Digitally Mediated Reality and Vulnerabilities in Decision-Making
Copyright (c) 2026 Porkoláb-Minarik Annamária
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Abstract
In modern military operations, decision-making increasingly takes place within a digitally mediated information environment. The application of artificial intelligence (AI)-based decision support systems and immersive technologies (VR/XR) offers significant advantages in information processing, situational awareness, and training. At the same time, these technologies do not merely function as sources of information; they actively shape decision-makers’ perception of reality, attentional focus, and the dimensions of trust in technology that influence judgment formation.
The aim of this study is to develop a theoretical framework that demonstrates how AI- and XR-based systems shape military decision making through underlying psychological mechanisms, with particular emphasis on vulnerabilities arising from digitally mediated decision contexts. Existing models typically focus on a single mechanism; therefore, to provide an integrated interpretation of these phenomena, this study introduces a novel theoretical framework – the psychological model of digitally mediated decision making – which interprets distortions in decision making through the interaction of perceptual, cognitive, and trust-related dimensions.
The study reviews the role of automation bias, cognitive overload and offloading, as well as the perceptual effects of immersive simulations in military decision making, and highlights that the combined influence of these factors may increase the risk of decision distortions.
Keywords:
References
AHMAD, Sayed F. et al. (2023): Impact of Artificial Intelligence on Human Loss in Decision Making, Laziness and Safety in Education. Humanities and Social Sciences Communications, 10(1). Online: https://doi.org/10.1057/s41599-023-01787-8
BRUNYÉ, Tad – GILES, Grace (2023): Methods for Eliciting and Measuring Behavioral and Physiological Consequences of Stress and Uncertainty in Virtual Reality. Frontiers in Virtual Reality, 4, 1–22. Online: https://doi.org/10.3389/frvir.2023.951435
BAWDEN, David – ROBINSON, Lyn (2009): The Dark Side of Information: Overload, Anxiety and Other Paradoxes and Pathologies. Journal of Information Science, 35(2), 180–191. Online: https://doi.org/10.1177/0165551508095781
BLANCHARD, Alexander – BRUUN, Laura (2025): Autonomous Weapon Systems and AI-enabled Decision Support Systems in Military Targeting. A Comparison and Recommended Policy Responses. Solna: SIPRI. Online: https://doi.org/10.55163/YQBY3151
DAVIS, Fred D. (1989): Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319–340. Online:
https://doi.org/10.2307/249008
ENDSLEY, Mica R. (2015): Situation Awareness Misconceptions and Misunderstandings. Journal of Cognitive Engineering and Decision Making, 9(1), 4–32. Online: https://doi.org/10.1177/15553434155726
FAN, Yizhou et al. (2025): Beware of Metacognitive Laziness: Effects of Generative Artificial Intelligence on Learning Motivation, Processes, and Performance. British Journal of Educational Technology, 56(2), 489–530. Online: https://doi.org/10.1111/bjet.13544
BANSAL, Gagan et al. (2019): Beyond Accuracy: The Role of Mental Models in Human-AI Team Performance. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 7(1), 2–11. Online: https://doi.org/10.1609/hcomp.v7i1.5285
GERLICH, Michael (2025): AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking. Societies, 15(1). Online: https://doi.org/10.3390/soc15010006
GLICKMAN, Moshe – SHAROT, Tali (2025): How Human–AI Feedback Loops Alter Human Perceptual, Emotional and Social Judgements. Nature Human Behaviour, 9(2), 345–359. Online: https://doi.org/10.1038/s41562-024-02077-2
HOFF, Kevin Anthony – BASHIR, Masooda (2015): Trust in Automation: Integrating Empirical Evidence on Factors That Influence Trust. Human Factors: The Journal of the Human Factors and Ergonomics Society, 57(3), 407–434. Online: https://doi.org/10.1177/0018720814547570
KJEKSRUD, Stian et al. (2026): Making Hybrid War Teachable and Learnable: Combining Artificial Intelligence and Virtual Reality. Scandinavian Journal of Military Studies, 9(1), 62–81. Online: https://doi.org/10.31374/sjms.414
KOSKELO, Jukka et al. (2024): Cardiac Autonomic Responses in Relation to Cognitive Workload during Simulated Military Flight. Applied Ergonomics, 121. Online: https://doi.org/10.1016/j.apergo.2024.104370
LEE, John Dee – SEE, Kathrina A. (2004): Trust in Automation: Designing for Appropriate Reliance. Human Factors: The Journal of the Human Factors and Ergonomics Society, 46(1), 50–80. Online: https://doi.org/10.1518/hfes.46.1.50.30392
MAITI, Jharna – YADAV, Alok Kumar – MURLIE, Sushma (2025): Trust Calibration in Human AI Surveillance Teams Supported by a Psychometric Framework for HR Decisions. TPM – Testing, Psychometrics, Methodology in Applied Psychology, 32(S2), 1847–1851. Online: https://tpmap.org/submission/index.php/tpm/article/view/987
NOURANI, Mashan et al. (2021): Anchoring Bias Affects Mental Model Formation and User Reliance in Explainable AI Systems. In Proceedings of the 26th International Conference on Intelligent User Interfaces (IUI ’21). New York, Association for Computing Machinery, 340–350. Online: https://doi.org/10.1145/3397481.3450639
PORKOLÁB Imre – PORKOLÁB-MINARIK Annamária (2025a): A mesterséges intelligenciával támogatott XR-technológiák hatása a valóságérzékelésre: pszichológiai perspektívák és kihívások. Honvédségi Szemle, 153(2), 83–93. Online: https://doi.org/10.35926/HSZ.2025.2.7
PORKOLÁB Imre – PORKOLÁB-MINARIK Annamária (2025b): Decentralizált döntéshozatal a mesterséges intelligencia korában. Katonai Logisztika, 33(1–2), 8–30. Online: https://doi.org/10.30583/2025-1-2-008
ROMEO, Giuseppe – CONTI, Daniela (2026): Exploring Automation Bias in Human–AI Collaboration: A Review and Implications for Explainable AI. AI & Society, 41, 259–278. Online: https://doi.org/10.1007/s00146-025-02422-7
SLATER, Mel – SANCHEZ-VIVES, Maria (2016): Enhancing Our Lives with Immersive Virtual Reality. Frontiers in Robotics and AI, 3, 1–47. Online: https://doi.org/10.3389/frobt.2016.00074
STEYVERS, Mark – KUMAR, Aakriti (2024): Three Challenges for AI-Assisted Decision-Making. Perspectives on Psychological Science, 19(5), 722–734. Online: https://doi.org/10.1177/17456916231181102
TVERSKY, Amos – KAHNEMAN, Daniel (1974): Judgment under Uncertainty: Heuristics and Biases. Science, 185(4157), 1124–1131. Online: https://doi.org/10.1126/science.185.4157.1124
VISSER, Roel – PETERS, Tobias M. – SCHARLAU, Ingrid – HAMMER, Barbara (2025): Trust, Distrust, and Appropriate Reliance in (X)AI: A Conceptual Clarification of User Trust and Survey of Its Empirical Evaluation. Cognitive Systems Research, 91. Online: https://doi.org/10.1016/j.cogsys.2025.101357