Expertise, Talent, and Social Encouragement - Earl Hunt

P1: JZG052184097Xc03 CB1040B/Ericsson 0 521 84087 X May 22 , 2006 10:14

C H A P T E R 3

Expertise, Talent, and SocialEncouragement

Earl Hunt

Introduction

There have literally been volumes of stud-ies of expertise (Chi, Glaser, & Farr, 1988;Ericsson, 1996; Ericsson & Smith, 1991;Sternberg & Grigorenko, 2001). The fieldscovered range from medicine to amateurwrestling. In spite of this diversity, regularthemes emerge.

Experts know a lot about their field ofexpertise. This is hardly surprising; an igno-rant expert would be an oxymoron. Expertswork at becoming experts. The revealed wis-dom is that this takes at least ten years(Richman et al., 1996). In some fields thetime is spent perfecting the minutiae ratherthan in the fun of solving problems or win-ning games. Amateur musicians spend agreat deal of time playing pieces, whereasprofessional musicians spend a great dealof time practicing sequences of movements(Ericsson, Krampe, & Tesch-Romer, 1993).Chess masters do not just play a lot of chess,they read a lot of the chess literature.

Because practice is so important, somepsychologists have minimized the contri-bution of talents developed before start-

ing on the path to expertise (Ericssonet al., 1993 ; Sloboda, 1996). This positionis consistent with well-established labora-tory findings showing that under certain cir-cumstances extended practice can lead toimprovements in performance by an orderof magnitude, along with a huge reductionin the range of interindividual differences(Schneider & Shiffrin, 1977).

In this chapter I explore the relationbetween studies of expertise and a few selec-ted results from different areas of psychol-ogy and economics. I shall argue that differ-ent types of expertise make different typesof cognitive demands. Accordingly the bal-ance between talent and practice may varywith the field, but it will vary in a predictableway. In addition, acquiring expertise is notsolely a cognitive matter. Personal interestsand social support are also very important.

Intelligence, Cognition,and Experience

Any discussion of the role of talent versusexperience has to begin with an analysis of

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32 the cambridge handbook of expertise and expert performance

the role of intelligence. Operationally, intel-ligence is usually defined by scores on testsof cognitive abilities. Based on the distri-butions of test scores, modern psychome-tricians have largely agreed on a hierarchi-cal model of intelligence, originally due toCattell (1971), in which general intelligence“g ” is inferred from positive correlationsbetween sets of broadly applicable but dis-tinct cognitive abilities. These include a gen-eralized reasoning ability (“fluid intelligence-Gf ”), the possession and use of knowledgeto solve problems (“crystallized intelligence-Gc”), spatial-visual reasoning, a general abil-ity to think quickly, and several other broadfactors (Carroll, 1993).

The distinction between g, Gf, and Gcoften drops out in discussions of the rela-tion between intelligence and social out-comes. This is unfortunate, for Gf and Gcare measured by different instruments. TheWechsler Adult Intelligence Scale (WAIS)confounds Gf and Gc (Horn, 1985). Twogroup tests that are widely used in industrialand academic settings, the Armed ServicesVocational Aptitude Battery (ASVAB) andthe Scholastic Assessment Test (SAT) areessentially tests of Gc, based on the generalknowledge and problem-solving skills thatone expects an American high school grad-uate to have (Roberts et al., 2000). The besttests of Gf, by contrast, are tests in which anexaminee must detect patterns in abstractand unusual material (Jensen, 1998).

The definition of Gc ensures that any Gctest is culture specific. Cattell (1971) antic-ipated this when he noted that within aperson Gc consists of two components, ageneral ability to use knowledge and thepossession of specific knowledge. He evensuggested that the proper evaluation of Gcwould require separate tests for every pro-fession. The same spirit can be found in theresearch of Sternberg et al. (2000) on “prac-tical intelligence,” which is evaluated by testsof culture- or subgroup-specific knowledge.

Gf and Gc are correlated, which makes itpossible to speak reasonably about g. How-ever, the correlations between measures ofdifferent types of cognitive abilities are high-est toward the low end of the general intelli-

gence scale, and markedly lower at the highend (Detterman & Daniel, 1989; Deary et al.,1996). This is important, as expertise is gen-erally associated with high levels of perfor-mance.

Measures of Gf have substantial corre-lations with measures of the performanceof working memory. A high-Gf person isprobably good at keeping track of severalthings at once and of concentrating his or herattention in the face of distractions (Engle,Kane & Tulhoski, 1999; Kyllonen & Christal,1990). These talents are good to have dur-ing the learning phase of most psychomo-tor activities (e.g., skiing, riding a bicycle,playing tennis). However, they are much lessneeded once an activity has been learned.Laboratory studies of how people learn to dopsychomotor tasks have shown that intelli-gence is a reasonably good predictor of per-formance early in learning but does not pre-dict asymptotic levels of learning very well(Ackerman 1996; Fleishman, 1972).

An important study by Ackerman andCianciolo (2000) modifies this conclusion.Ackerman and Cianciolo reasoned that ifa task taxes working memory after it hasbeen learned, the correlation with tests ofreasoning should remain. They then trainedpeople on two different, greatly reducedversions of an air traffic controller’s task.One could be solved by memorizing a not-too-complicated set of rules. To solve themore complicated task the participant hadto develop orderly patterns of traffic in thearea near a terminal. Participants practicedthe tasks for several days. The correlationbetween the first task and a measure offluid intelligence decreased over practicefrom .45 to .30. The correlations betweenthe intelligence measure and performanceincreased from .40 to .55 over the trainingperiod.

There are obvious parallels between thisstudy and the general study of expertise.Some aspects of expertise, such as swing-ing a golf club, require learning a con-stant relationship between stimulus andresponse. Others aspects, such as the ana-logical reasoning typical of the law, involvevaried mappings, the development of


expertise, talent, and social encouragement 33

mental models of a situation, and extensiveknowledge. Demands on both Gf and Gcnever cease.

A second important observation is basedon studies of natural decision making. Bydefinition, experts make better decisionsthan novices. However, this does not meanthat experts become better decision makersin the sense that they learn to avoid the mis-takes that have been documented in labora-tory studies of decision making (Kahneman,2003). Instead, experienced real-life deci-sion makers rely on analogical reasoning andschematic techniques for selecting and mon-itoring a plan of action (Klein, 1998). Thiskind of decision making depends on twothings: having the experiences on which theanalogies can be based and encoding thoseexperiences in a way that makes informationaccessible when needed. Gc again!

Findings from Industrial-Organizational Psychology

Although laboratory studies offer the advan-tage of control, they cannot replicate thevery long periods of time over which exper-tise is acquired in the workplace. The appro-priate studies are the domain of indus-trial-organizational, rather than cognitive,psychology.

In the late 1980s the US military evalu-ated various predictors of the performanceof enlisted men and women (Wigdor &Green, 1991) in military occupations rang-ing from artillerymen to cooks. Performanceincreased with experience, but appeared toasymptote after about three years. Asymp-totic level of performance was related toscores on a test of mental skills, the ArmedForces Qualifying Test (AFQT), taken attime of enlistment, but there was aninteraction.1 Enlisted personnel with highscores reached asymptotic performance in ayear, personnel with lower test scores tooklonger. Differences in performance could berelated to the AFQT after more than threeyears of service, but the differences were lessthan half those for personnel with only ayear’s service. (See Hunt [1995] for a further

discussion of the general issue of intelligenceand workplace performance.)

Similar observations have been made inthe civilian sector. Scores on tests of cog-nitive competence are related to workplaceperformance, and the correlations are some-what higher during training than during per-formance after training (Schmidt & Hunter,1998).2

The conclusions just offered were drawnfrom analyses of jobs that might be charac-terized as “blue collar” or “lower level whitecollar.” Although the data base is more lim-ited, the same thing seems to be true ofupper-level professional jobs. One large, par-ticularly well-designed study of managersfound a correlation of .38 between cogni-tive scores obtained at the outset of employ-ment and level of management reached aftermore than fifteen years on the job (Howard& Bray, 1988).

Evidently intelligence-as-reasoning andworking memory are always important dur-ing the early stages of learning, well beforethe expert level is reached. A task analy-sis is necessary to determine the extent towhich performance depends on reasoningand working memory after the expert levelhas been reached.

Specialized knowledge will always beimportant if expertise depends largely onthe execution of psychomotor sequences,as in ball-striking in golf. The sports exam-ple is obvious. Psychomotor sequences areimportant in other areas, including medicineand piloting high-performance aircraft. Inother cases (e.g., the law, physics), exper-tise requires the development of schema thatcan guide problem solving. To some extentthe use of such schema can reduce the bur-den on working memory, thus shifting thebalance between the Gf-and Gc-aspects ofintelligence.

Different types of expertise can be char-acterized by their location on the psychomo-tor/mental-modeling-and problem-solving/use-of-experience dimensions. Almost everytask in which expertise can be illustratedcontains some elements of each dimension.

Will we ever be able to test people atthe outset of their experience, say early in



high school, and predict who would becomeexperts solely on the basis of their talents?Probably not, for we have not yet consid-ered the social-personality aspect of expertdevelopment.

Why Become an Expert?

Sternberg (1996) has observed that intelli-gence is successful to the extent that it hasbeen used to meet one’s goals. It does notmake sense to do the work that it takes to bean expert unless you want to be one. In orderto understand expertise we have to under-stand interests.

Ackerman and his colleagues (Ackerman& Beier, 2001; Ackerman & Rolfhus, 1999;Rolfhus & Ackerman, 1999) have shown thatwithin American society interests fall intothree definable clusters:science and math-ematics, intellectual and cultural activity,and social activities. People have knowl-edge bases that correspond to their interests.They also show markedly different personal-ity profiles. Most important for our concernshere, the amount of knowledge a person haswithin his or her own interest area is bestpredicted by measures of Gc, or the extentto which a person has picked up knowledgeof the society in general.

Because intelligence is differentiated atthe upper end, one would expect differen-tial patterns of ability to be particularly pre-dictive of career choices of the gifted. Theyare. Lubinski, Benbow, and their colleagueshave conducted longitudinal studies of giftedstudents who, at age 13 , were in the topten-thousandth of examinees on tests of ver-bal and mathematical skill (Lubinski, Webb,Morelock, & Benbow, 2001). They differ-entiated between students who had signif-icantly higher verbal scores than mathemat-ics scores, or the reverse, and students whowere “high flat,” that is, verbal and mathe-matical scores were essentially the same. Itis important to remember that in this groupa “low” score corresponds to above averageperformance in the general population.

Overall the gifted students did very well.Several had doctorates at age 23 or less;

many others were attending some of themost prestigious graduate schools in thecountry. Some had made substantial con-tributions outside of academia. The typeof achievement differed by group. Studentswhose mathematics scores were higher thantheir verbal scores at age 13 gravitated tomathematics and science courses in college,students whose verbal scores were high-est gravitated toward the humanities andsocial sciences, and students with a flat pro-file (very high scores everywhere) showed amore even distribution of interests. Prefer-ences appeared relatively early. Reports offavorite class in high school mirrored laterprofessional specialization.

Talents are channeled by interests. In gen-eral, people are more interested in thingsthey are good at than things they find diffi-cult. The combination of talent and interestleads to specialized knowledge, and knowl-edge produces expertise. Society reacts tothe combination of talent and interest byoffering support, which leads to furtherspecialization.

Social Encouragement and Expertise

Because the acquisition of expertise re-quires substantial effort, the social sup-port provided during the learning phaseis extremely important. Chess expertsbegin early, often by participation in chessclubs (Charness, Krampe, & Mayr, 1996).Lubinski et al.’s gifted students made sub-stantial use of advanced placement coursesin high school and other educational accel-eration programs. If we look at individualcases, the amount of social support can bedramatic. Gardner’s (1993) biographic studyof exceptional contributors to society, suchas Einstein and Picasso, stresses how thesegreat contributors were able to be single-minded because they were supported byfamily, friends, and colleagues, often at con-siderable expense. At a less earthshakinglevel of expertise, the 2004 winner of theWimbledon woman’s tennis tournament,Maria Sharapova, received a scholarship toa tennis academy at age eight!



0

0.5

1

1.5

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2.5

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3.5

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10% 25% 50% 75% 90%

Percentile of income distribution

Ind

ex =

ear

nin

gs/

med

ian

ear

nin

gs Law, medicine,

dentistry

Financial-BusinessAdvisors

Economists,mathematicians,engineers

HSTeachers

Figure 3 .1. Differential reward indices as a function of type of occupation andthe percentile of the income distribution. Data derived from US Census 2000

income reports.

Because expertise requires motivationand support, society has considerable lever-age in deciding what types of exper-tise will be developed, by varying theextent to which rewards and support areoffered for expert compared to journeymanperformance.3 Where does our own societyreward expertise?

Rewarding expertise has to be distin-guished from rewarding an entire occupa-tion. This can be done by defining thedifferential reward index, Docc(x) for the xthpercentile of an occupation, as

Docc(x) = Incomeocc(x)MedianIncome

(3 .1)

Where Docc(x) is the value of the rewardindex at the xth percentile of the income dis-tribution in occupation “occ,” Incomeocc(x)is the income at the xth percentile, andMedianIncome is the median income forthe occupation. To illustrate, in 1999 themedian income for a physician or surgeon(Incomephysician(50)) was $120,000, whileIncomephysician(75) was $200,000. Therefore,for physicians and surgeons Dphsyician(75)was 1.67. For people who made their liv-ing fishing, Incomefisher(50) was $25 ,000, farless than the median income of physicians.

However, Incomefisher(75) was $40,000, soDfisher(75) = 1.6. Society rewarded physi-cians, as a group, far more than societyrewarded fishers, but within each groupthe relative rewards for expert comparedto journeyman performance were about thesame.

Figure 3 .1 shows the differential rewardindices for four groups of occupationswithin our society. Financial business advi-sors (including stock brokers) represent agroup whose compensation is closely tied totheir success. Three professions (physician-surgeons, lawyers [excluding judicial offi-cers], and dentists) generally derive incomeon a fee-for-service basis, including partici-pation in joint practices. Subgroups of pro-fessionals who develop specialized exper-tise (e.g., neurosurgeons, orthodontists, triallawyers) usually receive larger incomes thangeneral practitioners. Mathematicians (out-side of academia) and aerospace engineersalso have high degrees of specialization, andcould, in principle, be rewarded for exper-tise. Finally, high school teachers receiveincome from salaries that are almost entirelydetermined by their location of work andyears of seniority. Therefore they serve as acontrol group.



The differential reward index variedmarkedly across occupations. Financial andbusiness advisors in the 90th percentile oftheir profession earned 3 .5 times the medianincome for their profession, whereas those atthe 10th percentile earned half the medianincome. A similar but not-so-drastic acceler-ation was shown for the physician-dentist-lawyer group. The differential reward func-tion for mathematicians and engineers wasalmost identical to that for high schoolteachers. In all groups acceleration occurredat the top. The differential reward functionswere virtually linear from the 10th to the50th percentile.

These data suggest, but certainly do notprove, that our society encourages the devel-opment of expertise in business, law, andthe biomedical professions. The figures donot suggest very much encouragement forthe development of expertise in mathemat-ics and engineering. It is of interest to notethat as of 2004 educators and policy makerswere deploring the dearth of American stu-dents in engineering and mathematics, thebiomedical fields were prosperous, and busi-ness schools were booming.

Bleske-Recheck, Lubinski, and Benbow(2004) make a related point. They observedthat extremely gifted mathematics studentsreported liking Advanced Placement classesbecause it gave them an opportunity tostudy with, and proceed at the pace of, theiracademic peers. Bleske-Recheck et al. thenasked whether a well-documented trendtoward opening up Advanced Placementclasses to a greater range of students, in orderto encourage participation by students froma wider spectrum of society, might actu-ally make these classes less attractive to thevery gifted, and therefore channel talentedindividuals away from the areas where theymight maximize their contributions. This isnot the place to debate the overall socialmerits of opening up opportunities to non-traditional students versus offering specialnurturance to the gifted. (Bleske-Rechecket al. acknowledge that such benefits exist.)What is relevant here is that experiencesrelatively early in adolescence do motivatestudents to make particular career choices.

If we need experts in some field we mustencourage people to acquire appropriateexpertise and reward them when they havedone so.

Closing Remarks

In order to understand the development ofexpertise we have to distinguish betweenexpertise in perceptual-motor tasks and ex-pertise in cognitive activities. Perceptual-motor expertise requires automation inthe literal sense. Cognitive expertise re-quires experience, and probably depends tosome extent on automated “nonconscious”thought. It also depends very much on theacquisition of knowledge.

Working memory and attention are gener-ally considered to be the intellectual bottle-necks on human thought. These are the pro-cesses most taxed in the early stages of eitherperceptual-motor learning or knowledgeacquisition. Therefore it is harder to becomean expert than to be one!Nevertheless, insome areas of expert performance work-ing memory demands, and hence demandsfor high fluid intelligence, appear to extendbeyond the learning period.

This conclusion does not deny the impor-tance of practice. Becoming an expert inalmost anything requires literally years ofwork. People will do this only if they havesome initial success, enjoy the work, and aresupported by the social climate. Expertise isnot solely a cognitive affair.

Footnotes

1. The AFQT is a subset of the ASVAB, andtherefore a test of Gc.

2 . Schmidt and Hunter refer to tests of generalcognitive competence. However, the tests thatthey list appear to be tests mainly of Gc.

3 . My claim is not that expertise is the sole deter-miner of income. That would be silly. I doclaim, however, that expertise is one of thedeterminants of income. Therefore the differ-ential distribution of income within an occupa-tion partly reflects payment for expertise andpartly reflects other features, such as seniority.



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Expertise, Talent, and Social Encouragement - Earl Hunt

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