Supplementary analysis for non-computer condition split by levels of urges to be online and internet use groups (All the raw data are presented in Table urges to be online and internet use groups (All the raw data are presented in Table

3.6)

Dot-probe task performance

Attentional bias as assessed with eye movement data (fixation duration-direction of initial fixation)

Fixation Duration

There was a significant main effect of image type F(1, 60) = 6.18, p = .016 Ƞp² .09.

Overall, the results revealed that participants fixated longer on internet compared to the control non-computer images, t(67) = 2.52; p = .014, d = .62, 95% CI [13.78, 118.13].

However, this difference reached significance only for the problematic internet users with higher levels of urges to be online, t(11) = 2.61, p = .024, d =1.6, 95% CI [34.93, 410.01], and high engagers with higher levels of urges to be online, t(8) = 2.41, p = .043, d =1.7, 95% CI [2.88, 134.89], (Figure 3.13). Even though we can see from Figure 3.13 that non-problematic internet users showed a similar trend this difference failed to reached significance p = .126 which could be due to the small number in the group (n = 4) and large SD. Moreover there was a significant interaction between image type with levels of urges to be online, F(1, 60) = 8.28, p =.006 Ƞp² .12. The main effects of internet use group, F(3, 60) = 1.03, p = .38, levels of urges to be online group, F(1, 60) = 0.03, p = .86 and the interactions between levels of urges to be online group and internet use group, F(3, 60) = 2.06, p = .11, between image type and internet use group, F(3, 60) = 1.03, p = .39, and image type, internet use group and levels of urges to be online group, F(3, 60) = 0.69, p = .56 were not significant.

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Figure 3.13 Mean gaze dwell time (in milliseconds)on internet and control non-computer images, shown for internet users with higher levels of urges to be online for each internet use group (problematic, high engagers, moderate and non-problematic internet users). Error bars represent standard error of the means.

In order to investigate the significant interaction between image type with levels of urges to be online, a 2 x 2 mixed-way ANOVA was performed with image type (internet versus control non-computer) as a within factor and levels of urges to be online (internet users with higher levels of urges to be online, internet users with lower levels of urges to be online) as a between factor. There was a significant main effect of image type, F(1, 66) = 7.17, p = .009, Ƞp² .09 and image type with group interaction, F(1, 66) = 9.51, p = .003; Ƞp² .13.

The main effect of levels of urges to be online group was not significant, F(1, 66) = 0.034, p = .85. The group of internet users with higher levels of urges to be online spent significantly more time fixating on internet images compared to the control non-computer ones, t(33) = 3.5, p =.001, d =1.21, 95% CI [59.29, 224.52]. Group comparisons revealed that internet users with higher levels of urges to be online spent more time fixating on the internet images compared to internet users with lower levels of urges to be online and this difference approached significance, t(66) = 1.93, p = .058, d = .67, 95% CI [-2.47, 145.12]. Moreover,

* *

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internet users with lower levels of urges to be online spent significantly more time fixating on the control non-computer images compared to internet users with higher levels of urges to be online, t(66) = -2.43, p = .018, d = .85, 95% CI [-116.68, -14.49], (Figure 3.14).

Figure 3.14 Mean gaze dwell time (in milliseconds)on internet and control non-computer images, shown separately for internet users with higher and lower levels of urges to be online. Error bars represent standard error of the means.

Direction of initial fixation

Internet users with higher levels of urges to be online made their first fixation on internet-related pictures in 47.56% of the trials (SD = 10.79) and internet users with lower levels of urges to be online in 49.06% of the trials (SD = 11.21), which was not significantly different than 50% which indicated no bias, t(33) = -1.32, p = .19; t(33) = -0.49, p = .63, respectively.

Overall results revealed direction of initial fixation bias for control non-computer images.

Attentional bias as assessed with manual reaction times to probes

┌───── * ────┐

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The main effects of probe condition, F(3, 59) = 0.002, p = .96, of internet use group, F(3, 59) = 0.89, p = .45, levels of urges to be online group, F(1, 59) = 2.5, p = .12 and the interactions between levels of urges to be online group and internet use group, F(3, 59) = 1.83, p = .15, between probe condition and internet use group, F(3, 59) = 1.53, p = .22, probe condition and levels of urges to be online group, F(3, 59) = 0.98, p = .33, and probe condition, internet use group and levels of urges to be online group, F(3, 59) = 0.25, p = .86 were not significant.

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Table 3.6-Mean and standard deviations for RTs, direction of initial fixations and dwell gaze time for all trials, trials where control were computer and non-computer images, for problematic, high engagers, moderate and non-problematic internet users with higher and lower levels of urges to be online and separately for all internet users with higher and lower levels of urges to be online.

Internet use group

Problematic internet

users High engagers Moderate users

Non-problematic

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Further analysis was conducted aiming to investigate whether individuals with problematic internet use for specific internet applications, such as social networking sites (SNS) and online gaming would show an attentional bias for generic internet-related stimuli. It was assessed whether or not individuals who reported generic problematic internet use in the initial assessment also reported problematic SNS and online gaming use. From the seventeen general problematic internet users twelve also reported problematic SNS with the remaining five reporting only general problematic internet use (Table 3.7). Moreover, in the sample there were eight individuals with problematic SNS but not general problematic internet use (Table 3.7). Similarly, from the seventeen general problematic internet users only one also reported problematic online gaming with the remaining sixteen reporting only general problematic internet use. Further, in the sample there were three individuals with problematic online gaming but not general problematic internet use. Thus, due to the limited number of individuals who reported problematic online gaming use the analysis was focused only on SNS problematic internet use. Participants were divided in to three groups; 1) individuals who disclosed both general and SNS problematic internet use, 2) individuals with general problematic internet use, and, 3) individuals with SNS problematic internet use.

Table 3.7-Total number of participants in each group of internet users.

3.4.11 Dot-probe task performance split by subgroups of problematic