## 4 Results  
Our results show that implementing guidelines had a significant effect on participants' cognitive workload, as indicated by the NASA-TLX scores in different scenarios (\(\chi^{2}\) (2) = 8.24, \(p\) < 0.05). The median perceived effort levels were 1 (0 to 1.3), 1 (0 to 2) and 0(0 to 1) for trials 1, 2 and 3, respectively. Post hoc analysis with Wilcoxon signed-rank tests and Bonferroni correction showed a significant reduction in cognitive workload between scenarios 2 and 3 (Z = \(-\)2.646, \(p\) = 0.008), but no significant differences between scenarios 1 and 2 (Z = \(-\)0.816, \(p\) = 0.414) or between scenarios 1 and 3 (Z = \(-\)1.890, \(p\) = 0.059). However, there was a statistically significant reduction between scenario 2 and scenario 3 (Z = \(-\)2.646, \(p\) = 0.008).  
The number of fixations was significantly different between the scenarios (\(\chi^{2}\) (2) = 20.33, \(p\) < 0.05). The Bonferroni correction applied for the Wilcoxon signed-rank post hoc analysis was 0.036. The results showed that the number of fixations significantly decreased between scenario 1 and scenario 2 (Z = 55.8, \(p\) < 0.05). However, no statistical difference was reported between scenarios 2 and 3 (Z = \(-\)30.1, \(p\) > 0.05) nor between scenarios 1 and 3 (Z = 25.7, \(p\) > 0.05). The fixations duration did not statistically change across the three different scenarios (\(\chi^{2}\) (2) = 20.33, \(p\) > 0.05). Figure 3 shows the cognitive workload levels across the three different trials.  
Figure 4 shows the gaze behaviour in relation to the mean number of fixations and the mean duration of fixations for each scenario. Spearman correlation analysis was performed to assess the relationship between NASA-TLX and the eye-tracker data, and both measures were used to assess the cognitive workload in each scenario. In terms of the number of fixations, a strong and negative correlation was found between the objective and subjective measures in the first trial, which was statistically significant (r = \(-\)0.797, n = 11, \(p\) = 0.003). However, no correlation was found between the objective and subjective measures in trials 2 and 3. Similarly, no correlation was found between the objective and subjective measures for the fixation durations in the three trials.  
A linear regression was performed to determine whether acceptance significantly predicted cognitive workload. However, no statistically significant evidence was found for trials 1, 2 and 3.  
Figure 3: [PERSON]’s test mean ranks of cognitive workload across the three trials.  
Likewise, a linear regression was performed to determine whether acceptance significantly predicted perceived stress in the three trials. In the first scenario, acceptance accounted for 72.5% of the explained variability in the perceived stress (F (1,12) = 31.651, \(p\) < 0.001). The final predictive model was as follows: perceived stress = 4.578 + (\(-\)0.696 * acceptance). In the second scenario, acceptance explained 40.1% of the variability in the perceived stress (F (1,12) = 8.032, \(p\) < 0.05). The final predictive model was as follows: perceived stress = 2.886 + (\(-\)0.349 * acceptance). In the third scenario, acceptance accounted for 40.3% of the explained variability in perceived stress (F (1,12) = 8.113, \(p\) > 0.05). The final predictive model was as follows: perceived stress = 2.669 + (\(-\)0.219 * acceptance).  
A moderated regression analysis was used to investigate whether trust negatively moderated the relationship between cognitive workload and perceived stress. However, no significant evidence was found in the three different trials, meaning that we found no support that trust towards robots could influence how cognitive workload and perceived stress were related to each other.