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识别下一代的黑天鹅:预测非正常状态下的人类表现。

Identifying black swans in NextGen: predicting human performance in off-nominal conditions.

机构信息

Alion Science Corporation, Micro Analysis and Design Operations, 4949 Pearl East Circle, Suite 300, Boulder, CO 80301, USA.

出版信息

Hum Factors. 2009 Oct;51(5):638-51. doi: 10.1177/0018720809349709.

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.

摘要

目的

本研究旨在验证视觉注意的计算模型,该模型基于对飞行员未能注意到关键安全意外事件的元分析经验数据。

背景

许多飞机事故的部分原因是未能注意到外部非显著意外事件,这说明了变化盲视现象。一种名为 N-SEEV(注意显著性、预期、努力和价值)的视觉注意模型被开发出来,以预测这些失败。

方法

首先,确定了 25 项研究,这些研究报告了高保真驾驶舱模拟中意外事件漏报率的客观数据,并对这五项变量(飞行阶段、事件预期、事件位置、平视显示器的存在和高空航道显示的存在)的漏报率数据进行了汇总。其次,调整了 N-SEEV 模型的参数以模拟这些二分法。

结果

N-SEEV 模型的输出预测了所获得的漏报率的方差(r =.73)。所有六个二分条件的个体漏报率都在 14%的范围内进行了预测,其中四个条件的预测误差在 7%以内。

结论

基于独立数据集开发的 N-SEEV 模型能够成功预测从文献中收集到的飞行员对异常情况反应的这一关键安全措施的方差。

应用

随着未来空域新技术和新程序的设想,预测这些新技术和程序是否会影响飞行员未能注意到意外事件的安全性是很重要的。N-SEEV 等计算模型支持以具有成本效益的方式进行此类预测。

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