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The Study of Mathematically Precocious Youth (SMPY) was founded by Julian C. Stanley in September, 1971. The practical premise guiding the work of SMPY is and has been to conduct research through service to primarily mathematically talented students. By developing and providing innovative educational programs and educational guidance, its aim is to help start the individual toward the development of talented performance from talent potential (Stanley, 1977; Stanley & Benbow, 1986). In the process, SMPY attempts to discover effective mechanisms that promote both intellectual and social well-being among intellectually talented students. A story first told by Mark Twain captures the essence of SMPY's work with mathematically talented students and its concern with alleviating the loss of this human resource. This story depicts a long-term outcome that SMPY's work is designed to circumvent.

It is about a man who sought the greatest general who had ever lived. Upon inquiring as to where this individual might be found, he was told that the person he sought had gone to Heaven. At the Pearly Gates he informed St. Peter of the purpose of his quest, whereupon St. Peter pointed to a soul nearby. "But that," protested the inquirer, "isn't the greatest of all generals. I knew that person when he lived on earth, and he was only a cobbler." "I know that," replied St. Peter, "but if he had been a general he would have been the greatest of them all." (MacKinnon, 1962, p. 484)

When working with mathematically talented students, we attempt to find, and even provide, environments wherein their talents can best blossom and come to full fruition. What those environments consist of, we have learned, varies as a function of the individual (Benbow & Lubinski, 1994). It is not effective to treat gifted students categorically (i.e., by giving them the gifted program treatment-usually a one to two hour pull-out). Optimal educational programming needs to be responsive to individual differences in abilities and personality, both in quality and quantity (Benbow & Lubinski, 1995).

The topic of this paper is to describe what SMPY has learned about the characteristics of individuals who are likely to become our top scientists and engineers of the future. We will try to answer the following questions: Who are these individuals? What were their educational experiences? And how did they feel about them? Our goal in this paper is to distill what it takes to belong, at age 23, to the pool from which the top scientists and engineers of tomorrow will be drawn.

We will argue and present evidence that individuals possess certain attributes that make them differentially suited for excelling, with fulfillment, in contrasting educational and vocational tracks. That is, only a limited set of learning environments is educationally optimal for anyone individual, even a gifted individual. Students, for example, put forth their best effort when they intrinsically enjoy what they are doing, and world-class achievement is most likely to develop when gifted individuals are allowed to pursue what they love at their desired pace. Moreover, for true excellence to occur requires a considerable time commitment. Developing potential is a life-long task that begins early and involves responding successfully to opportunities, as well as building upon them.

Theoretical Basis
What is the basis for this approach to talent development? The conceptual framework for our educational philosophy draws on three theoretical perspectives (e.g., Dawis & Lofquist 1984; Tannenbaum 1983; Zuckerman 1977), while incorporating some of what is already known about the development of talent and personal preferences for contrasting educational/vocational paths (Benbow & Lubinski 1994; Lubinski & Benbow 1994; Stanley, 1977). Primarily, our work at SMPY is based upon a well-established model of vocational adjustment, the Theory of Work Adjustment (TWA), a model developed over the past 30 years by Rene V. Dawis and Lloyd H. Lofquist at the University of Minnesota (1984, 1969, 1991). Although formulated to better understand adjustment in the world of work, an attractive feature of this model is that it can be readily extended to critical antecedents to vocational adjustment, such as choice of educational program. SMPY has, in fact, extended the model to explain just that--educational adjustment (Benbow & Lubinski, 1994, 1995).

According to TWA as it relates to educational adjustment, to ascertain the optimal learning environment for an individual one must first parse the individual's academic personality and environment into two broad but yet complementary subdomains. An individual's academic personality primarily is comprised of his/her (a) repertoire of abilities and specific skills and (b) personal preferences for content found in contrasting educational environments. On the other hand, different environmental contexts (i.e., educational curricula) are classified in terms of (a) their ability and/or skill requirements and (b) their capability to reinforce personal preferences. The optimal educational environments for an individual are those which engender two levels of correspondence: satisfactoriness and satisfaction. Satisfactoriness refers to correspondence between an individual's abilities and the ability requirements of a particular educational curriculum, whereas satisfaction denotes correspondence between an individual's preferences and the types of reinforcers provided by the environment. Good educational choices maximize both satisfactoriness and satisfaction and, consequently, the degree of commitment to one's chosen field.

An important implication of this model is that both abilities and preferences must be assessed simultaneously to ascertain the readiness of a given individual for a particular educational track. Similarly, components of the educational ecology (response requirements and reward systems) need to be evaluated simultaneously to estimate whether both dimensions of correspondence are likely out-comes in that environment. Here it might be important to reiterate that optimal correspondence and, thus, personal fulfillment for anyone individual, whether gifted or not, is likely to be found only in a few educational tracks (Achter, Lubinski, & Benbow, under review). Individual differences function to differentially tailor people to enjoy and display competence in different subject matters.

It might be useful at this point to provide a practical illustration of TWA and its implications for talent development. Given the current popularity of and emphasis on engineering, we will focus on this educational choice. It should be noted, however, that the requirements are quite parallel for the physical sciences. Hence, our example is highly relevant for the topic of this chapter. For the engineering disciplines, we know that the ability requirements involve especially high mathematical reasoning ability (Benbow & Arjmand 1990; Davis 1965; Green 1989; Krutetskii 1976; Kuhn 1970; Walberg, Strykowski, Rovai, & Hung 1984). Yet high spatial/mechanical reasoning abilities are also important, probably the second most critical personal attribute for satisfactoriness (Humphreys, Lubinski, & Yao, 1993). High degrees of verbal ability are relatively less essential, but still important. In terms of preferences, theoretical (working with ideas), investigative (scientific), and realistic (working with gadgets and things) are among the most salient vocational interests and values for gravitating toward educational environments in engineering, finding their content reinforcing for developing one's intellectual talent, and maintaining a commitment towards it (Dawis, 1991; Dawis & Lofquist, 1984; Holland, 1985; Lubinski & Benbow, 1992; 1994; MacKinnon, 1962; Roe, 1953; Southern & Plant, 1968). It should be emphasized that, in comparison to other physical sciences and especially the biological sciences, engineering especially requires intense abilities and preferences for manipulating and working with sophisticated things and gadgets. Individuals with a pronounced need for people contact are not as readily reinforced in engineering due to its content. Moreover, among those individuals with scientific interests, individuals preferring to deal with content that is more organic rather than inorganic may be drawn to the biological rather than physical sciences. Nevertheless, these are the abilities and preferences important for not only good academic adjustment in engineering and the sciences but success. They must be assessed to ensure that they are in place for an individual considering this educational option (Benbow & Lubinski, 1995; Lubinski, Benbow, & Sanders, 1993).

It should be noted, however, that possessing this constellation of personal attributes (while rare) is still not sufficient for the manifestation of exceptional achievement in engineering or in the sciences, whether in school or professionally. That is even more rare. Truly exceptional achievement requires an intense commitment to mastery of one's chosen discipline and substantial energy-something Galton referred to as "capacity for work" or "zeal" and Spearman referred to as "will" (cf. Ericsson, Krampe, & Heizmann 1993; Simonton, 1994). It also requires special encounters with the appropriate environment to facilitate the emergence of world-class achievement. We turn our attention to this aspect next, which is important for those working in the area of talent development.

Bloom (1985) noted from his interviews of talented performers in a variety of disciplines that special experiences, sometimes interventions, are important in their development. Moreover, in her analysis of Nobel Laureates' careers, Zuckerman (1977) saw that their developmental paths fit well with the model of "the accumulation of advantage." That is, individuals who produce exceptional scientific advances almost universally show promise extremely early in their lives and this evidenced precocity appears not only to respond to but also to create greater opportunities for intellectual development. For example, most Laureates receive an advantage in graduate work by attending the most distinguished universities (10 universities produced 55% of the Laureates) and by studying with the best minds of the day.

Tannenbaum (1983), furthermore, postulated that great achievement results from a rare blend of superior general intellect, distinctive special aptitudes, the right combination of nonintellective traits, a challenging and supportive environment, and the smile of good fortune at crucial periods of life (i.e., the Zeitgeist and chance factors). According to Tannenbaum, every one of the five conditions is a necessary requisite for high achievement; none alone is sufficient to overcome inadequacies in the others. We have incorporated Tannenbaum's view as well into our theorizing on talent development. Specifically, we claim that, for the optimal development or actualization of talent to take place, not only must the individual possess the necessary personal attributes critical for success and satisfaction in his/her chosen educational track, but also he/she must be provided with the opportunity to develop, seek out, and create an appropriate learning environment.¹ Certain quasi-thresholds across all components are vital, but not necessarily at comparable levels.

Here we will explicate the theory guiding the work of SMPY. We will describe the personal characteristics and educational experiences of individuals enrolled in this nation's top math/science graduate programs who were surveyed in 1992 at age 23. We will contrast their characteristics and experiences with two groups of mathematically talented students in the SMPY longitudinal study--those who went to graduate school in the math/sciences and those who went to graduate school in other areas (to somewhat control for motivation, for securing advanced educational credentials).

The SMPY Longitudinal Study
Through the SMPY longitudinal study we are working towards developing a comprehensive and refined understanding of the processes whereby precocious forms of intellectual talent, identified before age 13, develop into noteworthy adult achievements and creative products. How various educational interventions or opportunities facilitate the translation of potential into tangible scientific products is of particular interest to SMPY. Moreover, do findings conform to the theoretical rationale guiding our work?

Design
There are five cohorts in all (see Lubinski & Benbow, 1994): four were assembled through talent searches, while a fifth cohort is composed of 715 graduate students (half females) in top U.S. mathematic and physical science departments. (Each of the first four cohorts is separated by a few years.) Combines the cohorts span 20 years, with findings from each of the first four cohorts serving in part as a replication for similar analyses conducted in other time frames. In addition, because the students in the first four cohorts were identified over a 20-year period using the same criteria and are studied at the same junctures, the study allows for a reasonable degree of quasi-control of historical influences.

Another unique aspect of this study is the ability to modify and add new assessment materials. Cohort 4 grows by approximately 200 participants each year, allowing us to ask questions not possible in the early 1970s. The currency of the study is, therefore, maintained. Finally, a retrospective but also longitudinal study of graduate students in this nation's top engineering, mathematics, and physical science departments was initiated (Cohort 5), partially so that we can ascertain whether such students differ in experiential or psychological ways from students identified via conventional talent searches. Data from Cohort 5 will help assess how well SMPY's findings based on students identified by the SAT at age 13 generalize to other groups of gifted individuals.

The first four SMPY cohorts were formed using different ability cut-offs on the SAT. The first three cohorts are successively more able, while the fourth, consisting of primarily Midwestern residents who are being identified through the Office of Precollegiate Programs for Talented and Gifted (OPPTAG) at Iowa State University, represents the same ability level as Cohort 2. A detailing of each cohort outlined in Figure 1 is given below.

Cohort 1 was identified in SMPY's March 1972, January 1973, and January 1974 Talent Searches as 7th- or 8th-graders scoring SAT-M 390 or SAT-V 370 (Benbow, 1992; Benbow & Arjmand, 1990). Those cut-off scores were selected because they represented the average performance of a random sample of high school females on the SAT at that time. The approximately 2,000 students were drawn primarily from the state of Maryland, with a heavy concentration from the greater Baltimore area. Cohort 2 is comprised of at least the top third of 7th-grade students from SMPY's December 1976, January 1978, and January 1979 Talent Searches (using cut-off scores at or above the top .5% in general intellectual ability). These 800 or so students were drawn from the Mid-Atlantic states. It should be noted that these first two cohorts are separated by at least three years. About 60% of the participants are male.

Cohort 3 is comprised of three groups and is national in its representation. It consists of approximately 300 students who scored at least 700 on SAT-M before age 13 between November 1980 and November 1983 (700Ms). It also includes more than 150 students scoring at or above 630 on SAT-V before age 13 (630Vs). (These scores represent the top 1 in 10,000 for mathematical and verbal reasoning abilities, respectively.) Finally, for comparison purposes, Cohort 3 includes 100 7th-grade students scoring slightly above chance on SAT (i.e., SAT-M + SAT-V 540) in the 1983 Talent Search conducted by the Center for Talented Youth (CTY) at Johns Hopkins University. Because chance performance tends to imply low ability, it is important to keep in mind that this last group's ability level is still in the top 3-5% on national norms (only students in the top 3-5% in ability can enter a Talent Search); thus, by most definitions they too would be considered at least modestly gifted.

Cohort 4 currently consists of over 1,000 students, primarily Midwesterners, scoring before age 13 at least 500 on SAT-M, 430 on SAT-V, or 930 or more on SAT-M + SAT-V. Like Cohort 2, they represent the top .5% in ability. Students in Cohort 4 had enrolled in Iowa State's summer program for intellectually talented youth (see Lubinski & Benbow, 1992 for a profile of their abilities and values), which is a program based purely on the SMPY model. Several comparison groups also are being formed from the Iowa Talent Search, which screens students with abilities in the top 3% in the nation, as well as from students in the normative ability range.

Finally, Cohort 5 contains almost 750 individuals from various engineering, mathematics, and physical science disciplines who are currently enrolled in this nation's top graduate programs (e.g., Stanford, Berkeley, MIT, Caltech). Top graduate programs in an area was determined using ratings from National Research Council (NRC) of the National Academy of the Sciences and the Gorman ratings. Approximately 50% of the sample consists of females. This sample was surveyed in the spring of 1992, with a response rate of 94%. Some of the findings from this survey are reported in Lubinski, Benbow, Sanjani, & Halvorson (in preparation).

Collectively, the five cohorts of SMPY comprise over 5,000 highly able students. This number is steadily increasing. All of the students in the five cohorts are being surveyed at critical junctures throughout their youth and adult lives. Each cohort, moreover, will be surveyed at the same ages to ensure comparability of findings across cohorts.

To date, we have surveyed Cohort 1 at ages 13, 18, 23, and 33 (in progress). Cohort 2 also has been surveyed at ages 13, 18, and 23, with the last survey just being completed. Cohort 3 has been surveyed at ages 13, 18, and 23 (in progress). Cohort 4 has been surveyed at age 13 and 18 (in progress). Cohort 5 has been surveyed at age 23 only, but that survey included much retrospective information. Response rates to our several follow-up surveys range from 75% to well over 90%. Respondents do not differ significantly from non-respondents on key variables including ability, family background, and college attendance (Benbow & Arjmand, 1990; Benbow & Stanley, 1982).

Characteristics of Students in Top Graduate Programs
Here we will describe some of the characteristics of the graduate students enrolled in the top graduate programs, particularly those that are germane for our theorizing about talent development. Hence, we begin with abilities. The mean score on the Graduate Record Exam-Quantitative (GRE-Q) approached 750 for individuals in Cohort 5 (Lubinski, Benbow, Eftekhari-Sanjani & Halvorson, in preparation); interestingly enough, less than 8% had GRE-Q's < 650. As a matter of fact, the modal score was 800, the top score possible; and this score was achieved by 134 of our 715 students. GRE-Verbal scores were somewhat lower, as predicted. Nonetheless, they averaged almost 620; and less than 8% were < 490. High school SAT-M and SAT-V scores were comparable to the GRE's obtained at the end of college. In short, these students were bumping their heads on the ceilings of these quantitative measures. They were actually more impressive than these instruments would allow them to reveal.

We were particularly interested in whether these individuals could have been identified as a seventh-grader by one of SMPY's talent searches as individuals with great potential for high achievement in mathematics and the sciences. We were quite surprised by the result. Only 7% felt that they would not have qualified for an SMPY Talent Search. Indeed, many had actually participated even though talent searches were relatively new when Cohort 5 was of 7th-grade age. Moreover, those who had participated in a talent search reported scores that were remarkably similar to those earned by Cohort 2. Cohort 2 certainly had the potential, in terms of ability, to pursue careers in the math/sciences and most did. This result seems to imply that talent searches can identify at age 12 to 13 almost all students who have the talent to be attractive to our top math/science graduate programs. Not many are missed. This, we feel, is an important finding.

Having exceptionally high mathematical reasoning ability and quite high verbal ability is not sufficient, however, for adjustment and success in the sciences. In SMPY's longitudinal study there are many individuals with such an ability profile, but not all go on to graduate school in the math/sciences or engineering-nor should they have done so. Most do pursue career option in these areas, but not all (Lubinski & Benbow, 1992; Lubinski, Benbow, & Sanders, 1993). How can we narrow the pool? As presented above, TWA would suggest that we look at preferences (viz., vocational interests and values). Hence, this class of variables is examined next.

Using the Strong, we determined students' normative standing on each occupational theme (Lubinski, Benbow, Eftekhari-Sanjani & Halvorson, in preparation). For Cohort 5, the Investigative theme, as anticipated, clearly dominates for both males and females (mean equals 58). The Realistic theme is also high for these individuals (mean equals 50), especially for the males. Artistic is high as well (i.e., about 50), but, especially for the females. In regards to the Study of Values, another measure of preferences, TWA would predict that students in Cohort 5 would have high Theoretical scores, and they do. Theoretical is clearly their dominant value (Lubinski, Benbow, Eftekhari-Sanjani & Halvorson, in preparation).

We can see, therefore, that the personality characteristics of students enrolled in top science and engineering graduate programs are indeed consistent with predictions emanating from TWA. These individuals do have exceptionally high math ability and strong investigative, realistic, and theoretical preferences. Moreover, many of them, especially the males, were frequently engaged in tinkering as adolescents.

Yet, as we discussed above, possessing the necessary personal attributes is insufficient. Individuals must work hard at developing their potential. They must be willing to commit many hours to work, and Table 2 reveals that they are. Many had no problem with committing to work 50 or even 60 hours per week.

In addition, they must be receptive to various educational opportunities that are presented to them, grasp them, and then be willing to work hard at maximizing the benefits afforded by them. Doing so positions the individual for further opportunities. When we look at the educational experiences of these students and the outcomes, the data paint an imposing picture. The individuals in Cohort 5 are simply impressive (Lubinski, Benbow, Eftekhari-Sanjani & Halvorson, in preparation). In elementary school and junior high school, they began to participate actively in the sciences. In Zuckerman's (1977) terminology, this is when they began the process of "accumulating advantage." Approximately 55% had enrolled in a special program. In high school, 68% reported participating in special programs and 55% in math or science contests. Momentum was building. Moreover, many had participated in gifted programs and most had used some form of educational acceleration to create a more challenging set of educational experiences (see Benbow & Lubinski, 1995). Their academic records at the end of high school are outstanding, for example, 75% were members of the national honor society. This put them in a good position during the college admissions process. With excellent records inside and outside of the classroom, they looked impressive to admissions committees and, hence, gained admittance into this country's most select undergraduate institutions.

Upon university matriculation, students in Cohort 5 continued to develop their talents in math and science-both inside and outside the classroom. They early on decided to concentrate their studies in the sciences (i.e., on the average by age 18). Their academic records remained at a stellar level. Yet most impressive was the amount of participation outside of the classroom in science. Over 21% participated in math or science contests and 84% took advantage of research opportunities during their undergraduate years. Together, taking advantage of the opportunities presented and performing well, as described above, coupled with their outstanding abilities (we do not select on the basis of preferences, but people self-select on that basis, Humphreys, Lubinski & Yao, 1993), made these individuals stand out when they applied for graduate school. They were selected and given the opportunity to attend our best graduate schools in the sciences. Eventually, a subset of these individuals will distinguish themselves further, by gaining acceptance into the next opportunity (an outstanding post-doctoral or faculty position), and the cycle continues.

We also asked about a special person or event and mentoring relationships (i.e., a relationship with someone who is advanced in a particular area and helped them learn about that area). Almost 70% reported having a special person or event stimulate their interest in science. With regard to mentoring, about 27% reported such a relationship in high school and over 59% in college. They were uniformly positive in their evaluation of the importance of this relationship. They report that this did positively influence their career and/or educational plans. Similarly, they reported positive effects for the other extra-curricular opportunities in science in which they had been engaged. Yet the mentoring relationship seemed to be the most influential, especially for the females.

Clearly, we have here a pattern of excellence begetting excellence. The individuals in Cohort 5 had the personal characteristic needed to excel in science and engineering and they worked hard at developing their potential. They took advantage of educational opportunities presented to them and this began early. It appeared to have a snowball effect on their achievement. With each stage, their academic credentials stood out more and more.

The above conclusion was reached using retrospective data, however. What is the result when prospective data from the SMPY longitudinal study is scrutinized? Can we use the above variables to predict who, among the mathematically talented, will be the ones to enter graduate school in science and engineering? We turn our attention to this question next.

Who Are the Mathematically Talented Students Who Go to Graduate School in Science?
To answer the above questions, we studied in depth individuals in Cohort 2. They had SAT and GRE profiles similar to those of Cohort 5, the individuals in our retrospective study. We studied two groups in Cohort 2: (1) those who entered graduate school in science and engineering, and (2) those who went to graduate school in another area. Only the males were studied. Too few females had entered graduate school in science or engineering.

As one would expect, the GRE-Q and SAT-M scores of those Cohort 2 males eventually entering graduate school in the sciences were higher than for those who did not (even though there was great restriction of range in the math scores at the end of high school and college), while the reverse was found for GRE-V and SAT-V. As for preferences, those eventually heading into graduate school in the sciences compared to those who did have higher Theoretical, Investigative, and Realistic scores (Lubinski, Benbow, Eftekhari-Sanjani & Halvorson, in preparation). This is consistent with our theoretical expectations. Interestingly, because of ceiling effects encountered on the quantitative assessments, the best predictor of entrance into engineering and the physical sciences was preferences. Benbow, Lubinski, Halvorson, and Sanders (in preparation) found that the best predictor was to combine the Investigate and Realistic Scores and subtract from that sum the Social score. Similarly, Theoretical minus Social scores on the Study of Values was a strong predictor. These composites were better predictors than family characteristics, which, like personal attitudes assessed in high school, contributed little in the way of predicting which students would embark on math/science careers (Benbow, Lubinski, Halvorson, & Sanders, in preparation).

In terms of developing their talents, both groups of students (those who were pursuing math/science careers and those who were not) were active in the process. It did not separate them much. Those not choosing to go into the sciences were actively developing their talents in other intellectually demanding domains. And this fits, given their contrasting constellation of abilities and preferences.

Conclusions
In this paper, we outlined the theoretical rationale guiding SMPY's work in the area of talent development. By drawing upon the Theory of Work Adjustment (TWA), we postulated that each academic track selects and attracts individuals with the right constellation of abilities and preferences. In the sciences, these are individuals with high mathematical and spatial abilities and strong theoretical, investigative, and realistic preferences. Verbal ability is important but not as important as the other abilities. Those with high verbal abilities tend to go into the humanities or law. Here we provided data to support these theoretical expectations. Students enrolled in our top math/science graduate programs do possess the necessary characteristics described above. So did students identified by SMPY at age 12 who later went into engineering and the physical sciences. Yet possessing the right constellation of abilities and preferences were not seen as sufficient. Those who go on to achieve at a high level must work hard at developing their talents. They respond and seize upon opportunities presented to them. By performing well, they make themselves stand out and hence more likely to receive other opportunities in the future. This pattern of achievement (i.e., the snowball effect of achievement) was clearly evident in the data. Those individuals attending our top graduate school in science began developing an impressive record of achievement inside and outside the classroom early. By high school they had begun building momentum and then continued doing so. This speaks to the importance of providing in our schools those opportunities needed by students who are actively developing their potential into exceptional performances at an accelerated rate. They cannot fully develop their talents without the availability of such fast-paced experiences.

Perhaps the most important finding in the data presented here is that we can identify at age 13 those students who have the potential to become our nation's great scientific achievers (Benbow, 1992; Lubinski & Benbow, 1992; Benbow, Lubinski, Halvorson, & Sanders, in preparation). Students labeled as mathematically talented on the basis of high SAT-M scores at age 13 do disproportionally enter careers in the math/science pipeline. Conversely, students within the math/science pipeline in this nation's elite graduate programs clearly had high SAT-M scores at age 13 as well as later on. Almost all thought that they would have qualified for an SMPY Talent Search.

Footnote
1. Explicating the following remarks is beyond the scope of this paper, but educators should be cognizant of the degree to which exceptional individuals actually seek out and create their environments. This is certainly apparent in Zuckerman's (1977) work. For the intellectually gifted, interventions frequently need only consist of providing the right kinds of learning opportunities for groups of "like minded" (comparable abilities and preferences) peers. Psychological science has had a tendency to view students, as well as adults in the work force, as passive agents whose behaviors are products of their socialization environments. It appears, however, that many similar environments are experienced differently, depending on the dispositional attributes that individuals bring to them. Much evidence suggests that people seek out dispositionally-congruent environments, and shy away from settings discorrespondent with their basic abilities, skills, and preferences. See Scarr (1992); Scarr and McCartney, 1983) for more detailed reading on this topic, and Scarr (in press) for implications for parents and policymakers.

The theoretical rationale for our work was based on previous descriptions in Benbow and Lubinski (1995) and Lubinski, Benbow, and Sanders (1993).

References

Achter, J., Lubinski, D., & Benbow, C. P. (under review). Multipotentiality among the intellectually gifted: It was never there in the first place, and already it's vanishing.

Benbow, C. P. (1992). Academic achievement in mathematics and science of students between ages 13 and 23: Are there differences among students in the top one percent of mathematical ability? Journal of Educational Psychology, 84, 51-61.

Benbow, C. P., & Arjmand, 0. (1990). Predictors of high academic achievement in mathematics and science by mathematically talented students: A longitudinal study. Journal of Educational Psychology, 82, 433-441.

Benbow, C. P., & Lubinski, D. (1994). Individual differences among the gifted: How can we best meet their educational needs? In N. Colangelo, S. G. Assouline, & D. L. Ambroson (Eds.), Talent development (Vol. 2, pp. 83-100). Dayton, OH: Ohio Psychology Press (now Gifted Psychology Press, Scottsdale, AZ).

Benbow, C. P., & Lubinski, D. (1995). Optimal development of talent: Responding educationally to individual differences in personality. Educational Forum.

Benbow, C. P., Lubinski, D., Halvorson, M. B., & Sanders, C.E. (In preparation). Competence and fulfillment along the math/science pipeline: Developmental implications of the early assessment of satisfaction and satisfactoriness.

Benbow, C. P., & Stanley, J. C. (1982). Consequences in high school and college of sex differences in mathematical reasoning ability: A longitudinal perspective. American Educational Research Journal, 19, 598-622.

Bloom, B. S. 1985. Developing talent in young people. NY: Ballantine.

Davis, J. A. 1965. Undergraduate career decisions: Correlates of academic choice. NORC Monographs in Social Research, No.2.

Dawis, R. V. 1991. Vocational interests, values, and preferences. In M. Dunnette & L. Hough (Eds.), Handbook of Industrial and Organizational Psychology, 2nd ed. Vol. 2 (pp. 833-871). Palo Alto: Consulting Psychologist Press.

Dawis, R. V., & Lofquist, L. H. (1984). A psychological theory of work adjustment: An individual differences model and its applications. Minneapolis: University of Minnesota Press.

Ericsson, K. A., Krampe, R. T., & Heizmann, S. (1993). Can we create gifted people. In G. R. Bock & K. Ackrill (Eds.), The origins and development of high ability (pp. 222-294). New York: Wiley.

Green, K. C. (1989). A profile of undergraduates in the sciences. American Scientist, 77, 475-480.

Holland, J. L. (1985). The making of vocational choices: A theory of vocational personalities and work environments (2nd ed.). Englewood Cliffs, NJ: Prentice-Hall.

Humphreys, L. G., Lubinski, D., & Yao, G. (1993). Utility of predicting group membership: Exemplified by the role of spatial visualization in becoming an engineer, physical scientist, or artist. Journal of Applied Psychology, 78, 250-261.

Krutetskii, V. A. (1976). The psychology of mathematical abilities in school children. Chicago: University of Chicago Press.

Kuhn, T. S. (1970). The structure of scientific revolutions (second edition). Chicago: University of Chicago Press.

Lofquist, L. H., & Dawis, R. V. (1969). Adjustment to work. New York: Appleton-Century-Crofts.

Lofquist, L. H., & Dawis, R. V. (1991). Essentials of person-environment-correspondence counseling. Minneapolis: University of Minnesota Press.

Lubinski, D., & Benbow, C. P. (1992). Gender differences in abilities and preferences among the gifted: Implication for the math-science pipeline. Current Directions in Psychological Science, 1, 61-66.

Lubinski, D., & Benbow, C. P. (1994). The Study of Mathematically Precocious Youth (SMPY): The first three decades of a planned 50-year longitudinal study of intellectual talent. In R. Subotnik & K. Arnold (Eds.), Beyond Terman: Longitudinal studies in contemporary gifted education (pp. 255-281). Norwood, NJ: Ablex.

Lubinski, D., Benbow, C. P., Eftekhari-Sanjani, H., & Halvorson, M. B. (in preparation). The psychological profile of our future scientific leaders.

Lubinski, D., Benbow, C. P., & Sanders, C. E. (1993). Reconceptualizing gender differences in achievement among the gifted. In K. A. Heller, F. J. Monks, & A. H. Passow (Eds.), International handbook for research on giftedness and talent (pp. 693-707). Oxford, England: Pergamon Press.

MacKinnon, D. W. (1962). The nature and nurture of creative talent. American Psychologist, 17, 484-495.

Roe, A. (1953). The making of a scientist. New York: Dodd, Mead.

Scarr, S. (1992). Developmental theories of the 1990's: Development of individual differences. Child Development, 63, 1-19.

Scarr, S. (in press). How people make their own environments: Implications for parents and policy makers. Psychology, Public Policy, and Law.

Scarr, S., & McCartney, K. (1983). How people make their own environments: A theory of genotype -> environment effects. Child Development, 54, 424-435.

Simonton, D. K. (1994). Greatness. Guilford Press.

Southern, M. L., & Plant W. T. (1968). Personality characteristics of very bright adults. Journal of Social Psychology, 75,119-126.

Stanley, J. C. (1977). Rationale of the Study of Mathematically Precocious Youth (SMPY) during its first five years of promoting educational acceleration. In J. C. Stanley, W. C. George, & C. H. Solano (Eds.), The gifted and the creative: A fifty-year perspective (pp. 73-112). Baltimore: Johns Hopkins University Press.

Stanley, J. C., & C. P. Benbow. (1986). Youth who reason exceptionally well mathematically. In R. J. Sternberg & J. E. Davidson (Eds.), Conceptions of giftedness (pp. 361-387). Cambridge, UK: Cambridge University Press.

Tannenbaum, A. (1983). Gifted children: Psychological and educational perspectives. New York: Macmillan.

Walberg, H. J., Strykowski, B. F. Rovai, E. & Hung, S. S. (1984). Exceptional performance. Review of Educational Research, 54, 84-112.

Zuckerman, H. (1977). Scientific elite: Nobel laureates in the United States. New York: Free Press.

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This article is provided as a service of the Davidson Institute for Talent Development, a 501(c)3 nonprofit dedicated to supporting profoundly gifted young people 18 and under. To learn more about the Davidson Institute’s programs, please visit www.DavidsonGifted.org.