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Related Concept Videos

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Related Experiment Video

Updated: May 28, 2026

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Mapping the Human Performance Envelope Through Multivariate Information Transfer.

Gianluca Borghini1, Khadija Latrach1, Gianluca Di Flumeri1

  • 1Department of Molecular Medicine, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185 Rome, Italy.

Brain Sciences
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

This study quantifies the human performance envelope (HPE) using neurophysiological data, linking higher HPE values and interconnected human factors to improved operator performance and resilience in air traffic control.

Keywords:
Least Absolute Shrinkage and Selection Operator (LASSO)air traffic controller (ATCO)conditional transfer entropy (cTE)human factors (HF)human performance envelope (HPE)mental statesneurophysiological

Related Experiment Videos

Last Updated: May 28, 2026

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

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.