Mapping the Human Performance Envelope Through Multivariate Information Transfer

Area of Science:

• Human Factors Research
• Cognitive Neuroscience
• Systems Engineering

Background:

• The Human Performance Envelope (HPE) is a theoretical model defining acceptable operator performance ranges.
• Current HPE models lack practical, real-world application and measurement validation.
• Translating HPE into a measurable concept is crucial for understanding operator limitations.

Purpose of the Study:

• To operationalize and quantify the theoretical Human Performance Envelope (HPE).
• To measure HPE manifestations in real-world operator contexts.
• To establish a neurophysiological framework for characterizing HPE.

Main Methods:

• Utilized Multivariate Autoregressive (MVAR) models and conditional transfer entropy (cTE) for complex system analysis.
• Collected behavioral, subjective, and neurophysiological data from professional Air Traffic Controllers (ATCs).
• Applied MVAR-cTE to analyze interactions among human factors and characterize HPE.

Main Results:

• Demonstrated significant correlations between controller performance and neurophysiological HPE values.
• Identified higher HPE values and inter-human factor connectivity in high-performance conditions.
• Established a link between densely interconnected human factors and operational resilience.

Conclusions:

• Successfully translated the theoretical HPE into a quantifiable, neurophysiological model.
• Validated the HPE model against objective operator performance metrics.
• Provided a novel framework for understanding directed interactions within human factors.