Identifying Black Swans in NextGen: Predicting Human Performance in Off-Nominal Conditions

Abstract

Objective: The objective is to validate a computational model of visual attention against empirical data—derived from a meta-analysis—of pilots’ failure to notice safety-critical unexpected events. Background: Many aircraft accidents have resulted, in part, because of failure to notice nonsalient unexpected events outside of foveal vision, illustrating the phenomenon of change blindness. A model of visual noticing, N-SEEV (noticing— salience, expectancy, effort, and value), was developed to predict these failures. Method: First, 25 studies that reported objective data on miss rate for unexpected events in high-fidelity cockpit simulations were identified, and their miss rate data pooled across five variables (phase of flight, event expectancy, event location, presence of a head-up display, and presence of a highway-in-the-sky display). Second, the parameters of the N-SEEV model were tailored to mimic these dichotomies. Results: The N-SEEV model output predicted variance in the obtained miss rate (r = .73). The individual miss rates of all six dichotomous conditions were predicted within 14%, and four of these were predicted within 7%. Conclusion: The N-SEEV model, developed on the basis of an independent data set, was able to successfully predict variance in this safety-critical measure of pilot response to abnormal circumstances, as collected from the literature. Applications: As new technology and procedures are envisioned for the future airspace, it is important to predict if these may compromise safety in terms of pilots’ failing to notice unexpected events. Computational models such as N-SEEV support cost-effective means of making such predictions.