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

Author:

Miao Haofei1,He Hao1,Hou Xianyun2,Wang Jinghui1,Chi Lizhong1

Affiliation:

1. School of Psychology, Beijing Sport University, Beijing, China

2. School of Philosophy, Wuhan University, Wuhan, China

Abstract

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.

Funder

The Open Funding Project of National Key Laboratory of Human Factors Engineering

Publisher

PeerJ

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