PE R SP EC TI V ES O N P SY CH O L O G I CA L S CIE N CE

Special Section: Doing Psychological Science

Study of Mathematically
Precocious Youth After 35 Years
Uncovering Antecedents for the Development ofMath-Science ExpertiseDavid Lubinski and Camilla Persson Benbow
Vanderbilt University

ABSTRACT—This review provides an account of the Studyof Mathematically Precocious Youth (SMPY) after 35
years of longitudinal research. Findings from recent
20-year follow-ups from three cohorts, plus 5- or 10-year
findings from all five SMPY cohorts (totaling more than
5,000 participants), are presented. SMPY has devoted
particular attention to uncovering personal antecedents
necessary for the development of exceptional math-science
careers and to developing educational interventions tofacilitate learning among intellectually precocious youth.
Along with mathematical gifts, high levels of spatial ability,
investigative interests, and theoretical values form a particularly promising aptitude complex indicative of potential for developing scientific expertise and of sustainedcommitment to scientific pursuits. Special educational
opportunities, however, can markedly enhance the development of talent. Moreover, extraordinary scientific accomplishments require extraordinary commitment both inand outside of school. The theory of work adjustment(TWA) is useful in conceptualizing talent identification anddevelopment and bridging interconnections among educational, counseling, and industrial psychology. The lensof TWA can clarify how some sex differences emerge ineducational settings and the world of work. For example,
in the SMPY cohorts, although more mathematically precocious males than females entered math-science careers,
this does not necessarily imply a loss of talent because thewomen secured similar proportions of advanced degrees
and high-level careers in areas more correspondent withthe multidimensionality of their ability-preference pattern
Address correspondence to David Lubinski or Camilla Benbow,
Department of Psychology and Human Development, Peabody 512,
Vanderbilt University, Nashville, TN 37203, e-mail: david.lubinski@
vanderbilt.edu or camilla.benbow@vanderbilt.edu.

316(e.g., administration, law, medicine, and the social sciences). By their mid-30s, the men and women appeared tobe happy with their life choices and viewed themselves as
equally successful (and objective measures support thesesubjective impressions). Given the ever-increasing importance of quantitative and scientific reasoning skills inmodern cultures, when mathematically gifted individuals
choose to pursue careers outside engineering and thephysical sciences, it should be seen as a contribution tosociety, not a loss of talent.
Society is becoming more knowledge based, technological, andinternational (Friedman, 2005); the physical and social systems
within which people operate are increasingly complex and dynamic, and economies are built upon ideas. Those countries thatflourish will be the ones most effective in developing their humancapital and in nurturing individuals who will come up with the best
ideas and innovations of tomorrow. A recent report issued by the
National Academy of Sciences (2005), Rising Above the Gathering
Storm, highlights the importance of these trends for all countries
concerned about their future prosperity and speaks to the importance of developing intellectual talent. Indeed, identifying anddeveloping talent for science, technology, engineering, and mathematics (STEM) was the dominant reason that Julian C. Stanley
founded the Study of Mathematically Precocious Youth (SMPY) in
1971. Longitudinal findings emerging out of SMPY have special
relevance today as the United States considers, for example,
launching an American Competitiveness Initiative (2006).
The purpose of this article is twofold. First, we outline the
SMPY longitudinal study and how its findings over the past
35 years have informed both basic and applied psychologicalscience on identifying and nurturing intellectual talent. Much
has been learned about effective ways to identify potential
for and to facilitate the development of scientific expertise.

Copyright r 2006 Association for Psychological Science

Volume 1—Number 4

David Lubinski and Camilla Persson Benbow

Because the work on educational facilitation has been summarized elsewhere (Benbow & Lubinski, 1996; Benbow &Stanley, 1996; Stanley, 2000),1 our second purpose is to focus
attention on the critical personal antecedents for developing
outstanding scientific careers. Which individuals are most
likely to become exceptional STEM professionals? We say lesshere on how to facilitate the educational development of suchindividuals.
SMPY was not designed for, nor is this article about, enhancing
the scientific literacy of the general population. Enhancing scientific literacy is clearly important. The public needs to makeinformed decisions on topics ranging from whether evolution
should be taught in the schools to whether tax dollars should be
used to fund stem cell research. Fostering scientific literacy,
however, is different from identifying future scientific leaders andcreating supportive environments for them. Although the two
certainly have common components, the talent and commitment
necessary to develop as a scientific leader require both personal
attributes and learning environments that are truly beyond thenorm. The intellectual abilities, personal commitment, and educational experiences needed to ameliorate global warming or tocreate an environmentally safe, oil-independent energy source areof a much different order of magnitude than those required fordeveloping scientific literacy (Simonton, 1994; Zuckerman, 1977).
An overabundance of STEM leaders has emerged from SMPY.
Their distinguishing psychological characteristics and the developmental choices structuring the paths they traversed from age 12
have become evident from experiential and personal data collected
at earlier time points. Not surprisingly, the personal attributes of
future STEM leaders reveal that it takes much more than exceptional abilities to develop truly exceptional scientific expertise.
SMPY

SMPY was founded by Stanley on September 1, 1971, at Johns
Hopkins University (Keating & Stanley, 1972; Stanley, 1996).
The study moved to Iowa State University in 1986, and was
directed by Camilla P. Benbow from 1986 to 1990; from 1991
through 1998, we co-directed SMPY at Iowa State, and then in
1998, we moved it to Peabody College of Vanderbilt University.
The initial idea motivating SMPY was to conduct research whileproviding services to intellectually talented adolescents, particularly those with mathematical talent. The underlying philosophy driving this study is based on the best educational
practices for all students, namely, ‘‘appropriate developmental
placement’’ (Lubinski & Benbow, 2000, p. 138)—providing
students with educational opportunities tailored to their rates oflearning (Benbow & Stanley, 1996; Colangelo, Assouline, &1

Many findings from SMPY’s first decade have been reported in edited volumes published by the Johns Hopkins University Press (Benbow & Stanley,
1983; Fox, Brody, & Tobin, 1980; George, Cohn, & Stanley, 1979; Keating,
1976; Stanley, George, & Solano, 1977; Stanley, Keating, & Fox, 1974). Stanley’s Festschrift (Benbow & Lubinski, 1996) concluded this series (cf. Benbow& Lubinski, 2006; ‘‘In Appreciation,’’ 2005).

Volume 1—Number 4Gross, 2004). In the words of Stanley (2000), the idea is to teachstudents ‘‘only what they don’t already know’’ (p. 216).
From the beginning, SMPY focused as much on serving thesocial and emotional well-being of intellectually precocious
youth as on experimenting with differential opportunities forpromoting intellectual development (Benbow, Lubinski, &Suchy, 1996). To inform these two agendas, Stanley planned anafter-high-school follow-up that ultimately evolved into a
50-year longitudinal study, which currently includes more than
5,000 intellectually talented individuals identified over a 25year period (1972–1997). The aim of this research is to develop abetter understanding of the unique needs of intellectually precocious youth, the determinants of the varying developmental
trajectories they display, and the role of education in talentdevelopment. ‘‘Study of Mathematically Precocious Youth’’ has
become a bit of a misnomer, however, because many of ourparticipants are more verbally than mathematically talented,
and the participants are now all adults. Nevertheless, to maintain consistency, we have chosen not to rename the study.
Above-Level Testing and Criteria Used to Identify SMPY
Participants
The members of four of SMPY’s five cohorts were selected(primarily) at around age 12 or 13, when they were in the seventhor eighth grade (a fifth cohort of top math-science graduate
students was identified for longitudinal study as well). The selection process was tiered: Almost all students were first required to earn scores within the top 3% on a conventional
achievement test routinely administered in their schools (e.g.,
the Iowa Test of Basic Skills). This select group was then given
the opportunity to take the SAT through a talent search (TS;Benbow & Stanley, 1996; Colangelo et al., 2004; Keating &Stanley, 1972; Stanley, 1996). The SATwas designed for collegebound high school juniors and seniors, to assess mathematical(SAT-M) and verbal (SAT-V) reasoning abilities critical forcollege work (Donlon, 1984). Because few of the potential cohort
members had received formal training in algebra or more advanced mathematics, and because they were 4 to 5 years
younger than the population for whom the SAT was designed,
this assessment falls into the category of above-level testing.
SAT-M and SAT-V score distributions among seventh and eighth
graders in the top 3% are indistinguishable from the score distributions observed among high school students (Benbow, 1988).
This finding is especially noteworthy for SAT-M, because despite alack of formal training in algebra, geometry, and other areas ofmath tested, several of these seventh and eighth graders scoreabove the admissions cutoff for many elite universities. Given the
abstract nature of this measuring tool and the sheer novelty of the
problems for this population, the SAT functions at more of ananalytical reasoning level for these students than it does for older
students who have been explicitly exposed to SAT content throughcourse work in high school (Benbow, 1983; Stanley & Benbow,
1986; Brody & Benbow, 1990; Minor & Benbow, 1996).

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Antecedents of Math-Science Expertise

Conventional age- or grade-based achievement tests routinely
administered in schools are insensitive to much of the individualityof intellectually talented young adolescents. Normatively based
instruments do not have high enough ceilings for these students,and most of their ability and achievement scores pile up at the top,with profile variation masked. This fosters an underappreciation
not only of their potential, but also of their unique strengths andrelative weaknesses. Superficial inferences based on such assessments amount to erroneous attributions of ‘‘multipotentiality,’’
namely, that they are all equally good at everything and can doalmost anything (cf. Achter & Lubinski, 2005; Achter, Lubinski, &Benbow, 1996). In reality, this special population (like almost all
special populations) contains an enormous amount of psychologicaldiversity, including diversity in relative strengths and weaknesses.
Above-level tests offer the kind of measuring stick needed forassessing the learning rates of highly able students because suchtests can uncover the profound range of individual differences inlearning readiness that these students display. SMPY chose theSAT for this purpose, but other instruments can be used in thisway as well. The SAT’s high ceiling for this age group distinguishes the able from the exceptionally able and, simultaneously,
assesses their relative math and verbal strengths. Capturing thelevel and pattern of their intellectual potential more precisely
than is possible with grade-level tests allows one to harness thenature and scope of their precocity to inform practice (Benbow &Stanley, 1996; Colangelo et al., 2004), research (Corno et al.,
2002; Vale & Vale, 1969), and theory development (Lubinski,
1996, 2000, 2004; Lubinski & Benbow, 2000; Underwood, 1975).
For example, intellectually able adolescents scoring 500 or
higher on SAT-M or SAT-V before age 13 (top 1 in 200) can
assimilate a full high school course (e.g., chemistry, English,
mathematics) in 3 weeks at a summer residential program forintellectually precocious youth; yet exceptionally able adolescents, those scoring 700 or more (top 1 in 10,000), can assimilate
at least twice this amount (Benbow & Stanley, 1996; Colangelo
et al., 2004; Stanley, 2000).2 Above-level assessments are2
That cognitive abilities can be enhanced through learning is, of course, true,
but a common finding is that the relationship is not linear. When all students areprovided with opportunities to learn at their desired rate, those who begin withmore ability typically profit more from these opportunities (Ceci & Papierno,
2005; Gagne, 2005; Jensen, 1991, p. 178; Kenney, 1975; Robinson, Abbott,
Berninger, & Busse, 1996; Robinson, Abbott, Berninger, Busse, & Mukhopadhyah, 1997). This nonlinearity is intensified when one considers the fullrange of ability. Students with developmental delays assimilate much less thantypically developing students even in the best of conditions, yet this fanning out
in achievement is observed throughout the ability spectrum and within these
populations as well (D. Fuchs et al., 2001; L.S. Fuchs, Fuchs, Karns, Hamlett,& Katzaroff, 1999). The idea that opportunities for optimal growth expand individual differences in achievement has been discussed periodically for decades (e.g., Pressey, 1946, 1955; Seashore, 1922; Thorndike, 1911; Thurstone,1948), yet it is conspicuously absent in many modern treatments. Two excellent
exceptions, however, are articles by Ceci and Papierno (2005) and Gagne(2005). Ceci and Papierno depicted this phenomenon nicely by subtitling theirtreatment ‘‘When the ‘Have Nots’ Gain but the ‘Haves’ Gain Even More.’’ With
this in mind, consider the following proposal for evaluating schools: ‘‘The goodschool, as I have suggested, does not diminish individual differences; it increases them. It raises the mean and increases the variance’’ (Eisner, 1999,
p. 660).

318

critical, therefore, for properly structuring educational curriculaand being responsive to individuality. The exceptionally able
certainly require different opportunities for optimal development than the able (Lubinski, Webb, Morelock, & Benbow,
2001; Muratori et al., 2006), the former needing a more abstract,
deeper, and faster-paced curriculum to avoid boredom. Furthermore, individual differences in learning rates between theable and the exceptionally able portend commensurate differences in occupational accomplishments many years later. Like
their earlier academic accomplishments, the occupational accomplishments of the profoundly gifted tend to develop at anaccelerated pace and with greater depth. The profoundly gifted
simply have greater capacity for accomplishment and creative
contributions.
In our 20-year follow-up studies of adolescents identified atage 12, for example, 30% of participants with SAT-M or SAT-V
scores of 500 or above secured doctorates, compared with 50%of those scoring 700 or above (Benbow, Lubinski, Shea, & Eftekhari-Sanjani, 2000; Lubinski, Benbow, Webb, & BleskeRechek, 2006). The base rate for earning a doctorate (i.e., J.D.,
M.D., or Ph.D.) in the United States is 1%. That a 2-hr test canidentify 12-year-olds who will earn this ultimate educational
credential at 50 times base-rate expectations is remarkable.
Moreover, a 200-point difference in SAT scores at age 12 (500vs. 700) eventuates, by middle age, in marked differences inincome, patents earned, and tenure-track academic positions
secured at top U.S. universities (cf. Lubinski et al., 2006; Wai,
Lubinski, & Benbow, 2005). More than one third of the ability
range is found within the top 1% of ability, above-level testing
affords valid assessments of individual differences within thisrange, and these differences make a difference in school, work,and life. Models of exceptional human development need toincorporate these individual differences.

The Five SMPY Cohorts
SMPY’s five cohorts were formed using different selection criteria (see Table 1). The first three cohorts are successively moreable (top 1.0%, 0.5%, and 0.01% in ability, respectively). The
fourth cohort consists of participants who met a top-3% criterion, but a large subset of these participants took the SAT at age
12 or 13 and achieved scores within the top 0.5%. Finally, a fifthcohort consists of top first- and second-year graduate students inmath and science; they were selected for a retrospective test of,among other things, the fidelity of TS procedures for identifying
scientific talent. Collectively, these five cohorts include morethan 5,000 participants.
THEORETICAL MODEL

Given the number of variables facilitating the development of
talent, we used a model from counseling psychology to helporganize findings and structure our longitudinal research andVolume 1—Number 4

David Lubinski and Camilla Persson Benbow

TABLE 1The Five Cohorts of the Study of Mathematically Precocious Youth
CohortN

Years whenidentified

Students’ age
when identified

1
2
3
4
5

2,188
778
501
1,130
714

1972–1974
1976–1979
1980–1983
1992–1997
1992

12–13
12
12
12–14
23–25

Identification criteria
SAT-M  390 or SAT-V  370 (top 1 in 100)
SAT-M  500 or SAT-V  430 (top 1 in 200)
SAT-M  700 or SAT-V  630 (top 1 in 10,000)
Top 3% on any subtest of a grade-level achievement test
Enrollment as a 1st- or 2nd-year student in a graduate programat a top-ranked engineering, math, or science department in
the United States

Note. SAT-M 5 score on the mathematics subtest of the SAT; SAT-V 5 score on the verbal subtest of the SAT. (Cutting scores were
established before the SAT-V was recentered in the mid-1990s.) The cutoffs selected for Cohort 1 represented the average performance ofa random sample of high school girls and also represented approximately the top 1% in ability when achieved by age 13. Participants weredrawn primarily from the state of Maryland, with a heavy concentration in the greater Baltimore area. Cohort 2 was drawn from the midAtlantic states, and Cohort 3 is national in its representation. Cohort 4 consisted primarily of Midwesterners; a subset of these participants, in addition to achieving a top-3% score on a subtest of a grade-level achievement test, also took the SAT and met at least one of the
top-0.5% criteria (i.e., SAT-V  430 or SAT-M  500). Talent-search participants have been or are to be followed up at ages 18, 23, 33,
50, and 65. Cohorts 1, 2, and 3 have completed follow-ups through age 33 (Benbow, Lubinski, Shea, & Eftekhari-Sanjani, 2000; Lubinski,
Benbow, Webb, & Bleske-Rechek, 2006). An age-50 follow-up of Cohort 1 will be launched in approximately 3 years. The age-18 follow-up(after high school) for Cohort 4 is complete (Webb, Lubinski, & Benbow, in press). A 10-year follow-up of Cohort 5 was conductedsimultaneously with the 20-year follow-up of Cohort 3 (Lubinski et al., 2006).

educational interventions (Lubinski & Benbow, 2000). Specifically, we extended the theory of work adjustment (TWA; Dawis& Lofquist, 1984; Lofquist & Dawis, 1969, 1991) to educationand talent development (Lubinski & Benbow, 1992; Lubinski& Humphreys, 1990b), just as Katzell (1994) drew on TWA
for modeling phenomena in industrial-organizational psychology. From our point of view, the applied psychological precincts of educational, counseling, and industrial psychology
may be viewed sequentially from a life-span developmental
perspective (Scarr & McCartney, 1983). All three specialties
involve the scientific study of how individual differences may be
used to structure interventions or opportunities to enhance
positive psychological growth, but each area concentrates ona somewhat different stage of development. TWA organizes
important variables common to all three areas (Lubinski &Benbow, 2000).
TWA is predicated on devoting equal attention to assessing
the individual and assessing the environment (see Fig. 1). The
individual’s learning or work personality is parsed into two major
components, abilities and preferences (interests and values),
whereas the environment is parsed into commensurate domainsof ability requirements (for meeting performance expectations)and reinforcer systems (for acknowledging and compensating
performance). Profound individual differences in capability andmotivation exist within cultures and subcultures, and evenamong siblings (Lubinski, 2000, 2004; Murray, 1998; Rowe,
1994; Scarr, 1996), and these differences eventuate in important
outcome differences in education and the world of work. This is
why assessing individual differences in psychological attributes
is so important for both personal and professional development(Dawis, 1992, 2001; Lubinski, 1996; Roe, 1956, p. ii; Tyler,

Volume 1—Number 41974; Williamson, 1965).3 According to TWA, educational
commitment and occupational tenure are a joint function of two
major dimensions of correspondence: satisfaction (correspondence between needs and rewards) and satisfactoriness (correspondence between ability and ability requirements). The former is determined subjectively (or intra-individually) by
students and workers and is based on their feelings; the latter isdetermined objectively (or interindividually) by teachers andsupervisors and is based on performance.
Satisfaction determines how much an individual is motivatedto maintain contact with a particular environment (commitment
to a discipline or occupation), whereas satisfactoriness determines how much the people in the environment who evaluate
performance are motivated to keep the person (or are committed
to retaining the person). Both must be present for the individual
to remain in the environment. If satisfaction is in place but
satisfactoriness is not, the person will be asked to leave (although the person may like to stay), whereas if the inverse is thecase, the person will choose to leave (although people in theenvironment may want to keep him or her). Satisfaction andsatisfactoriness determine the extent to which the person-environment relationship is likely to maintain harmony and reciprocally nurture personal and organizational development
over extended time frames (resulting in lengthy tenure). (For3
There are many different kinds of cognitive abilities, but it is generally
agreed that they are hierarchically organized (Carroll, 1993; Snow & Lohman,1989). In our treatment of specific abilities, we focus on those that are longitudinally stable and that have been shown to be widely generalizable in multiple contexts and to be related to individual differences in learning in
educational settings, as well as to learning and performance in the world of
work.

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Antecedents of Math-Science Expertise

Fig. 1. The theory of work adjustment (TWA). In this framework, personal attributes (abilities and interests) and the environment (abilities
requirements and reinforcer system) are given equal emphasis; occupational tenure is a joint function of the correspondence between abilities andability requirements (satisfactoriness) and of the correspondence between interests and the reinforcer system (satisfaction). We have extended TWA
to the domain of talent development. The diagrams on the left illustrate how cognitive abilities and interests have been conceptualized in our model(Lubinski & Benbow, 2000). In the diagram of the radex scaling of cognitive abilities, each region of concentration is denoted by a different letter,and higher numbers indicate increasing complexity. The diagram of Holland’s (1996) hexagon of interests (referred to as RIASEC, after the initialletters of the interests) shows a simplification following Prediger (1982); in this simplification, interests are modeled by a two-dimensional structureof independent dimensions: people-things and data-ideas.

further information about this model and its extension to otherdomains, see Achter & Lubinski, 2003, 2005; Dawis, 2005;
Dawis & Lofquist, 1984; Katzell, 1994; Lofquist & Dawis, 1991;
and Lubinski & Benbow, 2000.)
SMPY’S EMPIRICAL FINDINGS

We open this section by discussing cognitive abilities. First, we
present descriptive data highlighting the individual differencesin cognitive ability among young adolescents in the top 1% ofquantitative reasoning ability and showing that these individual
differences made a difference in education, the world of work,and creative accomplishments. We deal with this topic first
because there is a long-standing and widely held supposition ofan ‘‘ability threshold’’ in the scientific literature (i.e., an assumption that beyond a certain point, more ability does notmatter; Getzels & Jackson, 1962; Howe, 2001; Renzulli, 1986).
Second, the psychological significance of ability pattern is explicated. After treating cognitive abilities, we discuss the incremental validity of adding preferences to ability assessment
for modeling intellectual development more comprehensively
and then review the importance of time devoted to work and time
willing to devote to work. Next, the role of special educational

320

programs and the developmental particulars of top math-science
graduate students are touched upon. A focus on the math-science pipeline is maintained throughout, but the personal attributes relevant to other kinds of expertise are also covered. The
role of sex differences is also a focus of the discussion, and we
provide an appendix with some normative data illustrating how
sex differences in level and dispersion can collectively operate
to create disparate male:female ratios at high as well as low
extremes of ability. And finally, a concluding section places ourmodel and longitudinal findings in a broader historical context
and underscores the importance of the developmental sciences
and human-capital initiatives incorporating concepts and findings from the study of individual differences.
Cognitive Abilities
Ability Level
The statement that more than one third of the ability range
is contained in the top 1% gives one pause initially, but consider the following for a familiar illustration. Modern talent
searches and educational programs for intellectually talented
students have moved well beyond IQ, but let us take IQ asa reference point. IQs in the top 1% begin at approximately

Volume 1—Number 4

David Lubinski and Camilla Persson Benbow

when reflected on later in life, engendered uniformly high ratings of satisfaction with educational and subsequent career
choices, among both sexes (Benbow et al., 2000; Lubinski et al.,2006). By their mid-30s, both male and female participants
expressed few regrets about their educational and career
choices, and the nature of the regrets that were expressed did notcovary with sex.
However, these three cohorts were identified over a 10-year
period, and their 20-year follow-ups were offset by the same
number of years. Did cohort differences (as well as more ability)
contribute to the greater educational achievement of the more
recent cohorts?
To ascertain more precisely the importance of ability level, we
joined with Wai to conduct a within-cohort analysis of Cohorts 1
and 2 (Wai et al., 2005). Using 20-year follow-up data, we compared students in the top and bottom quartiles for SAT-M scoreswithin the top 1%, by cohort and sex, on an array of outcome
criteria having both high ceilings and low base rates: earning adoctorate (J.D., M.D., or Ph.D.), earning a STEM Ph.D., earning ahigh income, securing a patent, and achieving tenure at a U.S.
university ranked within the top 50. For the latter variable, we
examined the university Web sites of the academics in our sample
to ascertain who among them had achieved an associate professorship with tenure by the year 2004. ‘‘America’s Best Colleges
2004’’ (2003) was used to generate a reasonable list of the top 50
U.S. universities. Findings combined across cohorts and sexes arepresented in Figure 2.

137 and extend beyond 200 (an IQ range of more than 63
points is thus found in the top 1%). (The same phenomenon
occurs in physical measurements such as height and weight.)
The important psychological question is, do ability differences
within the top 1% make a difference in life? To answer thisquestion, we examine educational credentials, occupational
outcomes, and creative accomplishments as a function of SATM scores at age 13 (not all of our participants had SAT-V scoresavailable).
Table 2 contains 20-year follow-up data from Cohorts 1 and 2(Benbow et al., 2000) and Cohort 3 participants with SAT-M
scores of 700 and above (Lubinski et al., 2006). All three cohorts
had SAT-M means more than 100 points beyond the cuttingscore for the top 1% by age 13, but the three cohorts weresuccessively more able. Their mean SAT-M scores by age 13
were as follows: 537 and 505 for Cohort 1 males and females,
567 and 519 for Cohort 2 males and females, and 729 and 732
for Cohort 3 males and females. By age 33, approximately 25%
of Cohort 1 had secured doctorates, in comparison with morethan 30% for Cohort 2 and more than 50% for Cohort 3. Moreover, within each cohort, roughly equal proportions of males andfemales achieved advanced degrees, but the sexes were represented disproportionately across areas: Females were more
likely to secure degrees in the humanities, life sciences, andsocial sciences than in mathematics, computer science, engineering, and the physical sciences, whereas the reverse was true
for males. It should be noted, too, that these educational choices,
TABLE 2
Academic Credentials Secured by Age 33: Cohorts 1, 2, and 3

Major

Cohort 1 (840 males, 543 females)

Cohort 2 (403 males, 189 females)

Bachelor’s

Master’s

Doctorate

Bachelor’s

Master’s

Doctorate

Bachelor’s

Master’s

Doctorate

M

M

M

M

M

M

M

M

M

F

F

F

F

F

F

Cohort 3 (269 males, 24 females)F

F

F

Mathematics
7.5 6.3 1.0 0.9 0.4 0.2 10.3 9.7 2.2 2.1 2.2 0.5 30.6 13.6 7.4 9.1 8.7 0.0
Engineering
22.9 8.1 9.3 3.5 1.6 0.6 35.0 15.6 13.6 5.3 5.2 0.0 20.6
9.1 8.7 9.1 4.6 0.0
Computer science
7.0 4.4 3.9 2.4 1.2 0.0 10.3 2.7 6.5 0.5 2.0 0.0 13.1
4.5 9.6 0.0 5.5 0.0
Physical sciences
9.3 4.4 2.5 0.7 2.4 0.6 10.3 7.0 3.7 1.6 3.7 1.6 17.9 18.2 4.3 9.1 9.2 10.0
Biological sciences
8.1 13.5 0.5 2.4 1.1 1.3 5.8 9.6 0.5 1.6 2.2 2.1 7.1 27.3 1.3 0.0 1.8 0.0
Medicine, health
0.7 7.7 0.5 2.0 9.9 10.7 0.3 1.6 0.5 1.1 7.4 11.6 0.3
0.0 0.0 0.0 7.8 35.0
Social sciences
17.3 19.6 2.5 2.8 1.2 0.6 9.8 19.3 2.2 6.9 0.7 2.6 11.1
4.5 1.3 0.0 1.8 0.0
Arts, humanities
10.1 14.8 2.1 3.7 0.5 0.6 12.3 24.8 3.9 6.3 1.5 1.6 13.5 31.8 3.5 4.5 0.9 5.0
Law
—
—
—
— 7.9 6.5 ————6.7 11.6 ———— 8.6 10.0
Business
10.5 12.0 12.4 10.9 0.4 0.0 2.8 4.3 9.1 8.4 0.2 0.5 0.3
0.0 6.5 4.5 0.0 0.0
Education
0.5 3.1 1.1 3.1 0.1 0.2 0.0 1.6 0.0 2.6 0.0 0.0 0.0
0.0 1.3 4.5 0.4 0.0
Other fields
3.7 5.7 2.5 4.8 0.4 0.2 4.0 5.3 3.5 4.2 0.2 0.0 2.4
4.5 1.7 13.6 0.0 0.0
Combined majors
Math, inorganic
sciences
46.7 23.2 16.7 7.5 5.6 1.4 65.9 35.0 26.0 9.5 13.1 2.1 82.2 45.4 30.0 27.3 28.0 10.0
Life sciences,humanities
36.2 55.6 5.6 10.9 12.7 13.2 28.2 55.3 7.1 15.9 11.8 17.9 32.0 63.6 6.1 4.5 12.3 40.0
All majors
86.9 89.5 36.8 36.1 26.2 20.7 95.2 97.3 43.2 40.2 31.1 31.9 96.0 100
38.3 50.0 50.0 60.0
Note. For Cohort 3, only participants who scored at least 700 on the mathematics subtest of the SAT were included in this analysis. Numbers shown arepercentages. In the summary statistics, boldface type highlights a gender-differentiating trend for math and inorganic sciences and for life sciences and humanities: Males tended to receive more degrees in the former, females in the latter. F 5 females; M 5 males.

Volume 1—Number 4321Antecedents of Math-Science Expertise

Fig. 2. Outcomes as a function of ability level (top vs. bottom quartile of scores on the SAT math subtest). The graphs show
the percentage of Cohort 1 and 2 participants who earned a doctorate (upper left; doctorate in science, technology, engineering, or math—STEM—is indicated by black shading), had an income equal to or greater than the median for their sex,
earned a patent, and achieved tenure at a top-50 U.S. university. The mean SAT scores, at age 12, were 455 for the bottom
quartile (Q1) and 620 for the top quartile (Q4). Outcomes were assessed 201 years later. Adapted from Wai, Lubinski, andBenbow (2005).

Figure 2 reveals that the top quartile of the top 1% in ability
outperformed the bottom quartile. Note especially the proportion of tenured faculty within top U.S. universities. Individual
differences in the top 1% make a difference. It is important tobear in mind that these participants were initially assessed by
the SAT before age 13. By the time they reached high school,
both quartiles were bumping their heads on the ceiling of the
SAT (and the exceptionally able were not readily distinguishedfrom the able). Thus, when highly talented youth reach highschool, the SAT is no longer sensitive to the full range of theirintellectual capabilities; for them, the SAT in high schoolfunctions like age- or grade-based achievement tests did when
they were in seventh grade.
Data from Cohort 3, SMPY’s most able cohort (the top-1-in10,000 group), reinforce this idea (Lubinski et al., 2006). Someof these participants bumped their heads on the SAT’s ceiling as
young adolescents! As a group, they are more able than the top
quartile analyzed in the study just described (Wai et al., 2005),
and their adult accomplishments are correspondingly more
impressive. We compared their outcomes at their 20-year followup with the 10-year outcomes for SMPY’s Cohort 5: first- andsecond-year graduate students attending the top 15 STEM
programs in the United States in 1992 (Lubinski, Benbow, Shea,
Eftekhari-Sanjani, & Halvorson, 2001).
When the follow-up data were collected (Lubinski et al.,2006), the mean ages of the TS participants and the graduate
students were 33.6 and 35.4, respectively. Figure 3 graphs thepercentages of participants securing each of two important
outcomes (each parsed into three gradations): a tenure-track

322

position (at a U.S. university ranked within the top 25, from 26
through 50, or 51 or lower) or a high annual income ($100,000–$249,000, $250,000–$499,000, or $500,0001). Although the
TS participants were identified merely by having one very hightest score by age 13, and the graduate students were identified
after they had secured admission to a world-class STEM department, their achievements are highly comparable.
Achieving tenure, and especially a full professorship, at a
top-ranked university requires internal and external evaluations
by leaders in the field, who look for documentation of creativity
and advancing the discipline. Indeed, at such institutions,promotion to associate professor with tenure is consideredaround a $3 million investment in human capital. That abovelevel assessments can identify young adolescents who willachieve top-50 tenure-track positions at rates comparable tothose of graduate students attending the top U.S. math, science,and engineering doctoral programs is truly remarkable. Arguably, the TS participants could be viewed as a bit more successful overall, inasmuch as by their mid-30s, 21.7% of the TS
participants who were in tenure-track positions in the top 50
U.S. universities were already full professors, compared with‘‘only’’ 6.5% of the graduate students. Moreover, an inordinate
number of TS participants earned especially high incomes (e.g.,$500,0001), although the graduate students were 1.8 years
older than them at the follow-up.
Other outcome criteria in this study are noteworthy because
they underscore the creative accomplishments of both groups.
For example, earning patents is an excellent indicator of
creativity, or ‘‘inventive and scientific productivity’’ (Huber,Volume 1—Number 4

David Lubinski and Camilla Persson Benbow

Fig. 3. Percentage of graduate students (Cohort 5) and talent-search participants (Cohort 3, top 1 in 10,000) with tenure-track ortenured positions (left) and annual incomes of $100,000 or more (right) at follow-up. The data shown here are based on samples of
299 and 287 male and female graduate students, respectively, and 286 and 94 male and female talent-search participants, respectively. From Lubinski, Benbow, Webb, and Bleske-Rechek (2006).

1999, p. 49). Discussing the documentation on what constitutes
intellectual property, Huber (1998) remarked, ‘‘It would be hard
to find a field of study where so much effort has been expended inestablishing a definition. Perhaps the definition of invention is
the most solid definition in the field of creativity’’ (p. 61). The
percentages of graduate students (males: 32.1%, females:
20.9%) and TS participants (males: 17.8%, females: 4.3%) who
had earned patents was well beyond base-rate expectations.
Approximately 1% of the entire adult U.S. population holds atleast one patent (J.C. Huber, personal communication, October27, 2004). Epidemiologists and other scientists take notice whenbase rates double (Lubinski & Humphreys, 1997); therefore,
these percentages reflect an exceptional degree of creative accomplishment in science and technology. Graduate students
earned more patents than TS participants overall (26.6% vs.
14.5%, respectively), w2(1, N 5 966) 5 19.9, p < .001, which is
not surprising given that the graduate students were selectedfrom career tracks in which patents are commonly achieved, andsome TS participants were identified on the basis of their SAT-V,
rather than SAT-M, scores. When analyses were restricted to TS
participants who qualified on the basis of top-1-in-10,000 SATM scores, the percentages of males and females who earned
patents rose to 20.1% and 9.1%, respectively; the difference inthe rates for graduate students and TS participants was still

Volume 1—Number 4

statistically significant, but diminished (26.6% vs. 19.0%,
respectively), w2(1, N 5 807) 5 5.0, p < .05.4
SMPY’s findings on ability level are useful for multiple reasons. Perhaps the most important is that they call into question
the ability-threshold hypothesis, the belief in which has been
4
This analysis illustrates the differential validity of measures of quantitative
versus verbal reasoning ability for creative technical accomplishments. In
student selection, specific abilities also have differential validity in identifying
contrasting potentialities. For example, in what is arguably psychology’s most
famous longitudinal study (Terman, 1925, 1954), two future Nobel laureates
were assessed but rejected for longitudinal tracking (Shurkin, 1992): Luis Alvarez and William Shockley both came in under the cutting score on Terman’s
highly verbal Stanford-Binet. Contemporary TS programs have broadened talent-identification procedures by assessing both quantitative and verbal reasoning, and an assessment of the former undoubtedly would have identified bothof these distinguished individuals during adolescence. One of our most exceptional participants was identified well before adolescence using an abovelevel assessment of quantitative reasoning, SAT-M (Muratori et al., 2006); just a
few months ago, at age 31, he was awarded the Fields Medal (equivalent to theNobel prize in mathematics). But talent searches still could do better. They
could be expanded to include spatial ability, because talent searches currently
miss approximately 50% of the top 1% in three-dimensional spatial visualization (Humphreys, Lubinski, & Yao, 1993; Shea, Lubinski, & Benbow, 2001;
Webb, Lubinski, & Benbow, in press). Although modern talent searches areprobably missing few people with the quantitative potential of Alvarez andShockley (Lubinski, Benbow, et al., 2001), because of the inclusion of mathematical-reasoning measures, they are undoubtedly missing a number of people
with the intellectual strengths in nonverbal ideation characterizing individuals
such as Thomas Edison and Henry Ford, because of the exclusion of spatialability measures (Lubinski, 2004).

323

Antecedents of Math-Science Expertise

hard to shake. A recent letter published in Science, and signedby 79 academic administrators and research scientists, working
primarily at major universities (Muller et al., 2005), stated:‘‘There is little evidence that those scoring at the very top of therange in standardized tests are likely to have more successfulcareers in the sciences. Too many other factors are involved’’(p. 1043). However, SMPY findings falsify this idea. The SMPY
longitudinal follow-ups have resolved the methodological difficulties historically encountered in falsifying an ability threshold(Lubinski & Dawis, 1992), such as securing an appreciablesample of individuals within the top 1% of ability, being able todistinguish the exceptionally able from the able (within the top1%), finding criteria with sufficiently high ceilings and low baserates, and implementing an appreciable longitudinal delay sothat extraordinary accomplishments can develop. All of these
design features were met in the studies discussed in this section.
To be sure, factors other than ability level are important, andwe review a number of them later in this section. Nevertheless,other things being equal, more ability is always better, and there
was evidence to suggest this long before the letter appeared in
Science (Benbow, 1992; Benbow & Lubinski, 1996; Lubinski &Humphreys, 1990b; Lubinski, Webb, et al., 2001). In addition to
Science, psychological outlets routinely disregard the importance of distinguishing between the able and the exceptionallyable in modeling outstanding achievement. For example, a recent article in the American Psychologist stated:Standardized tests are thus not sufficiently predictive of future
performance. Individuals are not necessarily more meritorious if
they obtain the highest scores on standardized tests, thus rendering
invalid the argument that students with the highest scores shouldhave priority in admissions. (Vasquez & Jones, 2006, p. 138)

This is simply not the case.
Ability PatternThat quantitative reasoning ability contributes to securing an
advanced degree and, in particular, an advanced degree in
STEM areas is unquestionably true. However, given the generality of quantitative reasoning ability (Rivera-Batiz, 1992),
ability level speaks primarily to level of achievement, rather
than to the nature of achievement. To predict the latter, more
complex models are needed, and ability configuration is an
important consideration. Analyses reveal that patterns of spatial
and verbal abilities among mathematically gifted adolescents,
as for students in general (Humphreys, Lubinski, & Yao, 1993),
are critical for understanding qualitative differences in theirdevelopment (e.g., development that moves toward the humanities vs. the sciences vs. business, corporate, and marketingarenas). The following description illustrates how.
A subset of Cohort 2 participants (393 males, 170 females)
was assessed during early adolescence on all three ability dimensions found in our model (see Fig. 1): mathematical, verbal,

324

and spatial reasoning abilities (Shea, Lubinski, & Benbow,
2001). Figure 4 presents 5-, 10-, and 20-year longitudinalfindings on four criteria: favorite and least favorite high school
course (reported at age 18), college major (at age 23), and occupation (at age 33). These outcomes are graphed as a functionof all three abilities. The x-axis represents mathematical ability(SAT-M), the y-axis represents verbal ability (SAT-V), and thez-axis represents spatial ability. Position on the z-axis is indicated by the direction and length of the arrow emanating fromeach data point; a right-pointing arrow indicates spatial ability
above the grand mean, and a left-pointing arrow indicates spatial ability below the grand mean. The arrows were scaled on thesame units of measurement as the SAT scores (viz., z scores, orstandard deviation units).
Figure 4 shows that across all three time frames, exceptionalverbal ability, relative to mathematical and spatial ability, ischaracteristic of participants whose favorite courses, college
majors, and occupations were in the social sciences and humanities, whereas higher levels of math and spatial abilities,
relative to verbal abilities, characterize participants whosefavorite courses, college majors, and occupations were in engineering and math or computer science. Physical sciences
appear to require appreciable amounts of all three abilities.
These developmentally sequenced outcomes tell an important
story. Among other things, they illustrate that individual
differences factor into life outcomes whether or not they are usedin selecting individuals for opportunities with the potential tolead to these outcomes. Indeed, spatial ability is rarely, if ever,assessed among applicants for advanced degree programs.
Moreover, although bright students are well aware of the importance of scoring high on quantitative and verbal reasoning
abilities in order to gain access to subsequent educational opportunities, educational counselors and academic selection
committees hardly ever take spatial ability into consideration oreven mention it. Yet individual differences in this attributemarkedly influence whether students approach or avoid STEM
domains.
The importance of taking ability pattern into account also
holds for profoundly gifted individuals. Focusing exclusively on
one ability is never wise, because it could pale when viewed
alongside a stronger ability. Consider the following study. We(Lubinski, Webb, et al., 2001) divided Cohort 3 participants (top
1 in 10,000) into three groups based on their ability pattern: Twogroups had contrasting intellectual strengths, with greater
ability in one area than the other (i.e., SAT-M score more than 1
SD above SAT-V score or vice versa; high-math and high-verbal
groups), and one group was more intellectually uniform (i.e.,
SAT-M and SAT-V scores within 1 SD of each other; high-flatgroup). These three ability patterns, determined from assessments at age 12, eventuated in distinct developmental trajectories 10 years later. For example, the ability profiles predicteddifferential course preferences among these three groups in highschool and college (see Fig. 5). On average, the high-math group

Volume 1—Number 4

David Lubinski and Camilla Persson Benbow

Fig. 4. The relation between ability patterns and four educational and occupational outcomes: favorite high school class at age 18, least favorite highschool class at age 18, major of conferred bachelor’s degree at age 23, and occupation at age 33. The graph for each outcome plots the mean verbal,
math, and spatial ability of participants within various subcategories. Group ns are in parentheses. Verbal ability was measured by the verbal subtestof the SAT (SAT-V), math ability was measured by the mathematics subtest of the SAT (SAT-M), and spatial ability was measured by the combined
score on two subtests of the Differential Aptitude Test (Space Relations and Mechanical Reasoning). Results for high school courses are standardized
within sexes, and those for majors and occupations are standardized between sexes. Like the SAT-M and SAT-V scores, spatial-ability scores, indicated by the lengths of the arrows, are scaled in standard deviation units. Just as the bivariate points for the SAT scales illustrate how far apart thegroups are in two-dimensional space, as a function of their standing on math and verbal abilities, these arrowheads enable readers to envision how far
apart the groups are in three-dimensional space, as a function of all three abilities. The arrowhead for business majors has been enlarged to indicatethat this group’s relative weakness in spatial ability was actually twice as great as that indicated by the length shown. Adapted from Shea, Lubinski,
and Benbow (2001).

Volume 1—Number 4325

Antecedents of Math-Science Expertise

Fig. 5. Cohort 3 participants’ favorite course in high school and in college as a function of ability profile (Lubinski, Webb, Morelock, & Benbow,
2001). Participants in the high-math group had SAT-math (SAT-M) scores more than 1 standard deviation above their SAT-verbal (SAT-V) scores, andparticipants in the high-verbal group had the reverse profile; participants in the high-flat group had SAT-M and SAT-V scores within 1 standard
deviation of each other. Percentages in a given column do not necessarily sum to 100% because only data from participants indicating math-science orhumanities courses are displayed.

preferred math and science courses to humanities courses,
whereas the inverse was true for the high-verbal group; results
for the high-flat group were intermediate.
We grouped participants’ responses to open-ended queries
about their accomplishments and awards into three clusters:
humanities and arts, science and technology, and unclassifiable(‘‘other’’; see Tables 3 and 4) and then went back to ascertain
whether these three clusters were occupied differentially by thethree ability groups (Lubinski, Webb, et al., 2001). Table 4 reveals that three fourths of the classifiable accomplishments andawards of high-math participants were in science and technology; by comparison, two thirds of the classifiable accomplishments of high-verbal participants were in the humanities andarts. High-flat participants reported similar numbers of accomplishments in the two clusters. It appears that ability patternrelates to the time and effort devoted to various activities.
These findings on course preferences, individual awards, andcreative pursuits illustrate a common finding in counseling andvocational psychology: Ability pattern is critical for choice(Dawis, 1992; Dawis & Lofquist, 1984; Gottfredson, 2003;
Humphreys et al., 1993). Administering one test in isolation to agroup of talented adolescents is not enough to appreciate their326

psychological diversity. All three groups in the study just discussed had impressive mathematical and verbal abilities, buttilted profiles were highly related to differential development. If,
for instance, one were to focus exclusively on the quantitative
reasoning abilities of the high-verbal group (viz., mean SAT-M
5 556 before age 13), extraordinary promise for STEM domains
might be readily inferred. Yet despite the impressive quantitative reasoning abilities of these students, their even more
exceptional verbal prowess appears to have pushed their development in other directions.

Preferences
The foregoing review of ability level and pattern is helpful inunderstanding the magnitude of development of the exceptionally able and the distinct educational and occupational niches
that individuals with contrasting ability profiles seek out andultimately occupy. But there is more to the story. Abilities
capture only one class of TWA person variables: those relevant
to satisfactoriness. Also important are preferences, the person
variables relevant to satisfaction. Although research has demonstrated a number of small correlations between abilities andVolume 1—Number 4

David Lubinski and Camilla Persson Benbow

TABLE 3
Awards and Special Accomplishments of Cohort 3
Science and technology
Published scientific work (11)Developed software (8)Patented an invention (4)Received National Science Foundation fellowship (2)Designed image-correlation system for navigation for Mars Landing
Program (1)Received the American Physical Society’s Apker Award (1)Graduated from MIT in 3 years at age 19 with a perfect grade
point average and graduated from Harvard Medical School (M.D.)
at age 23 (1)Received teaching award for ‘‘Order of Magnitude Physics’’ (1)Humanities and arts
Published creative writing (7)Created noteworthy art or music (6)Received Fulbright award (2)Wrote proposal for a novel voting system for the new
South African Constitution (1)Made solo violin debut (age 13) with the Cincinnati
Symphony Orchestra (1)Named Mellon Fellow in the Humanities (1)Named Presidential Scholar for Creative Writing (1)Received Hopwood writing award (1)Received Creative Anachronisms Award of Arms (1)Won first place in midreal-medieval poetry (1)Received fellowship for foreign-language study (1)Received international predissertation award (1)Other
Inducted into Phi Beta Kappa (71)
Inducted into Tau Beta Pi (30)
Inducted into Phi Kappa Phi (14)Received award for entrepreneurial enterprises (2)Inducted into Omicron Delta Kappa (1)Received Olympiad silver medal (1)Finished bachelor’s and master’s degrees in 4 years (1)Received private pilot’s license in 1 month at age 17 (1)Note. The numbers in parentheses indicate the number of participants witheach accomplishment (also see Table 4). Adapted from Lubinski, Webb,
Morelock, and Benbow (2001).

interests (Ackerman, 1996; Ackerman & Heggestad, 1997;
Schmidt, Lubinski, & Benbow, 1998), they are sufficiently uncorrelated that they must be assessed independently for optimal
educational and vocational counseling (Dawis, 1992, 2001;
Lubinski, 1996, 2001; Savickas & Spokane, 1999).
Holland’s (1996) hexagon of interests (referred to as the
RIASEC model because the six interests are Realistic, Investigative, Artistic, Social, Enterprising, and Conventional) is themost widely accepted model of educational-vocational interests(Day & Rounds, 1998). We use this framework to represent thepreference component in our model (Fig. 1), although otherpreference inventories could be used instead (Dawis, 1991;
Dawis & Lofquist, 1984; Savickas & Spokane, 1999).
A conspicuous sex difference in interests and values, one that
goes back at least to Thorndike (1911), involves the prominence

Volume 1—Number 4

TABLE 4
Number of Accomplishments and Awards Classified as ScienceTechnology Versus Arts-Humanities as a Function of AbilityGroup in Cohort 3
Group
High mathHigh flat
High verbal

Sciences andtechnology

Humanities
and arts

16
6
7

5
6
13

Note. High flat 5 participants with SAT-mathematics (SAT-M) and SAT-verbal (SAT-V) scores within 1 standard deviation of each other; high math 5
participants with SAT-M scores more than 1 standard deviation beyond their
SAT-V scores; high verbal 5 participants with SAT-V scores more than 1
standard deviation beyond their SAT-M scores. All participants earned an
SAT-V score of 630 or more or an SAT-M score of 700 or more (or both) by age
13. The outcome data were collected in their 10-year after-college follow-up.

of a people-versus-things dimension (Lippa, 1998, 2006; Lubinski, 2000). This dimension runs from Social (people contact)
to Realistic (things and gadgets) in Holland’s hexagon, and it issalient in other preference instruments and models as well.
Females and males consistently display a mean difference larger
than 1 standard deviation on this dimension. Girls and women,
on average, tend to prefer to learn about and work with people(or, more generally, organic content), whereas boys and men, on
average, tend to prefer to learn about and work with things (or,more generally, inorganic content). Unfortunately, the contemporary social science literature often fails to highlight therelevance of male-female differences on this dimension for
STEM pursuits, despite voluminous evidence supporting its
importance for these pursuits (Achter et al., 1996; Campbell,
1971; Savickas & Spokane, 1999; Strong, 1943; Tyler, 1974;
Williamson, 1965).
Lippa (1998), for example, published three studies on thisrobust dimension of individuality and discussed the role it plays
in personality development. Examining the effect of gender on
preference for people versus things, he obtained an effect size
(female minus male) of 1.20 standard deviation units or greaterin each study (Lippa, 2006). This preference difference between
men and women, which is also conspicuous in intellectually
precocious samples, undoubtedly contributes to the preponderance of females with profound mathematical gifts who choose
to become physicians rather than engineers and physical scientists (see Table 2). Highly talented males, by contrast, aremuch more likely to become STEM professionals.
Preference Findings from SMPYOver the 1990s, a series of articles documented the utility of
conducting above-level preference assessments with intellectually precocious youth (i.e., studying this population using
preference questionnaires initially developed on older adolescents and young adults). In a study of Cohort 4, Achter et al. (1996)
documented the amount of their psychological diversity on the327Antecedents of Math-Science Expertise

TABLE 5
Rank Order of Preferences for Cohorts 1 through 4Interest/Value theme
Rank
order
1
2
3
4
5
6

Investigative/
Theoretical

Artistic/
Aesthetic

Enterprising/
Economic

Social/ Social

Realistic/
Political

Conventional/
Religious

M

F

M

F

M

F

M

F

M

F

M

F

52/45
24/27
11/15
8/7
4/5
1/2

37/19
27/16
19/18
10/20
6/15
2/12

5/7
11/8
11/9
17/22
24/22
32/32

35/22
26/19
9/20
11/13
9/14
11/12

7/15
11/28
22/21
29/17
22/13
10/6

3/7
7/12
15/13
20/20
26/26
29/22

2/8
5/11
10/18
11/24
30/28
42/12

9/29
21/25
22/18
16/14
14/10
18/4

19/21
22/22
20/26
19/17
13/11
7/3

4/12
5/18
18/18
19/20
26/16
27/16

18/10
28/5
27/9
15/10
7/20
4/47

16/15
15/13
18/10
21/12
17/16
13/33

Note. The table presents the percentage of male (M) and female (F) participants for whom each preference had the indicated rank. Themes designed tomeasure conceptually similar constructs were aligned as much as possible. In each cell, results for the Strong-Campbell Interest Inventory (RIASEC
dimensions) are given first, followed by results from the aligned dimension from the Study of Values (SOV). The data come from the initial assessments of
Cohorts 1 through 4. For the SOV, n 5 611 for males and 348 for females; for the Strong-Campbell inventory, n 5 474 for males and 211 for females.

individual differences indexed by the Strong-Campbell Interest
Inventory’s RIASEC dimensions (Harmon, Hansen, Borgen, &Hammer, 1994) and the Study of Values (SOV; Allport, Vernon, &Lindzey, 1970). Table 5 reports the rank orders of the six themeson each instrument, among all participants in Cohorts 1 through 4
at their initial assessment (but for Cohort 4, only participants withabilities in the top 1% were included in this analysis). Counselors
tend to focus on the top two or three themes in working withstudents and clients, and it is clear from these rank orders that thesexes differ in their priorities. For example, 72% of males and
35% of females had Theoretical values ranked as one of their top
two SOV dimensions. In contrast, 16% of males and 61% offemales had Artistic interests ranked as one of their top two
RIASEC dimensions, and 15% of males and 41% of females had
Aesthetic values ranked as one of their top two SOV dimensions.
Social interests and values were nearly 3 times as likely to be in
the top two for females as for males, whereas males were 4 times as
likely as females to rank Realistic interests as one of their top twothemes. Clearly, these results show a people-versus-things sex
difference. (Contrast these findings with the lack of sex differences on these measures among top math-science graduate students,
discussed later, in Top STEM Graduate Students.)
Two independent investigations documented the longitudinalstability of scores on these instruments for intellectually talented youth. RIASEC preferences were examined for Cohort 2(N 5 162, age 13 to age 28; Lubinski, Benbow, & Ryan, 1995),
and SOV preferences were examined for Cohort 1 (N 5 202, age
13 to age 33; Lubinski, Schmidt, & Benbow, 1996). Not only dothese studies support the temporal stability of scores on these
instruments, but the variability of the scores highlights thebreadth of psychological diversity among intellectually talented
youth on a class of variables that are relatively independent ofabilities and relevant to educational-vocational choice (Dawis,
1991, 1992). Other studies have provided evidence for theconstruct validity of these preference measures (Schmidt et al.,1998)—including the improvement in prediction obtained by328

including preferences along with abilities in forecasting college
degree major 10 years later (Achter, Lubinski, Benbow, & Eftekhari-Sanjani, 1999) and occupation 20 years later (Wai et al.,2005). The findings of the latter two studies are sufficiently
noteworthy to justify further review.
Teaming Abilities and Preferences
We conducted one of these studies (Achter et al., 1999) to ascertain whether the SOV provides incremental validity beyond
the SAT in predicting college majors. Across SMPY Cohorts 1, 2,
and 3, a total of 432 participants had scores for both instruments
and had reported earning a college degree by their 10-year
follow-up. These participants were grouped into three categories
based on whether they secured their degree in the humanities,
math or science, or something else. Then, we conducted a discriminant function analysis, utilizing the SAT and SOV scores,
to uncover the psychological variables differentiating thesegroups.
Table 6 provides the discriminant function loadings of the twodimensions derived from this analysis. These loadings form
distinct math-science and humanities amalgams, with mathability and Theoretical values loading most strongly on Function
1 (coupled with negative loadings for Social and Religious values), and verbal ability and Aesthetic values loading moststrongly on Function 2. Incremental-validity analyses revealed
the SAT-M and SAT-V measures accounted for 10% of thevariance among these three groups, and the five SOV scales
accounted for an additional 13% of the variance. Given theheterogeneity within these three broad degree groupings, andconsidering that the assessment occurred a decade earlier at age
13, the fact that the scores accounted for 23% of the variance isimpressive. By showing that age-13 preference assessments
provided incremental validity to SAT assessments in predicting
educational outcomes at age 23, this study established the importance of assessing both abilities and preferences for working
with talented youth in applied psychological services and forVolume 1—Number 4

David Lubinski and Camilla Persson Benbow

TABLE 6Discriminant Function Structure Matrix Containing Two
Functions for Predicting College Degree (Humanities, MathScience, or Other)
Variable
SAT verbal score
SAT math score
Study of Values
Theoretical
Aesthetic
SocialReligious
Economic

Function 1

Function 2

.09
.59

.56
.12

.87
.13
.60
.56
.47

.03
.81
.01
.03
.29

Note. The bivariate group means (Function 1 followed by Function 2) were.29 and .60 for humanities degrees, .43 and .05 for math-science degrees,and .57 and .21 for other degrees. From Achter, Lubinski, Benbow, andEftekhari-Sanjani (1999).

modeling their development theoretically in the context of TWA.
It should be noted, however, that the participants in the mathscience group were more readily distinguishable from the twoother groups than those two groups were from each other:
Function 1 accounted for 75% of the predictable variance, andFunction 2 accounted for the remaining 25%.
Figure 6 shows the three groups’ bivariate means on these twofunctions. Lines connecting these three bivariate means form
the unshaded triangle, and lines from each point running
through the midpoint of the other two parse the two-dimensional
space formed by these functions into three exhaustive regions,where participants earning degrees in the humanities (top left),science (right), and other disciplines (bottom left) would be
expected to fall (see Achter et al., 1999, for further details).
Subsequently, the generalizability of these regions for laterstages of development was examined.
In a study conducted a decade later (Wai et al., 2005), theseability-preference findings (Achter et al., 1999) predicting
educational outcomes were evaluated for their generalizability
to occupational outcomes. Specifically, we were interested inascertaining whether the functions were robust enough tomaintain their validity over another decade by predicting occupational-group membership at age 33. If age-33 occupational
data could be shown to occupy regions drawn with discriminantfunctions based on age-13 assessments and calibrated on age-23
educational criteria, the psychological significance of thesefunctions would accrue additional validity. Across Cohorts 1, 2,
and 3, there were 323 men and 188 women who had taken boththe SAT and the SOVat age 13 and who had 20-year longitudinal
data listing an occupation. These participants’ occupations wereclassified as in the humanities, math or science, or another area,
and their discriminant function scores were plotted in the space
derived in the previous study (Achter et al., 1999). Figure 6
shows the three occupational groups’ bivariate means on the two
discriminant functions. Lines connecting these three bivariate

Volume 1—Number 4means form the shaded triangle. (Bivariate means for some individual occupational groups are also plotted.) For each of thethree major categories, the percentage of hits (participantspredicted to be in the category and actually in the category) andmisses (participants predicted to be in the category but in oneof the other two categories) is provided in each region. Clearly,
the preponderance of each group is classified correctly, whichsuggests that teaming abilities and preferences is useful for
predicting qualitative differences in occupational choice. Indeed, a salient people-versus-things dimension (or, as we prefer,an organic-inorganic dimension), not illustrated in the figure,runs between nurses and homemakers along the dashed-lineboundary between the ‘‘Humanities’’ and ‘‘Other’’ regions andextends through the bivariate point for math-science occupations, continuing on under the positive x-axis between engineers
and computer scientists.
Function 1, the math-science function, accounted for the
majority of variance among the three groups, inasmuch as inboth studies, the math-science group was more readily distinguished from the other two groups than those two groups werefrom each other. Nevertheless, both functions provide powerful
analytic tools for conceptualizing educational and career
choices. Moreover, these findings also suggest how learning andwork environments can be adjusted to match the differential
proclivities of students and professionals who differ markedly onthese functions, although a discussion of such implications isbeyond the scope of this article. These functions afford insight
into how members of different professions (manifesting significant group differences on these two functions) approach intellectual problems and create contrasting but dispositionallycongruent organizational climates (Bouchard, 1997; Lubinski,
1996, 2000). In essence, these functions tell an important story
about the intellectual character of intellectually talented
populations and the environments they are motivated to seekout, avoid, and even create (Bouchard, 1997; Scarr, 1996). They
are also helpful in conceptualizing sex differences: In the occupational study (Wai et al., 2005), for example, the differences
between males and females (males minus females) on Function 1
and Function 2, measured in standard deviation units, were 1.54
and 1.05, respectively.
Modeling Talent Development More Comprehensively
One important dimension missing in the aforementioned studies
is spatial ability. The model depicted in Figure 1 has threecognitive abilities, and many investigators have made a compelling case for assessing all three in educational practice andresearch (Gohm, Humphreys, & Yao, 1998; Gottfredson, 2003;
Humphreys et al., 1993; Snow, 1994, 1999). But the studies that
we have summarized involved only mathematical and verbal
reasoning measures. Recently, we conducted a 5-year study inwhich we examined mathematical, verbal, and spatial abilities,
along with two broad preference inventories, the SOV and theStrong Interest Inventory (RIASEC dimensions), as predictors

329

Antecedents of Math-Science Expertise

Fig. 6. Location of college-degree and occupational categories (humanities, math-science, or other) in a two-dimensional space defined by
the discriminant functions in Table 6 (x 5 Function 1, y 5 Function 2). Lines connecting the bivariate means of these two functions for college
majors form the unshaded triangle, whereas lines connecting the bivariate means for occupations form the shaded triangle. A number of
specific occupations are plotted within this two-dimensional space (with sample sizes in parentheses). (Two occupations, physician andlawyer, were not classified in one of the three criterion groups, but their bivariate points are plotted to show where they fall in this space.)
This space was partitioned into three exhaustive regions by drawing a line from each college major’s bivariate mean through the midpoint of
the other two college majors’ bivariate means (see the dashed lines). Then, the percentage of participants with occupations corresponding to
the area that their bivariate points fell in was calculated. The boldface entry in the box in each region indicates the percentage of participantspredicted to be in that category who were in fact in that category (i.e., hits); the remaining percentages indicate the percentage of people
predicted to be in the category who were in each of the other categories (i.e., misses). From Wai, Lubinski, and Benbow (2005).

of educational outcomes and occupational intentions (Webb,
Lubinski, & Benbow, in press). This study constitutes the mostcomprehensive mapping of our talent-development model to
date.

330

Our outcome variables were based on information collectedfrom SMPY’s Cohort 4 at their age-18 follow-up (i.e., after highschool). The five criterion variables were favorite high school
course, least-favorite high school course, preferred leisure

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David Lubinski and Camilla Persson Benbow

activity, college major, and planned occupation. Two sets of
discriminant function analyses were performed—one for eachpreference inventory. For all five criterion variables, the incremental validity of spatial ability was evaluated, following theentry of the SAT and either the SOV or RIASEC themes. These
analyses are too extensive to be reviewed thoroughly here, but,on average, spatial ability added 2.4% incremental validity tothe prediction of these criteria. As before, the first discriminantfunction to emerge in these analyses was a math-science function, and Table 7 presents the results for this function, averaged
across all five criterion variables, for each preference inventory.
For both the SOV and the RIASEC themes, a salient math-science ability pattern emerges in Function 1. For example, salient
positive math and space loadings and negative verbal loadings
are found in this structure matrix, coupled with positive theoretical and negative social, aesthetic, and religious preferences.
In interpreting these functions and those from the discriminantfunction studies discussed in the previous section (Achter et al.,
1999; Wai et al., 2005), however, one should not surmise that thenegative weights for verbal ability mean that development of
math-science expertise does not involve verbal ability. Rather, it
would be more precise to say that individuals with extraordinary
verbal ability, especially when it is accompanied by somewhat
lower math and space abilities, are more inclined to pursue otherareas (as discussed earlier).
TABLE 7
Loadings for Two Sets of First Discriminant Functions Predicting
Three Criterion Groups in Cohort 4: Math-Science, Humanities,
and Other
Analysis
Study of Values

RIASEC dimensions

Variable

Loading

Variable

Loading

Theoretical
Aesthetic
SocialEconomic
Religious
SAT-V
SAT-M
Spatial ability

.57
.42
.36
.47
.17
.19
.39
.70

Realistic
Investigative
Artistic
SocialEnterprising
Conventional
SAT-V
SAT-M
Spatial ability

.11
.04
.69
.51
.42
.02
.24
.30
.64

Note. Interest and value themes designed to measure conceptually similar
constructs have been aligned as much as possible. Each loading in the structure matrix is the mean correlation taken over five criterion variables for
Function 1: favorite high school course, least favorite high school course(reverse-scored), preferred leisure activity, undergraduate major, and expected occupation. Two sets of five discriminant functions were run. Bothincluded three abilities: SAT verbal score (SAT-V), SAT math score (SAT-M),
and a spatial-ability composite of two Differential Aptitude Tests (Mechanical
Reasoning and Space Relations). One of the functions utilized the scales fromthe Study of Values (SOV), and the other utilized the RIASEC interests. Because the SOV is an ipsative measure (all profiles sum to 240), all the information is contained in any five scales, and the sixth is completely redundant;
the Political scale was arbitrarily omitted from our analyses. Adapted from
Webb, Lubinski, and Benbow (in press).

Volume 1—Number 4

Naturally, the findings involving spatial ability must be interpreted with caution, because they reflect only 5-year longitudinal data and intentions as opposed to actual outcomes for
4-year college degrees and occupations. Nevertheless, otherresearchers have observed the same pattern in more maturesamples (Austin & Hanisch, 1990; Gohm et al., 1998; Humphreys & Lubinski, 1996; Humphreys et al., 1993; Smith, 1964).
Moreover, given that the psychometric properties of the SAT,RIASEC dimensions, and SOV, when applied to intellectually
precocious young adolescents who are then followed for 20
years, have consistently mirrored findings on more mature
populations, these results hold promise. At the very least, all ofthese findings combine to suggest that modern talent searches
should be augmented to include measures of spatial visualization in the selection process, and doing so is likely to uncoversome STEM talent currently being lost. Indeed, it is estimatedthat contemporary talent searches miss approximately half of the
top 1% in spatial ability (Shea et al., 2001; Webb et al., in press),by exclusively restricting talent identification to mathematicaland verbal abilities.
Finally, although exploring sex differences is not a primarythrust of our work, they are hard to ignore. Sex differences on thetwo functions in Table 8 are especially revealing. The average
effect size for sex (males minus females) across the series of five
discriminant functions based on the SOV was 1.04, and theaverage effect size across the series of five discriminant functions based on the RIASEC dimensions was 1.04 as well. Thus,
rather large differences between the sexes are uncovered whenmodels aggregate different classes of relevant variables (abilities and preferences), and these variables all display sex
differences in the same direction when calibrated for STEM
pursuits. The perplexities encountered when focusing on one
class of relevant variables are, to a certain extent, resolved
when more complete models are considered. Moreover, personal
attributes beyond abilities and preferences are germane to
STEM pursuits; these attributes manifest large individual differences as well and are helpful in further clarifying individualand group differences in occupational outcomes (especially in
STEM areas).

Conative Factors
It takes more than the right mix of specific abilities, interests,
values, and opportunities to be attracted to and excel in STEM
areas (Lubinski & Benbow, 2000; Lubinski, Benbow, et al.,
2001; Tyler, 1974; Williamson, 1965). Conative variables arehighly important for mastering these intellectually demanding
and time-intensive disciplines, and they are underappreciatedrelative to abilities and interests. Regardless of the domain of
distinction (securing tenure at a top university, making partner
at a prestigious law firm, or becoming chief executive officer
of a major organization), notable accomplishments are rarely
achieved by people who work 40 hr per week or less (Campbell,331

Antecedents of Math-Science Expertise

TABLE 8
Rank Order of Preferences for Cohort 5Interest/Value theme
Rank
order
1
2
3
4
5
6

Investigative/
Theoretical

Artistic/
Aesthetic

Enterprising/
Economic

Social/Social

Realistic/
Political

Conventional/
Religious

M

F

M

F

M

F

M

F

M

F

M

F

64/43
25/30
7/14
3/8
0/4
1/1

52/44
33/26
11/13
2/9
1/7
1/2

16/21
24/21
25/16
12/20
11/14
11/8

23/20
23/19
19/25
13/15
12/12
10/8

2/9
1/14
4/16
12/16
28/25
53/19

2/12
2/20
5/17
15/18
28/20
47/14

10/14
12/16
20/23
32/20
15/20
11/8

9/13
12/12
19/21
28/21
17/24
15/8

11/7
29/10
26/20
18/26
10/23
6/15

13/6
21/12
27/21
23/23
8/22
8/15

3/12
7/9
17/8
21/9
36/13
16/48

5/10
10/10
17/5
19/10
32/13
16/52

Note. The table presents the percentage of male (M) and female (F) participants for whom each preference had the indicated rank. Themes
designed to measure conceptually similar constructs were aligned as much as possible. In each cell, results for the Strong-Campbell Interest
Inventory (RIASEC dimensions) are given first, followed by results from the aligned dimension from the Study of Values (SOV). For the SOV, n 5
362 for males and 342 for females; for the Strong-Campbell inventory, n 5 362 for males and 340 for females.

1977; Ericsson, 1996; Eysenck, 1995; Gardner, 1993; Jackson& Rushton, 1985; Simonton, 1988; Zuckerman, 1977). Worldclass performers work on average 60 to 80 hr per week withcommitment and passion. Galton’s (1869) observations about thecapacity for work, will, and zeal exhibited by extraordinary
performers are as true today as when he made them.
Consider the following remarks by a leading authority on thedevelopment of eminence and a distinguished biologist:
Making it big [becoming a star] is a career. People who wish to doso must organize their whole lives around a single enterprise. They
must be monomaniacs, even megalomaniacs, about their pursuits.
They must start early, labor continuously, and never give up thecause. Success is not for the lazy, procrastinating, or mercurial.
(Simonton, 1994, p. 181)
I have been presumptuous enough to counsel new Ph.D.’s in biology as follows: If you choose an academic career you will need
forty hours a week to perform teaching and administrative duties,
another twenty hours on top of that to conduct respectable research, and still another twenty hours to accomplish really important research. This formula is not boot-camp rhetoric. (Wilson,
1998, pp. 55–56)

In all of our age-33 follow-ups, we have examined how muchtime our participants devote to their careers and how much timethey are willing to devote to their careers. Figure 7 summarizes
responses to two questions from SMPY’s 20-year follow-up of
Cohorts 1 and 2 (Benbow et al., 2000; Lubinski & Benbow,
2000). At age 33, participants were asked how much they would
be willing to work in their ‘‘ideal job’’ and how much they actually did work. Figure 8 summarizes answers to the same twoquestions from SMPY’s Cohorts 3 and 5 (the top-1-in-10,000
group and the top math-science graduate students, respectively), when both samples were in their mid-30s (Lubinski et al.,2006). These data, taken from four extraordinarily able cohorts

332

Fig. 7. Number of hours participants in Cohorts 1 and 2 worked per
week and were willing to work per week in the ideal job, by sex. Participants were surveyed when they were in their mid 30s; they were asked
how many hours per week they typically worked (top panel; homemakers
were excluded from this question) and how many hours per week they
were willing to work, given their job of first choice (bottom panel).
Adapted from Lubinski and Benbow (2000).

Volume 1—Number 4

David Lubinski and Camilla Persson Benbow

Fig. 8. Number of hours graduate-student (GS) and talent-search (TS; Cohort 3—top 1 in 10,000) participants
worked per week and were willing to work per week in the ideal job, by sex (cf. Fig. 7). Participants were surveyed
when they were in their mid-30s. The data for hours worked are based on ns of 276 and 264 for male and female GSparticipants, respectively, and 217 and 54 for male and female TS participants, respectively. The data for hours
participants were willing to work are based on ns of 269 and 263 for male and female GS participants, respectively,
and 206 and 57 for male and female TS participants, respectively. From Lubinski, Benbow, Webb, and BleskeRechek (2006).

and assimilated over multiple time points, reveal considerable
variation in the noncognitive factor of willingness to work longhours. To understand the possible impact of this factor, one need
only imagine the differences in research productivity likely to
accrue over a 5- to 10-year interval between faculty members,
research scientists, or lawyers who work 45-hr weeks and those
who work 65-hr weeks (other things being equal). The same

Volume 1—Number 4

pattern would emerge for advancing knowledge or achieving
distinction in other occupational pursuits (see especially
Campbell, 1977, p. 58).
These figures also reveal another interesting sex difference
that is difficult to ignore: The percentage of individuals who were
working and preferred to work less than 40 hr per week wasappreciably greater among the women than the men. These data333

Antecedents of Math-Science Expertise

fit with a number of reports in the popular press indicating thatmany women graduating from elite colleges are opting out ofcareer tracks, preferring to become stay-at-home mothers (cf.
Story, 2005), as well as with reports on middle-aged womenvoluntarily leaving highly successful careers to pursue otherinterests (cf. Kuczynski, 2002). These findings align with genderdifferences uncovered in other studies of occupational aspirations and time devoted to work (Bhargava, 1986; Browne, 2002;
V.R. Fuchs, 1988; Hoffman & Reed, 1982; Shye, 1991; Wood,
Corcoran, & Courant, 1993). That these findings are observed inhighly talented men and women who are similarly able, similarly
aware of their abilities, and similarly satisfied with their current
careers and life in general at age 33 (Benbow et al., 2000; Lubinski et al., 2006) indicates that sex differences in occupational outcomes cannot be fully understood by examining only
levels and patterns of abilities, interests, values, and opportunity. Conative factors also need to be incorporated whenmodeling sex differences in outcomes.
If sex differences in time devoted to work persist, large sex
differences in creativity, occupational accomplishments, andwork-related outcomes will remain over time. Indeed, we(Benbow et al., 2000) found that controlling for number of hours
worked eliminated the commonly observed statistically significant sex differences in income. How much time individuals arewilling to devote to their careers also engenders different professional opportunities, particularly in STEM areas. Staying up
to date in these areas requires assimilating a continuous streamof not only substantive but also technical knowledge: One way inwhich STEM careers are more challenging than careers in otherdisciplines is the extent to which they are technologically richand ever-changing (almost continuous technical updating isrequired). Many disciplines are, of course, experiencing great
technological changes, but they are undoubtedly more intense in
STEM areas. In these disciplines, taking a leave of absence orworking part time for a number of years is possible, but doing so
reduces significantly the likelihood of reentering where one left
off or of keeping up to date and subsequently achieving a highimpact leadership role. Albert Einstein noted that one of themore powerful forces of nature is the compounding of interest.
The same holds for productivity. It too compounds, and thus, it ishard to catch up if earlier contributions are missing or unimpressive. Perhaps this is why it is often found that eminent individuals began to develop their talent early. The sooner aperson manifests exceptional performances, the sooner he or she
is likely to be recognized by others and to stimulate the provisionof opportunities for further development, which if seized upon
will make that person stand out even more and increase, in turn,the likelihood of even more opportunity, and so on—this is themultiplicative effect of taking advantage of opportunity (Zuckerman, 1977), or simply compounding.
For many individuals, however, balancing work and family
becomes a true challenge when pursuing a highly competitive
career (Browne, 2002; Rhoads, 2004), and it is not clear how to334

best meet this challenge. What is clear is that individual differences will likely factor into ultimate life choices (Tyler,
1974); people vary in how they choose to allocate their time,
because what some may consider a minor inconvenience, others
may view as totally unacceptable (cf. Lubinski & Benbow,
2001). What SMPY findings suggest, however, and what decadesof individual differences research have revealed for all of thepersonal attributes examined here (Lubinski, 2000; Tyler,1965), is that there is much more variation within groups thanbetween groups. Therefore, for a long time, the best science hassuggested that opportunities should be open and readily available to members of all groups on the basis of their individual
characteristics (Dawis, 1992; Tyler, 1974, 1992; Williamson,1965).

Educational Factors
The talent-development process in science, as in other areas(e.g., athletics, arts), typically begins early (e.g., in special mathor science programs), as early as fifth grade, and is then sustained(Lubinski, Benbow, et al., 2001). We (Lubinski, Benbow, et al.,2001) have concluded that developing exceptional scientificexpertise requires both special educational opportunities andparticular personal characteristics and that this attribute-plusopportunity tandem operates similarly for males and females.
Bloom (1985) also found that early intervention and stimulation
clearly stood out in the lives of talented and excelling adults.
From the start, SMPY has emphasized both talent identification and development. Its approach has been to tailor educational opportunities to be responsive to students’ individuality
and to build on their strengths (Benbow & Stanley, 1996; Lubinski & Benbow, 2000). Literally hundreds of studies haveshown that advancing talented students in their studies so thatthey are working with curricula designed for older students (i.e.,
acceleration) is effective in enhancing achievement (Benbow,
1991; Benbow & Stanley, 1996; Colangelo et al., 2004; Swiatek& Benbow, 1991a); positive effects are detectable up to 50 years
later (Cronbach, 1996). Because of SMPY’s initial and shortterm success with acceleration, and because the educational
efficacy of acceleration has been documented repeatedly byother investigators (Colangelo et al., 2004), we focus here on thelong-term evidence emerging from SMPY.
Swiatek and Benbow (1991b) conducted a 10-year follow-up
of 13-year-old participants in SMPY’s fast-paced mathematics
classes, which began with the expressed intent of challenging
students in mathematics and sparking or maintaining theirinterest in math and sciences until they reached college. Indeed,
relative to equally able comparison groups, SMPY participants
were twice as likely to be in math-science career tracks in their
mid-20s and in their mid-30s (Benbow, 2006). Other mathscience interventions conducted by SMPY revealed the same
trend (Benbow & Lubinski, 1996). Thus, being challenged byintellectually rigorous math-science educational opportunities

Volume 1—Number 4

David Lubinski and Camilla Persson Benbow

that are responsive to one’s learning needs increases the likelihood of being in a STEM career 20 years later.
The Advanced Placement (AP) program is becoming extremely popular in today’s schools as a means for challenging themost capable students in high school. When asked to look back,gifted students often cite AP courses as their favorite courses(Bleske-Rechek, Lubinski, & Benbow, 2004). At age 33, moreover, 70% of participants in Cohorts 1 and 2 who had taken one
or more AP courses or exams had obtained an advanced degree(master’s or beyond), compared with 43% of those who had nottaken an AP course or exam. Participation in an AP course
accounted for between 5 and 7% of additional variance in advanced-degree attainment after SAT-M scores were controlled.
Thus, through self-selection or something intrinsic to the AP
program itself, AP involvement is a positive predictor of educational success and satisfaction for intellectually talented
youth (Bleske-Rechek et al., 2004).

Top STEM Graduate Students: A Generalization Probe
As noted earlier, SMPY’s Cohort 5 was selected to conduct a
generalization probe to ascertain the fidelity of the TS model for
identifying math-science talent. In 1992, first- and second-year
graduate students attending the top 15 math-science training
programs in the United States were surveyed (Lubinski, Benbow,
et al., 2001). This cohort consisted of 368 male and 346 female
participants (the universities and disciplines included are listed
in Lubinski, Benbow, et al., 2001). To our knowledge, a large
group of approximately equal numbers of male and female
nascent scientists of this caliber has never been psychologicallyprofiled so comprehensively. Some of the information secured
included SAT scores for those who participated in talent
searches, high school SAT scores, Graduate Record Examination (GRE) scores, and scores on the Strong Interest Inventory,
the SOV, and the Adjective Check List (Gough & Heilburn,1983). In addition, much background information was collected.
One question in the initial survey, for example, asked the students if they felt they would have qualified for a talent searchwhen they were in seventh grade (and they were provided with anextensive description of the qualification criteria): Only 8% feltthat they would not have qualified.
Overall, this study confirmed that the salient attributes uncovered by SMPY’s longitudinal research were also prominent
psychological features among these graduate students, regardless of their sex. Talent searches were not as well known backwhen these students were in seventh grade, but 13 to 15% had
age-13 SAT scores. These assessments placed them just above
the top 1 in 200 in quantitative reasoning ability. The highschool SAT scores of Cohort 5 were analyzed for ability tilt (viz.,
SAT-M minus SAT-V), and the mean difference was 92 (Mdn 590) for males and 79 (Mdn 5 70) for females (Lubinski, Benbow,
et al., 2001). Throughout the 1980s, the maximum mean difference between math and verbal SAT scores was 66 points forVolume 1—Number 4males and 33 points for females (College Entrance Examination
Board, 1992). Thus, these math-science graduate students did
appear to display marked tilts. To evaluate the psychologicalsignificance of these tilts more concretely, we calculated thepercentages of math-science degrees among SMPY Cohort 2
participants, comparing those who did and did not have tilts
equivalent to the tilts found among the graduate students (i.e.,tilts  70 vs. < 70 for female participants and tilts  90 vs. < 90
for male participants (Lubinski, Benbow, et al., 2001). This
analysis showed that 75% of TS males and 51% of TS femaleswith SAT difference scores at or beyond the median of theirgender-equivalent graduate-student counterparts secured undergraduate math-science degrees. For TS participants with difference scores below these medians, the percentages receivingsuch degrees dropped significantly to 57% for males and 28%
for females.
The male and female graduate students in Cohort 5 displayed
the preference pattern (high investigative-theoretical preference and lower social and religious preferences) typically foundfor scientists, and were remarkably similar in other personal
attributes, especially when the sex differences uncovered among
the TS participants are considered. For example, males andfemales in Cohort 5 displayed congruent rank orderings in interests and values (Table 8); the lack of sex differences in these
profiles is striking, and hard to ignore given the conspicuous sex
differences in rank orderings for these dimensions among
SMPY’s TS participants (see Table 5). For each instrument,
summing the percentages of graduate students who ranked eachtheme first or second reveals sex differences of only a few percentage points. For example, 73% of males and 70% of females
ranked Theoretical as one of their top two SOV themes, as would
be expected of individuals pursuing scientific career tracks. The
male-female similarity in Cohort 5 was further supported bymore general personological assessments. The first- and lastranked scales among the 36 standard Adjective Check List
scales were the same for the two sexes: creative personality andsuccorance, respectively. This pattern is indicative of independently minded creative innovators and illustrates anotherway in which male and female top math-science graduate students are psychologically similar.
Tables 9 and 10 reveal additional similarities between top
male and female math-science graduate students, not only intheir educational experiences and accomplishments (e.g.,
seeking out advanced learning opportunities and special programs in math-science and excelling in them), but also in theircommitment to studying and doing research. In addition, amajority of the graduate students reported math or science as
their favorite high school class, and graduate students of both
sexes reported early participation in special programs andcontests for the gifted, such as math and science competitions(Table 9). (Sex differences had emerged on these items for TS
participants.) These findings reflect an early commitment tomath-science endeavors among the graduate students of both335

Antecedents of Math-Science Expertise

TABLE 9Percentage of Participants With Various Educational Experiences
Graduatestudents

TS participants

Experience

M

F

M

F

Interest in math-science stimulated by a special person
Math-science contest or special program before college
Accelerated primary or secondary education . . .
via advanced subject-matter placement
via Advance Placement or other exams for college credit
via college courses during high school
via grade skippingby any means
Reported influence of acceleration experience
Positive
Negative
Took biology, chemistry, physics, and calculus during high school
Favorite high school class was in math or science
Selected for the National Honor Society
Was National Merit finalist
Awarded National Merit scholarship
Was Presidential Scholar
Experienced mentoring relationship before college
Positive influence on educational-career plans
Negative influence on educational-career plans
Math-science contest or special program during college

61
58

69
54

68
54

73
37

58
66
33
11
88

62
67
33
13
91

68
92
37
23
92

60
88
29
28
92

78
2
68
79
70
23
15
13
28
96
3
20

80
1
66
74
79
21
17
13
28
97
0
21

70
10
65
64
63
42
23
3
33
95
2
25

70
8
60
39
70
38
21
5
34
89
2
11

Note. Items with important differences between groups are displayed in boldface. Group ns vary by item. Graduate students are
the participants in Cohort 5. TS participants are talent-search participants in Cohort 2. M 5 males; F 5 females. From Lubinski,
Benbow, Shea, Eftekhari-Sanjani, and Halvorson (2001).

sexes. As graduate students, they devoted much of their time totheir educational-vocational development, spending a medianof 50 hr per week on studying and research (Table 10). Overall,
the psychological profile and educational experiences and accomplishments of the graduate students, both in and outside ofschool, paralleled those of TS participants who went on to secure
STEM degrees (Lubinski, Benbow, et al., 2001; Wai et al., 2005;
Webb, Lubinski, & Benbow, 2002). The findings converged.
Collectively, these data indicate that regardless of sex, an individual’s abilities, preferences, and commitment to work
combine to promote the active pursuit of scientific excellence inthe scientific community as now constructed.

Entering and Exiting STEM Areas
Although it is well known that men are more frequently involvedin math-science domains than women are, analyses of individual
differences attributes among people who choose to pursue advanced training in math-science domains reveal that these attributes are more important than group membership (e.g., sex)
for understanding this educational choice. Findings from Webb
et al. (2002) support this idea.
We tracked 1,110 Cohort 1 and 2 participants who reported
plans to major in math or science at the onset of their undergraduate studies and then compared those who eventually336

completed a degree in math or science with those who completed
a degree outside these areas. More women than men eventuallychose to pursue degrees in areas outside of math and science, afinding that was not surprising given current statistics. Thispattern is often interpreted as negative because of a focus on
demographic parity. But in-depth analyses of the participants’educational, vocational, and life outcomes affords a different
and more positive interpretation.
First, individual differences in ability pattern and interests,
not biological sex, surfaced as the central predictors of who
actually completed a degree in math or science and who completed a degree outside these areas. Thus, it appears that group
status is a frail proxy variable for specific individual differences
attributes (Lubinski & Humphreys, 1997), such as ability andpreference patterns. The latter more centrally guide educational-vocational choices.
Second, students who completed degrees in math or scienceand those who completed degrees outside these areas showedsimilar levels of success, career satisfaction, and life satisfaction. For example, participants who completed their undergraduate degrees outside of math and science, regardless of sex,
were as likely to earn graduate degrees as participants who
majored in math or science; they merely secured their graduate
degrees in different areas. This finding mirrors other researchdemonstrating that SMPY women earn baccalaureate andVolume 1—Number 4

David Lubinski and Camilla Persson Benbow

TABLE 10
Educational Experiences of the Graduate Students in Cohort 5Experience
Participated in a talent search during junior high school
Believe would have been eligible for a talent search
Believe would not have been eligible for a talent search
Would have enrolled in a talent search
Gifted programs were available at some point
Participated in gifted program (given program was available)
Average number of years participated in a gifted program (SD)Participated in a summer program for the gifted
Positive experience from gifted programs
Negative experience from gifted programs
Worked on an independent research project during high school
Took honors course during high school in
Humanities
Social studies
Languages
Science
Changed undergraduate major
Changed from a program outside math-sciences
Age decided on undergraduate major (SD)Participated in an undergraduate research program
Positive influence on educational-career plans
Negative influence on educational-career plans
Experienced mentoring relationship as undergraduate
Positive influence on educational-career plans
Negative influence on educational-career plans
Member of undergraduate honor society (e.g., Phi Beta Kappa)
Median number of hours per week spent on
Studying
Research

Males

Females

15
63
7
65
74
86
5.2 (2.9)
26
67
3
25

13
62
8
72
78
84
5.4 (2.9)
23
71
3
23

52
42
30
66
29
12
17.7 (2.1)
83
88
5
57
96
1
71

59
45
38
68
35
11
18.1 (1.8)
83
88
4
61
94
3
76

20
30

20
30

Note. No significant differences between the sexes were found (a 5 .01). Statistics represent percentages, except
where specified otherwise. From Lubinski, Benbow, Shea, Eftekhari-Sanjani, and Halvorson (2001).

postbaccalaureate degrees at the same rates as SMPY men, butthat the women are more likely than the men to pursue theircredentials in organic fields, such as the social sciences, biology, and medicine, and the men are more likely than the women
to pursue their credentials in inorganic fields, such as engineering and the physical sciences (Achter et al., 1999; Benbow
et al., 2000; Lubinski, Webb, et al., 2001). To achieve parity,
should society try to push women away from organic sciences
into inorganic disciplines? If so, how much social engineering
would be appropriate? What is the appropriate way to produce
greater parity in engineering? How much parity is acceptable,
and how much is ideal?
Third, many individuals who completed their degrees in nonmath-science areas ultimately chose math-science occupations,and vice versa. Among mathematically talented populations, theleaking pipeline is not an appropriate metaphor. Upon reflection, this fluidity is not surprising for mathematically precocious
females. In samples of males and females selected for mathematical talent, females display higher levels of verbal ability

Volume 1—Number 4than males, and also more uniform levels of mathematical andverbal ability (Lubinski, Benbow, et al., 2001). Thus, mathematically precocious females more often than mathematicallytalented males are endowed with talents that enable them toexcel with distinction in domains that require highly developed
verbal-linguistic skills (these skills and the flexibility they lend
could perhaps propel these individuals in varied directions,in part because verbal-linguistic skills tend to co-occur withsocial, or organic, interests and values—Ackerman, 1996;
Schmidt et al., 1998). This versatility is useful in navigating
today’s multidimensional work environments, which are becoming increasingly scientific and technical. Scientific andquantitative reasoning skills, for example, are essential not onlyin physics, but also in environmental law or scientific journalism. Given the ever-changing nature of the modern world ofwork, it is not surprising that talented women are attracted toenvironments that capitalize on both their quantitative and theirverbal reasoning abilities (as well as their personal preferences)and that they may move in and out of careers formally labeled as

337

Antecedents of Math-Science Expertise

STEM careers more frequently than men do. They are working in
the interface areas that form connections with multiple disciplines and that they are particularly well suited for. Are suchcareer choices or shifts really a representation of a loss of talent?
Is saving a large segment of precious Alaskan land throughexpertise in environmental law less of a contribution to society
than publishing a discovery about the physical universe in
Nature? What is the right balance?

CONCLUDING PERSPECTIVES ON PSYCHOLOGICALSCIENCEOur model for talent development (TWA) emphasizes the importance of tailoring educational opportunities in accordancewith each student’s individuality (Lubinski & Benbow, 2000).
It is predicated on the importance of accurately assessing
individual differences and anticipating future individuality.
Psychological knowledge about individual differences and theeffective use of above-level assessments do help in identifying
extraordinary human potential. Appropriate psychometric measures of specific abilities do have the potential to uncover a wide
range of talent among diverse groups and, along with information
on interests and values, can facilitate a more precise tailoring of
educational opportunities (Corno et al., 2002). Teaming assessments of individual differences with differential opportunities harnesses both components of the ‘‘two disciplines ofscientific psychology’’ (Cronbach, 1957): aptitude (or personalreadiness) and treatment (or environmental opportunity).
Moreover, to the extent that educators do not respond to theindividuality of intellectually precocious students and alignlearning opportunities accordingly, academic underachievement becomes much more likely (Benbow & Stanley, 1996;
Bleske-Rechek et al., 2004; Colangelo et al., 2004; Lubinski &Humphreys, 1997; Stanley, 2000).
Fifty years ago, Donald G. Paterson (1957), a dominant force
in the applied psychological sciences, anticipated the importance the Association for Psychological Science is currently
placing on the scientific study of human capital. Paterson’s famous Bingham lecture, delivered at Ohio State University in
1956 and published in the American Psychologist the following
year, is full of wisdom and is still important reading. TWA wasdeveloped by two of his students (Dawis & Lofquist, 1984; Lofquist & Dawis, 1969, 1991), and we conclude with contributions
from two others to underscore its contemporary relevance in thecontext of findings from SMPY. The first quotation is from JamesJ. Jenkins. Jenkins was selected by Paterson to carry on his
applied psychological studies (Jenkins & Lykken, 1957; Jenkins& Paterson, 1961), but Jenkins’s (1981) passion and readinessfor psycholinguistics eventually propelled him toward otherpursuits. The second quotation is from Leona E. Tyler, arguably
the most distinguished counseling psychologist of the 20th
century.

338

If you are concerned with improving the output of some complex
system, you must study the component that produces the largest
variance first. Adjusting or correcting smaller sources of variancehas no appreciable effect on the output of the system as long as the
major source of variance is uncontrolled. (Jenkins, 1981, p. 224)
In our haste to abolish the unjust and the obsolete, we cannot
afford to ignore the psychological realities that generated such
systems in the first place. There are highly significant psychological differences among individuals, and the soundness of oursocial institutions depends upon how successfully we take theminto account. . . . A complex society cannot regard its members as
identical interchangeable parts of a social machine. Its complex
functioning depends upon the contributions of individuals specializing along different lines, equipped for carrying out different
specialized tasks.
For this reason we must not be content with any system of universal
education that provides identical treatment for all pupils. We must
look for ways of diversifying education to make it fit the diverse
individuals whose talents should be developed and utilized. (Tyler,
1974, pp. 6–7)

Psychology cannot afford to neglect the individuality foundwithin intellectually talented populations—nor can the study
and development of human capital initiatives.
Acknowledgments—An earlier version of this manuscript
profited from comments by David P. Campbell, Rene V. Dawis,
Kimberley Ferriman, Gregory Park, Jonathan Wai, and Rose
Mary Webb. Support for this article was provided by a Research
and Training Grant from the Templeton Foundation, by National
Institute of Child Health and Human Development Grant P30
HD 15052 to the Vanderbilt Kennedy Center for Research on
Human Development, and by the Strong Research Board.
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APPENDIX: SEX DIFFERENCES IN GENERAL AND IN
SPECIFIC ABILITIESSex differences are routinely observed among intellectuallytalented young adolescents, and it is useful to have somebenchmarks of the normative distributions that give rise to them.
Although most investigators find essentially no sex differencesin overall level of general intellectual ability (Jensen, 1998), sex

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Fig. A1. Distribution of IQs among Scottish boys and girls who were born in 1921
and tested at age 11 in the 1932 Scottish Mental Survey. The graph shows the percentage of each sex in each 5-point band of IQ scores. The numbers beside theplotted points indicate the absolute number of boys and girls in each 5-point band.
From Deary, Thorpe, Wilson, Starr, and Whalley (2003).

Fig. A2. Scores on four subtests of the Cognitive Abilities Test among a large and representative sample of pupils in the United Kingdom(Strand, Deary, & Smith, 2006). The sample constituted more than 320,000 students, ages 11 and 12 years (between September 2001 andAugust 2003). Results are shown for the nationally standardized tests of verbal reasoning, quantitative reasoning, and nonverbal reasoning, as well as for a composite of these three measures.

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Fig. A3. Sex differences (males minus females, in standard deviation or effect-size units) for eight Differential Aptitude Tests (DAT), whichwere developed to provide a standardized procedure for measuring abilities in grades 8 through 12 (Bennett, Seashore, & Wesman, 1982).
These norms for the DAT were based on a representative sample of more than 61,000 students in grades 8 through 12 (approximately 6,000
students per grade per sex). For each test, the effect size for each grade is shown separately. From Stanley, Benbow, Brody, Dauber, andLupkowski (1992).

differences in variability of general intellectual ability areroutinely observed, as are sex differences in both level andvariability of specific abilities. For example, Deary, Thorpe,
Wilson, Starr, and Whalley (2003) recently illustrated the groupdifferences in ability dispersion using a Scottish sample of morethan 80,000 children who were born in 1921 and assessed at age
11 in the 1932 Scottish Mental Survey (see Fig. A1).
More recently, Strand, Deary, and Smith (2006) analyzed arepresentative sample of 320,000 students in the United Kingdom. The students were assessed at ages 11 and 12, between
September 2001 and August 2003. The distributions in thissample, shown in Figure A2, highlight male-female differencesamong extreme scorers on measures of verbal reasoning,quantitative reasoning, and nonverbal reasoning. The fourth
panel is a composite measure of these three measures that, likethe earlier study of Deary et al. (2003), illustrates an inordinate

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number of males relative to females at both extremes in generalability. The effect-size difference favoring the females over the
males on the verbal test was 0.15, whereas the effect-size difference favoring the males on the other two tests was less than
this value. However, for all three tests, there were substantial sex
differences in the standard deviation of scores, with greater
variance among boys. Boys were overrepresented relative togirls at both the top and the bottom extremes for all tests, with the
exception of the top extreme in verbal reasoning. On some verbal
tests, more girls than boys were found at the extremes (as Strand
et al., and other investigators, have found); but other findings onvariability were mixed for this specific ability (cf. Lubinski &Dawis, 1992, p. 47, Table 7).
Finally, Stanley, Benbow, Brody, Dauber, and Lupkowski(1992) published an extensive analysis of the sex differences(males minus females in standard deviation or effect size units)

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on a variety of aptitude tests (see Fig. A3). These benchmarks
are useful to keep in mind when analyzing group differences atthe extreme tails of ability distributions (Arden & Plomin, 2006;

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Feingold, 1995). For further reading on this topic, see Hedges
and Nowell (1995), as well as Geary (1998), Halpern (2000),
Kimura (1999), and Lubinski and Humphreys (1990a).

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