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

This paper discusses the challenge of modeling in-flight startle causality as a precursor to enabling the development of suitable mitigating flight training paradigms. The article presents an overview of aviation human factors and their depiction in fuzzy cognitive maps (FCMs), based on the Human Factors Analysis and Classification System (HFACS) framework. The approach exemplifies system modeling with agents (causal factors), which showcase the problem space's characteristics as fuzzy cognitive map elements (concepts). The FCM prototype enables four essential functions: explanatory, predictive, reflective, and strategic. This utility of fuzzy cognitive maps is due to their flexibility, objective representation, and effectiveness at capturing a broad understanding of a highly dynamic construct. Such dynamism is true of in-flight startle causality. On the other hand, FCMs can help to highlight potential distortions and limitations of use case representation to enhance future flight training paradigms.

A Computational Model of Attention/Situation Awareness

A computational model of attention and situation awareness (SA) was developed and used to predict pilot errors in the task of taxiing from runway to terminal. The model incorporates a low-level perception/attention module and a higher-level belief-updating module. Attentional scanning is controlled by bottom-up and top-down processes, with the effectiveness of top-down guidance varying as a function of SA. Information sampled by the low-level module is fed forward to the higher-level module for consolidation within a working memory representation of the pilot's situation, with the quality of this representation reflecting the pilot's level of SA. The model was validated by comparing its predictions to the behavior of pilots performing a taxiway simulation. Results indicate that the model successfully predicts the improved performance associated with display augmentations, and provides construct validity regarding the effects of visibility, distraction, and degraded information quality.

Value of Human Performance Cognitive Predictions: A Free Flight Integration Application

Computational human performance modeling tools have been under development for over 50 years to generate human performance predictions for studying complex human behaviors. Models are particularly focussed in operations that possess high costs associated with failures. One such complex area that is being explored by international regulatory and airline operations groups is the concept known as “free flight”. “Free flight” in the United States is a Federal Aviation Administration (FAA) strategic goal for system capacity and for Air Traffic Services to improve accessibility, flexibility, and predictability in the national airspace in order to reduce flight times, crew resources, maintenance, and fuel costs. The experimental scenarios used to explore “free flight” are based on the full “free flight” concepts anticipated by the Requirements and Technical Concepts for Aviation, Inc. (RTCA) in the year 2025. These concepts explore the farthest out parameters of the aviation system. An evaluation of predicted behavioral costs associated with current day and “free flight” operations was performed using two “first principles” models, Air Man-machine Integration Design and Analysis System (Air MIDAS) and the Integrated Performance Modeling Environment (IPME). In analyses of a common scenario, both tools revealed increases along a seven-point, four-channel workload scale from current (MAirMIDAS = 0.77, MIPME = 1.24) to “free flight” operations (MAirMIDAS = 1.15, MIPME = 1.96). The models provided different performance profile predictions depending on the operator's role in the system change. Significant differences did exist in the predictions generated by the respective modeling software tools along the components that made up the overall workload predictions. Air MIDAS possessed significantly lower workload means on average (MVisual = 1.11, MAuditory = 1.08, MCognitive = 1.43, MPsychomotor = 0.22) than IPME (MVisual = 2.02, MAuditory = 0.49, MConitive = 2.46, MPsychomotor = 1.41). The only exception to this is the auditory modality. These findings support the notion of initially using models to screen for parameters for inclusion in costly simulation studies and demonstrate that human performance modeling tools differ in the representation of workload's impact on various modalities of the operator. A human-in-the-loop validation effort of these findings is required and anticipated.