Volume 3, Issue 2

Poker is a game of skill and chance involving economic decision-making under uncertainty. It is also a complex but well-defined real-world environment with a clear rule-structure. As such, poker has strong potential as a model system for studying high-stakes, high-risk expert performance. Poker has been increasingly used as a tool to study decision-making and learning, as well as emotion self-regulation. In this review, we discuss how these studies have begun to inform us about the interaction between emotions and technical skill, and how expertise develops and depends on these two factors. Expertise in poker critically requires both mastery of the technical aspects of the game, and proficiency in emotion regulation; poker thus offers a good environment for studying these skills in controlled experimental settings of high external validity. We conclude by suggesting ideas for future research on expertise, with new insights provided by poker.

Experts play a considerable role in society, as they have to evaluate the risk of policies in many fields of social life and in adversarial situations (e.g., the military). Yet, the influence of expertise on risk taking in adversarial situations has received little attention. An examination of the strategies used by chess players ranging from amateurs to masters in competitive games (n = 73,341) revealed an unexpected pattern of results. First, the majority of players favored the riskier strategy. This result is in line with the literature on economic decision making that indicates a tendency to take risks in situations where the outcome can be either positive or negative. More surprising is our second finding: As skill increased, the majority of players still adopted a risk seeking attitude but the proportion of players taking a more conservative approach increased. This result would tend to indicate that experts making decisions with impact on their own life become increasingly risk averse. Overall, our findings indicate that knowledge moderates but does not eliminate risk taking behavior. They also highlight that risk taking in adversarial situations might result from a complex set of factors. Further research should establish which psychological processes drive players to adopt a risk taking or conservative strategy in their games.

British-style cryptic crossword solving is an under-researched domain of expertise, relatively unburdened by confounds found in other expertise research areas, such as early starting age, practice regimes, and high extrinsic rewards. Solving cryptic crosswords is an exercise in code-cracking detection work, requiring the segregation and interpretation of multiple clue components, and the deduction and application of their controlling rules. Following the Grounded Expert Components Approach (GECA, Friedlander & Fine, 2016) an earlier survey demonstrated that solvers were typically educated to at least degree level, often in mathematics and science-related disciplines. This study therefore hypothesized that as a group they would show higher-than-average fluid intelligence compared to a general population, with experts showing higher levels than ordinary solvers. Twenty-eight crossword solvers (18 objectively defined experts, and 10 non-experts) solved a bespoke cryptic crossword and completed the Alice Heim tests of fluid intelligence (AH5), a timed high-grade test, measuring verbal and numerical (Part I) and diagrammatic (Part 2) reasoning abilities. In the 45m allowed, 17 experts and 2 non-experts correctly finished the crossword (times ranging between 11m and 40m). Both solver groups scored highly on the AH5 (both overall and for Part I) compared to manual test norms, suggesting that cryptic crossword solving has a high cognitive entry threshold. The experts scored higher than the non-experts, both overall (p = .032) and on Part I (p = .002). The overall and Part I AH5 scores correlated negatively (r_s = -.48; -.72 respectively) with extrapolated finishing times: faster finishing time being associated with higher AH5 scores. The experts and non-experts were matched in age, education, crossword solving experience, and weekly hours spent solving, leading to the suggestion that fluid intelligence differences between the groups may play an important role in cryptic crossword solving expertise. Although small in scale, the study thus adds to the growing body of literature which challenges the “deliberate practice only” framework of high expertise in a performance domain. Suggestions for future explorations in this domain are made.

The current study aimed to investigate the perceptual-motor abilities of esports players using an expert/nonexpert paradigm. A total of 75 participants (age: 24.17 ± 4.24 y, sex: male = 64, female = 11) were subdivided in accordance to their expertise level (i.e. professional: n = 25, recreational: n = 25 and control: n = 25). The perceptual-motor abilities assessed were manual dexterity, the speed-accuracy trade-off and a variety of response times. Groupwise differences were examined using multivariate and univariate analyses of variance. A significant multivariate effect of expertise level on performance characteristics was identified (p < .001, η_p² = .35). Significant univariate effects were identified on the movement time (p < .001, η_p² = .42), two-choice response time (p = .038, η_p² = .09), congruent precue response time (p = .010, η_p² = .12) and incongruent precue response time (p = .047, η_p² = .08). Professional esports players were less susceptible to the speed-accuracy trade-off when compared with recreational esports players and a control group. Furthermore, professional esports players demonstrated faster two-choice response times and were better at using or ignoring information preceding a stimulus to inform subsequent action when compared with the control group. Collectively, some perceptualmotor abilities may underlie expertise in esports, yet their ability to distinguish between professional and recreational esports players is limited. Future research should include more domain-specific measures to fully capture the underlying characteristics of expert esports players.

The present study compares the development experiences and the nature and microstructure of practice activities of super-elite and elite cricket batsmen, domains of expertise previously unexplored simultaneously within a truly elite sample. The study modeled the development of super-elite and elite cricket batsmen using non-linear machine learning (pattern recognition) techniques, examining a multitude of variables from across theoretically driven expertise domains. Results revealed a subset of 18 features, from 658 collected, discriminated between super-elite and elite batsmen with excellent classification accuracy (96%). The external validity of this new model is evidenced also by its ability to classify correctly the data obtained from six unseen batsmen with 100% accuracy. Our findings demonstrate that super-elite batsmen undertook a larger volume of skills-based practice that was both more random, and more varied in nature, at age 16. They subsequently adapted to, and transitioned across, the different levels of senior competition quicker. The findings suggest that optimizing challenge at a psychological and technical level is a catalyst for the development of (super-elite) expertise. Application of this holistically driven, non-linear methodological approach to talent pathways and other domains of expertise would likely prove productive.

Karl Anders Ericsson, widely regarded as the world’s foremost expert on expertise, died on June 17, 2020. One of the most influential psychological scientists of his generation, Anders authored some 275 articles, chapters, and books, which have been cited more than 80,000 times. He was best known for his work on the role of deliberate practice in acquiring expert performance. The original article on deliberate practice (Ericsson, Krampe, & Tesch-Römer, 1993) has been cited more than 11,000 times, making it one of the most cited articles in the psychological literature. In another highly influential article, he and Walter Kintsch proposed the concept of long-term working memory (Ericsson & Kintsch, 1995). Anders was lead editor of the field’s first handbook, the Cambridge Handbook of Expertise and Expert Performance (2006), as well as its second edition.