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

Area of Science:

• Human Factors
• Cognitive Psychology
• Systems Engineering

Background:

• Assessing cognitive load is crucial for optimizing human-machine interactions.
• Existing workload metrics often lack a unified theoretical framework.
• Understanding cognitive resource demands aids in diagnosing performance limitations.

Purpose of the Study:

• To validate Cognitive Metrics Profiling (CMP) against established workload measures.
• To decompose complex task workload into specific cognitive resource demands.
• To establish convergent validity between a cognitive model and physiological/behavioral metrics.

Main Methods:

• Developed a cognitive model for a complex unmanned vehicle control task.
• Utilized Cognitive Metrics Profiling (CMP) to capture workload variance.
• Examined model predictions against behavioral performance, subjective workload, and physiological data.

Main Results:

• The CMP model's composite workload accurately predicted physiological metrics, performance, and subjective workload.
• Model analysis revealed procedural, declarative, and temporal memory demands as key workload drivers.
• Preliminary evidence supports CMP's correspondence with physiological workload measures.

Conclusions:

• CMP shows promise in capturing workload aspects in complex environments.
• This approach may bridge behavioral, physiological, and subjective workload metrics.
• CMP can inform workload mitigation strategies and personnel/automation decision-making.