## 3 Results
### VR Goalkeeper Performance and Efficiency of the Attentional Networks (ANT)  
To confirm that the various types of warning cues and the flanker types influenced the pattern of performance on the ANT task as expected and to verify that we replicated past findings from studies that have used this task, we first carried out a repeated-measures Analysis of Variance (ANOVA) with terms for Flanker type and Warning type on the median response times for correct responses. An \(\alpha\) = 0.05 criterion for significance was set across all analyses. Post-hoc comparisons from the ANOVA were Bonferroni-corrected to reduce the risk of type I error arising from multiple statistical tests. The analysis revealed significant main effects for both the Flanker type and the Warning Type, F(2,198) = 557.2, \(p\) < 0.001, \(\eta^{2}\) = 0.35 and F(2,297) = 297.6, \(p\) < 0.001, \(\eta^{2}\) = 0.09, respectively. As seen in Table 1, response times were longer for the incongruent flankers than for either the congruent or neutral flankers (\(p\)'s < 0.001). Furthermore, response times were faster for trials with spatial cues and slower for trials with no cues, compared to either center cue or double cue trials (\(p\)'s < 0.001). A significant Flanker type x Warning type interaction was also found, F(6,594) = 20.6, \(p\) < 0.001, \(\eta^{2}\) = 0.01. The interaction was driven by performance in double cue trials being faster than performance in center cue trials in the incongruent flanker condition but not in the other two flanker conditions (congruent and neutral). Despite this interaction, the observed pattern of response times replicates closely the pattern reported by previous studies with the ANT (e.g., [12; 13]) and verifies that both warning cues and flankers induce the expected influence on response times. Therefore, we proceeded to compute network scores for each participant. As seen in Table 2, a significant negative correlation between alerting and conflict was obtained, suggesting that the networks are not completely independent. We then proceeded to examine whether the three attentional network scores predicted performance on the VR Goalkeeper task. Based on Signal Detection Theory, we computed d' scores for each participant in the VR Goalkeeper task using the hit rate (i.e., % of targets responded to from target-present trials) and the false alarm rate (i.e., % of distractors responded to from target-absent trials). We then carried out a hierarchical multiple regression with the d' score on the VR Goalkeeper task as the dependent variable. Given that the VR Goalkeeper task is one that men are more likely to be familiar with than women, gender was entered as a factor at step 1 of the analysis. Orienting was entered at step 2 as we deemed this sub-component of attention to be the most relevant to the VR Goalkeeper task used. Finally, Alerting and Conflict were entered at step 3 as we had no a priori hypothesis on whether they would predict performance in the VR Goalkeeper task. Hierarchical Regression statistics are reported in Table 3.  
The regression analysis revealed that at step 1, gender contributed significantly to the regression model, [F (1.98) = 8.98, \(p\) = 0.003] and accounted for 8.4% of the variation in VR goalkeeper performance. As the analysis showed, men had higher d' scores (M = 3.38, SE = 0.15) than women (2.84, SE = 0.11), \(p\) = 0.004. Introducing Orienting in step 2 explained an additional 4.2% of variation in goalkeeper d' and this change in R\({}^{2}\) was significant, F (1.97) = 4.62, \(p\) = 0.034. Adding Alerting and Conflict in step 3 accounted for an additional 12.63% of the variation of VR goalkeeper d' with the change in R\({}^{2}\) being again significant, F(2.95) = 8.02, \(p\) < 0.001. As seen in Table 3, when the three attentional networks and gender were included in step 3, they were all significant predictors of VR goalkeeper d'. The final