The purpose of this effort is to introduce a novel approach which can be used to determine how multiple minimally intrusive physiological sensors can be used together and validly applied to areas such as Augmented Cognition and Neuroergonomics. While researchers in these fields have established the utility of many physiological measures for informing when to adapt systems, the use of such measures together remains limited. Specifically, this effort will provide a contextual explanation of cognitive state, workload, and the measurement of both; provide a brief discussion on several relatively noninvasive physiological measures; explore what a modular cognitive state gauge should consist of; and finally, propose a framework based on the previous items that can be used to determine the interactions of the various measures in relation to the change of cognitive state.

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This effort investigated the ability of a neurophysiological measure to detect changes in workload during a task which is sensitive to cognitive function. A growing collection of research suggests that physiological measures such as EEG can be used to inform the adaptation of systems. However, it has been proposed that such measures often provide a gross interpretation of cognitive workload during complex tasks and are not sensitive to differences in specific cognitive function. To understand the utility of neurophysiological measures for human-machine interaction, we must know if these measures are sensitive to tasks which are sensitive to changes in cognitive function. To begin to answer this question, we investigated the sensitivity of Advanced Brain Monitoring's EEG-based measures to changes in workload experienced during a Stroop task. Results indicated that ABM's workload measure can detect changes associated with the attentional demands and cognitive processes linked to the ability to inhibit word naming during tasks involving semantic interference. This indicates that changes in workload associated with the ability to inhibit competing cognitive processes can be identified using neurophysiological workload measures.

As the modern workplace becomes more complex, the training community needs to develop new strategies to continute to create a competent workforce. The emerging field of augmented cognition may be able to contribute greatly to increasing training capability through the use of neurophysiological measures that support real-time performance and can be used to satisfy some of the requirements of an automated intelligent tutoring system. The first step to building this system is to design and validate a testbed that can be used with future efforts to target effective mitigation strategies with learning efficiency. In this study, participants watched a computerized instructional presentation and then engaged in a practice CFF scenario in the simulator. When finished, each participant was assigned to either a low or high task load test scenario. In both, the goal was to destroy five enemy tanks. Some participants were also asked to simultaneously execute a secondary radar monitoring task. Both the National Aeronautics and Space Administration's Task Load Index (NASA-TLX) and the MRQ were used to assess the validity of the testbed. Results from both measures indicate that a significant difference exists between the two levels of workload and further, we can distinguish between the subscales within each measurement tool. Thus, the overarching goal of this study was achieved. High and low workload scenarios were created and validated. Ultimatly, they will be used as a testbed of scenarios to address the questions surrounding the use of neurophysiological equipment to impact individual learning patterns.