The most popular genre of video games are action video games (AVGs), which are fast-paced, highly complex and feature dynamic environments. These games require fast motor responses, often under strict time limitations therefore, there is a potential for a positive effect of AVG gameplay on cognitive skills.
Several studies have shown improvements in processing speed as a result of AVG training however, the relationship between task switching and AVGs remains unclear (Dye, Green & Bavelier, 2009; Green et al., 2012; Van Ravenzwaaij et al., 2015; Sala et al., 2017; Bediou et al., 2018). Another aspect which may influence improved cognition is expertise, with the expectation that more expert players would show greater processing speed and task switching abilities.
Method: participants who self-identified as being a casual, expert or professional counter-strike: global offensive (CS: GO) players were interviewed to establish how to measure expertise in CS: GO players. Question topics included: player statistics, playstyle, their views regarding defining gaming expertise, roles in CS: GO, tournaments, performance aids, required skills and professional gaming. Expertise measures defined in Study 1 were then utilised in Study 2.
Results: Each interview participant ranged in their CS: GO expertise in terms of years of playtime, total hours of playtime and weekly hours of playtime. The casual reported playing for 6 years with 2,000 hours total but was currently playing zero hours per week. The expert reported playing for 12 years, similarly for 2,000 total hours and was currently playing 58 hours per week. The professional reported playing for 14 years, with a staggering 10,000 total hours and was currently playing 100 hours per week.
These statistics were significantly higher than any previous AVG study, exemplifying the importance of using samples of differentially experienced AVGs, and to use this line of questioning when defining AVG expertise in Study 2. Additionally, when asked how to measure expertise in CS: GO players, all three participants suggested that the existing in-game CS: GO ranking system was one of the most important factors in discerning between casual, expert and professional CS: GO players.
Method: 304 respondents completed the online experiment over a period of 4 weeks. This consisted of a colour-shape switching task via Tatool Web (www.tatool-web.com; von Bastian, Locher & Ruflin, 2013) which measured our cognitive outcomes (processing speed and task switching), followed by an in-depth questionnaire which assessed CS: GO experience and proficiency via Qualtrics (www.qualtrics.com). In total, the colour-shape task and questionnaire took no more than 30 minutes for participants to complete. Once data collection was complete we filtered out incomplete data sets, implausible answers and low accuracy scores was, resulting in a final sample of n = 273.
Results: k-means cluster analysis and drift-diffusion modelling (DDM) will be completed on this dataset, with the results comprising the first study of my PhD.
This study utilised a large and statistically stable correlation sample, which was more representative of the wider video game community than many previous all-male video game research studies. The study also adds valuable insight to the question: how do we measure video game expertise? A single multivariate measure of CS: GO expertise provided sophisticated allocation of individuals to expertise group based on cluster analysis, and improves on previous studies which have used arbitrary boundaries.
Further analysis of this data set is necessary during PhD study with the aim of publishing in 2022. Once published, we will upload the dataset to the Open Science Framework (OSF).