Changing Mind, Changing World Practical Intelligence and Tacit Kn.docx

Changing Mind, Changing World: Practical Intelligence and
Tacit Knowledge in Adult Learning
Bruce Torff
Hofstra University
Robert J. Sternberg
Yale University
Now well into their 40s, Bill and John came from similar
backgrounds. They did equally well in school and on college
admission tests, went to the same university where they
performed comparably, and embarked on careers in business.
However, whereas Bill has been very successful, consistently
gaining promotions in a top company, John has been unable to
climb the corporate ladder. Given that Bill and John began in so
similar a manner, what accounts for their differential success in
business? There could be many reasons, among them the
possibility that there are skills that are important in business—
and perhaps as well in adult life in general—that do not show
up in academic exercises such as schoolwork and tests.
This proposition is supported by research on the extent to which
intelligence test scores predict real-world performance. On
average, the validity coefficient between cognitive ability tests
and measures of job performance is about .2 (Wigdor & Garner,
1982). This means that only 4% of the variance in job
performance is accounted for by scores on ability tests. Even
after validity coefficients are corrected for unreliability in test
scores and criterion measures, and restriction of range caused
by the fact that only high scorers were hired, the average
validity coefficient rises only to .5 (J. Hunter & R. Hunter,
1984; Schmidt & J. Hunter, 1981). Thus, even with the
corrections, only 25% of the variance in job performance is
accounted for by ability test scores. Put the other way, even the
corrected estimates leave unexplained a full three-fourths of the
variance in job performance. Sternberg, Wagner, W. Williams,
and Horvath (1995) concluded that “even the most charitable

view of the relations between intelligence test scores and real-
world performance leads to the conclusion that the majority of
the variance in real-world performance is not accounted for by
intelligence test scores” (p. 913). Clearly, there is more
operating in adult success than the academic skills captured on
ability tests.
These findings—and the story of Bill and John—raise the issue
of how our society conceptualizes, teaches, and evaluates the
skills needed for a productive adult life. What sorts of skills are
needed for adult success? What have psychologists found about
the development of those skills? It turns out that recent theory
and research have veered away from previously held views
about human abilities. In this chapter we suggest that the
traditional view looks somewhat narrowly at human abilities
and the cultural contexts in which they operate, and thus
underestimates the importance of developmental changes in
both the person and the context. An alternative position holds
that because certain abilities develop over the life span, and
because the cultural contexts of abilities keep changing,
attaining a specific body of knowledge is less important than
the ability to learn. In a nutshell, the alternative model
encourages people not to “get an education” but to “stay in
education.”
OLD AND NEW VIEWS OF HUMAN ABILITIES
The Traditional View of Abilities
Before arguing for the alternative view, it would be useful to
review the more conventional point of view. Such a view
centers on three fundamental tenets. The first is a belief in
general intelligence—the idea that people have a general,
overarching ability (“g”) that works relatively evenly across the
diverse tasks that people encounter. General intelligence is
thought to exert an extremely powerful force on task
performance in both formal domains and everyday settings—a
view dubbed the “g-ocentric” position (Sternberg et al., 1995).
Second, this general ability is thought to develop in a manner
analogous to physical maturation—skills develop during

childhood and reach their final form in early adulthood. On this
view, people come at an early age to have a relatively fixed
potential for achievement that they may fulfill to a greater or
lesser degree. The third tenet of the conventional view is an
emphasis on school-based learning. The traditional view does
not deny that learning takes place outside the classroom, but it
holds that inschool learning is far and away the most important
factor in the development of intellectual ability.
The traditional view has a number of implications for our
society’s approach to adult learning. To begin with, the
traditional view is reflected in many contemporary educational
practices. Relative to schooling intervened in the first 2 decades
of life, adult learning receives little attention. Continuing
education is available, but the emphasis in our society clearly
falls on providing educational opportunities for school-age
children. This is true even in many professional settings; for
example, until recently schools devoted comparatively few
resources to professional development opportunities for
teachers. The traditional view is also reflected in the field of
psychology, which has focused more on children than on adult
learners, and on school type tasks more so than on job or
everyday tasks. Only recently have researchers turned their
attention to learning in the workplace and in everyday settings
(see, e.g., essays in Rogoff & Lave, 1984; Sternberg & Wagner,
1986; Sternberg, Wanger, & Okagaki, 1993). Overall, the
traditional view yields a rather grim picture of adult learning.
Assuming that human abilities are domain-general, relatively
fixed after childhood, and nurtured primarily in school settings,
psychologists and educators alike have devoted comparatively
little attention to adult learners or everyday learning.
An Alternative View of Human Abilities
Recent theory and research in educational psychology set out an
alternative position that sounds a more positive tone. In recent
decades psychologists have reconsidered the fundamental tenets
of the traditional model.
First, the notion of general intelligence has been opposed by a

number of recent theories of intelligence that point to multiple
faculties of the human mind (e.g., Ceci, 1990; Gardner, 1983;
Sternberg, 1988). For our part, we draw on Sternberg’s (1988)
triarchic theory of intelligence, which emphasizes creative and
practical skills in addition to the analytic and memory-based
skills typically highlighted on tests and schoolwork. Analytic,
creative, and practical skills constitute the basis of the triarchic
theory. Marshaling the triarchic theory to frame an account of
adult learning, we argue later that consideration of practical
intelligence (and attendant tacit knowledge) changes
significantly the picture of adult learning.
The second tenet of the traditional view, the notion that abilities
are fixed and tend to stabilize in early adulthood, has been
criticized by psychologists from diverse quarters (e.g., Ceci,
1990; Chi, Glaser, & Farr, 1988; Perkins, 1995; Renzulli, 1986;
Sternberg, 1988). Indeed, there is now substantial evidence that
abilities are modifiable to at least some degree (see Feuerstein,
1980; Herrnstein, Nickerson, deSanchez, & Swets, 1986;
Nickerson, 1986; Sternberg & Spear-Swerling, in press). The
developmental nature of human abilities is the focus of the
experiential subtheory of Sternberg’s triarchic theory.
Reconceptualizing abilities as developing expertise, the
experiential subtheory highlights the potential for, and the
importance of, learning across the life span.
Third, the traditional view’s emphasis on school-based learning
is not shared by many modern theorists and researchers, who
have shown how cultural context exerts a force on the
individual’s thinking (e.g., Ceci, 1990; Lave, 1988; Rogoff,
1990). The contextual subtheory of the triarchic model points to
significant learning that occurs on the job and in everyday
settings, and accounts for the influence of cultural context on
the development of human abilities.
In sum, the traditional model looks narrowly at human abilities
and cultural contexts, and thus yields a perspective of a
somewhat limited adult learner. The alternative model yields a
multidimensional, life-span-developmental, and context-

sensitive view of human abilities. As we see in the following
discussion, such a model paints a very different picture of adult
learning.
CHANGES IN ABILITIES, CHANGES IN CONTEXTS
What sorts of implications does the alternative model have for
the psychology of adult learning? In this section we suggest that
there are two sets of implications—ones concerning the
development of individual abilities, and others concerning the
cultural contexts in which abilities are embedded. We begin by
looking at the development of intellectual skills in the
individual. In what follows we make the case for practical
intelligence, and the related concept of tacit knowledge, as
important components in adult learning and development. Our
discussion begins with a review of recent theory and research
on practical intelligence.
Practical Intelligence and Adult Learning
Our society has long embraced a distinction between academic
intelligence (e.g., “book smarts”) and practical intelligence
(e.g., “street smarts,” common sense). This distinction figures
heavily in the implicit theories of intelligence held by both
laypeople and researchers. Sternberg, Conway, Ketron, and
Bernstein (1981) talked with people in a supermarket, a library,
and a bus station, as well as researchers who study intelligence,
and asked them to nominate and rate the importance of
characteristics of intelligent individuals. Factor analyses of the
ratings reveal that researchers and laypeople alike observe a
distinction between academic and practical intelligence.
Moreover, people believe that their practical abilities grow over
the years. S. Williams, Denney, and Schadler (1983) reported
that 76% of older adults believe that their ability to think,
reason, and solve problems has increased over the years. When
apprised of the research finding that scores on ability tests
decline after early adulthood, the older adults said that they
were talking about solving different kinds of problems from
those found on cognitive ability tests—“everyday” kinds of
problems.

Among the first psychologists to observe a distinction between
academic intelligence and practical intelligence was Neisser
(1976). Neisser described academic intelligence tasks as
formulated by others, often possessed of little or no intrinsic
interest, having all necessary information available from the
outset, remote from the individual’s ordinary experience,
typically well defined, having one correct answer, and often
having one correct path to solution. Academic enterprises as
such are common in schools and on intelligence tests. At the
same time, these features do not seem to apply to many of the
settings and problems that people face in everyday life.
Everyday problems are often unformulated or in need of
formulation, of personal interest, lacking information necessary
for solution, related to everyday experience, poorly defined,
characterized by multiple correct solutions, and endowed with
multiple methods for arriving at a solution.
Is the distinction between academic and practical intelligence
supported by research evidence? A brief review of studies
conducted in a single domain, mathematical reasoning, serves to
illustrate how academic and practical skills run separately.
Several researchers have conducted studies in which
participants were assessed on both academic and practical tasks
in the mathematical domain.
Scribner (1984) examined the strategies used by workers to fill
orders in a milk-processing plant. At the plant, workers called
assemblers fill orders for cases of assorted products (e.g., whole
milk, skim milk, buttermilk) and various quantities (gallons,
quarts, pints). Scribner found that the workers, rather than
employing typical mathematical procedures used in classrooms,
used complex strategies for combining partially filled cases,
with the goal of minimizing the number of moves required to
complete an order. The assemblers were among the least
educated workers at the plant, but they were able to calculate in
their heads quantities expressed in different base number
systems, and they routinely outperformed the more highly
educated white-collar workers who substituted when assemblers

were absent. Moreover, the order-filling performance of the
assemblers was unrelated to measures of academic performance
(intelligence test scores, arithmetic test scores, and grades).
Ceci and Liker (1986, 1988) took to the racetrack to conduct a
study of expert horse race handicappers. The researchers studied
strategies used by handicappers to predict posttime odds—
strategies that involved interactions among seven sources of
information, including a horse’s speed on a previous outing.
Applying a complex algorithm, handicappers adjusted times
posted for each quarter mile on a previous outing by factors
such as whether the horse was moving to pass other horses, and
if so, the speed of other horses passed and where the passes
were attempted. These adjustments are relevant because they
affect how much of the race is run away from the rail (causing
the horse to run a greater distance). Adjusting posted times for
these factors creates a better measure of a horse’s speed. Use of
the complex interaction in prediction would seem to require
considerable cognitive ability (as it is traditionally measured).
However, the degree to which a handicapper used the
interaction (determined by the regression weight for this term in
a multiple regression of the handicappers’ predicted odds) was
unrelated to the handicappers’ IQ (M = 97, r = −.07, p 05).
Remarkably, the average IQ among the handicappers was about
100.
Dorner and colleagues asked subjects to play the role of city
managers in a computer-simulated city (Dorner & Kreuzig,
1983; Dorner, Kreuzig, Reither, & Staudel, 1983). The
simulation provided subjects with a variety of problems, such as
how to raise revenue to build roads and power facilities. The
simulation involved more than 1,000 variables. Dorner and
colleagues quantified performances in terms of a hierarchy of
strategies ranging from the simplest (trial and error) to the most
complex (hypothesis testing with multiple feedback loops). No
relation was found between IQ and complexity of strategies
used. The researchers cross-validated this finding using a
different setting, called the Sahara problem, that required

participants to determine the number of camels that could be
kept alive by a small oasis. The results were the same—no
relation was found between IQ and the complexity of the
strategies employed.
These examples of everyday mathematics document how
practical intelligence runs apart from academic intelligence.
The question is raised, how does practical intelligence develop?
Do practical skills peak in early adulthood as do academic
skills?
The notion that practical and academic abilities follow different
courses in adult development is supported in a number of
studies. For example, Denney and Palmer (1981) conducted a
study in which 84 adults between the ages of 20 and 79 years
were given two types of reasoning problems, a traditional
cognitive measure (the Twenty Questions Task of Mosher &
Hornsby, 1966) and a problem-solving task involving real-life
situations. In the real-life measures, subjects were asked
questions such as, “If you were traveling by car and got
stranded out on an interstate highway during a blizzard, what
would you do?” or “Now let’s assume that you live in apartment
that doesn’t have any windows on the same side as the front
door. Let’s say that at 2:00 a.m. you heard a loud knock on the
door and someone yelled, ‘Open up. It’s the police.’ What
would you do?” Among other things, the researchers report
differences in the shape of the developmental function across
the two types of measures. Performance on the traditional
cognitive measure decreased linearly after age 20. Performance
on the practical problem-solving task increased to a peak in the
40- and 50 year-old groups and thereafter declined.
Cornelius and Caspi (1987) reported similar findings in a study
examining the relations between everyday problem solving and
fluid and crystallized forms of intelligence (a distinction we
discuss later). In a study of 126 adults of ages 20 to 78 years,
the researchers gave participants two sets of measures: (a)
traditional measures of fluid ability (Letter Series) and
crystallized ability (Verbal Meanings); and (b) an everyday

problem-solving inventory. The inventory invoked consumer
problems (a landlord who will not make repairs), information
seeking (additional information is needed to fill out a
complicated form), personal concerns (you want to attend a
concert but you are unsure if it is safe), family problems
(responding to criticism from a parent or child), and work
problems (you were passed over for a promotion). Cornelius and
Caspi found that performance on the measure of fluid ability
increased from ages 20 to 30, remained stable from 30 to 50,
and then declined. Performance on the everyday problem-
solving task and measures of crystallized ability increase
through age 70. Cornelius and Caspi’s participants showed peak
performance later in life than did Denny and Palmer’s; however,
the pattern of traditional cognitive task performance peaking
sooner than the practical task performance was consistent across
the studies.
In their theory of fluid versus crystallized abilities, Horn and
Cattell (1966) provided a set of concepts with which to describe
age-related changes in intellectual ability. According to Horn
and Cattell, fluid abilities are required to deal with novel
situations, such as those in the immediate testing situation (e.g.,
induction of the next letter in a letter-series task). Crystallized
abilities involve acculturated knowledge (e.g., the meaning of a
low-frequency vocabulary word). Several studies show that
whereas fluid abilities are vulnerable to age-related decline,
crystallized abilities are maintained well into adulthood (Horn,
1982; Labouvie-Vief, 1982; Schaie, 1977/1978).
These findings are consistent with a distinction between
academic and practical intelligence. As noted, practical
problems are characterized by apparent absence of information
necessary for a solution and relevance to everyday experience.
In contrast, academic problems are remote from the individual’s
experience and are characterized by the presence (in the
specification of the problem) of all the information necessary
for a successful solution. Thus, crystallized intelligence is more
relevant to the solution of practical problems than it is to the

solution of academic problems. Conversely, fluid abilities are
more relevant to the solution of academic problems than of
practical problems. Perhaps the growth in practical abilities that
older people report (S. Williams et al., 1983) reflects the
greater contribution of abilities that have not declined
(crystallized intelligence) to the solution of everyday problems.
We conclude that practical intelligence is separate from
academic intelligence, and unlike the academic variety,
practical skills are maintained or increased through late
adulthood. Given the considerable differences between
academic and practical problems, what’s needed are constructs
that help to frame these differences. In what follows we discuss
the role played by tacit knowledge in the development of
practical abilities.
Tacit Knowledge and Adult Learning
In academic problems, formal knowledge plays a key role. A
huge body of literature shows that expertise in solving academic
problems depends on availability and accessibility of formal
knowledge (e.g., Chi et al., 1988). Formal knowledge seems
much less relevant to practical problems, however. Formal
knowledge will not tell one, for example, what kinds of things
one should and should not say to a teacher or supervisor. In
nonacademic tasks, an important type of knowledge appears to
be comparatively informal—what we call tacit knowledge
(Sternberg et al., 1993, Wagner & Sternberg, 1985, 1986; see
also Polanyi, 1962). Tacit knowledge is practical know-how that
is usually not directly taught or even openly expressed or
stated. It is the kind of knowledge one acquires on the job or in
everyday kinds of situations, rather than through formal
instruction. For example, knowing how to convince others of
the worth of your idea or product is not the kind of knowledge
that is likely to be taught, but rather the kind of knowledge one
picks up through experience.
Sternberg et al. (1995) outlined three characteristic features of
tacit knowledge. First, tacit knowledge is procedural in nature,
taking the form of “knowing how” (procedural knowledge)

rather than “knowing that” (declarative knowledge). Second,
tacit knowledge is practically useful; it is directed toward
attainment of goals that people value. Third, tacit knowledge is
acquired under conditions of low environmental support; one
often gains tacit knowledge on one’s own, without much direct
instruction. In general, tacit knowledge is unspoken,
underemphasized, and conveyed in an indirect manner.
Wagner and Sternberg (1985) put forth a taxonomy of tacit
knowledge in the form of a three-by-two matrix. The model
begins by distinguishing among three kinds of tacit
knowledge—knowledge about managing (a) one’s self, (b)
others, and (c) tasks. These three kinds are crossed with two
orientations of tacit knowledge: local (oriented toward
attainment of short-term goals, e.g., how to organize daily
tasks), and global (oriented toward the long term, e.g., how to
get a promotion).
How does one measure tacit knowledge? Wagner and Sternberg
(1985) employed a method in which participants are presented
with scenarios that depict the kinds of problems faced by people
in a given life pursuit. The measurement instruments typically
consist of a set of scenarios followed by 5–20 response items.
The participants are asked to make judgments about these
scenarios that require them to possess and to exploit tacit
knowledge. These methods measure not only tacit knowledge,
but also the capacity to use it. Employing a similar strategy,
Williams and Sternberg (in press) developed a tacit-knowledge
instrument that contains statements describing actions taken in
the workplace. Examinees are asked to rate how characteristic
the actions are of their own behavior. W. Williams and
Sternberg also presented complex open-ended problems and
asked examinees to write plans of action that show how they
would handle the situations.
Sternberg and colleagues have employed three sets of methods
for scoring tacit-knowledge tests. Initially, Wagner and
Sternberg (1985) scored tacit knowledge by correlating ratings
on each response item with a categorical variable representing

group membership (e.g., experienced manager, business school
student, undergraduate). A positive correlation between item
and group membership indicated that higher ratings were
associated with greater levels of expertise in the domain. A
negative correlation indicated that higher ratings were
associated with lower levels of expertise. Items showing
significant correlations were retained for further analysis.
Ratings for these items were summed across items in a given
subscale, and these summed values served as predictor variables
in analyzing the relationship (between groups) between tacit
knowledge and job performance.
A second method for scoring tacit-knowledge tests involved a
sample of practically intelligent individuals (in this case,
academic psychologists) obtained through a nomination process
(Wagner, 1987). The tacit-knowledge test was administered to
the nominees and an expert profile was generated, reflecting the
central tendency of their responses. Tacit-knowledge tests for
participants were scored, separately for each item subscale, as
the sum of their squared deviations from the expert profile. This
scoring method allows for meaningful comparisons between
groups.
A third procedure for scoring tacit-knowledge tests was
employed by Wagner, Rashotte, and Sternberg (1992). In a
study conducted in the domain of sales, the researchers
collected “rules of thumb” that differentiated expert from
novice salespersons, such as, “Penetrate smokescreens by
asking ‘what if …?’ questions” and “In evaluating your success,
think in terms of tasks accomplished rather than hours spent
working.” The rules of thumb were placed in categories and
used to generate a set of work-related situations. Response
items were constructed so that some items represented
appropriate application of rules of thumb, and other items
represented incorrect or distorted applications. The tacit-
knowledge test was scored for the degree to which examinees
preferred response items that represented correct application of
rules of thumb.

For the researcher interested in adult learning, a key question
arises about tacit knowledge: Does acquisition and use of tacit
knowledge peak in early adulthood (like academic intelligence),
or does it increase through adulthood (like everyday problem
solving)?
Wagner and Sternberg (1985) administered a tacit-knowledge
inventory to three groups of individuals (n = 127) who differed
in breadth of experience and formal training in business
management. One group consisted of business managers,
another of business school graduates, and a third of
undergraduates. The means and standard deviations for amount
of managerial experience were, for the three groups
respectively, 16.2 (9.9), 2.2 (2.5), and 0.0 (0.0). Group
differences were found for 39 response items. A binomial test of
the probability of finding this many significant differences by
chance yielded p .0001. The researchers concluded that there
were significant differences in ratings across the three groups.
This finding was replicated in a study using tacit-knowledge
scores from three other groups—64 managers, 25 business
graduate students, and 60 undergraduates. The managers, whose
average age was 50, outperformed the business students, who
outperformed the undergraduates. These studies do not sample
the age range as comprehensively as do other studies on adult
learning (Cornelius & Caspi, 1987; Denney & Palmer,
1981); however, the studies suggest that the development of
tacit knowledge more closely resembles the development of
everyday problem solving than of academic abilities as
traditionally measured.
Taken together with findings on the developmental path of
practical intelligence, the research on tacit knowledge suggests
that there are developmental changes in the kinds of thinking
that people are best able to handle at different times of life. In
short, the individual’s abilities change as he or she grows and
learns—not just in youth, but throughout the life span.
Changing Cultural Contexts of Practical Intelligence and Tacit
Knowledge

Consider the many examples of accomplished individuals who
fail to adapt as times change and thus find their careers on the
wane. Musical artists, for example, occasionally find
themselves playing in smaller and smaller rooms as musical
tastes and technologies change; today’s arena rockers are often
tomorrow’s bar bands. There also are many examples of
individuals who adapt successfully and move forward even in
their later years. The artist Pablo Picasso makes a fine example.
Throughout his career, Picasso explored new ideas and
remained a vital force in his art. These examples illustrate that
there are factors outside the individual—in the domains and
fields in which human abilities are put to use and given
meaning—that change over time, and adaptation to these
changes is a key issue in adult success. The tacit knowledge
relevant to life’s challenges changes as the new technologies
and other factors introduce change to the types of skills needed
in the world.
Research on tacit knowledge demonstrates how important these
contextual changes can be. One study, for example, yields the
conclusion that there is specialized tacit knowledge for different
levels of expertise in a domain. W. Williams and Sternberg (in
press) used extensive interviews and observations to construct
both a general and a level-specific tacit-knowledge measure and
examined differences in tacit knowledge between levels of
management. The researchers obtained nominations from
managers’ superiors for “outstanding” and underperforming
managers, enabling them to delineate the specific content of
tacit knowledge for each level of management by examining at
each level what outstanding managers knew that the
underperforming ones did not.
The results support the notion of level-specific tacit knowledge.
As executives rise through the ranks, they gain tacit knowledge
specialized to higher levels of management expertise. Within
the domain of tacit knowledge about one’s self, tacit knowledge
about how to seek out, create, and enjoy challenges was
substantially more important to upper level executives than to

middle- or lower level executives. Tacit knowledge about
maintaining appropriate levels of control increased
progressively at higher levels of management. Tacit knowledge
about self-motivation, self-direction, self-awareness, and
personal organization was higher for upper level managers.
Tacit knowledge about completing tasks and working
effectively within the business environment was substantially
more important to upper level executives than to middle-level
ones, and substantially more important to middle-level
executives than to lower level ones. Within the domain of tacit
knowledge about other people, knowledge about influencing and
controlling others was essential for all managers, but especially
for upper level executives. Tacit knowledge about supporting,
cooperating with, and understanding others was highly
important to upper level executives, less important to middle-
level executives, and still less important to lower level ones.
Specialized knowledge as such shows how the context
surrounding human abilities plays a role in determining the
value and utility of those abilities. Changing social contexts
around the individual press different kinds of abilities to the
fore.
This contention is supported by studies examining the
relationship between tacit knowledge and demographic variables
such as education and experience. W. Williams and Sternberg
(in press) studied the relations of tacit knowledge with
demographic and experiential variables, and found that tacit
knowledge was related to the following measures of managerial
success: compensation (r = .39, p .001), age-controlled
compensation (r = .38, p .001), and level of position (r =
.36, p .001). These correlations were computed after controlling
for background and educational experience. Tacit knowledge
was weakly correlated with increased job satisfaction (r =
.23, p .001). The most important finding, however, was the lack
of correlation between years of management experience and
tacit knowledge, suggesting that it is not simply experience that
matters, but what a manager learns from experience. We

conclude that the ability to continue to acquire tacit knowledge
over time is an important element in continued success. Keeping
pace with the world means acquiring new tacit knowledge.
Conclusion: Changing Individuals, Changing Contexts
We have argued that there are two sets of factors that change
over time in human life. First, there are developmental changes
in the individual’s abilities. In general, academic abilities that
rely on fluid intelligence do not increase, but practical abilities
(and attendant tacit knowledge) that rely on crystallized forms
of intelligence often increase into later adulthood. Second, the
context of practical intelligence changes. Technological
advances and changing roles combine to give the adult an ever-
changing set of skills at which to aim. In a sense, both shooter
and target are moving.
Given these twin elements of constant change, the specific
content of tacit knowledge becomes less important. Skills
needed in the past may be less vital in the future—and fluid
abilities that undergirded learning in youth may be less potent
in adulthood. At issue, then, is the need for continual learning
and relearning of tacit knowledge. Adult success rests in large
measure on the extent to which the individual adapts to new
challenges necessitated by changing intellectual equipment and
changing environmental circumstances.
TEACHING FOR TACIT KNOWLEDGE IN ADULTHOOD
How can lifelong learning of tacit knowledge be fostered?
Preliminary steps in this direction might well include three
suggestions: building on extant skills, participating in
apprenticeship-type interactions with experts in a domain, and
employing tacit-knowledge acquisition strategies.
Building on Extant Skills
Baltes and Baltes (1990) provided evidence suggesting that
older individuals compensate for declining fluid abilities by
restricting their domains of activity to ones that they know well.
This tendency may have the effect of letting the world pass
folks by, at least in some cases. For example, a few years back
engineers with expertise in vacuum tube technology faced a

choice between learning some new moves (i.e., solid-state
electronics) or being left behind with obsolete skills. The
impulse to stick with what one knows did not serve vacuum tube
engineers well. Only those who adapted to new engineering
norms thrived. For lifelong learning of tacit knowledge,
individuals must accept the risks associated with taking on new
challenges.
At the same time, there also are indications that building on
extant skills can be a fruitful strategy. For example, there is
evidence that older adults compensate for declining fluid
abilities by applying specialized procedural and declarative
knowledge. Salthouse (1984) found that age-related decreases in
performance at the “molecular” level (e.g., speed in the
elementary components of typing skill) produce no observable
effects at the “molar” level (i.e., the speed and accuracy with
which work is completed). The author concluded that loss of
fluid abilities does not necessarily result in loss of productivity
in domains or tasks in which individuals have developed
expertise.
These findings also raise the possibility that extant skills can be
used in the service of learning new ones. At issue is the need to
select a learning environment that is neither too close nor too
far. If a new learning environment is too remote from one’s
experience, little knowledge transfers; and, with fluid abilities
that are somewhat less potent, a high level of performance
seems unlikely. In more familiar (but not too familiar) learning
environments, knowledge (tacit and otherwise) transfers and
provides a background for learning new moves.
To return to a previous example, it seems noteworthy that
vacuum tube engineers possess a wealth of skill in aspects of
engineering expertise not rendered obsolete by solid-state
electronics (i.e., basics of electronics and design). These extant
skills can be used a starting point for development of new
skills. The engineer is better off learning solid-state electronics
than starting over with, say, theoretical physics. Similarly, the
typists in Salthouse’s (1984) study may not find much typing

work any more, but they have more potential to acquire
advanced word processing
skills than do beginners. Here a new skill (word-processing) is
the focus of an extension of extant keyboarding skills. In these
examples, the key is bridging from older skills to newer ones.
Tacit Knowledge Through Apprenticeship
In general, support for acquisition of tacit knowledge is low.
People often come into tacit knowledge by figuring things out
for themselves. However, antecedents of tacit knowledge can
also be seen in the social realm—in the expert model presented
by people who have more expertise in a task or domain. When
the individual has the opportunity to observe, imitate, and
perhaps talk things over with experts, the individual has a rich
opportunity to acquire tacit knowledge about the domain or
task. Often this learning takes place in settings not conceived as
instructional by either the learner or the “teacher.”
Psychologists interested in the influence of cultural context on
human cognition have attempted to capture this social facet of
learning by invoking the notion of apprenticeship—an extended
sequence of interactions between a learner and a more
experienced practitioner in a domain or task (e.g., Lave, 1988;
Rogoff, 1990). It is easy to see how, say, private piano
instruction constitutes an apprenticeship. More recently the
notion of apprenticeship has been extended as an analytic tool
for describing all sorts of learning situations—schools, jobs,
avocations, everyday activities.
Linking aspects of learning to apprenticeship-type interactions
is fully consistent with the theory and research on tacit
knowledge. Michael Polanyi (1962), a seminal figure in the
history of studies of tacit knowledge, emphasized the
importance of apprenticeships in acquisition of the tacit
knowledge required for expertise in a domain. In essence,
getting connected to people in the know is a wise strategy for
acquisition of tacit knowledge.
Returning to the scene of Ceci and Liker’s (1986, 1988) study
of racetrack cognition helps to illustrate the point. Suppose one

wanted to learn the handicapper’s craft. One might begin by
attempting to specify just how they obtain positive outcomes,
presumably by interviewing and observing. But the
handicappers likely will not be able to say much about how they
make their selections—theirs is a more intuitive than scholarly
undertaking. Moreover, short-term exposure to their behavior
might not reveal much about the thinking involved in selecting
winners. It might well prove difficult to commit the
handicapper’s wisdom to a set of rules, or even to writing.
A more efficient means—and the one seen in many instances in
everyday life—is to acquire the tacit knowledge of
handicapping by participating in apprenticeship-type
interactions with an expert at the track. Over time, the
apprentice acquires the thinking behind successful
handicapping—the tacit knowledge—through experience,
through the right kinds of practice, and through the right kind of
reflective thinking (Schon, 1983).
Hence, a key strategy for lifelong learning of tacit knowledge is
to increase contact with expert models and boost participation
in apprenticeship-type interactions. Of course, not all modeling
situations have such positive results. Like any aspect of human
social life, apprenticeship-type interactions can be insufficient,
misleading, or abusive. But the research on apprenticeship
serves to underscore the fact that acquisition of tacit knowledge
is not always productively acquired through the more formal
means (using disciplinary notations, concepts, procedures, etc.),
and is often acquired in a less formal way, through everyday
social interactions.
Strategies for Acquisition of Tacit Knowledge
What psychological mechanisms can be marshaled for
acquisition of tacit knowledge? The componential subtheory of
Sternberg’s (1988) triarchic model of intelligence sets out three
knowledge-acquisition components: selective encoding
(distinguishing relevant from irrelevant information in the
learning of new material), selective combination (putting the
relevant information together to form a whole, unified cognitive

structure), and selective comparison (drawing on past
information to facilitate learning of new information).
Sternberg et al. (1993) conducted a study to examine how these
three components work in the acquisition of tacit knowledge. In
sequence, subjects took a tacit-knowledge test (in this case, in
sales), participated in a knowledge-acquisition task, and took a
posttest. In the knowledge-acquisition task, subjects were asked
to play the role of personnel manager in a business setting.
After reading a two-page description of the company, subjects
reviewed the transcripts of three job interviews with applicants
for sales positions and then recommended zero to three
candidates for hiring, with the goal of hiring only persons with
the most potential for productive employment in sales.
Subjects were divided into five groups, including two control
groups and three experimental groups. Control Group 1 received
the tacit-knowledge measure for sales as a pretest and posttest,
without intervening treatment. Control Group 2 received the
tests as well as a tacit-knowledge acquisition task with no
further cues to help them identify or use relevant information.
Experimental Group 1 received the acquisition task with cueing
for selective encoding. Relevant information for acquisition of
tacit knowledge was highlighted and the relevant rule of thumb
was provided. Experimental Group 2 received the acquisition
task with cueing for selective combination—relevant
information was highlighted, the rule of thumb given, and a
note-taking sheet with the appropriate categories was given to
subjects to help them combine information. Experimental Group
3 received the acquisition task with cueing for selective
comparison. This cueing was an evaluation that had been
completed by a “predecessor” in the company. Subjects were
thus able to use the predecessor’s performance to facilitate their
own. Relevant information was highlighted and rules of thumb
were also given to subjects in this group.
Analysis of the data from the tacit-knowledge acquisition task
revealed that there was an overall difference among four groups
(Control Group 1, experimental Groups 1–3). Control Group 2

was outperformed by the three experimental groups. Among the
experimental groups, the selective-combination group did the
best. The selective-encoding and selective-comparison groups
performed comparably—lower than the selective-combination
group but higher than the control group.
The researchers also examined posttest minus pretest
differences on the tacit-knowledge test across the five groups.
Control Group 1 showed the smallest gain. Among the
experimental groups, the selective-combination group showed
the largest gain, followed in descending order by the selective-
encoding group and the selective-comparison group. Thus, the
experimental groups outperformed the control groups and the
selective-encoding and selective-combination groups
outperformed the selective-comparison group. Overall, the
results suggest that the selective-comparison manipulation was
the weakest of the three, but that selective-encoding and
selective-combination were successful in facilitating acquisition
of tacit knowledge.
These findings suggest that tacit knowledge can be effectively
taught and can play a prominent role in adult education. At the
same time, additional studies are needed to specify ways in
which tacit knowledge can help individuals respond to changing
abilities and the changing world. A goal for future research is to
frame an account of, among other things, how bridging between
extant and new skills can be enhanced, how educational
interventions can draw on social interactions in the service of
tacit-knowledge, and how tacit-knowledge acquisition strategies
can be effectively infused into adult education.
CONCLUSION
In response to the traditional view of human abilities
as undifferentiated, fixed in early adulthood, and cultivated
through schoolwork, we have argued for an alternative approach
that views skills pluralistically, developmentally over the life
span, and broadly both in school and out. In particular, research
on practical intelligence and tacit knowledge paints a more
flattering portrait of the adult learner than does the traditional

model of human abilities. Whereas adults experience a leveling
off (or even a diminution) of certain kinds of skills, namely,
academic ones, others kinds of skills continue to grow into
adulthood, and these practical skills are vital to adult success.
The adult learner has the potential to bring practical know-how
and relevant experience to career pursuits and everyday tasks,
with positive results.
Moreover, such an approach emphasizes that changes outside
the individual—in the social contexts in which abilities are put
to use—exert a powerful force on the functioning of the adult
learner. Changing contexts call for changing skills,
underscoring the need for continual learning and relearning of
tacit knowledge. To this end we have made a few suggestions
about fostering tacit knowledge throughout the life span—
building on extant skills, participating in apprenticeship-type
interactions, and drawing on tacit-knowledge acquisition
strategies set out in the triarchic theory.
The alternative view acknowledges a complex set of forces—
internal and external ones—that operate on the intellectual
development of the adult. Such a view helps to explain why
ability tests account for so little of the variance in job
performance, and why Bill and John met such different fates in
the business world.
Changing Mind, Changing World: Practical Intelligence

and Tacit Knowledge in Adult Learning
Bruce Torff
Hofstra University
Robert J. Sternberg
Yale University
backgrounds. They did equally well in
school and on college admission tests, went to the same
university where they performed
comparably, and embarked on careers in business. However,
whereas Bill ha
s been very
successful, consistently gaining promotions in a top company,
John has been unable to climb the
corporate ladder. Given that Bill and John began in so similar a
manner, what accounts for their
differential success in business? There could be ma
ny reasons, among them the possibility that
there are skills that are important in business
—
and perhaps as well in adult life in general
—
that
do not show up in academic exercises such as schoolwork and
tests.
This proposition is supported by research on th
e extent to which intelligence test scores predict
real
-
world performance. On average, the validity coefficient between

cognitive ability tests and
measures of job performance is about .2 (Wigdor &
Garner, 1982). This means that only 4% of
the variance in
job performance is accounted for by scores on ability tests.
Even after validity
coefficients are corrected for unreliability in test scores and
criterion measures, and restriction of
range caused by the fact that only high scorers were hired, the
average
validity coefficient rises
only to .5 (J. Hunter & R. Hunter, 1984; Schmidt & J. Hunter,
1981). Thus, even with the
accounted for by ability test scores.
Put the other way, even the corrected esti
mates leave unexplained a full three
-
fourths of the
and Horvath (1995) concluded
that “even the most charitable view of the relations between
intelligence test scores and real
-
world performance l
eads to the conclusion that the majority of the variance in real
-
world
performance is not accounted for by intelligence test scores” (p.
913). Clearly, there is more
ability tests.
These findi
ngs

—
and the story of Bill and John
—
raise the issue of how our society
conceptualizes, teaches, and evaluates the skills needed for a
productive adult life. What sorts of
skills are needed for adult success? What have psychologists
found about the developme
nt of
those skills? It turns out that recent theory and research have
veered away from previously held
views about human abilities. In this chapter we suggest that the
traditional view looks somewhat
narrowly at human abilities and the cultural contexts in
which they operate, and thus
both the person and the context. An
alternative position holds that because certain abilities develop
over the life span, and because
the cultural contexts of abilities
keep changing, attaining a specific body of knowledge is less
important than the ability to learn. In a nutshell, the alternative
model encourages people not to
“get an education” but to “stay in education.”
Changing Mind, Changing World: Practical Intelligence
and Tacit Knowledge in Adult Learning
Bruce Torff
Hofstra University
Robert J. Sternberg
Yale University
backgrounds. They did equally well in
school and on college admission tests, went to the same

university where they performed
comparably, and embarked on careers in business. However,
whereas Bill has been very
successful, consistently gaining promotions in a top company,
John has been unable to climb the
corporate ladder. Given that Bill and John began in so similar a
manner, what accounts for their
differential success in business? There could be many reasons,
among them the possibility that
there are skills that are important in business—and perhaps as
well in adult life in general—that
do not show up in academic exercises such as schoolwork and
tests.
This proposition is supported by research on the extent to which
intelligence test scores predict
real-world performance. On average, the validity coefficient
between cognitive ability tests and
measures of job performance is about .2 (Wigdor & Garner,
1982). This means that only 4% of
the variance in job performance is accounted for by scores on
ability tests. Even after validity
coefficients are corrected for unreliability in test scores and
criterion measures, and restriction of
range caused by the fact that only high scorers were hired, the
average validity coefficient rises
only to .5 (J. Hunter & R. Hunter, 1984; Schmidt & J. Hunter,
1981). Thus, even with the
accounted for by ability test scores.
Put the other way, even the corrected estimates leave
unexplained a full three-fourths of the
and Horvath (1995) concluded
that “even the most charitable view of the relations between
intelligence test scores and real-
world performance leads to the conclusion that the majority of

the variance in real-world
performance is not accounted for by intelligence test scores” (p.
913). Clearly, there is more
ability tests.
These findings—and the story of Bill and John—raise the issue
of how our society
conceptualizes, teaches, and evaluates the skills needed for a
productive adult life. What sorts of
skills are needed for adult success? What have psychologists
found about the development of
those skills? It turns out that recent theory and research have
veered away from previously held
views about human abilities. In this chapter we suggest that the
traditional view looks somewhat
narrowly at human abilities and the cultural contexts in which
they operate, and thus
both the person and the context. An
alternative position holds that because certain abilities develop
over the life span, and because
the cultural contexts of abilities keep changing, attaining a
specific body of knowledge is less
important than the ability to learn. In a nutshell, the alternative
model encourages people not to
“get an education” but to “stay in education.”

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