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Fallout: an expertly crafted TV adaptation that manages to incorporate some of the best elements of gameplay

Peter Howell , University of Portsmouth

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Nick Butler , Stockholm University and Sverre Spoelstra , Lund University

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Andrés Porras Chaves , IE University

Video games like Snufkin: Melody of Moominvalley can inspire players to look after nature

Elliot Honeybun-Arnolda , University of East Anglia and Lucas Friche , Université de Lorraine

I created a ‘cosy game’ – and learned how they can change players’ lives

Adam Jerrett , University of Portsmouth

Playing Snufkin: Melody of Moominvalley is a reminder of Tove Jansson’s environmental message

Esme Miskimmin , University of Liverpool

Anyone can play Tetris, but architects, engineers and animators alike use the math concepts underlying the game

Leah McCoy , Wake Forest University

The video game industry is booming. Why are there so many layoffs?

Kenzie Gordon , University of Alberta ; Jennifer R. Whitson , University of Waterloo ; Johanna Weststar , Western University , and Sean Gouglas , University of Alberta

60% of Australian English teachers think video games are a ‘legitimate’ text to study. But only 15% have used one

Amanda Gutierrez , Australian Catholic University ; Kathy Mills , Australian Catholic University ; Laura Scholes , Australian Catholic University , and Luke Rowe , Australian Catholic University

From besting Tetris AI to epic speedruns – inside gaming’s most thrilling feats

James Dawes , Macalester College

Why we should take competitive video games more seriously

Thomas Burelli , L’Université d’Ottawa/University of Ottawa ; Haoran Liu , L’Université d’Ottawa/University of Ottawa , and Marie Dykukha , L’Université d’Ottawa/University of Ottawa

Vacuuming, moving house, unpacking are boring in real life – so why is doing them in a video game so fun?

Lesley Speed , Federation University Australia

E3: why the world’s biggest video game event just closed for good – and what’s next for the industry

Theo Tzanidis , University of the West of Scotland

Buying indie video games over the holidays can help make the industry more ethical and fair

Sarah Stang , Brock University

Baldurs Gate 3 wins game of the year at 2023’s Game Awards – an expert review

Emma Joy Reay , University of Southampton

Microsoft’s ban on third-party controllers on the Xbox excludes some disabled gamers from using the device

Juan Escobar-Lamanna , Western University

All the video games shortlisted for the 2023 Game Awards – reviewed by experts

Theo Tzanidis , University of the West of Scotland ; Adam Jerrett , University of Portsmouth ; David Stevenson , Trinity College Dublin ; Emma Joy Reay , University of Southampton ; Henryk Haniewicz , University of Southampton , and Michael Samuel , University of Bristol

‘Baldur’s Gate 3’ became the surprise hit of 2023 by upending conventional wisdom about what gives video games broad appeal

The Legend of Zelda film: past adaptations have gotten Link’s character wrong

José Blázquez , Bournemouth University

Australia’s media classification system is no help to parents and carers. It needs a grounding in evidence

Elizabeth Handsley , Western Sydney University and Fae Heaselgrave , University of South Australia

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Evolutionary Biologist, ARC Future Fellow, UNSW Sydney

Director of the International Gaming Research Unit and Professor of Behavioural Addiction, Nottingham Trent University

Senior Lecturer in Digital Cultures, SOAR Fellow., University of Sydney

Associate Professor in Politics, University of Leeds

Researcher: Digital Literacy and Digital Wellbeing, Western Sydney University

Associate Professor, Swinburne University of Technology

Lecturer in Digital Cultures, University of Sydney

Professor of Psychology, Stetson University

PhD Candidate in Recreation and Leisure Studies, University of Waterloo

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Professor in American Media and Culture, University of Kent

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• Published: 26 April 2022

Reaction time and working memory in gamers and non-gamers

• Gal Ziv 1 ,
• Ronnie Lidor 1 &
• Oron Levin 2  

Scientific Reports volume  12 , Article number:  6798 ( 2022 ) Cite this article

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• Human behaviour

The purpose of this pre-registered study was to examine whether asking gamers and non-gamers about their video game playing habits before or after they performed computerized cognitive-motor tasks affects their performance of those tasks. We recruited 187 participants from an online participants’ recruitment platform. Out of those participants, 131 matched our criteria as gamers or non-gamers. They were then divided to two subgroups, and performed a choice-RT task, a Simon task, an alternate task-switching task, and a digit span memory task either before or after answering a video-game playing habits questionnaire. The results showed that gamers who completed a video-games questionnaire before performing the tasks had faster reaction times (RTs) in the Simon task compared with gamers who answered the questionnaire after performing the tasks. In contrast, non-gamers who answered the questionnaire before the task had slower RTs in the Simon task and the alternate task-switching task compared with non-gamers who answered the questionnaire after performing the tasks. The results suggest that answering a video-games questionnaire before the start of a study can lead to a response expectancy effect—positive for gamers and negative for non-gamers. This may bias findings of studies examining video games and the performance of cognitive-motor tasks.

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Introduction.

With over 2.7 billion gamers worldwide 1 , playing video games can be considered as one of today's favorite pastimes. As the popularity of video games grows, research interest in the effects of playing video games on human behavior and psychology increases as well. In the past few decades, researchers have examined the relationship between video games and aggression (e.g., 2 , 3 ), depression (e.g., 4 , 5 ), addiction (e.g., 6 ), and cognitive processes—among them executive function (e.g., 7 , 8 ), attention (e.g., 9 ), reaction time (RT) (e.g., 10 ), and working memory (e.g., 11 ). It has been suggested that playing video games can have cognitive, motivational, emotional, and social benefits 12 .

A number of studies have shown positive relationships between playing video games and cognitive-motor skills (e.g., 13 , 14 , 15 , 16 , 17 ). For example, Boot et al. 13 showed that expert gamers are better than non-gamers in tracking moving objects, in detecting change, and in task switching. Colzato et al. 15 reported that experienced gamers who play first-person shooter games—action games that are played from a first-person view—were more accurate at an N-back task and reacted faster to go signals in a stop-signal task without compromising stopping performance, than non-gamers. Another study 17 used a stop-change paradigm (a variation of the stop-signal task with the addition of a cue to not only inhibit a response but to initiate another) and demonstrated that, compared with non-gamers, experienced first-person shooter players reacted faster in the go condition and in the change conditions without compromising accuracy.

While the abovementioned findings are promising, there are a number of methodological concerns that undermine our ability to show a causal relationship between playing video games and improved cognitive and motor performance. It is not clear, for example, whether the relationship between gaming and performance is caused by the gaming experience or if it represents pre-existing differences that lead to a self-selection effect, causing certain individuals to choose to play video games 13 . Boot et al. 18 suggested that several methodological shortcomings may undermine the positive effects of playing video games on cognitive/motor performance. Specifically, for studies that aim at examining differences between gamers and non-gamers, covert recruiting of participants is of importance.

Boot et al. 18 emphasize that gamers should not know that they are recruited for a study about gamers or about the benefits of playing video games, as this might bias the results. To prevent that bias from occurring, researchers should not ask participants about their video game-playing experience before the study. This methodological argument is supported by the concept of psychological suggestion. Psychological suggestion refers to a process by which individuals or environmental cues influence the way we think and behave 19 . Suggestions can be deliberate (e.g., directly influencing one's thought, beliefs, or behaviors), or unintentional (e.g., given by certain cues given by individuals or that are present in the environment). Examples of such unintentional cues can be found in various domains. For example, jurors' verdicts are affected by judges’ expectations of guilt and by subtle differences in the way they give instructions to the jury 20 . Ziv and colleagues 21 provided an example from the motor learning domain, where participants’ expectancies of success were manipulated by changing the task-success criterion. In their study, participants who practiced with an easy success criterion putted golf balls more accurately in a transfer task compared with participants who practiced with a difficult success criterion. In the abovementioned studies, subtle cues led to changes in decision making in jurors as well as in individuals who performed a motor task (golf putting). Similarly, gamers who learn that they are about to participate in a study on gamers' abilities, and who believe that gaming may be related to higher cognitive and motor performance, might expect to perform better—and indeed do so.

One theory that can explain how psychological suggestion works—and in the context of the current study how unintentional psychological suggestion can lead to changes in gamers' task performance, is the response expectancy theory 22 , 23 . Response expectancies can be defined as “the anticipation of automatic, subjective, and behavioral responses to particular situational cues” ( 23 , p. 69), and they can be a product of suggestion. Such response expectancies can lead individuals to automatically change their behavior in accordance with their expectancies 24 . For example, Clifasefi et al. 25 showed that telling participants that they are receiving a drug that enhances mental alertness and cognitive function, when they actually received a placebo, led to improved performance in a cognitive task (compared with participants who were told they were given a placebo). Similarly, Foroughi et al. 26 showed that individuals who were recruited overtly for a cognitive training session (i.e., recruitment flyer mentioned that training can improve cognitive function) improved their cognitive performance after a one-hour training session compared with participants who were recruited covertly (i.e., neutral recruitment flyer) for the same training and showed no improvements.

In accordance with psychological suggestion and response expectancy theory, Boot et al. 27 suggest that participants' expectancies can affect the results of studies. Gamers, for example, may expect to perform well in certain cognitive/motor tasks if they believe that there is a positive relationship between gaming and performance, and if they are aware of the fact that they were recruited for a certain study because they play video games. Such expectancy effects can also occur in video game training studies in which participants are told that such training should lead to improved performance in various cognitive tasks (e.g., 28 ).

To assess the effects of this possible bias directly, we devised a study in which a group of gamers and a group of non-gamers were covertly recruited and were asked to perform certain cognitive/motor tasks either before or after answering a video-games questionnaire. Covert recruitment can be accomplished, for example, by inserting the questions regarding gaming habits within various unrelated questions (e.g., questions about religious beliefs and preferred temperatures) 17 . However, it is even better to avoid asking such questions at all. In our study, we used an online participant recruitment platform that allowed us to recruit gamers and non-gamers without asking any preliminary questions.

Therefore, the purpose of the current study was to examine whether asking participants about their gaming experience prior to participation in the study affects their performance. We hypothesized that (a) asking gamers about their gaming experience before the study will lead to better performance in reaction time (RT)-based tasks compared to asking the same questions after the study; (b) asking non-gamers about their gaming experience before the study compared to after the study will not affect their performance in RT-based tasks; and (c) there will be no differences between gamers and non-gamers in a digit-span memory task.

The second hypothesis requires an explanation. First, although response expectancies and suggestions can be both positive and negative, there are relatively little data regarding these effects in simple cognitive-motor tasks. In addition, the few studies that examined these effects on motor performance showed contrasting results. While Ziv et al. 21 showed positive, not negative, effects in a golf-putting task, Fillmore and Vogel-Sprott 29 reported both negative and positive changes in the performance of a pursuit-rotor task corresponding to suggestions of negative or positive effects of caffeine (when the participants actually drank a decaffeinated drink). In addition, Harrell and Juliano 30 showed the opposite effect of placebo caffeine in a finger-tapping task (improved performance when told caffeine impairs performance, reduced performance when told caffeine enhances performance). Finally, we did not know whether non-gamers believe that gaming is related to performance of such tasks, or if such beliefs are necessary for the effect to occur. Therefore, we adopted a cautious approach in developing this hypothesis. The third hypothesis is based on the view that, as compared with attention and information processing capacity, working memory capacity is expected to be affected to a lesser extent by suggestions or response expectancy (in the context of the beliefs of gamers). Indeed, Boot et al. 27 have reported that expectancies that playing video games will improve memory stores are relatively low.

We selected RT-based tasks because these tasks are expected to produce better processing speeds, attentional control, and visuomotor transformation, which appear to be more elevated in gamers than non-gamers. Working memory, on the other hand, may be positively affected by gaming to a lesser extent, albeit some improvement might be expected as the memory network and the attentional network share overlapping neural pathways (e.g., dorsal attentional pathways 31 .

Pre-registration and raw data repository

The study’s main questions and hypotheses, experimental conditions and groups, and dependent variables, as well as the handling of outliers and data exclusion, sample size, and statistical analyses, were all pre-registered on aspredicted.org and can be accessed online ( https://aspredicted.org/wp53f.pdf ). Any deviations from the pre-registration are noted. Analyses that were not pre-registered are reported in the Exploratory Analyses sub-section of the Results section. We removed one hypothesis listed in our pre-registration (i.e., that gamers who play first-person shooter games will have faster reaction times but will make a similar number of errors in RT-based tasks, since the sample size of first-person shooter players playing over 10 h per week was too small ( n  = 14) compared with those playing less than three hours per week ( n  = 119). The raw dataset used for the statistical analyses can be accessed online as well on OSF ( https://osf.io/s2vcz/?view_only=88caada978f141f787684cc2e63b7673 ).

The results are reported for each of the experimental tasks separately. The RT data for all three RT-based tasks are presented in Fig.  1 .

Mean RTs for the choice-RT task ( a ), the Simon task ( b ), and the alternate task-switching task ( c ), for the four experimental groups (figure created using R software). NG-B Non-gamers, questionnaire at the beginning, NG-E Non-gamers, questionnaire at the end, G-B Gamers, questionnaire at the beginning, G-E Gamers, questionnaire at the end. Note that the y-axis limits differ between graphs. Errors bars represent standard error. Small light-gray circles represent individual participants.

Choice-RT task

A two-way ANOVA [Group × Questionnaire Timing (before or after the tasks)] revealed no group effect, F (1, 127) = 0.47, p  = 0.49, \(\eta_{p}^{2}\)  = 0.00 and no Questionnaire Timing effect, F (1, 127) = 1.68, p  = 0.20, \(\eta_{p}^{2}\)  = 0.01. In addition, no significant interaction was found, F (1, 127) = 0.59, p  = 0.44, \(\eta_{p}^{2}\)  = 0.01. The mean choice RT was 367.72 ± 62.73 ms.

Mean correct responses

There were no differences between questionnaire delivery time (before or after the task) in gamers (Mann–Whitney U = 638.00, p  = 0.74; mean: 23.73 ± 0.41) and non-gamers (Mann–Whitney U = 358.50, p  = 0.26; mean: 23.71 ± 0.53). There were also no differences in total correct responses between gamers and non-gamers (Mann–Whitney U = 2172, p  = 0.76).

A two-way ANOVA [Group × Questionnaire Timing (before or after the tasks)] revealed a significant interaction, F (1, 127) = 7.30, p  = 0.01, \(\eta_{p}^{2}\)  = 0.05, as can be seen in Fig.  2 . The mean RT of the non-gamers was higher when the questionnaire was delivered before performing the task (515.40 ± 70.26 ms) compared with after the task (479.51 ± 47.57 ms; Cohen’s d  = 0.61). In contrast, the mean RT of gamers was lower when the questionnaire was delivered before the task (487.26 ± 57.75 ms) compared with after the task (510.98 ± 70.57 ms, Cohen’s d  = 0.37). There was no Group effect, F (1, 127) = 0.02, p  = 0.88, \(\eta_{p}^{2}\)  = 0.00, and no Questionnaire Timing effect, F (1, 127) = 0.30, p  = 0.58, \(\eta_{p}^{2}\)  = 0.00.

The interaction between group (gamers vs. non-gamers) and the questionnaire delivery time (before vs. after the task) of the mean RT during the Simon task (error bars represent 95% confidence intervals) (figure created using Microsoft Excel).

There were no differences in the questionnaire delivery time (before or after the task) between gamers (Mann–Whitney U = 608.00, p  = 0.66; mean: 22.60 ± 1.32) and non-gamers (Mann–Whitney U = 404.50, p  = 0.81; mean: 22.35 ± 1.41). There were also no differences in total correct responses between gamers and non-gamers (Mann–Whitney U = 2309.50, p  = 0.29).

Alternate task-switching task

A two-way ANOVA [Group × Questionnaire Timing (before or after tasks)] revealed no group effect, F (1, 123) = 0.77, p  = 0.38, \(\eta_{p}^{2}\)  = 0.01, no Questionnaire Timing effect, F (1, 123) = 0.53, p  = 0.47, \(\eta_{p}^{2}\)  = 0.00, and no interaction, F (1, 123) = 3.12, p  = 0.08, \(\eta_{p}^{2}\)  = 0.03. The mean RT for this task was 967.43 ± 184.01 ms.

There were no differences in the questionnaire delivery time (before or after the task) between gamers (Mann–Whitney U = 495.50, p  = 0.83; mean: 21.93 ± 2.01) and non-gamers (Mann–Whitney U = 361.00, p  = 0.79; mean: 21.42 ± 2.73). There were also no differences in total correct responses between gamers and non-gamers (Mann–Whitney U = 1758.00, p  = 0.77).

Digit span task

A two-way ANOVA [Group × Questionnaire Timing (before or after the tasks)] revealed no group effect, F (1, 127) = 2.32, p  = 0.13, \(\eta_{p}^{2}\)  = 0.02, no Questionnaire Timing effect, F (1, 127) = 0.22, p  = 0.64, \(\eta_{p}^{2}\)  = 0.00, and no interaction, F (1, 127) = 0.70, p  = 0.70, \(\eta_{p}^{2}\)  = 0.00. The mean correct response was 5.88 ± 1.81.

Mean highest number of digits before the first error

A two-way ANOVA [Group × Questionnaire Timing (before or after the tasks)] revealed no group effect, F (1, 127) = 1.32, p  = 0.25, \(\eta_{p}^{2}\)  = 0.0 and no Questionnaire Timing effect, F (1, 127) = 0.63, p  = 0.43, \(\eta_{p}^{2}\)  = 0.01. In addition, no significant interaction was found, F (1, 127) = 0.64, p  = 0.43, \(\eta_{p}^{2}\)  = 0.01. The mean highest number of digits before the first error was 6.69 ± 1.93.

Stepwise multiple regression and LASSO regression analyses

We entered the following independent variables to the regression equations: hours per week playing video games; playing first-person shooter games, strategy games, and role-playing games; years playing video games; beliefs regarding a connection between playing video games and task performance; and, knowledge of media reports on a connection between playing video games and task performance. Table 1 presents the findings for both the stepwise and LASSO regressions. As can be seen in Table 1 , regardless of the type of regression used, the models led to a low R 2 of under 0.06.

Exploratory analyses

Gender differences.

We did not expect that gender differences would affect our results, and therefore we did not include an analysis such differences in our preregistration. However, we wanted to make sure that this assumption was indeed the case, and thus performed independent t-tests for all dependent variables in order to assess differences between males and females. Our assumption was correct, as all of these tests were statistically insignificant with low effect sizes (see Table 2 ).

Alternate task switching task including all data

For the alternate task-switching task, we decided before the study to remove all RT values over 1500 ms. However, because there was no time limit to the stimulus, durations of over 1500 ms may have been valid as well. Therefore, we ran the two-way ANOVA [Group × Questionnaire Timing (before or after the tasks)] without excluding values over 1500 ms. This analysis revealed a significant interaction, F (1, 127) = 4.35, p  = 0.04, \(\eta_{p}^{2}\)  = 0.03, as can be observed in Fig.  3 . A post-hoc analysis showed that the non-gamers reduced their RT from 1135.45 ± 605.75 ms when the questionnaire was completed before performing the tasks to 911.01 ± 161.57 ms when the questionnaire was answered after performing the tasks (Cohen’s d  = 0.51). In contrast, the gamers had similar RTs in the beginning questionnaire (1007.94 ± 272.63 ms) and the end questionnaire (1054.83 ± 332.94 ms). There was neither a Group effect, F (1, 127) = 0.02, p  = 0.90, \(\eta_{p}^{2}\)  = 0.00, nor a Questionnaire Timing effect, F (1, 127) = 1.86, p  = 0.18, \(\eta_{p}^{2}\)  = 0.01.

The interaction between group (gamers vs. non-gamers) and the questionnaire delivery time (before vs. after the task) of the mean RT during the alternate task-switching task (error bars represent 95% confidence intervals) (figure created using Microsoft Excel).

Beliefs about a connection between playing video games and the ability to perform cognitive-motor tasks

An independent t-test revealed no differences between the beliefs of gamers and non-gamers regarding the connection between playing video games and the ability to perform cognitive-motor tasks, t (128) = 1.44, p  = 0.15, Cohen’s d  = 0.25. The mean response (on a scale of 1–10) was 8.05 ± 1.93 and 7.60 ± 1.64, for gamers and non-gamers, respectively. The median for both groups was eight.

Awareness of media reports on the benefits of video games

In non-gamers (< 3 h of play per week), 25 participants reported that they were aware of media reports discussing the benefits of video games in regards to the performance of cognitive-motor tasks, and 32 participants reported that they were not aware of such reports. In gamers (> 10 h of play per week), 43 reported that they were aware and 30 reported that they were not aware of these reports. However, a χ 2 test revealed no differences between the groups, χ 2 (1) = 2.90, p  = 0.09, φ = 0.15.

Bayesian analyses of null results

In null-hypothesis significance testing, a lack of significance does not allow us to demonstrate the probability of the null hypothesis itself 32 . Therefore, we used Bayesian statistics to assess the probability of the null hypotheses for the dependent variables that did not produce significant main effects or interactions. The Bayes factors supporting the null hypothesis (BF 01 ) compared to the possible combinations of main effects and interactions are presented in Table 3 .

We also analyzed the Bayes factors to exclude the interaction effect alone. This analysis showed that the Bayes factors for excluding the interaction were 16.51, 6.3, 15.70, and 14.94 for the choice-RT task RT, the alternate task switching RT, the correct response, and the highest number of digits before first error in the digit-span task, respectively.

The current study examined whether cognitive/motor task performance in gamers and non-gamers was affected by whether they completed a video-games questionnaire prior to performing those tasks. We had three hypotheses. First, we expected that asking gamers about their gaming experience before the study would lead to better performance in RT-based tasks compared with asking the same questions after the study. This hypothesis was partially supported. Gamers had faster RTs when they performed the Simon task (but not the other two RT-based tasks) after answering the video-games questionnaire compared with before answering the questionnaire. The Bayes factors associated with these tasks mostly suggested that the data are more likely to be accurate under the null hypothesis (except for inconclusive findings regarding the models with the separate main effects) (see Table 3 ). Second, we hypothesized that this effect would not be found in non-gamers. This hypothesis was not supported by our data. In the Simon task, non-gamers had faster RTs when performing the task before completing the questionnaire compared with after answering the questionnaire. In addition, our exploratory analysis showed that this also occurred in the alternate task-switching task. These results suggest that answering a video-games questionnaire before performing such tasks may have an adverse effect on performance in non-gamers. Finally, our hypothesis that similar effects would not be found for the digit-span memory task was supported by the data of the current experiment.

The finding that the timing of questionnaire delivery affects both gamers and non-gamers can explain, at least in part, the observed differences between groups in previous studies in which all participants answered a video-games questionnaire prior to their participation in the study. According to the results obtained in our study, not only do gamers perform better after answering questions about their gaming habits, but non-gamers perform worse after answering such questions. In fact, our data suggest that it is possible that the effect on non-gamers is greater than the opposite effect on gamers, since answering questions about video-games habits negatively affected the non-gamers in two tasks—the Simon task and the alternate task-switching task (although this is an exploratory finding), whereas this only positively affected gamers in the Simon task. Moreover, there were no differences between groups in the participants' responses to the question “Do you think there is a connection between playing video games and the ability to perform cognitive-motor tasks, such as the ones you just performed?”. In both groups, the mean response was ~ 7.5–8 (on a scale of 1—not at all to 10—very much so). In addition, there were no differences between groups in the number of participants who were familiar with media reports or research regarding the benefits of playing video games in relation to the ability to perform cognitive-motor tasks. Both gamers and non-gamers appeared to believe that playing video games can enhance performances of cognitive-motor tasks, and therefore it is possible that the video-games questionnaire caused gamers to perform better and caused non-gamers to perform worse. Therefore, the results of our study support the concept of psychological suggestion as well as the response expectancy theory.

Similar effects can be found in the literature on stereotypes and test performance. These effects suggests that individuals show suboptimal task performance when they know they are "expected to be" weak at that task 33 . The belief that one is supposed to be weak at a task can be due to prior experience, common knowledge, media reports, or a direct manipulation. All those causes are forms of psychological suggestion—a phenomenon in which what individuals are made to believe, think, or feel can influence their cognition and patterns of behavior positively or negatively 19 . For example, Beilock et al. 34 (Exp. 1) randomly assigned 40 male expert golfers to a stereotype-threatened group or a control group and asked them to putt from three distances. The participants in the stereotype group were told that women tend to perform these putting tasks better than men and that these differences are supported by statistics from the Professional Golf Association and the Ladies Professional Golf Association. While there were no differences in putting performances between groups in a pre-test, golfers in the stereotype-threatened group performed worse in a post-test compared with their counterparts in the control group.

Psychological suggestion can affect, among other factors, the motivation of the participants. Therefore, it is possible that once gamers realized that the study is about gaming, their motivation to perform better was directly elevated. This elevated motivation may have led to behavioral changes that led to the improved performance in gamers when they performed the tasks after answering the video games questionnaire. Regardless of the reason for the elevated motivation, it has been shown that such motivation can increase focus on the task at hand, and therefore leading to improved performance and learning 35 . While the effects of psychological suggestion and/or motivation are plausible mechanisms for improved performance, the actual underlying mechanism are still to be examined directly in additional studies.

In the current study, the effects of the questionnaire timing on performance in both gamers and non-gamers were found only in some of the performed tasks. In the digit-span task, despite previous studies showing improved working memory performance in gamers (e.g., N-back task 15 ), we did not expect any differences between gamers and non-gamers in memorizing digits, as this is not usually a beneficial attribute in video games. However, it is possible that working memory may still benefit from video games, as this cognitive function could be facilitated by improvement in attention or processing speed since the brain networks mediating memory functions and executive function appear to overlap 36 . There were no differences between groups and conditions in the choice-RT task as well. We suspect that this is because the task was too easy, and therefore was not sensitive enough to account for the possible priming effect of the questionnaire. The Simon task was of moderate difficulty and presented the greatest effect of questionnaire timing. Finally, the alternate task-switching task, the most challenging of the three RT-based tasks, showed a questionnaire timing effect only for the non-gamers in the exploratory post-hoc analysis. It is possible that task difficulty serves as a moderator for such stereotype effects 37 , 38 . Barber et al. 37 , for example, showed that negative age-based stereotyping negatively affected the gait of older adults in a difficult gait task but not in a simple gait task. Additional studies that examine the effects of playing video games on generic or practical cognitive-motor performance should address task difficulty as a possible moderator.

Finally, it is possible that our inconsistent and relatively modest findings are because the priming effect itself was subtle as it required participants to indirectly realize that the study is about video gaming (when the questionnaire was introduced prior to the performance of the tasks). It is possible that larger and more consistent effect sizes would have been found if participants were overtly recruited for this study. However, it was our purpose to examine the effects of subtle and indirect cues on performance, and thus we chose covert recruitment and indirect cues. It should also be noted that in psychological research, as Funder and Ozer 39 suggested, “small effect sizes from large- N studies are the most likely to reflect the true state of nature” (p. 164), and that “Smaller effect sizes are not merely worth taking seriously. They are also more believable” (p. 166). The results of our study, taking into account variability in human behavior, the large sample size, and the relatively subtle intervention, are in line with the abovementioned statements.

Strengths of the current study

The primary strength of the current study is the covert recruitment of participants. The online participant recruitment platform we used ( www.prolific.co ) allows the researcher to employ many variables to exclude or include participants based on preliminary answers they supplied when they registered on the website (e.g., demographics, health, hobbies). Furthermore, the researcher can exclude participants who had participated in previous studies completed by the researcher. Once a study is published on that website, participants receive a message that they are eligible to participate, but they do not know the criteria for participation. Hence, the participants in the current study did not know that this was a study that examined the relationships between playing video games and cognitive-motor performance, nor did they participate in any of our previous studies in which similar tasks were used. This is a major methodological issue in video-game research 18 , and therefore we believe that our methodology allowed us to provide meaningful answers to our research questions.

Another strength of the current study is the large sample size and ample statistical power. Many of the studies that compared gamers to non-gamers used relatively small sample sizes [e.g., 36 participants 17 , 21 participants 13 , 35 participants 14 ]. In the current study, we were able to recruit 131 participants, who provided us with at least 80% of statistical power. Finally, conducting the study online ensured that it was double-blinded. In addition to the covert recruitment, the researchers in such an online study do not have any contact with the participants, and thus cannot influence their performance in any way.

One final strength is the computerized randomization to experimental groups. This randomization is performed without the knowledge or the intervention of the researchers, and therefore prevents bias in assigning participants to groups.

Limitations of the current study

One limitation of the current study is that the sample size did not include enough participants who were first-person shooter players or action video-game players. In previous studies, it was mainly playing action video games that was associated with improved cognitive-motor performance. However, we would have been required to implement an overt recruitment process of participants to specifically recruit those participants, and that would have prevented us from answering our research questions.

Second, in an online study, variables such as type and size of keyboard, screen size, participants’ motivation, and environmental conditions cannot be controlled. However, all the participants used a computer to complete the experimental tasks and did not use a smartphone or a tablet. In addition, Woods et al. 40 suggested that large sample sizes in online studies can make up for the relative lack of control.

Third, it is possible that self-selection bias led participants who received an invitation to participate in a study on RT and memory. Such self-selection may create a sample that consider themselves as proficient at such tasks. However, if this was the case, our findings may suggest that both negative and positive priming can lead to differences in performance even in a biased sample of participants who perform such tasks well.

Finally, in order to maintain covert recruitment, we could not ask detailed questions about videogame playing habits prior to the study. Therefore, we have no knowledge of the distribution of playing time over the week. It is possible that some of the participants play mostly on weekends (similar to massed practice) while others distribute their playing time more evenly throughout the week (similar to distributed practice). Massed and distributed practice may affect learning differently (e.g., 41 ), and thus this can be an important moderating variable that should be examined in additional studies on gaming.

The results of the current study suggest that asking participants about their gaming experience before they perform cognitive-motor tasks can either positively or negatively affect their performance, depending on whether they are gamers or non-gamers. In addition, task difficulty is a probable moderator of these effects. The results obtained in our study have methodological implications for future research that examines the differences between gamers and non-gamers, and for research in video-game training aimed at facilitating cognitive-motor performance. Finally, these findings support the concept of psychological suggestion and the response expectancy theory.

Participants

We used G*Power 42 to perform a priori power analysis for our two-way analysis of variance (ANOVA) [Group (gamers/non-gamers) × Questionnaire Delivery Time (before/after the performance of tasks comparing)]. To the best of our knowledge, no previous studies have directly examined the effects of suggestion or response expectancy on simple cognitive/motor tasks in gamers and non-gamers. However, there are studies from the motor learning literature showing that enhanced expectancies of success which are caused by providing easy criteria of success (e.g., 21 , 43 ) or visual illusions that lead to a perceived larger target 44 can lead to improved performance and learning, with effect sizes varying from moderate (Cohen's d  = 0.54; 44 to large (Cohen’s d  = 0.8 21 , calculated from the reported \(\eta_{p}^{2}\)  = 0.14). Because these studies had a small sample size ( N between 36 and 45), effect sizes could have been overestimated (the Winner’s curse 45 . Therefore, in our study we took a more cautious approach and selected a moderate effect size (Cohen’s d  = 0.5/Cohen’s f  = 0.25) for our power analysis. We entered this effect size into the power analysis with the following parameters: alpha (two-sided) = 0.05, power = 0.80, allocation ratio 1:1. The results of the power analysis suggested that 128 participants are required to detect differences between groups or to find an interaction with 80% power.

Therefore, our goal was to recruit 128 participants between the ages of 18–35 years and to randomize them to four groups of 32 participants each: (a) gamers who answered a video-games questionnaire at the beginning of the study (G-B), (2) gamers who answered a video-games questionnaire at the end of the study (G-E), (c) non-gamers who answered a video-games questionnaire at the beginning of the study (NG-B), and (d) non-gamers who answered a video-games questionnaire at the end of the study (NG-E).

We recruited participants through Prolific ( www.prolific.co )—an online participant database platform that allows the researcher to use various exclusion and inclusion criteria (based on information individuals provide in their profile) and allows the participants to participate in an online study from their own computer.

In such an online study, we cannot know if the participants who begin the study will complete it. Therefore, we recruited 160 participants in two projects. In one project we recruited 80 participants who, according to their information on Prolific, play video games more than 13 h per week (a more stringent criterion than our pre-registered requirement of > 10 h per week), and in another project we recruited 80 participants who play video games less than three hours per week. We were aware of the possibility that the information individuals entered when they created an account on Prolific may not be current, and indeed, out of 159 participants, only 110 matched our pre-registered criteria: 70 participants who reported playing over 10 h per week and 40 participants who reported playing fewer than three hours per week. Therefore, we added 28 participants in another project in order to increase the number of non-gamers. This addition led to a total of 187 participants who completed the study. Out of those, 131 participants (27 females, one participant who did not report gender, mean age = 23.51 ± 4.33 years) matched our gamers and non-gamers inclusion criteria, and they are analyzed in the current study: 34 participants in the G-B group (one female, one unreported gender), 39 participants in the G-E group (seven females), 28 participants in the NG-B group (10 females), and 30 participants in the NG-E group (nine females). It is important to note that the participants were not recruited based on information entered when signing up to our specific study. The participants on Prolific.ac answer general questions regarding demographics, hobbies, health, etc. when joining the database. Based on these data, we were able to filter participants who filled in specific responses. However, the participants did not know why they received an invitation to participate. This allowed us to covertly recruit participants for the study, without them knowing that the study had anything to do with gaming.

Randomization to groups was performed automatically by the web-based platform. Importantly, the prospective participants in Prolific did not know that they were recruited based on their video game playing habits. The participants also reported being fluent in English and were paid 2.5 British Pounds for their participation. The study was approved by the Ethics Committee of The Academic College at Wingate (approval # 303), and all participants filled out an electronic informed consent form on the study’s website prior to their participation. In addition, all methods were performed in accordance with the relevant guidelines and regulations.

Participants were asked to perform the following four tasks.

In this task, the participants pressed as quickly as possible the “j” key if the word “right” appeared on the right side and the “f” key if the word “left” appeared on the left side of a centralized cross on the computer screen. The words “right” or “left” were presented for 900 ms, followed by 600 ms during which only the centralized cross was displayed 46 , 47 .

This task is a variation of the choice-RT task. The words “right” or “left” could be displayed on either the right or the left side of the cross. The participants were required to press the “j” key if they saw the word “right” (even if it appeared on the left side of the cross) and to press the “f” key if they saw the word “left” (even if it appeared on the right side of the cross) 48 , 49 . Similar to the choice-RT task, the words “right” or “left” were presented for 900 ms, followed by 600 ms during which only the centralized cross was displayed.

In this task, a square or a rectangle in either a blue or green color appeared at the top or at the bottom of the screen. If a shape appeared at the top of the screen, the participants were asked to press the “f” key if the shape was blue and the “j” key if the shape was green (regardless of whether it was a square or a rectangle). However, if the shape appeared at the bottom of the screen, participants were asked to press the “f” key if the shape was a square and the “j” key if the shape was a rectangle (regardless of the color). In this task, each stimulus was presented for an unlimited duration until a key press was recorded. The above-mentioned three RT tasks are presented in Fig.  4 .

An example of the choice-RT task ( a ), the Simon task ( b ), and the alternate task-switching task ( c , d ) (figure created using Microsoft PowerPoint).

Digit-span memory task

In this task, participants were asked to remember the digits that were presented to them on the screen. The first number included three digits and each consecutive number had one additional digit up to 11 digits. Digits were shown one at a time for a period of one second each. Digits were randomly selected using a random number sampling of the digits 0 to 9 without replacement up to 10 digits. For the 11-digit number an additional (duplicate) digit was randomly added to the 10 digits. All digit randomizations were conducted in R 50 . If the random sample included a series of ascending or descending numbers (e.g., 1, 3, 5; 7, 5, 3; 3, 6, 9; 8, 6, 4) the series was deleted, and another random sample was generated. A similar approach to the presentation of this task has been used in previous experiments (e.g., 51 ).

This study was conducted online using a web-based platform ( www.gorilla.sc 52 ). This platform is integrated with the participants' database ( www.prolific.co ) and the participants perform the experiment on their own computer. Web-based studies have been shown to provide accurate measures of RT that are similar to those attained in lab-based studies (e.g., 53 , 54 ).

After the completion of an electronic informed consent form that was presented at the beginning of the study, participants in the G-B and NG-B groups answered a questionnaire regarding their video game playing habits. Specifically, they were asked how many hours they spend playing video games per week in general, and how many hours they specifically spend playing first-person shooter games, strategy games, or role-playing games. The participants chose one answer from a list (I do not play video games, 1–3 h, 4–6 h, 7–9 h, 10–12 h, 13 h per week or more). In addition, they were asked how many years they have been playing video games (< 1 year, 1–2 years, 3–4 years, 5–6 years, > 7 years). Participants in the G-E and NG-E groups answered a neutral questionnaire with the same number of questions (e.g., how many hours per week do you watch TV, how long is your commute to work, how many books have you read in the last year). After answering the questionnaires, the participants were familiarized with the four tasks in a counterbalanced order. Each participant performed one block of eight trials of the three RT-based tasks (i.e., choice-RT, Simon task, alternate task-switching task) and one block of four trials of the digit span task that consisted of remembering one digit, two digits, three digits, and four digits.

After completing the familiarization stage, the main part of the study began. For the three RT-based tasks, the participants performed two blocks of 24 trials each. For the digit span task, they performed two blocks starting with three digits and ending with 11 digits (in increments of one). The four tasks were presented in a counterbalanced order between participants. After completing the four tasks, participants in the G-E and NG-E answered the same video game playing habits questionnaire. In addition, all four groups answered the following two questions: (a) “Do you think there is a connection between playing video games and the ability to perform cognitive-motor tasks, such as the ones you just performed?” (answers on a scale of 1—not at all, to 10—very much so), and (b) “Are you familiar with media reports or research regarding the benefits of playing video games in relation to the ability to perform cognitive-motor tasks?” (yes or no). These two questions were presented to all groups at the end of the study, because if they were presented at the beginning of the study they could have explicitly exposed the study’s purpose 18 . In all of the questions presented throughout the experiment, the option to answer, “Prefer not to say” was included as well.

Data exclusion

During pre-registration, we decided that for the choice-RT and the Simon task, RT values of over 1000 ms would be removed because they represented RTs that were longer than the presentation of the stimulus (900 ms). However, this did not occur. For the alternate task-switching task, based on our pre-registration, RT values of over 1500 ms were removed. This resulted in a removal of 17 blocks (out of a total of 262 blocks, 6.5% of the blocks). If there were more than 50% incorrect key presses in a block of 24 trials, the block was deleted, as this most likely shows that the participant did not understand the task. This happened only three times in the Simon task (1.1% of blocks), and eight times in the alternate task-switching task (3.1% of the blocks). During pre-registration, we also decided that if there were over 50% incorrect key presses in both blocks of two of the three RT-based tasks for one participant, this participant would be removed from the study. However, this did not occur with any of the participants.

Data analyses

For each of the three RT-based tasks we measured RTs (ms) and the number of correct responses. These were averaged for the two blocks of trials in each task. For the digit span task, we measured the maximum number of digits remembered before the first error and the total number of correct answers. These two variables were averaged for the two blocks of trials.

Based on skewness and kurtosis values, RTs were mostly normally distributed and were analyzed using a 2-way ANOVA [Group (gamers/non-gamers) × Timing of questionnaire (before/after tasks)]. The number of correct key presses in the three RT-based tasks was not normally distributed, and because there is no non-parametric equivalent for a two-way ANOVA we used the Mann–Whitney test to examine, for the group of gamers and non-gamers separately, the differences in correct responses between the condition in which the video-games questionnaire was completed before performing the task and the condition in which it was presented after performing the tasks. The variables measured in the digit span memory task were normally distributed and were analyzed using two-way ANOVAs like those used for the analyses of RTs.

In our pre-registration, we wanted to conduct the statistical analyses separately for each type of game played (e.g., general, first-person shooter, strategy games, role-playing). However, the separate sample sizes were too small, and therefore these analyses could not be performed. We also performed a stepwise multiple regression to examine whether video game playing habits and conceptions of the effects of video games on performance could predict RTs and correct responses in the performed tasks. For this analysis only, we used the data of all 187 participants who completed the study. Because stepwise regression can lead to overfitting and over-estimation of models, we also conducted LASSO (Least Absolute Shrinkage and Selection Operator) regression—an accepted alternative to stepwise regression that deals with such problems 55 . To better understand the non-significant effects or interactions, we used Bayesian statistics in our exploratory analyses.

Statistical analyses were conducted using the SPSS version 25 (SPSS Statistics, IBM, USA), R 50 for LASSO regression, and JASP 56 for all Bayesian analyses. Bonferroni post-hoc analyses and 95% confidence intervals were used for post-hoc testing when appropriate, and alpha was set at 0.05.

Data availability

The raw data for this study is available in a raw data repository: https://osf.io/s2vcz/?view_only=88caada978f141f787684cc2e63b7673 . The pre-registration is available here: https://aspredicted.org/wp53f.pdf .

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Ziv, G., Lidor, R. & Levin, O. Reaction time and working memory in gamers and non-gamers. Sci Rep 12 , 6798 (2022). https://doi.org/10.1038/s41598-022-10986-3

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The Changing Face of Video Games and Video Gamers: Future Directions in the Scientific Study of Video Game Play and Cognitive Performance

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Research into the perceptual, attentional, and cognitive benefits of playing video games has exploded over the past several decades. However, the methodologies in use today are becoming outdated, as both video games and the gamers themselves are constantly evolving. The purpose of this commentary is to highlight some of the ongoing changes that are occurring in the video game industry, as well as to discuss how these changes may affect research into the effects of gaming on perception, attention, and cognition going forward. The commentary focuses on two main areas: (1) the ways in which video games themselves have changed since the early 2000s, including the rise of various “hybrid” genres, the emergence of distinct new genres, and the increasing push toward online/open-world games, and (2) how video game players have changed since the early 2000s, including shifts in demographics, the decreasing specialization of gamers, and the fact that gamers today now have a long gaming history. In all cases, we discuss possible changes in the methods used to study the impact of video games on cognitive performance that these shifts in the gaming landscape necessitate.

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Dale, G., Shawn Green, C. The Changing Face of Video Games and Video Gamers: Future Directions in the Scientific Study of Video Game Play and Cognitive Performance. J Cogn Enhanc 1 , 280–294 (2017). https://doi.org/10.1007/s41465-017-0015-6

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Community, feedback, bug reporting, complete guide to video game market research.

According to Newzoo, the video game market hit $175.8bn in 2021 – more than global movie and music industries combined.

This article covers the key areas of video game market research. It gives a general industry overview. It covers types of research and the main sources of data.

This article was written by the Video Game Insights team – a free video game market research and analytics platform.

What is the games industry?

Games industry is a huge and fast growing sector with a widening ecosystem. It’s no longer just about development and distribution of video games.

eSports has become a billion dollar industry on its own and bound to grow way past that. Professional teams have superstar statuses much like top footballers or tennis players.

Video game streaming has become incredebly popular for the generation Z. Limelight suggests that the 18-25 year olds now spend more time watching people play video games than watching traditional sports.

Cloud gaming platforms are the next battleground for the likes of Microsoft, Amazon and Google. That’s a whole other industry section that hardly existed a couple of years ago.

NFTs and Metaverse are recent buzzwords that surround the games industry. In reality, collectables and social gathering places have been in games for decades. These are just new fancy words (and somewhat new technologies) used on old concepts. Time will tell how imprtant part these will play.

How to think about games market research?

For the purpose of this article, we’ll cover traditional video games market, not including streaming, eSports etc.

As with any market, the size of the market only tells so much. Segmentation of that size and trends is what provides most value.

Traditional segmentations also work in games industry:

• Revenue distribution within the value chain
• Size of the companies
• Monetisation model

On top of that, more granular game industry specific segmentations apply:

• Genre of the game
• Type of player
• Other micro segments like art style, multiplayer vs single player, game themes etc

We’ll briefly cover each of the segmentation methods below.

How to segment the video game industry

1. games industry size by geography.

Video games globally are ever more dominated by Asia as a region. China is now the #1 country by revenue with almost £41bn according to Newzoo. That’s followed by USA, Japan, South Korea and only then Germany, UK and France.

Looking at future growth, Asia is bound to continue growing its importance in video games, driven by social acceptance of gaming as a pastime as well as large middle class population.

2. Revenue distribution within the gaming ecosystem

Developers don’t get all of the $60 from selling a copy of the new game. In fact, often they don’t even get half of it.

In console, PC and mobile games alike, the important part of the valeu chain is the store platform – from Steam to Google Play to Playstation Store. They typically take 12-30% of the revenues, mostly on the higher end.

Publishers are another important, but somewhat declining part of the ecosystem. As the games are more and more sold digitally and the store platforms do a lot of the marketing, publisher’s role is diminishing. However, they still typically take 40-70% of the game revenue.

Note that this is an average range. This typically includes publishers fully paying for the game and marketing costs from their own pockets. There are deals where this is not the case and their cut is much smaller.

That leaves the developers with significantly less than the original revenue estimated from selling $60 games. This varies a lot depending on the developer size.

Some developers also publish their own games, saving that loss of revenue.

3. Size of the video game companies

A typical classification of games is whether they were published by a AAA publisher or by indie developers. AA has become a new, also acceptable classification to categorise the inbetweeners.

AAA games – Large budget ($100m+) games with large marketing budgets (another $100m+).

AA games – Produced by professional mid-sized studios, but lack the budget that goes to 100s of millions.

Indie games – Produced as anything from 1 person hobby project to a team of 100 people self-publishing their first big game. Broad definition of the smaller developers and new firms not owned by the industry giants.

4. Segementing by Platform

As mentioned above, games can be made for PC, consoles or mobile. Handheld is another platform that’s recently made a comeback in the face of Nintendo Switch. Arcade used to be a popular, but now mostly forgotten platform for games to be realsed for. VR is the newcomer on the block that hasn’t got a substantial share yet.

The platforms vary a lot by types of games developed for them and will be covered under 6. point.

5. Monetisation models

For the last 20 years, large AAA games have cost $60 to buy. People keep to own a physical or digital copy of the game and that’s it. However, more than ever before, other monetisation methods have started to dominate.

Buy to own – One time purchase of the game. Subscription – First popularised by the likes of World of Warcraft – people pay a monthly subscription to continue playing (mostly online). Free to Play (FtP) – Popularised by mobile games. Free to download, monetised in other ways. In App Purchases – One of the key ways FtP games monetise Ads – Showing ads to players is the second way some of the FtP, especially hypercasual, games monetise. Battle-passes – Similar to subscription, but people buy a number of plays instead. For example, they can buy a bundle of 10 matches – allowing them to enter online matches 10 times. Less popular as a monetisation metric. Cloud platforms – Platforms like Game Pass can offer developers a wide range of monetisation options from money upfront to pay per play. On top of that, in app purchases are viable complementary revenue source.

6. Genre of the game

Genres can vary from the general 6-8 inclusing Action, Adventure etc to tens of subgenres like rougelike and grand strategy. Video Game Insights’s Steam Analytics platform allows you to see and filter by a variety of genres and sub-genres.

7. Type of player

There are actually several ways to categorise a player, but ultimately it all comes down to spend. For games relying on in app purchases, only a handful of players can make up majority of their revenue.

According to Swrve, only c. 2.2% of the players ever pay anything and up to 46% of the revenue comes from 0.22% of the player base. Clearly, identifying the top payers is key.

Types of players are also important for other business models – keeping fans engaged increases the likelyhood of them purchasing sequals or other games developed by the same developer.

Player analytics has reached huge complexity and there are whole analytics teams dealing with terabytes of player data to figure out how to best monetise each player type.

8. Other segments

There are other ways to segment the games that are less to do with market research and more about product management and competitor research.

It’s important to know which other games fit the same theme, art style etc. This allows game developers to understand what works and doesn’t in their space as well as how to better estimate revenue potentials.

Where to find video game market trends, data and analytics?

Mobile games industry is spoiled with choice when it comes to data. Platforms like Appannie are expensive, but provide extensive and high quality data.

It’s a lot harder to access high quality information on PC and consule markets. Below summarises some of the options available.

Newzoo is the leading global games industry data platform. It’s expensive, but great for market size, eSports and much more. Widely used by large game studios.

Video Game Insights

Video Game Insights was created to provide a mostly free and generally cheaper high quality alternative to existing solutions.

Steam Analytics platform allows indie developers, enthusiasts and large studios alike to quickly understand how the Steam game market is evolving.

Top charts show which games are the best selling, most played or have the most hype around the upcoming launch.

It is excellent for market and competitor research. It is also a great tool for quick back of the envelope revenue estimates for a game idea.

Steamspy has long been the go to free / cheap source of PC games data. It offers a large amount of data on Steam games, but doesn’t package it into a convenient format for market / competitor analysis.

Other video game data sources

There are other video game industry data providers out there from GSD to SteamDB, each with their own strengths and weakenesses.

On top of that, there are some sites doing an incredible job covering Twitch straming statistics such as Twitchtracker and SullyGnome.

Final takeaways

Games industry is a huge and complex ecosystem and there’s a tool or data source out there for each specific need.

Video Game Insights covers a range of articles from tutorials on how to do market and customer analysis to tips on video game marketing and pricing.

If you found this insightful, you might want to check out more games industry trends on our free Steam Analytics platform !

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Review article, neural basis of video gaming: a systematic review.

• 1 Cognitive NeuroLab, Faculty of Health Sciences, Universitat Oberta de Catalunya, Barcelona, Spain
• 2 Laboratory for Neuropsychiatry and Neuromodulation, Massachusetts General Hospital, Boston, MA, USA

Background: Video gaming is an increasingly popular activity in contemporary society, especially among young people, and video games are increasing in popularity not only as a research tool but also as a field of study. Many studies have focused on the neural and behavioral effects of video games, providing a great deal of video game derived brain correlates in recent decades. There is a great amount of information, obtained through a myriad of methods, providing neural correlates of video games.

Objectives: We aim to understand the relationship between the use of video games and their neural correlates, taking into account the whole variety of cognitive factors that they encompass.

Methods: A systematic review was conducted using standardized search operators that included the presence of video games and neuro-imaging techniques or references to structural or functional brain changes. Separate categories were made for studies featuring Internet Gaming Disorder and studies focused on the violent content of video games.

Results: A total of 116 articles were considered for the final selection. One hundred provided functional data and 22 measured structural brain changes. One-third of the studies covered video game addiction, and 14% focused on video game related violence.

Conclusions: Despite the innate heterogeneity of the field of study, it has been possible to establish a series of links between the neural and cognitive aspects, particularly regarding attention, cognitive control, visuospatial skills, cognitive workload, and reward processing. However, many aspects could be improved. The lack of standardization in the different aspects of video game related research, such as the participants' characteristics, the features of each video game genre and the diverse study goals could contribute to discrepancies in many related studies.

Introduction

Nowadays, video gaming is a highly popular and prevalent entertainment option, its use is no longer limited to children and adolescents. Demographic data on video gaming shows that the mean age of video game players (VGPs) (31 years old, as of 2014) has been on the rise in recent decades ( Entertainment Software Association, 2014 ), and it is a common activity among young adults. Moreover, the increasing ubiquity of digital technologies, such as smart-phones and tablet computers, has exposed most of the population to entertainment software in the form of casual video games (VGs) or gamified applications. Therefore, an important segment of society, over 30% in tablet computers and 70% in smart phones, has been exposed to these technologies and can be considered now, in some form, casual gamers ( Casual Games Association, 2013 ).

It is not uncommon to hear both positive and negative health claims related to VGs in the mass media. Most of the time, these are unverified and sensationalist statements, based on “expert” opinions, but lacking evidence behind them. On the other side, as VGs become more complex (due to improvements in computer hardware), they cater to audiences other than children, appealing to older audiences, and VGs have gained prevalence as a mainstream entertainment option. Consequently, the number of people who spend hours daily playing these kinds of games is increasing.

There is interest in knowing the possible effects of long-term exposure to VGs, and whether these effects are generally positive (in the shape of cognitive, emotional, motivation, and social benefits) (e.g., Granic et al., 2014 ) or negative (exposure to graphic violence, contribution to obesity, addiction, cardio-metabolic deficiencies, etc.) (e.g., Ivarsson et al., 2013 ; Turel et al., 2016 ). Moreover, VGs possess a series of intrinsic features which make them suitable for use in experimental procedures: they seem to increase participants' motivation better than tasks traditionally used in neuropsychology (e.g., Lohse et al., 2013 ) and, in the case of purpose-made VGs, they offer a higher degree of control over the in-game variables.

For all the reasons mentioned above, VGs have recently sparked more scientific interest. The number of publications that study or use some form of gaming has been increasing, since 2005, at a constant rate of 20% per year. While during the 90's around 15 VG-related articles were published per year, in 2015 that number was over 350 (see Figure 1 ).

Figure 1. Increasing trend in VG-related articles . Since 2005, the average annual growth is around 20%. (Source: MEDLINE).

However, the concept of VG is extremely heterogeneous and within the category we find a myriad of hardly comparable genres. The behavioral effects and the neural correlates derived from the use of VGs depend both on the nature of the VG, the exposition to the game (hours of game play, age of onset, etc.) ( Kühn and Gallinat, 2014 ), and, to a large extent, the individual characteristics of each participant ( Vo et al., 2011 ).

Furthermore, due to the popularity of VG genres where graphic violence is prevalent (shooters, survival horror, fantasy), many studies have chosen to focus on this variable. Therefore, there is a reasonable amount of scientific literature devoted to the study of violent behaviors and violence desensitization as a consequence of violence in VGs (e.g., Wang et al., 2009 ; Engelhardt et al., 2011 ). Lastly, in particular since the emergence of online VG play, there are concerns about the addictive properties of VGs, akin to gambling and substance abuse, consequently making it another recurrent topic in the literature (e.g., Young, 1998 ).

For the time being, this whole body of knowledge is a complex combination of techniques, goals and results. On one hand, there are articles which study the effects of VG exposure over the nervous system and over cognition (e.g., Green and Seitz, 2015 ); it seems that there is solid evidence that exposure to certain kinds of VGs can have an influence on behavioral aspects, and therefore, we should be able to appreciate changes in the neural bases ( Bavelier et al., 2012a ). Actually, assessing the cognitive and behavioral implications of VG exposure has already been the object of study in recent systematic reviews and meta-analysis that used neuropsychological tasks to measure the influence of these games in healthy individuals. This is highly relevant since they evaluate the possible transfer effects of VG training to wider cognitive domains, providing a global perspective on how experimental and quasi-experimental designs differ in the size of the effect depending on the cognitive function ( Powers et al., 2013 ), and how aging interferes with cognitive training by means of computerized tasks ( Lampit et al., 2014 ) and VGs ( Toril et al., 2014 ; Wang et al., 2016 ). Knowledge obtained about transfer effects is very important since it allows us to establish a link between VGs and cognition, indirectly helping us understand its neural basis, which in this case acts as a bridge between them. From an applied perspective, this knowledge can be used to design more effective rehabilitation programs, especially those focusing on older populations, keeping the most useful components and reducing those which are shown to have less benefits.

On the other hand, VGs have been used as a research tool to study the nervous system. In this group of studies, it is common to find exposure to VGs as the independent variable, especially in most studies that use unmodified commercial VGs. However, it is not unusual to employ custom designed VGs, such as the widely used Space Fortress, where in-game variables can be fine-tuned to elicit certain mental processes in consonance with the research hypothesis (e.g., Smith et al., 1999 ; Anderson et al., 2011 ; Prakash et al., 2012 ; Anderson et al., 2015 ). Nevertheless, in both cases, the study of the VG exposure over the nervous system and the use of VGs as a research tool, VGs are used to obtain information about the underlying neural processes relevant to our research interest.

As yet there is no systematic review on this topic. The aim of this article is to gather all the scientific information referring to neural correlates of VGs and synthesize the most important findings. All articles mentioning functional and structural changes in the brain due to video gaming will be analyzed and information about the most relevant brain regions for each kind of study will be extracted; the main objective of many VG-related articles is not to study their neural correlates directly. Studies focusing on the addictive consequences or the effects of violence will be categorized independently.

Our final goal is to highlight the neural correlates of video gaming by making a comprehensive compilation and reviewing all relevant scientific publications that make reference to the underlying neural substrate related to VG play. This is the first effort in this direction that integrates data regarding VGs, neural correlates and cognitive functions that is not limited to action-VGs or cognitive training programs, the most frequently found research topics.

In order to structure reliably the gathered information in this systematic review, the guidelines and recommendations contained in the PRISMA statement ( Liberati et al., 2009 ) have been followed.

Eligibility Criteria

All articles which included neural correlates (both functional and structural) and included VG play in the research protocol or studied the effects of exposure to VGs were included in the review. Both experimental and correlational studies were included. No restrictions regarding publication date were applied.

Healthy participants of any age and gender were considered. Studies include both naive and experienced VG participants. Participants that reported gaming addiction or met criteria for internet gaming disorder (IGD) were also included in the review owing to the interest in observing neural correlates in these extreme cases. Other pathologies were excluded in order to avoid confounding variables.

Articles employing several methodologies were included. These can be organized into three main groups: studies where naive participants were trained in the use of a VG against a control group, studies comparing experienced players vs. non-gamers or low-experience players, and studies comparing differential characteristics of two VG or two VG genres.

The primary outcome measures were any kind of structural and functional data obtained using neuroimaging techniques including computerized tomography (CT) scan, structural magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET), single-photon emission computed tomography (SPECT), magneto encephalography (MEG), transcranial direct current stimulation (tDCS), electroencephalogram (EEG), event-related potentials (ERP), event-related spectral perturbation (ERSP), steady state visually evoked potential (SSVEP), Doppler, and near-infrared spectroscopy (NIRS), following or related to VG use.

Information Sources

Academic articles were located using two electronic databases: MEDLINE and Web of Science, and by scanning reference lists in other studies in the same field. Only the results from these two databases are reported since results from other sources (Scopus, Google Scholar) did not provide any relevant new results. The search was not limited by year of publication and only articles published in English, Spanish, or French were considered for inclusion. The first studies relevant to the topic are from 1992, while the most recent studies included in this review were published in February 2016.

A systematic search was performed using a series of keywords which were expected to appear in the title or abstract of any study containing neural correlates of VGs. These keywords were grouped in two main categories. First of all, a group of keywords trying to identify articles which used VG as a technique or as a study goal. These keywords included search terms related to “video games” proper (in different orthographic variants), types of players (casual, core, and hardcore gamers) and references to serious gaming. In second place, two groups of keywords were used to detect articles which studied the neural basis: (1) keywords related to anatomical features, such as structural or functional changes, gray, or white matter (WM) volumes, cortical features, and connectivity and (2) keywords which mentioned the neuroimaging technique used to obtain that data, such as EEG, MRI, PET, or NIRS. (See Appendix)

Study Selection

Due to the large amount of results obtained by the previous search terms, strict exclusion criteria were applied to limit the final selection of studies. The same criteria were applied in a standardized way by two independent reviewers, and disagreements between reviewers were resolved by consensus. Due to high variability in the terminology and the diversity of keywords used in the search, a large number of false positive studies (65% of items found) appeared during the review process (see Figure 2 ).

Figure 2. Study selection diagram flow . * Articles in these sections may not be mutually exclusive.

By performing a search using standardized terms, a list of studies from the two databases was extracted. A large number of studies (62% of those that met the inclusion criteria) were found to be duplicates in both databases, so a careful comparison was made in order to merge the references.

No unpublished relevant studies were considered. Studies relevant to the topic but not published in peer-reviewed journals, such as conference posters and abstracts were considered.

Data Collection Process

All the relevant information was classified in a spreadsheet, according to the variables listed below. Variables related to violence and abuse of VGs were also categorized, since a significant portion of the studies focused on these behaviors. A small number of articles ( n = 7) were found in sources other than the two databases, mainly through references in other articles.

For each study, the following data was extracted: (1) characteristics of the sample, including sample size, average age and range, inclusion and exclusion criteria, and gaming experience; (2) aim of the study, specially noting if it is focused on gaming abuse or exposure to violent content; (3) name and genre of the VG used during the study, if applicable; (4) study design; (5) main neuroimaging technique applied in the study, and whether the technique was applied while participants played; (6) functional and structural neural correlates observed in the study. Studies were then classified in several groups as to whether they provided structural or functional data, and whether they addressed violent or addictive aspects.

Moreover, in order to understand the outcomes derived from the neural correlates, most of the studies establish a connection between these correlates and their cognitive correspondence, either by directly measuring the outcomes using cognitive tasks and questionnaires, or by interpreting their results based on existing literature.

In the discussion section of this review, we attempted to summarize the main findings by associating the neural changes to their cognitive and behavioral correspondences. Whereas, in many cases the original articles provided their own explanation for the phenomena, we also worked on integrating the general trends from a cognitive perspective. We therefore indicate which studies provide and interpret empirical cognitive or/and behavioral data (non-marked), those which discuss cognitive or/and behavioral implications without assessing them (marked with * ), and those which did not provide any cognitive nor behavioral information (marked with ** ).

The combined search of MEDLINE and Web of Science provided a total of 306 unique citations. Of these, 205 studies were discarded because they did not seem to meet the inclusion criteria after reviewing the abstract. The main reasons for exclusion were: being a review article ( n = 22), absence of neural correlates ( n = 70), presence of pathology in the participants ( n = 65), not being related to VGs or using simple computerized tasks which could not be considered VGs ( n = 69), testing of new technologies in which the brain correlates were a mere by-product ( n = 25), articles focused on motor functions ( n = 15), pharmacological studies ( n = 2), and finally, articles in languages other than English, Spanish, or French ( n = 18). Excluded articles often met more than one exclusion criteria. As mentioned in the eligibility criteria, an exception were those articles in which the pathology consisted of gaming overuse or addiction and articles which featured psychopathology and included groups of healthy participants from whom neural data was provided.

Fifteen extra articles that met the inclusion criteria were found after examining the contents and following the references in the previously selected studies. As expected, articles written in English comprised the vast majority; among the rest (8.9%), 10 of them (4.9%) were discarded from the review solely for language reasons. No unpublished relevant studies were considered. Studies relevant to the topic but not published in peer-reviewed journals, such as conference posters and abstracts were considered. Ultimately, a total of 116 studies were identified for inclusion in the review (see flow diagram in Figure 2 ).

Most studies ( n = 100; 86.2%) provided functional data, while only 22 (18.9%) of them studied structural changes in the brain. A few ( n = 6; 5.2%) provided both structural and functional data. A significant number of the studies focused their attention on excessive playing or VG addiction. That was the case for 39 (33.6%) of the reviewed articles, so we considered it appropriate to analyze them in their own category. Likewise, 16 studies (13.8%) focusing on the violent component of VGs were also placed in their own category. These categories were not always exclusive, but there was only one case where the two criteria were met. (See Table 1 for a breakdown by category).

Table 1. Article breakdown by category .

Characteristics of Included Studies

Based on their methodology, studies in this review could be classified as experimental ( n = 54; 46.6%), randomly assigning the participant sample to the experimental groups, and quasi-experimental ( n = 62; 53.4%), where the groups were usually constructed according to the participants' characteristics. While studies involving excessive gaming almost always followed a quasi-experimental design comparing experienced gamers against low-experience VG players, articles studying normal gaming and the effects of violence exposure used both experimental and quasi-experimental designs. A fraction of the studies ( n = 15; 13%), both experimental and quasi-experimental, compared the results to a baseline using a pretest-posttest design. That was the case for most studies involving a training period with VGs.

The cumulative sample included in this review exceeds 3,880 participants. The exact number cannot be known since participants could have been reused for further experiments and in some cases the sample size was not available. Most studies used adolescents or young adults as the primary experimental group, since that is the main demographic target for video gaming. In many cases, only male participants were accepted. In the cases where VG experience was compared, the criteria varied greatly. For the low video gaming groups, VG usage ranged from <5 h/week to none at all. For the usual to excessive VG groups, it could typically start at 10 h/week. In some cases, where the level of addiction mattered, the score in an addiction scale was used instead.

In more than half of the studies ( n = 67; 57.8%) participants actually played a VG as part of the experimental procedure. In the rest, either neural correlates were measured in a resting-state condition or VG related cues were presented to the participants during the image acquisition.

Structural changes in the gray matter (GM) were measured in the form of volumetric changes, whereas WM was assessed using tractography techniques. Functional changes were typically measured comparing activation rates for different brain regions. Nearly half ( n = 55; 47.4%) of the assessed studies used fMRI as the neuroimaging technique of choice, while other functional techniques remained in a distant second place. Functional connectivity was assessed in several studies employing resting-state measures. EEG in its multiple forms was also widely used ( n = 32; 27.6%) to obtain functional data, either to measure activation differences across regions or in the form of event related potentials. (See Table 2 for a breakdown by neuroimaging technique).

Table 2. Neuroimaging techniques used in the reviewed studies .

The high variability in the study designs, participants and objectives meant we focused on describing the studies, their results, their applicability, and their limitations on a qualitative synthesis rather than meta-analysis.

Structural Data

Data regarding structural changes following VG use was available from 22 studies, fourteen of which provided structural data for more than 800 participants that had a normal VG use and included both VGPs and non-VGPs (see Table 3 ). The remaining eight studies examined aspects concerning the excessive or professional use of VG (see Table 4 ).

Table 3. Studies providing structural data dealing with healthy, non-expert participants .

Table 4. Studies providing structural data dealing with VG experts or excessive gaming .

In studies dealing with healthy, non-addicted participants, eight studies used MRI to provide structural information for the GM, while six focused on the WM using diffusion tension imaging (DTI).

Three studies compared lifetime VG experience prior to the study, while the rest used a training paradigm where participants were exposed to a VG during the experimental sessions prior to the neuroimaging procedure and compared to a baseline. Seven studies provided WM integrity data using the DTI technique while the rest analyzed cortical thickness variations using regular structural MRI.

The most researched areas in studies examining volumetric differences found relevant changes in prefrontal regions, mainly the dorsolateral prefrontal cortex (dlPFC) and surrounding areas, superior and posterior parietal regions, the anterior cingulate cortex (ACC), the cerebellum, the insula, and subcortical nuclei, as well as the striatum and the hippocampus. In addition to this, structural connectivity studies observed changes in virtually all parts of the brain, such as in fibers connecting to the visual, temporal and prefrontal cortices, the corpus callosum, the hippocampus, the thalamus, association fibers like the external capsule, and fibers connecting the basal ganglia.

Functional Data

A 100 articles provided functional data combined with VG use. Of these, around half ( n = 51) were studies which did not include violence or addiction elements (See Table 5 ). A third ( n = 34) corresponded to articles aiming at understanding the neural bases of IGD (See Table 6 ), often drawing parallels with other behavioral addictions and trying to find biomarkers for VG addiction. The rest ( n = 16) were devoted to study the effects of violence exposure in VGs (See Table 7 ). In total, these studies provided functional data for 3,229 experimental subjects, including control groups. Note that there is some overlap with the structural section, since a few ( n = 6) studies provided both structural and functional data.

Table 5. Studies providing functional data dealing with healthy, non-expert participants, without violent content .

Table 6. Studies providing functional data dealing with VG experts or excessive gaming .

Table 7. Studies providing functional data focused on the violent contents of VG .

The rich diversity of methodologies and research goals means that the study of functional brain correlates covers practically all regions of the brain. The most studied areas are found in frontal and prefrontal regions and are concerned with high-order cognitive processes and motor/premotor functions. Activity changes in parietal regions, like the posterior and superior parietal lobe, relevant for diverse functions such as sensory integration and visual and attentional processing, are also a common find. The anterior and posterior cingulate cortices, together with other limbic areas, such as the amygdala, and the entorhinal cortex, display activity changes possibly as a consequence of learning and emotion processing and memory. Structures in the basal nuclei also have a prominent role, particularly the striatum, in studies related to VG addiction. Finally, we must not overlook a series of brain regions which do not appear as frequently, such as occipital and temporal cortices, the cerebellum, the thalamus, and the hippocampus, where distinctive activity patterns have also been observed as a result of VG play.

Due to the given amount of data provided in the reviewed articles, we decided to categorize all the information based on the cognitive functions which are associated with the neurophysiological correlates, rather than focusing on the main research goal for each study. Thus, the discussion has been grouped into six main sections: attention, visuospatial skills, cognitive workload, cognitive control, skill acquisition, and reward processing. These cognitive processes are not clearly independent since they present some degree of overlap. This is particularly relevant in the cases of cognitive workload, which may be linked to virtually any cognitive function, and attention, which is also closely related to cognitive control, among other functions. Nevertheless, after analyzing the literature, virtually all the articles included in this review focused on one or more of the mentioned cognitive functions in order to explain their findings. Thus, the proposed categories have sufficient presence in the literature to justify their use as separate domains for the study of cognition. While they should not be understood as independent aspects of cognition, the chosen categorization will allow a link between the underlying neural correlates and corresponding behavior to be easily established.

Within each one of the sections, structural and functional correlates are discussed according to their contributions to cognitive functioning, including possible inconsistencies between studies and the presence of transfer effects. Owing to the close link between VG violence, limbic and reward systems, and the possible abnormal reward mechanisms in addicted players, studies previously classified with violence in VGs and VG addiction are predominantly discussed in the reward processing section.

Attentional resources are one of the main cognitive domains in which VGs are involved and one of the most researched. The involvement of attentional networks during gameplay is closely related with other brain regions responsible for cognitive control, especially when more complex operations toward a specific goal are required. Many brain regions are involved in attention, particularly nodes in the dorsal frontoparietal system, mediating top-down attentional processes in goal-oriented behavior, but also nodes in the ventral network, responsible for bottom-up sensory stimulation (e.g., Vossel et al., 2014 ) dealing with those salient stimuli to which the player must pay attention.

There is evidence that VGPs display enhanced performance in a range of top-down attentional control areas, such as selective attention, divided attention, and sustained attention ( Bavelier et al., 2012b ). The ACC is an area that consistently shows functional activity during VG play due to its involvement as the main hub in top-down attentional processes (selective or focused attention) and goal-oriented behavior (e.g., Anderson et al., 2011 * ; Bavelier et al., 2012b ).

Non-VGPs, compared to VGPs, showed greater frontoparietal recruitment, a source of selective attention, as task demands increased, showing that habitual gamers have more efficient top-down resource allocation during attentional demanding tasks ( Bavelier et al., 2012a ). That resource optimization effect can also be observed in attentional control areas, such as the right middle frontal gyrus (MFG), right superior frontal gyrus (SFG), and the ventromedial prefrontal cortex (vmPFC) ( Prakash et al., 2012 * ). Functional connectivity changes in the attentional ventral stream, particularly in occipitotemporal WM, responsible for bottom-up reorienting toward novel stimuli, have also been observed as a result of VG training and were linked to cognitive improvement ( Strenziok et al., 2014 * ). Integration between attentional and sensoriomotor functions has been observed in expert VGPs in the form of increased structural GM and functional connectivity in anterior and posterior insular sub regions where long-term exposure to attentional VG demands coordinated with the fine skills involved in using the VG controller may have resulted in plastic changes in these two regions that are respectively involved in attentional and sensoriomotor networks ( Gong et al., 2015 * ).

Using electrophysiological techniques, it seems that VG play correlates with an increment of the frontal midline theta rhythm, associated with focused attention ( Pellouchoud et al., 1999 * ), and increases with VG practice ( Sheikholeslami et al., 2007 ** ; Smith et al., 1999 ), both in an action and a puzzle VG, attributable to ACC activity. Likewise, amplitudes in the P200 ( Wu et al., 2012 ), an early visual stimuli perceptual component, and P300 components ( Mishra et al., 2011 ; Wu et al., 2012 ), which involved in early stages of decision-making, were also linked to top-down spatial selective attention improvements after training and lifetime exposure to action VG. Action VGPs and non-action VGPs seem to respond differently in the way they deploy attention to central and peripheral targets in visual attention tasks, as measured by the N2pc component ( West et al., 2015 ), which is also linked to selective attention.

If we consider different VG genres, it seems that action VGs are better at improving selective attention than other slow-paced VGs such as role-playing games (RPG) ( Krishnan et al., 2013 ), puzzle ( Green and Bavelier, 2003 ), or strategy VGs ( Tsai et al., 2013 ) which require high planning skills and other forms of proactive cognitive control. This is probably due to the extensive use of attentional systems, paired with precise timings that action VGs require. While these improved attentional skills are typically observed in habitual VGPs, it is possible to achieve long-lasting improvements as a result of a single VG training procedure ( Anguera et al., 2013 ).

Visuospatial Skills

Visuospatial skills encompass processes that allow us to perceive, recognize, and manipulate visual stimuli, including visuomotor coordination and navigational skills, and VGs are predominantly interactive visual tasks.

The areas implicated in visuospatial processing have traditionally been classified along a visual ventral stream (responsible for object recognition) and a visual dorsal stream (responsible for spatial location). Both depart from the visual cortex, in the occipital lobe, and reach the posterior parietal cortex (dorsal stream) and the inferior temporal cortex (ventral stream). More recent proposals have refined that model, broadening the traditional conceptualization of the two-stream model (for further details see Kravitz et al., 2011 ). Among other nodes, the role of the hippocampus stands out for its function in higher order visual processing and memory ( Kravitz et al., 2011 ; Lee A. C. H. et al., 2012 ).

Neural correlates related to visuospatial skills have been detected in relationship with structural volume enlargements of the right hippocampus (HC), both in long-term gamers and experimentally after a VG training period ( Kühn et al., 2013 ; Kühn and Gallinat, 2014 * ). Increased hippocampal volumes were also found by Szabó et al. (2014 ** ), although the authors do not attribute that effect to the VG training. The entorhinal cortex, associated with navigational skills ( Schmidt-Hieber and Häusser, 2013 ), which together with the HC is involved in spatial memory ( Miller et al., 2015) , was also correlated with lifetime experience in logic/puzzle and platform VG ( Kühn and Gallinat, 2014 * ).

Decreased activation in occipitoparietal regions, associated with the dorsal visuospatial stream ( Goodale and Milner, 1992 ), has also been linked to improved visuomotor task performance, suggesting a reduction of the cognitive costs as a consequence of the VG training, dependent on the training strategy used in the VG ( Lee H. et al., 2012 ). Earlier N100 latencies in the visual pathways are another feature found in long-term VGPs, which may contribute to faster response times in visual tasks after years of practice ( Latham et al., 2013 ).

Reduced WM integrity in interhemispheric parietal networks for spatially-guided behavior could be another symptom for a decreased reliance on specific visuospatial networks after VG training as performance improved ( Strenziok et al., 2013 * ). However, other studies found that increased WM integrity in visual and motor pathways was directly responsible for better visuomotor performance in long term VGPs ( Zhang et al., 2015 * ). Despite these connectivity changes, brain functional differences between VGPs and non-VGPs do not always reflect performance in visuospatial skills, which were best predicted by non-visual areas ( Kim Y. H. et al., 2015 * ).

Cognitive Workload

Brain activation patterns depend on the cognitive demands of the environment and also on the associated level of workload ( Vogan et al., 2016 ), which is directly related to the allocation of resources to the working memory and its associated attentional processes ( Barrouillet et al., 2007 ). When we manipulate this variable and observe its neural correlates, it is likely that we are seeing the result of neural recruitment mechanisms as the cognitive demands increase ( Bavelier et al., 2012a ). VGs have often been employed to obtain cerebral measures of cognitive workload, given the ability to adjust many of their features, particularly in a purpose-made VG, such as the popular Space Fortress. Due to the nature of this task, it is likely that functional changes related to the manipulation of cognitive load appear along the attentional networks and in specific key nodes related to executive functions, mainly in prefrontal and parietal cortices.

Cognitive workload is not a unitary concept; some studies have been able to identify different activation patterns by manipulating the difficulty of a task (e.g., Anderson et al., 2011 * ). Namely, the number of stimuli appearing simultaneously on the screen and the complexity of each stimulus seem to elicit different responses from the brain. For instance, in the context of an air traffic control simulator, when directly manipulating the task difficulty by increasing the number of planes that a participant had to attend, the theta band power increased ( Brookings et al., 1996 ). Theta band power also displayed higher power compared to a resting condition, and gradually increased during gameplay ( Sheikholeslami et al., 2007 ** ). The theta band seems to be directly related to the level of cognitive demand in a wide range of cognitive abilities, such as attention, memory, and visuospatial processes, although this finding is not universal and decreased theta band power has been observed as a feature of sustained attention. So it appears that it is both related to task complexity and levels of arousal and fatigue. On the other hand, beta band power seemed to be more associated with the complexity of the task, especially in frontal and central areas, likely indicating a qualitative change in the cognitive strategy followed by the participant or the type of processing done by the brain ( Brookings et al., 1996 ).

Assessing cognitive workload with ERP shows that during VG play, amplitudes tend to correlate negatively with game difficulty in expert VGPs, with most ERP (P200, N200) having its maximum amplitude in frontoparietal locations, with the exception of the P300, being larger in parietal regions ( Allison and Polich, 2008 ). This is consistent with previous literature about cognitive workload related to attention and working memory demands and ERP peak amplitude decrements ( Watter et al., 2001 ).

Frontoparietal activity, linked to attentional processes, also exhibits recruitment effects as game difficulty increases, which also affects reaction times, making them slower ( Bavelier et al., 2012a ). As mentioned above, comparing habitual VGPs with non-VGPs, it appears that the former show less recruitment of frontoparietal networks when compared to the non-gamers, which could be attributed to their VG experience and the optimization of their attentional resources ( Bavelier et al., 2012a ). Increased blood flow in prefrontal areas like dlPFC was also associated with increasing cognitive demands related to attention, verbal and spatial working memory and decision making ( Izzetoglu et al., 2004 * ).

The intensity of the events displayed in the VG was also linked with certain electrophysiological correlates. High intensity events, such as the death of the VG character, were associated with increased beta and gamma power when compared with general gameplay ( McMahan et al., 2015 ).

Cognitive Control

During the course of a VG, the player can encounter many situations in which he has to use one of several possible actions. For instance, while playing a game, the player might be required to interrupt and quickly implement an alternate strategy, or manipulate a number of elements in a certain way in order to solve a puzzle and progress in the storyline. All these abilities can be characterized under the “umbrella” of cognitive control, which includes reactive and proactive inhibition, task switching and working memory ( Obeso et al., 2013 ). These cognitive control aspects are key to overcoming the obstacles found the VG. In fact, they are frequently used in parallel ( Nachev et al., 2008 ) in order to engage in goal-directed behavior. These processes have their neural substrate in the prefrontal cortex, supported by posterior parietal areas and the basal ganglia ( Alvarez and Emory, 2006 ). Therefore, most changes regarding cognitive control observed after VG play will likely be detected in these regions.

Indeed, prefrontal regions are one of the brain areas in which GM volumetric changes have been observed as a result of a cognitive training with a VG, which is remarkable if we consider that the common VG training period spans from a few weeks to a couple of months. These regions, such as the dlPFC, determinant for cognitive control ( Smith and Jonides, 1999 ), show volumetric changes that seem to correlate with VG performance and experience, likely as a result of the continuous executive demands found in a VG, such as attentional control and working memory ( Basak et al., 2011 ). These volumetric changes even result in correlations with transfer effects in cognitive control tasks ( Hyun et al., 2013 ). Volumetric-behavioral correlations work both ways, since individuals with decreased orbitofrontal cortex (OFC) volumes as a consequence of VG addiction show poorer performance in similar tasks ( Yuan et al., 2013a ).

During VG play, these prefrontal regions increase their activation in response to the cognitive demands (game difficulty) and display a positive correlation with performance measures ( Izzetoglu et al., 2004 * ). Still, prefrontal activity is not only affected by the complexity of the task, but also by the nature of the task and the individual differences of the participants ( Biswal et al., 2010 ). Some research groups have found deactivation of dorsal prefrontal regions during gameplay. A possible explanation for this phenomenon could be the interference effect of attentional resources during visual stimuli, since activity in the dlPFC remained stable while passively watching a VG, but not while actively playing it ( Matsuda and Hiraki, 2004 * ). Likewise, the same team also found that finger movement while handling the game controller did not seem to contribute as a source of prefrontal deactivation. Further studies also noted that the observed prefrontal deactivation was not affected by age or performance level ( Matsuda and Hiraki, 2006 * ), although some authors have challenged that finding, claiming that prefrontal activation during video gaming was age-dependent, where most adults tended to show increased prefrontal activity while it was attenuated in some of the children. So prefrontal activation could be a result of age, game performance, level of interest and attention dedicated to the VG ( Nagamitsu et al., 2006 ** ).

It has been possible to establish a causal relationship between dlPFC activation and cognitive control using non-invasive stimulation methods. Stimulating the left dlPFC using tDCS results in a perceptible improvement in multitasking performance in a three-dimensional VG ( Hsu et al., 2015 ).

Changes in functional activity after a training period in other executive-related nodes, such as the superior parietal lobe (SPL) have also been associated with working memory improvements ( Nikolaidis et al., 2014 ).

Connectivity-wise, Martínez et al. (2013) found resting-state functional connectivity changes in widespread regions (frontal, parietal, and temporal areas) as a result of a VG training program, which were attributed to the interaction of cognitive control and memory retrieval and encoding.

Despite the observed structural and functional changes in prefrontal areas, executive functions trained in a VG show poor transfer effects as measured with cognitive tasks ( Colom et al., 2012 ; Kühn et al., 2013 ). Others, showing neural correlates related to executive functions, visuospatial navigation and fine motor skills, failed to observe far transfer effects even after a 50 h training period, as measured by neuropsychological tests ( Kühn et al., 2013 ). By studying lifelong experts or professional gamers, some studies have detected structural GM changes that correlated with improved executive performance, involving posterior parietal ( Tanaka et al., 2013 ), and prefrontal ( Hyun et al., 2013 ) regions. Regarding structural connectivity, WM integrity changes in thalamic areas correlated with improved working memory, but integrity of occipitotemporal fibers had the opposite effect ( Strenziok et al., 2014 ). VG experience also seems to consolidate the connectivity between executive regions (dlPFC and the posterior parietal cortex -PPC-) and the salience network, composed by the anterior insula and the ACC, and responsible for bottom-up attentional processes ( Gong et al., 2016 ).

Different VG genres seem to affect which cognitive skills will be trained. Training older adults in a strategy VG seemed to improve verbal memory span ( McGarry et al., 2013 ), but not problem solving or working memory, while using a 2D action VG improved everyday problem solving and reasoning. Transfer effects were even more relevant in the case of a brain training/puzzle VG, where working memory improvements were also observed ( Strenziok et al., 2014 ). Using a younger sample, working memory improvements were detected after training with a 2D action VG (Space Fortress, Nikolaidis et al., 2014 ). Nevertheless, training periods found in scientific literature vary greatly and it is difficult to ascertain if a lack of transferred skills cannot be due to a short training period.

Regarding electrophysiological methods, electroencephalography studies have shown functional correlations with alpha oscillations in the frontal cortex that could reflect cognitive control engagement in the training VG ( Mathewson et al., 2012 ).

Skill Acquisition

Several studies have attempted to determine which regions could act as predictors for skill acquisition. Since this is a domain in which multiple cognitive functions are involved, volumetric and functional changes will appear in a wide range of cortical regions. Most of the learning in VGs is non-declarative, including visuospatial processing, visuomotor integration, and motor planning and execution. Improvements in these areas will generally lead to decreased cortical activation in the involved areas due to the optimization of resources, whereas this is not the case for striatal and medial prefrontal areas, which display a distinctive pattern of activation and typically increase their activity due to skill acquisition ( Gobel et al., 2011 ).

Striatal volumes were determined as predictors for skill acquisition, although structural changes in the hippocampal formation were not ( Erickson et al., 2010 ). Particularly, the anterior half of the dorsal striatum was the region which more accurately predicted skill acquisition in a complex VG ( Vo et al., 2011 ). Other areas identified as predictors were the medial portion of the Brodmann area 6, located in the frontal cortex and associated with motor control in cognitive operations and response inhibition and the cerebellum, likely associated with motor skill acquisition ( Basak et al., 2011 ). The same authors also considered the post-central gyrus, a somatosensory area that could be related to a feedback mechanism between prefrontal and motor regions, while the volume of the right central portion of the ACC also correlated with skill acquisition and is responsible for monitoring conflict. Finally, dlPFC volumes, with a central role on the executive functions, also showed correlation with VG performance over time ( Basak et al., 2011 ).

On a functional level, Koepp et al. (1998 ** ) was the first team to identify a relationship between striatum activity, associated with learning and the reward system, and performance level in a VG. The study by Anderson et al. (2015) also support the notion that the striatum, particularly the right dorsal striatum, composed of the caudate nucleus and the claustrum, is a key area in skill acquisition. However, the same team was able to predict learning rates more accurately by comparing whole sequential brain activation patterns to an artificial intelligence model.

Learning gains seemed to be best predicted by individual differences in phasic activation in those regions which had the highest tonic activation ( Anderson et al., 2011 * ). Differences related to learning rates were also observed in the activation of the default mode network, especially when different training strategies were employed by the participants. Using electrophysiological methods, the best predictors were the alpha rhythms ( Smith et al., 1999 ), particularly frontal regions, and alpha and delta ERSP, which are associated with cognitive control (task switching and inhibition) and attentional control networks ( Mathewson et al., 2012 ). Frontal midline theta rhythms, linked with focused concentration and conscious control over attention, seemed to increase over the course of the training sessions with a VG ( Smith et al., 1999 ).

Reward Processing

VG addiction is understood as an impulse-control disorder with psychological consequences, not unlike other addictive disorders, especially non-substance addictions such as pathological gambling ( Young, 1998 ). Internet Gaming Disorder (IGD) has been recently proposed for inclusion as a psychiatric diagnosis under the non-substance addiction category in the Diagnostic and Statistical Manual for Mental Disorders 5th ed. (DSM-5) ( American Psychiatric Association, 2013 ), with its diagnostic criteria being adapted from those of pathological gambling. Efforts in order to find a consensus regarding its assessment are still ongoing ( Petry et al., 2014 ). In some cases, VG addiction is included as a subset within the broader definition of Internet addiction, although this categorization is not always consistent, since many VGs in which addiction is studied do not have an online component. Several instruments have been developed to assess gaming addictions: the Internet Addiction Test (IAT) by Young (1998) and the Chen Internet Addiction Scale (CIAS) ( Chen et al., 2004 ) being the most used in research and clinical practice.

Within the VG literature, there is a great deal of interest in knowing the neurobiological basis of VG addiction and whether it can be related to other behavioral addictions by observing abnormal reward processing patterns. This seems to be the case, since many regions involved in the reward system have been found affected in people with VG addiction (e.g., Liu et al., 2010 * ; Hou et al., 2012 * ; Hahn et al., 2014 ). Among the complex set of structures that are involved in the reward system, the cortico-ventral basal ganglia circuit is the center of the network responsible for assessing the possible outcomes of a given behavior, especially in those situations where, during a goal-oriented behavior, complex choices must be made and the value and risk of secondary rewards must be weighed ( Haber, 2011 ).

Differential structural and functional changes in addicted individuals can be found throughout the reward system. The main components of this circuit are the OFC, the ACC, the ventral striatum, ventral pallidum, and midbrain dopaminergic neurons ( Haber, 2011 ), but many other regions seem to be involved in the wider context of addiction.

By exposing the participants to gaming cues, it is possible to elicit a craving response and study which regions show stronger correlation in IGD patients compared to controls. The model proposed by Volkow et al. (2010) involves several regions, which are mentioned consistently across studies, to explain the complexity of the craving. First, the precuneus, which showed higher activation in addicted individuals ( Ko et al., 2013 * ), is an area associated with attention, visual processes, and memory retrieval and integrates these components, linking visual information (the gaming cues) to internal information. Regions commonly associated with memory and emotional functions are also involved: the HC, the parahippocampus and the amygdala seem responsible for providing emotional memories and contextual information for the cues ( Ding et al., 2013 * ), regions where subjects showed higher activation ( O'Brien et al., 1998 ). Central key regions of the reward system, like the limbic system and the posterior cingulate have a role in integrating the motivational information and provide expectation and reward significance for gaming behaviors ( O'Doherty, 2004 ). The OFC and the ACC are responsible for the desire for gaming and providing a motivational value of the cue-inducing stimuli ( Heinz et al., 2009 ), contributing to the activation and intensity of the reward-seeking behavior ( Kalivas and Volkow, 2005 ; Brody et al., 2007 ; Feng et al., 2013 * ). In the last step, prefrontal executive areas such as the dlPFC have also shown involvement during craving responses ( Han et al., 2010a * ; Ko et al., 2013 * ), and are linked to the formation of behavioral plans as a conscious anticipation of VG play. All these frontal regions[dlPFC, OFC, ACC, and the supplementary motor area (SMA)] tend to show reduced GM volumes in participants with IGD ( Jin et al., 2016 * ).

Striatal volumes, particularly the ventral striatum, responsible for a key role in reward prediction, were reduced in people with excessive internet gaming compared to healthy controls ( Hou et al., 2012 * ) and in the insula, with its role in conscious urges to abuse drugs ( Naqvi and Bechara, 2009 ).

Overall, these features are characteristic of reward deficiencies that entail dysfunctions in the dopaminergic system, a shared neurobiological abnormality with other addictive disorders ( Ko et al., 2009 * , 2013 * ; Cilia et al., 2010 ; Park et al., 2010 ; Kim et al., 2011 ).

Several regions seem to be related to the intensity of the addiction. In a resting state paradigm, connectivity between the left SPL, including the posterior cingulate cortex (PCC), and the right precuneus, thalamus, caudate nucleus, nucleus accumbens (NAcc), SMA and lingual gyrus (regions largely associated with the reward system) correlated with the CIAS score, while at the same time, functional connectivity with the cerebellum and the superior parietal cortex (SPC) correlated negatively with that score ( Ding et al., 2013 * ). The distinctive activation and connectivity patterns related to the PCC ( Liu et al., 2010 * ), an important node in the DMN and reward system ( Kim H. et al., 2015 ), could be used as a biomarker for addiction severity, both in behavioral and substance dependence. As the addiction severity increases, changing from a voluntary to a compulsive substance use, there is a transition from prefrontal to striatal control, and also from a ventral to a dorsal striatal control over behavior ( Everitt and Robbins, 2005 ), Matching evidence in the form of weaker functional connectivity involving the dorsal-caudal putamen has been found in IGD patients ( Hong et al., 2015 * ).

It is important to note that, even controlling the amount of time playing VGs, professional and expert gamers display very different neural patterns compared to addicted VGPs. Gamers falling into the addiction category show increased impulsiveness and perseverative errors that are not present in professional gamers and, on a neural level, they differ in GM volumes in the left cingulate gyrus (increased in pro-gamers) and thalamus (decreased in pro-gamers), which together may be indicative of an unbalanced reward system ( Sánchez-González et al., 2005 ; Han et al., 2012b ).

Exposure to Violent Content

Many articles use violent VGs in their designs as a way to study the effects of violence exposure, emotional regulation and long-term desensitization. Exposure to violent content has been associated with reduced dlPFC activity and interference in executive tasks (inhibition, go/no-go task) ( Hummer et al., 2010 ), which cannot be interpreted without studying the link with the limbic and reward systems. It is likely that repeated exposure to violent content will trigger desensitization processes that affect regions linked to emotional and attentional processing, particularly a frontoparietal network encompassing the left OFC, right precuneus and bilateral inferior parietal lobes ( Strenziok et al., 2011 ). It is hypothesized that this desensitization may result in diminished emotional responses toward violent situations, preventing empathy and lowering the threshold for non-adaptive behaviors linked to aggressiveness ( Montag et al., 2012 ).

Limbic areas are associated with violence interactions, shown by the activation changes detected in the ACC and the amygdala in the presence of violent content ( Mathiak and Weber, 2006 * ; Weber et al., 2006 * ). Lateral (especially left) prefrontal regions might be involved as well, integrating emotion and cognition and therefore working as a defense mechanism against negative emotions by down-regulating limbic activity ( Montag et al., 2012 ). Wang et al. (2009) also provided evidence of that regulation mechanism by observing differing functional correlations between the left dlPFC and the ACC, and medial prefrontal regions & the amygdala during an executive task after a short-term exposure to a violent VG.

The reward circuit also seems to be implicated in the presence of violent content. Activation decreases in the OFC and caudate appeared in the absence of an expected reward. However, it does not seem that violence events were intrinsically rewarding ( Mathiak et al. (2011 * ). Zvyagintsev et al. (2016 * ) found that resting-state functional connectivity was reduced within sensory-motor, reward, default mode and right frontotemporal networks after playing a violent VG, which could be linked to short-term effects on aggressiveness.

Gender differences in neural correlates were observed in one study ( Chou et al., 2013 * ) after being exposed to violent content, with reduced blood flow in the dorsal ACC after playing a violent VG in males, but not females, possibly as a result of the role of the ACC in regulating aggressive behavior in males.

The effect of certain personality traits, particularly empathy, have been assessed using violent VG exposure ( Lianekhammy and Werner-Wilson, 2015 * ). However, while empathy scores correlated with neural activity (frontal asymmetry during EEG), they were not affected by the presence of violent content. Markey and Markey (2010) found that some personality profiles, especially those with high neuroticism and low conscientiousness and agreeableness, are more prone to be affected by the exposure to violent VGs.

VG player's perspective may also be determinant to the level of moral engagement; while ERP N100 amplitudes were greater during a first person violent event, if the player was using a distant perspective, general alpha power was greater, which is indicative of lower arousal levels ( Petras et al., 2015) .

Montag et al. (2012) , observed that regular gamers have been habituated to violence exposure and show less lateral prefrontal activation, linked to limbic down-regulation, compared to non-gamers. However, gamers have not lost the ability to distinguish real from virtual violence, as Regenbogen et al. (2010 * ) found, although that also depended on each person's learning history.

While attenuated P300 amplitudes have been linked to violence desensitization, both in short and long term exposure ( Bartholow et al., 2006) , these amplitudes did not increase using a pro-social VG ( Liu Y. et al., 2015 ). Engelhardt et al. (2011) , experimentally linked the lower P300 amplitudes to violence desensitization and their effects on aggression. Bailey et al. (2010) also supported the link between violent VG exposure and desensitization to violent stimuli, associating it with early processing differences in attentional orienting.

Flow and boredom states during VG play have also been the subject of research using neural correlates. The concept of flow, described by Csikszentmihalyi (1990) , is understood as a mind state of being completely focused on a task that is intrinsically motivating. Among other characteristics, the state of flow implies a balance between the task difficulty and the person's skills, the absence of ambiguity in the goals of the task, and is commonly accompanied by a loss of awareness of time. Considering that the concept of flow is a complex construct which itself cannot be directly measured, it is necessary to operationalize its components. Some authors have identified some of these components as sustained attention (focus), direct feedback, balance between skill and difficulty, clear goals and control over the activity ( Klasen et al., 2012 * ) and it has been theorized to be firmly linked to attentional and reward processes ( Weber et al., 2009 ).

VGs provide the appropriate context in which flow states are encouraged to occur, since feedback is offered continuously and the level of difficulty is programmed to raise progressively, in order to match the improving skills of the player ( Hunicke, 2005 ; Byrne, 2006 ). Therefore, VGs are perfect candidates to operationalize the components involved in the flow theory.

During gameplay in an action VG, Klasen et al. (2012 * ) could not relate the feedback component to any meaningful neural activity, but the four remaining flow-contributing factors showed joint activation of somatosensory networks. Furthermore, motor regions were implicated in the difficulty, sustained attention and control components. Together, the authors identify this sensorimotor activity as a reflection of the simulated physical activity present in the VG, which can contribute to the state of flow. The rest of the components elicited activity in several different regions. The reward system was involved in the skill-difficulty balance factor, observed by activation in the ventral striatum and other basal nuclei, rewarding the player in successful in-game events. In addition to activity in reward regions, this factor also correlated with simultaneous activity in a motor network comprised of the cerebellum and premotor areas. The factor comprising concentration and focusing during the VG was associated with changes in attentional networks and the visual system, as players switched away from spatial orientation to processing the numerous elements of the VG in high focus settings. Goal-oriented behavior showed decreased activity in the precuneus and regions of the ACC, while activity in bilateral intraparietal sulcus and right fusiform face area (associated with face processing) increased, which the authors explain as a result of a shift from navigation in a known environment to seeking new game content ( Klasen et al., 2012 * ).

When manipulating the VG settings to elicit states or boredom, operationalized as the absence of goal-oriented behavior, one of the main aspects of flow, affective states appear. While the lack of goal-directed behavior resulted in an increase of positive affect, the neural correlates were characterized by lower activation in the amygdala and the insula ( Mathiak et al., 2013) . However, a different neural circuit was responsible when negative affect increased, characterized by activation in the ventromedial prefrontal cortex and deactivation of the HC and the precuneus, that seemed to counteract the state of boredom, possibly by planning future actions during inactive periods ( Mathiak et al., 2013 ). Involvement of frontal regions was also observed by Yoshida et al. (2014) related to flow and boredom states. During the state of flow, activity in bilateral ventrolateral prefrontal cortex (vlPFC) [comprising the inferior frontal gyrus (IFG) and lateral OFC] increased, and it decreased when participants were subject to a boredom state. The OFC is linked to reward and emotion processing ( Carrington and Bailey, 2009 ), and monitoring punishment ( Kringelbach and Rolls, 2004 ). However, this study employed boredom differently, using a low difficulty level in the VG instead of the suppressing goal-directed behavior.

Brain-computer interfaces, using electrophysiological methods to measure brain activity, have been able to differentiate states of flow and boredom, created by adjusting the level of difficulty of a VG. The EEG frequencies that were able to discern between flow states were in the alpha, low-beta and mid-beta bands, measured in frontal (F7 and F8) and temporal (T5 and T6) locations ( Berta et al., 2013 ).

Gender Differences

Although some studies have already discussed the presence of gender differences in cognitive processes related to VG playing, the lack of studies dealing with this topic and providing neural data are notable. The most relevant study of gender differences ( Feng et al., 2007 * ) found that a 10-h training in an action VG (but not in a non-action VG) was enough to compensate for baseline gender differences in spatial attention, and to reduce the gap in mental rotation skills. Whether the initial difference was innate or a product of lesser exposure to this kind of activities in women is a matter of debate ( Dye and Bavelier, 2010 ). Actually, one of the reasons men do not improve as much as women could be explained by a ceiling effect due to previous exposure to VGs. On the other hand, women with less experience in these activities are able to achieve equal performances in visuospatial skills that reach the same ceiling effect with a short training period. In this respect, Dye and Bavelier comment on the possible effects of lifetime VG exposure since the gender gap in attentional and non-attentional skills is smaller or non-existent during childhood compared to adult life, and the greater development of these skills in male individuals is partially due to games targeting a male audience.

Other authors ( Ko et al., 2005 ) have focused on other psychosocial factors to explain gender differences in online VG addictions. Considering most online VGPs are men and this difference is also observed in addiction cases, they studied the possible factors and observed that lower self-esteem and lower daily life satisfaction are determinant in men, but not women. They attribute these differences to the reasons on why they play VGs: while men declared to play to pursue feelings of achievement and social-bonding, it was not the case for women. This aspect is not new to VG addiction and is shared aspect with other addictions. It is likely that VGs are used as a way to cope with these problems, leading up to the development of the addiction.

Limitations

The study of neural correlates of VGs entails a number of inherent difficulties. The main limitation encountered during the development of this review was the dual nature of studies with regard to VGs as a research tool or as an object of study. The lack of standardization in study objectives is another limitation that should be addressed. Despite the recent popularity of VG-related studies, there are a multitude of similar research lines that offer hardly comparable results, making it difficult to draw general conclusions. We aimed to unify all sorts of studies in order to interpret and generalize the results.

First of all, we compared a large number of studies that not only used completely different techniques, but also had very heterogeneous research goals. We grouped them together with the aim of extracting all the available neuroimaging information, but it is likely that some information that would have been relevant for us was missed in the studies because their research objectives differed greatly from our own. In fact, in certain cases, VGs were almost irrelevant to the aim of the study and were only used as a substitute for a cognitive task, so the provided results may not directly reflect the VG neural correlates. Similarly, VGs were sometimes used as tools to provide violence exposure or to study the effects of behavioral addictions without the VG being the central object of study.

Another issue was the lack of a proper classification for VG genres. While the most common division is between action and non-action VGs, it would be interesting to establish which variables determine this classification. For instance, both first person shooters and fighting games could be considered action VGs. Both demand quick response times and high attentional resources, but first person shooter games require much higher visuospatial skills while fighting games do not. Consequently, efforts should be made to determine which aspects of each VG genre are related with each cognitive process and its associated neural correlates.

Apart from these aspects, comparisons between gamers and non-gamers are common in VG literature. Nevertheless, there is no consensus on the inclusion requirements for each group and it seems that no scientific criterion has been used to establish a cut-off line. Current dedication to VGs, measured in hours per week, seems to be the most common classification method. Non-gamer groups sometimes are so strict as to exclude any gaming experience, but on other occasions, for the same category, several weekly VG hours are tolerated. This is problematic since, in some cases, cognitive changes have been found after just a few weeks of VG training. However, in most cases, the onset age of active VG play, which is a particularly relevant aspect ( Hartanto et al., 2016 ), is not taken into account. Another relevant variable, which tends to be forgotten, is lifetime VG experience, usually measured in hours. Moreover, despite the clearly different outcomes caused by different VG genres, this variable is not included when describing a participant's VG experience. Therefore, VG experience should be measured taking into account all the variables mentioned above: onset age, lifetime VG experience (in hours), current VG dedication (hours per week) and VG genres.

With regard to this review, it was really difficult to extract all the relevant information because of the limitations of the existing literature about the topic. But we did our best to clarify the results and to extract valuable conclusions.

Another limitation was the link between neural changes and cognitive functions. The neural correlates of VGs are the focus of this review, and we found it essential to complement this data by discussing their cognitive implications. In most cases these implications were directly assessed by the individual studies, but in some cases they were extrapolated based on previous literature. Furthermore, even when functional or structural changes are detected, they do not always reflect cognitive changes. This may be due to a lack of sensitivity in the cognitive and behavioral tasks employed. In order to detect both neural and cognitive changes, specific research designs, with sufficiently sensitive measurements of the three dimensions (functional, structural, and cognitive) are needed. Ideally, to determine when each change starts to appear as a result of VG exposure, an experimental design, including a VG training period, should be used. In this design, the neural and cognitive data would be assessed along a series of time points until the three types of changes were detected. An exhaustive discussion of the cognitive implications of VGs is beyond our scope since there are already other works that deal with this particular issue ( Powers et al., 2013 ; Lampit et al., 2014 ; Toril et al., 2014 ; Wang et al., 2016 ).

Efforts should be made to systematize VG-related research, establishing VG training protocols and determining the effects of lifetime VG exposure, in order that more comparable results can be obtained and to improve the generalizability of results.

Conclusions

The current work has allowed us to integrate the great deal of data that has been generated during recent years about a topic that has not stopped growing, making it easier to compare the results of multiple research groups. VG use has an effect in a variety of brain functions and, ultimately, in behavioral changes and in cognitive performance.

The attentional benefits resulting from the use of VG seem to be the most evidence-supported aspect, as many studies by Bavelier and Green have shown ( Green and Bavelier, 2003 , 2004 , 2006 , 2007 , 2012 ; Dye et al., 2009 ; Hubert-Wallander et al., 2011 ; Bavelier et al., 2012b ). Improvements in bottom-up and top-down attention, optimization of attentional resources, integration between attentional and sensorimotor areas, and improvements in selective and peripheral visual attention have been featured in a large number of studies.

Visuospatial skills are also an important topic of study in VG research, where optimization of cognitive costs in visuomotor task performance is commonly observed. Some regions show volumetric increases as a result of VG experience, particularly the HC and the entorhinal cortex, which are thought to be directly related to visuospatial and navigational skills. Optimization of these abilities, just like in attention and overall skill acquisition, is usually detected in functional neuroimaging studies as decreased activation in their associated pathways (in this case, in regions linked to the dorsal visual stream). It is likely that the exposure to a task first leads to an increase of activity in the associated regions, but ultimately, as the performance improves after repeated exposures, less cortical resources are needed for the same task.

Likewise, although not always consistent, even short VG training paradigms showed improvements in cognitive control related functions, particularly working memory, linked to changes in prefrontal areas like the dlPFC and the OFC. How to achieve far transfer in these functions remains one of the most interesting questions regarding cognitive control. Despite VGs being good candidates for cognitive training, it is still not well-known what the optimum training parameters for observing the first effects are. It seems intuitive that longer training periods will have a greater chance of inducing far transfer, but how long should they be? We also commented on how VG genre can have differential effects on cognitive control, so we cannot expect to observe these effects without first controlling this variable, since different VG genres often have little in common with each other.

Cognitive workload studies have offered the possibility of observing neural recruitment phenomena to compensate for the difficulty and complexity of a cognitive task and a number of studies have pointed to the importance of frontoparietal activity for this purpose.

It has been also possible to link skill acquisition rates with certain cerebral structures. Several brain regions are key in this regard, mainly the dlPFC, striatum, SMA, premotor area, and cerebellum. Moreover, as suggested by Anderson et al. (2015) , models of whole-brain activation patterns can also be used as an efficient tool for predicting skill acquisition.

The role of the reward system is always present when we talk about VGs, due to the way they are designed. Addiction has a heavy impact throughout the neural reward system, including components like the OFC, the ACC, the ventral striatum, ventral pallidum, and midbrain dopaminergic neurons, together with diverse regions that have support roles in addiction. The role of structures that link addiction to its emotional components, such as the amygdala and the HC should not be underestimated. Limbic regions work together with the PCC to integrate the motivational information with the expectation of reward.

Exposure to violent content has implications regarding the reward circuits and also emotional and executive processing. Reduced functional connectivity within sensory-motor, reward, default mode and right frontotemporal networks are displayed after playing a violent VG. The limbic system, interacting with the lateral prefrontal cortex, has a role in down-regulating the reaction to negative emotions, like those found in violent contexts, which may lead to short-term violence desensitization.

Despite the difficulties in locating the main components of flow in the brain, it seems that several networks are involved in this experience. General activation of somatosensory networks is observed while being in this state, whereas activation in motor regions is only linked to three components of flow: skill-difficulty balance, sustained attention and control over the activity. The reward system has key implications in the experience of flow, showing that the ventral striatum and other basal ganglia are directly linked to the skill-difficulty balance in a task. When seeking new content in order to avoid boredom, the bilateral intraparietal sulcus and the right fusiform face area seem to be the most implicated regions. During a flow-evoking task, the absence of boredom is shown by activity in the IFC, the OFC, and the vmPFC. Flow is also linked to emotional responses, and both positive and negative affect during a VG have shown changes in the amygdala, insula, vmPFC and the HC.

It is also worth commenting on the negative effects of VGs. While much has been written about the possible benefits of VG playing, finding articles highlighting the negative outcomes in non-addicted or expert VGPs is much less common. To our knowledge, only four studies pointed out neural correlates which predicted hindered performance in a range of cognitive domains. VG use has been linked with reduced recruitment in the ACC, associated with proactive cognitive control and possibly related to reduced attentional skills ( Bailey et al., 2010 ). Likewise, exposure to violent content in VG is associated with lower activity in the dlPFC, interfering with inhibitory control. The same team ( Bailey and West, 2013 ) observed how VG play had beneficial effects on visuospatial cognition, but in turn had negative effects on social information processing. Lastly, VG exposition has been linked to delayed microstructure development in extensive brain regions and lower verbal IQ ( Takeuchi et al., 2016 ).

Finally, although this review is focused on the neural correlates of VG, not their cognitive or behavioral effects, we believe in the importance of integrating all these aspects, since raw neuroimaging data often offer little information without linking it to its underlying cognitive processes. Despite the fact that this integration is increasingly common in the literature, this is not always the case and it is an aspect that could be addressed in future studies.

Author Contributions

All authors had an equal involvement during the process of making this review article. The article's design, data acquisition, and analysis of its content has been made by consensus among all the authors.

This study has been supported by the doctoral school of the Open University of Catalonia, Spain, under the IN3-UOC Doctoral Theses Grants Programme 2013-2016 ( http://in3.uoc.edu ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The reviewer JMRA and handling Editor declared their shared affiliation, and the handling Editor states that the process nevertheless met the standards of a fair and objective review.

Acknowledgments

We would like to sincerely thank our colleague Cristina García Palma for her assistance during the whole process of extracting and processing information from the scientific databases and for her valuable contributions during the course of this work. We would also like to express our gratitude to Nicholas Lumsden, who assisted in the proof-reading and English-language correction of the manuscript.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/article/10.3389/fnhum.2017.00248/full#supplementary-material

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Keywords: addiction, cognitive improvement, functional changes, internet gaming disorder, neural correlates, neuroimaging, structural changes, video games

Citation: Palaus M, Marron EM, Viejo-Sobera R and Redolar-Ripoll D (2017) Neural Basis of Video Gaming: A Systematic Review. Front. Hum. Neurosci . 11:248. doi: 10.3389/fnhum.2017.00248

Received: 16 September 2016; Accepted: 26 April 2017; Published: 22 May 2017.

Reviewed by:

Copyright © 2017 Palaus, Marron, Viejo-Sobera and Redolar-Ripoll. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Marc Palaus, [email protected]

This article is part of the Research Topic

Cognitive and Brain Plasticity Induced by Physical Exercise, Cognitive Training, Video Games and Combined Interventions

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Monday, October 24, 2022

Video gaming may be associated with better cognitive performance in children

Additional research necessary to parse potential benefits and harms of video games on the developing brain.

On Monday, April 10, 2023, a Notice of Retraction and Replacement published for the article featured below . The key findings remain the same. The press release has been updated, in line with the retracted and replacement article, to clarify that attention problems, depression symptoms, and attention-deficit/hyperactivity disorder (ADHD) scores were significantly higher among children who played three hours per day or more compared to children who had never played video games.

A study of nearly 2,000 children found that those who reported playing video games for three hours per day or more performed better on cognitive skills tests involving impulse control and working memory compared to children who had never played video games. Published today in JAMA Network Open , this study analyzed data from the ongoing  Adolescent Brain Cognitive Development (ABCD) Study , which is supported by the National Institute on Drug Abuse (NIDA) and other entities of the National Institutes of Health.

“This study adds to our growing understanding of the associations between playing video games and brain development,” said NIDA Director Nora Volkow, M.D. “Numerous studies have linked video gaming to behavior and mental health problems. This study suggests that there may also be cognitive benefits associated with this popular pastime, which are worthy of further investigation.”

Although a number of studies have investigated the relationship between video gaming and cognitive behavior, the neurobiological mechanisms underlying the associations are not well understood. Only a handful of neuroimaging studies have addressed this topic, and the sample sizes for those studies have been small, with fewer than 80 participants.

To address this research gap, scientists at the University of Vermont, Burlington, analyzed data obtained when children entered the ABCD Study at ages 9 and 10 years old. The research team examined survey, cognitive, and brain imaging data from nearly 2,000 participants from within the bigger study cohort. They separated these children into two groups, those who reported playing no video games at all and those who reported playing video games for three hours per day or more. This threshold was selected as it exceeds the American Academy of Pediatrics screen time guidelines , which recommend that videogaming time be limited to one to two hours per day for older children. For each group, the investigators evaluated the children’s performance on two tasks that reflected their ability to control impulsive behavior and to memorize information, as well as the children’s brain activity while performing the tasks.

The researchers found that the children who reported playing video games for three or more hours per day were faster and more accurate on both cognitive tasks than those who never played. They also observed that the differences in cognitive function observed between the two groups was accompanied by differences in brain activity. Functional MRI brain imaging analyses found that children who played video games for three or more hours per day showed higher brain activity in regions of the brain associated with attention and memory than did those who never played. At the same time, those children who played at least three hours of videogames per day showed more brain activity in frontal brain regions that are associated with more cognitively demanding tasks and less brain activity in brain regions related to vision.  

The researchers think these patterns may stem from practicing tasks related to impulse control and memory while playing videogames, which can be cognitively demanding, and that these changes may lead to improved performance on related tasks. Furthermore, the comparatively low activity in visual areas among children who reported playing video games may reflect that this area of the brain may become more efficient at visual processing as a result of repeated practice through video games.

While prior studies have reported associations between video gaming and increases in violence and aggressive behavior, this study did not find that to be the case. Though children who reported playing video games for three or more hours per day scored higher on measures of attention problems, depression symptoms, and attention-deficit/hyperactivity disorder (ADHD) compared to children who played no video games, the researchers found that these mental health and behavioral scores did not reach clinical significance in either group, meaning, they did not meet the thresholds for risk of problem behaviors or clinical symptoms. The authors note that these will be important measures to continue to track and understand as the children mature.

Further, the researchers stress that this cross-sectional study does not allow for cause-and-effect analyses, and that it could be that children who are good at these types of cognitive tasks may choose to play video games. The authors also emphasize that their findings do not mean that children should spend unlimited time on their computers, mobile phones, or TVs, and that the outcomes likely depend largely on the specific activities children engage in. For instance, they hypothesize that the specific genre of video games, such as action-adventure, puzzle solving, sports, or shooting games, may have different effects for neurocognitive development, and this level of specificity on the type of video game played was not assessed by the study.

“While we cannot say whether playing video games regularly caused superior neurocognitive performance, it is an encouraging finding, and one that we must continue to investigate in these children as they transition into adolescence and young adulthood,” said Bader Chaarani, Ph.D., assistant professor of psychiatry at the University of Vermont and the lead author on the study. “Many parents today are concerned about the effects of video games on their children’s health and development, and as these games continue to proliferate among young people, it is crucial that we better understand both the positive and negative impact that such games may have.”

Through the ABCD Study, researchers will be able to conduct similar analyses for the same children over time into early adulthood, to see if changes in video gaming behavior are linked to changes in cognitive skills, brain activity, behavior, and mental health. The longitudinal study design and comprehensive data set will also enable them to better account for various other factors in the children’s families and environment that may influence their cognitive and behavioral development, such as exercise, sleep quality, and other influences.

The ABCD Study, the largest of its kind in the United States, is tracking nearly 12,000 youth as they grow into young adults. Investigators regularly measure participants’ brain structure and activity using magnetic resonance imaging (MRI) and collect psychological, environmental, and cognitive information, as well as biological samples. The goal of the study is to understand the factors that influence brain, cognitive, and social-emotional development, to inform the development of interventions to enhance a young person’s life trajectory.

The Adolescent Brain Cognitive Development Study and ABCD Study are registered service marks and trademarks, respectively, of the U.S. Department of Health and Human Services

About the National Institute on Drug Abuse (NIDA): NIDA is a component of the National Institutes of Health, U.S. Department of Health and Human Services. NIDA supports most of the world’s research on the health aspects of drug use and addiction. The Institute carries out a large variety of programs to inform policy, improve practice, and advance addiction science. For more information about NIDA and its programs, visit www.nida.nih.gov .

About the National Institutes of Health (NIH): NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit www.nih.gov .

NIH…Turning Discovery Into Health ®

  B Chaarani, et al.  Association of video gaming with cognitive performance among children .  JAMA Open Network.  DOI: 10.1001/jamanetworkopen.2022.35721 (2022).

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1. Although video games were first developed for adults, they are no longer exclusively reserved for the grown ups in the home. In 2006 , Rideout and Hamel reported that as many as 29 percent of preschool children (children between two and six years old) in the United States had played console video games, and 18 percent had played hand-held ones . Given young children’s insatiable eagerness to learn, coupled with the fact that they are clearly surrounded by these media, we predict that preschoolers will both continue and increasingly begin to adopt video games for personal enjoyment . Although the majority of gaming equipment is still designed for a much older target audience, once a game system enters the household it is potentially available for all family members, including the youngest . Portable systems have done a particularly good job of penetrating the younger market.

2. Research in the video game market is typically done at two stages: some time close to the end of the product cycle, in order to get feedback from consumers, so that a marketing strategy can be developed ; and at the very end of the product cycle to ‘fix bugs’ in the game. While both of those types of research are important, and may be appropriate for dealing with adult consumers, neither of them aids in designing better games, especially when it comes to designing for an audience that may have particular needs, such as preschoolers or senior citizens. Instead, exploratory and formative research has to be undertaken in order to truly understand those audiences, their abilities, their perspective, and their needs .

3. In the spring of 2007 , our preschool-game production team at Nickelodeon had a hunch that the Nintendo DS  - with its new features, such as the microphone, small size and portability, and its relatively low price point - was a ripe gaming platform for preschoolers. There were a few games on the market at the time which had characters that appealed to the younger set, but our game producers did not think that the game mechanics or design were appropriate for preschoolers. What exactly preschoolers could do with the system, however, was a bit of a mystery. So we set about doing a study to answer the query: What could we expect preschoolers to be capable of in the context of hand-held game play, and how might the child development literature inform us as we proceeded with the creation of a new outlet for this age group?

Our context in this case was the United States, although the games that resulted were also released in other regions, due to the broad international reach of the characters. In order to design the best possible DS product for a preschool audience we were fully committed to the ideals of a ‘user-centered approach’, which assumes that users will be at least considered, but ideally consulted during the development process. After all, when it comes to introducing a new interactive product to the child market , and particularly such a young age group within it, we believe it is crucial to assess the range of physical and cognitive abilities associated with their specific developmental stage.

4. Revelle and Medoff (2002) review some of the basic reasons why home entertainment systems, computers, and other electronic gaming devices, are often difficult for preschoolers to use. In addition to their still developing motor skills (which make manipulating a controller with small buttons difficult), many of the major stumbling blocks are cognitive . Though preschoolers are learning to think symbolically, and understand that pictures can stand for real-life objects, the vast majority are still unable to read and write. Thus, using text-based menu selections is not viable. Mapping is yet another obstacle since preschoolers may be unable to understand that there is a direct link between how the controller is used and the activities that  appear before them on screen. Though this aspect is changing, in traditional mapping systems real life movements do not usually translate into game-based activity.

5. Over the course of our study, we gained many insights into how preschoolers interact with various platforms, including the DS . For instance, all instructions for preschoolers need to be in voice-over, and include visual representations, and this has been one of the most difficult areas for us to negotiate with respect to game design on the DS. Because the game cartridges have very limited memory capacity, particularly in comparison to console or computer games, the ability to capture large amounts of voice-over data via sound files or visual representations of instructions becomes limited . Text instructions take up minimal memory, so they are preferable from a technological perspective. Figuring out ways to maximise sound and graphics files, while retaining the clear visual and verbal cues that we know are critical for our youngest players, is a constant give and take. Another of our findings indicated that preschoolers may use either a stylus, or their fingers , or both although they are not very accurate with either. One of the very interesting aspects of the DS is that the interface, which is designed to respond to stylus interactions, can also effectively be used with the tip of the finger. This is particularly noteworthy in the context of preschoolers for two reasons. Firstly, as they have trouble with fine motor skills and their hand-eye coordination is still in development, they are less exact with their stylus movements; and secondly, their fingers are so small that they mimic the stylus very effectively, and therefore by using their fingers they can often be more accurate in their game interactions. 

Questions 1-5

Do the following statements agree with the claims of the writer in Reading Passage 3?

YES               if the statement agrees with the claims of the writer

NO                 if the statement contradicts the claims of the writer

NOT GIVEN if it is impossible to say what the writer thinks about this

1 YES NO NOT GIVEN     Video game use amongst preschool children is higher in the US than in other countries. Answer: NOT GIVEN

2 YES NO NOT GIVEN     The proportion of preschool children using video games is likely to rise. Answer: YES

3 YES NO NOT GIVEN     Parents in the US who own gaming equipment generally allow their children to play with it. Answer: NOT GIVEN

4 YES NO NOT GIVEN     The type of research which manufacturers usually do is aimed at improving game design. Answer: NO

5 YES NO NOT GIVEN     Both old and young games consumers require research which is specifically targeted. Answer: YES

Questions 6-10

Complete the summary using the list of words/phrases, A-l , below.  

Problems for preschool users of video games

Preschool children find many electronic games difficult, because neither their motor skills nor their 6 A B C D E F G H I are sufficiently developed. Answer: C

Certain types of control are hard for these children to manipulate, for example, 7 A B C D E F G H I can be more effective than styluses. Answer: E

Also, although they already have the ability to relate 8 A B C D E F G H I Answer: F to real-world objects, preschool children are largely unable to understand the connection between their own 9 A B C D E F G H I Answer: A and the movements they can see on the screen. Finally, very few preschool children can understand 10 A B C D E F G H I Answer: I .

Questions 11-14

C hoose the correct letter, A, B, C or D

11     In 2007, what conclusion did games producers at Nickelodeon come to?

A The preschool market was unlikely to be sufficiently profitable.

B One of their hardware products would probably be suitable for preschoolers.

C Games produced by rival companies were completely inappropriate for preschoolers.

D They should put their ideas for new games for preschoolers into practice. Answer: B

12    The study carried out by Nickelodeon

A was based on children living in various parts of the world.

B focused on the kinds of game content which interests preschoolers.

C investigated the specific characteristics of the target market.

D led to products which appealed mainly to the US consumers. Answer: C

13     Which problem do the writers highlight concerning games instructions for young children?

A Spoken instructions take up a lot of the available memory.

B Written instructions have to be expressed very simply.

C The children do not follow instructions consistently.

D The video images distract attention from the instructions. Answer: A

14     Which is the best title for Reading Passage 3?

A An overview of video games software for the preschool market

B Researching and designing video games for preschool children

C The effects of video games on the behaviour of young children

D Assessing the impact of video games on educational achievement Answer: B

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Super Mario hackers' tricks could protect software from bugs

Video gamers who exploit glitches in games can help experts better understand buggy software, students at the University of Bristol suggest.

Known as 'speedrunners', these types of gamers can complete games quickly by working out their malfunctions.

The students examined four classic Super Mario games, and analysed 237 known glitches within them, classifying a variety of weaknesses. This research explores whether these are the same as the bugs exploited in more conventional software.

Nintendo's Super Mario is the quintessential video game. To understand the sorts of glitches speedrunners exploit, they examined four of the earliest Mario platforming games -- Super Mario Bros (1985), Super Mario Bros. 3 (1988), Super Mario World (1990) and Super Mario 64 (1996). Whilst these games are old, they are still competitively run by speedrunners with new records reported in the news. The games are well also well understood, having been studied by speedrunners for decades, ensuring that there are large numbers of well researched glitches for analysis.

Currently the world record time for conquering Super Mario World stands at a blistering 41 seconds. The team set out to understand 237 known glitches within them, classifying a variety of weaknesses to see if they can help software engineers make applications more robust.

In the Super Mario platforming games Mario must rescue Princess Peach by jumping through an obstacle course of various platforms to reach a goal, avoiding baddies or defeating them by jumping on their heads. Players can collect power-ups along the way to unlock special abilities, and coins to increase their score. The Mario series of games is one of Nintendo's flagship products, and one of the most influential video game series of all time.

Dr Joseph Hallett from Bristol's School of Computer Science explained: "Many early video games, such as the Super Mario games we have examined, were written for consoles that differ from the more uniform PC-like hardware of modern gaming systems.

"Constraints stemming from the hardware, such as limited memory and buses, meant that aggressive optimization and tricks were required to make games run well.

"Many of these techniques (for example, the NES's memory mapping) are niche and can lead to bugs, by being so different to how many programmers usually expect things to work."

"Programming for these systems is closer to embedded development than most modern software, as it requires working around the limits of the hardware to create games. Despite the challenges of programming these systems, new games are still released and retro-inspired."

Categorizing bugs in software allows developers to understand similar problems and bugs.

The Common Weakness Enumeration (CWE) is a category system for hardware and software weaknesses and vulnerabilities. The team identified seven new categories of weakness previously unspecified.

Dr Joseph Hallett from Bristol's School of Computer Science explained: "We found that some of the glitches speed runners use don't have neat categorizations in existing software defect taxonomies and that there may be new kinds of bugs to look for in more general software."

The team thematically analysed with a code book of existing software weaknesses (CWE) -- a qualitative research method to help categorize complex phenomena.

Dr Hallett continued: "The cool bit of this research is that academia is starting to treat and appreciate the work speedrunners do and study something that hasn't really been treated seriously before.

"By studying speedrunners' glitches we can better understand how they do it and whether the bugs they use are the same ones other software gets hacked with.

"It turns out the speedrunners have some tricks that we didn't know about before."

Now the team have turned their hand to studying Pokémon video games.

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Navigating the Esports Betting Market: The Stakes of Competitive Gaming

Understanding the Esports Landscape 

The video games industry has grown rapidly over the past decade, thanks to various developments and innovations among companies and major properties in the market. In a previous post, we highlighted the video games market , which Emergen Research researchers predicted to reach USD 752.86 billion in 2032, with a significant increase from USD 221.3 billion recorded in 2022.

Exploring Multiplayer Games and Their Popularity

Multiplayer games are one of the most popular segments in the global market. Today, multiplayer games are among the most popular titles, attracting thousands of daily players and allowing gamers to socialize and engage in healthy competition. In 2020, there were 932 million online players. With the space increasingly saturated, it's natural for some competition to emerge, which leads us to esports.

The Evolution of Esports: From Hobby to Industry

eSports allows playing with individuals from all over the world instantly from the comfort of the home using an internet connection. ESports is segmented into First Person Shooter (FPS), Multiplayer Online Battle Arena (MOBA), Real-Time Strategy (RTS), and Player versus Player (PvP). FPS is currently the most popular and wanted game genre. In this genre, a player can control one avatar in a single game. Counter-Strike: Go, Call of Duty, and a number of other games are among the most prominent currently.

The esports industry has grown rapidly over the past decade due to various factors like emergence of online sports cafés and increasing competitiveness of gamers globally, increasing live esports coverage platforms, another reason eSports is popular is streaming, which allows users to watch other people (including professional gamers) play on websites like Twitch. Twitch, owned by Amazon, has 15 million daily visitors in 2017, with 355 billion minutes watched on the platform. Game creators, gamers, influencers, and event organizers have all benefitted from the industry's professionalization. The global esports market size reached USD 1,165.6 Million in 2020 and is expected to reach USD 5,199.8 Million in 2028 registering CAGR of 20.7%.

The Rise of Esports Betting

The rise of the esports industry has also led to a subsegment — esports betting. Today, the esports betting market is expected to reach USD 3.3 billion by 2028, from  expected market valuation of USD 2.5 billion in 2024. Riot Games' MOBA title, League of Legends (LoL), is one of the many dominant titles in the segment, with LoL accounting for 45% of the market's revenue share in 2019.

Below, we'll take a closer look at the various factors impacting the rise of esports betting and how they contribute to the esports and video game industry as a whole:

Factors Driving Esports Betting

Comparison to sports betting.

Various aspects of esports borrow from its traditional counterpart — real-life sports. The competitive nature of esports allows video gamers to take their hobbies and interests a step further, as real-life athletes do. Another key aspect of real-life sports that esports borrow from, of course, is betting. For many esports fans, betting is an indirect way to participate in the community while showing support for their favorite teams and players. Betting also provides an avenue for esports fans to have their own healthy competition with fellow fans.

Just as sports fans bet on NBA and Super Bowl outcomes, esports fans tune into their favorite esports games' tournaments and events to root for their favorites. Following esports betting tips can help new and advanced bettors understand the betting odds depending on the game. Like traditional bettors, esports bettors should also consider the most optimal esports betting strategies to prevent going over their betting budget. As with traditional sports betting, esports betting does not guarantee a win. After all, even odds from bookmakers can lead to surprises, as teams and players can have off days, get sick, and undergo a variety of other circumstances that can impact the outcome of a game or tournament.

Ease of Access

Another factor contributing to the rise of esports betting is users' ease of access, thanks to constant technological developments. The Internet, the backbone of online games and competitive gaming, also makes it easy for esports fans and viewers to tune into their respective titles' events and tournaments. Many esports games today are live-streamed on various video platforms like Twitch, YouTube, Facebook, and other localized streaming options in different countries.

In fact, esports livestreams have been dominating streaming platform Twitch's top categories. This includes tournament streams of popular esports titles like League of Legends, Valorant, Counter-Strike, and Dota 2, many of which have established circuit systems where tournaments and matches occur throughout the year. Many esports players, teams, and coaches also livestream on their days off, allowing fans to form connections beyond tournaments.

Accessing esports tournaments and betting sites digitally makes betting much more accessible and doable. Staying tuned to recent developments in your preferred esports title is important for making the most optimal bets. Of course, esports tournaments are also accessible physically, as most major esports titles today hold tournaments in esports venues and stadiums worldwide. Some examples include Counter-Strike's PGL Major Copenhagen 2024, Dota 2's BetBoom Dacha Dubai 2024, and ESL One Birmingham.

Economic Opportunities

Finally, a crucial component of esports betting is the economic opportunities it provides for esports fans. Of course, a lot of money goes into and flows through esports. Some of the biggest esports prize pools are in the millions of dollars, such as a Dota 2 tournament in 2023, boasting a cumulative prize pool of $30.82 million, followed by Arena of Valor ($20.59 million). While these numbers are tempting, professional esports players represent a tiny percentage of the video game market, as they are the best of the best at their respective games.

As such, esports betting is a great way for esports fans to contribute and participate in the ecosystem and create their own economic opportunities. After all, esports betting requires extensive research, studying, and in-game knowledge to improve one's chances of winning, making it a good outlet for gamers who may not have the opportunity to compete professionally or enter the esports or video game industry for other roles.

Ultimately, the esports betting market will continue to grow alongside the general esports market. As video games continue to improve and innovate, players will continue to flock to them for entertainment and other opportunities. The esports industry also offers an alternative for bettors to pursue different opportunities.

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Brock students make mark at provincial video game showcase

Monday, April 22, 2024 | by Gillian Minaker

It was a next-level weekend for Brock University game design students, faculty and staff.

Students from the Interactive Arts and Science (IASC) and GAME programs competed against 150 student teams from across Ontario, showcasing their video games at the 2024 Level Up Showcase and securing multiple top awards in highly sought-after categories.

Among the winning games were Cosmic Justice , which was awarded first place in the Accessibility in Games category, and Dead on Arrival , a high-stakes racing game that took first place in the coveted People’s Choice award category.

Not far behind was Unnatural Selection , which clinched second place in the same category.

Reverex , a collaborative project between Brock and Niagara College third-year students, was honoured with third place for Innovating Technology, highlighting Brock’s commitment to merging technical skill with artistic expression in video game creation.

Aaron Mauro, Associate Professor and Chair of the Department of Digital Humanities, said this weekend’s performance continues to strengthen Brock’s position as a leader in the interactive digital media sector in the province.

“Seeing our game design students excel on such a significant platform continues to enhance our work in bridging academic excellence with industry expectations,” Mauro said.

Known for its competitive spirit and creativity, the annual Level Up Showcase held at the Westin Harbour Castle in Toronto celebrates student talent in game design and allows teams to share their work with the public and industry professionals.

In addition to industry networking and celebrating community, the event also provides a platform and key experiential learning opportunity for students to receive live feedback from people testing their games.

Mauro said the awards reflect the creativity and hard work of DDH students, staff and faculty.

“We are committed to providing a supportive environment that encourages creativity and practical application of knowledge, combining cutting-edge technology and practical experience,” he said.

Read more stories in: Digital Displays , Featured , Humanities , News Tagged with: Aaron Mauro , digital humanities , GAME , humanities , interactive arts and science , Level Up Showcase

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Pokémon Go Rediscover Kanto: all Research Tasks

Check out all tasks and rewards of this surprise event

• Author: Marco Wutz

Wasn’t it supposed to be Sustainability Week in Pokémon Go right now? Yes, it sure was – but Niantic wants you to Rediscover Kanto in a surprise event lasting until May 9, 2024, even though the majority of the player base is absolutely sick of seeing Pokémon from that region with many creatures from newer locations of the series still missing from the game.

So, back to Kanto we go. Aside from some bonuses and the fact that Charizard, Blastoise, and Venusaur can get their Community Day moves if you evolve them during this event, it has a few Special Research and Field Research Tasks in store for you.

Here are all Pokémon Go Rediscover Kanto Research Tasks and rewards .

Pokémon Go Rediscover Kanto: Special Research Tasks

You can claim this Special Research by simply logging into the game between now and May 9, 2024, at 8pm local time. Once you’ve acquired the missions in this way, you can complete all five steps and claim their rewards whenever you want.

Here are all Pokémon Go Rediscover Kanto Special Research Tasks :

Step 1 of 5

• Use 5 Berries to help catch Pokémon : Bulbasaur
• Feed your buddy 5 times : Charmander
• Catch 20 Pokémon from the Kanto region : Squirtle
• Complete all tasks in this step : XP x5,000, Stardust x2,500, Lucky Egg x1

Step 2 of 5

• Use 10 Berries to help catch Pokémon : Poké Ball x20
• Play with your buddy 3 times : Great Ball x15
• Catch 30 Pokémon from the Kanto region : Ultra Ball x10
• Complete all tasks in this step : XP x5,000, Stardust x2,500, Fast TM x3

Step 3 of 5

• Use 15 Berries to help catch Pokémon : Razz Berry x10
• Earn 25 hearts with your buddy : Nanab Berry x10
• Catch 40 Pokémon from the Kanto region : Pinap Berry x10
• Complete all tasks in this step : XP x5,000, Stardust x2,500, Charge TM x3

Step 4 of 5

• Use 20 Berries to help catch Pokémon : Bulbasaur Candy x25
• Earn 4 Candies exploring with your buddy : Charmander Candy x25
• Catch 50 Pokémon from the Kanto region : Squirtle Candy x25
• Complete all tasks in this step : XP x5,000, Stardust x2,500, Lure x1

Step 5 of 5

• Use 151 Berries to help catch Pokémon : XP x15,100
• Earn 15,100 Stardust : XP x15,100
• Catch 151 Pokémon from the Kanto region : XP x15,100
• Complete all tasks in this step : Stardust x10,000, Incubator x1

Pokémon Go Rediscover Kanto: Field Research Tasks

There is one Field Research Task you can get by spinning the photo discs at PokéStops during the event – so until May 9, 2024, at 8pm local time. Once you’ve got it in your inventory, you can finish it whenever you want.

Here are all Pokémon Go Rediscover Kanto Field Research Tasks :

• Power up Pokémon 5 times : Mega Energy (Venusaur) x25, Mega Energy (Charizard) x25, or Mega Energy (Blastoise) x25

That’s that – welcome back to Kanto.

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Haiti PM Henry out as transitional government takes power amid gang violence

Haitian Prime Minister Ariel Henry resigned on Thursday and a transitional council took power with the aim of returning stability to a country where gang violence has caused chaos and misery.

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New Group Joins the Political Fight Over Disinformation Online

The group intends to fight what its leader, Nina Jankowicz, and others have described as a coordinated campaign by conservatives and their allies to undermine researchers who study disinformation.

By Steven Lee Myers and Jim Rutenberg

Two years ago, Nina Jankowicz briefly led an agency at the Department of Homeland Security created to fight disinformation — the establishment of which provoked a political and legal battle over the government’s role in policing lies and other harmful content online that continues to reverberate.

Now she has re-entered the fray with a new nonprofit organization intended to fight what she and others have described as a coordinated campaign by conservatives and others to undermine researchers, like her, who study the sources of disinformation.

Already a lightning rod for critics of her work on the subject, Ms. Jankowicz inaugurated the organization with a letter accusing three Republican committee chairmen in the House of Representatives of abusing their subpoena powers to silence think tanks and universities that expose the sources of disinformation.

“These tactics echo the dark days of McCarthyism, but with a frightening 21st-century twist,” she wrote in the letter on Monday with the organization’s co-founder Carlos Álvarez-Aranyos, a public-relations consultant who in 2020 was involved in efforts to defend the integrity of the American voting system.

The inception of the group, the American Sunlight Project, reflects how divisive the issue of identifying and combating disinformation has become as the 2024 presidential election approaches. It also represents a tacit admission that the informal networks formed at major universities and research organizations to address the explosion of disinformation online have failed to mount a substantial defense against a campaign, waged largely on the right, depicting their work as part of an effort to silence conservatives.

Taking place in the courts, in conservative media and on the Republican-led House Judiciary Select Subcommittee on the Weaponization of the Federal Government, the campaign has largely succeeded in eviscerating efforts to monitor disinformation, especially around the integrity of the American election system.

Many of the nation’s most prominent researchers, facing lawsuits, subpoenas and physical threats, have pulled back.

“More and more researchers were getting swept up by this, and their institutions weren’t either allowing them to respond or responding in a way that really just was not rising to meet the moment,” Ms. Jankowicz said in an interview. “And the problem with that, obviously, is that if we don’t push back on these campaigns, then that’s the prevailing narrative.”

That narrative is prevailing at a time when social media companies have abandoned or cut back efforts to enforce their own policies against certain types of content.

Many experts have warned that the problem of false or misleading content is only going to increase with the advent of artificial intelligence.

“Disinformation will remain an issue as long as the strategic gains of engaging in it, promoting it and profiting from it outweigh consequences for spreading it,” Common Cause, the nonpartisan public interest group, wrote in a report published last week that warned of a new wave of disinformation around this year’s vote.

Ms. Jankowicz said her group would run advertisements about the broad threats and effects of disinformation and produce investigative reports on the backgrounds and financing of groups conducting disinformation campaigns — including those targeting the researchers.

She has joined with two veteran political strategists: Mr. Álvarez-Aranyos, formerly a communications strategist for Protect Democracy, a nonpartisan group that seeks to counter domestic authoritarian threats, and Eddie Vale, formerly of American Bridge, a liberal group devoted to gathering opposition research into Republicans.

The organization’s advisory board includes Katie Harbath, a former Facebook executive who was previously a top digital strategist for Senate Republicans; Ineke Mushovic, a founder of the Movement Advancement Project , a think tank that tracks threats to democracy and gay, lesbian and transgender issues; and Benjamin Wittes, a national security legal expert at the Brookings Institution and editor in chief of Lawfare .

“We need to be a little bit more aggressive about how we think about defending the research community,” Mr. Wittes said in an interview, portraying the attacks against it as part of “a coordinated assault on those who have sought to counter disinformation and election interference.”

In the letter to congressional Republicans, Ms. Jankowicz noted the appearance of a fake robocall in President Biden’s voice discouraging voters in New Hampshire from voting in the state’s primary and artificially generated images of former President Donald J. Trump with Black supporters, as well as renewed efforts by China and Russia to spread disinformation to American audiences.

The American Sunlight Project has been established as a nonprofit under the section of the Internal Revenue Code that allows it greater leeway to lobby than tax-exempt charities known as 501(c)(3)s. It also does not have to disclose its donors, which Ms. Jankowicz declined to do, though she said the project had initial commitments of $1 million in donations.

The budget pales in comparison with those behind the counteroffensive like America First Legal, the Trump-aligned group that, with a war chest in the tens of millions of dollars, has sued researchers at Stanford and the University of Washington over their collaboration with government officials to combat misinformation about voting and Covid-19.

The Supreme Court is expected to rule soon in a federal lawsuit filed by the attorneys general of Missouri and Louisiana accusing government agencies of using the researchers as proxies to pressure social media platforms to take down or restrict the reach of accounts.

The idea for the American Sunlight Project grew out of Ms. Jankowicz’s experience in 2022 when she was appointed executive director of a newly created Disinformation Governance Board at the Department of Homeland Security.

From the instant the board became public, it faced fierce criticism portraying it as an Orwellian Ministry of Truth that would censor dissenting voices in violation of the First Amendment, though in reality it had only an advisory role and no enforcement authority.

Ms. Jankowicz, an expert on Russian disinformation who once served as an adviser to Ukraine’s Ministry of Foreign Affairs, stepped down shortly after her appointment. Even then, she faced such a torrent of personal threats online that she hired a security consultant. The board was suspended and then, after a short review, abolished.

“I think we’re existing in an information environment where it is very easy to weaponize information and to make it seem sinister,” Mr. Álvarez-Aranyos said. “And I think we’re looking for transparency. I mean, this is sunlight in the very literal sense.”

Ms. Jankowicz said that she was aware that her involvement with the new group would draw out her critics, but that she was well positioned to lead it because she had already “gone through the worst of it.”

Steven Lee Myers covers misinformation and disinformation from San Francisco. Since joining The Times in 1989, he has reported from around the world, including Moscow, Baghdad, Beijing and Seoul. More about Steven Lee Myers

Jim Rutenberg is a writer at large for The Times and The New York Times Magazine and writes most often about media and politics. More about Jim Rutenberg

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• Front Psychol

The Association Between Video Gaming and Psychological Functioning

Juliane m. von der heiden.

1 Department of Psychology, Johannes Gutenberg University Mainz, Mainz, Germany

Beate Braun

2 Department of Psychosomatic Medicine, University Medical Center, Mainz, Germany

Kai W. Müller

Boris egloff, associated data.

Video gaming is an extremely popular leisure-time activity with more than two billion users worldwide ( Newzoo, 2017 ). However, the media as well as professionals have underscored the potential dangers of excessive video gaming. With the present research, we aimed to shed light on the relation between video gaming and gamers’ psychological functioning. Questionnaires on personality and psychological health as well as video gaming habits were administered to 2,734 individuals (2,377 male, 357 female, M age = 23.06, SD age = 5.91). Results revealed a medium-sized negative correlation between problematic video gaming and psychological functioning with regard to psychological symptoms, affectivity, coping, and self-esteem. Moreover, gamers’ reasons for playing and their preferred game genres were differentially related to psychological functioning with the most notable findings for distraction-motivated players as well as action game players. Future studies are needed to examine whether these psychological health risks reflect the causes or consequences of video gaming.

Introduction

Video gaming is a very popular leisure activity among adults ( Pew Research Center, 2018 ). The amount of time spent playing video games has increased steadily, from 5.1 h/week in 2011 to 6.5 h/week in 2017 ( The Nielsen Company, 2017 ). Video gaming is known to have some benefits such as improving focus, multitasking, and working memory, but it may also come with costs when it is used heavily. By spending a predominant part of the day gaming, excessive video gamers are at risk of showing lower educational and career attainment, problems with peers, and lower social skills ( Mihara and Higuchi, 2017 ). On the one hand, video game use is widespread, and it may come with certain precursors as well as consequences. On the other hand, little is known about the relations between various video gaming habits and psychological functioning. This study aims to shed light on these important relations using a large sample.

A video game is defined as “a game which we play thanks to an audiovisual apparatus and which can be based on a story” ( Esposito, 2005 ). In the last few years, the amount of scientific research devoted to video game playing has increased (e.g., Ferguson, 2015 ; Calvert et al., 2017 ; Hamari and Keronen, 2017 ). Most scientific studies in this area of research have focused on the extent of video game play and its diverse correlates. While some researchers have emphasized the benefits of game playing and even suggested a therapeutic use of video games ( Primack et al., 2012 ; Granic et al., 2014 ; Colder Carras et al., 2018 ), others have been intrigued by its potential dangers ( Anderson et al., 2010 ; Müller and Wölfling, 2017 ).

Parents and professionals may be worried about their excessively playing children being “addicted.” However, problematic and potentially addictive video game use goes beyond the extent of playing (in hours per week; Skoric et al., 2009 ). It also includes such issues as craving, loss of control, and negative consequences of excessive gaming. While it is still a matter of debate whether problematic video game play should be considered a behavioral addiction , its status as a mental disorder has been clarified since the release of the DSM-5 in 2013. In the DSM-5, the American Psychiatric Association (2013) defined Internet Gaming Disorder with diagnostic criteria closely related to Gambling Disorder. Generally, this decision has been supported by many researchers (e.g., Petry et al., 2014 ) but has also caused controversies. Researchers have criticized the selection of diagnostic criteria and the vague definition of the Internet Gaming Disorder construct, which excludes offline games from being related to addictive use (e.g., Griffiths et al., 2016 ; Bean et al., 2017 ).

Several studies, literature reviews, and meta-analyses have focused on the correlates of problematic video gaming, usually assessed as a continuum with addiction marking the upper end of the scale (e.g., Ferguson et al., 2011 ; Kuss and Griffiths, 2012 ). The degree of addictive video game use has been found to be related to personality traits such as low self-esteem ( Ko et al., 2005 ) and low self-efficacy ( Jeong and Kim, 2011 ), anxiety, and aggression ( Mehroof and Griffiths, 2010 ), and even to clinical symptoms of depression and anxiety disorders ( Wang et al., 2018 ). Potential consequences of video game use have been identified as well, such as a lack of real-life friends ( Kowert et al., 2014a ), stress and maladaptive coping ( Milani et al., 2018 ), lower psychosocial well-being and loneliness ( Lemmens et al., 2011 ), psychosomatic problems ( Müller et al., 2015 ; Milani et al., 2018 ), and decreased academic achievement ( Chiu et al., 2004 ; Gentile, 2009 ). Effect sizes have varied widely across studies ( Ferguson et al., 2011 ). There seem to be sex and age differences with regard to video gaming behavior: potentially problematic video gaming was found to be more likely among males than females (e.g., Greenberg et al., 2010 ; Estévez et al., 2017 ), and among younger gamers ( Rehbein et al., 2016 ).

In addition to looking at problematic video game use and its relation to psychological functioning, it is relevant to also focus on why individuals play video games. Players use video games for very different reasons ( Ryan et al., 2006 ; Yee, 2006 ) such as to distract themselves from daily hassles or because they enjoy the social relationships they have developed in the virtual world. Potentially problematic video gaming has been found to be related to various reasons for playing such as coping and escape ( Hussain and Griffiths, 2009 ; Schneider et al., 2018 ), socialization ( Laconi et al., 2017 ), and personal satisfaction ( Ng and Wiemer-Hastings, 2005 ). Coping ( Laconi et al., 2017 ), social interaction, and competition were among the main reasons for gaming among males but not among females ( Lucas and Sherry, 2004 ). Mixed results emerged concerning age differences ( Greenberg et al., 2010 ), but especially younger gamers seemed to be motivated for video gaming by social interactions ( Hilgard et al., 2013 ). However, so far it remains unclear to what extent people’s various reasons for playing video games are differentially related to their psychological functioning.

Besides investigating the links between potentially problematic video game use and psychological functioning as well as between reasons for playing video games and psychological functioning, it is relevant to also look at which game genres individuals prefer. Correlates of preferences for certain game genres (e.g., simulation, strategy, action, role-playing) are cognitive enhancement ( Dobrowolski et al., 2015 ; Bediou et al., 2018 ), but also the amount of time spent playing ( Lemmens and Hendriks, 2016 ; Rehbein et al., 2016 ) and psychopathological symptoms ( Laconi et al., 2017 ). Males were shown to prefer action and strategy games, whereas females showed a preference for games of skill ( Scharkow et al., 2015 ; Rehbein et al., 2016 ). Younger gamers seemed to prefer action games, older players more so games of skill ( Scharkow et al., 2015 ). However, it is not yet understood to what extent preferences for certain video game genres are differentially related to psychological functioning.

Typically, research has focused merely on violent video games (e.g., Anderson and Bushman, 2001 ; Elson and Ferguson, 2014 ) or one specific game within one specific game genre (frequently World of Warcraft; Graham and Gosling, 2013 ; Visser et al., 2013 ; Herodotou et al., 2014 ), thereby neglecting the variety of possible gaming habits across various game genres.

In the present study, our objective was to examine the relation between video gaming and psychological functioning in a fine-grained manner. For this purpose, we examined psychological functioning by employing various variables such as psychological symptoms, coping strategies, and social support. Likewise, we assessed video gaming in a similarly detailed way, ranging from (a) problematic video game use, (b) the reasons for playing, to (c) the preferred game genres. This strategy prevented us from making potentially invalid generalizations about video gaming in general and allowed us to examine the spectrum of gaming habits and the respective relations between such habits and a diverse set of variables representing psychological functioning.

Playing video games excessively should be appealing to individuals with poor psychological functioning because games allow people to avoid their everyday problems and instead immerse themselves in another environment ( Taquet et al., 2017 ). Moreover, video games offer people a chance to connect with other people socially despite any more or less evident psychological problems they may have ( Kowert et al., 2014b ; Mazurek et al., 2015 ). On the other hand, potentially problematic video game use may also lead to psychological problems because it reduces the amount of time and the number of opportunities gamers have to practice real-life behavior ( Gentile, 2009 ). Thus, we expected to find a negative correlation between problematic video gaming and variables representing psychological functioning such that we expected more potentially problematic video game use to be related to dysfunctional coping strategies ( Wood and Griffith, 2007 ), negative affectivity ( Mathiak et al., 2011 ), and poor school performance ( Mihara and Higuchi, 2017 ). Moreover, we expected to find differential correlates of people’s reasons for playing video games and their psychological functioning: Playing for escape-oriented reasons such as distraction should go along with diverse indices of poor psychological functioning ( Király et al., 2015 ), whereas playing for gain-oriented reasons such as the storyline or the social connections in the game should be related to adequate psychological functioning ( Longman et al., 2009 ). Also, we expected to find people’s preferred game genres (e.g., strategy, action) to be differentially related to their psychological functioning ( Park et al., 2016 ). Finally, we aimed to shed light on the unique contribution of each measure of psychological functioning to the prediction of problematic video game use.

Materials and Methods

Participants 1.

A total of N = 2,891 individuals (2,421 male, 470 female) with a mean age of 23.17 years ( SD = 5.99, Range: 13–65) participated in our study. Of these participants, N = 2,734 (95%) confirmed their use of video games and were thus included in further analyses (2,377 male, 357 female, with a mean age of 23.06 years; SD = 5.91, Range: 13–65). The distribution of participants with regard to sex and age mirrors the findings of past research with males and younger individuals being more likely to play video games (e.g., Griffiths et al., 2004 ). Participants’ place of residence was Germany.

Procedure and Instruments 2

We posted links to our online questionnaire on various online forums as well as on popular online game sites. To achieve heterogeneity of the sample, no exclusion criteria other than having access to the Internet and understanding German were specified. As an incentive to participate in the study, four vouchers of 50€ were raffled.

Video Gaming

Potentially problematic video game use.

The AICA-S, the Scale for the Assessment of Internet and Computer game Addiction ( Wölfling et al., 2016 ), was used to assess participants’ gaming behavior with regard to potential problematic use. Based on the DSM criteria for Internet Gaming Disorder (tolerance, craving, loss of control, emotion regulation, withdrawal, and unsuccessful attempts to cut back), this standardized self-report scale consists of 15 items usually with a five-point scale ranging from 1 ( never ) to 5 ( very often ). The final score (Min = 0, Max = 27 points) is computed using weighted scoring (items with an item-total correlation > 0.55 in the norm sample are weighted double; Wölfling et al., 2011 ). The AICA-S score can be used to differentiate between regular (0–6.5 points) and problematic use of video games (7–13 points: abuse; 13.5–27 points: addiction). In our sample, N = 2,265 (83%) were identified as regular gamers, and N = 469 (17%) as problematic gamers. We used the AICA-S as a continuous variable for all further analyses ( M = 3.98, SD = 3.22, Range: 0–24). The instrument has been validated for different age groups in the general population and in clinical samples ( Müller et al., 2014a , 2019 , but note small sample size; Müller et al., 2014b ). Cronbach’s alpha was α = 0.70. As expected, the AICA-S score was correlated with male sex ( r = 0.17 ∗∗∗ ) and age ( r = –0.15 ∗∗∗ ). On average, participants played video games for M = 4.09 hours per weekday ( SD = 4.44, Range: 0–24), and M = 4.21 h per day at the weekend ( SD = 2.99, Range: 0–24).

Reasons for playing

Gamers indicated how often they played video games for certain reasons. They rated each of 10 reasons separately on Likert scales ranging from 1 ( never ) to 4 ( very often ). The most prevalent reasons were relaxation ( M = 2.96, SD = 0.91), amusement ( M = 2.94, SD = 0.85), and because of the storyline ( M = 2.67, SD = 1.10).

Game genres

Gamers were asked how often they usually played various video game subgenres such as first-person shooter, round-based strategy, massively multiplayer online role-playing games (MMORPGs), life simulations, and others. Ratings were made on Likert scales ranging from 1 ( never ) to 4 ( very often ). Using Apperley’s (2006) classification of game genres, we categorized the subgenres into the main genres action ( M = 2.54, SD = 0.84), strategy ( M = 2.13, SD = 0.80), role-playing ( M = 2.01, SD = 0.73), and simulation ( M = 1.58, SD = 0.44). A cluster for unclassified subgenres ( M = 1.54, SD = 0.39) was added to additionally account for such subgenres as jump’n’runs and games of skill. Descriptive statistics and intercorrelations for all measures (including sex and age) are presented in Supplementary Tables S1–S4 .

Psychological Functioning

Participants provided ratings of their psychological functioning on the following constructs:

General psychopathology

The SCL-K-9 ( Klaghofer and Brähler, 2001 ), a short version of the SCL-90-R ( Derogatis, 1975 ), was administered to assess participants’ subjective impairment regarding psychological symptoms (somatization, obsessive-compulsive, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, paranoid ideation, and psychoticism). The SCL-K-9 score is strongly correlated with the original score of the SCL-90-R ( r = 0.93). The 9 items were answered on 5-point Likert-type scales ranging from 1 ( do not agree at all ) to 5 ( agree completely ). Cronbach’s alpha was satisfactory (α = 0.77).

We assessed 10 coping strategies with the Brief COPE ( Carver, 1997 ; German version by Knoll et al., 2005 ), which is the shorter version of the COPE ( Carver et al., 1989 ): self-distraction, denial, substance use, venting, self-blame, behavioral disengagement, acceptance, active coping, planning, and positive reframing. The two items per subscale were administered on 5-point Likert-type scales ranging from 1 ( never ) to 5 ( very often ). Intercorrelations of the two items per subscale ranged from r = 0.32, p < 0.001 for positive reframing to r = 0.78, p < 0.001 for substance use (with one exception: r = -0.05, p = 0.01 for self-distraction).

We measured general affect as a trait and affect during video gaming as a state using the German version ( Krohne et al., 1996 ) of the Positive and Negative Affect Schedule (PANAS; Watson et al., 1988 ). On a 5-point Likert-type scale ranging from 1 ( not at all ) to 5 ( completely ), participants rated the intensity of 20 adjectives. Cronbach’s alpha was α = 0.78 for general positive affect, α = 0.83 for general negative affect, α = 0.85 for positive affect while playing, and α = 0.83 for negative affect while playing.

The measure for the assessment of shyness in adults ( Asendorpf, 1997 ) consists of 5 items that were answered on a 5-point Likert-type scale ranging from 1 ( not at all ) to 5 ( completely ). Cronbach’s alpha was excellent (α = 0.86).

We administered the German version ( Elbing, 1991 ) of the NYU Loneliness Scale ( Rubenstein and Shaver, 1982 ). The 4 items were answered on 5- to 6-point Likert-type scales. Cronbach’s alpha was satisfactory (α = 0.79).

Preference for solitude

A 10-item measure of preference for solitude ( Nestler et al., 2011 ) was answered on a 6-point Likert-type scale ranging from 1 ( not at all ) to 6 ( completely ). Cronbach’s alpha was excellent (α = 0.86).

Life satisfaction

Participants answered a one-item life satisfaction measure on a 4-point Likert-type scale ranging from 1 ( not at all ) to 4 ( completely ).

Self-esteem

We administered the German version ( von Collani and Herzberg, 2003 ) of the Rosenberg Self-Esteem Scale (RSES; Rosenberg, 1979 ). The 10 items were answered on a 4-point Likert-type scale ranging from 1 ( not at all ) to 4 ( completely ). Cronbach’s alpha was excellent (α = 0.88).

Self-efficacy

We administered a 10-item generalized self-efficacy scale ( Schwarzer and Jerusalem, 1995 ), which was answered on a 4-point Likert-type scale ranging from 1 ( not at all ) to 4 ( completely ). Cronbach’s alpha was excellent (α = 0.86).

Social support and friends

We administered the perceived available social support subscale from the Berlin Social Support Scales (BSSS; Schwarzer and Schulz, 2003 ). The 8 items were answered on a 5-point Likert-type scale ranging from 1 ( not at all ) to 5 ( completely ). Cronbach’s alpha was excellent (α = 0.94). Participants indicated how many offline friends and offline acquaintances they had ( r = 0.44, p < 0.001) as well as how many online friends and online acquaintances they had ( r = 0.33, p < 0.001). Due to left-skewed distributions, we logarithmized the data before aggregation.

Participants reported their grade point average. German grades are assessed on a scale that ranges from 1 ( excellent ) to 6 ( insufficient ). Thus, higher scores indicate worse grades.

Participants further reported their sex and age. Both were used as control variables in further analyses.

In a first step, we computed zero-order correlations between the video gaming variables and the measures of psychological functioning. In a second step, we computed partial correlations in which we controlled for sex and age because past research has repeatedly shown that sex and age are correlated with both video gaming ( Homer et al., 2012 ; Mihara and Higuchi, 2017 ) and psychological functioning ( Kessler et al., 2007 ; Nolen-Hoeksema, 2012 ). Finally, we explored the unique contribution of each measure of psychological functioning to the prediction of potentially problematic video gaming. Therefore, we computed regressions with potentially problematic video gaming as the dependent variable and sex, age, and the measures of psychological functioning as predictors (entered simultaneously into the regression equation). By employing this procedure, we were able to determine the effect that each variable had over and above the other ones. For instance, we could identify whether general psychopathology was predictive of potentially problematic video game use when the influence of all other variables (e.g., shyness, loneliness, and others) was held constant.

Additionally, we included analyses regarding sex and age differences in the link between video gaming and psychological functioning. Since we collected a self-selected sample where different sexes and age groups were not represented equally, our findings are only preliminary, but may stimulate future research.

Potentially Problematic Video Game Use and Psychological Functioning

First, we examined whether potentially problematic video game use was related to various psychological functioning variables. As can be seen in Table 1 , the results for the zero-order correlations were similar to those for the partial correlations in which we controlled for sex and age. A medium-sized positive relation to the potentially problematic use of video games emerged for the presence of psychological symptoms including depression, anxiety, and hostility. Furthermore, several coping strategies were differentially associated with the potentially problematic use of video games: Self-blame and behavioral disengagement showed the strongest positive relations to potentially problematic video game use, followed by denial, acceptance, substance use, self-distraction, and venting. Planning, active coping, and, to a lesser extent, positive reframing were negatively associated with the potentially problematic use of video games. Moreover, the association with potentially problematic video game use was negative for general positive affect and positive and larger in size for general negative affect. However, potentially problematic video game use was clearly positively associated with the experience of both positive and negative affect while playing. Further, a preference for solitude, shyness, and loneliness were positively correlated with the potentially problematic use of video games. Lower self-esteem, lower life satisfaction, and, to a lesser extent, poorer perceived social support and lower self-efficacy went along with potentially problematic video game use. There was an association between fewer offline friends and acquaintances but more online connections with potentially problematic video gaming. Finally, poorer performance in school (i.e., higher grades) was related to the potentially problematic use of video games. These results suggest that potentially problematic video gaming goes along with poor psychological functioning and vice versa.

Associations between potentially problematic video gaming and psychological functioning.

Reasons for Playing Video Games and Psychological Functioning

Second, we investigated whether players’ reasons for playing video games were differentially related to the psychological functioning variables. Table 2 presents the partial correlations, controlling for sex and age. Using video games to distract oneself from stress was clearly connected to a high level of psychological symptoms. Distraction-motivated gamers preferred coping strategies such as self-blame, behavioral disengagement, self-distraction, denial, substance use, venting, and acceptance, but they neglected active coping and planning. They showed less general positive affect and more negative affect both in general and while playing as well as more positive affect while playing. These gamers further reported low self-esteem and low life satisfaction, loneliness, a preference for solitude, shyness, a lack of self-efficacy and social support, and poor achievement in school. A similar but somewhat less extreme picture was revealed for gamers who played video games in order to have something to talk about . However, these gamers reported more online connections. Gamers who played video games to improve their real-life abilities also reported more online connections. In addition, these gamers showed higher levels of general positive affect. The strongest association with online friends and acquaintances emerged, as expected, for gamers who played because of the social relations in the virtual world. Although all reasons for playing video games were related to positive affect while playing, the strongest associations emerged for gamers who played because of the social relations , to stimulate their imagination , and for curiosity . It is interesting that, for gamers who played video games because of the storyline and for relaxation , there was a relation only to positive but not to negative affect while playing. Reasons for playing were only weakly related to sex and age (see Supplementary Table S2 ). In sum, several reasons for playing video games were differentially associated with psychological functioning.

Associations between reasons for playing video games and psychological functioning.

Video Game Genre and Psychological Functioning

Third, we examined whether players’ preferences for different video game genres were differentially associated with the measures of psychological functioning. Table 3 shows the partial correlations in which we controlled for sex and age. There was a weak connection between general psychological symptoms and all of the video game genres we investigated except strategy. A preference for action games had the strongest association with affect while playing. Thus, action games seem to be both rewarding and a source of frustration. A preference for action games went along with poorer school performance. Gamers who preferred role-playing games scored higher on shyness and a preference for solitude and lower on self-esteem; they also reported fewer offline connections. By contrast, preferences for games of the unclassified category on average went along with a larger number of offline friends and more positive affect, both while playing and in general. Two game genres (i.e., role-playing and unclassified games) were related to the coping strategy of self-distraction. Because preferred game genre was related to participants’ sex (see Supplementary Table S3 ), we had a more detailed look at the correlations between preferred game genre and psychological functioning separately for both sexes: For males ( n = 2,377), the strongest correlation between general psychopathology and game genre emerged for action ( r = 0.08, p < 0.001), followed by role playing ( r = 0.07, p < 0.01), and unclassified ( r = 0.07, p < 0.01). For females ( n = 357), the strongest relation between general psychopathology and game genre emerged for simulation ( r = 0.17, p < 0.01). Differences were also found regarding the strength of the relation between number of friends online and the genre action: r = 0.06, p < 0.01 for males, and r = 0.27, p < 0.001 for females. Similarly, preferred game genre was related to participants’ age (see Supplementary Table S3 ). However, there were merely differences with regard to the relation of psychological functioning and game genre, when analyzed separately for different age groups (<19 years, n = 557; 19–30 years, n = 1916; >31 years, n = 261). In sum, our results speak to the idea that individuals with different levels of psychological functioning differ in their choices of game genres and vice versa.

Associations between preferred video game genre and psychological functioning.

Predicting Potentially Problematic Video Game Use by Psychological Functioning Variables

In a final step, we entered all of the investigated psychological functioning variables as well as sex and age as predictors of the potentially problematic use of video games. By employing this procedure, we were able to determine the unique contribution of each psychological functioning variable when the influence of all other variables was held constant. As Table 4 shows, the number of online friends and acquaintances as well as positive affect while playing were most predictive of potentially problematic video game use over and above all other variables. General psychopathology, a lack of offline connections, and poor school performance were weaker but still relevant predictors of potentially problematic video game use.

Prediction of potentially problematic video game use by psychological functioning variables.

With this study, we aimed to shed light on the association of diverse video gaming habits with gamers’ psychological functioning. Drawing on a large sample, our results revealed a medium-sized relation between potentially problematic video game use and poor psychological functioning with regard to general psychological symptoms, maladaptive coping strategies, negative affectivity, low self-esteem, and a preference for solitude as well as poor school performance. These findings are in line with those of prior work (e.g., Kuss and Griffiths, 2012 ; Milani et al., 2018 ). Also, reasons for playing video games were differentially related to psychological functioning with the most pronounced findings for escape-oriented in contrast to gain-oriented motives. Specifically, distraction-motivated gaming went along with higher symptom ratings, lower self-esteem, and more negative affectivity, whereas playing to establish social relationships in the virtual world was related to a larger number of online connections and more positive affect while playing. Furthermore, there were only weak relations between the preferred game genres and psychological functioning. The action games genre was associated with the strongest ratings of affect while playing. These results on reasons and genres may help to explain conflicting findings of former studies, because in our work we examined various reasons for playing, several game genres, and various aspects of psychological functioning simultaneously. Finally, positive affect while playing and a larger number of online friends were the strongest unique predictors of potentially problematic video game use, followed by psychological symptoms, a lack of offline connections, and poor school performance. These findings suggest that, on the one hand, independent of one’s psychological conditions, enjoying oneself during gaming (i.e., experiencing positive affect, connecting with online friends) may go along with potentially problematic use of video games. On the other hand, poor psychological functioning seems to be a unique risk factor for potentially problematic video gaming.

The presented results are generally in line with previous work that has identified a connection between video gaming and psychological health, academic problems, and social problems ( Ferguson et al., 2011 ; Müller et al., 2015 ). However, our study moved beyond prior research by providing in-depth analyses of both video gaming habits (including potentially problematic use, reasons for playing, and preferred game genre) and psychological functioning (including psychological symptoms, coping styles, affectivity, as well as variables that are related to individuals and their social environments). In addition, we identified unique predictors of potentially problematic video game use.

How can the findings on differential relations between video gaming and various indices of psychological functioning – ranging from beneficial results ( Latham et al., 2013 ) to unfavorable results ( Barlett et al., 2009 ; Möller and Krahé, 2009 ; Anderson et al., 2010 ) – be integrated? According to Kanfer and Phillips (1970) , problematic behavior (e.g., excessive video gaming) can be understood as a function of the situation (e.g., being rejected by a peer); the organism (e.g., low self-esteem); the person’s thoughts, physical reactions, and feelings (e.g., sadness, anger); and finally, the short- as well as long-term consequences of the behavior (termed SORKC model). In the short run, according to our results, playing video games may be a way to distract oneself from everyday hassles and may lead to positive affect while playing and a feeling of being connected to like-minded people, all of which are factors that have an immediate reinforcing value. In the long run, however, spending many hours per day in front of a computer screen may prevent a person from (a) developing and practicing functional coping strategies, (b) finding friends and support in the social environment, and (c) showing proper school achievement, factors that are potentially harmful to the person. Thus, differentiating between short- and long-term perspectives may help us understanding the differential correlates of intensive video gaming.

When is it appropriate to speak of video game addiction? More and more researchers have suggested a continuum between engagement ( Charlton and Danforth, 2007 ; Skoric et al., 2009 ) and pathological gaming/addiction, instead of a categorical perspective. In part, this recommendation has also been followed in the DSM-5 ( American Psychiatric Association, 2013 ) where Internet Gaming Disorder is classified with different degrees of severity, ranging from mild to moderate to severe, according to the functional impairment associated with it. The AICA-S also allows for a differential perspective on gaming behavior by providing ways to assess both the time spent playing video games and the main DSM criteria that indicate Internet Gaming Disorder. However, in our study we did not aim at making a diagnosis, but at having a closer look at potentially problematic gaming behavior and its correlates in a non-clinical sample.

In sum, it seems relevant to assess not only the extent of video game use but also the reasons behind this behavior (e.g., distraction) and the concrete rewards that come from playing (e.g., the experience of strong affect while playing action games) to fully understand the relation between video gaming and psychological functioning.

Limitations and Future Directions

With the present study, we aimed to uncover the association between video gaming and psychological functioning. Our approach was cross-sectional and warrants interpretative caution because correlations cannot determine the direction of causation. It remains unclear whether potentially problematic gaming is a factor that contributes to the development of psychological dysfunction or whether psychological dysfunction contributes to potentially problematic gaming. Also, a third factor (e.g., preexisting mental difficulties) may produce both psychological dysfunction and potentially problematic gaming. Thus, longitudinal studies that are designed to identify the causal pathway may provide a promising avenue for future research. Future studies may also answer the question whether the link between video gaming and psychological functioning is moderated by sex, age, the reasons for playing, or the preferred game genre. In addition, it is important not to forget that the present results are based on a self-selected sample in which potentially problematic video gamers were overrepresented (e.g., Festl et al., 2013 , for a representative sample). Thus, future research should replicate our findings in a representative sample. Further, we relied on self-reported data, which is a plausible method for assessing inner affairs such as people’s reasons for their behaviors, but it would be helpful to back up our findings with evidence derived from sources such as peers, caregivers, and health specialists. Our work reflects only a first approach to the topic, and future work may additionally collect in-game behavioral data from the players ( McCreery et al., 2012 ; Billieux et al., 2013 ) to objectively and more specifically investigate diverse patterns of use. Furthermore, one must not forget that the used taxonomy to classify video game genres is only one of various possible options and one should “think of each individual game as belonging to several genres at once” ( Apperley, 2006 , p. 19). Finally, some of the effects reported in our paper were rather modest in size. This is not surprising considering the complexity and multiple determinants of human behavior. In our analyses, we thoroughly controlled for the influence of sex and age and still found evidence that video gaming was differentially related to measures of psychological functioning.

The current study adds to the knowledge on gaming by uncovering the specific relations between video gaming and distinct measures of psychological functioning. Potentially problematic video gaming was found to be associated with positive affect and social relationships while playing but also with psychological symptoms, maladaptive coping strategies, negative affectivity, low self-esteem, a preference for solitude, and poor school performance. Including gamers’ reasons for playing video games and their preferred game genres helped deepen the understanding of the specific and differential associations between video gaming and psychological health. This knowledge might help developing adequate interventions that are applied prior to the occurrence of psychological impairments that may go along with potentially problematic video gaming.

Ethics Statement

In our online survey, participants were given information on voluntary participation, risks, confidentiality/anonymity, and right to withdraw. Whilst participants were not signing a separate consent form, consent was obtained by virtue of completion. We implemented agreed procedures to maintain the confidentiality of participant data.

Author Contributions

BB, BE, JH, and KM conceived and designed the study. BB, JH, and KM collected and prepared the data. JH analyzed the data. BE and JH wrote the manuscript.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

1 The data were gathered as part of a larger project ( Stopfer et al., 2015 ; Braun et al., 2016 ). However, the analyses in the present article do not overlap with analyses from previous work.

2 Other measures were administered, but they were not relevant to the present research questions and are thus not mentioned in this paper. The data set and analysis script supporting the conclusions of this manuscript can be retrieved from https://osf.io/emrpw/?view_only=856491775efe4f99b407e258c2f2fa8d .

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2019.01731/full#supplementary-material

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