## 3 Results
### Algorithmically Extracted Workload Measure  
The same analysis was conducted using data derived from a \"cognitive workload\" algorithm, which subtracts the light-elicited pupillary changes ([19]). Quantitatively, the pattern of results is overall similar to that using the raw pupillary changes, with the exception of that of the NEGH Group (see Figure 7).  
The increase in cognitive load between the baseline and the manipulation was, again, found in every single participant. When comparing the cognitive-load changes between groups, the largest average change between the baseline and the manipulations occurred in the high NEGL condition (NEGL: with a delta of 6.27), followed by participants in the high-workload/neutral-content group (NEUH: delta of 5.45), and lastly the participants in the NEGH group (delta of 4.47) and the NEUL group (delta of 3.45). Figure 8 illustrates the difference in cognitive-load change between the baseline and the manipulation in all groups. While participants in the NEUL group showed the smallest increase in cognitive load when the baseline average was subtracted from the average during manipulation, participants in the NEUH group as well as those in the NEGL group showed similar increases. Participants in the NEGH group showed a rather small increase in cognitive load as well.  
Figure 6: Pupil diameter on the \(y\) axis as a function of the question answered on the \(x\) axis and whether participants believed the answers were veridical (red graph) or fake (blue graph). The colored horizontal lines depict the averages of the baseline (lower respective lines) and the manipulation (upper respective lines).  
Figure 8: Median cognitive-load changes (black horizontal lines), corresponding quartiles (colored areas above and below the horizontal lines), minima and maxima (whiskers), and individual values (colored dots) of cognitive-load (CogLoad) differences between baseline and manipulation on the \(y\) axis as a function of condition/Group (low-workload manipulation on the left, high-workload manipulation on the right; neutral-content conditions in blue, negative-content conditions in red) on the \(x\) axis.  
Figure 7: Average cognitive workload (according to the algorithm) on the \(y\) axis as a function of the answer on the \(x\) axis, separately for different groups or conditions (NEUL: neutral content/low load; NEUH: neutral content/high load; NEGL: negative content/low load; NEGH: negative content/high load). The two dotted lines in the colors of each graph show the corresponding condition’s difference in average calculated cognitive workload between baseline and manipulation (questions).  
An ANOVA yielded a significant interaction between the workload and emotional content manipulation, \(F(1,35)=4.77\), \(p=0.036\), \(\eta^{2}=0.13\), but neither the workload manipulation, \(F(1,35)=1.38\), \(p=0.248\), \(\eta^{2}=0.04\), nor the main effect of emotion, \(F(1,35)=0.44\), \(p=0.509\), \(\eta^{2}=0.01\), were significant. In neutral emotion conditions, a pairwise post hoc \(t\)-test between low- (\(M=4.64\), \(SD=3.43\)) and high-workload conditions (\(M=6.96\), \(SD=3.21\)) showed significantly smaller pupillary changes in low-workload conditions compared to high-workload conditions, \(t(35)=-2.108\), \(p=0.047\), \(\eta^{2}=0.7\). In contrast, in negative emotion conditions, the \(t\)-test between low- (\(M=5.42\), \(SD=3.49\)) and high-workload conditions (\(M=6.18\), \(SD=3.52\)) did not show a significant change in pupil size, \(t(35)=-0.651\), \(p=0.047\), \(\eta^{2}=0.7\). This result was due to a load effect in the neutral conditions that numerically even slightly reversed under negative conditions.