Leveling Up: Using the Tracer Method to Address Training Needs for Esports Players

This paper describes a new method, the Tracer Method, that maps multiple types of decision-making activities to visual search behaviors to improve the acquisition of expertise. The method combines Critical Decision Method (CDM) with eye tracking to identify expert strategies for performance in interface-based tasks. The Tracer Method uses a network analysis of eye movement data, contingent on the critical decisions, to produce new information that neither method alone can generate. Twenty-five experienced Over-watch players were eye-tracked while playing a competitive game, followed by a CDM interview. Three types of decisions: sensemaking, uncertainty management, and coordination resulted in different eye-movement network diagrams of transitional probabilities. These different strategies can inform Esport team training. Although we demonstrate the usefulness of the Tracer Method in an Esports context, the method could apply in other human factors domains.

Alterations in Violinists’ Practice Behaviors in Response to Differing Technical Demands in the Material

The pedagogical literature strongly suggests that when a musician works on a specific piece of repertoire, one should choose practice strategies tailored to the challenges presented by that material. Such a behavioral choice could represent an instantiation of focused and deliberate practice, a critical aspect of the relationship between experience and the acquisition of expertise. However, most of the literature investigating individual practice has used a single stimulus for all participants, or else has employed surveys or other proxy measures of practicing behavior. In this study, participants of three different experience levels (high school, collegiate, or professional violinists) practiced three excerpts, each featuring a different signature challenge. Results suggest that practice is highly idiosyncratic, that participants do adjust their approaches to the challenges of the material, but that individuals of differing experience levels identify remarkably similar problems within the material.

Accelerating the Acquisition of Expertise

What accounts for the striking variability in how readily people acquire expertise and the ultimate level of performance they attain? In this article, we discuss sources of individual differences in skill acquisition. We begin by describing a first-of-its-kind case study of golf expertise: Dan McLaughlin’s attempt to make the PGA Tour through deliberate practice. Contrasting engineered vs. ecological approaches to developing expertise, we then discuss three factors that may have contributed to McLaughlin’s inability to attain his goal: domain selection, starting age, and design of training. We conclude by discussing future directions for expertise research. With a better understanding of what accounts for variability across people in the development of expertise, it may be possible to accelerate the acquisition of expertise.

Modeling Experts with Fast-and-Frugal Heuristics

According to the program reviewed in this chapter, people—including experts—use fast-and-frugal heuristics. These heuristics are models of bounded rationality that function well under limited knowledge, memory, and computational capacities. These heuristics are ecologically rational: they are fitted to the structure of information in the environment. While studying experts in the context of this program amounts to modeling them with fast-and-frugal heuristics, studying the acquisition of expertise focuses on how laypeople learn such heuristics. Experts can be viewed as intuitive statisticians. They have larger samples than novices and they are—through experience, education, and feedback—able to construct both accurate models of their environment and smart heuristics. Because fast-and-frugal heuristics do not require complex calculation and are typically easy to set up, this program offers a straightforward way to aid experts: After the heuristics’ performance has been determined under various environmental conditions, experts can be educated about these results.

Domain-General Models of Expertise

This chapter reviews evidence concerning the contribution of cognitive ability to individual differences in expertise. The review covers research in traditional domains for expertise research such as music, sports, and chess, as well as research from industrial–organizational psychology on job performance. The specific question that we seek to address is whether domain-general measures of cognitive ability (e.g., IQ, working memory capacity, executive functioning, processing speed) predict individual differences in domain-relevant performance, especially beyond beginning levels of skill. Evidence from the expertise literature relevant to this question is difficult to interpret, due to small sample sizes, restriction of range, and other methodological limitations. By contrast, there is a wealth of consistent evidence that cognitive ability is a practically important and statistically significant predictor of job performance, even after extensive job experience. The chapter discusses ways that cognitive ability measures might be used in efforts to accelerate the acquisition of expertise.

Deliberate Practice and the Acquisition of Expertise

The American medical system has room for improvement in the area of quality. Many systems-level approaches have been tried, but most have not yielded significant improvements in healthcare quality. This chapter focuses on strategies that mediate individual-level expert performance in a variety of domains. A central strategy underlying expert performance is deliberate practice. Deliberate practice is supported by having a learning orientation and “grit,” which is defined as long-term perseverance and passion for a goal, even if the goal is arduous. A general approach to performance improvement for individuals is also discussed. A reinvestment model for performance improvement proposes that individuals invest their time, effort, and cognitive resources, such as working memory capacity, in the design and implementation of deliberate practice for performance improvement.