The concept of expertise is discussed, in the context of fluid and crystallized intelligence. Methods for the study of individual differences in expertise are reviewed, along with the acquisition of open and closed skills. The theoretical and empirical basis for the role of intellectual abilities are considered, along with both deliberate practice and transfer, in the development and maintenance of expertise.

“Psychometrics” refers to the scientific discipline that combines psychological inquiry with quantitative measurement. Though psychometric theory and practice pertain to all aspects of measurement, in the current context, psychometric approaches to expertise pertain to the measurement and prediction of individual differences and group differences (e.g., by gender, age) and, in particular, high levels of proficiency including expertise and expert performance. The scientific study of expertise involves several important psychometric considerations, such as reliability and validity of measurements, both at the level of predictors (e.g., in terms of developing aptitude measures that can predict which individuals will develop expert levels of performance), and at the level of criteria (the performance measures themselves). We will discuss these basic aspects of psychometric theory first, and then we will provide an illustration of psychometric studies that focus on the prediction of expert performance in the context of tasks that involve the development and expression of perceptual-motor skills, and tasks that involve predominantly cognitive/intellectual expertise. Finally, we will discuss challenges for future investigations.

Before we start, some psychological terms need to be defined. The first terms are “traits” and “states.” Traits refer to relatively broad and stable dispositions. Traits can be physical (e.g., visual acuity, strength) or psychological (e.g., personality, interests, intelligence). In contrast to traits, states represent temporary characteristics (e.g., sleepy, alert, angry). The second set of terms to be defined are “interindividual differences” and “intraindividual differences.”

At the most fundamental level, the relationship between intelligence and learning is close and convincing. Indeed, the modern era of intelligence assessment is identified with the critical success of Binet and Simon (1905) in their development of a set of scales that provided valid predictions of school success. These scales, or similar assessments inspired by this approach (such as the Wechsler Intelligence Scale for Children; Wechsler, 1949), continue to represent the best predictors of school success. School success, at least for children and adolescents, is considered by many to be the indicator of learning achievement. While this analysis works quite well for global measures of learning, there is far less utility of omnibus IQ-type measures for predicting individual differences in narrower domains of learning. If we want to predict which students will excel in learning a musical instrument, mastering power tools, or becoming adept at a particular sport, or even which students will become the fastest typists, the relationship between intelligence and learning appears to be much more complicated.

Part of the reason why IQ-type measures are less valid for predicting individual differences in skilled performance has to do with the relative “bandwidth” of the assessment instrument and the breadth of the criterion, or what has been referred to as a lack of Brunswik symmetry (Wittmann & Süß, 1999). That is, IQ tests have high bandwidth — they are typically constructed from as many as a dozen different scales (e.g., memory, reasoning, vocabulary, math, etc.).