As discussed earlier, academic problems are well-defined, abstract problems that do not necessarily reflect real-world tasks (Neisser, 1976; Sternberg, 1988, 1997a). Therefore, overall cognitive ability tests and similar tests measure problem-solving skills that are relatively different from the skills needed to solve everyday, practical problems. For this reason, we do not view measures of tacit knowledge as proxies for measures of academic cognition. Although general cognitive skill may support the acquisition and use of tacit knowledge in important ways, tacit knowledge is not reducible to academic cognition. Of course, it is an empirical question whether measures of tacit knowledge do in fact correlate with measures of crystallized cognition. This question is addressed in subsequent sections.

3.3.3 Tacit knowledge is not sufficient for effective performance

Although we do not consider tacit knowledge to be a proxy for general cognition, we do recognize that so-called g and other factors contribute to successful performance in many jobs, based on traditional criteria of success (such as performance ratings). The performance of many everyday tasks requires general academic cognition in (at least) the normative range, motivation to succeed, nontacit domain knowledge, and many other resources. We recognize and basically are in concurrence with the results of numerous meta-analyses that show the significant contribution of these variables to understanding performance (see Schmidt and Hunter, 1998). But we attempt to supplement these variables and improve upon conventional approaches to understanding, predicting, and improving performance in real-world settings.

Measures of practical cognition, like all measures of cognition, are, at best, indicators of the underlying cognitive functions we seek to understand. As such, we can talk about practical cognition, and more specifically tacit knowledge, at different levels of abstraction. That is, we can conceptualize tacit knowledge at the level of its cognitive representation, and at the level which it is measured in the behavior and articulated knowledge of the individual. We discuss these different levels of abstraction below.

3.4 Describing tacit knowledge at different levels of abstraction

Tacit knowledge can be conceptualized at qualitatively different levels of abstraction. At the lowest, least abstract level, tacit knowledge can be described as mentally-represented knowledge structures. We believe that these knowledge structures take the form of complex, condition-action mappings. At this level of description, tacit knowledge takes on its psychological reality and has its consequences for intelligent behavior.

Ideally, we would measure the possession of tacit knowledge directly at the level of its cognitive representation. However, we must infer possession of tacit knowledge from the knowledge that people articulate. When knowledge is articulated, often it is greatly simplified. That is, the complex knowledge structures that map sets of antecedent conditions onto consequent actions are summarized and abbreviated into general rules and procedures. It is at this level, that we measure people's tacit knowledge.

At a higher, more abstract level of description, tacit-knowledge items can be grouped into categories of functionally-related items. Describing tacit knowledge at this level adds value to the identification of tacit knowledge by highlighting the broad, functional areas or competencies that tacit knowledge represents. In other words, in addition to specific items of tacit knowledge, we can identify more generally the types of knowledge that are likely to be tacit.