Volume 2, Issue 2

Some areas of human performance have clear outcome metrics—such as chess or running—which ease the testing of expertise models. However, there are areas of expertise (which may lead to eminence) where cultural context and other factors may have varying levels of importance, but where expertise models should still be tested to inform more comprehensive theoretical models of development. In this study, we examine the presence of elite education and inferred cognitive ability for the development of eminent creative expertise as determined by the gatekeepers who have historically identified the TIME 100—arguably a set of individuals who are highly influential in the cultural context of their time. Overall, we uncover that top 1% in IQ people are overrepresented in the TIME 100 by an effect size, or relative risk, of about 42. This ranged at the low end with Artists/Entertainers being overrepresented by a factor of about 19 and at the high end with Scientists/Thinkers overrepresented by a factor of about 70. These findings inform our understanding of the presence of highly selective educational institutions, as well as cognitive abilities, in the development of eminence across the domains included in the TIME 100. These findings also simultaneously inform the literatures on the relationship between cognitive abilities and creative expertise, and how these elements contribute to our understanding of the development of expertise in traditionally understudied domains.

Research exploring the development of expertise has mostly adopted linear methods to identify precursors of expertise, assessing statistical differences between groups of isolated features (variables) by way of attaching importance; e.g., deliberate practice hours (Ericsson et al., 1993). However, confining the complex nature of expertise development to linear investigations alone may be overly simplistic. Consequently, to better understand the multidimensional and complex nature of expertise development, we applied (non-linear) pattern recognition analyses to a set of 93 features obtained from a sample of 15 elite (International) and 13 sub-elite (First Class County) cricket spin bowlers. Our study revealed that a subset of 12 developmental features, from a possible 93, discriminated between the elite and sub-elite groups with very good accuracy. The 12-feature subset forms a holistic development profile, reflecting the elite’s earlier engagement in cricket, greater quantity of domain-specific practice and competition, and superior adaptability to new levels of competition. Evidence for the external validity of this new model is offered by its ability to correctly classify data obtained from 5 unseen spin bowlers with 100% accuracy. After consideration of these quantitative findings, the content of qualitative data provided by the cricketers was subsequently analyzed to obtain a deeper understanding of the features that discriminate between the elite and sub-elite groups. Supplemental online material is provided here: https://www.journalofexpertise.org/articles/volume2_issue2/JoE_2019_2_2_Jones_SM.pdf

This study investigated the development of player skill and cognitive understanding of a game over repeated plays to (a) bridge separate research traditions on skill acquisition and games learning and (b) provide deeper insight into the process of developing mental models of games. 325 participants responded to an online questionnaire with questions concerning their experience with the game, Hive, as well as both open- and closed-ended items designed to compare their understanding of the game to an expert’s understanding. Open-ended items were content analyzed and modeled as a latent variable. As predicted, both player skill and mental model matching were positively associated with number of plays. Additionally, while player skill had a curvilinear relationship with number of plays that indicated diminishing returns on additional plays, that between cognitive understanding and plays appeared to be linear. The implications of these findings for the cognitive underpinnings of player skill—and for mental model matching theory in particular—are discussed. Supplemental online material is provided here: https://osf.io/3yeg2/

I argue that Wai and his colleagues (2019) argue for a causal relationship between general cognitive ability and creativity that goes beyond their data. Although there certainly is a correlational relationship, the data they present do not clearly allow for causal conclusions of any kind. I discuss three issues with their article: the usefulness of a measure of admission to elite universities as a proxy for a measure of general cognitive ability; the desirability of limiting causal conclusions based on correlational data; and the desirability of having a comparison or control group when drawing scientific conclusions.

Robert Sternberg (2019) provides a critique of our work that is more of a commentary or reflection, because he is not in disagreement with us but simply has a different point of view. We appreciate that Sternberg, a scholar who has contributed prolifically to the literatures on intelligence, creativity, and expertise, is thinking deeply about our work. In this response, we discuss three main points. First, our study is correlational, not causal. Contrary to Sternberg’s assertion in his title, we made reasonable correlational interpretations and conclusions about our data, especially when linking our findings to the broader literature, which is robust. Second, we leveraged a unique sample and “ingenious methodology” (Sternberg, p. 140) to add to the large body of work linking elite colleges—and by proxy inferred general intelligence—to later eminent creative expertise. Third, we had multiple comparison groups.