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Both examples had the unexpected finding that the MSGD to the display and controls of the device was correlated with the RT and miss rate to the events in the 2-D metric loadings space, and not with any of the physical demand metrics, including other glance metrics (such as TGT and number of glances).
That is, these tasks had relatively long MSGD and relatively poor event detection, placing them in the upper left quadrant of Figure 10 (red squares).
One explanation for the clustering of MSGD with event detection metrics and not physical demand metrics in the visual-manual tasks analyzed in the current examples is that there is a common underlying cause of both the relatively long single glances and the relatively poor event detection; namely, the attentional effects of cognitive demand.
The "cassette tape," "turnSignal," and "Sentinel" tasks were dropped because the total glance time (TGT) did not equal the product of mean single glance time (MSGD) and number of glances (Glances) for those tasks, as it did for the other 23 tasks with non-missing data.
* MSGD to the road, because other metrics pertinent to glances to the road were not published: total glances to the road, total eyes-on-road time, and percent time gazing to the road (gaze concentration).
Also consistent with the first two Examples, MSGD loads onto Dimension 2 (cognitive demand).
The PCA of the data in the current study suggests that MSGD and LGP are independent indicators of event detection and response performance (cognitive demand), and the physical demand metrics have little or no influence on event detection and response.
This anomaly results from the indirect interaction effects of the high correlation between LGP and MSGD on TEORT (see Appendix C).
The reason for these surprising result has to do with the high correlation between MSGD and LGP, which acts a "suppressor" on TEORT (see Appendix C).
This result is useful, because the eye glance metrics of TEORT, MSGD, and LGP have complex interactions between them, which causes a task's TEORT value in a univariate analysis method to underestimate the physical demand of the task.
There is also considerable debate in the driving safety field whether the glance-based metrics (such as TGT and MSGD to a device) are better or worse than driving performance (lateral and longitudinal variability) as outcome measures for Visual-Manual secondary tasks performed while driving.
With respect to cognitive demand, Alliance Principle 2.1A contains MSGD, which is a metric loading onto cognitive demand (i.e., event detection and response), at least for the Visual-Manual tasks that are the scope of the Alliance Guidelines.