Personnel are often required to perform multiple simultaneous tasks at the limits of their cognitive capacity. In research surrounding cognitive performance resources for tasks during stress and high cognitive workload, one area of deficiency is measurement of individual differences. To determine the amount of attentional demand a stressor places on a subject, one must first know that all subjects are performing at the same level with the same amount of available capacity in the control condition. By equating the baselines of performance across all subjects (“baselining”) we can control for differing amounts of performance capacity or attentional resources in each individual. For example, a given level of task performance without a time restriction may be equated across subjects to account for attentional resources. Training to a fixed level of proficiency with time limits might obliterate individual differences in mental resources. Eye movement parameters may serve as a real-time measure of attentional demand. In implementing a baselining technique to control for individual differences, eye movement behaviors will be associated with the true cognitive demands of task loading or other stressors. Using eye movement data as a proxy for attentional state, it may be possible to “close the loop” on the human-machine system, providing a means by which the system can adapt to the attentional state of the human operator. In our presentation, eye movement data will be shown with and without the benefit of the baselining technique. Experimental results will be discussed within the context of this theoretical framework.