Distinguishing experts from novices by the Mind's Hand and Mind's Eye

Zone In Not Out! The Key to Winning High-Level Tetris

Automating a perceptual-motor task will not win you a perceptual-motor contest. Despite claims that mindless automaticity is the essence of expertise, the view espoused here is that automaticity is worthwhile only because it enables the expert to plan and strategize. Indeed, the purpose of learning to manually shift gears is to eventually ignore that function to focus instead on actual driving. To perform well, the expert must transition their attention from a task's low-level components to its high-level nuances. This is best understood in real-world scenarios (e.g. driving, in which performance is dynamic and sometimes competitive). This argument is based on a years-long, longitudinal case study of learning to play the puzzle game, Tetris. Tetris is intensively perceptual-motor with complicated manual routines needed to manage expert game speeds. For this case study, the player began as an advanced novice but successfully transitioned to championship level in the 2020 Classic Tetris World Championship. Initially, the challenge was gaining enough skill to make and execute perceptual-motor decisions in a fraction of a second. However, once that process became automatic, the player could spend those freed mental resources elsewhere. Performance was better for all games when the player was mentally engaged and used their focused attention to plan ahead rather than just automatically respond to the game pieces. We argue that the end goal for automating perceptual-motor skills in competitive, dynamic environments is to free cognitive space in the brain for the user to excel strategically.

Cognitive expertise in esport experts: a three-level model meta-analysis

Objectives

The cognitive expertise of experts has been an intriguing theme; there has been rapid growth in cognitive research related to esports. Given the close association between esports activities and cognition, esports holds promise in offering new perspectives for understanding cognitive expertise. This meta-analysis aims at quantitatively delineating the cognitive disparities between esports experts and amateurs.

Methods

The expert group comprised professional video game players and high-ranking players (top 1%), while amateurs were assigned to the control group. Research studies published between January 2000 and December 2023 were systematically searched in databases. A three-level model with cluster-robust variance estimation was used to calculate the overall effect size. The moderating variables included professional level, cognitive abilities, dependent variable type, game genre, gender and age.

Results

A total of 15 studies containing 142 effect sizes and 1085 participants were included in this meta-analysis. The results indicated that, compared to amateurs, video game experts demonstrated superior cognitive abilities with a small effect size (Hedges' g = 0.373, 95% CI [0.055-0.691], p = .012). The differences between experts and amateur players mainly manifest in spatial cognition and attention. Sensitivity analysis, risk of bias, and publication bias results indicated the reliability of these findings.

Conclusions

This meta-analysis confirms that esports experts possess superior cognitive abilities compared to amateurs, particularly in aspects of spatial cognition and attention. These can provide an effective reference for future selection and training in esports.

Constructing Expertise: Surmounting Performance Plateaus by Tasks, by Tools, and by Techniques

Acquiring expertise in a task is often thought of as an automatic process that follows inevitably with practice according to the log-log law (aka: power law) of learning. However, as Ericsson, Chase, and Faloon (1980) showed, this is not true for digit-span experts and, as we show, it is certainly not true for Tetris players at any level of expertise. Although some people may simply "twitch" faster than others, the limit to Tetris expertise is not raw keypress time but the techniques acquired by players that allow them to use the tools provided by the hardware and software to compensate for the game's relentlessly increasing drop speed. Unfortunately, these increases in drop speed between Tetris levels make performance plateaus very short and quickly followed by game death. Hence, a player's success at discovering, exploring, and practicing new techniques for the tasks of board preparation, board maintenance, optimal placement discovery, zoid rotation, lateral movement of zoids, and other tasks important to expertise in Tetris is limited. In this paper, we analyze data collected from 492 Tetris players to reveal the challenges they confronted while constructing expertise via the discovery of new techniques for gameplay at increasingly difficult levels of Tetris.

The Role of Experience, Perceived Match Importance, and Anxiety on Cortisol Response in an Official Esports Competition

The aim of the present study was to analyse the neuroendocrine stress response, psychological anxiety response, and perceived match importance (PMI) between expert and non-expert control gamers in an official competitive context. We analyzed, in 25 expert esports players and 20 control participants, modifications in their somatic anxiety, cognitive anxiety, self-confidence, PMI, and cortisol in a League of Legends competition. We found how expert esports players presented higher cortisol concentrations (Z = 155.5; p = 0.03; Cohen’s d = −0.66), cognitive anxiety (Z = 99.5; p = 0.001), and PMI (Z = 50.5; p < 0.001) before the competition than non-experts participants. We found a greater statistical weight in the cognitive variables than in the physiological ones. The results obtained suggest that real competitive context and player’s expertise were factors associated with an anticipatory stress response. The PMI proved to be a differentiating variable between both groups, highlighting the necessity to include subjective variables that contrast objective measurements.