System interaction influences on cognitive-affective states to enhance performance,workload,and knowledge acquisition in teams conducting close air support simulations.

Abstract

The purpose of this research is to shed light on the effects of an automated feedback system to optimize cognitive-affective states and increase effectiveness of using remotely piloted aerial system team members training to conduct Close Air Support missions in a simulation training environment. Feedback manipulations in this study utilize attributes of engagement as an optimal cognitive-affective state in order to assess state and effectiveness differences. Understanding these effects could enable predictions of aspects that might be adapted to optimize future approaches in training teams in complex situations. If states of learners can be impacted via feedback experiences to an engagement like state and thereby benefit from increased learning and effectiveness, then training approaches utilizing feedback may advance in capability. Thus, designs of automated feedback systems in human-computer interfaces may help advance training of complex military tasks such as close air support with remotely piloted aerial systems through decreasing workload, increasing knowledge acquisition, and enabling better performance.

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