Abstract
Background and aims
Uncontrollable gaming behavior is a core symptom of Internet Gaming Disorder (IGD). Attentional bias towards game-related cues may contribute to the difficulty in regulating online gaming behavior. However, the context-specific attentional bias and its cognitive mechanisms in individuals with IGD have not been systematically investigated.
Methods
We compared individuals with IGD to healthy controls (HC) using a rapid serial visual presentation (RSVP) task to measure temporal attentional bias. By applying game-related and neutral stimuli as targets, we specifically assessed how attentional resources were allocated to game-related stimuli compared to neutral stimuli.
Results
The IGD group showed enhanced attentional blink effect when a game-related stimulus was the first target and a neutral target was the next, reflecting IGD's difficulty in disengaging from game-related stimuli. Both IGD and HC individuals exhibited decreased accuracy in identifying a neutral first target followed by a game-related second target at shorter lags, indicating increased attentional engagement with game-related stimuli in general.
Discussion
The results provide a cognitive basis for recurrent and uncontrollable gaming behaviors in individuals with IGD. Game cues have priority in the allocation of attentional resources in individuals with IGD. The results shed new light on the development of specific treatments for IGD.
Introduction
Internet gaming, as a major entertainment activity is one of the most important online niches. However, Internet gaming also has adverse effects at both individual and societal levels due to its addictive feature. The Diagnostic and Statistical Manual of Mental Disorders 5th edition (DSM-5) and the International Classification of Diseases 11th revision have been indexed excessive internet gaming and defined Internet gaming disorder (IGD) as “persistent, recurrent use of the Internet to engage in games, often with other players, leading to clinically significant impairment or distress” (American Psychiatric Association, 2013). A meta-analysis covering 29 countries found that the prevalence of IGD in the general population reached 4.7% (Feng, Ramo, Chan, & Bourgeois, 2017), and individuals with IGD have maladaptive problems such as interpersonal problems, loneliness, low self-esteem, and low academic performance (He, Xia, Jiang, & Wei, 2012; Heuer, Mennig, Schubö, & Barke, 2021; J Kuss, D Griffiths, Karila, & Billieux, 2014; Savci & Aysan, 2017). Since the development and maintenance of IGD is complex (Petry et al., 2014), systemic investigation to the mechanism is required.
The development and maintenance mechanisms of IGD can be explained through the Interaction of Person-Affect-Cognition-Execution model (I-PACE) (Brand, 2022; Brand et al., 2016, 2019). The model of specific Internet use disorders (IUD) suggests that several cognitive factors are associated with IUD, especially IGD (Wegmann & Brand, 2020), involving automatic, impulsive and cognitive control deficits. In these cognitive processes, attentional biases in relation to craving have been investigated in both substance and behavioral addictions (Field & Cox, 2008). Attentional bias refers to the tendency for specific stimuli related to attention maintenance and allocation (Field & Cox, 2008; Field et al., 2009, 2016). Previous studies on specific IUDs suggested maladaptive attentional bias in these populations such as IGD (Heuer et al., 2021; Jeromin, Nyenhuis, & Barke, 2016; Kim et al., 2019; Lorenz et al., 2013; Metcalf & Pammer, 2011; van Holst et al., 2012; Zhang et al., 2016; Zhou, Zhou, Zhou, Shen, & Zhang, 2022), social networking (Jiang, Zhao, & Li, 2017; Nikolaidou, Fraser, & Hinvest, 2019; Zhao et al., 2022), and pornography (He, Zheng, Nie, & Zhou, 2018; Wang & Huang, 2022). In addition, individuals with IGD exhibited cue-reactivity and enhanced craving when exposed to game-related cues (Liu et al., 2017). Problematic Internet pornography users built up automated associations between online porn cues and positive outcomes, prompting cue-reactivity and craving (Snagowski, Wegmann, Pekal, Laier, & Brand, 2015). The internet-related attentional biases and craving/approaching responses are consistent with the idea that attentional biases may play a fundamental role in guiding addictive behavior as well as the dual-mode theories of addictive behaviors (Stacy & Wiers, 2010; Wiers & Stacy, 2006). In addition, according to previous evidence attentional bias has been suggested to be associated with craving or flow experience in individuals with IGD (Zhou et al., 2022), problematic pornography use (Allen, Kannis-Dymand, & Katsikitis, 2017; Brahim, Cruz, Courtois, May, & Khazaal, 2023; Marino et al., 2023), and social networking (Nikolaidou et al., 2019). The results may reflect the strengthened cravings to internet-related cues and weakened reflective processes (Brand et al., 2019).
Most studies on attentional bias in individuals with IGD were conducted with the dot-probe task (DPT) or the Addiction Stroop task (Liu et al., 2024). In DPT, word or picture pairs briefly appear on the screen before a probe, and participants quickly respond to the probe's position. Faster responses to probes at congruent locations indicate an attentional bias toward the addictive stimulus (Wang & Huang, 2022; Zhao et al., 2022). Previous studies have demonstrated that individuals with IGD have an attentional bias towards game-related stimuli compared to neutral images in DPT (Lorenz et al., 2013), and attentional bias can predict their craving for games in a short period (Zhou et al., 2022). The Addiction Stroop task, adapted from the classic Stroop task, requires participants to identify the color of words while ignoring the semantic information. Longer times to identify the color of addiction-related words indicate a greater attentional bias. Attentional bias in individuals with IGD has been identified in studies using the Addiction Stroop task (e.g. Metcalf & Pammer, 2011; Zhang et al., 2016). Furthermore, Kim et al. (2019) by applying eye tracking, found a higher anti-saccade error rate for game pictures, indicating the underlying mechanism of attentional bias in IGD group. Though a few studies with DPT or Addiction Stroop task found no significant difference between IGD and HC groups (Jeromin, Rief, & Barke, 2016; Wang et al., 2018; Zhang et al., 2016), most research suggested a spatial attentional bias toward game-related cues in IGD.
Most existing studies have focused mainly on the attentional bias of spatial selectivity, e.g., how game-related stimuli capture attention spatially (He et al., 2018; Lorenz et al., 2013), but much less on how attentional selectivity unfolds over time, i.e., the temporal dimension of attentional bias (Le Pelley, Seabrooke, Kennedy, Pearson, & Most, 2017; MacLean & Arnell, 2012). However, our ability to quickly detect and switch between events is critical for navigating in dynamic environments. When faced with serial stimuli in the online environment, individuals may find it difficult to efficiently switch between important stimuli (e.g., in-game events vs. work notifications), reflecting deficits in attentional engagement and disengagement. Though a few studies have preliminarily investigated attentional engagement and disengagement based on spatial attention by adjusting the presentation time of game-related stimuli (Wang & Huang, 2022; Zhou et al., 2022), the deficit of attentional engagement and disengagement in temporal dimension in individuals with IGD is still lacking. By manipulating the timing of game-related stimuli within a series, we can directly and clearly examine both attentional engagement and disengagement biases to game-related stimuli in IGD and their relationship to game craving and addictive behavior.
The joint examination of attentional engagement and disengagement (i.e., temporal dimension of attentional bias) can be conducted through the rapid serial visual presentation (RSVP) task (MacLean & Arnell, 2012). In RSVP trials, visual stimuli are typically presented in sequence, with each stimulus presented in short (e.g., 100 ms). Each stimulus sequence contains 1 or 2 target stimuli, and participants need to identify the target stimulus after each serial. The stimuli onset asynchronization (SOA) between 2 target stimuli (i.e., lag) is manipulated as an independent variable. Compared to long SOA conditions, the performance of recognizing the second target (T2) following short SOA (lag < 500 ms) will be disrupted, known as attentional blink (AB). The recognition performance of T2 will gradually bounce back with the increase of lag, and after the transient period (>500 ms) return to normal levels (MacLean & Arnell, 2012). Specifically, the RSVP task can measure 3 aspects of temporal attentional bias: (1) the AB enhancement; (2) the AB antagonism; and (3) the backward interference (Neimeijer, de Jong, & Roefs, 2013).
The AB enhancement can occur in healthy participants when the first target stimulus (T1) is salient (e.g., an emotional picture) (De Jong, Koster, van Wees, & Martens, 2010; De Martino, Kalisch, Rees, & Dolan, 2009; Neimeijer et al., 2013), which may due to the elaborate processing of salient information (Kavanagh, Andrade, & May, 2005). Incentive salience of game-related stimuli has been demonstrated (Kim et al., 2019, 2021), leading to an increased attentional bias and delayed attention disengagement from game-related cues. A previous study identified the attention disengagement bias in individuals with IGD (Heuer et al., 2021). The antagonistic effect of AB may occur when T2 is salient due to the priority of the salient information entering attention processing (De Martino et al., 2009; Shapiro, Caldwell, & Sorensen, 1997). In behavioral addiction studies, individuals with gambling disorder showed biased attentional engagement toward gamble-related stimuli (Brevers et al., 2011; Hønsi, Mentzoni, Molde, & Pallesen, 2013), suggesting that incentive salient stimuli may be preferentially captured in the addicted population. Though the incentive salience of game-related stimuli has been demonstrated, the features of the AB effects in IGD still require investigation. In addition, previous studies in IGD reported deficits in attentional process due to the impairment in ignoring previous task demand (Kim et al., 2019; Liu et al., 2014), indicating a problem with attention interference (Facoetti, Ruffino, Peru, Paganoni, & Chelazzi, 2008; Neimeijer et al., 2013). However, IGDs' ability to attention interference in specific dynamic situations still needs systemic exploration.
The present study applied a game-relevant RSVP task to explore the temporal biases of attention engagement and disengagement in individuals with IGD. We hypothesized that for individuals with IGD: (1) when T1 is the game-related stimulus, the T2 performance will appear AB enhancement effect, and it will be reflected in the difference of accuracy at different lag levels. (2) When T2 was the game-related stimulus, individuals with IGD will show AB antagonism effect, and the antagonism effect was also manifested in the change of accuracy at the specific lag level; (3) When T2 is the game-related stimulus, it will interfere with the recognition of T1 by individuals with IGD; (4) The IGD individual's use of problematic online games, game craving, and game flow would be positively associated with those temporal attentional bias indicators.
Methods
Participants
As the most common Internet game type played in IGD (He, Pan, Nie, Zheng, & Chen, 2021; Nuyens et al., 2016), the Multiplayer Online Battle Arena (MOBA) games were considered. IGD participants endorsed one of the three most popular MOBA games were recruited for the present study [League of Legends (LOL), Honor of Kings, Defense of the Ancients 2 (DotA 2)].
The participants were recruited through online questionnaires. The dichotomous version of the Internet Gaming Disorder Scale-short form 9 item (IGD-SF9) (Lemmens, Valkenburg, & Gentile, 2015) was used to screen participants of IGD. The weekly game usage questionnaire was used to survey the frequency and duration of their gaming behavior. Basic demographic information was also collected. The inclusion criteria of the IGD group were as follows: (1) met 5 symptoms or more in the IGD-SF9; (2) more than 2 hours/day engaged in Internet gaming, and this behavior has lasted for more than a year; (3) spent most of the gaming time on one of the MOBA games. The inclusion criteria for the HC group, based on the previous study of low-frequency gaming healthy controls (Dong, Li, Wang, & Potenza, 2017), were as follows: (1) met no more than 3 symptoms of the IGD-SF9; (2) spent less than 1 hour/day engaged in Internet gaming. This ensured that the HC group had a basic concept of the games and achieved similar representativity to previous studies (Lei et al., 2020; Ma et al., 2024; Wang et al., 2018), which ensured that the group could identify game stimuli during the experiment. Exclusion criteria were as follows: (1) substance dependence (e.g., tobacco) or other behavioral addiction disorders (e.g., gambling); (2) a history of mental disorders, brain diseases, or medication of the central nervous system.
According to the criteria above, a total of 46 college students were selected for this study and classified into two groups. The IGD group included 24 participants, and the HC group included 22 participants, meeting the minimum sample size of 44 estimated a priori using MorePower version 6.0 (Campbell & Thompson, 2012) with α = 0.05, power = 0.8, and ηp2 = 0.08 (Brevers et al., 2011; Schmitz, Naumann, Biehl, & Svaldi, 2015). All participants were adult native Chinese speakers and had normal or corrected visual acuity. Every participant provided a signed informed consent after being informed of the purpose of the study.
Measures
Internet Gaming Disorder Scale-Short Form 9 item
The Internet Gaming Disorder Scale-Short Form 9 item (IGD-SF9) developed by Lemmens et al. (2015) was conducted to recruit participants in the present study. The items on this scale correspond to the 9 diagnostic criteria for IGD in the DSM-5. In previous studies, this scale showed solid psychometric properties (Cronbach's alpha = 0.83, CFI = 0.997, RMSEA = 0.016), and could distinguish between people with disordered gamers, heavy gamers, and light gamers (Lemmens et al., 2015).
Problematic Online Game Use Scale
The Problematic Online Game Use Scale (POGUS) developed by Kim and Kim (2010) measures an individual's use of problematic online games from five dimensions: euphoria, health problem, conflict, failure of self-control, and preference of virtual relationship. There were 20 items in the scale and 5 points were used (1 = strongly disagree, 5 = strongly agree). The higher score indicates higher severity of problematic online game use. The Chinese version of the scale showed good reliability and validity among Chinese college students, and the internal consistency coefficient of the whole scale is 0.92 (Zhang et al., 2012). The internal consistency coefficient in the current study was 0.975.
Game craving scale
The Game Craving Scale (GCS) was developed by Stoeber, Harvey, Ward, and Childs (2011). It included 9 items in 3 dimensions (expectation, desire, and relief) and was scored on a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree). Higher scores indicate greater desire to Internet gaming. This scale has shown good reliability and validity in previous studies (Stoeber et al., 2011). In the current study, the internal consistency coefficient was 0.932.
Game Flow Scale
The Gaming Flow Scale (GFS) was revised from the Facebook Flow Scale developed by Kwak, Choi, and Lee (2014). The current study referred to the approach of Brailovskaia, Meier-Faust, Schillack, and Margraf (2022), replacing “Facebook” with “Internet games” in the original scale to measure an individual's flow experience in games. The scale consists of 15 items, which are composed of 5 dimensions: focused attention, enjoyment, curiosity, telepresence, and time-distortion. Each item is rated on a 5-point scale ranging from 1 (totally disagree) to 5 (totally agree), with higher scores indicating a greater flow experience during the game. The original scale showed acceptable psychometric properties, with an internal consistency coefficient varying from 0.667–0.921 in different dimensions (Kwak et al., 2014). In the current study, the internal consistency coefficient was 0.933.
Rapid serial visual presentation task
To elicit the specific AB effect for game-related stimuli in the temporal dimension, a modified RSVP task was conducted. Each trial of the task presented images at a continuous speed of 100 ms per image without inter-stimulus interval. There were two kinds of stimulus in the presentation stream: the target stimulus surrounded by a 10-pixel wide blue border and the distractor surrounded by a 10-pixel wide black border. Participants were instructed to detect the target(s) and report the number and content of the target stimulus (“natural landscape”, “man-made landscape”, “gaming scene”) after each series. Each image presentation stream was fixed with four distractor images after the last target image to avoid interference from working memory. Depending on the position of T1 and T2, the number of images presented per trial varies from 10–19 (Fig. 1).
An example of an RSVP series. Participants were instructed to report the number and content of target stimuli surrounded by a 10-pixel wide blue border while ignoring distractors surrounded by a 10-pixel wide black border. The target stimuli can be game-related (e.g., T1) or neutral (e.g., T2), with the lag between them ranging from 200 to 800 ms (shown here with a lag of 300 ms). The proportions of the stimuli in this figure have been adjusted for clarity
Citation: Journal of Behavioral Addictions 2025; 10.1556/2006.2024.00075
The RSVP task included dual-target trials, single-target trials, and non-target trials. In the dual-target trials, T1 appeared randomly in 3 positions (T1 position: 4, 6, 8) to avoid participants' expectation effect, and T2 appeared after T1 with varying lag delays (T2 lag time: 200 ms, 300 ms, 600 ms, 800 ms). Among these conditions, 200 and 300 ms (lag-2 and lag-3) are in the AB period, and 600 and 800 ms (lag-6 and lag8) are outside the AB period. Besides, in dual-target trials, the category of T1 and T2 formed 3 combinations: neutral T1 + neutral T2, game-related T1 + neutral T2, neutral T1 + game-related T2. Each random combination of different conditions from the above variables was repeated 8 times, consisting of 288 dual-target trials. For the neutral target, the frequency of “natural landscape” and “man-made landscape” was equal to avoid participants' dominant reaction. To avoid participants' expectation of the number of target pictures, the experiment also included a total of 36 trials for single-target design (18 trials for single game-related stimulus and 18 trials for single neutral stimulus) and another 36 trials without target, to prevent participants from presuming that 2 target stimuli would always appear. Therefore, participants should complete 360 trials in total, which consisted of 5 blocks, each of which included 72 trials. A minimum of 30 s rest time was set between the blocks. The RSVP task was compiled and presented via E-prime version 3.0.
The stimuli in the task were presented at 550 × 550 pixels in the center of the screen. Neutral stimuli: 44 neutral target stimuli and distractors were selected from the International Affective Picture System (IAPS) (Bradley & Lang, 2017). Neutral image content covered life scenes irrelevant to internet/computer games, buildings, vehicles, natural scenery, plants, etc. Game-related stimuli (64 images) were captured from the public video website of the game video screenshots to the in-game scene as the main content. Physical features of neutral pictures, game-related pictures, and distractor stimuli were controlled, and differences in grayscale, brightness, contrast, and complexity of various pictures were reduced by Matlab Image Processing Toolbox.
Procedure
On the testing day, participants first received and signed informed consent in the laboratory. Next, participants completed an 8-trial RSVP practice session which was set in the same way as the formal experiment session, including 4 dual-target trials, 2 single-target trials, and 2 non-target trials. The target stimuli used in the practice session did not reappear in the formal experiment session. Then, participants were instructed to complete the formal experiment session. Finally, participants rated the valence, arousal, familiarity, and craving of the target stimuli presented in the RSVP task and completed the scales in the laboratory.
Statistical analysis
Statistics analysis was performed using SPSS version 26.0 and R version 4.2.3. Demographic variables were compared between groups using independent t-tests or χ2 tests.
The dual-target trials were analyzed using mixed-design analysis of variance (ANOVA) for (1) the AB enhancement effect, (2) the AB antagonism effect, and (3) attentional backward interference. The AB enhancement was characterized by difficulty in detaching attention from T1, and recognition performance of T2 will be slower to recover to normal levels. The specific AB enhancement effect only included trials with neutral stimuli as T2. The independent variables were the lag of T2 relative to T1 (lag-2, lag-3, lag-6, lag-8), the type of T1 (game-related, neutral), and the group (IGD, HC). The dependent variable included was: the recognition accuracy rate of T2 following correctly identified T1 (i.e., T2|T1). The AB antagonism effect referred to a rapid attentional shift to T2, which resulted in a weakening of the AB. The AB antagonism analysis included only neutral stimulus T1 trials. Independent variables were the lag condition, T2 types, and groups. The dependent variable was T2|T1. Attention interference referred to the effect that T2 disrupting T1 processing backwardly (Neimeijer et al., 2013). The analysis focused on trials with neutral T1. Independent variables considered were lag, T2 type, and participant group. The dependent variable was T1 accuracy when participants detected two targets in a series. To ensure an adequate number of trials for analysis, all data were included regardless the correction of T2 recognition. In this part of the analysis, one participant in the IGD group who never recognized two targets was excluded.
Simple effect analysis and post hoc comparison of Bonferroni correction were used to analyze significant interaction effects and main effects. In the ANOVA, when the assumption of sphericity was not met, the Huynh-Feldt correction method was used. The present study also calculated each individual's area above curve (AAC) of accuracy (MacLean & Arnell, 2012) between 200 and 600 ms (lag-6 performance as a ceiling level) for each condition: (1) neutral T1 + neutral T2, (2) game-related T1 + neutral T2, (3) neutral T1 + game-related T2 (named AACbaseline, AACT1game, AACT2game). The difference obtained by subtracting AACbaseline from AACT1game (named ΔAACenhance) represents the AB enhancement effect due to the game-related T1. The difference obtained by subtracting AACT2game from AACbaseline (named ΔAACantagonize) represents the AB antagonism effect from the game-related T2. These AAC variables were used for correlation analysis with other self-reported variables.
Ethics
This study was approved by the Ethical Review Committee for the Protection of Human Participants of the Department of Psychology, Sun Yat-sen University (ID: 2023-0615-0305).
Results
Demographic and gaming characteristics
There was no significant difference in age, years of education, gender, and weekly non-gaming online entertainment time between the IGD group and the HC group (ps > 0.05). Group differences were found in weekly online gaming time (t23.938 = 7.698, p < 0.001), POGUS (t43 = 5.613, p < 0.001), game flow (t43 = 3.963, p < 0.001) and game craving (t43 = 5.985, p < 0.001) (Table 1).
Demographic and Internet-related characteristics
| Variable | M(SD) | t (df) or χ2 | p | |
| IGD (n = 24) | HC (n = 22) | |||
| Age | 20.04 (1.81) | 21.14 (2.44) | −1.742 (44) | 0.089 |
| Gender (male/female) | 14/10 | 10/12 | 0.763 | 0.382 |
| Education years | 14.79 (1.50) | 15.41 (2.02) | −1.169 (38.687) | 0.249 |
| Weekly online gaming time (hrs) | 25.81 (14.46) | 1.63 (2.21) | 8.089 (24.173) | <0.001 |
| Weekly non-gaming online entertainment time (hrs) | 9.27 (5.63) | 9.84 (12.24) | −0.203 (44) | 0.840 |
| IGD-SF9 | 6.13 (1.19) | 0.73 (1.03) | 16.358 (44) | <0.001 |
| POGUS | 59.96 (13.56) | 39.36 (10.01) | 5.814 (44) | <0.001 |
| Game flow | 3.74 (0.52) | 2.96 (0.76) | 4.091 (44) | <0.001 |
| Game craving | 4.52 (0.85) | 2.98 (0.85) | 6.141 (44) | <0.001 |
Note. IGD: Internet gaming disorder; HC: healthy control; M: mean; SD: standard deviation; IGD-SF9: the Internet gaming disorder scale-short form 9 item. POGUS: the problematic online game use scale.
The AB enhancement effect in the IGD group
The accuracy of T1 recognition was calculated for the two groups under various conditions. We measured the accuracy of T2 recognition following correctly identified T1 (represented by T2|T1, Table 2).
Descriptive information about the RSVP task accuracy
| Groups | T1 recognition (M±SD) | T2|T1 (M±SD) | |||
| lag-2 | lag-3 | lag-6 | lag-8 | ||
| IGD | 0.605 ± 0.075 | 0.235 ± 0.132 | 0.293 ± 0.180 | 0.530 ± 0.178 | 0.561 ± 0.156 |
| HC | 0.622 ± 0.102 | 0.179 ± 0.101 | 0.258 ± 0.134 | 0.610 ± 0.155 | 0.606 ± 0.159 |
Note. IGD: Internet gaming disorder; HC: healthy control; T1 recognition: accuracy of T1 recognition; T2|T1: accuracy of T2 recognition when T1 was identified correctly; M: mean; SD: standard deviation.
A mixed ANOVA analysis was conducted with group (IGD, HC) as the between-subjects variable, type of T1 (game-related, neutral), and lag (lag-2, lag-3, lag-6, lag-8) as within-subjects variables. The third-order interaction was significant (F(3, 132) = 3.261, p = 0.024, ηp2 = 0.069). The main effect of lag was significant (F(2.707, 119.099) = 139.562, p < 0.001, ηp2 = 0.760). The larger the lag between T2 and T1, the higher the recognition accuracy of T2. The results showed that T2 accuracy at lag-2 was significantly lower than that at lag-3 (p = 0.001), and lag-3 was significantly lower than lag-6 (p < 0.001), but there was no significant difference between lag-6 and lag-8 (p = 1.000; lag-2 < lag-3 < lag-6 = lag-8). The results indicated an AB enhancement effect. However, the main effects of groups and T1 type were not significant (ps > 0.05).
The T1 type by lag interaction was significant (F(3, 132) = 17.273,p < 0.001, ηp2 = 0.282). To be specific, the results showed that the accuracy of T2 following neutral T1 (i.e., T2|T1neutral) was significantly higher than the accuracy of T2 following game-related T1 (i.e., T2|T1game-related) at lag-2 and lag-3 conditions (lag-2: p < 0.001; lag-3: p = 0.003). There was no significant difference between T2|T1neutral and T2|T1game-related at lag-6 and lag-8 conditions (ps > 0.05).
In the IGD group, there was a significant main effect of lag (F(3, 69) = 53.786,p < 0.001, ηp2 = 0.700) and a significant interaction between T1 type and lag (F(3, 69) = 17.594, p < 0.001, ηp2 = 0.433). The simple effect analysis showed that the T2|T1game-related was significantly lower than the T2|T1neutral at lag-2 and lag-3 (ps < 0.001), and the T1 type differences were switched at lag-6 (p = 0.036) and lag-8 (p = 0.002).
In the HC group, there was a significant main effect of lag (F(2.305, 48.396) = 88.558, p < 0.001, ηp2 = 0.808) and a significant interaction between T1 type and lag (F(3, 63) = 4.042, p = 0.022, ηp2 = 0.161). The simple effect analysis showed that the T2|T1game-related was significantly lower than the T2|T1neutral at lag-2 (p = 0.002). The T1 type differences were not significant at the other 3 lag conditions (ps = 1.000). The interaction effects of lag by groups, and T1 type by groups were not significant (ps > 0.05) (Fig. 2). In short, the difference in second-order interaction effect size between groups (IGD: ηp2 = 0.416 vs. HC: ηp2 = 0.161) indicated a more pronounced AB enhancement in the IGD group.
The results of attentional blink enhancement analysis. Response accuracy of T2 (ACC) following neutral T1 (green line) and game-related T1 (red line) in different lag conditions for (A) Internet gaming disorder (IGD) and (B) healthy control (HC) groups. Error bars show 95% confidence interval. The asterisk indicates significant differences between T2 accuracy following neutral T1 and game-related T1 (*p < 0.05, **p < 0.01, ***p < 0.001)
Citation: Journal of Behavioral Addictions 2025; 10.1556/2006.2024.00075
The AB antagonism effect enhanced by game-related T2
A mixed design ANOVA of T2 accuracy with group (IGD, HC) as the between-subjects variable, type of T2 (neutral, game-related), and lag (lag-2, lag-3, lag-6, lag-8) as within-subjects variables showed significant main effects of lag (F(2.353, 103.551) = 76.834, p < 0.001, ηp2 = 0.636) and T2 type (F(1,44) = 37.104, p < 0.001, ηp2 = 0.457). Post hoc showed that T2 accuracy differed among lag conditions (ps <0.05; lag-2 = lag-3 < lag-6 = lag-8) and differed between game-related T2 and neutral T2 (p < 0.001; game-related T2 > neutral T2) (Fig. 3). The group by lag interaction was significant (F(2.353, 103.551) = 9.675, p < 0.001, ηp2 = 0.180). At lag-2, the T2 accuracy of the IGD group was significantly higher than that of the HC group (p = 0.023). However, no group difference was found in T2 accuracy at other lag levels. The results may indicate the group difference in the quality of attention and the AB antagonism effect affected by game-related cues in the IGD group.
The results of attentional blink antagonism analysis. Response accuracy (ACC) of neutral T2 (green line) and game-related T2 (red line) in different lag conditions for (A) Internet gaming disorder (IGD) and (B) healthy control (HC) groups. Error bars show 95% confidence interval
Citation: Journal of Behavioral Addictions 2025; 10.1556/2006.2024.00075
Besides, the T2 type by group interaction reached a marginally significant level (F(1, 44) = 3.978, p = 0.052, ηp2 = 0.083). Simple effect analysis showed that both the IGD and HC groups' accuracy of game-related T2 was higher than the accuracy of neutral T2, and this discrepancy in the IGD group was larger than that in the HC group (IGD: ΔT2 = 0.278, p < 0.001; HC: ΔT2 = 0.141, p = 0.007). The second-order interaction effects of lag by T2 type, and the third-order interaction among lag, T2 type, and group were not significant (ps > 0.05).
Game-related T2 influenced backward interference
A mixed design ANOVA of T1 accuracy was conducted with group (IGD, HC) as the between-subjects variable, T2 type (neutral, game-related), and lag (lag-2, lag-3, lag-6, lag-8) as within-subjects variables. The main effect of lag was significant (F(2.749, 118.198) = 4.211, p = 0.009, ηp2 = 0.089). Post hoc showed that T1 accuracy at lag-2 condition was marginally significantly lower than that at lag-3 condition (p = 0.057), and the difference between any of the other two lag conditions was not significant (ps > 0.05). Furthermore, the main effect of T2 type was significant (F(1, 43) = 5.201, p = 0.028, ηp2 = 0.108). The accuracy of T1 followed by game-related T2 (i.e., T1|T2game-related) was significantly lower than that of T1 followed by neutral T2 (i.e., T1|T2neutral). The main effect of group and all interaction effects were not significant (ps > 0.05) (Fig. 4).
The results of attentional backward interference analysis. Response accuracy of T1 (ACC) before neutral T2 (green line) and game-related T2 (red line) indifferent lag conditions. Error bars show 95% confidence interval
Citation: Journal of Behavioral Addictions 2025; 10.1556/2006.2024.00075
Gaming characteristics correlated with AB effects
Correlation analyses were conducted between gaming characteristics, craving ratings of images, and the AB effect of the RSVP task, with ΔAACenhance and ΔAACantagonize as AB effect indicators. Within the full sample, the ΔAACenhance was positively correlated with the score of POGUS (r = 0.294, p = 0.047) and the craving rating difference (Δcraving) between game-related and neutral images (r = 0.404, p = 0.005). And the Δcraving was also significantly correlated with ΔAACenhance (r = 0.426, p = 0.048) and ΔAACantagonize (r = −0.455, p = 0.033) in the HC group. For the IGD group, no significant correlation was found between craving ratings and the attentional bias effects (ps > 0.05). Thus, the correlations between the score of game flow scale, game craving scale, and AB effect indicators were not significant in the full sample or in each group (ps > 0.05).
Discussion
In the current study, the temporal attentional bias in individuals with IGD was preliminarily investigated by applying a game-relevant RSVP task. We found an enhanced AB effect in the condition with game-related targets as T1 in individuals with IGD. When T2 was the game-related stimulus, there was no significant group difference in the AB antagonistic effect. The findings revealed the temporal attentional biases in individuals with IGD, which may promote the persistent gaming behavior.
The results of the present study are generally in line with the perspectives and further suggestions of the I-PACE model (Brand, 2022; Brand et al., 2019). The model highlights the importance of the interaction between individuals' predisposing variables and certain inner circle aspects that specific situations deliver. The model suggests that the external addiction-relevant stimuli may trigger affective and cognitive response, such as increased attention to these stimuli and craving for online activities, leading to specific behaviors (Brand et al., 2019). As a consequence, the affective and cognitive responses over time may lead to conditioning processes. Furthermore, the suggestions of the model posits two paths that may increase the engagement in online activities: the “feel better” path and the “must do” path. The former relates to pleasurable experiences and craving, whereas, the latter relates to habitual and compulsive behaviors (Brand, 2022). Self-control may moderate the effects of both paths. In our study, the observed game-cue T2 related AB antagonism effect, as automated responses to game-related stimuli, may suggest an enhanced “must do” path and an impairment of self-control. In addition, the game-cue T1 enhanced AB effect may indicate cue-triggered craving that promotes more attentional resources involvement, reflecting increased specific game-cue sensitivity and blunted self-control (e.g., inhibitory control) (Petrucci & Pecchinenda, 2018). The game-cue-related AB enhancement and antagonism effects found in our study should be considered as a manifestation of multiple factors, suggesting that individuals with online addictive behaviors may be assumed to have an imbalance between the driving paths and self-control (Brand, 2022; Brand et al., 2016, 2019).
Firstly, individuals with IGD showed an enhanced AB effect in terms of game influence on attentional bias. Under the condition of correct identification of the game-related T1, the IGD group showed decreased neutral T2 identification within the AB in the period, reflecting an enhanced AB effect in the IGD group. The result supported hypothesis 1. Consistently, a previous study with computer-related DPT also identified attentional biases in IGD participants. Compared to the HC group, when the cue was computer-related, the IGD group showed decreased accuracy in probe judgments with short SOA (Jeromin, Nyenhuis, & Barke, 2016). The elaborated intrusion theory of desire (EI) may explain the present results. EI theory suggests that associations of cues in the context (such as game-related cues) can trigger cravings, and the emotional dynamics of the cravings drive individuals to elaborate to create mental representations that satisfy the cravings. This process requires more cognitive resources and makes subsequent other cognitive processing less efficient (Brahim et al., 2023; Kavanagh et al., 2005). Individuals with IGD have difficulty disengaging from game-related cues. In terms of the time dimension, once a game cue appears, the game craving triggered by the association of game cues causes people to process the game-related information that has entered their working memory more elaborately to satisfy their craving, thus tying up more cognitive resources in processing game information. The privilege of elaborated processing given to game cues in individuals with IGD will make the consolidation process of game cues more stable and less intrusive, and at the same time more durable to prevent subsequent new information from entering working memory.
In addition, individuals with IGD exhibit a more subdued response to neutral T1. The attentional blink patterns in the condition of neutral T1 and T2 differed between groups, with the IGD group displaying a greater change in T2 accuracy compared to HC individuals. This may be attributed to the anhedonia of general stimuli in those with IGD, which was demonstrated in both the field of behavioral addiction and substance use disorders (Sescousse, Barbalat, Domenech, & Dreher, 2013). Consequently, their insensitivity to general stimuli indicated less motivation for cognitive effort. In summary, the craving and elaborate processing for game-related stimuli, along with the blunted response and anhedonia for neutral stimuli in certain dynamic environments may indicate a core deficit in IGD that underlies uncontrollable and persistent gaming behavior.
Secondly, when the game-related stimulus was presented as a second target after a neutral stimulus, individuals with IGD showed a weakened AB effect, suggesting an AB antagonism effect. The results of the current study were consistent with the results of previous studies using short SOAs. Previous attentional bias studies using shorter SOAs to examine attentional engagement have identified spatial attentional biases in individuals with IGD (Lorenz et al., 2013; Metcalf & Pammer, 2011). The automatic capture of attention for game-related T2 can be explained by the automatic cognitive processing theory (Billieux et al., 2020; He et al., 2018; Tiffany, 1990), which presumes that addictive stimuli are automatically captured, rather than driven by individuals' experience of craving. The result may provide evidence for the I-PACE model and relevant suggestions that addiction related attentional biases may guide addictive behaviors (Brand, 2022; Brand et al., 2016).
In addition, an enhanced backward interference of the game-related T2 on the preceding neutral T1 was found, the effect of which was comparable between the IGD and the HC groups. The results illuminated the specificity of Internet games independent of gaming disorder. Although the game-related stimulus appeared later, it may attract more attention. This specificity has also been verified in previous studies of IGD (Kim et al., 2018, 2019, 2021), suggesting the specific effects of Internet games on attentional biases.
The present study investigated the temporal attentional bias in IGD and revealed a game-related T1 enhanced AB effect, which empirically supported and bridged the I-PACE model and EI theory (Brand, 2022; Brand et al., 2019; Kavanagh et al., 2005). The game-cue triggered craving may promote elaborate processing that satisfy gaming-related needs, the process of which may lead to the difficulty of attentional disengagement. The game-related T2 enhanced AB antagonism effect found in the study may reflect an automatic responses to game-related cues, which support the I-PACE model and relevant suggestions of the impaired “must do” path and self-control in individuals with IGD (Brand, 2022). All together, the attentional biases guide IGD individuals' attention to the potential immediate reward of gaming, which may further drive their online gaming behaviors. Furthermore, a review study highlighted the efficacy of specific neuropsychological modification approaches that closely related to attentional bias inhibition training (Verdejo-García, Alcázar-Córcoles, & Albein-Urios, 2019). Thus, the development of effective intervention for IGD could consider targeting on specific cognitive factors (e.g., attentional biases, craving, and expectation).
Several limitations in the current study should be noted. First, the study had a relatively small sample size. Although the present study has met the minimum sample size requirement based on power analysis, a larger sample size may provide more robust evidence of group differences and correlations between AB and problematic online gaming behaviors. The second limitation of this study was the imbalance between male and female participants. Although IGD is most prevalent in young adult males, further research is needed to examine the AB effects in both males and females with IGD.
Conclusions
The present study investigated the temporal attentional bias in individuals with IGD using a game-related RSVP task. Using 3 different aspects of attentional bias indices, the IGD group showed an enhanced AB effect related to attentional disengagement from game-related cues. A positive correlation between the game-related AB enhancement effect and problematic online game use similarly was found. The mal-adaptive attentional bias may trigger game craving in individuals with IGD and ultimately lead to their excessive gaming behavior. The current study deepened our understanding of attentional bias in IGD and paved the way for developing and refining more effective interventions.
Funding sources
This study was supported by the National Natural Science Foundation of China (No. 32200910, No. 32171082, and No. 32471136), the Funding by Science and Technology Projects in Guangzhou (Grant No. SL2023A04J00648, No. 2024A04J3695), the National Social Science Foundation of China (Grant No. 17ZDA323), the Neuroeconomics Laboratory of Guangzhou Huashang College (2021WSYS002), and the Science and Technology Planning Project of Guangdong Province (2023B1212060018).
Authors' contribution
M.J.H.: conceptualization; data curation; investigation; project administration; visualization; writing - original draft. Y.X.K.: conceptualization; supervision; writing - review & editing. L.L.: conceptualization; supervision; funding acquisition; project administration; writing - original draft. All authors reviewed and revised the manuscript.
Conflicting of interest
The authors declared no potential conflicts of interest for the research, authorship, and/or publication of this article.
Supplementary material
Supplementary data to this article can be found online at https://doi.org/10.1556/2006.2024.00075.
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