Event-related brain response to visual cues in individuals with Internet gaming disorder: relevance to attentional bias and decision-making

Influence of reinforcement learning on the inhibitory control of Internet gaming disorder

Reward processing dysfunction and inhibition control deficiency have been observed in Internet gaming disorder (IGD). However, it is still unclear whether the previous reinforcement learning depends on reward/punishment feedback influences on the cognitive inhibitory control of IGD. This study compared the differences between an IGD group and healthy people without game experiences in the probability selection task and the subsequent stop signal task by the method of behavioral experiments, in order to explore whether the reward learning ability is impaired in the IGD group. We also discuss the influence of previous reward learning on subsequent inhibition control. The results showed that (1) during the reward learning phase, the IGD group's accuracy was significantly lower than that of the control group; (2) compared with the control group, the IGD group's reaction times were longer in the transfer phase; (3) for no-go trials of the inhibitory control phase after reward learning, the accuracy of the reward-related stimulation in the IGD group was lower than that of punishment-related or neutral stimulation, but there was no significant difference among the three conditions in the control group. These findings indicated that the reinforcement learning ability of the IGD group was impaired, which further caused the abnormal response to reinforcement stimuli.

Approach bias in individuals with Internet gaming disorder: Evidence from an event-related potential-based approach-avoid task

Individuals with Internet gaming disorder (IGD) often exhibit an approach bias towards gaming cues compared to non-gaming cues. Although previous studies suggested a positive correlation between approach bias and the severity of game use, the neuropsychological mechanisms that underpin the automatic action tendencies remain largely unexplored. The present study measured event-related potentials (ERPs) in 22 IGD and 23 healthy control (HC) participants who met the inclusion criteria, both groups conducted the Stimulus-Response Compatibility task (SRC), with their ERPs recorded during the task. Results revealed that the IGD group showed a significantly larger approach bias towards gaming cues (avoidance versus approach reaction time) compared to the HC group. The amplitude of P300 significantly increased, whereas N100 significantly decreased for game-approach compared to game-avoid for IGD compared to HC participants. The findings suggested that the enhanced integrated motivational value under compatible conditions as well as increased stimulus-response conflicts under incompatible conditions may contribute to the approach bias in IGD individuals. Further investigation on the intervention is prompted through longitudinal studies.

Severity identification for internet gaming disorder using heart rate variability reactivity for gaming cues: a deep learning approach

Background

The diminished executive control along with cue-reactivity has been suggested to play an important role in addiction. Hear rate variability (HRV), which is related to the autonomic nervous system, is a useful biomarker that can reflect cognitive-emotional responses to stimuli. In this study, Internet gaming disorder (IGD) subjects' autonomic response to gaming-related cues was evaluated by measuring HRV changes in exposure to gaming situation. We investigated whether this HRV reactivity can significantly classify the categorical classification according to the severity of IGD.

Methods

The present study included 70 subjects and classified them into 4 classes (normal, mild, moderate and severe) according to their IGD severity. We measured HRV for 5 min after the start of their preferred Internet game to reflect the autonomic response upon exposure to gaming. The neural parameters of deep learning model were trained using time-frequency parameters of HRV. Using the Class Activation Mapping (CAM) algorithm, we analyzed whether the deep learning model could predict the severity classification of IGD and which areas of the time-frequency series were mainly involved.

Results

The trained deep learning model showed an accuracy of 95.10% and F-1 scores of 0.995 (normal), 0.994 (mild), 0.995 (moderate), and 0.999 (severe) for the four classes of IGD severity classification. As a result of checking the input of the deep learning model using the CAM algorithm, the high frequency (HF)-HRV was related to the severity classification of IGD. In the case of severe IGD, low frequency (LF)-HRV as well as HF-HRV were identified as regions of interest in the deep learning model.

Conclusion

In a deep learning model using the time-frequency HRV data, a significant predictor of IGD severity classification was parasympathetic tone reactivity when exposed to gaming situations. The reactivity of the sympathetic tone for the gaming situation could predict only the severe group of IGD. This study suggests that the autonomic response to the game-related cues can reflect the addiction status to the game.

The Influence of Green Product Type, Message Framing, and Anticipated Pride on Green Consumption Behavior: An Event-Related Potential (ERP) Study

Different types of green products require different marketing approaches to promote individual green purchasing behaviors. Previous studies have focused only on the effects of message framing on the promotion of different types of green products; however, little is known about the role of underlying emotions. Using event-related potentials (ERPs), this study investigated the neural responses to message framings and anticipated pride in green product types to assess their level of influence on green consumption. Participants in this study were randomly assigned to the anticipated pride versus control groups, and asked to make green consumption decisions involving different types (self- vs. other-interested) of green products, utilizing both gain and loss framing. The behavioral results demonstrated that participants in the anticipated pride group made more green product purchase choices than those in the control group. The ERP results showed that within the loss framing of the control group, other-interested green products induced larger N400 and smaller late positive potential (LPP) amplitudes than self-interested green products, whereas the results showed the opposite trend for the anticipated pride group. These results indicate that although individuals might have biases in their motivation that lead them to focus on self-interested green products, anticipating pride reduces cognitive conflicts and increases their motivation to focus on other-interested green products in the context of loss.

Neuropsychological Deficits in Disordered Screen Use Behaviours: A Systematic Review and Meta-analysis

Over the last few decades, excessive and disordered screen use has become more prevalent, prompting investigations into its associated consequences. The extent to which disordered screen use behaviours impact neuropsychological functioning has been reportedly mixed and at times inconsistent. This review sought to synthesise the literature and estimate the magnitude of overall cognitive impairment across a wide range of disordered screen use behaviours. We also sought to determine the cognitive domains most impacted, and whether the observed impairments were moderated by the classification of screen-related behaviours (i.e., Internet or gaming) or the format of cognitive test administration (i.e., paper-and-pencil or computerised). A systematic search of databases (Embase, PsycINFO, MEDLINE) identified 43 cross-sectional articles that assessed neuropsychological performance in disordered screen use populations, 34 of which were included in the meta-analysis. A random-effects meta-analysis revealed significant small/medium (g = .38) cognitive deficits for individuals with disordered screen use behaviours relative to controls. The most affected cognitive domain with a significant medium effect size (g = .50) was attention and focus followed by a significant reduction in executive functioning (g = .31). The classification of disordered screen use behaviours into Internet or gaming categories or the format of cognitive testing did not moderate these deficits. Additionally, excluding disordered social media use in an exploratory analysis had little effect on the observed outcomes. This study highlights a number of methodological considerations that may have contributed to disparate findings and shows that disordered screen use can significantly impact cognitive performance. Recommendations for future research are also discussed. Data for this study can be found at https://osf.io/upeha/ .

Bored with boredom? Trait boredom predicts internet addiction through the mediating role of attentional bias toward social networks

Internet addiction is an emerging issue, impacting people's psychosocial functioning and well-being. However, the prevalence and the mechanisms underlying internet misuse are largely unknown. As with other behavioral addiction disorders, the increase and persistence of internet addiction may be favored by negative affect such as boredom. In this study, we examined the role of boredom susceptibility, as a personality trait, in predicting the risk of internet addiction. Furthermore, we analyzed the attentional mechanisms that may exacerbate dysfunctional internet behaviors. Specifically, we assessed the mediating role of attentional bias toward social media cues on the relation between boredom susceptibility and internet addiction. Sixty-nine young adults were administered a dot-probe task assessing internet-related attentional bias (AB) and questionnaires measuring internet addiction (IAT) and boredom susceptibility (BS-BSSS). Correlation and t-test analyses confirmed that the tendency to experience boredom and selective attention toward social network information was related to internet addiction. Furthermore, the mediation model indicated that AB fully explains the link between BS-BSSS and IAT. The study highlighted the crucial role of selective attentional processing behind internet addiction. The current results are useful for both researchers and clinicians as they suggest that intervention programs for internet addiction should include strategies to cope with dysfunctional cognitive processes.

Intertemporal Decision-making and Risk Decision-making Among Habitual Nappers Under Nap Sleep Restriction: A Study from ERP and Time-frequency

Sleep restriction affects people's decision-making behavior. Nap restriction is a vital subtopic within sleep restriction research. In this study, we used EEG to investigate the impact of nap sleep restriction on intertemporal decision-making (Study 1) and decision-making across risky outcomes (Study 2) from ERP and time-frequency perspectives. Study 1 found that habitual nappers restricting their naps felt more inclined to choose immediate, small rewards over delayed, large rewards in an intertemporal decision-making task. P200s, P300s, and LPP in our nap-restriction group were significantly higher than those in the normal nap group. Time-frequency results showed that the delta band (1 ~ 4 Hz) power of the restricted nap group was significantly higher than that of the normal nap group. In Study 2, the nap-restriction group was more likely to choose risky options. P200s, N2s, and P300s in the nap deprivation group were significantly higher than in the normal nap group. Time-frequency results also found that the beta band (11 ~ 15 Hz) power of the restricted nap group was significantly lower than that of the normal nap group. The habitual nappers became more impulsive after nap restriction and evinced altered perceptions of time. The time cost of the LL (larger-later) option was perceived to be too high when making intertemporal decisions, and their expectation of reward heightened when making risky decisions-believing that they had a higher probability of receiving a reward. This study provided electrophysiological evidence for the dynamic processing of intertemporal decision-making, risky decision-making, and the characteristics of nerve concussions for habitual nappers.

Overview on brain function enhancement of Internet addicts through exercise intervention: Based on reward-execution-decision cycle

Internet addiction (IA) has become an impulse control disorder included in the category of psychiatric disorders. The IA trend significantly increased after the outbreak of the new crown epidemic. IA damages some brain functions in humans. Emerging evidence suggests that exercise exerts beneficial effects on the brain function and cognitive level damaged by IA. This work reviews the neurobiological mechanisms of IA and describes the brain function impairment by IA from three systems: reward, execution, and decision-making. Furthermore, we sort out the research related to exercise intervention on IA and its effect on improving brain function. The internal and external factors that produce IA must be considered when summarizing movement interventions from a behavioral perspective. We can design exercise prescriptions based on exercise interests and achieve the goal of quitting IA. This work explores the possible mechanisms of exercise to improve IA through systematic analysis. Furthermore, this work provides research directions for the future targeted design of exercise prescriptions.

Different Educational Interventions on Individual Cognition of Garbage Classification Based on EEG Monitoring

Improvement in an individuals’ cognition is the key to promote garbage classification. This study takes university students as the research subjects, through three educational interventions, including the self-learning, heuristic learning, and interactive learning ways, to seek the most effective intervention based upon event-related potentials (ERPs) that is beneficial to enhance cognition of garbage classification. The results show that the experimental subjects induced P300 and LPP components, representing attentional changes and cognitive conflicts in classification judgments. There are differences in the amplitudes and peak latency of the two components corresponding to different interventions, indicating that the three educational interventions are able to improve the individual’s cognition level of garbage classification within a certain period of time. The interactive-learning intervention triggers the largest amplitudes of P300 and LPP, as well as the smallest peak latency, indicating its effect is the best. Such results provide insight into the design for an appropriate strategy in garbage classification education. The study also shows that an EEG signal can be used as the endogenous neural indicator to measure the performance of garbage classification under different educational interventions.

Interoception Dysfunction Contributes to the Negative Emotional Bias in Major Depressive Disorder

Background

Previous research studies have demonstrated that impaired interoception is involved in emotional information processing in major depressive disorder (MDD). Heartbeat-evoked potential (HEP) amplitudes, an index for interoception, could be manipulated by emotional faces in healthy people. Considering negative emotional bias is the core characteristic in MDD, we hypothesized that interoception dysfunction was associated with the negative emotional bias in MDD.

Methods

An electroencephalogram (EEG) study under an emotional faces task was applied to explore the relationship between interoception and emotional bias. HEPs before emotional faces stimuli were used to predict the late positive potential (LPP) amplitudes and it worked as an index of emotional bias. Twenty-seven patients with MDD and 27 healthy controls (HCs) participated in this study. Source analysis gave an auxiliary description for results in sensory level.

Results

Major depressive disorders (MDDs) had poor performance in the heartbeat count task (HCT) and attenuate HEP average amplitudes (455-550 ms). Compared with HCs, cluster-based permutation t-tests revealed that MDDs had attenuated LPP amplitudes (300-1,000 ms) over centroparietal regions and enhanced LPP amplitudes over frontocentral regions. Furthermore, abnormal attenuated HEPs could predict aberrant LPPs under sad face stimuli in MDDs, which could be associated with the dysfunction of the anterior cingulate cortex (ACC) and right insula.

Conclusion

Mediated by ACC and insula, interoception dysfunction contributes to the negative emotional bias of MDD, highlighting the importance of interoception in the disorder.

Slow-Wave EEG Activity Correlates with Impaired Inhibitory Control in Internet Addiction Disorder

Impaired inhibitory control is a core feature of internet addiction disorder (IAD). It is therefore of interest to determine the neurophysiological markers associated with it. The present study aimed to find such biomarkers with a resting-state electroencephalogram (EEG). We specifically used scores on the Chinese Internet Addiction Scale revised edition (CIAS-R) to divide 46 participants into two groups: the IAD group (>53, n = 23) and control group (<46, n = 23). Both behavioral aspects (Go/NoGo responses and impulsivity) and EEG were measured in the lab. The results suggest that the IAD group presented a decreased slow-wave (1−8 Hz) absolute power across the whole brain. The slow-wave activities in the frontal areas were also correlated with the commission error rate in the Go/NoGo task in the IAD group. These results imply that the frontal slow-wave EEG activity may serve as a neurophysiological marker of IAD, helping to understand the underlying neural mechanisms of inhibitory control deficits in IAD and point to possible interventions.

Brain variability in dynamic resting-state networks identified by fuzzy entropy: a scalp EEG study

Objective.Exploring the temporal variability in spatial topology during the resting state attracts growing interest and becomes increasingly useful to tackle the cognitive process of brain networks. In particular, the temporal brain dynamics during the resting state may be delineated and quantified aligning with cognitive performance, but few studies investigated the temporal variability in the electroencephalogram (EEG) network as well as its relationship with cognitive performance.Approach.In this study, we proposed an EEG-based protocol to measure the nonlinear complexity of the dynamic resting-state network by applying the fuzzy entropy. To further validate its applicability, the fuzzy entropy was applied into simulated and two independent datasets (i.e. decision-making and P300).Main results.The simulation study first proved that compared to the existing methods, this approach could not only exactly capture the pattern dynamics in time series but also overcame the magnitude effect of time series. Concerning the two EEG datasets, the flexible and robust network architectures of the brain cortex at rest were identified and distributed at the bilateral temporal lobe and frontal/occipital lobe, respectively, whose variability metrics were found to accurately classify different groups. Moreover, the temporal variability of resting-state network property was also either positively or negatively related to individual cognitive performance.Significance.This outcome suggested the potential of fuzzy entropy for evaluating the temporal variability of the dynamic resting-state brain networks, and the fuzzy entropy is also helpful for uncovering the fluctuating network variability that accounts for the individual decision differences.