Abstract
Optical See-Through (OST) Augmented Reality (AR) is beginning to be used more widely in the public domain. However, addressing manipulative content is necessary for the widespread adoption of OST AR technology. For instance, in a cybersecurity context, attackers might try to influence and reduce user performance by changing the quality of AR information, introducing misleading content, irrelevant data, and other adverse factors. This research investigates how helpful, misleading, and irrelevant information in OST AR affects human performance. The study used a memory task and employed a repeated measures design involving 19 participants. The findings revealed that the participants needed more time to complete the task when presented with irrelevant information compared to when they had access to useful AR information or when AR content was not presented. In addition, helpful AR information allowed users to complete the task more effectively with fewer errors than irrelevant and misleading AR information. The results suggest that AR enhances user memory, enabling them to perform tasks more efficiently. Moreover, when malicious information is introduced, manipulative content can effectively increase the decision-making time of their targets by disrupting memory-based judgments.
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Acknowledgments
This work was partially supported by the U.S. National Science Foundation under Grant No. DMS 2123761, CNS 1822118, AMI, NewPush, Cyber Risk Research, ARL, NIST under Award No. 60NANB23D152, the State of Colorado (grant #SB 18-086), and Colorado State University internal funds. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation, or other organizations and agencies.
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Sturgeon, M., Anspach, E., Ortega, F., Ray, I., Safayet Arefin, M. (2025). Impact of Relevant Augmented Reality Information on Human Performance. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2024. Lecture Notes in Computer Science, vol 15047. Springer, Cham. https://doi.org/10.1007/978-3-031-77389-1_15
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