Attention Dynamics and Adaptive Decision Support in C5ISR: A Recurrence Quantification Analysis of Visual and Multimodal Attention Guidance Effects on Mission Performance
For designers of C5ISR decision support systems, this work provides evidence that multimodal attention guidance improves performance and that recurrence-based gaze metrics can characterize attentional dynamics.
This study evaluated gaze-driven adaptive decision support tools in a simulated military command center, finding that a multimodal tool outperformed a visual-only one. Recurrence quantification analysis of eye-tracking data revealed that gaze metrics like entropy and diagonal line length predict performance, with nonlinear patterns following the Yerkes-Dodson law.
Modern command, control, communications, computers, cyber, intelligence, surveillance, and reconnaissance (C5ISR) environments place substantial attentional demands on mission commanders. Failures in attention allocation in these high-risk settings can have severe operational consequences. This study investigates the efficacy of gaze-driven, attention-guided adaptive decision support tools, including visual-only and multimodal designs, in a high-fidelity simulated military command center. To characterize gaze and attentional dynamics during interaction with these tools, recurrence quantification analysis was applied to eye-tracking data. Stepwise regression using the Bayesian information criterion was then used to identify recurrence-based gaze metrics associated with performance. Results showed that the multimodal adaptive decision support tool was associated with significantly higher performance than the visual-only attention-guided tool. Average diagonal line length showed a negative linear association with performance, whereas entropy showed a positive linear association. Recurrence rate, determinism, and entropy also showed nonlinear quadratic relationships with performance. In particular, recurrence rate and determinism followed an inverted-U pattern consistent with the Yerkes-Dodson law. These findings suggest that effective performance in dynamic C5ISR contexts depends on a balance between structured and flexible visual scanning, and that recurrence-based gaze metrics can help characterize attentional dynamics during interaction with adaptive decision support systems.