FORUM
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.
