Possible End to an Endless Quest? Cognitive Bias Modification for Excessive Multiplayer Online Gamers

Abstract

Cognitive biases have previously been recognized as key mechanisms that contribute to the development, maintenance, and relapse of addictive behaviors. The same mechanisms have been recently found in problematic computer gaming. The present study aims to investigate whether excessive massively multiplayer online role-playing gamers (EG) demonstrate an approach bias toward game-related cues compared to neutral stimuli; to test whether these automatic action tendencies can be implicitly modified in a single session training; and to test whether this training affects game urges and game-seeking behavior. EG (n = 38) were randomly assigned to a condition in which they were implicitly trained to avoid or to approach gaming cues by pushing or pulling a joystick, using a computerized intervention (cognitive bias modification via the Approach Avoidance Task). EG demonstrated an approach bias for gaming cues compared with neutral, movie cues. Single session training significantly decreased automatic action tendencies to approach gaming cues. These effects occurred outside subjective awareness. Furthermore, approach bias retraining reduced subjective urges and intentions to play, as well as decreased game-seeking behavior. Retraining automatic processes may be beneficial in changing addictive impulses in EG. Yet, large-scale trials and long-term follow-up are warranted. The results extend the application of cognitive bias modification from substance use disorders to behavioral addictions, and specifically to Internet gaming disorder. Theoretical implications are discussed.

Introduction

Massively multiplayer online role-playing games (MMORPG) can be defined as games in which numerous players around the globe simultaneously inhabit a virtual realm, adopt alternative personas, and interact with one another.¹ Of all online gamers, 46% play MMORPGs,² confirming their position as the most popular online games, with a recent estimation of 20 million MMORPG players around the world.³ MMORPG players report increased rates of problematic use and life interference compared with non-MMORPG players.^4,5 Furthermore, problematic and uncontrolled involvement in playing MMORPGs is the most frequently reported complaint by people seeking help for an Internet-related problem.⁶ MMORPGs are the most prevalent subcategory of online gaming addiction due to their characteristics of being socially interactive, endless, and time-consuming, as well as by enabling escapism, immersion, fast achievements, and adventures unmet in real life.^7–9

Many studies indicated that neuro-bio-psychosocial signs of excessive Internet/online gaming parallel characteristics of substance and behavioral addictions, mainly pathological gambling.^10–12 For example, brain structural and functional correlates of excessive gaming, such as changes in reward-learning circuitry and abnormalities in frontal brain regions that are believed to be responsible for cognitive control and control of inhibition resemble those found in problematic alcohol users.^13–15 Symptoms include behavioral and cognitive salience, mood modification, tolerance, withdrawal, preoccupation, relapse, and craving.^9,16,17 Excessive MMORPGers (EG) also express high novelty-seeking, risk-taking tendency and low self-esteem,^18–20 and suffer significant psychological and health negative consequences.^11,21 Therefore, the DSM-5 committee, considering substance use and addictive disorders, proposed diagnostic criteria of Internet gaming disorder (IGD), thus providing scientists and clinicians with a consistent classification for research that will enable IGD to be included in future versions of the DSM.^22–24

Impulsivity, a central factor involved in the etiology of addictive behaviors, has been reported to be associated with and predict IGD.^25–27 Several other aspects of compromised executive functions—for example impaired response inhibition, error processing, cognitive flexibility, decision making, and attentional bias for gaming-related stimuli—have been recently reported in EG.^25,28–35

It is suggested that the dual process perspective of addiction^14,36,37 may apply to IGD. Accordingly, impaired executive function and diminished cognitive control over reward-seeking motivational drives contribute to unconscious automatic processes that lead to persistent online gaming, despite reflective knowledge of the negative consequences.^10,38 Cognitive biases (e.g., attentional and approach biases) are thought to be part of the cue-reactivity process, integral to the development and maintenance of addiction. When addicts are exposed to addiction-related cues, an automatic process occurs that manifests as physiological arousal, subjective craving, and cognitive biases.^39–41 Indeed, IGD, much like substance use disorders, shows a cue-induced brain craving response^11,42,43 and attentional bias toward addiction-related cues.^25,33,35 However, to date, no research has investigated the process of approach bias, an automatically activated action tendency to approach addiction-related stimuli,⁴⁴ in IGD.

Cognitive bias modification (CBM) refers to procedures designed to change particular styles of cognitive processing that are thought to contribute to the development and maintenance of undesirable emotional reactions or disorders (e.g., anxiety), using systematic practice in an alternative processing style via computer-based tasks.⁴⁵ CBM experiments have demonstrated that biased cognitive processes of attention and interpretation can reliably be modified, although research on the effectiveness of attention CBM on decreasing emotional reactivity in clinical populations is limited and still in its early stage.^46–48

CBM programs have been developed to target disorder-specific maladaptive cognitive processes in addiction, either through strengthening control processes⁴⁹ or through changing biases in attention and action tendencies.³⁶ In the alcohol Approach Avoidance Task (AAT), participants are instructed to pull or push a joystick in response to a stimulus feature (picture position) unrelated to the pictures' content. Using a retraining variety of this task, participants are trained to avoid alcohol indirectly by pushing alcohol pictures away while responding to picture position. It was demonstrated that alcohol approach bias could be modified using a single session retraining version of this task.⁵⁰ Moreover, in randomized controlled trials with alcohol-dependent patients,^51,52 avoid trained patients not only reduced immediate approach bias but relapsed 10–13% less often than control patients in the following year.

The rationale of the present study was to apply the experimental logic of alcohol approach avoid retraining paradigm^50,52 to retrain automatic action tendencies for gaming cues. Thus, the aims of the present study were to demonstrate an approach bias toward game-related cues in EG; to test whether approach bias to gaming cues can be implicitly modified in a single session training; and to examine whether this affects game urges and game-seeking behavior of EG.

Methods

Sample

Participants (N = 38) were recruited through online gaming groups and forums, and were eligible to enroll if they were 18–30-year-old single males, who regularly engage in MMORPGs for >10 hours a week for at least 1 year,²⁵ were free from significant medical or neurological conditions, and did not meet criteria for any DSM-5 Axis I disorder. Participants were Caucasian, reported an average of 16.1 weekly gaming hours, and had a high problematic gaming level (M = 2.5 on the Game Addiction Scale [GAS]).^25,33 There were no significant baseline differences by experimental condition (p > 0.05). See Table 1 for demographics and gaming variables.

Table 1.

Demographics and Baseline Gaming Measures by Group

	Approach (n = 19)	Avoidance (n = 19)	Statistic	p
Age, years	23.1 (4.0)	22.5 (4.0)	t(36) = 0.45	n.s.
Years of education	12.9 (1.2)	12.8 (1.5)	t(36) = 0.36	n.s.
ADHD			χ²(1) = 1.95	n.s.
Yes	11 (57.9%)	15 (78.9%)
No	8 (42.1%)	4 (21.1%)
Gambling	1.3 (0.6)	1.2 (0.5)	t(36) = 0.61	n.s.
Smoking	1.3 (0.7)	1.3 (0.9)	t(36) = 0.00	n.s.
Alcohol drinking	1.8 (0.9)	1.9 (0.8)	t(36) = −0.38	n.s.
Psychoactive drug use	1.2 (0.4)	1.2 (0.5)	t(36) = 0.00	n.s.
Body mass index	23.1 (4.6)	23.4 (3.1)	t(36) = −0.28	n.s.
General health	4.4 (0.8)	4.4 (0.6)	t(36) = −0.17	n.s.
Emotional distress	21.7 (7.4)	22.9 (7.0)	t(36) = −0.55	n.s.
Gaming addiction	2.5 (0.5)	2.5 (0.5)	t(36) = −0.08	n.s.
Hours of weekly gaming	14.9 (7.0)	17.2 (7.1)	t(36) = −1.06	n.s.

Note. Values shown are M (SD). Comparisons between the groups were made with a two-tailed Student's t test or chi square tests with Yates' continuity correction, as appropriate. Gambling, smoking, alcohol drinking, and psychoactive drug use were rated on one-item scales (1 = “never” to 5 = “daily”). General health was rated on a one-item scale (1 = “very bad” to 5 = “very good”). Emotional distress was measured by the General Health Questionnaire,⁷⁷ with high scores indicating higher emotional distress. Gaming addiction was measured using the Game Addiction Scale.

The study was approved by the Institutional Review Board for Ethical Research of University of Haifa. Written informed consent was obtained from all participants after a complete description of the study.

Materials and measures

Weekly hours of gaming

Weekly hours of gaming were calculated by multiplying results from two questions⁵³: (a) days per week of gaming, rated on a 5-point Likert scale (1 = “never” to 5 = “almost daily”), and (b) average hours of use on a gaming day, rated on a 7-point Likert scale (1 = “don't use” to 7 = “8 hours or more”).

GAS

The GAS⁵⁴ assesses problematic gaming using 21 questions covering aspects of salience, tolerance, mood modification, relapse, withdrawal, conflict, and problems. Each statement is scored on a 5-point Likert scale (1 = “never” to 5 = “very often”), with higher average scores indicating higher likelihood of gaming addiction.

AAT

The AAT^50,55 consisted of four consecutive phases that changed without participants' awareness:

(a) Practice phase (20 trials): participants learned to push or pull a joystick in response to image orientation (tilt 5° to left or right). The modified gaming version of the AAT employed a reversed movements' paradigm, since MMORPGers are used to pushing the joystick to zoom in, approaching objects within the game virtual world, and extending their virtual hand to grasp an object in the game. Therefore, pushing the joystick resulted in zooming in and increased picture size and sense of approaching, whereas pulling decreased the picture size and indicated avoidance.

(b) Pretest assessment (80 test trials): participants were asked to respond to image orientation by pushing/pulling a joystick. Pictures of MMORPGs and animated movies came equally often in the push/pull format. The format was counterbalanced, with half of the participants pulling pictures tilted to the left and half pulling pictures tilted to the right. Experimental gaming pictures were in-game screenshots from six computer games that were selected according to: (a) most played popular MMORPGs in 2014,⁵⁶ and (b) most played computer games reported by participants prior to participation. Control pictures consisted of animated cartoons from popular films, similar in shade, number of characters, and overall composition, and were not present in any computer or video game.³³

(c) Approach/avoid training (440 trials with a 1 minute break every 60 trials): Breaks were inserted to decrease fatigue and enhance motivation, were the same for both conditions, and included encouraging feedback on participants' progress and number of trials left for task completion. In the avoid gaming condition, 90% of the game pictures appeared in the pull format and 10% in the push format, with reversed contingencies for movie pictures. In the approach gaming condition, all contingencies were reversed.

(d) Posttest assessment (80 trials): identical to the pretest.

Behavioral measure of urge to play

Participants were presented with a blue background screen presenting dummy icons of 12 popular games, half of which appeared in the AAT (i.e., Diablo III) and half did not (i.e., Starcraft II), among other general program icons (i.e., my computer, Internet Explorer, etc.). The icons did not start the actual game, but rather recorded the number of times participants pressed each icon, thus providing a measure for game-seeking behavior.

Subjective current gaming urge

The subjective current gaming urge was assessed using a 10-point visual analogue scale (0 = “not at all” to 10 = “very much”).⁴² This measure is widely used to assess craving for psychoactive substances.^57,58

Game playing intentions

Game playing intentions were measured by a time allocation task. Participants were presented with names and icons of 12 popular MMORPGs, six of which appeared in the AAT. Participants were instructed to allocate 1,000 minutes of gaming time between these games according to their will to play. The index is the total time allocated to games presented in the AAT.

Procedure

The study design was a single session, randomized controlled pilot trial. A week prior to participation, participants filled in an online screening questionnaire asking about gaming habits and the GAS. Participants were randomized 1:1 into two groups: avoid training (index group; n = 19), or approach training (control group; n = 19).

Upon arrival, participants indicated their craving to play computer games. The AAT was then performed. Immediately afterwards, participants indicated their craving for gaming again and were given a short demographic questionnaire. In order to measure game-seeking behavior, the experimenter told the participant he was going to change money in order to pay them later, and instructed them to fill in the given questionnaire. In case they finished before he returned, they could use the laboratory computer at their convenience. The experimenter left the computer screen presenting the desktop with popular game dummy icons, and left the room. The experimenter returned after eight minutes. Participants completed the time allocation task, and were debriefed, thanked, and paid €15 for participation.

Statistical approach

The strength of motivational biases (i.e., to approach or avoid) toward the two image categories (gaming/movies) was calculated by subtracting the median^50,59,60 “pull = avoid” reaction time from the median “push = approach” reaction time (RT_pull − RT_push) for each image category, with positive difference scores indicating an approach bias (i.e., faster to pull than to push). These delta scores were then standardized for further analyses. Mixed analyses of variance (ANOVA) were computed to test for differences pre- and post-CBM. A priori power analysis using Gpower⁶¹ indicated that a sample size of 32 would be sufficient to detect a significant medium-large effect (f = 0.30) with alpha level of 0.05.

Results

Automatic action tendencies to gaming among problematic gamers

Pretest delta AAT scores for each picture category (gaming, movies) were analyzed using a paired samples t test, in order to examine baseline approach tendencies to gaming among EG. Analysis revealed higher approach tendencies to gaming pictures (M = 50.1, SE = 11.1) than for movie pictures (M = 25.1, SE = 9.6), t(37) = 2.08, p < 0.05, d = 0.34, thus indicating that EG show approach bias to gaming pictures.

CBM effects on gaming approach tendencies

A significant interaction between time, picture type, and experimental condition was found using 2 × 2 × 2 mixed ANOVA, with picture type (gaming, movies) and time (pretest, posttest) as within-subjects factors, and experimental condition (approach or avoid retraining) as the between-subjects factor F(1, 36) = 7.86, p < 0.01, η²_p = 0.18. Follow up analyses using paired samples t tests indicated that approach bias for gaming pictures decreased, t(17) = 2.24, p < 0.05, d = 0.53, following avoid gaming training (pretest: M = 0.03, SE = 0.12; posttest: M = –0.31, SE = 0.15), whereas approach bias to gaming pictures increased, t(19) = –2.10, p < 0.05, d = 0.47 following approach training (pretest: M = –0.14, SE = 0.13; posttest: M = 0.32, SE = 0.24; see Fig. 1). The change in AAT scores was not significant for movie pictures (p > 0.3). Participants reported no awareness of the change in contingencies from pre- to posttest AAT.

FIG. 1.

D scores on the games approach/avoidance task for participants in the two groups (approach and avoid). D scores were standardized difference scores, derived at pretest and posttest from the difference between mean response latencies for avoidance movements to pictures (pulling a joystick) and approach movements to pictures (pushing a joystick); a positive value indicates an approach bias, and a negative value indicates an avoidance bias. Error bars indicate ± 1 standard error of the mean.

CBM effects on game playing intentions

Following a significant correlation between intentions to play and gaming addiction, r(38) = 0.33, p < 0.05, a one-way analysis of covariance with GAS as a covariate was used to analyze the effect of CBM on intentions to play MMORPGs. Following CBM, participants in the avoid gaming condition allocated less time to games that appeared in the AAT (M = 352.2, SE = 70.1) compared with time allocated to these games by participants in the approach condition (M = 534.4, SE = 71.6), F(1, 37) = 4.32, p < 0.05, η²_p = 0.11.

CBM effects on self-report and behavioral measures of urges to play

Using a 2 × 2 mixed ANOVA, with time (pre- and post-CBM) as the within-subjects factor, and experimental condition (approach or avoid retraining) as the between-subjects factor, a significant interaction between time and experimental condition was found, F(1, 33) = 11.41, p = 0.002, η²_p = 0.26. Follow-up analyses using paired samples t tests indicated that craving for computer gaming decreased, t(15) = 7.57, p < 0.001, d = 1.89, following avoid gaming training (pretest: M = 8.6, SE = 0.29; posttest: M = 5.2, SE = 0.43), whereas approach training did not affect craving, t(18) = 0.88, p = n.s., d = 0.20 (pretest: M = 8.2, SE = 0.47; posttest: M = 7.6, SE = 0.55). Analysis also revealed a significant main effect of time, F(1, 33) = 22.81, p < 0.001, η²_p = 0.41: craving for computer-gaming was higher pretraining (M = 8.4, SE = 0.29) compared with post-training (M = 6.4, SE = 0.36). Also, a main effect of condition, F(1, 33) = 4.30, p < 0.05, η²_p = 0.12, revealed that the approach group reported higher subjective craving (M = 7.9, SE = 0.34) than the avoid group (M = 6.9, SE = 0.37).

Using an independent samples t test, it was found that participants who were trained to avoid gaming pressed less frequently on game icons (M = 2.7, SE = 0.3) compared with participants who were trained to approach gaming (M = 5.5, SE = 1.4), thus showing lower game-seeking behavior, t(13) = 2.43, p = 0.03; d = 1.30.

Discussion

This study demonstrated an approach bias toward game-related cues in excessive MMORPGers. In a randomized, controlled pilot trial using a modified gaming version of the well-validated AAT, it was shown that a single session CBM resulted in a significant decrease in automatic action tendencies to approach gaming cues. Furthermore, approach bias retraining reduced subjective urges and intentions to play MMORPGs, as well as decreased game-seeking behavior.

The finding that EG were faster to approach than to avoid gaming cues indicates an approach bias in EG specific for gaming-related images, but not for neutral movie cues. Previous research using the AAT has found similar bias for cigarettes,^62,63 cannabis,⁶⁴ and alcohol,⁶⁰ suggesting that EG have yet another behavioral feature comparable to established addictions. By demonstrating approach bias in a behavioral addiction for the first time, results lend further support to the current conceptualization of addictive behaviors mirroring the clinical phenotypes seen in substance use disorders.^17,65

Whereas evidence for attention bias, impaired response inhibition, and cognitive flexibility were previously reported in IGD,^28–34 the current research demonstrates yet another cognitive bias thought to be part of the dual processes integral to the development and maintenance of addiction. These perspectives suggest that persistent engagement in addictive behavior is the result of an imbalance between an impulsive system that promotes automatic, habitual, and salient behaviors, and impaired reflective system for decision making. Future work should explore whether the cognitive deficit identified is evident before addiction, thus signaling risk, or is a consequence of repetitive engagement in habitual behavior.⁶⁵ Findings could imply that because of their automatic action tendencies toward game-related cues, high-level problematic gamers might have more difficulty restraining from starting a game or disengaging from a gaming session once started.

Studies of other appetitive targets have demonstrated a link between approach bias and consumption. For example, approach bias for alcohol has been associated with greater weekly alcohol consumption⁶⁶ and higher frequency of binge drinking⁴⁴ in heavy drinkers. Similarly, approach bias for cannabis has been shown to predict increased cannabis use in heavy cannabis users.⁶⁴ Future research will need to determine whether a similar link exists between approach bias for gaming cues, gaming behavior, and gaming addiction severity.

Based on the proposed neurocognitive perspective for IGD,^10,11 a modified gaming version of the AAT was used in a randomized, controlled pilot trial. Results indicate that a single session CBM in EG reduced automatic action tendencies to approach gaming cues. These results are in line with previous studies that successfully retrained approach bias in cigarette smokers,⁶⁷ heavy alcohol drinkers, and alcohol-dependent patients,^51,52,68,69 providing further evidence for a generalized effect of the AAT training. CBM increased control over gaming cue reactivity, suggesting that dual process perspectives of addiction^14,36,37,70 may be useful in understanding IGD.^10,11 Furthermore, this minimal intervention reduced subjective urges and intentions to play, as well as decreased game-seeking behavior. Thus, training not only improved performance on the cognitive bias test, but the benefits of training were also seen to transfer to other untrained emotions, cognitions, and behaviors (for a related discussion, see Owen et al.⁷¹) that are associated with addiction severity and relapse.^41,72

Prior studies that failed to show short-term effects of CBM on craving proved clinically beneficial in reducing subsequent consumption and relapse rates when coinciding with other treatment for addictive disorders.^50,51,68 It is yet to be determined whether these short-term behavioral and subjective effects of CBM on EG will generalize outside the lab setting and for how long. In addition, the optimal number of training sessions for EG is a question that still needs to be addressed, as former studies with long-lasting clinical results have used multiple sessions.^{50–52,68,69} Nonetheless, the promising immediate decrease in craving and game-seeking behavior suggest a causal influence of CBM,⁴⁸ and therefore provides further support for theoretical models that posit a causal influence of cognitive bias on addictive behaviors,^40,73 including IGD. As prior CBM administered in participants' home environments promote reductions in bias, craving, and substance use,^67,74,75 future automated web-based and mobile CBM might be even more beneficial for gamers than the classic lab version of the AAT.

Limitations

Several limitations of this study are noted. First, only men were analyzed, with the majority of MMORPGers characteristically being men.⁷⁶ Men are more likely to play games excessively and are more prone to experience negative consequences due to their gaming behavior.^33,38 Thus, these results may generalize to most gamers. However, further research may provide valuable insights into the differences between male and female gamers. Second, this study excluded participants with multiple addictions and those with psychiatric disorders. Thus, the results should be generalized cautiously to these groups. Third, this study lacks a control group of nonexcessive MMORPGers, following former CBM studies that include only hazardous/dependent drug users. Including participants who engage in other forms of video gaming might reveal the processes in which cognitive biases relate to IGD development. Fourth, participants reported an average of only 16 weekly gaming hours. However, they were all working adults with a high problematic gaming level. Future studies should try to replicate the present findings in a sample with higher daily gaming hours.

Conclusion

This study provides the first evidence for the existence of approach bias to gaming cues in EG, thus lending further support for the similarities between substance and behavioral addictions. Furthermore, by showing that CBM affects approach bias, craving, and game-seeking behavior, this study lays the groundwork for understanding the development of IGD through mechanisms of the dual process model. Future studies should explore the mechanisms behind IGD and the long-term effects of CBM.

Footnotes

Acknowledgments

An earlier version of this work has been presented at the 2015 International Conference on Behavioral Addictions (ICBA), Budapest, Hungary.

Author Disclosure Statement

No competing financial interests exist.

References

Kuss

, Louws

, Wiers

. Online gaming addiction? Motives predict addictive play behavior in massively multiplayer online role-playing games. Cyberpsychology, Behavior, & Social Networking, 2012; 15:480–485.

Nagygyörgy

, Urbán

, Farkas

, et al. Typology and sociodemographic characteristics of massively multiplayer online game players. International Journal of Human–Computer Interaction, 2013; 29:192–200.

Billieux

, Deleuze

, Griffiths

, et al. 2015. Internet gaming addiction: the case of massively multiplayer online role-playing games. In el-Guebaly

, Carra

, Glanter

, eds. Textbook of addiction treatment: international perspectives. Milan, Italy: Springer-Verlag, pp. 1515–1525.

Berle

, Starcevic

, Porter

, et al. Are some video games associated with more life interference and psychopathology than others? Comparing massively multiplayer online role‐playing games with other forms of video game. Australian Journal of Psychology, 2015; 67:105–114.

Collins

, Freeman

, Chamarro-Premuzic

. Personality traits associated with problematic and non-problematic massively multiplayer online role playing game use. Personality & Individual Differences, 2012; 52:133–138.

Thorens

, Achab

, Billieux

, et al. Characteristics and treatment response of self-identified problematic Internet users in a behavioral addiction outpatient clinic. Journal of Behavioral Addictions, 2014; 3:78–81.

Leménager

, Gwodz

, Richter

, et al. Self-concept deficits in massively multiplayer online role-playing games addiction. European Addiction Research, 2013; 19:227–234.

Lee

, Ko

, Song

, et al. Characteristics of Internet use in relation to game genre in Korean adolescents. CyberPsychology & Behavior, 2007; 10:278–285.

Charlton

, Danforth

. Validating the distinction between computer addiction and engagement: online game playing and personality. Behaviour & Information Technology, 2010; 29:601–613.

10.

Dong

, Potenza

. A cognitive-behavioral model of Internet gaming disorder: theoretical underpinnings and clinical implications. Journal of Psychiatric Research, 2014; 58:7–11.

11.

Kuss

, Griffiths

. Internet gaming addiction: a systematic review of empirical research. International Journal of Mental Health & Addiction, 2012; 10:278–296.

12.

Griffiths

. A “components” model of addiction within a biopsychosocial framework. Journal of Substance Use, 2005; 10:191–197.

13.

Harper

. The neuropathology of alcohol-related brain damage. Alcohol & Alcoholism, 2009; 44:136–140.

14.

Bechara

. Decision making, impulse control and loss of willpower to resist drugs: a neurocognitive perspective. Nature Neuroscience, 2005; 8:1458–1463.

15.

Kühn

, Gallinat

. Brains online: structural and functional correlates of habitual Internet use. Addiction Biology, 2015; 20:415–422.

16.

Young

. Psychology of computer use: XL. Addictive use of the Internet: a case that breaks the stereotype. Psychological Reports, 1996; 79:899–902.

17.

Kuss

. Internet gaming addiction: current perspectives. Psychology Research & Behavior Management, 2013; 6:125–137.

18.

Lin

, Zhou

, Dong

, et al. Impaired risk evaluation in people with Internet gaming disorder: fMRI evidence from a probability discounting task. Progress in Neuro-Psychopharmacology & Biological Psychiatry, 2015; 56:142–148.

19.

C-H

, Yen

J-Y

, Chen

C-C

, et al. Tridimensional personality of adolescents with Internet addiction and substance use experience. Canadian Journal of Psychiatry, 2006; 51:887–894.

20.

C-H

, Hsiao

, Liu

G-C

, et al. The characteristics of decision making, potential to take risks, and personality of college students with Internet addiction. Psychiatry Research, 2010; 175:121–125.

21.

Hsu

, Wen

M-H

, Wu

M-C

. Exploring user experiences as predictors of MMORPG addiction. Computers & Education, 2009; 53:990–999.

22.

American Psychiatric Association. (2013) Diagnostic and statistical manual of mental disorders. 5th ed. Arlington, VA: American Psychiatric Publishing.

23.

Petry

, O'Brien

. Internet gaming disorder and the DSM‐5. Addiction, 2013; 108:1186–1187.

24.

Petry

, Rehbein

, Gentile

, et al. An international consensus for assessing Internet gaming disorder using the new DSM‐5 approach. Addiction, 2014; 109:1399–1406.

25.

Metcalf

, Pammer

. Impulsivity and related neuropsychological features in regular and addictive first person shooter gaming. Cyberpsychology, Behavior, & Social Networking, 2014; 17:147–152.

26.

Dong

, Huang

, Du

. Enhanced reward sensitivity and decreased loss sensitivity in Internet addicts: an fMRI study during a guessing task. Journal of Psychiatric Research, 2011; 45:1525–1529.

27.

Lee

, Choi

J-S

, Shin

Y-C

, et al. Impulsivity in Internet addiction: a comparison with pathological gambling. Cyberpsychology, Behavior, & Social Networking, 2012; 15:373–377.

28.

C-H

, Hsieh

T-J

, Chen

C-Y

, et al. Altered brain activation during response inhibition and error processing in subjects with Internet gaming disorder: a functional magnetic imaging study. European Archives of Psychiatry & Clinical Neuroscience, 2014; 264:661–672.

29.

Pawlikowski

, Brand

. Excessive Internet gaming and decision making: do excessive World of Warcraft players have problems in decision making under risky conditions?. Psychiatry Research, 2011; 188:428–433.

30.

Dong

, Lin

, Zhou

, et al. Cognitive flexibility in Internet addicts: fMRI evidence from difficult-to-easy and easy-to-difficult switching situations. Addictive Behaviors, 2014; 39:677–683.

31.

Littel

, Berg

, Luijten

, et al. Error processing and response inhibition in excessive computer game players: an event‐related potential study. Addiction Biology, 2012; 17:934–947.

32.

Zhou

, Yuan

, Yao

. Cognitive biases toward Internet game-related pictures and executive deficits in individuals with an Internet game addiction. PloS One, 2012; 7:e48961.

33.

van Holst

, Lemmens

, Valkenburg

, et al. Attentional bias and disinhibition toward gaming cues are related to problem gaming in male adolescents. Journal of Adolescent Health, 2012; 50:541–546.

34.

Decker

, Gay

. Cognitive-bias toward gaming-related words and disinhibition in World of Warcraft gamers. Computers in Human Behavior, 2011; 27:798–810.

35.

Lorenz

, Krüger

, Neumann

, et al. Cue reactivity and its inhibition in pathological computer game players. Addiction Biology, 2013; 18:134–146.

36.

Wiers

, Gladwin

, Hofmann

, et al. Cognitive bias modification and cognitive control training in addiction and related psychopathology mechanisms, clinical perspectives, and ways forward. Clinical Psychological Science, 2013; 1:192–212.

37.

Volkow

, Fowler

, Wang

G-J

. The addicted human brain viewed in the light of imaging studies: brain circuits and treatment strategies. Neuropharmacology, 2004; 47:3–13.

38.

Hahn

, Notebaert

, Dresler

, et al. Linking online gaming and addictive behavior: converging evidence for a general reward deficiency in frequent online gamers. Frontiers in Behavioral Neuroscience, 2014; 8:385.

39.

Thalemann

, Wölfling

, Grüsser

. Specific cue reactivity on computer game-related cues in excessive gamers. Behavioral Neuroscience, 2007; 121:614–618.

40.

Field

, Cox

. Attentional bias in addictive behaviors: a review of its development, causes, and consequences. Drug & Alcohol Dependence, 2008; 97:1–20.

41.

Robinson

, Berridge

. The incentive sensitization theory of addiction: some current issues. Philosophical Transactions of the Royal Society B: Biological Sciences, 2008; 363:3137–3146.

42.

, Liu

, Yen

, et al. Brain correlates of craving for online gaming under cue exposure in subjects with Internet gaming addiction and in remitted subjects. Addiction Biology, 2013; 18:559–569.

43.

Yen

J-Y

, Yen

C-F

, Chen

C-S

, et al. Cue-induced positive motivational implicit response in young adults with Internet gaming addiction. Psychiatry Research, 2011; 190:282–286.

44.

Palfai

, Ostafin

. Alcohol-related motivational tendencies in hazardous drinkers: assessing implicit response tendencies using the modified-IAT. Behaviour Research & Therapy, 2003; 41:1149–1162.

45.

Koster

, Fox

, MacLeod

. Introduction to the special section on cognitive bias modification in emotional disorders. Journal of Abnormal Psychology, 2009; 118:1–4.

46.

Hallion

, Ruscio

. A meta-analysis of the effect of cognitive bias modification on anxiety and depression. Psychological Bulletin, 2011; 137:940–958.

47.

Fox

, Mackintosh

, Holmes

. Travellers' tales in cognitive bias modification research: a commentary on the special issue. Cognitive Therapy & Research, 2014; 38:239–247.

48.

Clarke

, Notebaert

, MacLeod

. Absence of evidence or evidence of absence: reflecting on therapeutic implementations of attentional bias modification. BMC Psychiatry, 2014; 14:8.

49.

Houben

, Wiers

, Jansen

. Getting a grip on drinking behavior training working memory to reduce alcohol abuse. Psychological Science, 2011; 22:968–975.

50.

Wiers

, Rinck

, Kordts

, et al. Retraining automatic action‐tendencies to approach alcohol in hazardous drinkers. Addiction, 2010; 105:279–287.

51.

Wiers

, Eberl

, Rinck

, et al. Retraining automatic action tendencies changes alcoholic patients' approach bias for alcohol and improves treatment outcome. Psychological Science, 2011; 22:490–497.

52.

Eberl

, Wiers

, Pawelczack

, et al. Approach bias modification in alcohol dependence: do clinical effects replicate and for whom does it work best?. Developmental Cognitive Neuroscience, 2013; 4:38–51.

53.

Van Rooij

, Schoenmakers

, Vermulst

, et al. Online video game addiction: identification of addicted adolescent gamers. Addiction, 2011; 106:205–212.

54.

Lemmens

, Valkenburg

, Peter

. Development and validation of a game addiction scale for adolescents. Media Psychology, 2009; 12:77–95.

55.

Fleming

, Bartholow

. Alcohol cues, approach bias, and inhibitory control: applying a dual process model of addiction to alcohol sensitivity. Psychology of Addictive Behaviors, 2014; 28:85–96.

56.

Statista. Most played PC games on gaming platform Raptr in December 2014, by share of playing time 2014. www.statista.com/statistics/251222/most-played-pc-games/ (accessed January 15, 2015).

57.

Sinha

, Fuse

, Aubin

L-R

, et al. Psychological stress, drug-related cues and cocaine craving. Psychopharmacology, 2000; 152:140–148.

58.

Bordnick

, Traylor

, Copp

, et al. Assessing reactivity to virtual reality alcohol based cues. Addictive Behaviors, 2008; 33:743–756.

59.

Rinck

, Becker

. Approach and avoidance in fear of spiders. Journal of Behavior Therapy & Experimental Psychiatry, 2007; 38:105–120.

60.

Wiers

, Rinck

, Dictus

, et al. Relatively strong automatic appetitive action‐tendencies in male carriers of the OPRM1 G‐allele. Genes, Brain & Behavior, 2009; 8:101–106.

61.

Faul

, Erdfelder

, Lang

A-G

, et al. G* Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 2007; 39:175–191.

62.

Watson

, de Wit

, Hommel

, et al. Motivational mechanisms underlying the approach bias to cigarettes. Journal of Experimental Psychopathology, 2013; 4:250–262.

63.

Wiers

, Kühn

, Javadi

, et al. Automatic approach bias towards smoking cues is present in smokers but not in ex-smokers. Psychopharmacology, 2013; 229:187–197.

64.

Cousijn

, Goudriaan

, Wiers

. Reaching out towards cannabis: approach‐bias in heavy cannabis users predicts changes in cannabis use. Addiction, 2011; 106:1667–1674.

65.

Grant

, Chamberlain

. Impulsive action and impulsive choice across substance and behavioral addictions: cause or consequence?. Addictive Behaviors, 2014; 39:1632–1639.

66.

Field

, Kiernan

, Eastwood

, et al. Rapid approach responses to alcohol cues in heavy drinkers. Journal of Behavior Therapy & Experimental Psychiatry, 2008; 39:209–218.

67.

Wittekind

, Feist

, Schneider

, et al. The approach-avoidance task as an online intervention in cigarette smoking: a pilot study. Journal of Behavior Therapy & Experimental Psychiatry, 2015; 46:115–120.

68.

Schoenmakers

, de Bruin

, Lux

, et al. Clinical effectiveness of attentional bias modification training in abstinent alcoholic patients. Drug & Alcohol Dependence, 2010; 109:30–36.

69.

Fadardi

, Cox

. Reversing the sequence: reducing alcohol consumption by overcoming alcohol attentional bias. Drug & Alcohol Dependence, 2009; 101:137–145.

70.

Noël

, Brevers

, Bechara

. A neurocognitive approach to understanding the neurobiology of addiction. Current Opinion in Neurobiology, 2013; 23:632–638.

71.

Owen

, Hampshire

, Grahn

, et al. Putting brain training to the test. Nature, 2010; 465:775–778.

72.

Robinson

, Berridge

. The neural basis of drug craving: an incentive-sensitization theory of addiction. Brain Research Reviews, 1993; 18:247–291.

73.

Cox

, Fadardi

, Intriligator

, et al. Attentional bias modification for addictive behaviors: clinical implications. CNS Spectrums, 2014; 19:215–224.

74.

Kerst

, Waters

. Attentional retraining administered in the field reduces smokers' attentional bias and craving. Health Psychology, 2014; 33:1232–1240.

75.

McGeary

, Meadows

, Amir

, et al. Computer-delivered, home-based, attentional retraining reduces drinking behavior in heavy drinkers. Psychology of Addictive Behaviors, 2014; 28:559–562.

76.

Yee

. The demographics, motivations, and derived experiences of users of massively multi-user online graphical environments. Presence, 2006; 15:309–329.

77.

Goldberg

. Manual of the General Health Questionnaire. Windsor, England. National Foundation for Educational Research, 1978.