic learners.

Beyond these three general categories, many theories of and approaches toward human potential have been developed. Among them is the theory of multiple intelligences, developed by Howard Gardner, Ph.D., Professor of Education at Harvard University. Gardner’s early work in psychology and later in human cognition and human potential led to the development of the initial six intelligences. Today there are nine intelligences and the possibility of others may eventually expand the list.

These intelligences (or competencies) relate to a person’s unique aptitude set of capabilities and ways they might prefer to demonstrate intellectual abilities.


People have different strengths and intelligences. For example, students who are “interviewed” as a means to gain access to a course may be mislabeled as being less than desirable because of inappropriate assessment (poorly written interview questions, bias toward a perceived “perfect student,” and other narrow criteria).

“In life, we need people who collectively are good at different things. A well-balanced world, and well-balanced organizations and teams, are necessarily comprised of people who possess different mixtures of intelligences. This gives that group a fuller collective capacity than a group of identical able specialists”

Gardner’s multiple intelligences theory can be used for curriculum development, planning instruction, selection of course activities, and related assessment strategies. Instruction which is designed to help students develop their strengths can also trigger their confidence to develop areas in which they are not as strong. Students’ multiple learning preferences can be addressed when instruction includes a range of meaningful and appropriate methods, activities, and assessments.

In summary, integrate educational theories, teaching strategies, and other pedagogic tools in meaningful and useful ways to better address the needs of students. Gardner himself asserts that educators should not follow one specific theory or educational innovation when designing instruction but instead employ customized goals and values appropriate to their teaching and student needs. Addressing the multiple intelligences and potential of students can help instructors personalize their instruction and methods of assessment.

The Nine Types of Intelligence

1. Naturalist Intelligence (“Nature Smart”)

Designates the human ability to discriminate among living things (plants, animals) as well as sensitivity to other features of the natural world (clouds, rock configurations). This ability was clearly of value in our evolutionary past as hunters, gatherers, and farmers it continues to be central in such roles as botanist or chef. It is also speculated that much of our consumer society exploits the naturalist intelligences, which can be mobilized in the discrimination among cars, sneakers, kinds of makeup, and the like.

2. Musical Intelligence (“Musical Smart”)

Musical intelligence is the capacity to discern pitch, rhythm, timbre, and tone. This intelligence enables us to recognize, create, reproduce, and reflect on music, as demonstrated by composers, conductors, musicians, vocalist, and sensitive listeners. Interestingly, there is often an affective connection between music and the emotions and mathematical and musical intelligences may share common thinking processes. Young adults with this kind of intelligence are usually singing or drumming to themselves. They are usually quite aware of sounds others may miss.

3. Logical-Mathematical Intelligence (Number/Reasoning Smart)

Logical-mathematical intelligence is the ability to calculate, quantify, consider propositions and hypotheses, and carry out complete mathematical operations. It enables us to perceive relationships and connections and to use abstract, symbolic thought sequential reasoning skills and inductive and deductive thinking patterns. Logical intelligence is usually well developed in mathematicians, scientists, and detectives. Young adults with lots of logical intelligence are interested in patterns, categories, and relationships. They are drawn to arithmetic problems, strategy games and experiments.

4. Existential Intelligence (Spirit Smart)

Sensitivity and capacity to tackle deep questions about human existence, such as the meaning of life, why do we die, and how did we get here.

5. Interpersonal Intelligence (People Smart”)

Interpersonal intelligence is the ability to understand and interact effectively with others. It involves effective verbal and nonverbal communication, the ability to note distinctions among others, sensitivity to the moods and temperaments of others, and the ability to entertain multiple perspectives. Teachers, social workers, actors, and politicians all exhibit interpersonal intelligence. Young adults with this kind of intelligence are leaders among their peers, are good at communicating, and seem to understand others’ feelings and motives.

6. Bodily-Kinesthetic Intelligence (“Body Smart”)

Bodily kinesthetic intelligence is the capacity to manipulate objects and use a variety of physical skills. This intelligence also involves a sense of timing and the perfection of skills through mind–body union. Athletes, dancers, surgeons, and craftspeople exhibit well-developed bodily kinesthetic intelligence.

7. Linguistic Intelligence (Word Smart)

Linguistic intelligence is the ability to think in words and to use language to express and appreciate complex meanings. Linguistic intelligence allows us to understand the order and meaning of words and to apply meta-linguistic skills to reflect on our use of language. Linguistic intelligence is the most widely shared human competence and is evident in poets, novelists, journalists, and effective public speakers. Young adults with this kind of intelligence enjoy writing, reading, telling stories or doing crossword puzzles.

8. Intra-personal Intelligence (Self Smart”)

Intra-personal intelligence is the capacity to understand oneself and one’s thoughts and feelings, and to use such knowledge in planning and directioning one’s life. Intra-personal intelligence involves not only an appreciation of the self, but also of the human condition. It is evident in psychologist, spiritual leaders, and philosophers. These young adults may be shy. They are very aware of their own feelings and are self-motivated.

9. Spatial Intelligence (“Picture Smart”)

Spatial intelligence is the ability to think in three dimensions. Core capacities include mental imagery, spatial reasoning, image manipulation, graphic and artistic skills, and an active imagination. Sailors, pilots, sculptors, painters, and architects all exhibit spatial intelligence. Young adults with this kind of intelligence may be fascinated with mazes or jigsaw puzzles, or spend free time drawing or daydreaming.

For your Naturalist Learners
From: Overview of the Multiple Intelligences Theory, Association for Supervision and Curriculum Development and Thomas Armstrong.com and Howard Gardner’s Theory of Multiple Intelligences by Northern Illinois University, Faculty Development and Instructional Design Center.

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Is there a basic mathematical relationship between IQ and learning speed? - Psychology

Arguing that "reason, intelligence, logic, knowledge are not synomous. . .", Howard Gardner (1983) proposed a new view of intelligence that is rapidly being incorporated in school curricula. In his Theory of Multiple Intelligences, Gardner expanded the concept of intelligence to also include such areas as music, spacial relations, and interpersonal knowledge in addition to mathematical and linguistic ability.

This digest discusses the origins of Gardner's Theory of Multiple Intelligences, his definition of intelligence, the incorporation of the Theory of Multiple Intelligences into the classroom, and its role in alternative assessment practices.

Gardner defines intelligence as "the capacity to solve problems or to fashion products that are valued in one or more cultural setting" (Gardner & Hatch, 1989). Using biological as well as cultural research, he formulated a list of seven intelligences. This new outlook on intelligence differs greatly from the traditional view which usually recognizes only two intelligences, verbal and computational. The seven intelligences Gardner defines are:

Logical-Mathematical Intelligence--consists of the ability to detect patterns, reason deductively and think logically. This intelligence is most often associated with scientific and mathematical thinking.

Linguistic Intelligence-- involves having a mastery of language. This intelligence includes the ability to effectively manipulate language to express oneself rhetorically or poetically. It also allows one to use language as a means to remember information.

Spatial Intelligence-- gives one the ability to manipulate and create mental images in order to solve problems. This intelligence is not limited to visual domains-- Gardner notes that spatial intelligence is also formed in blind children.

Musical Intelligence-- encompasses the capability to recognize and compose musical pitches, tones, and rhythms. (Auditory functions are required for a person to develop this intelligence in relation to pitch and tone, but it is not needed for the knowledge of rhythm.)

Bodily-Kinesthetic Intelligence-- is the ability to use one's mental abilities to coordinate one's own bodily movements. This intelligence challenges the popular belief that mental and physical activity are unrelated.

The Personal Intelligences-- includes interpersonal intelligence -- the ability to understand and discern the feelings and intentions of others-- and intrapersonal intelligence --the ability to understand one's own feelings and motivations. These two intelligences are separate from each other. Nevertheless, because of their close association in most cultures, they are often linked together.

Although the intelligences are anatomically separated from each other, Gardner claims that the seven intelligences very rarely operate independently. Rather, the intelligences are used concurrently and typically complement each other as individuals develop skills or solve problems. For example, a dancer can excel in his art only if he has 1) strong musical intelligence to understand the rhythm and variations of the music, 2) interpersonal intelligence to understand how he can inspire or emotionally move his audience through his movements, as well as 3) bodily-kinesthetic intelligence to provide him with the agility and coordination to complete the movements successfully.

Gardner argues that there is both a biological and cultural basis for the multiple intelligences. Neurobiological research indicates that learning is an outcome of the modifications in the synaptic connections between cells. Primary elements of different types of learning are found in particular areas of the brain where corresponding transformations have occurred. Thus, various types of learning results in synaptic connections in different areas of the brain. For example, injury to the Broca's area of the brain will result in the loss of one's ability to verbally communicate using proper syntax. Nevertheless, this injury will not remove the patient's understanding of correct grammar and word usage.

In addition to biology, Gardner (1983) argues that culture also plays a large role in the development of the intelligences. All societies value different types of intelligences. The cultural value placed upon the ability to perform certain tasks provides the motivation to become skilled in those areas. Thus, while particular intelligences might be highly evolved in many people of one culture, those same intelligences might not be as developed in the individuals of another.

Using Multiple Intelligences in the Classroom

Accepting Gardner's Theory of Multiple Intelligences has several implications for teachers in terms of classroom instruction. The theory states that all seven intelligences are needed to productively function in society. Teachers, therefore, should think of all intelligences as equally important. This is in great contrast to traditional education systems which typically place a strong emphasis on the development and use of verbal and mathematical intelligences. Thus, the Theory of Multiple Intelligences implies that educators should recognize and teach to a broader range of talents and skills.

Another implication is that teachers should structure the presentation of material in a style which engages most or all of the intelligences. For example, when teaching about the revolutionary war, a teacher can show students battle maps, play revolutionary war songs, organize a role play of the signing of the Declaration of Independence, and have the students read a novel about life during that period. This kind of presentation not only excites students about learning, but it also allows a teacher to reinforce the same material in a variety of ways. By activating a wide assortment of intelligences, teaching in this manner can facilitate a deeper understanding of the subject material.

Everyone is born possessing the seven intelligences. Nevertheless, all students will come into the classroom with different sets of developed intelligences. This means that each child will have his own unique set of intellectual strengths and weaknesses. These sets determine how easy (or difficult) it is for a student to learn information when it is presented in a particular manner. This is commonly referred to as a learning style. Many learning styles can be found within one classroom. Therefore, it is impossible, as well as impractical, for a teacher to accommodate every lesson to all of the learning styles found within the classroom. Nevertheless the teacher can show students how to use their more developed intelligences to assist in the understanding of a subject which normally employs their weaker intelligences (Lazear, 1992). For example, the teacher can suggest that an especially musically intelligent child learn about the revolutionary war by making up a song about what happened.

Toward a More Authentic Assessment

As the education system has stressed the importance of developing mathematical and linguistic intelligences, it often bases student success only on the measured skills in those two intelligences. Supporters of Gardner's Theory of Multiple Intelligences believe that this emphasis is unfair. Children whose musical intelligences are highly developed, for example, may be overlooked for gifted programs or may be placed in a special education class because they do not have the required math or language scores. Teachers must seek to assess their students' learning in ways which will give an accurate overview of the their strengths and weaknesses.

As children do not learn in the same way, they cannot be assessed in a uniform fashion. Therefore, it is important that a teacher create an "intelligence profiles" for each student. Knowing how each student learns will allow the teacher to properly assess the child's progress (Lazear, 1992). This individualized evaluation practice will allow a teacher to make more informed decisions on what to teach and how to present information.

Traditional tests (e.g. multiple choice, short answer, essay. . .) require students to show their knowledge in a predetermined manner. Supporters of Gardner's theory claim that a better approach to assessment is to allow students to explain the material in their own ways using the different intelligences. Preferred assessment methods include student portfolios, independent projects, student journals, and assigning creative tasks. An excellent source for a more in-depth discussion on these different evaluation practices is Lazear (1992).

Schools have often sought to help students develop a sense of accomplishment and self-confidence. Gardner's Theory of Multiple Intelligences provides a theoretical foundation for recognizing the different abilities and talents of students. This theory acknowledges that while all students may not be verbally or mathematically gifted, children may have an expertise in other areas, such as music, spatial relations, or interpersonal knowledge. Approaching and assessing learning in this manner allows a wider range of students to successfully participate in classroom learning.

Blythe, T., & Gardner H. (1990). A school for all intelligences.Educational Leadership. 47(7), 33-37.

Fogarty, R., & Stoehr, J. (1995). Integrating curricula with multiple intelligences. Teams, themes, and threads. K-college. Palatine, IL: IRI Skylight Publishing Inc. (ERIC Document Reproduction Service ED No. 383 435)

Gardner, H. (1983). Frames of Mind. New York: Basic Book Inc.

Gardner, H. (1991) The unschooled mind: how children think and how schools should teach.New York: Basic Books Inc.

Gardner, H., & Hatch, T. (1989). Multiple intelligences go to school: Educational implications of the theory of multiple intelligences. Educational Researcher, 18(8), 4-9.

Kornhaber, M., & Gardner, H. (1993, March). Varieties of excellence: identifying and assessing children's talents. A series on authentic assessment and accountability. New York: Columbia University, Teachers College, National Center for Restructuring Education, Schools, and Teaching. (ERIC Document Reproduction Service No. ED 363 396)

Lazear, David. (1991). Seven ways of teaching: The artistry of teaching with multiple intelligences. Palatine, IL: IRI Skylight Publishing Inc. (ERIC Document Reproduction Service No. ED 382 374) (highly recommended)

Lazear, David (1992). Teaching for Multiple Intelligences. Fastback 342 Bloomington, IN: Phi Delta Kappan Educational Foundation. (ERIC Document Reproduction Service No. ED 356 227) (highly recommended)


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The Definitive Word on Intelligence

Arthur R. Jensen, The g Factor, Praeger Publishers, 1998, 648 pp.

Arthur Jensen of U.C. Berkeley is one of the greatest social scientists of our time. He virtually single-handedly resurrected the scientific study of intelligence, and he has been at the center of many breakthroughs in this field. Needless to say, he is a courageous man, who has never let hysterical opposition or even death threats keep him from studying some of the most important and contentious issues we face.

The g Factor is only the latest of the many publications that resulted from what can now be seen as a watershed event: the 1969 appearance in the Harvard Educational Review of Prof. Jensen’s famous article on the heritability of IQ and how difficult it is to raise. This article not only reestablished the connection between genetics and intelligence but set the direction of Prof. Jensen’s career. He has since written countless articles in this field and three major books: Educability and Group Differences (1973), Bias in Mental Testing (1980), and now, The g Factor.

These books chart the recent remarkable progress in the study of intelligence. If Prof. Jensen had so dominated any less controversial field he would certainly be a candidate for the Nobel Prize. Unfortunately, his real stature is recognized only by a small number of specialists and professional colleagues, but the implications of his work continue to reverberate through the larger society. Whatever recognition he may ultimately receive, his work has gone far to set the study of mental ability once more on a firmly scientific basis.

This book is an investigation of the nature of intelligence, the extent to which it is under genetic control, and its uneven distribution between individuals and groups. The first part is a complete and sometimes technical treatment of “the g factor” itself, which appears to be a unitary mental ability underlying all activities we think of as requiring intelligence. “Factors” are the end result of a mathematical procedure called factor analysis, and the g factor is the “general” factor of intelligence, first hypothesized by the British psychologist, Charles Spearman (1863-1945). Spearman thought of g as a direct analogy to the “G” of physics, that is Newton’s gravitational constant. Spearman’s view, substantiated by almost a century of research, was that g is of central importance to psychology just as g was to Newtonian physics.

G can be thought of as the undifferentiated raw cognitive power of the brain. It cannot be directly measured, but it manifests itself in all types of cognitive activity, and people who are good at one kind of mental test tend to be good at all of them. To use the statistical term, a person’s different abilities are correlated, and similar abilities tend to correlate most closely with each other. For example, someone who is exceptionally good at any mathematical test is likely to be very good at all mathematical tests — but he is likely to perform well on verbal tests, too. As we will see, g is at work when even the smallest demands are made on the mind.

If people take enough different kinds of mental tests, their scores can be analyzed for factors, or the tendency of the correlations between similar abilities to cluster in groups. There will be factors for such things as verbal, musical, mathematical, and spatial manipulation abilities. Further analysis of these factors reveals a fundamental factor common to them all, which is the g factor.

We can therefore imagine a series of different factories in the brain, all powered by the same energy source. One of the factories manufactures solutions to mathematical problems, while another produces correct understandings of words and sentences. Other factories produce solutions to other kinds of mental problems, but all of them can be thought of as running off a common power source, which is g.

People differ in the efficiency of their individual factories, which is why smart people have different strengths in different areas despite being smart in a general sort of way. But people differ most significantly in the level of the general power source, or g. Someone with an IQ of 100 may have a math factory that is relatively more efficient than his verbal or music factory, but even in math he is likely to fall well behind someone with an IQ of 130 whose math factory is relatively less efficient than his verbal factory. It is the difference in levels of power available to all of a person’s factories that produce the marked differences in ability that characterize our species.

Many kinds of mental performance can be taught and people can show improvement, but what is improving is an ability that is not g. As Prof. Jensen explains, “At the level of psychometrics [mental testing], ideally, g may be thought of as a distillate of the common source of individual differences in all mental tests, completely stripped of their distinctive features, of information content, skill, strategy, and the like.”

Interestingly, Prof. Jensen reports that it is at the highest levels of g that people show the most variation in abilities that are independent of g. Thus, very intelligent people may have markedly different mental ability profiles despite similar levels of g. If all the factories are getting lots of power from their common source, some of the factories are likely to be unusually efficient so that the pattern of different levels of efficiency can differ considerably from one smart person to another.

Some critics have complained that g is not real because it cannot be measured directly and must be derived by a complex statistical process. Prof. Jensen shows that it is not, for this reason, artificial. If there were no g factor, sophisticated mathematics could not coax it into existence. Moreover, the same g factor is found in all human populations, and can be derived from the results of mental tests prepared by people who have never heard of g or who have even doubted there was such a factor. g can be calculated only because it exists, and in that sense is purely objective. Prof. Jensen believes that it reflects one of the basic functions of the brain, and that although all normal people share the same biological structures they differ greatly in the efficiency of certain neurological processes.

Direct assessment of brain functions gives strong evidence that g is a real, physiological phenomenon, and Prof. Jensen has been a pioneer in using what are called elementary cognitive tasks (ECTs) to study intelligence. The simplest sort of ECT involves a test device with two push-buttons. The subject holds down the black button while he waits for a light to go on inside the smaller, white button. He then presses the illuminated button as quickly as possible. This measures two things. The first is reaction time: the time between the light going on and the subject taking his finger off the black button. The second is movement time: the time it takes the subject to move his finger from the black button to the illuminated button.

Obviously, this is a very simple (indeed, elementary) task, though tests of this kind can be made more complicated. For example, there can be a number of smaller buttons that can light up in different patterns, requiring the subject to make slightly more complicated decisions before moving his finger. We do not think of this sort of thing as mentally demanding — no one ever “fails” these tests — but the neurological processing that goes into these very simple tasks is closely related to intelligence.

Prof. Jensen has found that reaction speed is strongly correlated with g level, but that the highest correlation is between g and consistency of reaction time. With a set of scores from various different ECTs, it is possible to achieve a 0.7 correlation with g as calculated from conventional IQ tests. This approaches the g correlation (0.8) of Ravens Progressive Matrices, the IQ test that comes the closest to measuring g itself. Surprising as it may seem, careful monitoring of the processes that underlie ECTs can give results that are so reliable they rival pencil-and-paper tests.

ECT performance matches group differences in intelligence. It is worse in children than in adults, and better in gifted children than in normal children. Blacks have quicker movement times than whites while whites have quicker and more consistent reaction times. Asians do slightly better than whites, and performance for no group improves with practice ECTs appear to measure something basic to the brain.

Another direct assessment of mental processing is the inspection time test. This uses an instrument called a tachistoscope to throw an image on a screen for a very brief period. Starting at the millisecond level, which is too quick for anyone to see the image, the exposure is gradually increased until a subject can just make it out. There is a correlation of .54 between speed of inspection time and IQ — remarkably high for a task that is so different from an IQ test. Once again, the test seems to be measuring a neurological process closely associated with mental processing.

Yet another direct assessment is the study of brain waves. Prof. Jensen explains that a wave pattern called average evoked potential can be analyzed in specialized ways that show a surprisingly high correlation with IQ.

Finally, researchers have devised something that is essentially a direct test of brain efficiency. The brain’s fuel is glucose, or simple sugar. When a radioactive isotope of glucose is injected into a subject’s blood stream it is possible to measure the rate at which the brain takes it up and metabolizes it. When rate of metabolism is measured while subjects are taking an IQ test, the high scorers use less sugar than the low scorers, with a remarkable correlation with IQ of around .7 or .8. The less powerful brains get wrong answers despite burning more fuel. If we return to the analogy of the brain as composed of factories, the common power supply simply appears to be less efficient.

If advances continue to be made in direct assessment of the brain, conventional IQ testing may be superseded. This would certainly silence any complaints about “test bias.”

Because the issue of whether education or environment can influence IQ levels is central to so much policy-making, The g Factor thoroughly covers the question of heritability. Kinship and adoption studies have provided some of the most illuminating data on this question, and Prof. Jensen reports them in detail.

Some of the most significant findings are the correlations of IQs of identical twins reared in the same family (.86), identical twins separated at birth and reared in different families (.75) and fraternal twins reared in the same family (.60). That identical twins separated at birth should have more similar IQs than fraternal twins reared by the same parents is perhaps the single most powerful argument for the view that genes have a greater effect on IQ than environment. As Prof. Jensen points out, “similarities in the MZA’s [monozygotic (identical) twins reared apart] environments cannot possibly account for more than a minute fraction of the IQ correlation of +.75 between MZAs.”

Studies of siblings and adopted children likewise confirm the power of heredity in determining differences in IQ, and it is now generally agreed among specialists that 60 to 80 percent of human IQ variation is due to genes. This does not mean, however, that the remaining environmental influences are well understood or can be used to raise IQ. As Prof. Jensen explains, “a large part of the specific environmental variance appears to be due to the additive effects of a large number of more or less random and largely physical events — developmental ‘noise’ — with small, but variable positive and negative influences on the neurophysiological substrate of mental growth.”

What is this developmental “noise”? “[S]uch effects as childhood diseases, traumas, and the like, as well as prenatal effects such as mother-fetus incompatibility of blood antigens, maternal health, and perinatal effects of anoxia and other complications in the birth process, could each have a small adverse effect on mental development.” These appear to be the kind of non-genetic factors that influence IQ, and they are not the sort of thing that can be easily manipulated.

As Prof. Jensen makes emphatically clear, the non-genetic influence comes only slightly, if at all, from what are called between-family differences: education of parents, social status, family income, school quality, etc. Liberals believe that these are the crucial factors that make people different from each other, but liberals are wrong. IQ (like other personality traits) is astonishingly impervious to any but the most degraded and unfavorable environments.

Prof. Jensen calls the environmentalist view “the sociologist’s fallacy.” It is true that children from wealthy homes tend to be smarter than children from poor homes, but wealth does not make them smart. They get genes for intelligence from their smart parents, and their parents are likely to be well off (and have homes full of books and speak in complete sentences) because they are smart. Of course, children do differ from their parents in intelligence, and these differences explain how families rise and fall. A person’s IQ has a correlation of .7 with his own adult socio-economic status but only about .4 with that of his parents.

Error though it be, the sociologist’s fallacy has driven not only an enormous number of government uplift programs but several well-publicized private efforts to raise the IQs of poor black children. Prof. Jensen reviews the results of the Milwaukee Project, Head Start, and the Abecedarian Project, some of which made extraordinary attempts to improve environments.

In some cases, the early results were very encouraging: gains of 20 or even 30 points compared to control groups. But as Prof. Jensen convincingly argues, what the children learned at intensive “infant stimulation centers” and the like was information and strategies that helped them take the tests. g very probably did not change. In most cases, administrators did not give a battery of tests and attempt to calculate g. Instead, they gave the same test at different ages and rejoiced to find improvement.

Professor Jensen gives a striking example of how training can improve test results without raising g. He notes many children’s IQ tests have a memory component: How long a string of letters or numbers can the child repeat back to the tester? Most adults can’t remember more than about seven numbers, but with lots of practice and training, people can remember as many as 70 or even 100 digits. They can do this because they develop a specific strategy or skill, not because their memory or g level has improved. The tricks a person uses to remember 70 digits are so specialized, in fact, that they do not even help the same person remember more than an average number of letters (rather than digits)!

Children who took part in these widely-acclaimed IQ-raising programs probably learned specific skills of this kind during the thousands of hours of instruction they received. But even the most intensive enrichment programs had virtually no permanent effect on school performance or IQ, which suggests that g itself was unchanged. Prof. Jensen concludes that IQ cannot be appreciably increased by specialized education.

It is true that the IQ test scores of children are affected to some degree by the environment their parents make for them. This is almost certainly because they learn more facts and absorb test-taking strategies and not because the love and care of good parents improves g. In fact, as children grow older they create environments that suit their own genetic endowments, and Prof. Jensen is categorical about what then happens: “By adulthood, all of the IQ correlation between biologically related persons is genetic . . . [T]he environmental contribution to the familial correlations is nil.” Surprising as it may seem, once a child grows up, his IQ score is similar to that of family members only because he is genetically related to them, not because they spent many years in the same household.

Prof. Jensen is equally forthright in explaining that genes account for the well-established IQ differences between the races. First, he points out that approximately half — or 50,000 — of the genes that vary in human beings play a role in brain functions, and that 30,000 affect the brain exclusively. It would be astonishing if genes did not play a central role in intelligence and if the races, which differ physically in so many ways, did not differ in brain function.

He also offers an arresting refutation of the fashionable view that race is purely a social construct and is not biological. Prof. Jensen likens race to the visible colors. A rainbow forms when the wave-length of light changes continuously and uniformly, but we do not perceive a continuous change. Instead, we see distinct bands of color. Though there may be some blurring of race at the edges because of cross mating, races are as distinct as the bands of visible color. Prof. Jensen also cites the increasingly persuasive genetic evidence for the biological distinctness of different populations (see figure, below).

A number of elegant demonstrations based on the principle of regression toward the mean strongly suggest a genetic origin for group differences. This principle is a biological law according to which parents who are at the extremes of any trait are likely to have children who are less extreme. Two very tall parents are likely to have children who are not quite so tall, and two very short parents are likely to have children who are not quite so short. In the children, these traits revert toward the average, or the mean. The same effect is found in intelligence, but the mean toward which the black IQ regresses is a full 15 points lower than the white mean.

Therefore, when black couples and white couples are matched for IQ, the black/white IQ difference in their children increases as parental IQ increases. In other words, high IQ is an anomaly in all races, but more of an anomaly for blacks than for whites, and the children of high-IQ blacks regress further because they are regressing toward a lower mean.

Prof. Jensen reports a study of high-IQ children in one school district that provides more evidence for the difference in means. When white and black students were perfectly matched for IQs of 120, the average IQs of the siblings of the whites was 113 whereas the average IQs for the siblings of the blacks was 99. Among blacks, an IQ of 120 is simply a much greater deviation from the norm than it is for whites, and this is reflected in the IQs of their more ordinary siblings.

Genetic distance between any two groups is represented by the total length
of the lines separating them.

Regression toward the mean explains something that has always baffled the “sociologists:” children of low-income whites (and Asians) get better SAT scores than the children of high-income blacks. If environment controls IQ, the children of wealthy blacks should be enjoying the benefits of good environment. They are, but those benefits are meager and do not make up for the effects of heredity and the lower mean toward which black children regress.

There is no non-genetic explanation for group differences that can account for phenomena of this kind, but they are perfectly consistent with widely accepted principles of genetics. Specialists understand the force of arguments of this kind, which is why the view that “racism” and other environmental factors cause the black/white IQ gap persists mostly among the ignorant — who are the great majority.

More strong evidence for a substantially different biological mean for IQ is found in studies of the low end of the IQ distribution curve as well. Mental retardation — IQs below 70 — is generally of two types, familial and organic. Familial retardation occurs in children who are otherwise normal but were simply dealt a very poor hand of the genes that affect intelligence. Given a normal distribution of intelligence, a few people are inevitably going to have very low IQs, just as a few will have very high ones. Organic retardation, on the other hand, is caused by clear biological defects, like Down’s syndrome (Mongolism) and children who suffer from it are obviously abnormal.

An important racial difference lies in the fact that half of whites with IQs below 70 are organic retardates but only 12.5 percent of the blacks are. The source of this difference is the racial disparity in naturally occurring distributions of intelligence. Given that the distribution curve for black intelligence is shifted approximately 15 points toward the left, a substantially larger proportion of otherwise normal blacks will fall below an IQ of 70.

The opposite is true at the high end of the curve. The percentage of whites with IQs higher than 130 is 20 times that of blacks. Because there are approximately six times as many whites as blacks in America, in real terms there are perhaps 120 times more whites than blacks with IQs at this level. This is why, without racial preferences, it is impossible to admit large numbers of blacks to competitive universities or to promote them to challenging positions.

Brain and head size studies likewise confirm the biological origins of group differences. It is now well established that brain size correlates with intelligence, and Prof. Jensen reports that the heads of black newborns are a full .4 standard deviation smaller than those of whites.

Likewise, it has long been known that near-sightedness, or myopia, is correlated with intelligence children with IQs over 130 are three to five times more likely to be nearsighted than children with normal IQs. There seems to be no functional, cause-and-effect connection between myopia and intelligence, but a pleiotropic relationship exists in that some of the same genes affect both traits. Intelligence and myopia are somehow “side effects” of each other to some degree. Prof. Jensen finds that myopia is most common in Jews, next in Asians, then in whites, and least common in blacks — precisely the distribution one would expect. Moreover, reading does not cause myopia. An oculist can examine the eyes of children who are too young to read and who are not yet near-sighted, and accurately predict whether they will need glasses later in life.

It is well known that the test score gap between blacks and whites varies from one IQ test to another, and that the gap narrows on the least abstract, most information-laden tests. Prof. Jensen explains that the real difference lies in the extent to which a test measures g the more g-“loaded” a test is and the fewer specific non-g abilities it measures, the greater the black/white gap.

Like many others who have studied the question, Prof. Jensen finds that the racial gap in IQ is increasing because of dysgenic birth patterns. In both races, less intelligent mothers are having more children than more intelligent mothers, but the disproportions are higher among blacks than whites. Also, since blacks have children, on average, two years earlier than whites, the generation time for blacks is shorter and dysgenic effects spread more rapidly.

One of Prof. Jensen’s most interesting racial findings is that the average IQ difference for blacks and whites in the same social class is 12 points — almost as great as the average difference between the two races (there is an average 17-point difference between any two people in the population picked at random). This is explained not only by preferential policies but also by racial differences in IQ distribution. If, for example, a demanding profession requires a minimum IQ of 125, blacks in that profession will tend to have IQs that cluster at the minimum, whereas whites will show greater variety. Because of this effect, the IQ gap between blacks and whites in the same social class narrows as one moves down the social scale.

Prof. Jensen finds that the geographic distribution of IQ is also uneven. For both blacks and whites, there is a continuous gradient that rises from the south towards the north and west. The gradient is sharper for blacks than whites, and both gradients are apparent in pre-school children, so regional differences in education do not explain it.

It has been widely reported that from infancy black children develop motor skills more rapidly than whites. Interestingly, Prof. Jensen finds that lower-class children (both white and black) develop more quickly than upper-class children, which suggests that slow maturation and high intelligence are correlated not just between races but within races.

For the most part, Prof. Jensen does not make policy recommendations the facts alone are persuasive enough. He does point out, though, that life itself is a kind of continuous intelligence test, and that high g is one of the most important ingredients of success. He explains that scores on a highly g-loaded test are the best indicators of performance on any but the most specialized jobs. IQ is an excellent predictor for performance even on jobs that require manual dexterity and coordination. To a remarkable degree, g is the central mental characteristic of humans. Of course, intelligence is not everything. It takes more than brains to become a doctor — it takes persistence and discipline, too — but persistence is not enough. For many things, a certain level of g is indispensable, and low g cuts off desirable options at every stage of life. Low g is therefore a more accurate predictor of achievement than high g, since a lack of intelligence cannot usually be made up for by other qualities whereas high intelligence can be wasted.

When people with low g are scattered through otherwise normal communities it affects only individuals. Friends and relatives step in to help them. However, as Prof. Jensen points out, when people of low intelligence gather in large numbers, as they do in welfare housing, society falls apart. Prof. Jensen notes that in America there are now entire apartment blocks in which, even with welfare, the residents cannot get by without help from social workers. Dysgenic trends and increased immigration of low-g stock mean areas like this will only expand.

In this connection, Prof. Jensen makes some interesting observations about adult illiteracy. Most people assume that the cause is poor schooling, but he argues that the problem is usually not the process of decoding written language but understanding it. Most illiterates do no better on reading comprehension tests when the selections are read to them than when they try to do the reading themselves! Illiteracy, in Prof. Jensen’s view, is much more a problem of low g than of somehow not learning how to read.

There are a few points on which Prof. Jensen’s data differ from results AR has reported elsewhere. Some researchers have found that although the average IQs of men and women are the same, a greater standard deviation for men means that more of them are bunched at both high and low IQs. Prof. Jensen does not find sufficient evidence to draw this conclusion. He does confirm the standard sex differences in verbal and spatial abilities and even reports that some higher mammals show the typical male superiority in spatial ability. He also writes that in addition to their well-known advantage in verbal ability, one of the largest sex differences favoring women is in something called “speed and accuracy,” which is similar to clerical checking.

Prof. Jensen also takes up the question of why black women are so much more successful than black men. They are more likely to graduate from high school and college, pass high-level civil service tests, and enter skilled professions. This difference is not found among whites, and some researchers have wondered if black women may have a higher average IQ than black men. Once again, Prof. Jensen finds no such difference — but he offers no other explanation.

Prof. Jensen also differs from researchers who explain part of the black/white crime rate difference in terms of high black testosterone levels and an inability to defer gratification. He argues that population differences in g alone explain differences in crime rates. He notes that criminals of all races have IQs that are some 10 points below those of their siblings, and finds that within the same ranges of IQ, blacks and whites have essentially the same crime rates.

Needless to say, Prof. Jensen has spent his career disagreeing with others, and from time to time in The g Factor he must explain why his critics are wrong — and he is always a gentleman. Even with those who have disagreed with him in strong terms, he is more than generous in pointing out the parts of their theories that may be correct, and couches his own criticism in the gentlest terms. He treats his wildest, least scientific critics to nothing more than dignified silence: The names of Leon Kamin and Stephen Jay Gould do not even appear in an otherwise exhaustively researched and footnoted work.

The g Factor is not an easy book to read. Prof. Jensen writes clearly and repeats explanations when it would be unreasonable to expect perfect recall in his readers, but he writes for an informed, even specialist audience. He has already begun collaboration with a journalist on a more popular version of The g Factor. But those who are willing to invest the effort this book requires, will find that it is the monumental work of an extraordinary mind. A review can only begin to touch on its breadth and detail. This book is likely to become one of the landmark works in psychology, and it is the great good fortune of our society that a man of Prof. Jensen’s stature has made his career in this crucially important but thankless field.


What are learning styles?

Google has a field day when you look up learning styles. As is the case with most complicated terminology, some of the definitions make sense and some don’t. Here are three explanations of learning styles that sit right:

    states that “Learning Styles (LS) can be defined as the way humans prefer to concentrate on, store, and remember new and difficult information.” OK, so far so good. That makes sense.
    describes learning styles as “the characteristic of cognitive, affective, social, and physiological behaviors that serve as relatively stable indicators of how learners perceive, interact with, and respond to the learning environment.”
  1. On the other hand, Brown University jumps more to the point and expresses that learning styles are how you process and comprehend information in learning situations.

Brown also breaks up learning styles into six categories (which we’ll revisit later):

  • Visual learning
  • Kinesthetic learning
  • Individual learning
  • Tactile learning
  • Auditory learning
  • Group learning

In short, learning styles have to do with an individual’s preferred way of learning — or, as Gardner put it, how students approach a task.


Nassim Taleb on IQ

Nassim Taleb has published an attack on intelligence research that is getting a lot of attention and so I thought I would respond to it.

As summarized in this useful chart from Strenze (2015), meta-analyses of hundreds of studies have demonstrated that IQ is predictive of life success across many domains.

This is the basic validating fact when it comes to IQ: the use of IQ tests can help us predict things we want to predict and to explain things we want to explain.

Does IQ Linearly Predict Success?

Some people wonder if IQ’s relationship with success weakens above a certain threshold such that it is better described by a curvilinear trend rather than a simple linear one. Taleb brings this up and displays this graph:

This graph does show a decrement in IQ’s predictive validity as we move up the IQ scale. But there is still a positive correlation between SAT scores and IQ among those with IQs over 100. Just compare the distribution of scores among those with IQs of 110 and 130.

We can find other examples of this. For instance, Hegelund et al. (2018) analyzed data on over a million Danish men and various life outcomes. For several outcomes, IQ made little difference among those with IQs over 115.

However, for income the relationship was entirely linear.

We see the same thing in America if we look at the relationship between IQ and traffic incidents:

So this happens sometimes, but other times it doesn’t. Importantly, these situations do not arise with equal frequency. Coward and Sackett (1990) analyzed data from 174 studies on the relationship between IQ and job performance. A non-linear trend fit the relation better than a purely linear one only between 5 and 6 percent of the time, roughly what one would expect on the basis of chance alone. Similarly, Arneson et al. (2011) analyzed four large data sets on the relationship between IQ and education or military training outcomes and found in all four cases that the relationship was best described with a linear model. Thus, IQs relationship with occupational and educational outcomes is normally adequately described with a linear function.

I’ll say more about this below, but here note in passing that Taleb never explains why a non-linear trend would invalidate IQ in the first place.

IQ and Job Performance

Often times, IQ tests are used by employers in their hiring process because IQ scores are a good predictor of job performance. Taleb doesn’t see the point in this and writes that “If you want to detect how someone fares at a task, say loan sharking, tennis playing, or random matrix theory, make him/her do that task we don’t need theoretical exams for a real world function by probability-challenged psychologists.”

This argument has a lot of intuitive appeal and is probably convincing to people who aren’t familiar with this field of research. Within the field, however, it has long been known not only that IQ adds to an employer’s predictive ability even if they’ve also administered a work sample test but that, in fact, IQ is sometimes a better predictor of job performance than work sample tests are.

Given this, Taleb’s argument against using IQ tests in hiring is not compelling.

On Normality

Taleb also writes the following: “If IQ is Gaussian by construction and if real world performance were, net, fat tailed (it is), then either the covariance between IQ and performance doesn’t exist or it is uninformational.”

Taleb is correct to say that the distribution of many real world measures depart significantly from normality, that IQ scores are normally distributed by design, and that departures from normality can cause problems in statistical analysis. However, his conclusion from these facts, that IQ research is essentially meaningless, seems totally unwarranted.

Firstly, not all distributions are non-normal. Secondly, not all departures from normality are large enough to cause serious problems for standard statistical models. Thirdly, when departures from normality are large researchers typically do things like running variables through log transformations to achieve acceptable levels of normality, or run a different sort of analysis that doesn’t depend on a normal distribution. For Taleb’s criticism to be compelling, he would need to cite specific studies in which normality was departed from in a way which renders the actual statistical analysis done invalid and show that the removal of such studies from the IQ literature changes an important conclusion of said literature. He does nothing of the sort.

Moreover, Taleb’s conclusion, that the results of IQ research are meaningless, is clearly wrong. If such results were totally “uninformational”, they wouldn’t follow a sensible pattern. Yet, IQ correlates with job performance, and correlates better within jobs where IQ would be expected to matter more, and these correlates are consistent across studies. IQ correlates more strongly among identical twins than fraternal twins. IQ predicts performance in education. Etc. The probability of this theoretically expected pattern of relationships emerging if the analyses were so flawed that they were utter nonsense is extremely small, and so we are warranted in thinking that Taleb’s conclusion is false.

Taleb’s Measurement Standards

A consistent theme in Taleb’s article is that IQ tests don’t meet his standards for measurement. However, his standards for measurement are not standard in psychometrics, not justified by Taleb, and intuitively implausible.

Taleb writes that IQ is “not even technically a measure — it explains at best between 13% and 50% of the performance in some tasks (those tasks that are similar to the test itself), minus the data massaging and statistical cherrypicking by psychologists it doesn’t satisfy the monotonicity and transitivity required to have a measure. No measure that fails 60–95% of the time should be part of “science””.

Let’s break this down. First, Taleb says that a measurement must explain more than 50% of the variance in tasks it is used to predict. That is, if we have a measure the use of which reduces our degree of predictive error by 50%, said measure is invalid according to Taleb. Taleb gives no argument justifying this standard. I’m going to give two arguments to reject it.

First, reducing our error by such a degree could be very useful. Actually, its hard to think of any situation in which a 50% reduction in error wouldn’t be useful.

Secondly, if real world behavior is complex in the sense that it is caused by many variables of small to moderate effect then it will be impossible to create measures of single variables which explain more than 50% of the variance in behavior. In the social sciences, single variables normally explain less than 5% of the variance in important outcomes, suggesting that human behavior is, in this sense, complex. Given this, Taleb’s standards would be totally inappropriate for the behavioral sciences.

A related aspect of Taleb’s standards is that a measure not fail 60% or more of the time. Unfortunately, Taleb doesn’t define what “fail” means and it isn’t obvious what it would mean in the case of IQ research. It’s equally unclear where he got this number from.

However, even without knowing any of this it seems clear that Taleb’s standard is problematic. Consider a case in which your probability of correctly solving a problem is 1% without a given measure and 40% with said measure. This measure thus increases your probability of success by a factor of 40 and would be extremely useful. Yet, it has a fail rate of 60% and so, according to Taleb, can’t be used in science. This seems clearly irrational and so rejecting Taleb’s standard seems justified.

Finally, let’s consider Taleb’s standard of montonicity. This is getting back to the idea that IQ’s relationship with an outcome, say job performance, needs to be the same at all levels of job performance. As I’ve already reviewed, IQ’s relationship with important outcomes is largely linear. But this standard seems unwarranted to begin with. IQ is useful in so far as it let’s you make predictions. If IQ has a non-linear relation with some outcome, one merely needs to know that IQ will still be able to help us make useful predictions.

In fact, IQ can help us make predictions even if its relation with an outcome is nonlinear and we think its linear. For instance, if IQ’s relationship with some outcome becomes non-existent after an IQ of 120, it will still be predictive in the vast majority of cases and so our predictive accuracy will probably be greater than if we hadn’t used IQ at all.

Against Taleb’s standards for measurement, I prefer a practical standard. Firms and colleges are trying to predict success in their respective institutions and social scientists are trying to explain differences in interesting life outcomes. IQ tests help us do these things. Even with IQ tests, prediction is far from perfect. But it is better than it would be without them and that fact more than any other legitimizes their use.

Are High IQ People Pencil-Pushing Conformists?

Taleb also attributes various negative attributes to people who score highly on IQ tests. He says that people who score highly on IQ tests are paper shuffling obedient “intellectuals yet idiots” who are uncomfortable with uncertainty or not answering questions. Such people also lack critical thinking skills. In fact Taleb goes as far as saying that IQ “measures best the ability to be a good slave.” and that people with high IQs are “losers”.

Taleb’s treatment of this issue is entirely theoretical. He cites no empirical evidence nor does he make reference to empirical constructs by which his claims might be tested. However, it seems reasonable to suppose that, if Taleb is right, we should see a positive correlation between IQ and measures of conformity and risk aversion, and a negative correlation between IQ and leadership as well as critical thinking. But this is the opposite of what the relevant literature suggests.

First, consider conformity. Rhodes and Wood (1992) conducted a meta-analysis and found that people scoring high on IQ tests were less likely than average to be convinced by either conformity driven or persuasion driven rhetorical tactics. People who score high on intelligence tests are also more likely to be atheists and libertarians (Zuckerman et al. 2013, Carl 2014, Caplan and Miller 2010). These are minority viewpoints and not what we would expect if IQ correlated with conformity.

With respect to risk , Andersson et al. (2016) show the majority of research linking cognitive ability to risk preference either finds no relation between the two variables or a finds that high IQ individuals tend to be less risk averse than average.

Beauchamp et al. (2017) found that intelligence is positively associated with people’s propensity to take risk in a sample of 11,000 twins. This was true of risk seeking behavior in general as well as risk seeking behavior specifically with reference to finances.

With respect to leadership, Levine and Rubinstein (2015) find that IQ is positively correlated with the probability of someone being an entrepreneur. In a meta-analysis of 151 previous samples, Judge and Colbert (2004) found a weak positive relationship between a person’s IQ and their effectiveness as, or probability of becoming, a leader. This is hardly what we would expect if IQ measured a person’s ability to “a slave”.

With respect to critical thinking, IQ is strongly correlated with formal tests of rationality which gauge people’s propensity to incorrectly use mental heuristics or think in biased ways (Ritchie, 2017).

And finally, with respect to real world problems as measured by situational judgement tests, McDaniel et al. (2004) found a .46 correlation between people’s scores on SJTs and IQ tests in a meta-analysis of 79 previous correlations.

Thus, Taleb’s assertions about the psychological correlates of IQ are entirely at odds with what the relevant data suggests.

Population Differences in IQ

Taleb also makes four remarks about population differences in IQ.

First, he says “Another problem: when they say “black people are x standard deviations away”. Different populations have different variances, even different skewness and these comparisons require richer models. These are severe, severe mathematical flaws (a billion papers in psychometrics wouldn’t count if you have such a flaw)”

It is true that Black and White Americans differ in their degree of variance in IQ. Specifically, the Black standard deviation is smaller than the White standard deviation. This has been known about, and written about, for decades. But this doesn’t pose a problem for talking about the distance between groups in standard deviation units both because you can simply aggregate both groups into one and use a pooled standard deviation and because you can simply specify which standard deviation you are using.

Taleb’s second remark is that “The argument that “some races are better at running” hence [some inference about the brain] is stale: mental capacity is much more dimensional and not defined in the same way running 100 m dash is.”

I think the argument Taleb is imagining can be more charitably stated as follows: there are genetically driven differences between ethnic groups for many, indeed nearly all, variable physical traits outside the brain, so, unless we have specific reason to think otherwise, our default assumption should be that the same is true of the brain.

Put more precisely, we might say that the presence of genetically driven differences for most variable traits outside the brain increases the prior probability of genetically driven differences for variable traits within the brain. We might further explain that the distinction between brain and non-brain, while important to us, is not important to evolution, and that the same processes which cause non-brain differences can also cause brain differences. Thus, in the absence of other evidence, the prior probability of neurologically variable traits differing between ethnic groups due to genetics is high.

Whatever one may think of this argument, Taleb’s response, that we define mental traits differently than physical traits, is impotent. After all, Taleb doesn’t explicate why the difference in how we define physical and mental traits should be relevant to the logic of the argument. Nor, in fact, does he specify how said definitions differ at all. He merely asserts that some unspecified difference in definition exists and implies that this difference is relevant to the argument in an unspecified way. Obviously, this is not a compelling rebuttal.

Taleb’s third remark is as follows: “If you looked at Northern Europe from Ancient Babylon/Ancient Med/Egypt, you would have written the inhabitants off… Then look at what happened after 1600. Be careful when you discuss populations.”

Taleb is correct in the sense that the populations who are most developed today are always not the ones who were most developed in the ancient world. However, it is nonetheless true that we could have predicted which populations would end up being more economically developed if we had a more compelling model. Specifically, you can predict the majority of modern day variation in national economic development on the basis of ecological facts concerning, for instance, potential crop yield and animal domesticatability, of a region in pre-historic times (Spoalore et al. 2012).

The relationship between this fact and the idea that long run national development is influenced partially by genetically driven population differences is complicated since such ecological differences might directly cause differences in development, but might also cause differences in behavior via impacting selective pressures, or may do both.

Thus, the relationship between ancient and current variation in national development poses no obvious problem for partially biological narratives.

Finally, Taleb remarks “The same people hold that IQ is heritable, that it determines success, that Asians have higher IQs than Caucasians, degrade Africans, then don’t realize that China for about a Century had one order of magnitude lower GDP than the West.”

This comment suggests that Taleb simply hasn’t read the authors who argue that IQ is an important driver of national differences in wealth. The most famous proponents of this hypothesis are, easily, Richard Lynn and Tatu Vanhanen. In their 2012 book “Intelligence: a Unifying Construct for the Social Sciences“, they report that IQ can explain as much as 35% of national variation in wealth. They go on to posit several variables which might explain when nations strongly deviate from their expected wealth based on IQ, including, for instance, possessing large oil reserves and having a socialist economy.

Like individual differences, national differences are not caused by a single factor. Many variables are involved and IQ is only one of them. The fact that some variation in national wealth cannot be explained by IQ does nothing to diminish the proportion of variation in national wealth that canbe explained by IQ.

Can We Believe Psychological Research?

Now, Taleb actually admits that what he said had no evidence behind it. He gives a reason for this, stating that: “I have here no psychological references for backup: simply, the field is bust. So far

50% of the research does not replicate, and papers that do have weaker effect. ”

Presumably Taleb is referring to the Open Science Collaboration results form 2015. OSC (2015) replicated 100 psychological experiments and in only 47% of cases did the replications find the same thing as the original study. We might therefore think that the probability of some hypothesis being true is roughly 1 in 2 if it has been previously confirmed by a novel psychological study.

It’s important to realize that this has nothing specifically to do with psychology. Camerer et al. (2016) replicated 18 experiments in economics and found that 61% of them replicated. In fact, both psychology and experimental economics have far higher replication rates than do several other fields. For instance, Begeley and Ellis (2012) found that cancer research replicated only 11% of the time. Even worse, an attempt to replicate 17 brain imagining studies completely failed. That is, not a single finding replicated, suggesting that the replication rate in brain imagining research is, at most, 5.5%.

I am unaware of any attempts to directly measure the replication rates of most physical sciences, but Nature conducted a large survey of scientists and asked them to estimate the proportion of work in their fields that would replicate. I’ve averaged the results by field and as you can see, in no field do researchers expect work to replicate as much as 75% of the time.

Discipline Estimated Replication Rate
Physics 0.73
Other 0.52
Medicine 0.55
Material Science 0.60
Engineering 0.55
Earth and Environmental Science 0.58
Chemistry 0.65
Biology 0.59
Astronomy 0.65

Now, Taleb doesn’t tell us what replication rate he requires to care about what a science says. Still, one can easily imagine that his argument against caring about psychological data could also be used as an argument against caring about scientific data in general.

Regardless, let’s suppose that the probability of a social scientific finding replicating is roughly 50% and the probability of a hard science finding replicating is roughly 60%. How should we react to this purported fact?

First, it’s important the realize that the probability of some randomly formulated hypothesis about the world being true can be construed as being less than one half. This requires a certain way of looking at probability, but it doesn’t seem unreasonable to say that there are lots of ways the world isn’t and only one way the world is, so the vast majority of possible descriptions of the world are false. By contrast, replication research might be taken to suggest that something like half of hypotheses that have been confirmed by an initial study are true. Looked at this way, such rates actually represent significant epistemic progress.

More importantly, we can easily guess ahead of time which studies are going to replicate. Consider, for instance, what happens if we use a single metric, p values, to predict whether a study will replicate. That 2015 study on replication in psychology found a replicate rate of only 18% for findings with an initial p value between .04 and .05 and 63% for findings with an initial p value of less than .001. Similarly, that 2016 study on replication in economics found a replication rate of 88% for findings with an initial p value of less than .001.

Using these and similar clues, multiple papers have found that researchers are able to correctly predict which of a set of previous findings will successfully replicate the strong majority of the time(Camerer et al., 2018 Forsell et al., 2018).

Thus, if we consumer research intelligently, we can be a lot less worried about buying into false positive results.

Returning to psychology, and intelligence research in particular, it is important to note that a lack of statistical power is one important cause of low replication rates which does not apply to IQ research to the degree that it applies to most disciplines.

Specifically, while no field has the sort of statistical power we would theoretically like it to have, intelligence research comes a lot closer than most fields do.

Citation Discipline Mean / Median Power
Button et al. (2013) Neuroscience 21%
Brain Imaging 8%
Smaldino and McElreath (2016) Social and Behavioral Sciences 24%
Szucs and Ioannidis (2017) Cognitive Neuroscience 14%
Psychology 23%
Medical 23%
Mallet et al (2017) Breast Cancer 16%
Glaucoma 11%
Rheumatoid Arthritis 19%
Alzheimer’s 9%
Epilepsy 24%
MS 24%
Parkinson’s 27%
Lortie-Forgues and Inglis (2019) Education 23%
Nuijten et al (2018) Intelligence 49%
Intelligence – Group Differences 57%

Thus, intelligence research should replicate better than most research does. Given this, whatever our general level of skepticism about social science is, our skepticism about intelligence research should be lesser.

Of course, low power isn’t the only reason that research fails to replicate, and the most important solution to this problem is to simply not rely on un-replicated research.


Additional information

Isabel S. Campos. Researcher, ISPA – Instituto Universitário, Rua Jardim do Tabaco, 34, Lisbon 1149-041, Portugal. E-mail: [email protected]

Current themes of research:

Psychology of Education. Emotion and Decision-Making.

Most relevant publications in the field of Psychology of Education:

Campos, I. S., Almeida, L. S., Ferreira, A. I., & Martinez, L. F. (2012). Working memory as separable subsystems: A study with Portuguese primary school children. The Spanish Journal of Psychology, 15(2), forthcoming.

Leandro S. Almeida. Full Professor, Instituto de Educação, Universidade do Minho, Campus de Gualtar, 4710-057 Braga, Portugal. E-mail: [email protected]

Current themes of research:

Learning and Cognition. Psychology of Education. Adjustment and Success in Higher Education.

Most relevant publications in the field of Psychology of Education:

Caires, S., & Almeida, L. S. (2007). Positive aspects of the teacher training supervision: The student teachers' perspective. European Journal of Psychology of Education, 22(4), 515–528.

Campos, I. S., Almeida, L. S., Ferreira, A. I., & Martinez, L. F. (2012). Working memory as separable subsystems: A study with Portuguese primary school children. The Spanish Journal of Psychology, 15(2), forthcoming.

Diniz, A., Pocinho, M., & Almeida, L. (2011). Cognitive abilities, sociocultural background and academic achievement. Psicothema, 23(4), 695–700.

Ferrando, M., Prieto, M., Almeida, L. S., Ferrandiz, C., Bermejo, R., Lopez-Pina, J., & Fernandez, M. (2011). Trait emotional intelligence and academic performance: Controlling for the effects of IQ, personality, and self-concept. Journal of Psychoeducational Assessment, 29(2), 150–159.

Soares, A., Guisande, A. M., Almeida, L. S., & Paramo, F. M. (2009). Academic achievement in first-year Portuguese college students: The role of academic preparation and learning strategies. International Journal of Psychology, 44(3), 204–212.

Aristides I. Ferreira. Assistant Professor of Human Resource Management and Organizational Behavior, Business Research Unit, Instituto Universitário de Lisboa (ISCTE-IUL), Av. Forças Armadas, 1649-026 Lisboa, Portugal. E-mail: [email protected]

Current themes of research:

Cognition. Psychological Assessment. Psychometrics. Organizational Behavior. Human Resource Management.

Most relevant publications in the field of Psychology of Education:

Brito, L., Almeida, L. S., Ferreira, A. I., & Guisande, M. A. (2011). Contribución de los procesos y contenidos en la diferenciación cognitiva en la infancia: Un estudio con escolares Portugueses. Revista Infancia Y Aprendizage, 34(3), 323–336.

Campos, I. S., Almeida, L. S., Ferreira, A. I., & Martinez, L. F. (2012). Working memory as separable subsystems: A study with Portuguese primary school children. The Spanish Journal of Psychology, 15(2), forthcoming.

Ferreira, A. I., Almeida, L., & Prieto, G. (2011). The role of processes and contents in human memory: An Item Response Theory approach. Journal of Cognitive Psychology, 23(7), 873–885.

Ferreira, A. I., & Hill, M. M. (2008). Organisational cultures in public and private portuguese universities: A case study. Higher Education, 55(6), 637–650.

Ferreira, A. I., & Martinez, L. F. (2012). Presenteeism and burnout among teachers in public and private Portuguese elementary schools. The International Journal of Human Resource Management, forthcoming.

Ferreira, A. I., Martinez, L. F., & Guisande, M. A. (2009). Risky behavior, personality traits and road accidents among university students. European Journal of Education and Psychology, 2(2), 79–98.

Luis F. Martinez. Assistant Professor of Human Resource Management and Organizational Behavior, Business Research Unit, Instituto Universitário de Lisboa (ISCTE-IUL), Av. Forças Armadas, 1649-026 Lisboa, Portugal. E-mail: [email protected]

Current themes of research:

Emotion and Decision-Making. Economic Psychology. Organizational Behavior. Human Resource Management.

Most relevant publications in the field of Psychology of Education:

Campos, I. S., Almeida, L. S., Ferreira, A. I., & Martinez, L. F. (2012). Working memory as separable subsystems: A study with Portuguese primary school children. The Spanish Journal of Psychology, 15(2), forthcoming.

Ferreira, A. I., & Martinez, L. F. (2012). Presenteeism and burnout among teachers in public and private Portuguese elementary schools. The International Journal of Human Resource Management, forthcoming.

Ferreira, A. I., Martinez, L. F., & Guisande, M. A. (2009). Risky behavior, personality traits and road accidents among university students. European Journal of Education and Psychology, 2(2), 79–98.

Martinez, L. M. F., Zeelenberg, M., & Rijsman, J. B. (2011). Behavioural consequences of regret and disappointment in social bargaining games. Cognition and Emotion, 25, 351–359.

Glória Ramalho. Associate Professor, ISPA – Instituto Universitário, Rua Jardim do Tabaco, 34, Lisbon 1149-041, Portugal and Researcher at Gabinete de Avaliação Educacional do Ministério da Educação (GAVE). E-mail: [email protected]

Current themes of research:

Most relevant publications in the field of Psychology of Education:

Costa, A., Martins, M. R. D., & Ramalho, G. (Eds.) (2000). Literacia e sociedade: Contribuições pluridisciplinares. Lisboa: Caminho.

Ramalho, G., Perrenoud, P., & Ferrer, A. T. (2003). Avaliação dos resultados escolares: Medidas para tornar o sistema mais eficaz. Lisboa: Edições ASA.


Other Popular Topics

Rules for Operations on Inequalities

TI-Nspire For Dummies Cheat Sheet

How to Convert between Fractions and Repeating Dec.

Important Terms in Game Theory

10 Math Concepts You Can’t Ignore

How to Calculate Monthly Payments for a Sinking Fund

Using Probability When Hitting the Slot Machines

Change between Slope-Intercept and Standard Form

Solve a Minimization Problem Using Linear Programm.

Dummies has always stood for taking on complex concepts and making them easy to understand. Dummies helps everyone be more knowledgeable and confident in applying what they know. Whether it’s to pass that big test, qualify for that big promotion or even master that cooking technique people who rely on dummies, rely on it to learn the critical skills and relevant information necessary for success.

Copyright © 2021 & Trademark by John Wiley & Sons, Inc. All rights reserved.


What are learning styles?

Google has a field day when you look up learning styles. As is the case with most complicated terminology, some of the definitions make sense and some don’t. Here are three explanations of learning styles that sit right:

    states that “Learning Styles (LS) can be defined as the way humans prefer to concentrate on, store, and remember new and difficult information.” OK, so far so good. That makes sense.
    describes learning styles as “the characteristic of cognitive, affective, social, and physiological behaviors that serve as relatively stable indicators of how learners perceive, interact with, and respond to the learning environment.”
  1. On the other hand, Brown University jumps more to the point and expresses that learning styles are how you process and comprehend information in learning situations.

Brown also breaks up learning styles into six categories (which we’ll revisit later):

  • Visual learning
  • Kinesthetic learning
  • Individual learning
  • Tactile learning
  • Auditory learning
  • Group learning

In short, learning styles have to do with an individual’s preferred way of learning — or, as Gardner put it, how students approach a task.


Other Popular Topics

Rules for Operations on Inequalities

TI-Nspire For Dummies Cheat Sheet

How to Convert between Fractions and Repeating Dec.

Important Terms in Game Theory

10 Math Concepts You Can’t Ignore

How to Calculate Monthly Payments for a Sinking Fund

Using Probability When Hitting the Slot Machines

Change between Slope-Intercept and Standard Form

Solve a Minimization Problem Using Linear Programm.

Dummies has always stood for taking on complex concepts and making them easy to understand. Dummies helps everyone be more knowledgeable and confident in applying what they know. Whether it’s to pass that big test, qualify for that big promotion or even master that cooking technique people who rely on dummies, rely on it to learn the critical skills and relevant information necessary for success.

Copyright © 2021 & Trademark by John Wiley & Sons, Inc. All rights reserved.


Additional information

Isabel S. Campos. Researcher, ISPA – Instituto Universitário, Rua Jardim do Tabaco, 34, Lisbon 1149-041, Portugal. E-mail: [email protected]

Current themes of research:

Psychology of Education. Emotion and Decision-Making.

Most relevant publications in the field of Psychology of Education:

Campos, I. S., Almeida, L. S., Ferreira, A. I., & Martinez, L. F. (2012). Working memory as separable subsystems: A study with Portuguese primary school children. The Spanish Journal of Psychology, 15(2), forthcoming.

Leandro S. Almeida. Full Professor, Instituto de Educação, Universidade do Minho, Campus de Gualtar, 4710-057 Braga, Portugal. E-mail: [email protected]

Current themes of research:

Learning and Cognition. Psychology of Education. Adjustment and Success in Higher Education.

Most relevant publications in the field of Psychology of Education:

Caires, S., & Almeida, L. S. (2007). Positive aspects of the teacher training supervision: The student teachers' perspective. European Journal of Psychology of Education, 22(4), 515–528.

Campos, I. S., Almeida, L. S., Ferreira, A. I., & Martinez, L. F. (2012). Working memory as separable subsystems: A study with Portuguese primary school children. The Spanish Journal of Psychology, 15(2), forthcoming.

Diniz, A., Pocinho, M., & Almeida, L. (2011). Cognitive abilities, sociocultural background and academic achievement. Psicothema, 23(4), 695–700.

Ferrando, M., Prieto, M., Almeida, L. S., Ferrandiz, C., Bermejo, R., Lopez-Pina, J., & Fernandez, M. (2011). Trait emotional intelligence and academic performance: Controlling for the effects of IQ, personality, and self-concept. Journal of Psychoeducational Assessment, 29(2), 150–159.

Soares, A., Guisande, A. M., Almeida, L. S., & Paramo, F. M. (2009). Academic achievement in first-year Portuguese college students: The role of academic preparation and learning strategies. International Journal of Psychology, 44(3), 204–212.

Aristides I. Ferreira. Assistant Professor of Human Resource Management and Organizational Behavior, Business Research Unit, Instituto Universitário de Lisboa (ISCTE-IUL), Av. Forças Armadas, 1649-026 Lisboa, Portugal. E-mail: [email protected]

Current themes of research:

Cognition. Psychological Assessment. Psychometrics. Organizational Behavior. Human Resource Management.

Most relevant publications in the field of Psychology of Education:

Brito, L., Almeida, L. S., Ferreira, A. I., & Guisande, M. A. (2011). Contribución de los procesos y contenidos en la diferenciación cognitiva en la infancia: Un estudio con escolares Portugueses. Revista Infancia Y Aprendizage, 34(3), 323–336.

Campos, I. S., Almeida, L. S., Ferreira, A. I., & Martinez, L. F. (2012). Working memory as separable subsystems: A study with Portuguese primary school children. The Spanish Journal of Psychology, 15(2), forthcoming.

Ferreira, A. I., Almeida, L., & Prieto, G. (2011). The role of processes and contents in human memory: An Item Response Theory approach. Journal of Cognitive Psychology, 23(7), 873–885.

Ferreira, A. I., & Hill, M. M. (2008). Organisational cultures in public and private portuguese universities: A case study. Higher Education, 55(6), 637–650.

Ferreira, A. I., & Martinez, L. F. (2012). Presenteeism and burnout among teachers in public and private Portuguese elementary schools. The International Journal of Human Resource Management, forthcoming.

Ferreira, A. I., Martinez, L. F., & Guisande, M. A. (2009). Risky behavior, personality traits and road accidents among university students. European Journal of Education and Psychology, 2(2), 79–98.

Luis F. Martinez. Assistant Professor of Human Resource Management and Organizational Behavior, Business Research Unit, Instituto Universitário de Lisboa (ISCTE-IUL), Av. Forças Armadas, 1649-026 Lisboa, Portugal. E-mail: [email protected]

Current themes of research:

Emotion and Decision-Making. Economic Psychology. Organizational Behavior. Human Resource Management.

Most relevant publications in the field of Psychology of Education:

Campos, I. S., Almeida, L. S., Ferreira, A. I., & Martinez, L. F. (2012). Working memory as separable subsystems: A study with Portuguese primary school children. The Spanish Journal of Psychology, 15(2), forthcoming.

Ferreira, A. I., & Martinez, L. F. (2012). Presenteeism and burnout among teachers in public and private Portuguese elementary schools. The International Journal of Human Resource Management, forthcoming.

Ferreira, A. I., Martinez, L. F., & Guisande, M. A. (2009). Risky behavior, personality traits and road accidents among university students. European Journal of Education and Psychology, 2(2), 79–98.

Martinez, L. M. F., Zeelenberg, M., & Rijsman, J. B. (2011). Behavioural consequences of regret and disappointment in social bargaining games. Cognition and Emotion, 25, 351–359.

Glória Ramalho. Associate Professor, ISPA – Instituto Universitário, Rua Jardim do Tabaco, 34, Lisbon 1149-041, Portugal and Researcher at Gabinete de Avaliação Educacional do Ministério da Educação (GAVE). E-mail: [email protected]

Current themes of research:

Most relevant publications in the field of Psychology of Education:

Costa, A., Martins, M. R. D., & Ramalho, G. (Eds.) (2000). Literacia e sociedade: Contribuições pluridisciplinares. Lisboa: Caminho.

Ramalho, G., Perrenoud, P., & Ferrer, A. T. (2003). Avaliação dos resultados escolares: Medidas para tornar o sistema mais eficaz. Lisboa: Edições ASA.


Nassim Taleb on IQ

Nassim Taleb has published an attack on intelligence research that is getting a lot of attention and so I thought I would respond to it.

As summarized in this useful chart from Strenze (2015), meta-analyses of hundreds of studies have demonstrated that IQ is predictive of life success across many domains.

This is the basic validating fact when it comes to IQ: the use of IQ tests can help us predict things we want to predict and to explain things we want to explain.

Does IQ Linearly Predict Success?

Some people wonder if IQ’s relationship with success weakens above a certain threshold such that it is better described by a curvilinear trend rather than a simple linear one. Taleb brings this up and displays this graph:

This graph does show a decrement in IQ’s predictive validity as we move up the IQ scale. But there is still a positive correlation between SAT scores and IQ among those with IQs over 100. Just compare the distribution of scores among those with IQs of 110 and 130.

We can find other examples of this. For instance, Hegelund et al. (2018) analyzed data on over a million Danish men and various life outcomes. For several outcomes, IQ made little difference among those with IQs over 115.

However, for income the relationship was entirely linear.

We see the same thing in America if we look at the relationship between IQ and traffic incidents:

So this happens sometimes, but other times it doesn’t. Importantly, these situations do not arise with equal frequency. Coward and Sackett (1990) analyzed data from 174 studies on the relationship between IQ and job performance. A non-linear trend fit the relation better than a purely linear one only between 5 and 6 percent of the time, roughly what one would expect on the basis of chance alone. Similarly, Arneson et al. (2011) analyzed four large data sets on the relationship between IQ and education or military training outcomes and found in all four cases that the relationship was best described with a linear model. Thus, IQs relationship with occupational and educational outcomes is normally adequately described with a linear function.

I’ll say more about this below, but here note in passing that Taleb never explains why a non-linear trend would invalidate IQ in the first place.

IQ and Job Performance

Often times, IQ tests are used by employers in their hiring process because IQ scores are a good predictor of job performance. Taleb doesn’t see the point in this and writes that “If you want to detect how someone fares at a task, say loan sharking, tennis playing, or random matrix theory, make him/her do that task we don’t need theoretical exams for a real world function by probability-challenged psychologists.”

This argument has a lot of intuitive appeal and is probably convincing to people who aren’t familiar with this field of research. Within the field, however, it has long been known not only that IQ adds to an employer’s predictive ability even if they’ve also administered a work sample test but that, in fact, IQ is sometimes a better predictor of job performance than work sample tests are.

Given this, Taleb’s argument against using IQ tests in hiring is not compelling.

On Normality

Taleb also writes the following: “If IQ is Gaussian by construction and if real world performance were, net, fat tailed (it is), then either the covariance between IQ and performance doesn’t exist or it is uninformational.”

Taleb is correct to say that the distribution of many real world measures depart significantly from normality, that IQ scores are normally distributed by design, and that departures from normality can cause problems in statistical analysis. However, his conclusion from these facts, that IQ research is essentially meaningless, seems totally unwarranted.

Firstly, not all distributions are non-normal. Secondly, not all departures from normality are large enough to cause serious problems for standard statistical models. Thirdly, when departures from normality are large researchers typically do things like running variables through log transformations to achieve acceptable levels of normality, or run a different sort of analysis that doesn’t depend on a normal distribution. For Taleb’s criticism to be compelling, he would need to cite specific studies in which normality was departed from in a way which renders the actual statistical analysis done invalid and show that the removal of such studies from the IQ literature changes an important conclusion of said literature. He does nothing of the sort.

Moreover, Taleb’s conclusion, that the results of IQ research are meaningless, is clearly wrong. If such results were totally “uninformational”, they wouldn’t follow a sensible pattern. Yet, IQ correlates with job performance, and correlates better within jobs where IQ would be expected to matter more, and these correlates are consistent across studies. IQ correlates more strongly among identical twins than fraternal twins. IQ predicts performance in education. Etc. The probability of this theoretically expected pattern of relationships emerging if the analyses were so flawed that they were utter nonsense is extremely small, and so we are warranted in thinking that Taleb’s conclusion is false.

Taleb’s Measurement Standards

A consistent theme in Taleb’s article is that IQ tests don’t meet his standards for measurement. However, his standards for measurement are not standard in psychometrics, not justified by Taleb, and intuitively implausible.

Taleb writes that IQ is “not even technically a measure — it explains at best between 13% and 50% of the performance in some tasks (those tasks that are similar to the test itself), minus the data massaging and statistical cherrypicking by psychologists it doesn’t satisfy the monotonicity and transitivity required to have a measure. No measure that fails 60–95% of the time should be part of “science””.

Let’s break this down. First, Taleb says that a measurement must explain more than 50% of the variance in tasks it is used to predict. That is, if we have a measure the use of which reduces our degree of predictive error by 50%, said measure is invalid according to Taleb. Taleb gives no argument justifying this standard. I’m going to give two arguments to reject it.

First, reducing our error by such a degree could be very useful. Actually, its hard to think of any situation in which a 50% reduction in error wouldn’t be useful.

Secondly, if real world behavior is complex in the sense that it is caused by many variables of small to moderate effect then it will be impossible to create measures of single variables which explain more than 50% of the variance in behavior. In the social sciences, single variables normally explain less than 5% of the variance in important outcomes, suggesting that human behavior is, in this sense, complex. Given this, Taleb’s standards would be totally inappropriate for the behavioral sciences.

A related aspect of Taleb’s standards is that a measure not fail 60% or more of the time. Unfortunately, Taleb doesn’t define what “fail” means and it isn’t obvious what it would mean in the case of IQ research. It’s equally unclear where he got this number from.

However, even without knowing any of this it seems clear that Taleb’s standard is problematic. Consider a case in which your probability of correctly solving a problem is 1% without a given measure and 40% with said measure. This measure thus increases your probability of success by a factor of 40 and would be extremely useful. Yet, it has a fail rate of 60% and so, according to Taleb, can’t be used in science. This seems clearly irrational and so rejecting Taleb’s standard seems justified.

Finally, let’s consider Taleb’s standard of montonicity. This is getting back to the idea that IQ’s relationship with an outcome, say job performance, needs to be the same at all levels of job performance. As I’ve already reviewed, IQ’s relationship with important outcomes is largely linear. But this standard seems unwarranted to begin with. IQ is useful in so far as it let’s you make predictions. If IQ has a non-linear relation with some outcome, one merely needs to know that IQ will still be able to help us make useful predictions.

In fact, IQ can help us make predictions even if its relation with an outcome is nonlinear and we think its linear. For instance, if IQ’s relationship with some outcome becomes non-existent after an IQ of 120, it will still be predictive in the vast majority of cases and so our predictive accuracy will probably be greater than if we hadn’t used IQ at all.

Against Taleb’s standards for measurement, I prefer a practical standard. Firms and colleges are trying to predict success in their respective institutions and social scientists are trying to explain differences in interesting life outcomes. IQ tests help us do these things. Even with IQ tests, prediction is far from perfect. But it is better than it would be without them and that fact more than any other legitimizes their use.

Are High IQ People Pencil-Pushing Conformists?

Taleb also attributes various negative attributes to people who score highly on IQ tests. He says that people who score highly on IQ tests are paper shuffling obedient “intellectuals yet idiots” who are uncomfortable with uncertainty or not answering questions. Such people also lack critical thinking skills. In fact Taleb goes as far as saying that IQ “measures best the ability to be a good slave.” and that people with high IQs are “losers”.

Taleb’s treatment of this issue is entirely theoretical. He cites no empirical evidence nor does he make reference to empirical constructs by which his claims might be tested. However, it seems reasonable to suppose that, if Taleb is right, we should see a positive correlation between IQ and measures of conformity and risk aversion, and a negative correlation between IQ and leadership as well as critical thinking. But this is the opposite of what the relevant literature suggests.

First, consider conformity. Rhodes and Wood (1992) conducted a meta-analysis and found that people scoring high on IQ tests were less likely than average to be convinced by either conformity driven or persuasion driven rhetorical tactics. People who score high on intelligence tests are also more likely to be atheists and libertarians (Zuckerman et al. 2013, Carl 2014, Caplan and Miller 2010). These are minority viewpoints and not what we would expect if IQ correlated with conformity.

With respect to risk , Andersson et al. (2016) show the majority of research linking cognitive ability to risk preference either finds no relation between the two variables or a finds that high IQ individuals tend to be less risk averse than average.

Beauchamp et al. (2017) found that intelligence is positively associated with people’s propensity to take risk in a sample of 11,000 twins. This was true of risk seeking behavior in general as well as risk seeking behavior specifically with reference to finances.

With respect to leadership, Levine and Rubinstein (2015) find that IQ is positively correlated with the probability of someone being an entrepreneur. In a meta-analysis of 151 previous samples, Judge and Colbert (2004) found a weak positive relationship between a person’s IQ and their effectiveness as, or probability of becoming, a leader. This is hardly what we would expect if IQ measured a person’s ability to “a slave”.

With respect to critical thinking, IQ is strongly correlated with formal tests of rationality which gauge people’s propensity to incorrectly use mental heuristics or think in biased ways (Ritchie, 2017).

And finally, with respect to real world problems as measured by situational judgement tests, McDaniel et al. (2004) found a .46 correlation between people’s scores on SJTs and IQ tests in a meta-analysis of 79 previous correlations.

Thus, Taleb’s assertions about the psychological correlates of IQ are entirely at odds with what the relevant data suggests.

Population Differences in IQ

Taleb also makes four remarks about population differences in IQ.

First, he says “Another problem: when they say “black people are x standard deviations away”. Different populations have different variances, even different skewness and these comparisons require richer models. These are severe, severe mathematical flaws (a billion papers in psychometrics wouldn’t count if you have such a flaw)”

It is true that Black and White Americans differ in their degree of variance in IQ. Specifically, the Black standard deviation is smaller than the White standard deviation. This has been known about, and written about, for decades. But this doesn’t pose a problem for talking about the distance between groups in standard deviation units both because you can simply aggregate both groups into one and use a pooled standard deviation and because you can simply specify which standard deviation you are using.

Taleb’s second remark is that “The argument that “some races are better at running” hence [some inference about the brain] is stale: mental capacity is much more dimensional and not defined in the same way running 100 m dash is.”

I think the argument Taleb is imagining can be more charitably stated as follows: there are genetically driven differences between ethnic groups for many, indeed nearly all, variable physical traits outside the brain, so, unless we have specific reason to think otherwise, our default assumption should be that the same is true of the brain.

Put more precisely, we might say that the presence of genetically driven differences for most variable traits outside the brain increases the prior probability of genetically driven differences for variable traits within the brain. We might further explain that the distinction between brain and non-brain, while important to us, is not important to evolution, and that the same processes which cause non-brain differences can also cause brain differences. Thus, in the absence of other evidence, the prior probability of neurologically variable traits differing between ethnic groups due to genetics is high.

Whatever one may think of this argument, Taleb’s response, that we define mental traits differently than physical traits, is impotent. After all, Taleb doesn’t explicate why the difference in how we define physical and mental traits should be relevant to the logic of the argument. Nor, in fact, does he specify how said definitions differ at all. He merely asserts that some unspecified difference in definition exists and implies that this difference is relevant to the argument in an unspecified way. Obviously, this is not a compelling rebuttal.

Taleb’s third remark is as follows: “If you looked at Northern Europe from Ancient Babylon/Ancient Med/Egypt, you would have written the inhabitants off… Then look at what happened after 1600. Be careful when you discuss populations.”

Taleb is correct in the sense that the populations who are most developed today are always not the ones who were most developed in the ancient world. However, it is nonetheless true that we could have predicted which populations would end up being more economically developed if we had a more compelling model. Specifically, you can predict the majority of modern day variation in national economic development on the basis of ecological facts concerning, for instance, potential crop yield and animal domesticatability, of a region in pre-historic times (Spoalore et al. 2012).

The relationship between this fact and the idea that long run national development is influenced partially by genetically driven population differences is complicated since such ecological differences might directly cause differences in development, but might also cause differences in behavior via impacting selective pressures, or may do both.

Thus, the relationship between ancient and current variation in national development poses no obvious problem for partially biological narratives.

Finally, Taleb remarks “The same people hold that IQ is heritable, that it determines success, that Asians have higher IQs than Caucasians, degrade Africans, then don’t realize that China for about a Century had one order of magnitude lower GDP than the West.”

This comment suggests that Taleb simply hasn’t read the authors who argue that IQ is an important driver of national differences in wealth. The most famous proponents of this hypothesis are, easily, Richard Lynn and Tatu Vanhanen. In their 2012 book “Intelligence: a Unifying Construct for the Social Sciences“, they report that IQ can explain as much as 35% of national variation in wealth. They go on to posit several variables which might explain when nations strongly deviate from their expected wealth based on IQ, including, for instance, possessing large oil reserves and having a socialist economy.

Like individual differences, national differences are not caused by a single factor. Many variables are involved and IQ is only one of them. The fact that some variation in national wealth cannot be explained by IQ does nothing to diminish the proportion of variation in national wealth that canbe explained by IQ.

Can We Believe Psychological Research?

Now, Taleb actually admits that what he said had no evidence behind it. He gives a reason for this, stating that: “I have here no psychological references for backup: simply, the field is bust. So far

50% of the research does not replicate, and papers that do have weaker effect. ”

Presumably Taleb is referring to the Open Science Collaboration results form 2015. OSC (2015) replicated 100 psychological experiments and in only 47% of cases did the replications find the same thing as the original study. We might therefore think that the probability of some hypothesis being true is roughly 1 in 2 if it has been previously confirmed by a novel psychological study.

It’s important to realize that this has nothing specifically to do with psychology. Camerer et al. (2016) replicated 18 experiments in economics and found that 61% of them replicated. In fact, both psychology and experimental economics have far higher replication rates than do several other fields. For instance, Begeley and Ellis (2012) found that cancer research replicated only 11% of the time. Even worse, an attempt to replicate 17 brain imagining studies completely failed. That is, not a single finding replicated, suggesting that the replication rate in brain imagining research is, at most, 5.5%.

I am unaware of any attempts to directly measure the replication rates of most physical sciences, but Nature conducted a large survey of scientists and asked them to estimate the proportion of work in their fields that would replicate. I’ve averaged the results by field and as you can see, in no field do researchers expect work to replicate as much as 75% of the time.

Discipline Estimated Replication Rate
Physics 0.73
Other 0.52
Medicine 0.55
Material Science 0.60
Engineering 0.55
Earth and Environmental Science 0.58
Chemistry 0.65
Biology 0.59
Astronomy 0.65

Now, Taleb doesn’t tell us what replication rate he requires to care about what a science says. Still, one can easily imagine that his argument against caring about psychological data could also be used as an argument against caring about scientific data in general.

Regardless, let’s suppose that the probability of a social scientific finding replicating is roughly 50% and the probability of a hard science finding replicating is roughly 60%. How should we react to this purported fact?

First, it’s important the realize that the probability of some randomly formulated hypothesis about the world being true can be construed as being less than one half. This requires a certain way of looking at probability, but it doesn’t seem unreasonable to say that there are lots of ways the world isn’t and only one way the world is, so the vast majority of possible descriptions of the world are false. By contrast, replication research might be taken to suggest that something like half of hypotheses that have been confirmed by an initial study are true. Looked at this way, such rates actually represent significant epistemic progress.

More importantly, we can easily guess ahead of time which studies are going to replicate. Consider, for instance, what happens if we use a single metric, p values, to predict whether a study will replicate. That 2015 study on replication in psychology found a replicate rate of only 18% for findings with an initial p value between .04 and .05 and 63% for findings with an initial p value of less than .001. Similarly, that 2016 study on replication in economics found a replication rate of 88% for findings with an initial p value of less than .001.

Using these and similar clues, multiple papers have found that researchers are able to correctly predict which of a set of previous findings will successfully replicate the strong majority of the time(Camerer et al., 2018 Forsell et al., 2018).

Thus, if we consumer research intelligently, we can be a lot less worried about buying into false positive results.

Returning to psychology, and intelligence research in particular, it is important to note that a lack of statistical power is one important cause of low replication rates which does not apply to IQ research to the degree that it applies to most disciplines.

Specifically, while no field has the sort of statistical power we would theoretically like it to have, intelligence research comes a lot closer than most fields do.

Citation Discipline Mean / Median Power
Button et al. (2013) Neuroscience 21%
Brain Imaging 8%
Smaldino and McElreath (2016) Social and Behavioral Sciences 24%
Szucs and Ioannidis (2017) Cognitive Neuroscience 14%
Psychology 23%
Medical 23%
Mallet et al (2017) Breast Cancer 16%
Glaucoma 11%
Rheumatoid Arthritis 19%
Alzheimer’s 9%
Epilepsy 24%
MS 24%
Parkinson’s 27%
Lortie-Forgues and Inglis (2019) Education 23%
Nuijten et al (2018) Intelligence 49%
Intelligence – Group Differences 57%

Thus, intelligence research should replicate better than most research does. Given this, whatever our general level of skepticism about social science is, our skepticism about intelligence research should be lesser.

Of course, low power isn’t the only reason that research fails to replicate, and the most important solution to this problem is to simply not rely on un-replicated research.


Howard Gardner’s 9 Types of Intelligence

Many of us are familiar with three general categories in which people learn: visual learners, auditory learners, and kinesthetic learners.

Beyond these three general categories, many theories of and approaches toward human potential have been developed. Among them is the theory of multiple intelligences, developed by Howard Gardner, Ph.D., Professor of Education at Harvard University. Gardner’s early work in psychology and later in human cognition and human potential led to the development of the initial six intelligences. Today there are nine intelligences and the possibility of others may eventually expand the list.

These intelligences (or competencies) relate to a person’s unique aptitude set of capabilities and ways they might prefer to demonstrate intellectual abilities.


People have different strengths and intelligences. For example, students who are “interviewed” as a means to gain access to a course may be mislabeled as being less than desirable because of inappropriate assessment (poorly written interview questions, bias toward a perceived “perfect student,” and other narrow criteria).

“In life, we need people who collectively are good at different things. A well-balanced world, and well-balanced organizations and teams, are necessarily comprised of people who possess different mixtures of intelligences. This gives that group a fuller collective capacity than a group of identical able specialists”

Gardner’s multiple intelligences theory can be used for curriculum development, planning instruction, selection of course activities, and related assessment strategies. Instruction which is designed to help students develop their strengths can also trigger their confidence to develop areas in which they are not as strong. Students’ multiple learning preferences can be addressed when instruction includes a range of meaningful and appropriate methods, activities, and assessments.

In summary, integrate educational theories, teaching strategies, and other pedagogic tools in meaningful and useful ways to better address the needs of students. Gardner himself asserts that educators should not follow one specific theory or educational innovation when designing instruction but instead employ customized goals and values appropriate to their teaching and student needs. Addressing the multiple intelligences and potential of students can help instructors personalize their instruction and methods of assessment.

The Nine Types of Intelligence

1. Naturalist Intelligence (“Nature Smart”)

Designates the human ability to discriminate among living things (plants, animals) as well as sensitivity to other features of the natural world (clouds, rock configurations). This ability was clearly of value in our evolutionary past as hunters, gatherers, and farmers it continues to be central in such roles as botanist or chef. It is also speculated that much of our consumer society exploits the naturalist intelligences, which can be mobilized in the discrimination among cars, sneakers, kinds of makeup, and the like.

2. Musical Intelligence (“Musical Smart”)

Musical intelligence is the capacity to discern pitch, rhythm, timbre, and tone. This intelligence enables us to recognize, create, reproduce, and reflect on music, as demonstrated by composers, conductors, musicians, vocalist, and sensitive listeners. Interestingly, there is often an affective connection between music and the emotions and mathematical and musical intelligences may share common thinking processes. Young adults with this kind of intelligence are usually singing or drumming to themselves. They are usually quite aware of sounds others may miss.

3. Logical-Mathematical Intelligence (Number/Reasoning Smart)

Logical-mathematical intelligence is the ability to calculate, quantify, consider propositions and hypotheses, and carry out complete mathematical operations. It enables us to perceive relationships and connections and to use abstract, symbolic thought sequential reasoning skills and inductive and deductive thinking patterns. Logical intelligence is usually well developed in mathematicians, scientists, and detectives. Young adults with lots of logical intelligence are interested in patterns, categories, and relationships. They are drawn to arithmetic problems, strategy games and experiments.

4. Existential Intelligence (Spirit Smart)

Sensitivity and capacity to tackle deep questions about human existence, such as the meaning of life, why do we die, and how did we get here.

5. Interpersonal Intelligence (People Smart”)

Interpersonal intelligence is the ability to understand and interact effectively with others. It involves effective verbal and nonverbal communication, the ability to note distinctions among others, sensitivity to the moods and temperaments of others, and the ability to entertain multiple perspectives. Teachers, social workers, actors, and politicians all exhibit interpersonal intelligence. Young adults with this kind of intelligence are leaders among their peers, are good at communicating, and seem to understand others’ feelings and motives.

6. Bodily-Kinesthetic Intelligence (“Body Smart”)

Bodily kinesthetic intelligence is the capacity to manipulate objects and use a variety of physical skills. This intelligence also involves a sense of timing and the perfection of skills through mind–body union. Athletes, dancers, surgeons, and craftspeople exhibit well-developed bodily kinesthetic intelligence.

7. Linguistic Intelligence (Word Smart)

Linguistic intelligence is the ability to think in words and to use language to express and appreciate complex meanings. Linguistic intelligence allows us to understand the order and meaning of words and to apply meta-linguistic skills to reflect on our use of language. Linguistic intelligence is the most widely shared human competence and is evident in poets, novelists, journalists, and effective public speakers. Young adults with this kind of intelligence enjoy writing, reading, telling stories or doing crossword puzzles.

8. Intra-personal Intelligence (Self Smart”)

Intra-personal intelligence is the capacity to understand oneself and one’s thoughts and feelings, and to use such knowledge in planning and directioning one’s life. Intra-personal intelligence involves not only an appreciation of the self, but also of the human condition. It is evident in psychologist, spiritual leaders, and philosophers. These young adults may be shy. They are very aware of their own feelings and are self-motivated.

9. Spatial Intelligence (“Picture Smart”)

Spatial intelligence is the ability to think in three dimensions. Core capacities include mental imagery, spatial reasoning, image manipulation, graphic and artistic skills, and an active imagination. Sailors, pilots, sculptors, painters, and architects all exhibit spatial intelligence. Young adults with this kind of intelligence may be fascinated with mazes or jigsaw puzzles, or spend free time drawing or daydreaming.

For your Naturalist Learners
From: Overview of the Multiple Intelligences Theory, Association for Supervision and Curriculum Development and Thomas Armstrong.com and Howard Gardner’s Theory of Multiple Intelligences by Northern Illinois University, Faculty Development and Instructional Design Center.

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Is there a basic mathematical relationship between IQ and learning speed? - Psychology

Arguing that "reason, intelligence, logic, knowledge are not synomous. . .", Howard Gardner (1983) proposed a new view of intelligence that is rapidly being incorporated in school curricula. In his Theory of Multiple Intelligences, Gardner expanded the concept of intelligence to also include such areas as music, spacial relations, and interpersonal knowledge in addition to mathematical and linguistic ability.

This digest discusses the origins of Gardner's Theory of Multiple Intelligences, his definition of intelligence, the incorporation of the Theory of Multiple Intelligences into the classroom, and its role in alternative assessment practices.

Gardner defines intelligence as "the capacity to solve problems or to fashion products that are valued in one or more cultural setting" (Gardner & Hatch, 1989). Using biological as well as cultural research, he formulated a list of seven intelligences. This new outlook on intelligence differs greatly from the traditional view which usually recognizes only two intelligences, verbal and computational. The seven intelligences Gardner defines are:

Logical-Mathematical Intelligence--consists of the ability to detect patterns, reason deductively and think logically. This intelligence is most often associated with scientific and mathematical thinking.

Linguistic Intelligence-- involves having a mastery of language. This intelligence includes the ability to effectively manipulate language to express oneself rhetorically or poetically. It also allows one to use language as a means to remember information.

Spatial Intelligence-- gives one the ability to manipulate and create mental images in order to solve problems. This intelligence is not limited to visual domains-- Gardner notes that spatial intelligence is also formed in blind children.

Musical Intelligence-- encompasses the capability to recognize and compose musical pitches, tones, and rhythms. (Auditory functions are required for a person to develop this intelligence in relation to pitch and tone, but it is not needed for the knowledge of rhythm.)

Bodily-Kinesthetic Intelligence-- is the ability to use one's mental abilities to coordinate one's own bodily movements. This intelligence challenges the popular belief that mental and physical activity are unrelated.

The Personal Intelligences-- includes interpersonal intelligence -- the ability to understand and discern the feelings and intentions of others-- and intrapersonal intelligence --the ability to understand one's own feelings and motivations. These two intelligences are separate from each other. Nevertheless, because of their close association in most cultures, they are often linked together.

Although the intelligences are anatomically separated from each other, Gardner claims that the seven intelligences very rarely operate independently. Rather, the intelligences are used concurrently and typically complement each other as individuals develop skills or solve problems. For example, a dancer can excel in his art only if he has 1) strong musical intelligence to understand the rhythm and variations of the music, 2) interpersonal intelligence to understand how he can inspire or emotionally move his audience through his movements, as well as 3) bodily-kinesthetic intelligence to provide him with the agility and coordination to complete the movements successfully.

Gardner argues that there is both a biological and cultural basis for the multiple intelligences. Neurobiological research indicates that learning is an outcome of the modifications in the synaptic connections between cells. Primary elements of different types of learning are found in particular areas of the brain where corresponding transformations have occurred. Thus, various types of learning results in synaptic connections in different areas of the brain. For example, injury to the Broca's area of the brain will result in the loss of one's ability to verbally communicate using proper syntax. Nevertheless, this injury will not remove the patient's understanding of correct grammar and word usage.

In addition to biology, Gardner (1983) argues that culture also plays a large role in the development of the intelligences. All societies value different types of intelligences. The cultural value placed upon the ability to perform certain tasks provides the motivation to become skilled in those areas. Thus, while particular intelligences might be highly evolved in many people of one culture, those same intelligences might not be as developed in the individuals of another.

Using Multiple Intelligences in the Classroom

Accepting Gardner's Theory of Multiple Intelligences has several implications for teachers in terms of classroom instruction. The theory states that all seven intelligences are needed to productively function in society. Teachers, therefore, should think of all intelligences as equally important. This is in great contrast to traditional education systems which typically place a strong emphasis on the development and use of verbal and mathematical intelligences. Thus, the Theory of Multiple Intelligences implies that educators should recognize and teach to a broader range of talents and skills.

Another implication is that teachers should structure the presentation of material in a style which engages most or all of the intelligences. For example, when teaching about the revolutionary war, a teacher can show students battle maps, play revolutionary war songs, organize a role play of the signing of the Declaration of Independence, and have the students read a novel about life during that period. This kind of presentation not only excites students about learning, but it also allows a teacher to reinforce the same material in a variety of ways. By activating a wide assortment of intelligences, teaching in this manner can facilitate a deeper understanding of the subject material.

Everyone is born possessing the seven intelligences. Nevertheless, all students will come into the classroom with different sets of developed intelligences. This means that each child will have his own unique set of intellectual strengths and weaknesses. These sets determine how easy (or difficult) it is for a student to learn information when it is presented in a particular manner. This is commonly referred to as a learning style. Many learning styles can be found within one classroom. Therefore, it is impossible, as well as impractical, for a teacher to accommodate every lesson to all of the learning styles found within the classroom. Nevertheless the teacher can show students how to use their more developed intelligences to assist in the understanding of a subject which normally employs their weaker intelligences (Lazear, 1992). For example, the teacher can suggest that an especially musically intelligent child learn about the revolutionary war by making up a song about what happened.

Toward a More Authentic Assessment

As the education system has stressed the importance of developing mathematical and linguistic intelligences, it often bases student success only on the measured skills in those two intelligences. Supporters of Gardner's Theory of Multiple Intelligences believe that this emphasis is unfair. Children whose musical intelligences are highly developed, for example, may be overlooked for gifted programs or may be placed in a special education class because they do not have the required math or language scores. Teachers must seek to assess their students' learning in ways which will give an accurate overview of the their strengths and weaknesses.

As children do not learn in the same way, they cannot be assessed in a uniform fashion. Therefore, it is important that a teacher create an "intelligence profiles" for each student. Knowing how each student learns will allow the teacher to properly assess the child's progress (Lazear, 1992). This individualized evaluation practice will allow a teacher to make more informed decisions on what to teach and how to present information.

Traditional tests (e.g. multiple choice, short answer, essay. . .) require students to show their knowledge in a predetermined manner. Supporters of Gardner's theory claim that a better approach to assessment is to allow students to explain the material in their own ways using the different intelligences. Preferred assessment methods include student portfolios, independent projects, student journals, and assigning creative tasks. An excellent source for a more in-depth discussion on these different evaluation practices is Lazear (1992).

Schools have often sought to help students develop a sense of accomplishment and self-confidence. Gardner's Theory of Multiple Intelligences provides a theoretical foundation for recognizing the different abilities and talents of students. This theory acknowledges that while all students may not be verbally or mathematically gifted, children may have an expertise in other areas, such as music, spatial relations, or interpersonal knowledge. Approaching and assessing learning in this manner allows a wider range of students to successfully participate in classroom learning.

Blythe, T., & Gardner H. (1990). A school for all intelligences.Educational Leadership. 47(7), 33-37.

Fogarty, R., & Stoehr, J. (1995). Integrating curricula with multiple intelligences. Teams, themes, and threads. K-college. Palatine, IL: IRI Skylight Publishing Inc. (ERIC Document Reproduction Service ED No. 383 435)

Gardner, H. (1983). Frames of Mind. New York: Basic Book Inc.

Gardner, H. (1991) The unschooled mind: how children think and how schools should teach.New York: Basic Books Inc.

Gardner, H., & Hatch, T. (1989). Multiple intelligences go to school: Educational implications of the theory of multiple intelligences. Educational Researcher, 18(8), 4-9.

Kornhaber, M., & Gardner, H. (1993, March). Varieties of excellence: identifying and assessing children's talents. A series on authentic assessment and accountability. New York: Columbia University, Teachers College, National Center for Restructuring Education, Schools, and Teaching. (ERIC Document Reproduction Service No. ED 363 396)

Lazear, David. (1991). Seven ways of teaching: The artistry of teaching with multiple intelligences. Palatine, IL: IRI Skylight Publishing Inc. (ERIC Document Reproduction Service No. ED 382 374) (highly recommended)

Lazear, David (1992). Teaching for Multiple Intelligences. Fastback 342 Bloomington, IN: Phi Delta Kappan Educational Foundation. (ERIC Document Reproduction Service No. ED 356 227) (highly recommended)


The Definitive Word on Intelligence

Arthur R. Jensen, The g Factor, Praeger Publishers, 1998, 648 pp.

Arthur Jensen of U.C. Berkeley is one of the greatest social scientists of our time. He virtually single-handedly resurrected the scientific study of intelligence, and he has been at the center of many breakthroughs in this field. Needless to say, he is a courageous man, who has never let hysterical opposition or even death threats keep him from studying some of the most important and contentious issues we face.

The g Factor is only the latest of the many publications that resulted from what can now be seen as a watershed event: the 1969 appearance in the Harvard Educational Review of Prof. Jensen’s famous article on the heritability of IQ and how difficult it is to raise. This article not only reestablished the connection between genetics and intelligence but set the direction of Prof. Jensen’s career. He has since written countless articles in this field and three major books: Educability and Group Differences (1973), Bias in Mental Testing (1980), and now, The g Factor.

These books chart the recent remarkable progress in the study of intelligence. If Prof. Jensen had so dominated any less controversial field he would certainly be a candidate for the Nobel Prize. Unfortunately, his real stature is recognized only by a small number of specialists and professional colleagues, but the implications of his work continue to reverberate through the larger society. Whatever recognition he may ultimately receive, his work has gone far to set the study of mental ability once more on a firmly scientific basis.

This book is an investigation of the nature of intelligence, the extent to which it is under genetic control, and its uneven distribution between individuals and groups. The first part is a complete and sometimes technical treatment of “the g factor” itself, which appears to be a unitary mental ability underlying all activities we think of as requiring intelligence. “Factors” are the end result of a mathematical procedure called factor analysis, and the g factor is the “general” factor of intelligence, first hypothesized by the British psychologist, Charles Spearman (1863-1945). Spearman thought of g as a direct analogy to the “G” of physics, that is Newton’s gravitational constant. Spearman’s view, substantiated by almost a century of research, was that g is of central importance to psychology just as g was to Newtonian physics.

G can be thought of as the undifferentiated raw cognitive power of the brain. It cannot be directly measured, but it manifests itself in all types of cognitive activity, and people who are good at one kind of mental test tend to be good at all of them. To use the statistical term, a person’s different abilities are correlated, and similar abilities tend to correlate most closely with each other. For example, someone who is exceptionally good at any mathematical test is likely to be very good at all mathematical tests — but he is likely to perform well on verbal tests, too. As we will see, g is at work when even the smallest demands are made on the mind.

If people take enough different kinds of mental tests, their scores can be analyzed for factors, or the tendency of the correlations between similar abilities to cluster in groups. There will be factors for such things as verbal, musical, mathematical, and spatial manipulation abilities. Further analysis of these factors reveals a fundamental factor common to them all, which is the g factor.

We can therefore imagine a series of different factories in the brain, all powered by the same energy source. One of the factories manufactures solutions to mathematical problems, while another produces correct understandings of words and sentences. Other factories produce solutions to other kinds of mental problems, but all of them can be thought of as running off a common power source, which is g.

People differ in the efficiency of their individual factories, which is why smart people have different strengths in different areas despite being smart in a general sort of way. But people differ most significantly in the level of the general power source, or g. Someone with an IQ of 100 may have a math factory that is relatively more efficient than his verbal or music factory, but even in math he is likely to fall well behind someone with an IQ of 130 whose math factory is relatively less efficient than his verbal factory. It is the difference in levels of power available to all of a person’s factories that produce the marked differences in ability that characterize our species.

Many kinds of mental performance can be taught and people can show improvement, but what is improving is an ability that is not g. As Prof. Jensen explains, “At the level of psychometrics [mental testing], ideally, g may be thought of as a distillate of the common source of individual differences in all mental tests, completely stripped of their distinctive features, of information content, skill, strategy, and the like.”

Interestingly, Prof. Jensen reports that it is at the highest levels of g that people show the most variation in abilities that are independent of g. Thus, very intelligent people may have markedly different mental ability profiles despite similar levels of g. If all the factories are getting lots of power from their common source, some of the factories are likely to be unusually efficient so that the pattern of different levels of efficiency can differ considerably from one smart person to another.

Some critics have complained that g is not real because it cannot be measured directly and must be derived by a complex statistical process. Prof. Jensen shows that it is not, for this reason, artificial. If there were no g factor, sophisticated mathematics could not coax it into existence. Moreover, the same g factor is found in all human populations, and can be derived from the results of mental tests prepared by people who have never heard of g or who have even doubted there was such a factor. g can be calculated only because it exists, and in that sense is purely objective. Prof. Jensen believes that it reflects one of the basic functions of the brain, and that although all normal people share the same biological structures they differ greatly in the efficiency of certain neurological processes.

Direct assessment of brain functions gives strong evidence that g is a real, physiological phenomenon, and Prof. Jensen has been a pioneer in using what are called elementary cognitive tasks (ECTs) to study intelligence. The simplest sort of ECT involves a test device with two push-buttons. The subject holds down the black button while he waits for a light to go on inside the smaller, white button. He then presses the illuminated button as quickly as possible. This measures two things. The first is reaction time: the time between the light going on and the subject taking his finger off the black button. The second is movement time: the time it takes the subject to move his finger from the black button to the illuminated button.

Obviously, this is a very simple (indeed, elementary) task, though tests of this kind can be made more complicated. For example, there can be a number of smaller buttons that can light up in different patterns, requiring the subject to make slightly more complicated decisions before moving his finger. We do not think of this sort of thing as mentally demanding — no one ever “fails” these tests — but the neurological processing that goes into these very simple tasks is closely related to intelligence.

Prof. Jensen has found that reaction speed is strongly correlated with g level, but that the highest correlation is between g and consistency of reaction time. With a set of scores from various different ECTs, it is possible to achieve a 0.7 correlation with g as calculated from conventional IQ tests. This approaches the g correlation (0.8) of Ravens Progressive Matrices, the IQ test that comes the closest to measuring g itself. Surprising as it may seem, careful monitoring of the processes that underlie ECTs can give results that are so reliable they rival pencil-and-paper tests.

ECT performance matches group differences in intelligence. It is worse in children than in adults, and better in gifted children than in normal children. Blacks have quicker movement times than whites while whites have quicker and more consistent reaction times. Asians do slightly better than whites, and performance for no group improves with practice ECTs appear to measure something basic to the brain.

Another direct assessment of mental processing is the inspection time test. This uses an instrument called a tachistoscope to throw an image on a screen for a very brief period. Starting at the millisecond level, which is too quick for anyone to see the image, the exposure is gradually increased until a subject can just make it out. There is a correlation of .54 between speed of inspection time and IQ — remarkably high for a task that is so different from an IQ test. Once again, the test seems to be measuring a neurological process closely associated with mental processing.

Yet another direct assessment is the study of brain waves. Prof. Jensen explains that a wave pattern called average evoked potential can be analyzed in specialized ways that show a surprisingly high correlation with IQ.

Finally, researchers have devised something that is essentially a direct test of brain efficiency. The brain’s fuel is glucose, or simple sugar. When a radioactive isotope of glucose is injected into a subject’s blood stream it is possible to measure the rate at which the brain takes it up and metabolizes it. When rate of metabolism is measured while subjects are taking an IQ test, the high scorers use less sugar than the low scorers, with a remarkable correlation with IQ of around .7 or .8. The less powerful brains get wrong answers despite burning more fuel. If we return to the analogy of the brain as composed of factories, the common power supply simply appears to be less efficient.

If advances continue to be made in direct assessment of the brain, conventional IQ testing may be superseded. This would certainly silence any complaints about “test bias.”

Because the issue of whether education or environment can influence IQ levels is central to so much policy-making, The g Factor thoroughly covers the question of heritability. Kinship and adoption studies have provided some of the most illuminating data on this question, and Prof. Jensen reports them in detail.

Some of the most significant findings are the correlations of IQs of identical twins reared in the same family (.86), identical twins separated at birth and reared in different families (.75) and fraternal twins reared in the same family (.60). That identical twins separated at birth should have more similar IQs than fraternal twins reared by the same parents is perhaps the single most powerful argument for the view that genes have a greater effect on IQ than environment. As Prof. Jensen points out, “similarities in the MZA’s [monozygotic (identical) twins reared apart] environments cannot possibly account for more than a minute fraction of the IQ correlation of +.75 between MZAs.”

Studies of siblings and adopted children likewise confirm the power of heredity in determining differences in IQ, and it is now generally agreed among specialists that 60 to 80 percent of human IQ variation is due to genes. This does not mean, however, that the remaining environmental influences are well understood or can be used to raise IQ. As Prof. Jensen explains, “a large part of the specific environmental variance appears to be due to the additive effects of a large number of more or less random and largely physical events — developmental ‘noise’ — with small, but variable positive and negative influences on the neurophysiological substrate of mental growth.”

What is this developmental “noise”? “[S]uch effects as childhood diseases, traumas, and the like, as well as prenatal effects such as mother-fetus incompatibility of blood antigens, maternal health, and perinatal effects of anoxia and other complications in the birth process, could each have a small adverse effect on mental development.” These appear to be the kind of non-genetic factors that influence IQ, and they are not the sort of thing that can be easily manipulated.

As Prof. Jensen makes emphatically clear, the non-genetic influence comes only slightly, if at all, from what are called between-family differences: education of parents, social status, family income, school quality, etc. Liberals believe that these are the crucial factors that make people different from each other, but liberals are wrong. IQ (like other personality traits) is astonishingly impervious to any but the most degraded and unfavorable environments.

Prof. Jensen calls the environmentalist view “the sociologist’s fallacy.” It is true that children from wealthy homes tend to be smarter than children from poor homes, but wealth does not make them smart. They get genes for intelligence from their smart parents, and their parents are likely to be well off (and have homes full of books and speak in complete sentences) because they are smart. Of course, children do differ from their parents in intelligence, and these differences explain how families rise and fall. A person’s IQ has a correlation of .7 with his own adult socio-economic status but only about .4 with that of his parents.

Error though it be, the sociologist’s fallacy has driven not only an enormous number of government uplift programs but several well-publicized private efforts to raise the IQs of poor black children. Prof. Jensen reviews the results of the Milwaukee Project, Head Start, and the Abecedarian Project, some of which made extraordinary attempts to improve environments.

In some cases, the early results were very encouraging: gains of 20 or even 30 points compared to control groups. But as Prof. Jensen convincingly argues, what the children learned at intensive “infant stimulation centers” and the like was information and strategies that helped them take the tests. g very probably did not change. In most cases, administrators did not give a battery of tests and attempt to calculate g. Instead, they gave the same test at different ages and rejoiced to find improvement.

Professor Jensen gives a striking example of how training can improve test results without raising g. He notes many children’s IQ tests have a memory component: How long a string of letters or numbers can the child repeat back to the tester? Most adults can’t remember more than about seven numbers, but with lots of practice and training, people can remember as many as 70 or even 100 digits. They can do this because they develop a specific strategy or skill, not because their memory or g level has improved. The tricks a person uses to remember 70 digits are so specialized, in fact, that they do not even help the same person remember more than an average number of letters (rather than digits)!

Children who took part in these widely-acclaimed IQ-raising programs probably learned specific skills of this kind during the thousands of hours of instruction they received. But even the most intensive enrichment programs had virtually no permanent effect on school performance or IQ, which suggests that g itself was unchanged. Prof. Jensen concludes that IQ cannot be appreciably increased by specialized education.

It is true that the IQ test scores of children are affected to some degree by the environment their parents make for them. This is almost certainly because they learn more facts and absorb test-taking strategies and not because the love and care of good parents improves g. In fact, as children grow older they create environments that suit their own genetic endowments, and Prof. Jensen is categorical about what then happens: “By adulthood, all of the IQ correlation between biologically related persons is genetic . . . [T]he environmental contribution to the familial correlations is nil.” Surprising as it may seem, once a child grows up, his IQ score is similar to that of family members only because he is genetically related to them, not because they spent many years in the same household.

Prof. Jensen is equally forthright in explaining that genes account for the well-established IQ differences between the races. First, he points out that approximately half — or 50,000 — of the genes that vary in human beings play a role in brain functions, and that 30,000 affect the brain exclusively. It would be astonishing if genes did not play a central role in intelligence and if the races, which differ physically in so many ways, did not differ in brain function.

He also offers an arresting refutation of the fashionable view that race is purely a social construct and is not biological. Prof. Jensen likens race to the visible colors. A rainbow forms when the wave-length of light changes continuously and uniformly, but we do not perceive a continuous change. Instead, we see distinct bands of color. Though there may be some blurring of race at the edges because of cross mating, races are as distinct as the bands of visible color. Prof. Jensen also cites the increasingly persuasive genetic evidence for the biological distinctness of different populations (see figure, below).

A number of elegant demonstrations based on the principle of regression toward the mean strongly suggest a genetic origin for group differences. This principle is a biological law according to which parents who are at the extremes of any trait are likely to have children who are less extreme. Two very tall parents are likely to have children who are not quite so tall, and two very short parents are likely to have children who are not quite so short. In the children, these traits revert toward the average, or the mean. The same effect is found in intelligence, but the mean toward which the black IQ regresses is a full 15 points lower than the white mean.

Therefore, when black couples and white couples are matched for IQ, the black/white IQ difference in their children increases as parental IQ increases. In other words, high IQ is an anomaly in all races, but more of an anomaly for blacks than for whites, and the children of high-IQ blacks regress further because they are regressing toward a lower mean.

Prof. Jensen reports a study of high-IQ children in one school district that provides more evidence for the difference in means. When white and black students were perfectly matched for IQs of 120, the average IQs of the siblings of the whites was 113 whereas the average IQs for the siblings of the blacks was 99. Among blacks, an IQ of 120 is simply a much greater deviation from the norm than it is for whites, and this is reflected in the IQs of their more ordinary siblings.

Genetic distance between any two groups is represented by the total length
of the lines separating them.

Regression toward the mean explains something that has always baffled the “sociologists:” children of low-income whites (and Asians) get better SAT scores than the children of high-income blacks. If environment controls IQ, the children of wealthy blacks should be enjoying the benefits of good environment. They are, but those benefits are meager and do not make up for the effects of heredity and the lower mean toward which black children regress.

There is no non-genetic explanation for group differences that can account for phenomena of this kind, but they are perfectly consistent with widely accepted principles of genetics. Specialists understand the force of arguments of this kind, which is why the view that “racism” and other environmental factors cause the black/white IQ gap persists mostly among the ignorant — who are the great majority.

More strong evidence for a substantially different biological mean for IQ is found in studies of the low end of the IQ distribution curve as well. Mental retardation — IQs below 70 — is generally of two types, familial and organic. Familial retardation occurs in children who are otherwise normal but were simply dealt a very poor hand of the genes that affect intelligence. Given a normal distribution of intelligence, a few people are inevitably going to have very low IQs, just as a few will have very high ones. Organic retardation, on the other hand, is caused by clear biological defects, like Down’s syndrome (Mongolism) and children who suffer from it are obviously abnormal.

An important racial difference lies in the fact that half of whites with IQs below 70 are organic retardates but only 12.5 percent of the blacks are. The source of this difference is the racial disparity in naturally occurring distributions of intelligence. Given that the distribution curve for black intelligence is shifted approximately 15 points toward the left, a substantially larger proportion of otherwise normal blacks will fall below an IQ of 70.

The opposite is true at the high end of the curve. The percentage of whites with IQs higher than 130 is 20 times that of blacks. Because there are approximately six times as many whites as blacks in America, in real terms there are perhaps 120 times more whites than blacks with IQs at this level. This is why, without racial preferences, it is impossible to admit large numbers of blacks to competitive universities or to promote them to challenging positions.

Brain and head size studies likewise confirm the biological origins of group differences. It is now well established that brain size correlates with intelligence, and Prof. Jensen reports that the heads of black newborns are a full .4 standard deviation smaller than those of whites.

Likewise, it has long been known that near-sightedness, or myopia, is correlated with intelligence children with IQs over 130 are three to five times more likely to be nearsighted than children with normal IQs. There seems to be no functional, cause-and-effect connection between myopia and intelligence, but a pleiotropic relationship exists in that some of the same genes affect both traits. Intelligence and myopia are somehow “side effects” of each other to some degree. Prof. Jensen finds that myopia is most common in Jews, next in Asians, then in whites, and least common in blacks — precisely the distribution one would expect. Moreover, reading does not cause myopia. An oculist can examine the eyes of children who are too young to read and who are not yet near-sighted, and accurately predict whether they will need glasses later in life.

It is well known that the test score gap between blacks and whites varies from one IQ test to another, and that the gap narrows on the least abstract, most information-laden tests. Prof. Jensen explains that the real difference lies in the extent to which a test measures g the more g-“loaded” a test is and the fewer specific non-g abilities it measures, the greater the black/white gap.

Like many others who have studied the question, Prof. Jensen finds that the racial gap in IQ is increasing because of dysgenic birth patterns. In both races, less intelligent mothers are having more children than more intelligent mothers, but the disproportions are higher among blacks than whites. Also, since blacks have children, on average, two years earlier than whites, the generation time for blacks is shorter and dysgenic effects spread more rapidly.

One of Prof. Jensen’s most interesting racial findings is that the average IQ difference for blacks and whites in the same social class is 12 points — almost as great as the average difference between the two races (there is an average 17-point difference between any two people in the population picked at random). This is explained not only by preferential policies but also by racial differences in IQ distribution. If, for example, a demanding profession requires a minimum IQ of 125, blacks in that profession will tend to have IQs that cluster at the minimum, whereas whites will show greater variety. Because of this effect, the IQ gap between blacks and whites in the same social class narrows as one moves down the social scale.

Prof. Jensen finds that the geographic distribution of IQ is also uneven. For both blacks and whites, there is a continuous gradient that rises from the south towards the north and west. The gradient is sharper for blacks than whites, and both gradients are apparent in pre-school children, so regional differences in education do not explain it.

It has been widely reported that from infancy black children develop motor skills more rapidly than whites. Interestingly, Prof. Jensen finds that lower-class children (both white and black) develop more quickly than upper-class children, which suggests that slow maturation and high intelligence are correlated not just between races but within races.

For the most part, Prof. Jensen does not make policy recommendations the facts alone are persuasive enough. He does point out, though, that life itself is a kind of continuous intelligence test, and that high g is one of the most important ingredients of success. He explains that scores on a highly g-loaded test are the best indicators of performance on any but the most specialized jobs. IQ is an excellent predictor for performance even on jobs that require manual dexterity and coordination. To a remarkable degree, g is the central mental characteristic of humans. Of course, intelligence is not everything. It takes more than brains to become a doctor — it takes persistence and discipline, too — but persistence is not enough. For many things, a certain level of g is indispensable, and low g cuts off desirable options at every stage of life. Low g is therefore a more accurate predictor of achievement than high g, since a lack of intelligence cannot usually be made up for by other qualities whereas high intelligence can be wasted.

When people with low g are scattered through otherwise normal communities it affects only individuals. Friends and relatives step in to help them. However, as Prof. Jensen points out, when people of low intelligence gather in large numbers, as they do in welfare housing, society falls apart. Prof. Jensen notes that in America there are now entire apartment blocks in which, even with welfare, the residents cannot get by without help from social workers. Dysgenic trends and increased immigration of low-g stock mean areas like this will only expand.

In this connection, Prof. Jensen makes some interesting observations about adult illiteracy. Most people assume that the cause is poor schooling, but he argues that the problem is usually not the process of decoding written language but understanding it. Most illiterates do no better on reading comprehension tests when the selections are read to them than when they try to do the reading themselves! Illiteracy, in Prof. Jensen’s view, is much more a problem of low g than of somehow not learning how to read.

There are a few points on which Prof. Jensen’s data differ from results AR has reported elsewhere. Some researchers have found that although the average IQs of men and women are the same, a greater standard deviation for men means that more of them are bunched at both high and low IQs. Prof. Jensen does not find sufficient evidence to draw this conclusion. He does confirm the standard sex differences in verbal and spatial abilities and even reports that some higher mammals show the typical male superiority in spatial ability. He also writes that in addition to their well-known advantage in verbal ability, one of the largest sex differences favoring women is in something called “speed and accuracy,” which is similar to clerical checking.

Prof. Jensen also takes up the question of why black women are so much more successful than black men. They are more likely to graduate from high school and college, pass high-level civil service tests, and enter skilled professions. This difference is not found among whites, and some researchers have wondered if black women may have a higher average IQ than black men. Once again, Prof. Jensen finds no such difference — but he offers no other explanation.

Prof. Jensen also differs from researchers who explain part of the black/white crime rate difference in terms of high black testosterone levels and an inability to defer gratification. He argues that population differences in g alone explain differences in crime rates. He notes that criminals of all races have IQs that are some 10 points below those of their siblings, and finds that within the same ranges of IQ, blacks and whites have essentially the same crime rates.

Needless to say, Prof. Jensen has spent his career disagreeing with others, and from time to time in The g Factor he must explain why his critics are wrong — and he is always a gentleman. Even with those who have disagreed with him in strong terms, he is more than generous in pointing out the parts of their theories that may be correct, and couches his own criticism in the gentlest terms. He treats his wildest, least scientific critics to nothing more than dignified silence: The names of Leon Kamin and Stephen Jay Gould do not even appear in an otherwise exhaustively researched and footnoted work.

The g Factor is not an easy book to read. Prof. Jensen writes clearly and repeats explanations when it would be unreasonable to expect perfect recall in his readers, but he writes for an informed, even specialist audience. He has already begun collaboration with a journalist on a more popular version of The g Factor. But those who are willing to invest the effort this book requires, will find that it is the monumental work of an extraordinary mind. A review can only begin to touch on its breadth and detail. This book is likely to become one of the landmark works in psychology, and it is the great good fortune of our society that a man of Prof. Jensen’s stature has made his career in this crucially important but thankless field.


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How Is Math Used in Psychology?

Psychological researchers use statistical methods to determine if certain treatments are effective, and clinical psychologists must be able to interpret statistical data to interpret diagnostic material and studies. Psychologists working with groups also rely on statistics for measurements.

As with all sciences, psychology is partially based on a mathematical foundation. Hypotheses need to be tested, and statistical analysis provides a means of determining whether treatments appear to be effective or not. Psychologists working with patients must read studies to determine what scientific literature shows to be most effective, and they must have a strong understanding of statistics to do so.

Diagnosing problems is an essential part of clinical psychology, but the results rarely show up on brain scans or other tools. As a result, psychologists rely on surveys and information provided by patients. Psychologists must be able to interpret these results to make the best diagnosis possible. Understanding the prevalence of various problems also helps.

Psychologists often work with groups of people in schools, offices and other organizations, and much of their work requires them to locate and measure various trends. However, they must be able to determine whether trends are real effects or just statistical noise. Again, statistics play a key role, and using p-values and Bayesian statistical methods allows psychologists to make important observations.