# Studying the Role of Visuospatial Attention in the Multi-Attribute Task Battery II
## 3 Results
### Analyses of Pupillary Responses in the Multi-Attribute Task Battery (MATB-II)  
The analysis of the pupillary responses was conducted, with the two variables workload (high, low) and performance (high, low). As explained, we used an algorithm to automatically extract a workload measure. Figure 8 shows an example of how this works. Here, one can see how the pupil size of a participant changed during the MATB-II's subtasks, as well as the calculated change in cognitive load, and the calculated light changes that were modelled via the Pupillary-Light-Response (PLR) model [25; 26]. This model predicts the pupillary light reflex behavior to brightness via an individually trained empirical model. The model uses brightness measures from the eye tracker's world video data. The algorithm uses the modelled PLR to subtract it from the raw data and arrive at a cognitive load measure. The top left of Figure 8 shows the changes in pupil diameter over the course of the Multi-Attribute Test Battery (MATB-II; 5 min). Here, the black line corresponds to the pupil diameter in pixels. The colored vertical lines indicate the start and stop of a subtask in the MATB-II. Here, the dotted green line corresponds to a change in the pump's function in the resource-management task (\"pump error\" = pump cannot be used; \"pump repaired\" = pump can be used again). A dotted yellow line represents a change in the tracking task (\"manual\" = participant must control the crosshair; \"auto\" = the crosshair moves by itself). The dash-dotted blue line indicates the start of a communication task. The blue line corresponds to a participant's response to a communication task. The dash-dotted red line represents the start of a system monitoring task. The red line indicates that the participant responds to a system monitoring task. The top right of Figure 8 shows the Pupillary-Light-Response (PLR) that was calculated on the basis of the light changes, presented at the bottom right. These PLRs were subtracted from the pupil diameter changes to derive the cognitive or workload changes. The bottom left of Figure 8 shows the cognitive or workload changes during the MATB-II. Using the raw average pupil diameter sizes in pixels during the MATB-II, no significant differences between the 24 participants in the high workload group (\(M=34.08\), \(SD=6.59\)) and the 25 participants in the low workload group (\(M=36.33\), \(SD=7.14\)) were found, \(t(47)=1.14\), \(p=0.259\). The same is true for the cognitive load indices derived from the raw data: the average cognitive load of the high workload group (\(M=3.16\), \(SD=1.13\)) did not differ significantly from the average cognitive load of the low workload group (\(M=3.46\), \(SD=1.21\)), \(t(47)=0.89\), \(p=0.380\). There were also no significant differences in average raw pupil size diameter during the MATB-II between the 24 low performers (\(M=36.22\), \(SD=5.79\)) and the 25 high performers (\(M=34.27\), \(SD=7.81\)), \(t(47)=0.99\), \(p=0.328\), and between average cognitive load measures derived from raw pupil sizes between the 24 low performers (\(M=3.39\), \(SD=1.10\)) and the 25 high performers (\(M=3.25\), \(SD=1.26\)), \(t(47)=0.42\), \(p=0.678\). This latter finding is particularly striking, as clear performance differences and accompanying self-assessments of felt cognitive load or workload were found between the two groups of high versus low performers (see Supplementary Table S1). This means that the average of measured pupil sizes across a task battery such as the MATB-II per se is not very revealing. Among the reasons are possible differences between different tasks and between high versus low performers concerning the rates of saccades and, thus, their contributions to measured pupil sizes. In general, saccades tend to diminish pupil size responses to task characteristics [33].