Profiling cognitive workload in an unmanned vehicle control task with cognitive models and physiological metrics

ABSTRACT

In the present study, we use Cognitive Metrics Profiling (CMP) to capture variance in cognitive load within a complex unmanned vehicle control task. We aim to demonstrate convergent validity with existing workload measurement methods, and to decompose workload into constituent cognitive resources to aid in diagnosing causes of workload. A cognitive model of the task was developed and examined to determine the extent to which it could predict behavioral performance, subjective workload, and validated physiological workload metrics. We also examined model activity to draw insights regarding loaded cognitive capacities. We found that composite workload from the model predicted physiological metrics, performance, and subjective workload. Moreover, the model indicates that differences in workload were driven largely by procedural, declarative, and temporal memory demands. We have found preliminary evidence of correspondence between workload predictions of a CMP model and physiological measures of workload. This suggests our approach captures interesting aspects of workload in a complex task environment and may provide a theoretical link between behavioral, physiological, and subjective metrics. This approach may provide a means to design effective workload mitigation interventions and improve decision-making about personnel tasking and automation.

KEYWORDS:

• Cognitive workload
• computational modeling
• EEG
• cognitive architecture
• memory

Acknowledgments

A portion of these results were reported as a paper in the proceedings of the 17th annual meeting of the International Conference on Cognitive Modelling and as an abstract and poster in the 41st annual meeting of the Cognitive Science Society. This work was supported by a Venture Fund grant from the 711^th Human Performance Wing Chief Scientist’s Office. The authors thank Sarah Spriggs, Jessica Bartik, Gloria Calhoun, Kyle Behymer, and Heath Ruff for overseeing data collection and for helpful feedback on analysis and interpretation. The authors thank Allen Dukes, Justin Estepp, Samantha Klostermann, and Chelsey Credlebaugh for setting up the physiological data collection system and for providing data analysis scripts for physiological data. A majority of this work was completed by M. Morris as a contractor for Ball Aerospace and by C. Fisher as a contractor with Oak Ridge Institute for Science and Education (ORISE). The opinions expressed herein are solely those of the authors and do not necessarily represent the opinions of the United States Government, the U.S. Department of Defense, the U.S. Air Force, or any of their subsidiaries or employees. The contents have been reviewed and deemed Distribution A. Approved for public release. Case number: AFRL-2022-4251.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data is not available.

Notes

1. A special case worth mentioning here is the procedural module. In ACT-R, every 50 milliseconds, the procedural module searches memory for a production to fire. Sometimes an appropriate production is found. Other times, the system must wait for new information. We consider the procedural module to be active only when a production has matched and is firing.

2. Unlike Jo et al., we did not increase the workload estimate with frequency of module errors. Module errors were infrequent in our module and it was not clear how to apply these frequencies to our estimates in this context.

3. Per a reviewer request, we examined gender as an additional fixed effect in the LMM analyses. The effect of gender was only significant for EI, suggesting lower EI values for male participants. CMP and Condition fixed effects were still significant despite the inclusion of Gender.