Integrating the HFACS Framework and Fuzzy Cognitive Mapping for In-Flight Startle Causality Analysis

Area of Science:

• Aviation Psychology
• Cognitive Systems Engineering
• Human Factors in Aviation

Background:

• In-flight startle responses pose significant challenges in aviation safety.
• Existing models often struggle to capture the dynamic nature of startle causality.
• Developing effective pilot training requires a robust understanding of human factors.

Purpose of the Study:

• To model in-flight startle causality using fuzzy cognitive maps (FCMs).
• To explore the application of FCMs within the Human Factors Analysis and Classification System (HFACS) framework.
• To inform the development of advanced flight training paradigms for mitigating startle effects.

Main Methods:

• Utilized fuzzy cognitive maps (FCMs) for system modeling.
• Represented causal factors as agents and problem space characteristics as FCM concepts.
• Leveraged the Human Factors Analysis and Classification System (HFACS) framework.

Main Results:

• Developed an FCM prototype with explanatory, predictive, reflective, and strategic functions.
• Demonstrated FCMs' flexibility and effectiveness in representing dynamic constructs like startle causality.
• Highlighted FCMs' utility in identifying potential distortions in use case representations.

Conclusions:

• Fuzzy cognitive maps provide a powerful tool for modeling complex aviation phenomena.
• FCMs can enhance the development of targeted and effective flight training programs.
• This approach aids in understanding and mitigating in-flight startle causality for improved pilot performance and safety.