In this PhD a method was developed to identify systematic patterns of change in visual attention allocation (change patterns). The change patterns were then integrated into the Functional Resonance Analysis Method (FRAM) for the generation of models of work-as-done. The change patterns were validated against known changes in visual attention allocation due to shifts in functions of work-as-done in several eye-tracking studies: three simulator studies, one field study and one experimental study. In total approx. 50 hours of eye-tracking data was analyzed. The results of the method were validated quantitatively and qualitatively. In the quantitative validation, the changes in visual attention allocation due to changes in functions were covered with a mean deviation of approx. 13 seconds averaged over all datasets (2% deviation relative to the recording lengths). In the qualitative validation, the change patterns produced were found to be plausible for the evaluated studies. Finally, it was demonstrated how the change patterns can be integrated into FRAM and potentially contribute to the understanding of emergent effects in industries with high levels of automation.