Related Experiment Video
Updated: Jun 15, 2026
06:37
Artificial Intelligence-Based System for Detecting Attention Levels in Students
Published on: December 15, 2023
Identifying black swans in NextGen: predicting human performance in off-nominal conditions
Christopher D Wickens1, Becky L Hooey, Brian F Gore
1Alion Science Corporation, Micro Analysis and Design Operations, 4949 Pearl East Circle, Suite 300, Boulder, CO 80301, USA. cwickens@alionscience.com
Human Factors
|March 4, 2010
Summary
A computational model accurately predicted pilots' failures to notice unexpected events, validating its use for aviation safety. This model supports predicting risks with new technologies.
Area of Science:
- Aviation psychology
- Human factors in aviation
- Cognitive modeling
Background:
- Pilot failure to notice unexpected events contributes to aircraft accidents.
- Change blindness, or failure to notice visual changes, is a key factor.
- The N-SEEV (noticing-salience, expectancy, effort, and value) model was developed to predict these failures.
Purpose of the Study:
- To validate the N-SEEV computational model of visual attention.
- To compare model predictions against empirical data from pilots' failure to notice critical events.
Main Methods:
- A meta-analysis pooled miss rate data from 25 high-fidelity cockpit simulation studies.
- Data included variables like flight phase, event expectancy, and display types.
- N-SEEV model parameters were tailored to match these empirical data dichotomies.
Main Results:
- The N-SEEV model successfully predicted variance in pilot miss rates (r = .73).
- Individual miss rates for six conditions were predicted within 14%, with four within 7% accuracy.
Conclusions:
- The N-SEEV model, validated against independent data, accurately predicts pilot responses to abnormal circumstances.
- Computational models like N-SEEV offer cost-effective methods for assessing safety risks of future aviation technologies and procedures.