Human beings have likely been trying to understand expertise since the first cave dweller wondered why Grog was so much better at hunting, or why Norg seemed to always know where the best berries were.   Efforts to identify, and more precisely to predict expertise have pretty much been ongoing ever since. It’s no wonder, since a McKinsey report showed that high-performers could generate significantly greater productivity (40%), profit (49%) and revenue (67%) depending on their role when compared to even average performers (Cornet, Rowland, Axelrod, Handfield-Jones, & Welsh, 2001). While we are still not very good at predicting future expertise, or even how to objectively quantify it, we have learned a few things along the way. Expertise is not necessarily an innate ability. Nor is expertise necessarily what you know but how you know it.

Scientific assessment of individual differences seems to have hit critical mass in the mid- to late- 19th century, culminating in the development of the general theory of intelligence (Spearman, 1904). Spearman was attempting to create a unified way of looking at and evaluating innate capability, sans training or experience. This idea that certain human beings were simply destined for greatness was the impetus for the intelligence testing that we still use today for assessing potential (e.g. IQ). While many people (including businesses) put a lot of stock into general measures of intelligence, it turns out that actual, real world performance is not simply a matter of innate ability. For instance, IQ measures proved to be useless in predicting the rankings of internationally ranked chess players. In fact, studies have shown intelligence measures to only account for between 4% and 30% of real world performance (Sternberg, Grigorenko, & Bundy, 2001). Even at the high-end of that range, more than two-thirds of the reason for an individual’s real world performance is unaccounted for by standard intelligence measures. Real world performance is more than innate ability, but the product of ability informed by experiential knowledge and skills.

The mid- 20th century ushered in the idea that, perhaps, expert performance was the result of specialized knowledge developed over time. Michael Polanyi famously defined tacit knowledge by suggesting we know more than we can tell (Peck, 2006; Polanyi, 1966). As opposed to explicit knowledge, which can be written down, easily expressed and taught, tacit knowledge remains elusive even to those who have it (Mahroeian & Forozia, 2012). While explicit knowledge is what we know, tacit knowledge is the ability to apply that knowledge successfully; experts exhibit some form of meta-knowledge enabling them to better apply their knowledge. Experts achieve automaticity in both their thoughts and actions, making complex processes appear effortless and simple. Yet, experts are generally unable to explain how they do this. The result is that experts appear to solve problems intuitively, not because they specifically know more, but because they know better.

One explanation of where tacit knowledge originates is through the development of superior mental models of domain knowledge. Research comparing the mental models of expert and novice practitioners show that experts organize their knowledge in ways uniquely different from novices (Chi, Glaser, & Rees, 1982; Gogus, 2013). This research substantiates that a principal difference between an expert and a novice is the structure of their mental models, not necessarily the contents of their knowledge. The mental models of expert practitioners appear to coalesce to a point of maximum efficiency regardless of how the skills develop (Schack, 2004). These efficient mental models allow experts immediate access to (more) knowledge and procedures relevant for efficient use in daily application (Feltovich, Prietula, & Ericsson, 2006). In short, experts generate the best solutions under time constraints, better perceive the relevant characteristics of problems, are more likely to apply appropriate problem solving strategies, are better at self-monitoring to detect mistakes and judgment errors, and perform with greater automaticity and minimal cognitive effort (Chi, 2006). Experts perform faster and more accurately with less effort.

A recent study comparing more-experienced and less-experienced soccer players utilized iris-scanning technology to make this point exceptionally salient (Lex, Essig, Knoblauch, & Schack, 2015). This study determined that while more-experienced and less-experienced players fixated on visuals of game situations for the same amount of time per pixel, more-experienced players focused on four specific aspects of the visual while less-experienced players fixated on many areas irrelevant to the decision-making process; the result was that more-experienced players made effective decisions much faster than their less-experienced counterparts. The point here is experts are capable of screening out extraneous information and focusing solely on the details that matter in order to make effective, efficient, and accurate choices. While all of the players had the same basic knowledge of the game, more-experienced players applied that knowledge more efficiently to make accurate decisions more quickly.

So, what makes an expert, an expert? Much like the number of licks to reach the center of a tootsie-pop, the world may never really know. Despite apocryphal notions, we don’t know how long it takes for someone to become an expert, or even if all individuals are capable of becoming experts. We don’t even have a universal means of determining if someone has truly become an expert or easily differentiating experts from novices objectively. What we do know is that expertise is not something you are born with and it is not something achieved simply by obtaining knowledge or training.  It is a metamorphosis from knowing what, to knowing how.

One might say that expertise is simply a state of mind.

References

Chi, M. T. H., Glaser, R., & Rees, E. (1982). Expertise in problem solving. In R. J. Sternberg (Ed.), Advances in the psychology of human intelligence (Vol. 1, pp. 7–75). Hillsdale: Lawrence Erlbaum Associates.

Cornet, A., Rowland, P. J., Axelrod, E. L., Handfield-Jones, H., & Welsh, T. A. (2001). War for talent, part two. McKinsey Quarterly, (2), 9–12. Retrieved from http://www.mckinsey.com/

Gogus, A. (2013). Evaluating mental models in mathematics: A comparison of methods. Educational Technology Research and Development, 61(2), 171–195. doi:10.1007/s11423-012-9281-2

Mahroeian, H., & Forozia, A. (2012). Challenges in managing tacit knowledge: A study on difficulties in diffusion of tacit knowledge in organizations. International Journal of Business and Social Science, 3(19), 303–308. Retrieved from http://ijbssnet.com/

Peck, D. A. (2006). Tacit knowledge and practical action: Polanyi, Hacking, Heidegger and the tacit dimension. ProQuest Dissertations and Theses. University of Guelph (Canada), Ann Arbor. Retrieved from http://search.proquest.com.library.capella.edu/docview/305337938?accountid=27965

Polanyi, M. (1966). The Tacit Dimension. Knowledge in Organizations. Butterworth-Heinemann. doi:10.1016/B978-0-7506-9718-7.50010-X

Spearman, C. (1904). “General intelligence,” objectively determined and measured. The American Journal of Psychology, 15(2), 201–292. doi:10.2307/1412107

Sternberg, R. J., Grigorenko, E. L., & Bundy, D. a. (2001). The predictive value of IQ. Merrill – Palmer Quarterly, 47(1), 1. doi:10.1353/mpq.2001.0005