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Abstract

In recent years, esports (the competitive video game industry) have evolved significantly, revolutionizing not only the competitive scene but also the teams and their professional staff. Sports psychologists need to face with cognitive demands related to this activity primarily mental, to respond to the specific needs of the sector (e.g., high performance interventions). The aim of this study was to determine the relationship between cognitive performance in tests and the MOBA League of Legends (LOL) game scores. A group of 36 pure-genre LOL players was evaluated with behavioral tests of fluid intelligence, attentional control inhibition, working memory, cognitive flexibility, and decision making. Furthermore, their in-game performance (rank percentile, kills death assists, minions per minute, and vision score) was recorded during a complete season (approximately 1 year). Data was analyzed by means of correlation and multiple regression analysis. Results showed that cognitive flexibility was positively related to the “rank percentile” (p < .001; R = .442; 95% CI [2.97, 74.76]). Besides, a positive relationship was found between decision making and the in-game performance variable “minions per minute” (p = .009; R = .236; 95% CI [−8.46, 5.26]). These findings point that it is possible predict performance on specific variables, in esports with high cognitive impact throughout neuropsychological assessment. These results provide a starting point for research in the field of esports, sports psychology, and high-performance-oriented intervention.

Keywords

neuropsychology experimental psychology psychology social sciences cognitive psychology sport psychology applied psychology neuroscience behavioral sciences esports

Introduction

In the past few years, the video game industry has revolutionized our understanding of entertainment, generating a social and economic movement around it, especially since the advent of esports (the competitive video gaming industry). In 2019, esports generated worldwide profits of 1.096 billion dollars; these market figures represent a 26.7% increase compared to the previous year and are expected to reach 1.790 billion in 2022 (Newzoo, 2020). Among all esports, the MOBA League of Legends (LOL) game stands out from the rest of esports and in just 10 years since its launch around 100 million people play the game every month (Kollar, 2016).

Following the success of LOL, competitions soon began to develop and 2,208 tournaments have been held so far (Pedraza-Ramirez et al., 2020). The competitive scene has evolved remarkably, from amateur tournaments to national and regional leagues. The World Championship is the most important competition and the one that generates the greatest interest, reaching a peak audience of 45.95 million viewers in 2020 (Lolesports, 2020).

Contribution of Neuroscience to Psychology of Esports

With the professionalization of the industry, esports teams have imitated the operating model of traditional sports by including professionals from various disciplines in their teams to improve the performance of their players (Fiore et al., 2008). Despite the working goals for these professionals are similar to traditional sports (Leis et al., 2021), the high cognitive demands involved in these games stablishes new challenges and targets for them. Some authors emphasize the advantage of including sports psychologists in these teams (Vaamonde & Chirivella, 2020; Watson et al., 2021). Consequently, the potential of psychologists to advance research, knowledge, and training in human cognition makes them a key player in facing the challenges posed by the peculiarities of esports.

As regards the cognitive side of esports, neuroscience is postulated as a suitable field of study for research into cognitive performance of players (Campbell et al., 2018; Martin-Niedecken & Schättin, 2020). In the same direction that other sports with a high cognitive component, such as chess, neuroscience has shown that it is possible to substantiate the mechanisms of performance. For example, it has been possible to detect structural differences in the brain between expert players and non-players (Hänggi et al., 2014), differentiate cognitive features between expert and non-expert players (Unterrainer et al., 2006), and relate cognitive abilities of players with their performance (r = .24; Burgoyne et al., 2016). These findings underline the role of neuroscience and its possible contribution to the sector of esports. Despite the large number of studies carried out in recent decades, findings about how games are related to human cognition are not conclusive. Sala et al. (2018) performed a meta-analysis and found a trivial association between cognitive performance and skill in the game (r = .07). However, it is important to note that data was drawn for a variety of mixed video games, and then it is not applicable to specific games as LOL. As far as we know, there are only two studies that analyzed how cognition affects performance in LOL players. Kokkinakis et al. (2017) found a small to moderate correlation (r = .44, .30, and .24) between fluid intelligence and players’ classification in the game. In contrast, Ding et al. (2018) found no association between player ranking and any of the multiple cognitive variables of the study.

Although evidence on cognition and specific games is scarce, findings from general action games suggest that certain cognitive constructs may be important for game performance. For example, it has been found that experienced players of action games have better working memory ability than non-players (Colzato et al., 2013). Cognitive flexibility has been linked to game performance in systematic reviews and meta-analyses (g = 0.55; Bediou et al., 2018; Nuyens & Kuss, 2019; Palaus et al., 2017). Top-down attention control inhibition, visual-spatial skills, and decision making have also been postulated as key elements involved in video games. However, Dale and Green (2017) warn that many studies about action games have notable methodological deficiencies. These limitations include heterogeneity of samples, lack of control of psychopathology and discrepancies in the categorization of the video game genre studied (category of games related by resembling gameplay characteristics and conditions the way in which the player interacts; Adams, 2013). Thus, it is complicated to drawn solid conclusions, and therefore, researchers need to undertake studies with common standards which eliminate many of the confusing variables (Campbell et al., 2018).

Game Expertise and Cognitive Performance in LOL

Studies developed on a single game may avoid certain of the above-mentioned limitations. Features of LOL make it a suitable instrument for the study of human computer-interaction and cognitive expertise. First of all, if we analyze the structure of this game from the perspective of “a problem” (Goel & Grafman, 2000), LOL would be considered a highly structured one and therefore more stable than other games. Despite the evident difference, LOL would be like chess, since each game is always played on the same board, with similar rules and conditions. This makes it possible to analyze the execution in the same context over time, without finding distortions between different games as happens in most games, in which there are structural changes such as the rules, the objects, and the map of the game. After that, LOL, like chess, is based on an Elo rating system (Berg, 2020), a general classification where players can move up or down depending on their performance in games. This distribution, similar to a normal distribution bell, makes it possible to differentiate among players depending on their skill level in a massive database, which compiles objectively data from million players objectively and in real time. Finally, LOL is a game that records a large number of performance variables (e.g., win percentage, gold won, etc.). Unlike other games, it is possible to obtain automatically a great quantity of stable results in a controlled environment for long periods (Pluss et al., 2019).

Therefore, under these conditions, the analysis of the relations between game performance and cognitive abilities should be able to be studied more rigorously. However, due to the limited studies and findings, no cognitive models have been developed to explain how cognitive components are related to specific esports as LOL. Pedraza-Ramirez et al. (2020) encourage the understanding of the cognitive demands of esports and they systematically reviewed the available literature on esports. These authors performed a relevant effort to set the scientific stage in the field, but also warn that their proposal is based on several esports and be cautious about the possible overlapping of cognitive functions between games. Giving in mind this consideration, Pedraza-Ramirez et al. (2020) propose a heuristic model of esports cognitive performance, highlighting the importance of cognitive flexibility, inhibitory control, working memory, and high order functions (e.g., decision making, intelligence).

From a cognitive point of view, LOL is a game that demands attentional control, reactive skills, and rapidity to execute precise actions. In general, large part of the actions during the game consist of selecting a stimulus and inhibiting others to execute actions (e.g., defeat neutral enemies). As a result, based on findings from other action games and in the same direction that point other authors (Bediou et al., 2018; Raab et al., 2015) competitive esport players may require attention control inhibition to meet the demands of the game. In addition, it is important to know how objects or skills work, as well as the temporal or statistical information associated with them. Generation, transformation, and managing information in real-time are processes needed in games that demand high cognitive load as LOL. For hence, working memory may be postulated as a key process to understand overall performance in LOL players. On the other hand, actions in the game must be executed in a continuous process of decision making in which the risks of the actions should be analyzed quickly and under pressure. Different types of decision making have been widely studied in sports and expertise has been highly related to accurate decisions (Afonso et al., 2012). In the same direction, risk taking could be associated in LOL with several performance variables in which the risk is present (e.g., number of times that a player die each game). Eventually, LOL is a frenetic game in which players need switch between different tasks, as changes in the position of the other players or the vision of the map, among others. Finally, cognitive flexibility has been related to video game use in various systematic reviews and meta-analyses (Bediou et al., 2018; Nuyens et al., 2019; Palaus et al., 2017) and is postulated a core candidate to understand the adaptive demands of the game (Pedraza-Ramirez et al., 2020).

Aim of This Study

In summary, esports has experienced remarkable growth over the last decade, which is in contrast to the scarce number of studies (Pedraza-Ramirez et al., 2020). LOL game could be a good study model, but the only available evidence is an association between fluid intelligence and position of players’ in classification ranking (Kokkinakis et al., 2017). The main cognitive requirements of the LOL based on the heuristic model of Pedraza-Ramirez et al. (2020) seem to include processes of fluid intelligence, attention control inhibition, working memory, cognitive flexibility, and decision making. Moreover, the professionalization of the sector highlights the need to develop methodologically sound studies on the relationship between specific esports variables and the cognitive skills involved in them. Consequently, the aim of this study is to determine the relationship between the five cognitive processes proposed by Pedraza-Ramirez et al. (2020) in their heuristic model of esports performance (fluid intelligence, attention control inhibition, working memory, cognitive flexibility, and decision making) and the achievement in a sample of pure-genre players of the MOBA LOL game. Our hypothesis is that a moderate positive relationship will be found between the performance on these five cognitive processes and several in-game performance variables.

Methodology

Participants

Thirty-six LOL male players were recruited using paper advertisements posted on physical boards in the UCAM University premises and advertisements on social media. The inclusion criteria for this study were: (i) LOL players who have played more than 100 games, according to Kokkinakis’s et al. (2017); (ii) Male players, a criterion based on the recommendations of Pedraza-Ramirez et al. (2020) in order to avoid possible confusing influence of sex differences in cognition (Spets & Slotnick, 2021); (iii) Pure-genre players, which meant spending at least 62.5% of all hours spent on video games on LOL, according to the criteria of Dale and Green (2017) so as to reduce the influence on cognitive ability overlapping other type of games; and (iv) Players who have been classified in the 2019 LOL season, this criterion was stablished to analyze the in-game performance in a specific space of time.

The exclusion criteria were: (i) suffering from a disease affecting central nervous system functioning, (ii) reporting having consumed drugs in the last 48 hours (except for coffee and tobacco), and (iii) exceeding the cut-off score of 75 points on the Internet Gaming Disorder Test (IGD-20; Fuster et al., 2016), which might indicate a video game-related disorder. All criteria were measured through self-reporting. The sociodemographic and gaming-related data are shown in Table 1.

Table 1.

Sociodemographic Data and Game Features of the Sample.

	M (SD)	95% CI
Age	21.92 (3.28)	[20.81–23.03]
Years of education	18.89 (1.83)	[18.27–19.51]
IGD-20: total score
VRI: Days per week played (last year)	5.25 (1.78)	[5.25–4.64]
VRI: Hours per week played to LOL (last year)	4.23 (2.77)	[3.29–5.17]
VRI: % of hours played to LOL (last year)	89.28 (0.14)	[84.41–94.14]
LOL Season 2019: Number of hours played	330.75 (195.54)	[264.58–396.91]
LOL Season 2019: Number of games played	593.72 (375.63)	[460.53–726.92]
	n (%)
LOL Season 2019: Rank achieved
Bronze	4 (11.1)
Silver	1 (2.8)
Gold	15 (41.7)
Platinum	5 (13.9)
Diamond	8 (22.2)
Máster o superior	3 (8.3)

Note. SD = standard deviation; CI = confidence interval; IGD-20 = internet gaming disorder test; VRI = videogame research interview; LOL = league of legends.

Instruments

Assessment of cognition

The instruments were chosen according to the heuristic model proposed by Pedraza-Ramirez et al. (2020) and previous studies performed on LOL gamers (Kokkinakis et al., 2017) in order to analyze five core cognitive constructs, fluid intelligence, attention control inhibition, working memory, cognitive flexibility, and decision-making.

Kaufman Brief Intelligence Test (KBIT; Kaufman, 1990). This is a screening test that evaluates crystallized and fluid intelligence. The matrix subtest was administered in this study, which makes it possible to assess fluid intelligence, a construct that is completely independent of acquired knowledge, and related to logical thinking, including inductive and deductive reasoning, and solving problems in novel situations. This subtest consists of 48 classic matrix problems of increasing difficulty. The outcome variable is the Fluid Intelligence Quotient (IQ) score.

Antisaccade task (AT; Roberts et al., 1994) from the computerized PEBL battery task (Mueller & Piper, 2014). This task measures attention control inhibition. During this task, an arrow is shown in the center of the screen and the participants are asked to focus their attention on this stimulus. Right after, a square (for 225 ms) is shown either on the left or on the right side of the screen (3.4 in from the cross), then another square is shown in the opposite side where the first square appeared. The second square contains the target stimulus, which remains on the screen for 150 ms before it disappears. The target stimulus is an arrow that can point in four directions (↑, ↓, ←, and →). In this task, the participants are asked to try to ignore the first square and to pay attention to the second, and to respond as quickly as possible to the direction of the arrow shown. Before the task starts, a practice trial is conducted with 22 stimuli; the final test consists of a total of 90 stimuli. The outcome variable is the percentage of correct response.

The Corsi block tapping test (Kessels et al., 2008) from the computerized PEBL battery task (Mueller & Piper, 2014). It is a classic measure of visual spatial working memory. At the beginning of this test, nine fixed blue squares are shown, then a sequence is reproduced in which the squares are illuminated (one second per square). The objective of the participants is to remember this sequence. The initial sequence consists of two squares, and as long as the participant correctly remembers one of the two trials, they will go on onto to the next sequence, where a new square will be added. The task ends when two trials of the same length are played incorrectly. This task consists of two conditions; “forward,” which the sequence must be repeated in the same order in, and “backward,” where the sequence must be performed in reverse order. The outcome variables are the total score.

The Reversal Learning Task (RLT; Swainson, 2000). This is a computerized task responsible for measuring cognitive flexibility. In this test, the participants are shown two squares with two different colors, and they are told that one of them is the “correct” one and will most likely give them points, but sometimes it will also make them lose them; the other will be the “wrong” one, which will mostly subtract points but sometimes give them points. The task consists of 160 trials in four phases; every 40 stimuli (one phase), the “correct” square changes to the “wrong” square, exchanging the feedback each provides. In phases 1 and 2, the percentage of the “correct” square provides 80% of the trials, while in phases 2 and 3, the percentage is 70%. The participants receive feedback on their success or error in all the trials and are conscious of the points they have achieved during the task at all times. The ultimate aim of the task is to obtain as many points as possible. The outcome variable was the percentage of correct response in block 4. Block 4 represented the percentage of responses executed on the correct square after having changed the correct/incorrect criterion three times.

Number letter task (NLT; Miyake et al., 2000). A computerized task managed using the Inquisit 5 Lab battery (Inquisit 5, 2016). This task measures cognitive flexibility. In this task, a 2 × 2 matrix is shown in which a pair of characters (a digit and a letter, e.g., AG) rotate in a predictive clockwise direction. When the pair of characters appears at the top of the matrix, the participants must respond by deciding if the letter is either a consonant or a vowel. If the pair of characters appears at the bottom of the matrix, the participants must respond by deciding if the number is either even or odd. The task consists of three phases of practice. The first one focused on the letter criterion (32 trials); the second focused on numbers (32 trials); and finally a practice of both conditions (16 trials). The final task consists of a total of 128 trials, and both the characters chosen and the start of their starting position are assigned randomly. The outcome variable is the latency switch cost (difference between mean correct latency of switch trials and non-switch trials).

The Angling Risk Task (ART; Pleskac, 2008). A computerized task managed using the Inquisit 5 Lab battery (Inquisit 5, 2016). This task makes possible to assess risky decision-making under circumstances of certainty. In this task, the participants play in a 30-round fishing tournament, where in they must obtain the most money. In the pool there are 127 red fish and 1 blue fish; each time a red fish is caught, the participant wins 0.05 cents, while if the blue fish is caught, all the money won from that round is lost and the participant automatically passes to the next round. To avoid losing the money won in each round, the participants have the option to stick, forcing passage to the next round, but ensuring the winnings, since the money is safe in the bank. The probability of catching the blue fish is random and it depends on the number of red fish available. The players know all the information about fish that are available and have been caught during the entire task. The outcome variable is the total amount of money earned.

Balloon Analog Risk Task (BART; Lejuez, 2002). A computerized task from the PEBL battery. This task measures risk-taking under uncertainty conditions. The participants are given a balloon which must be blown up to win money by them; the objective of the task is to win as much money as possible. Each time a balloon is blown up, the participant wins 0.05 cents, but if the balloon explodes, they lose the money won from that round. The effective way to make money is by sticking; if this is done, the money is stored in the bank and the next balloon begins, thus until a total of 90 trials are completed. It ought to be highlighted that there are three balloons with different probabilities of exploding. A 30 purple balloons have the probability of 1/128, 30 orange balloons have a probability of 1/32, and 30 yellow balloons have a probability of 1/8; each time the balloon is blown up, the probability of its exploding progressively increases. For instance, in the case of purple balloons, the first time the balloon is blown up, the probability of its exploding is 1/128 and if the balloon does not explode, the next time it is blown up, the probability of its exploding will be 1/127. The participants are not reported about the chances of explosion and the only information available on the screen is the amount of money won with the previous balloons. The main outcome variable is the total amount of money earned.

Video games Research Interview (VRI): An ad hoc structured interview was developed based on a substance consumption precedent (Verdejo-García et al., 2005). Consumption data are recorded for the 10 most-consumed games over the last year as the average of hours of game use per day and the number of the days per week. Moreover, in each game, the name, genre, platform played, average days played per week, average number of hours played per day, and number of years playing are noted.

Internet Gaming Disorder Test (IGD-20; Pontes et al., 2014). A questionnaire validated and adapted to the Spanish population (Fuster et al., 2016) that assesses the possible existence of an internet gaming disorder using 20 items.

In-game performance

LOL has a database of real-time information about the performance of game players. This information is stored in the Riot Games API database and can be compiled from the game’s client himself/herself using the nickname of each participant. This database collects information about long periods of time, detailed by season, playing mode, roles performed, etc. For this study, the recorded data obtained during the 2019 season were analyzed, and different variables were compiled. Each player’s game rank was selected at the main variable; this was considered the best indicator of performance in the game and represents the situation that would occupy the player within the general classification of the game (divided into divisions). In addition to what has been said other variables of interest were selected that are considered indicators of good performance, such as the KDA index, farm per minute, % control over the map. These variables are broadly used in the competitive scene as indicators of good performance, are highly related to success and are described below.

Rank percentile. Considered the variable that best reflects the overall player’s performance. LOL includes a game mode which includes a tier-based classification system (see Supplemental Appendix I) where the players go up and down based on their own victories and defeats.

Kills Death Assists (KDA). An indicator represents the average of [Kills + Assists/Deaths]. This is a general index of the player’s account and includes information about all the game modes.

Minions per minute. A variable that states the number of neutral enemies killed per minute. This specific index corresponds to the “Solo/Duo Q” game mode and the most-played character.

Vision score. A variable that states the contribution to the control of the in favor of the team. This specific index corresponds to the “Solo/Duo Qualifier” game mode and the most-played character.

Procedures

The participants were assessed between September 2019 and March 2020 in person at UCAM University. Participants were informed of the study conditions and signed an informed consent form. The assessment session lasted about 90 minutes, with a 20-minute break. The participants were paid €10 as compensation for their participation.

The study followed the code of ethics for human research in the Declaration of Helsinki and was subject to a favorable report from the Ethics Committee of the UCAM University (CEO21906).

Statistical Analysis

Descriptive analyses were carried out to determine the sociodemographic characteristics of participants. The Kolmogorov-Smirnov test was used to analyze the normal distribution of the variables. Pearson correlations were performed for the variables with a normal distribution. These correlations were made between the cognitive performance variables derived from the cognitive tests and the game performance variables. Multiple regression analysis was conducted to analyze how game performance can be explained from cognitive variables. The factors in the models were those cognitive variables which showed significant correlation with the in-game variables. The number of predictors was aligned with statistical recommendations for sample size in regression analyses (Hair et al., 2009). All statistical analyses were carried out using SPSS statistics v26.

Results

Sample normality was confirmed trough Kolmogorov-Smirnov Test. The scores on cognitive tests are shown in Table 2 and their correlations with the in-game variables in Table 3. The results showed significant correlations between the rank percentile and the NLT, ART and RLT tests; Minions per minute with the AT, ART, and BART; and the KDA with the KBIT.

Table 2.

M (SD) and Confidence Interval (95%) of Cognitive Performance Variables and In-Game Performance Variables.

Test	M (SD)	95% CI
KBIT	111.33 (7.932)	[108.65, 114.02]
NLT latency switch cost	584.165 (314.743)	[477.671, 690.659]
AT % correct responses	87.848 (8.231)	[85.063, 90.633]
ART total money gained	39.245 (9.939)	[35.882, 42.609]
BART total money gained	31.258 (12.627)	[26.985, 35.530]
RLT block 4	60.972 (22.774)	[53.266, 68.678]
CORSI total score	68.77 (24,21)	[60.58, 76.96]
Rank percentile	67.556 (29.679)	[57.361, 77,751]
Minions per minute	4.574 (2.100)	[3.852, 5.295]
KDA	2.409 (0.437)	[2.258, 2.559]
Vision score	46.240 (19.924)	[39.395, 53.084]

Note. CI = confidence interval; KBIT = Kaufman brief intelligence test; NLT = number-letter task; AT = antisaccade task; ART = the angling risk task; BART = balloon analog risk task; RLT = the reversal learning task; KDA = kill death assist.

Table 3.

Pearson Correlation Matrix Between Cognitive Performance Variables and In-Game Performance Variables.

	Variables	1	2	3	4	5	6	7	8	9	10	11
1	KBIT	1
2	NLT (latency switch cost)	−.208	1
3	AT (% correct responses)	.220	−.015	1
4	ART (total money gained)	.150	.041	.185	1
5	BART (total money gained)	.043	.052	.344*	−.033	1
6	RLT (block 4)	.081	−.167	.212	.340*	.256	1
7	CORSI total score	.100	−.079	−.002	.234	−.014	.194	1
8	Rank percentile	.117	−.512**	.105	.333*	−.007	.504**	.140	1
9	Minions per minute	−.005	−.029	.402*	.422*	.379*	.074	.079	.191	1
10	KDA	.356*	−.261	.203	.073	−.042	.070	−.059	.319	−.048	1
11	Vision score	.184	.205	−.100	−.024	−.111	.147	.021	.367*	−.481**	.435**	1

Note. KDA = kill death assist; KBIT = Kaufman brief intelligence test; NLT = number-letter task; AT = antisaccade task; ART = the angling risk task; BART = balloon analog risk task; RLT = the reversal learning task.

p < .05. **p < .001.

The in-game performance variables rank percentile and minions per minute were included as dependent variables in two multiple linear regression models. Variables showing correlations with the previous indices were included as predictors. Regression models were not run for the variable because it was not related to cognitive scores and in the case of the KDA, only one variable correlated with the index. No variable showed collinearity; high-tolerance and low-VIF (Variance Inflation Factor) values were found (model 1: tolerance > 0.851, VIF < 1.175; model 2; tolerance > 0.843, VIF < 1.186; Craney & Surles, 2002). The number of predictors was aligned with statistical recommendations for sample size in regression analyses (Hair et al., 2009). The results are shown in Table 4.

Table 4.

Multiple Regression Models: Contribution of Cognitive Performance Variables to In-Game Performance Variables, Rank Percentile, and Minions per Minute.

Dependent variables	Independent variables	R ²	R² adjusted	Full model F [95% CI]	Full model p	Predictor variables t [95% CI]	Predictor variables p	β (standarized β)
Rank percentile		.489	.442	10.226 [2.973, 74.764]	<.001
	NLT latency switch cost					−3,596 [−0.068, −0.019]	.001	−.043 (−.463)
	ART total money gained					1.730 [−0.122, 1.503]	.093	.69 (.234)
	RLT block 4					2.533 [0.087, 0.806]	.016	.447 (.347)
Minions per minute		.302	.236	4.608 [−8.463, 5.266]	.009
	BART total money gained					2.519 [0.010, 0.139]	.025	.066 (.356)
	ART total money gained					2.355 [0.013, 0.119]	.017	.075 (.399)
	AT % correct responses					0.344 [−0.069, 0.097]	.733	.014 (.055)

Note. CI = confidence interval; NLT = number-letter task; ART = the angling risk task; RLT = the reversal learning task; BART = balloon analog risk task; AT = antisaccade task.

The linear regression model to predict the rank percentile was statistically significant, with a total explained variance of 44.2%. The variables which contributed to the model were RLT and NLT. The linear regression for predicting the Minions per minute was statistically significant, with a total explained variance of 23.6%. The variables that contributed to the model were BART and ART.

Discussion

This study analyses for the first time specific in-game performance variables (rank classification, minions per minute, KDA, and vision score) in the MOBA LOL video game. Based on the heuristic model proposed by Pedraza-Ramirez et al. (2020), the aim of this study was to determine the relationship between five core cognitive constructs (fluid intelligence, attention control inhibition, working memory, cognitive flexibility, and decision making) and in-game performance variables in a group of pure-genre players. The results partially confirm our hypothesis and show that it is possible study cognitively esports players and predict their performance. Results from the correlation and regression analyses showed that four out of five of the studied variables from the LOL performance are associated with fluid intelligence, attention control inhibition, cognitive flexibility, and decision-making, but not with working memory.

Findings indicate that the classification in the game ranking (one of the most important outcome for LOL players) can be mainly predicted by their cognitive flexibility abilities (scores in the NLT and RLT tests were significant predictors), and secondly by their decision-making abilities (score in the ART test was a marginally significant predictor). These findings might have theoretical implications for the two cognitive flexibility tasks used. Albeit both tasks measure flexibility, each one would reflect different sub-processes of the construct. Shafritz et al. (2005) reports that cognitive flexibility could be divided on response switching and cognitive set switching. On the one hand, the NLT task would imply response switching processes (the ability to move between tasks). The dependence on the ability to response changing to get a good score in the game seems clear. LOL is a frenzied game that requires quick changes to respond properly to different and simultaneous tasks, such as the elimination of neutral enemies, controlling nearby enemies and map visualization. On the other hand, the RLT task seems to be related to set switching (the ability to detect a rule change consciously). The ability to recognize the changes that take place over the course of the LOL or, in other words, knowing how to detect the implicit rules that modify the course of a game, can make the difference between victory and defeat. Previous findings in action video game have shown that players are faster than non-players in response switching task (Bediou et al., 2018; Nuyens et al., 2019; Palaus et al., 2017). Regarding LOL, Li (2020) also found better performance on expert players respect regular players in response switching task. However, as far as we know, the study of how cognitive set switching is related to in-game performance is still unexplored, and this is the first study to highlight the relevance of both cognitive flexibility dimensions in esports players. According to Kokkinakis et al. (2017) study, we expected that the classification in the ranking was related to fluid intelligence, but we do not find this relationship. In any case, our results are aligned with findings from a study performed with Dota 2 players (a MOBA game similar to LOL) in which no relation was found about performance and fluid intelligence (Röhlcke et al., 2018).

About the number of minions killed per minute (the second most important outcome in LOL) could be predicted by the decision-making abilities (ART test for context with certainty and BART for uncertainty as significant predictors) along with the attentional control (AT test score as non-significant as independent predictor). In LOL killing neutral enemies provides gold earnings for players, and total amount of gold won is usually related to a victory. Our results highlight the relevance of decision making tasks (BART and ANG) on this variable. In both tasks, the participants must obtain as much money as possible in a continuous process in which they assess the risks and benefits of their decisions. These results would indicate that monitoring behavior and analyzing the costs and benefits of decisions taken would be key elements in the performance of LOL players in this variable. No previous studies have analyzed the influence of decision-making processes on game performance in LOL players. The uniqueness of these results and the complexity of decision-making process (Mishra, 2014) point the importance of confirming the findings in future studies. In contrast, attentional task (AT) correlated with the variable minions killed per minute; despite its significance in regression analysis was close to significance. This is not surprising since the number of minions killed in a game not only depends on decisions made for controlling the waves, but also on the attentional abilities that allows you to select the appropriate stimulus and ignore irrelevant stimuli to defeat enemies in the precise moment. These results are consistent with the general conclusions obtained by systematic reviews and meta-analyses (Bediou et al., 2018; Choi et al., 2020; Sala et al., 2018) that highlight the importance of attention control in video games. Regarding specific studies carried out on LOL players, Qiu et al. (2018) did not proved its relationship with performance, but they found that expert players had better performance than regular players in a visual attentional task (useful field of view).

In relation to KDA in-game performance variable, we only found a positive correlation with fluid intelligence. The lack of more associations with the KDA index could be explained by the complexity of this variable. This index would be affected by several conditions during the game or by the characteristics of the players’ role, among others. Fluid intelligence is considered a complex construct that is related to various components of executive functions or creativity (Benedek et al., 2014). Therefore, the relationship between cognitive performance and complex indices such as KDA would require a deeper and more specific analysis. Finally, regarding the last in-game performance variable, vision score, we did not find any significant association with the cognitive constructs assessed. Vision score requires the implementation of strategic skills that make possible to predict the enemy team’s movements at key moments. The lack of meaningful results might be explained by the absence of strategic planning tests in this study. Furthermore, this variable depends largely on the player’s role, being more relevant for the “support role” and our sample was mixed composed and heterogeneous.

This study is the first to analyze game-specific variables with performance in the MOBA LOL game. As far as we know, there have only been two previous studies of LOL players in which cognitive performance is related to game performance, although they focused exclusively on the ranking performance variable. Kokkinakis et al. (2017) found a relationship between fluid intelligence and players’ performance in the game; by contrast, in our study the correlation with this construct was not significant. It must also be noted that the aforementioned study only used measures related to fluid intelligence, and cognitive flexibility measures were not used. The lack of a relationship between fluid intelligence and performance in our study may be due to methodological differences between both studies. Firstly, the authors used another kind of measure to assess performance in the game, the MMR (Matchmaking Ranking). Although MMR is a highly performance-related variable, it is not currently possible to obtain these data in an objective fashion, hence it is impossible for us to examine that relationship. Secondly, in this study the in-game performance variable was obtained from an entire season (2019). Furthermore, in the aforementioned study no temporal information about data collection is provided. On the other hand, Ding et al. (2018) did not find any significant correlation between rank classification and an extensive battery of cognitive tests. Regardless, it is important to highlight the fact that the ranking performance variable was calculated based on the range of players, but this was codified on a linear scale. This manner of quantifying performance would not be representative of the distribution of the players in the ranking, as we show in Supplemental Appendix I.

Our results confirm partially the heuristic model proposed by Pedraza-Ramirez et al. (2020). Cognitive flexibility and decision-making were the two main cognitive constructs that predict performance in the LOL esport. We also found a positive correlation between attentional control inhibition and fluid intelligence in some in-game variables; however, their contribution in regression analysis were not significant as independent predictors. Finally, any association was found on working memory construct. These findings support in part the above-mentioned model, but also stand out the need to develop specific models based on single games taking into account specific in-game variables. Most studies have been developed from a global genre perspective (e.g., action games), but despite their similarities each game has specific demands, so future studies should focus on their singularities. On the other hand, performance in a game can be measured by different variables, and how cognitive processes are related to these variables should be individually studied.

In respect of general results found in videogame literature, our results contrast with the meta-analysis carried out by Sala et al. (2018). These authors concluded that there is a limited relationship between cognitive abilities and the in game performance. Our investigation contrasts with these results and found a stronger relationship between these two features. We think there are various reasons, mainly methodological that would explain the strength of our results. First, in the aforementioned meta-analysis, skill in the game was analyzed based on the number of hours spent per week and the score in the game. The number of practice hours as a mediator’s skill is not an appropriate variable since it has been pointed that deliberate practice partially explains the execution in several activities, and therefore, this relationship is far from being perfect (Macnamara et al., 2014). On the other hand, while the score in a game may be of interest for assessing performance, there is considerable variability on how the scores are recorded in various games, even if they are part of the same genre. Even within the same game there may be variabilities in their score recording, since in general, video games are characterized by their changing conditions, diverse complexity, and an unstable problem structure (Shute & Emihovich, 2018). This idea would be supported by the Sala et al. (2018) meta analysis that found a stronger relationship between cognitive abilities and in-game performance when the genre of the game was considered (r range from .09 to .30). Although the effort to and interest in segregating into types of genres is the correct approach, their classification remains too general. Dale and Green (2017) point out that the various types of genres show similar mechanics that overlap with each other; this hinders the relational study with certain cognitive constructs. It is important to indicate that many studies scarcely describe the characteristics of the sample (e.g., their experience objectively measured) and consequently it is hard to analyze these relations in terms of normality. Another important feature is the absence of control to confusing variables such as the presence of psychopathology (e.g., Internet Gaming Disorder) or the fact that the participants are not classified as pure-genre or mixed gamers.

This investigation is one of the first in the field of esports to analyze specific performance variables with a more precise methodology. Until now, the massive growth of esports has contrasted with the low number of scientific publications. These early manuscripts are important for the field because they lay the foundations for future studies. Despite this, these investigations use criteria that make it difficult to draw clear conclusions. For example, the definition of expert players varies between the studies. Qiu et al. (2018) considered that an expert player is one with 2 years of professional experience, and who is among the 7% best players according to the ranking system. By contrast, Ding et al. (2018) considered experts’ players only professionals who played in a secondary league in China. On the other hand, these studies do not clearly defined the variables used to assess in game performance. For example, Bertran and Chamarro (2016) used the KDA index in games that do not count for the purposes of the classification, and they do not explain how they treated the player’s classification in the overall ranking. Similarly, Ding et al. (2018) coded the classification of the players into a linear system of 10 to 70 points, which would not be representative of the distribution of the real game classification (see Supplemental Appendix I). Furthermore, these studies provide scarce information about the gaming players’ profile (e.g., the number of hours of play spent on other games, other genres of games played, etc.; Chang et al., 2017; Gray et al., 2018). Finally, they do not assess relevant aspects such as whether the players are multi-genre or genre-pure (Dale & Green, 2017).

The implications of this study, besides being important for characterizing professional players, point at the way to achieve high performance among esports players as well. In recent years, the importance of mindfulness techniques on cognitive functioning has been emphasized. Various studies have shown that mindfulness meditation causes positive long-term changes in attention switching (Chiesa et al., 2011), as well as in attentional functions and cognitive flexibility (Moore & Malinowski, 2009). Mindfulness practice has demonstrated to improve the performance of players versus a control group in sports that require mental abilities like precision, as in darts, or shooting (SMD = 1.35; Bühlmayer et al., 2017). So, if cognitive flexibility is related to achievement, then mindfulness meditation could have a positive impact on esports players’ performance, specifically on LOL players.

In spite of this, there are various points to keep in mind that limit this study. The sample used in this study is small; hence, the results should be considered exploratory; despite this, it should be noted that, from our point of view, the selection criteria involve a methodological improvement that differs from the studies undertaken to this point. Even so, there are variables that were not controlled for, and which affect performance, such as knowledge of the game, the type of practice, or the influence of updates.

Future studies should control the methodological criteria, standardizing them with respect to all the other studies. After decades of study in the field, it is essential to establish more specific sample selection criteria. Video games are becoming more heterogeneous every day, hence it is necessary to analyze the participants’ player profile, prioritizing some aspects, like genre-pure players. Moreover, it would be important to analyze specific games because, despite the similarities between games in the same genre, there might be differences among them. Similarly, the study variables must be homogeneous across the studies. As regards LOL studies, it would be necessary to explore issues such as the differences according to the player’s role or to perform specific analyses between expert players (high rank) and novice players (low rank). Finally, according to the field of neuroscience, it is important to use standard assessment instruments that have been previously shown reliable and through which valid results have been obtained.

Conclusions

In conclusion, and based on the preliminary data shown in this study, our findings support the heuristic model proposed by Pedraza-Ramirez et al. (2020), highlighting the relationship between cognitive flexibility and decision-making processes with some in-game performance variables in the esport LOL. These results provide a starting point for researching in the field of esports, sports psychology, and high-performance-oriented intervention.

Supplemental Material

sj-docx-1-sgo-10.1177_21582440221142728 – Supplemental material for Cognitive Flexibility and Decision Making Predicts Expertise in the MOBA Esport, League of Legends

Supplemental material, sj-docx-1-sgo-10.1177_21582440221142728 for Cognitive Flexibility and Decision Making Predicts Expertise in the MOBA Esport, League of Legends by Carlos Valls-Serrano, Cristina de Francisco, Eduardo Caballero-López and Alfonso Caracuel in SAGE Open

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work is supported by “Ayudas a la realización de Proyectos de Grupos de Investigación” from “Plan propio de investigación, Universidad Católica de Murcia (UCAM)” [PMAFI-03/19].

ORCID iDs

Carlos Valls-Serrano

Alfonso Caracuel

Availability of Data and Materials

The data that support the findings of this study are available from the corresponding author Valls-Serrano, C., upon reasonable request.

Supplemental Material

Supplemental material for this article is available online.

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Supplementary Material

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Cognitive Flexibility and Decision Making Predicts Expertise in the MOBA Esport,League of Legends

Abstract

Keywords

Introduction

Contribution of Neuroscience to Psychology of Esports

Game Expertise and Cognitive Performance in LOL

Aim of This Study

Methodology

Participants

Instruments

Assessment of cognition

In-game performance

Procedures

Statistical Analysis

Results

Discussion

Conclusions

Supplemental Material

sj-docx-1-sgo-10.1177_21582440221142728 – Supplemental material for Cognitive Flexibility and Decision Making Predicts Expertise in the MOBA Esport, League of Legends

Footnotes

Declaration of Conflicting Interests

Funding

ORCID iDs

Availability of Data and Materials

Supplemental Material

References

Supplementary Material