Annals of Psychometry: 35 years of talent selection

David Lubinski kindly shared the recent paper linked below. He and I will both be at ISIR 2019, the annual meeting of the International Society for Intelligence Research.

Psychological Constellations Assessed at Age 13 Predict Distinct Forms of Eminence 35 Years Later (Psychological Science 2019, Vol. 30(3) 444–454).

The paper studies two populations:

1. 13 year olds identified through talented and gifted programs, all of whom scored in the top 1% in at least one of Mathematical or Verbal ability (based on SAT score; some scored at the 1 in 10k level). They were also assessed using a preference inventory (SOV = Study of Values). About 10% of this cohort of 677 were identified 35 years later as having achieved "eminence" in their careers -- e.g., full professor at R1 university, senior executive status, ...

2. Exceptional STEM graduate students at top 15 PhD programs, evaluated using GRE and SOV. If I'm not mistaken many or all of these students were NSF Graduate Fellows. About 20% of this population of 605 had achieved STEM eminence 25 years later.

I would estimate that only about one in a thousand individuals drawn randomly from the general population attains eminence as defined in the paper. Thus, the talent selection used to form cohorts 1&2 (e.g., SAT administered at age 13) produced success rates as much as 100 times higher than in the base population.

See related posts: 1 2 3

Psychological Constellations Assessed at Age 13 Predict Distinct Forms of Eminence 35 Years Later

Psychological Science 2019, Vol. 30(3) 444–454

Brian O. Bernstein, David Lubinski, and Camilla P. Benbow
Department of Psychology & Human Development, Vanderbilt University

Abstract
This investigation examined whether math/scientific and verbal/humanistic ability and preference constellations, developed on intellectually talented 13-year-olds to predict their educational outcomes at age 23, continue to maintain their longitudinal potency by distinguishing distinct forms of eminence 35 years later. Eminent individuals were defined as those who, by age 50, had accomplished something rare: creative and highly impactful careers (e.g., full professors at research-intensive universities, Fortune 500 executives, distinguished judges and lawyers, leaders in biomedicine, award-winning journalists and writers). Study 1 consisted of 677 intellectually precocious youths, assessed at age 13, whose leadership and creative accomplishments were assessed 35 years later. Study 2 constituted a constructive replication—an analysis of 605 top science, technology, engineering, and math (STEM) graduate students, assessed on the same predictor constructs early in graduate school and assessed again 25 years later. In both samples, the same ability and preference parameter values, which defined math/scientific versus verbal/humanistic constellations, discriminated participants who ultimately achieved distinct forms of eminence from their peers pursuing other life endeavors.

Note that even within both cohorts SAT / GRE were useful in predicting achievement outcomes. Click figures below for larger versions.

One hundred years of research on intellectual precocity

David Lubinski sent me this comprehensive review of 100 years of research on intellectual precocity. Someone has already posted an un-gated copy online at the link below. Many of the stunning SMPY graphs summarizing their longitudinal (30+ year) study of a population of gifted individuals (including one group measured at the 1 in 10,000 ability level at age 13) appear in the paper. More SMPY.

From Terman to Today: A Century of Findings on Intellectual Precocity

David Lubinski
Vanderbilt University

One hundred years of research (1916–2016) on intellectually precocious youth is reviewed, painting a portrait of an extraordinary source of human capital and the kinds of learning opportunities needed to facilitate exceptional accomplishments, life satisfaction, and positive growth. The focus is on those studies conducted on individuals within the top 1% in general or specific (mathematical, spatial, or verbal reasoning) abilities. Early insights into the giftedness phenomenon actually foretold what would be scientifically demonstrated 100 years later. Thus, evidence-based conceptualizations quickly moved from viewing intellectually precocious individuals as weak and emotionally liable to highly effective and resilient individuals. Like all groups, intellectually precocious students and adults have strengths and relative weaknesses; they also reveal vast differences in their passion for different pursuits and their drive to achieve. Because they do not possess multipotentiality, we must take a multidimensional view of their individuality. When done, it predicts well long-term educational, occupational, and creative outcomes.

New Yorker: Practice Doesn't Make Perfect (Zach Hambrick, MSU Psychology)

MSU Psychology Professor Zach Hambrick is featured in this New Yorker article about Nature vs Nurture. How far the pendulum has swung since the naive days of Malcolm Gladwell's Outliers and its credulous embrace of Anders Ericsson's nurturist claims. David Lubinski and SMPY also make an appearance.

New Yorker: Practice Doesn't Make Perfect

... So how much did practice actually explain? In a 2014 meta-analysis that looked specifically at the relationship between deliberate practice and performance in music, games like chess, sports, education, and other professions, Hambrick and his team found a relationship that was even more complex than they had expected. For some things, like games, practice explained about a quarter of variance in expertise. For music and sports, the explanatory power accounted for about a fifth. But for education and professions like computer science, military-aircraft piloting, and sales, the effect ranged from small to tiny. For all of these professions, you obviously need to practice, but natural abilities matter more.

What’s more, the explanatory power of practice fell even further when Hambrick took exact level of expertise into account. In sports—one of the areas in which deliberate practice seems to make the most difference—it turned out that the more advanced the athlete, the less of a role practice plays. Training an average athlete for a set number of hours yields far more results than training an élite athlete, which, in turn, yields greater results than training a super-élite athlete. Put differently, someone like me is going to improve a great deal with even a few hundred hours of training. But within an Olympic team tiny differences in performance are unlikely to be the result of training: these athletes train together, with the same coach, day in and day out. Those milliseconds come from somewhere else. Some may be due to the fact that genetic differences can account for some of the response to training. ...

So where else, exactly, do performance differences come from? While Hambrick’s work has been focussed more explicitly on practice and genetics, David Lubinski, a professor of psychology at Vanderbilt University, has been approaching the question from a slightly different angle: through what’s called the Study of Mathematically Precocious Youth (smpy), a longitudinal study of the lives of students who, by the age of thirteen, had scored in the top one per cent of mathematical-reasoning ability and were then selected to take part in an enriched educational environment. (The study, co-directed for many years by Lubinski and his wife, Vanderbilt’s education-school dean, Camilla Benbow, was described in detail in a recent article in Nature.) It’s a crucial supplement to work like Hambrick’s; the data you get from close observation of the same sample and the same individuals over time can answer questions other approaches can’t. “What kinds of practice are more effective? What approaches more effective for some people than others?” Hambrick asks. “We need all the pieces to the puzzle to maximize people’s potential. Lubinski’s work on mathematically precocious youth is an essential piece.”

SMPY in Nature

No evidence of diminishing returns in the far tail of the cognitive ability distribution.

How to raise a genius: lessons from a 45-year study of super-smart children (Nature)

A long-running investigation of exceptional children reveals what it takes to produce the scientists who will lead the twenty-first century.

Tom Clynes 07 September 2016

On a summer day in 1968, professor Julian Stanley met a brilliant but bored 12-year-old named Joseph Bates. The Baltimore student was so far ahead of his classmates in mathematics that his parents had arranged for him to take a computer-science course at Johns Hopkins University, where Stanley taught. Even that wasn't enough. Having leapfrogged ahead of the adults in the class, the child kept himself busy by teaching the FORTRAN programming language to graduate students.

Unsure of what to do with Bates, his computer instructor introduced him to Stanley, a researcher well known for his work in psychometrics — the study of cognitive performance. To discover more about the young prodigy's talent, Stanley gave Bates a battery of tests that included the SAT college-admissions exam, normally taken by university-bound 16- to 18-year-olds in the United States.

Bates's score was well above the threshold for admission to Johns Hopkins, and prompted Stanley to search for a local high school that would let the child take advanced mathematics and science classes. When that plan failed, Stanley convinced a dean at Johns Hopkins to let Bates, then 13, enrol as an undergraduate.

Stanley would affectionately refer to Bates as “student zero” of his Study of Mathematically Precocious Youth (SMPY), which would transform how gifted children are identified and supported by the US education system. As the longest-running current longitudinal survey of intellectually talented children, SMPY has for 45 years tracked the careers and accomplishments of some 5,000 individuals, many of whom have gone on to become high-achieving scientists. The study's ever-growing data set has generated more than 400 papers and several books, and provided key insights into how to spot and develop talent in science, technology, engineering, mathematics (STEM) and beyond.

...

At the start, both the study and the centre were open to young adolescents who scored in the top 1% on university entrance exams. Pioneering mathematicians Terence Tao and Lenhard Ng were one-percenters, as were Facebook's Mark Zuckerberg, Google co-founder Sergey Brin and musician Stefani Germanotta (Lady Gaga), who all passed through the Hopkins centre.

“Whether we like it or not, these people really do control our society,” says Jonathan Wai, a psychologist at the Duke University Talent Identification Program in Durham, North Carolina, which collaborates with the Hopkins centre. Wai combined data from 11 prospective and retrospective longitudinal studies2, including SMPY, to demonstrate the correlation between early cognitive ability and adult achievement. “The kids who test in the top 1% tend to become our eminent scientists and academics, our Fortune 500 CEOs and federal judges, senators and billionaires,” he says.

Such results contradict long-established ideas suggesting that expert performance is built mainly through practice — that anyone can get to the top with enough focused effort of the right kind. SMPY, by contrast, suggests that early cognitive ability has more effect on achievement than either deliberate practice or environmental factors such as socio-economic status.

...

The study's first four cohorts range from the top 3% to the top 0.01% in their SAT scores. The SMPY team added a fifth cohort of the leading mathematics and science graduate students in 1992 to test the generalizability of the talent-search model for identifying scientific potential.

“I don't know of any other study in the world that has given us such a comprehensive look at exactly how and why STEM talent develops,” says Christoph Perleth, a psychologist at the University of Rostock in Germany who studies intelligence and talent development.

...

Roe's scientists: original published papers

Gwern has provided scans of the original papers published by Anne Roe on studies of 64 eminent scientists. These papers include details concerning the selection of these individuals and the psychometric testing performed on them. Roe's scientists -- selected in their 40's and 50's for outstanding research contributions -- scored much higher on a set of high ceiling psychometric tests than the general population of scientists or PhDs.

Roe's work, combined with SMPY and Duke TIP longitudinal studies, and the earlier Terman studies, supports the claim that measured cognitive ability in the far tail significantly increases the likelihood of important contributions to science and technology.

See Annals of psychometry: IQs of eminent scientists.

1. Roe 1949, "Psychological Examinations of Eminent Biologists": http://www.gwern.net/docs/iq/1949-roe-biologists.pdf

2. Roe 1951, "A Psychological Study of Eminent Biologists": http://www.gwern.net/docs/iq/1951-roe-biologists.pdf

3. Roe 1951, "A Study of Imagery in Research Scientists": http://www.gwern.net/docs/iq/1951-roe-imagery.pdf

4. Roe 1951, "Psychological Tests of Research Scientists": http://www.gwern.net/docs/iq/1951-roe-scientists.pdf

5. Roe 1953, "A Psychological Study of Eminent Psychologists and Anthropologists, and a comparison with Biological and Physical Scientists": http://www.gwern.net/docs/iq/1953-roe-psychologists.pdf

6. Roe 1953, _The Making of a Scientist_: https://www.dropbox.com/s/i7raf2aup5pdpgy/1953-roe-makingscientist.pdf

7. Roe 1951, "A psychological study of physical scientists" (physicists/chemists) now available: https://www.dropbox.com/s/qh34xcxl0pzc9lr/1951-roe-physicalscientists.pdf

The individuals in the study are listed below.

Physicists will recognize names such as Luis Alvarez, Julian Schwinger, Wendell Furry, J.H. Van Vleck and others. Also in the group were Carleton Coon, B.F. Skinner, Linus Pauling and Sewall Wright.

Allport, Gordon W.(Gordon Willard), 1897-1967
Alvarez 1911-1988, Luis Walter
Anderson, Edgar, 1897-1969
Babcock, Horace W., 1912-2003
Beach, Frank A., (Frank Ambrose), 1911-1988
Beadle, George Wells, 1903-1989
Beams, Jesse W., (Jesse Wakefield), 1898-1977
Bearden, J.A. (Joyce Alvin), 1903-1987
Bonner, James Frederick, 1910-1996
Bruner, Jerome S. (Jerome Seymour), 1915-
Cleland, Ralph E., (Ralph Erskine), 1892-1971
Coon, Carleton S., (Carleton Stevens), 1904-1981
Corner, George Washington, 1889-1981
Doisy, Edward Adelbert, 1893-1986
Epling, Carl, 1894-1968
Ewing, W. Maurice, (William Maurice), 1906-1974
Furry, W.H. (Wendell Hinkle) , 1907-1984
Guilford, J. P. , (Joy Paul), 1897-1987
Hallowell, A. Irving , (Alfred Irving), 1892-1974
Hansen, William Webster, 1909-1949
Harlow, Harry Freerick, 1905-1981
Hilgard, Ernest R., (Ernest Ropiequet), 1904-2001
Joseph Edward, Mayer, 1904-1983
Kirkwood, John Gamble, 1907-1959
Kluckhohn, Clyde, 1905-1960
Knudsen, Vern Oliver, 1893-1974
Lashley, Karl Spencer, 1890-1958
Lindsey, Donald B.
Linton, Ralph, 1893-1953
Mayer, Joseph Edward, 1904-1983
McMillan, Edwin M. (Edwin Mattison), 1907-1991
Morse, Philip M., (Philip McCord), 1903-1985
Mueller, J. Howard, (John Howard), 1891-1954
Muller, H. J., (Hermann Joseph), 1890-1967
Mulliken, Robert Sanderson, 1896-1986
Muskat , M. (Morris) , 1906-1998
Northrop, John Howard, 1891-1987
Pauling, Linus, 1901-1994
Rhoades, Marcus M., (Marcus Morton), 1903-1991
Ritcher, Curt Paul, 1894-1994
Robbins, William Jacob, 1890-1978
Robertson, H. P., (Howard Percy), 1903-1961
Rogers, Carl R., (Carl Ransom), 1902-1987
Romer, Alfred Sherwood, 1894-1973
Schwinger, Julian Seymour, 1918-1994
Sears, Robert R., (Robert Richardson)
Shapiro, Harry L., (Harry Lionel), 1902-1990
Skinner, B. F. (Burrhus Fredric), 1904-1990
Smith, Homer William, 1895-1962
Sonneborn, T.M., (Tracy Morton), 1905-1981
Stanley, Wendell M., (Wendell Meredith), 1904-
Stebbins, G. Ledyard, (George Ledyard), 1906-2000
Stevens, S. S., (Stanley Smith), 1906-1973
Stewart, Homer Joseph, 1915-2007
Stratton, Julius Adams, 1901-1994
Strong, William Duncan, 1899-1962.
Sturtevant, A.H. (Alfred Henry), 1891-1970
Tuve, Merle Antony, 1901-1982
Van Vleck, J. H., (John Hasbrouck), 1899-1980
Willey, Gordon R., (Gordon Randolph), 1913-2002
Wright, Sewall, 1889-1988

SMPY at 50: Research Associate position

I'm posting the job ad below for David Lubinski. The Study of Mathematically Precocious Youth (SMPY) is the most systematic long term study of individuals of high cognitive ability since the Terman Study.

SMPY helps to establish a number of important facts about individuals of high ability:

1. We can (at least crudely) differentiate between individuals at the 99th, 99.9th and 99.99th percentiles. Exceptional talent can be identified through testing, even at age 13.

2. Probability of significant accomplishment, such as STEM PhD, patents awarded, tenure at leading research university, exceptional income, etc. continues to rise as ability level increases, even within the top 1%.

3. There are systematic differences in cognitive abilities and profiles in different fields (business, medicine, engineering, physics, etc.)

4. Men and women of exceptional ability differ in life aspirations and preferences.

No one can claim to understand high level human capital, technological innovation, scientific progress, or exceptional achievement without first familiarizing themselves with these results.

Needless to say, I think this Research Associate position will entail important and fascinating work.

Research Associate:

The Study of Mathematically Precocious Youth (SMPY) seeks a full-time post-doctoral Research Associate for study oversight, conducting research, writing articles, laboratory management, and statistical analyses using the vast SMPY data base. SMPY is a four-decade longitudinal study consisting of 5 cohorts and over 5,000 intellectually talented participants. One chief responsibility of this position will be to manage laboratory details associated with launching an age-50 follow-up of two of SMPY’s most exceptional cohorts: a cohort of 500 profoundly gifted participants initially identified by age 13 in the early 1980s, and a second cohort of over 700 top STEM graduate students identified and psychologically profiled in 1992 as first- and second-year graduate students. Candidates with interests in assessing individual differences, talent development, and particularly strong statistical-technical skills are preferred. Send vitae, cover letter stating interests, (pre)reprints, and three letters of recommendation to: Dean Camilla P. Benbow, Department of Psychology & Human Development, 0552 Peabody College, Vanderbilt University, Nashville, TN, 37203. The position will remain open until a qualified applicant is selected. For additional information, please contact either co-director: Camilla P. Benbow, camilla.benbow@vanderbilt.edu, or David Lubinski, david.lubinski@vanderbilt.edu.

http://www.vanderbilt.edu/Peabody/SMPY/. Vanderbilt University is an Equal Opportunity/Affirmative Action Employer.

We are aiming for a June 30th start date but that’s flexible.

Some relevant figures based on SMPY results of Lubinski, Benbow, and collaborators. See links above for more discussion of the data displayed.

The cult of genius?

In one of his early blog posts, Terence Tao (shown above with Paul Erdos in 1985) wrote

Does one have to be a genius to do maths? The answer is an emphatic NO. In order to make good and useful contributions to mathematics, one does need to work hard, learn one’s field well, learn other fields and tools, ask questions, talk to other mathematicians, and think about the “big picture”. And yes, a reasonable amount of intelligence, patience, and maturity is also required. But one does not need some sort of magic “genius gene” that spontaneously generates ex nihilo deep insights, unexpected solutions to problems, or other supernatural abilities.

The popular image of the lone (and possibly slightly mad) genius – who ignores the literature and other conventional wisdom and manages by some inexplicable inspiration (enhanced, perhaps, with a liberal dash of suffering) to come up with a breathtakingly original solution to a problem that confounded all the experts – is a charming and romantic image, but also a wildly inaccurate one, at least in the world of modern mathematics. We do have spectacular, deep and remarkable results and insights in this subject, of course, but they are the hard-won and cumulative achievement of years, decades, or even centuries of steady work and progress of many good and great mathematicians; the advance from one stage of understanding to the next can be highly non-trivial, and sometimes rather unexpected, but still builds upon the foundation of earlier work rather than starting totally anew. (This is for instance the case with Wiles‘ work on Fermat’s last theorem, or Perelman‘s work on the Poincaré conjecture.)

Actually, I find the reality of mathematical research today – in which progress is obtained naturally and cumulatively as a consequence of hard work, directed by intuition, literature, and a bit of luck – to be far more satisfying than the romantic image that I had as a student of mathematics being advanced primarily by the mystic inspirations of some rare breed of “geniuses”. This “cult of genius” in fact causes a number of problems, since nobody is able to produce these (very rare) inspirations on anything approaching a regular basis, and with reliably consistent correctness. (If someone affects to do so, I advise you to be very sceptical of their claims.) The pressure to try to behave in this impossible manner can cause some to become overly obsessed with “big problems” or “big theories”, others to lose any healthy scepticism in their own work or in their tools, and yet others still to become too discouraged to continue working in mathematics. Also, attributing success to innate talent (which is beyond one’s control) rather than effort, planning, and education (which are within one’s control) can lead to some other problems as well.

These are insightful comments, and deserve to be taken very seriously, coming as they do from the one of the youngest Fields Medalists in history and a legendary child prodigy.

But many readers misinterpreted Tao's remarks as minimizing the impact of native ability on success in research. Recently, Tao corrected this impression in the comment thread to his original post.

4 December, 2015 at 12:40 pm Terence Tao

It appears my previous comment may have have been interpreted in a manner differently from what I intended, which was as a statement of (lack of) empirical correlation rather than (lack of) causation. More precisely, the point I was trying to make with the above quote is this: if one considers a population of promising young mathematicians (e.g. an incoming PhD class at an elite mathematics department), they will almost all certainly have some reasonable level of intelligence, and some subset will have particularly exceptional levels of intelligence. A significant fraction of both groups will go on to become professional mathematicians of some decent level of accomplishment, with the fraction likely to (but not necessarily) be a bit higher when restricted to the group with exceptional intelligence. But if one were to try to use “exceptional levels of intelligence” as a predictor as to which members of the population will go on to become exceptionally successful and productive mathematicians, I believe this to be an extremely poor predictor, with the empirical correlation being low or even negative (cf. Berkson’s paradox).

Now, at the level of theoretical causation rather than empirical correlation, I would concede that if one were to take a given mathematician and somehow increase his or her level of intelligence to extraordinary levels, while keeping all other traits (e.g. maturity, work ethic, study habits, persistence, level of rigor and organisation, breadth and retention of knowledge, social skills, etc.) unchanged, then this would likely have a positive effect on his or her ability to be an extraordinarily productive mathematician. However, empirically one finds that mathematicians who did not exhibit precocious levels of intelligence in their youth are likely to be stronger in other areas which will often turn out to be more decisive in the long-term, at least when one restricts to populations that have already reached some level of mathematical achievement (e.g. admission to a top maths PhD program).

For instance, many difficult problems in mathematics require a slow, patient approach in which one methodically digests all the existing techniques in the literature and applies various combinations of them in turn to the problem, until one gets a deep enough understanding of the situation that one can isolate the key obstruction that needs to be overcome and the key new insight which, in conjunction with an appropriate combination of existing methods, will resolve the problem. A mathematician who is used to using his or her high levels of intelligence to quickly find original solutions to problems may not have the patience and stamina for such a systematic approach, and may instead inefficiently expend a lot of energy on coming up with creative but inappropriate approaches to the problem, without the benefit of being guided by the accumulated conventional wisdom gained from fully understanding prior approaches to the problem. Of course, the converse situation can also occur, in which an unusually intelligent mathematician comes up with a viable approach missed by all the more methodical people working on the problem, but in my experience this scenario is rarer than is sometimes assumed by outside observers, though it certainly can make for a more interesting story to tell.

Some comments on Tao's comment:

1. Individuals accepted into elite PhD programs in mathematics are already highly selected. I would guess, based on my familiarity with test scores of applicants to similar programs in theoretical physics, that a typical person in this population is well beyond +3 SD in overall cognitive (or at least mathematical) ability, which means fewer than one in a thousand in the general population. Tao doesn't say what he thinks the chances are for someone who has significantly less ability than this; I would say their chances at a research career in math are poor. Individuals with what Tao refers to as “exceptional levels of intelligence” would be at least +4 SD or more, making them fewer than one in ten thousand in the general population, or even much more rare. (To be totally frank I think a large fraction of good mathematicians are +4 SD and Tao is really talking about people who are exceptional even relative to them.)

2. Tao describes a schematic model with several quasi-independent input factors (raw cognitive ability, work ethic, maturity, breadth of knowledge, etc.) contributing to success. This is my working model as well. The claim that within the population of PhD students at top departments there might be only small or even negative correlation between factors such as raw ability and work ethic also seems plausible to me given a minimum threshold of undergraduate achievement (which can be obtained using various combinations of the individual factors) necessary for admission.

3. Tao's comments seem entirely consistent with results from SMPY (Study of Mathematically Precocious Youth), a longitudinal study of gifted children that finds increasing probability of success (e.g., STEM tenure at top research university) as ability increases from 99th to 99.99th percentile.

4. Should young people be made aware of the brute facts presented above? It seems terrible to limit one's ambitions based on some crudely measured construct like general cognitive ability or math ability. On the other hand, we do this all the time. When was the right time in my life to wise up about the fact that I would probably never make it to the NFL? After playing linebacker at 200 lbs for Division III Caltech (which doesn't even have a football team now), I was considering walking on at UC Berkeley as a 19 year old grad student. Should I have clung to my dream, or wised up about my dim future in Division I sports? :-)

5. Related to Tao's last remark the converse situation can also occur, in which an unusually intelligent mathematician comes up with a viable approach missed by all the more methodical people, see Sidney Coleman on Feynman:

"I think if he had not been so quick people would have treated him as a brilliant quasi crank, because he did spend a substantial amount of time going down what later turned out to be dead ends," said Sidney Coleman, a theorist who first knew Feynman at Caltech in the 50's.

"There are lots of people who are too original for their own good, and had Feynman not been as smart as he was, I think he would have been too original for his own good," Coleman continued. "There was always an element of showboating in his character. He was like the guy that climbs Mont Blanc barefoot just to show that it can be done."

Feynman continued to refuse to read the current literature, and he chided graduate students who would begin their work on a problem in the normal way, by checking what had already been done. That way, he told them, they would give up chances to find something original.

"I suspect that Einstein had some of the same character," Coleman said. "I'm sure Dick thought of that as a virtue, as noble. I don't think it's so. I think it's kidding yourself. Those other guys are not all a collection of yo-yos. Sometimes it would be better to take the recent machinery they have built and not try to rebuild it, like reinventing the wheel. Dick could get away with a lot because he was so goddamn smart. He really could climb Mont Blanc barefoot."

Related posts:

Success, Ability and All That

One hundred thousand brains

Bezos on the Big Brains

Annals of psychometry: IQs of eminent scientists

What is the difference?

Colleges ranked by Nobel, Fields, Turing and National Academies output

Out on the tail

Gender trouble in the valley

This NYTimes article looks at the gender disparity in technology career success within the Stanford class of 1994.

NYTimes: In the history of American higher education, it is hard to top the luck and timing of the Stanford class of 1994, whose members arrived on campus barely aware of what an email was, and yet grew up to help teach the rest of the planet to shop, send money, find love and navigate an ever-expanding online universe. ...

I found this reader comment to be realistic -- it is consistent with my own experience both as a parent and as a startup founder.

tiddle nyc

I've been in tech field for some years now. Being a working mother, with two kids (one boy, one girl), this subject hits close to home.

When I first started, there were more women in the ranks than it is now. I never experienced any sexism or discrimination in workplace, nor did I ever feel pushed aside. But I have to say this to my fellow female peers, in order to get ahead, you have to stay in the field. Dropping out or even scaling back will not help, and you can't blame others for not entrusting you with high profile projects because you might not be here next week.

Naturally it helps to have a spouse who share chores and childrearing, rather than having the woman/mother/wife to have-it-all, but really do-it-all which is practically impossible. That's how we stay the course and allow a pathway for younger generations of female to move up the ranks.

Looking at my kids - and we raise them to have the same aspirations, ambitions, and aggressiveness - there is indeed certain nature-vs-nurture difference. Justified or not, my son is almost always over-confident in his ability in all situations whereas my daughter is more circumspect and tentative (even if she's more than capable). It takes a lot more encouragement to prompt my daughter to be aggressive, whereas my son naturally does it on his own. As I look around all those in fields like VC and startups, I see mirrors of how men and women behavior.

This article doesn't surprise me.

See also Gender differences in preferences, choices, and outcomes: SMPY longitudinal study. A longitudinal study of mathematically precocious men and women (SMPY) showed significant gender differences in life and career preferences:

... According to the results, SMPY men are more concerned with money, prestige, success, creating or inventing something with impact, etc. SMPY women prefer time and work flexibility, want to give back to the community, and are less comfortable advocating unpopular ideas. Some of these asymmetries are at the 0.5 SD level or greater. Here are three survey items with a ~ 0.4 SD or more asymmetry:

# Society should invest in my ideas because they are more important than those of other people.

# Discomforting others does not deter me from stating the facts.

# Receiving criticism from others does not inhibit me from expressing my thoughts.

I would guess that Silicon Valley entrepreneurs and leading technologists are typically about +2 SD on each of these items! One can directly estimate M/F ratios from these parameters ...

The anecdote below about serial entrepreneur David Sacks is amusing:

NYTimes: ... Mr. Sacks almost wasn't hired because of doubts that he could work well with others; during his job interview, he put the chief financial officer on notice that his own job would be totally different once Mr. Sacks arrived, Mr. Thiel remembered. But his lack of social grace became an asset, according to Mr. Thiel and other former colleagues. He did not waste time on meetings that seemed pointless, and he bluntly insisted that the engineers whittle an eight-page PayPal registration process down to one.

Everyone knew Mr. Sacks was politically conservative, but in the office, he was less bombastic. He had become a manager, he said in an interview, and did not want to hurt the cohesion of his team. But he and Mr. Thiel now had a setting in which to try out their ideas about diversity and meritocracy. 'In the start-up crucible, performing is all that matters,' Mr. Sacks wrote about that time. He wanted to give all job applicants tests of cognitive ability, according to his colleague Keith Rabois, and when the company searched for a new chief executive, one of the requirements was an I.Q. of 160 -- genius level.

The goal was 'pure meritocracy,' said Amy Klement, one of a small number of women to rise high within the organization. She and other women called Mr. Sacks an effective, relentless, generous boss. But some also wondered how comfortable the men running the company were around them. Lauri Schultheis said that when she interviewed to be PayPal's office manager, and its first female employee -- before even Mr. Sacks arrived -- an engineer asked her, 'Does this mean I have to stop looking at porn? ...

Gender differences in preferences, choices, and outcomes: SMPY longitudinal study

The recent SMPY paper below describes a group of mathematically gifted (top 1% ability) individuals who have been followed for 40 years. This is precisely the pool from which one would hope to draw STEM and technological leadership talent. There are 1037 men and 613 women in the study.

The figures show significant gender differences in life and career preferences, which affect choices and outcomes even after ability is controlled for. (Click for larger versions.) According to the results, SMPY men are more concerned with money, prestige, success, creating or inventing something with impact, etc. SMPY women prefer time and work flexibility, want to give back to the community, and are less comfortable advocating unpopular ideas. Some of these asymmetries are at the 0.5 SD level or greater. Here are three survey items with a ~ 0.4 SD or more asymmetry:

# Society should invest in my ideas because they are more important than those of other people.

# Discomforting others does not deter me from stating the facts.

# Receiving criticism from others does not inhibit me from expressing my thoughts.

I would guess that Silicon Valley entrepreneurs and leading technologists are typically about +2 SD on each of these items! One can directly estimate M/F ratios from these parameters ...

Life Paths and Accomplishments of Mathematically Precocious Males and Females Four Decades Later  (Journal: Psychological Science)

David Lubinski, Camilla P. Benbow, and Harrison J. Kell
Vanderbilt University

Two cohorts of intellectually talented 13-year-olds were identified in the 1970s (1972–1974 and 1976–1978) as being in the top 1% of mathematical reasoning ability (1,037 males, 613 females). About four decades later, data on their careers, accomplishments, psychological well-being, families, and life preferences and priorities were collected. Their accomplishments far exceeded base-rate expectations: Across the two cohorts, 4.1% had earned tenure at a major research university, 2.3% were top executives at “name brand” or Fortune 500 companies, and 2.4% were attorneys at major firms or organizations; participants had published 85 books and 7,572 refereed articles, secured 681 patents, and amassed $358 million in grants. For both males and females, mathematical precocity early in life predicts later creative contributions and leadership in critical occupational roles. On average, males had incomes much greater than their spouses’, whereas females had incomes slightly lower than their spouses’. Salient sex differences that paralleled the differential career outcomes of the male and female participants were found in lifestyle preferences and priorities and in time allocation.

See also these poll results from the Harvard Crimson.

Crimson: ... The gender gap was also apparent in career choice. Men were far more likely to hope to eventually work in finance and entrepreneurship than women, while women were much more likely to aspire to careers in nonprofits and public service, health, and media or publishing. [ Note: these are super high achieving HARVARD kids in the survey, not state-U types ... no one has more "privilege" than they do, so I think it's fair to conclude that they might be expressing their relatively unconstrained actual preferences here. ]

Talent selection

How good is high school talent scouting for football? The star system used in college recruiting seems to have good validity in predicting an NFL career.

SBNation: ... The chance of a lesser-rated recruit being drafted in the first round is nowhere close to what it is for a blue-chipper.

Consider this: While four- and five-star recruits made up just 9.4 percent of all recruits, they accounted for 55 percent of the first and second round. Any blue-chip prospect has an excellent shot of going on to be a top pick, if he stays healthy and out of trouble.

For those who don't like percentages, here are some more intuitive breakdowns based on the numbers from the entire 2014 draft:

A five-star recruit had a three-in-five chance of getting drafted (16 of 27).
A four-star had a one-in-five chance (77 of 395).
A three-star had a one-in-18 chance (92 of 1,644).
A two-star/unrated recruit had a one-in-34 chance (71 of 2,434).

Compare to standardized testing and intellectual achievements later in life:

Success, Ability, and all that: ... In the SMPY study probability of having published a literary work or earned a patent was increasing with ability even within the top 1%. The "IQ over 120 doesn't matter" meme falls apart if one measures individual likelihood of success, as opposed to the total number of individuals at, e.g., IQ 120 vs IQ 145, who have achieved some milestone. The base population of the former is 100 times that of the latter!

In other words, if you find similar numbers of IQ 120 and IQ 145 individuals achieving some milestone (e.g., CEO of tech-focused startup or STEM research tenure; roughly speaking, 120 and 145 might be equally likely for those populations), then the odds ratio at an individual level is ~ 100 to 1 in favor of the 145s.

Chief Executives: brainpower, personality, and height

This paper uses Swedish conscript data to examine characteristics of CEOs of large and medium sized companies. Also discussed on Marginal Revolution. Thanks to Carl Shulman for the link.

It looks like large company CEOs are roughly +1, +1.5 and +0.5 SD on cognitive ability, non-cognitive ability (see below) and height, respectively. Apparently Swedish medical doctors are also only about +1 SD in cognitive ability (see article).

Horizontal axes are on a stanine (STAndard NINE) scale. On this scale a normal distribution is divided into nine intervals, each of which has a width of 0.5 standard deviations excluding the first and last.

Match Made at Birth? What Traits of a Million Swedes Tell Us about CEOs

Abstract: This paper analyzes the role three personal traits — cognitive and non-cognitive ability, and height — play in the market for CEOs. We merge data on the traits of more than one million Swedish males, measured at age 18 in a mandatory military enlistment test, with comprehensive data on their income, education, profession, and service as a CEO of any Swedish company. We find that the traits of large-company CEOs are at par or higher than those of other high-caliber professions. For example, large-company CEOs have about the same cognitive ability, and about one-half of a standard deviation higher non-cognitive ability and height than medical doctors. Their traits compare even more favorably with those of lawyers. The traits contribute to pay in two ways. First, higher-caliber CEOs are assigned to larger companies, which tend to pay more. Second, the traits contribute to pay over and above that driven by firm size. We estimate that 27-58% of the effect of traits on pay comes from CEO’s assignment to larger companies. Our results are consistent with models where the labor market allocates higher-caliber CEOs to more productive positions.

... The cognitive-ability test consists of four subtests designed to measure inductive reasoning (Instruction test), verbal comprehension (Synonym test), spatial ability (Metal folding test), and technical comprehension (Technical comprehension test).

[Non-cognitive ability:] Psychologists use test results and family characteristics in combination with one-on-one semi-structured interviews to assess conscripts’ psychological fitness for the military. Psychologists evaluate each conscript’s social maturity, intensity, psychological energy, and emotional stability and assign a final aptitude score following the stanine scale. Conscripts obtain a higher score in the interview when they demonstrate that they have the willingness to assume responsibility, are independent, have an outgoing character, demonstrate persistence and emotional stability, and display initiative. Importantly, a strong desire for doing military service is not considered a positive attribute for military aptitude (and may even lead to a negative assessment), which means that the aptitude score can be considered a more general measure of non-cognitive ability.

See related post Creators and Rulers:

I went to Harvard Business School, a self-styled pantheon for the business elite.

The average person was:
- top decile intellect (though probably not higher)
- top decile emotional intelligence (broadly construed - socially aware, self-aware, persuasion skills, etc.)
- highly conscientious / motivated

Few were truly brilliant intellectually. Few were academically distinguished (plenty of good ivy league degrees, but very few brilliant mathematical minds, etc.).

A good number will be at Davos in 20 years time.

Performance beyond a certain level in the vast majority of fields (and business is certainly one of them) is principally a function of having no cognitive and personal qualities which fall below a (high, but not insanely high) hygene threshold -- and then multiplied by determination, of course.

Conscientiousness, in fact, is the best single stable predictor of job success for complex jobs (well established in personality psychometrics).

Very high intelligence actually negatively correlates with career success (Kotter), probably because smart people enjoy solving problems, rather than making money selling things -- which outside of quant trading, show business and sport is really the only way of being really successful.

There are some extremely intelligent people in business (by which I mean high IQ, not just wise or experienced), but you tend to find them in the corners of the business landscape with the richest intellectual pastures: some areas of law, venture capital, some cutting edge technology fields.

See also Human capital mongering: M-V-S profiles. Note deviation scores (SDs) here are relative to the average among the gifted kids in the sample, not relative to the general population. The people in this sample are probably above average in the general population on each of M-V-S.

The figure below displays the math, verbal and spatial scores of gifted children tested at age 12, and their eventual college majors and career choices. This group is cohort 2 of the SMPY/SVPY study: each child scored better than 99.5 percentile on at least one of the M-V sections of the SAT.





Scores are normalized in units of SDs. The vertical axis is V, the horizontal axis is M, and the length of the arrow reflects spatial ability: pointing to the right means above the group average, to the left means below average; note the arrow for business majors should be twice as long as indicated but there was not enough space on the diagram. The spatial score is obviously correlated with the M score.

Upper right = high V, high M (e.g., physical science)
Upper left = high V, lower M (e.g., humanities, social science)
Lower left = lower V, lower M (e.g., business, law)
Lower right = lower V, high M (e.g., math, engineering, CS)

Success, Ability, and all that

I came across this nice discussion at LessWrong which is similar to my old post Success vs Ability. The illustration below shows why even a strong predictor of outcome is seldom able to pick out the very top performer: e.g., taller people are on average better at basketball, but the best player in the world is not the tallest; smarter people are on average better at making money, but the richest person in the world is not the smartest, etc.

This seems like a trivial point (as are most things, when explained clearly), however, it still eludes the vast majority. For example, in the Atlantic article I linked to in the earlier post Creative Minds, the neuroscientist professor who studies creative genius misunderstands the implications of the Terman study. She repeats the common claim that Terman's study fails to support the importance of high cognitive ability to "genius"-level achievement: none of the Termites won a Nobel prize, whereas Shockley and Alvarez, who narrowly missed the (verbally loaded) Stanford-Binet cut for the study, each won for work in experimental physics. But luck, drive, creativity, and other factors, all at least somewhat independent of intelligence, influence success in science. Combine this with the fact that there are exponentially more people a bit below the Terman cut than above it, and Terman's results do little more than confirm that cognitive ability is positively but not perfectly correlated with creative output.

In the SMPY study probability of having published a literary work or earned a patent was increasing with ability even within the top 1%. The "IQ over 120 doesn't matter" meme falls apart if one measures individual likelihood of success, as opposed to the total number of individuals at, e.g., IQ 120 vs IQ 145, who have achieved some milestone.

It is plausible that, e.g., among top execs or scientists or engineers there are roughly equal numbers of IQ 120 and IQ 145 individuals (the actual numbers could vary depending on how the groups are defined). But the base population of the former group is 100 times that of the latter! (IQ 120 is about top 10% and IQ 145 is roughly top 0.1% in the population.) This means, e.g., that the probability that an IQ 145 person becomes a top scientist could be ~100x higher than for an IQ 120 person.

This topic came up last night in Hong Kong, at dinner with two hedge funders (Caltech/MIT guys with PhDs) who have had long careers in finance. Both observed that 20 years ago it was nearly impossible to predict which of their colleagues and peers would go on to make vast fortunes, as opposed to becoming merely rich.

Life impacts of personality and intelligence

Here are two nice figures I came across recently.

The first, based on SMPY data, displays odds ratios for various accomplishments (doctorate degree, STEM publication, patent, high income, tenure) as a function of SAT-M score at age 13. The quartiles correspond roughly to 1 in 200 ability (Q1) to 1 in 10k ability (Q4). This data soundly refutes the "IQ above 120 doesn't matter" Malcolm Gladwell nonsense. See earlier post Horsepower matters; Psychometrics works.

In (imprecise) words: "Profoundly gifted children are 10+ times more likely than merely gifted children to, e.g., earn a patent or gain tenure at a top research university. They are at least several times more likely to earn exceptionally high incomes." (Note "merely gifted" is somewhat below the Q1 SMPY cut -- most school systems use top few percent vs top 0.5 percent.)

The second figure shows regression coefficients of income (at various ages) vs IQ and personality traits (standardized, so returns for each SD of trait). This was originally discussed in Earnings effects of personality, education and IQ for the gifted; see also this paper (Miriam Gensowski, Copenhagen). Note the IQ returns may be underestimated for average individuals since the data source is Terman and there is significant restriction of range (everyone tested at better than 1 in 200 or so on the Stanford-Binet). Nevertheless there are still positive returns to above average IQ within the Terman group (analogous to SMPY results above).

It pays to be Smart, Disciplined/Focused, Extraverted, and Mean! 8-(

Technical innovation and spatial ability

A new paper from David Lubinski and collaborators looks at spatial ability measured at age 13 to see whether it adds predictive power to (SAT) Math and Verbal ability scores. The blobs in the figure above (click for larger version) represent subgroups of individuals who have published peer reviewed work in STEM, Humanities or Biomedical research, or (separately) have been awarded a patent. Units in the figure are SDs within the SMPY population.

Creativity and Technical Innovation: Spatial Ability’s Unique Role
DOI: 10.1177/0956797613478615

In the late 1970s, 563 intellectually talented 13-year-olds (identified by the SAT as in the top 0.5% of ability) were assessed on spatial ability. More than 30 years later, the present study evaluated whether spatial ability provided incremental validity (beyond the SAT’s mathematical and verbal reasoning subtests) for differentially predicting which of these individuals had patents and three classes of refereed publications. A two-step discriminant-function analysis revealed that the SAT subtests jointly accounted for 10.8% of the variance among these outcomes (p < .01); when spatial ability was added, an additional 7.6% was accounted for—a statistically significant increase (p < .01). The findings indicate that spatial ability has a unique role in the development of creativity, beyond the roles played by the abilities traditionally measured in educational selection, counseling, and industrial-organizational psychology. Spatial ability plays a key and unique role in structuring many important psychological phenomena and should be examined more broadly across the applied and basic psychological sciences.

Note that SAT composite accounted for 10 percent of variance in research success even within this already gifted subpopulation. This non-zero result, despite the restriction of range, contradicts the Gladwellian claim that IQ above 120 does not provide additional returns. In fact, the higher the IQ score above the 99.5 percentile cutoff for this group, the greater the likelihood that an individual has been awarded a patent or has published a research paper.

Human capital mongering: M-V-S profiles

The figure below displays the math, verbal and spatial scores of gifted children tested at age 12, and their eventual college majors and career choices. This group is cohort 2 of the SMPY/SVPY study: each child scored better than 99.5 percentile on at least one of the M-V sections of the SAT.





Scores are normalized in units of SDs. The vertical axis is V, the horizontal axis is M, and the length of the arrow reflects spatial ability: pointing to the right means above the group average, to the left means below average; note the arrow for business majors should be twice as long as indicated but there was not enough space on the diagram. The spatial score is obviously correlated with the M score.

Upper right = high V, high M (e.g., physical science)
Upper left = high V, lower M (e.g., humanities, social science)
Lower left = lower V, lower M (e.g., business, law)
Lower right = lower V, high M (e.g., math, engineering, CS)

Because of the selection criteria I wouldn't be surprised if the SDs are large in this population. Many of the SMPY qualifiers could have relatively average V scores and vice versa for SVPY. So the variation between the highest and lowest scores in each ability could be larger than in the general population.

High V, Low M

I sent the message below to a social scientist I know who (like many, perhaps understandably) is confused about Stephen J. Gould's status as an evolutionary theorist. Many Gould readers are surprised to learn that his main expertise was the paleontology of snails and that he struggled with higher mathematics. When I first encountered Gould's essays as a kid, I concluded that there was just no there there. He was all literary flourish and little depth.

Which brings me to an observation I've been meaning to write about. It is that high verbal ability (which Gould certainly had) is useful for appearing to be smart, or for winning arguments and impressing other people, but it's really high math ability that is useful for discovering things about the world -- that is, discovering truth or reasoning rigorously. The importance of math ability manifests in two distinct ways:

1. Powerful (deep) models of Nature (e.g., electrodynamics or evolutionary theory) are themselves mathematical. Most of the incredible progress in our understanding of the universe is just not available to people who do not understand math. For example, we can talk until we are blue in the face about the Uncertainty Principle, but there is no precise understanding without actual equations.

2. The statistical techniques used to analyze data obtained in a messy, complex world require mathematical ability to practice correctly. In almost all realistic circumstances hypothesis testing is intrinsically mathematical. It is quite easy to fool yourself statistically if you don't have strong math ability, but rather are simply following cookbook recipes.

High verbal ability is useful for more than just impressing others -- it typically implies a certain facility with concepts and relationships between ideas -- but high V alone is a dangerous thing. The most confused people I meet in the academy tend to be high V, low (modest) M types.

More on the V / M split in this longitudinal study of gifted children (SMPY / SVPY -- see esp. figure 4).

Gould on Gould (NY Review of Books, March 29 1984):

"I am hopeless at deductive sequencing... I never scored particularly well on so-called objective tests of intelligence because they stress logical reasoning ..."

This is from the email I sent to a colleague:

Gould appeals to high V low M people who do not actually understand evolutionary theory at a mathematical level. He never made any important contribution to evolutionary theory other than as a popularizer.

Note this is distinct from his deliberate obfuscation of topics like IQ in Mismeasure of Man. He wrote some incorrect things there about factor and statistical analysis, but perhaps those distortions were intentional. See The Mismeasure of Science: Stephen Jay Gould versus Samuel George Morton on Skulls and Bias.


Paul Krugman:

Now it is not very hard to find out, if you spend a little while reading in evolution, that Gould is the John Kenneth Galbraith of his subject. That is, he is a wonderful writer who is beloved by literary intellectuals and lionized by the media because he does not use algebra or difficult jargon. Unfortunately, it appears that he avoids these sins not because he has transcended his colleagues but because he does does not seem to understand what they have to say; and his own descriptions of what the field is about - not just the answers, but even the questions - are consistently misleading. His impressive literary and historical erudition makes his work seem profound to most readers, but informed readers eventually conclude that there's no there there. (And yes, there is some resentment of his fame: in the field the unjustly famous theory of "punctuated equilibrium", in which Gould and Niles Eldredge asserted that evolution proceeds not steadily but in short bursts of rapid change, is known as "evolution by jerks").

What is rare in the evolutionary economics literature, at least as far as I can tell, is references to the theorists the practitioners themselves regard as great men - to people like George Williams, William Hamilton, or John Maynard Smith. This is serious, because if you think that Gould's ideas represent the cutting edge of evolutionary theory (as I myself did until about a year and a half ago), you have an almost completely misguided view of where the field is and even of what the issues are.

http://www.pkarchive.org/theory/evolute.html


John Maynard Smith:

"Gould occupies a rather curious position, particularly on his side of the Atlantic. Because of the excellence of his essays, he has come to be seen by non-biologists as the preeminent evolutionary theorist. In contrast, the evolutionary biologists with whom I have discussed his work tend to see him as a man whose ideas are so confused as to be hardly worth bothering with, but as one who should not be publicly criticized because he is at least on our side against the creationists. All this would not matter, were it not that he is giving non-biologists a largely false picture of the state of evolutionary theory."


John Tooby:

"Although Gould characterizes his critics as "anonymous" and "a tiny coterie," nearly every major evolutionary biologist of our era has weighed in in a vain attempt to correct the tangle of confusions that the higher profile Gould has inundated the intellectual world with. The point is not that Gould is the object of some criticism -- so properly are we all -- it is that his reputation as a credible and balanced authority about evolutionary biology is non-existent among those who are in a professional position to know...

These [major evolutionary biologists] include Ernst Mayr, John Maynard Smith, George Williams, Bill Hamilton, Richard Dawkins, E.O. Wilson, Tim Clutton-Brock, Paul Harvey, Brian Charlesworth, Jerry Coyne, Robert Trivers, John Alcock, Randy Thornhill, and many others."

http://cogweb.ucla.edu/Debate/CEP_Gould.html  

 

Richard Lewontin: 

"Steve and I taught evolution together for years and in a sense we struggled in class constantly because Steve, in my view, was preoccupied with the desire to be considered a very original and great evolutionary theorist. So he would exaggerate and even caricature certain features, which are true but not the way you want to present them. For example, punctuated equilibrium, one of his favorites. He would go to the blackboard and show a trait rising gradually and then becoming completely flat for a while with no change at all, and then rising quickly and then completely flat, etc. which is a kind of caricature of the fact that there is variability in the evolution of traits, sometimes faster and sometimes slower, but which he made into punctuated equilibrium literally. Then I would have to get up in class and say “Don’t take this caricature too seriously. It really looks like this…” and I would make some more gradual variable rates. Steve and I had that kind of struggle constantly. He would fasten on a particular interesting aspect of the evolutionary process and then make it into a kind of rigid, almost vacuous rule, because—now I have to say that this is my view—I have no demonstration of it—that Steve was really preoccupied by becoming a famous evolutionist." 

https://johnhawks.net/weblog/topics/history/biology/lewontin-wilson-gould-interview-2015.html

 

See Human capital mongering: M-V-S profiles for further explanation of this figure.

Vanderbilt

Sorry for the lack of posts! I've been at Vanderbilt since leaving Fermilab. I was pretty busy giving two talks and meeting with a lot of physics faculty during my visit. As always I am amazed at the variety of things people are working on in a single department. Vanderbilt has a beautiful campus :-)

I've collaborated with two Vanderbilt theorists: Tom Kephart and Bob Scherrer, but this was my first visit to the university.




I had a brief window of time to meet with psychology professors Camilla Benbow and David Lubinski, who co-direct The Study of Mathematically Precocious Youth (SMPY). The data set they've been accumulating is unique and valuable.

SMPY:

The Study of Mathematically Precocious Youth (SMPY) was founded by Julian C. Stanley, on 1 September 1971, at Johns Hopkins University. Camilla P. Benbow and David Lubinski co-direct SMPY at Peabody College of Vanderbilt University. They are planning to complete a 50-year longitudinal study of five cohorts, consisting of over 5,000 intellectually talented individuals, identified over a 25-year period (1972-1997). The aim of this research is to develop a better understanding of the unique needs of intellectually precocious youth and the determinants of the contrasting developmental trajectories they display over the lifespan. The Study of Mathematically Precocious Youth is a bit of a misnomer, however, because verbally precocious youth have been included for longitudinal tracking, and participants are now all adults. Nevertheless, "SMPY" has been chosen to be retained to maintain consistency.

Four of SMPY's five cohorts were identified by talent searches by age 13. These cohorts vary in ability level ranging from the top 3% to the top .01% in quantitative or verbal reasoning ability. A fifth cohort of 714 participants, identified as first- or second-year graduate students attending top U.S. math/science programs in 1992, complements the first four cohorts of talent search participants by, among other things, affording an opportunity to assess the generalizability of the talent search model for identifying scientific potential. A 10-year follow-up of these math/science graduate students is now available (Lubinski et al., 2006). For the first three SMPY cohorts (identified in 1972-1974, 1976-1978, & 1980-1983, respectively), their 20-year follow-ups are available (Benbow et al., 2000; Lubinski et al., 2006). SMPY either has or is planning to follow-up all four cohorts of talent search participants at ages 18, 23, 33, 50 and 65. Cohort 5, the math-science graduate students, will be followed-up at ages 35 (complete) as well as 50 and 65. So far, seven books and over 300 articles have been based on SMPY; many recent articles are on PDF files on Benbow and Lubinski's individual web sites. For further and more detailed information on SMPY's history, the selection criteria for each cohort, and major longitudinal findings, a recent monograph has just appeared (Lubinski & Benbow, 2006).