Statistically significant results: SCtR A one sample Kolmogorov-Smirnov showed that the data was not normally distributed, because of a subject that had abnormally very high skin conductivity (more than 16 microSiemens). Therefore, this subject was removed for the analysis of skin conductivity. A multiple ANOVA was performed to find out the influence of gender, workload level and IM flow on SCtR. Only one independent parameter showed to affect SCtR significantly: IM flow (p<0.014, with 0.727 of power). A set of correlation tests (Pearson correlation, Kendall's tau and Spearman's rho non parametric correlations) was performed. A very significant result (p<0.01) appeared: SCtR had a tendency to increase with SCpR, meaning that, the higher the tonic component is, the bigger the phasic modifications will be. SCpR A multiple ANOVA was performed to find out the influence of gender, workload level and IM flow on SCpR. Two independent parameters showed to affect SCtR significantly: IM flow (p<0.005, with 0.883 of power) and workload (p<0.003, with 0.908 of power). Post-hoc analysis showed that the difference between no IM and high IM is extremely significant (p<0.004). A set of correlation tests (Pearson correlation, Kendall's tau and Spearman's rho non parametric correlations) was performed. The first very significant result (p<0.002) that appeared was with score: SCpR had a tendency to decrease when score increased, meaning that, subjects performing well showed less skin conductivity variation. The second significant result was with delay (p<0.022). SCpR has a tendency to increase with time delay: when a subject delayed its responses to IM, its phasic component had a tendency to be higher. As mentioned before, there is a significant correlation between SCtR and SCpR. Delay A multiple ANOVA test was run, but no statistical result appeared. A set of correlation tests was performed (Pearson correlation, Kendall's tau and Spearman's rho non parametric correlations). It turned out that delay was inversely correlated to score (p<0.012). This was expected: subjects performin well on the game (high scores) would have more time to answer the IM, and thus have shorter delays. Score A one sample Kolmogorov-Smirnov showed that the data was not normally distributed, because almost all subjects had a score of 100% for the easiest scenario. This made the data really skewed to the right. Therefore, the data for the scenario with low workload and no IM was removed. The remaining data was normal enough to perform the following tests. A multiple ANOVA test was performed. Three results came out positive: workload was a significant factor (p<0.005 with power at 0.869); IM was significant (p<0.028 and power of 0.620) and gender*IM was also significant (p<0.037, power = 0.568). This last result was unexpected, especially since gender itself is not significant (p=0.581). The correlation between score and delay was explained before.