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The texture discrimination task (TDT) stimulus. An adapted version of the standard TDT used frequently in VPL experimentation. The first target is highlighted in blue where subjects report either the presence of an L or T, which is designed to hold fixation. This is referred to as the fixation task. The second target (peripheral orientation task) is highlighted in orange and requires the subject to respond with an H or V depending if the targets orientation was horizontal or vertical. The peripheral orientation task is the primary measure of performance in the experiment. Note that the blue and orange circles are provided just for illustrative purposes. They did not appear in the actual experiment. The shorter the SOA, the more difficult the task becomes. doi:10.1371/journal.pone.0120011.g001 

The texture discrimination task (TDT) stimulus. An adapted version of the standard TDT used frequently in VPL experimentation. The first target is highlighted in blue where subjects report either the presence of an L or T, which is designed to hold fixation. This is referred to as the fixation task. The second target (peripheral orientation task) is highlighted in orange and requires the subject to respond with an H or V depending if the targets orientation was horizontal or vertical. The peripheral orientation task is the primary measure of performance in the experiment. Note that the blue and orange circles are provided just for illustrative purposes. They did not appear in the actual experiment. The shorter the SOA, the more difficult the task becomes. doi:10.1371/journal.pone.0120011.g001 

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Playing certain types of video games for a long time can improve a wide range of mental processes, from visual acuity to cognitive control. Frequent gamers have also displayed generalized improvements in perceptual learning. In the Texture Discrimination Task (TDT), a widely used perceptual learning paradigm, participants report the orientation of...

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... the present study, subjects were given a modified version of the TDT paradigm, used in the Yotsumoto and colleagues (2009) study, where subjects were trained on two different TDT backgrounds in immediate succession. This training produced an interference effect where the training from each background interfered with the training from the other. This notion refers back to the concept of consolidation, where a performance improvement would be seen in this paradigm if an hour of rest were allowed in between learning the two backgrounds. TDT has been originally developed by Karni and Sagi [22] and can be manipulated to form both task-relevant and task-irrelevant signals that impact perceptual training ( Fig. 1). The primary goal for the subject is to discriminate an orientation (by responding either with H or V keys for horizontal or vertical) of a target array of oblique lines imbedded in a series of background horizontal or vertical line segments. The target can be presented in any of the four visual quadrants in the subject ’ s periphery and is referred to as the peripheral orientation task. For the present study specifically, all subjects were trained in the lower left visual quadrant. An additional task is employed as well in order to hold fixation and attention in the center of the stimulus, where subjects are required to report the presence of an L or T at the fixation cross by pressing the corresponding keys. This task is known as the fixation task. TDT can be varied in difficulty through changing the length of Stimulus-to-mask Onset Asynchrony (SOA), which is the time elapsed between the presentation onset of the stimulus and the onset of the mask. If the mask appears more closely following stimulus presentation (small SOA), the task becomes more difficult. After the presentation of the mask, subjects first enter their response to the fixation task followed by their response to the peripheral orientation task. The experiment in the present study consisted of two sessions, which spanned for 2 conse- cutive days. Each session was 24 hours apart and localized to the afternoon in order to avoid any form of circadian effect. One session was divided into 2 parts. The first half of the first session consisted of either all vertical or all horizontal background lines and the second half of the first session consisted of the opposite orientation for the background (Fig. 2). For example, if one subject performed the first half of session 1 with a horizontal background, then their second half would be a vertical background stimulus. The second session (24 hours later) con- tained the same stimuli parameters as the first session. This procedure was counterbalanced across subject to ensure that the actual stimuli themselves were not confounding the results. The purpose of changing the background line orientation within a session is to cause interference in learning [17], where if this had not occurred one would normally see improvement. The core idea behind this involves the concept of learning consolidation, where not enough time is allowed between different background training for learning to be solidified within the brain ’ s memory systems [9]. Both frequent gamers and non-gamers were trained with the same number of trials and blocks. In each session, there were 7 blocks, in each of which was conducted with a single SOA with 39 trials. Thus, there were 7 SOAs used and the total number of trials was 273. The SOAs were 180ms, 160ms, 140ms, 120ms, 100ms, 80ms, 60ms, and presented in this order. Through- out the training, the target was presented at a consistent quadrant of the visual field for each subject. For analysis, our results have been divided into two separate performance measures, considering both techniques from prior literature [17]. The first measure was the 75% threshold SOA, which was computed as follows. First, we obtained the correct response ratio for the peripheral orientation task computed for each SOA and then fitted this data to a logistic psychometric function. This psychometric curve allows us to easily see performance trends across each SOA and define the threshold for each respective session. The threshold is the point on the psychometric curve that corresponds to the SOA that subjects were able to achieve 75% correct response rate. As noted in our methods, shorter SOAs indicated that the task was more difficult. Thus, the SOA on the curve where subjects had a 75% threshold was the most difficult SOA subjects could handle before dropping below optimal performance rate. If the threshold becomes shorter after training, this indicates that the subject learned the task. The second measure was a simple percent correct for each SOA, which was supplementary to the first threshold measure. While these two measures are correlated, the sensitive aspects may be different. First, Fig. 3A shows the 75% threshold for each session of the 2-day training in both frequent gamers and non-gamers. In order to confirm learning of the task, a 2x2x2 repeated measures mixed-design ANOVA was conducted on this data, using day, background, and group as factors. The ANOVA revealed a significant main effect of day for both frequent gamers and non-gamers (F(1,17) = 12.840, p = 0.002), suggesting overall performance improvement. Additionally, the main effect of background was also significant (F(1,17) = 4.811, 0 = 0.043) suggesting different trends in performance on each background. The interaction between day, background, and group however, was not significant (F(1,17) = 0.736, p = 0.404), as well as the overall group difference examined through the ANOVA (F(1,17) = 1.430, p = 0.249). Since different trends ...

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... To account for the diversity of playing engagement patterns, sufficiently broad inclusion criteria were used to invite players to participate in the survey: being at least 18 years old and playing at least 5 h per week. Although there is no consensus on how much is a "significant amount" of time spent gaming, some authors have proposed a minimum of 5 h of playing per week as a criterion for a player to be considered as a "frequent video gamer" (Berard, Cain, Watanabe, & Sasaki, 2015) or an "heavy gamer" (Hollis, 2014). Thus, a minimum of 5 h per week have been set as an inclusion criterion in the present study. ...
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Background Online competitive practice of video games has recently known a significant worldwide expansion. However, this practice can be associated to problematic use and deterioration of quality of life depending on multiple determinants, among which motivation is central. The purpose of this study was to identify motivational clusters and to compare them regarding quality of life, problematic use of video game, and personality traits. Methods Participants (N=256) in this cross-sectional study were recruited through specialized websites to complete self-reported questionnaires assessing motivation to play online (MOGQ), personality (BFI-Fr), quality of life (WHOQOL-BREF), and problematic use (IGD-Scale). A hierarchical clustering analysis and intergroup comparative analyses were conducted. Findings: Three motivational clusters were identified (“recreational”, “competitive” and “escapers”). “Competitive” and “escapers” players reported higher IGD scores than the “recreational” players (p<.001). However, “escapers” players had lower psychological health scores (p<.001), were more neurotic (p<.001), and less extroverted (p<.001) than the others. Based on IGD scores, “competitive” and “escapers” players were considered as problematic albeit only “escapers” exhibited a functional impairment. Therefore, engaged and problematic players cannot be differentiated with IGD scores. Discussion IGD scores were insufficient to differentiate between players at risk of evolution toward pathological states (i.e., “escapers” players) and those whose strong engagement is not detrimental to their quality of life (i.e., “competitive” players). Consequently, considering both psychological health and motivation is necessary to assess the problematic nature of competitive videogame practice. Better definitions and assessment tools are essential in order to avoid over-diagnosis of non-pathological behaviors.
... f. No frequent action video game playing determined by video game questionnaire (Tamaki et al., , 2020aBerard et al., 2015;Green and Bavelier, 2003) g. Able to undergo EEG and MRI experiments (for instance, not too sensitive to surgical tapes or no metal in the body) h. ...
... Data generated in the previous study Tamaki et al. (2020b) https://www.nature.com/ articles/s41593-020-0666-y Berard et al. (2015), and Green and Bavelier (2003) n/a Pittsburg Sleep Quality Index (PSQI) Buysse et al. (1989) n/a Stanford sleepiness scale (SSS) Hoddes et al. (1972Hoddes et al. ( , 1973 n/a Pillow n/a n/a Towels n/a n/a Cotton n/a n/a Cushions n/a n/a Gauze n/a n/a Bouffant cap n/a n/a Ear plugs n/a n/a (Roenneberg et al., 2003) o Sleep-wake habits questionnaire (Tamaki et al., , 2020a o Video game questionnaire (Tamaki et al., , 2020aBerard et al., 2015;Green and Bavelier, 2003) o MEQ (Horne and Ostberg, 1976) o PSQI (Buysse et al., 1989) o Stanford sleepiness scale (SSS) (Hoddes et al., 1972(Hoddes et al., , 1973 Psychomotor vigilance test (PVT) (Dinges and Powell, 1985). Pillow, towels, cotton, gauze, bouffant cap, ear plugs, alcohol bottle (67%), EEG prep pad, cotton swabs EEG gel, and chest band (to stabilize EEG cap) ( Approximately 1 week after the adaptation session, start the main experiment. ...
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... ► Video gaming performed 5 hours or more per week. 25 Exclusion criteria Willing participants did not qualify for the study if they met the following characteristics: ► Younger than 18 years of age. ► Video gaming performed less than 5 hours/week. ...
... ► Video gaming performed less than 5 hours/week. 25 ► Not currently living in South Africa. ► No access to Wi-Fi/internet. ...
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... Third, people who frequently play action video games were excluded because extensive video game playing has been shown to affect visual and attention processing (Berard et al., 2015;Green & Bavelier, 2003;Li et al., 2009). Frequent gamers were defined as those who played . ...
... https://doi.org/10.1101/2020 action video games at least 5 hours a week for a continuous period of 6 months or more as defined by previous research (Berard et al., 2015;Green & Bavelier, 2003). Fourth, subjects who were included had a regular sleep schedule, i.e., differences in average bedtimes and wake-up times between weekdays and weekends were less than 2 hours, and the average sleep duration regularly ranged from 6 to 9 hours using a sleep-wake habits questionnaire (Tamaki et al., 2020b; and the Munich chronotype questionnaire (Roenneberg et al., 2003). ...
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... Overall, our study contrasts with a growing body of work showing that AVGPs outperform NVGPs on a variety of perceptual, attentional and cognitive skills (Bediou et al., 2018; but see Sala, Tatlidil, & Gobet, 2018). Regarding visual processing in particular, AVGPs have shown superior performance in a number of perceptual decision making tasks measuring contrast sensitivity (Li et al., 2009), orientation identification (Bejjanki et al., 2014;Berard et al., 2015), or motion perception (Green This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Hutchinson & Stocks, 2013). ...
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... The interval between the first and the second training sessions for each group was 12 h. Each training session consisted of 16 blocks, each of which had 39 trials with a constant SOA based on our previous study (69). Eight SOAs were considered: 400 ms, 180 ms, 160 ms, 140 ms, 120 ms, 100 ms, 80 ms, and 60 ms. ...
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... All subjects had no prior experience in VPL tasks, as experiences in VPL tasks may cause a long-term visual sensitivity change (Karni & Sagi, 1991;Lu et al., 2011;Sagi, 2011;Sasaki et al., 2010;Schwartz et al., 2002;Seitz et al., 2005;Yotsumoto, Chang, Watanabe, & Sasaki, 2009). People who frequently play action video games were excluded because extensive video game playing affects visual and attention processing (Berard, Cain, Watanabe, & Sasaki, 2015;Green & Bavelier, 2003;Li, Polat, Makou, & Bavelier, 2009). Frequent gamers were defined as those who participated in action video game playing at least 5 hr a week for a continuous period of 6 months or more as defined by previous research (Berard et al., 2015;Green & Bavelier, 2003). ...
... People who frequently play action video games were excluded because extensive video game playing affects visual and attention processing (Berard, Cain, Watanabe, & Sasaki, 2015;Green & Bavelier, 2003;Li, Polat, Makou, & Bavelier, 2009). Frequent gamers were defined as those who participated in action video game playing at least 5 hr a week for a continuous period of 6 months or more as defined by previous research (Berard et al., 2015;Green & Bavelier, 2003). In addition, subjects who were included had a regular sleep schedule, i.e., differences in average bedtimes and wake-up times between on weekdays and weekends were less than 2 hr, and the average sleep duration regularly ranged from 6 to 9 hr. ...
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Training-induced performance gains in a visual perceptual learning (VPL) task that take place during sleep are termed "offline performance gains." Offline performance gains of VPL so far have been reported in the texture discrimination task and other discrimination tasks. This raises the question as to whether offline performance gains on VPL occur exclusively in discrimination tasks. The present study examined whether offline performance gains occur in detection tasks. In Experiment 1, subjects were trained on a Gabor orientation detection task. They were retested after a 12-hr interval, which included either nightly sleep or only wakefulness. Offline performance gains occurred only after sleep on the trained orientation, not on an untrained orientation. In Experiment 2, we tested whether offline performance gains in the detection task occur over a nap using polysomnography. Moreover, we tested whether sigma activity during non-rapid eye movement (NREM) sleep recorded from occipital electrodes, previously implicated in offline performance gains of the texture discrimination task, was associated with the degree of offline performance gains of the Gabor orientation detection task. We replicated offline performance gains on the trained orientation in the detection task over the nap. Sigma activity during NREM sleep was significantly larger in the occipital electrodes relative to control electrodes in correlation with offline performance gains. The results suggest that offline performance gains occur over the sleep period generally in VPL. Moreover, sigma activity in the occipital region during NREM sleep may play an important role in offline performance gains of VPL.
... Accordingly, action video game players have been shown to not only benefit from better perceptual templates, but also to develop such templates faster (Bejjanki et al., 2014). Although this effect needs replication, the possibility of enhanced perceptual learning abilities in AVGPs has also been illustrated not just in terms of faster learning overall, but also in terms of a reduction in interference between learning episodes (Berard et al., 2015). In this work, the learning of a first perceptual task led to lesser proactive interference on the learning of a second task in AVGPs as compared to NVGPs. ...
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