Human Factors Associated with Startle and Surprise Events in Aviation: A Large-Scale Analysis of NASA ASRS Reports
Abstract
This study examined the prevalence and human factors correlates of startle and surprise events in commercial aviation using a large-scale analysis of NASA Aviation Safety Reporting System (ASRS) narratives. While startle and surprise effects have been identified as contributors to loss-of-control events and other critical incidents, prior empirical research has relied primarily on small-sample surveys, laboratory studies, or case analyses of individual accidents. The present study extends this work by analyzing the co-occurrence of pre-coded human factors with startle-related language across a decade of confidential incident reports. A keyword-matching algorithm was applied to 38,655 ASRS reports (2012–2022) to identify 2,642 reports (6.8%) containing startle/surprise-related language. Chi-square tests, odds ratio analyses with 95% confidence intervals, and logistic regression were used to compare human factors, flight phase distributions, anomaly types, and outcomes between startle-flagged and non-startle reports. All major human factors—including Fatigue (OR = 1.86, p < .001), Physiological factors (OR = 2.11, p < .001), Workload (OR = 1.60, p < .001), and Confusion (OR = 1.38, p < .001)—were significantly over-represented in startle reports. Logistic regression confirmed Physiological factors (β = 0.75) and Fatigue (β = 0.48) as the strongest independent predictors. Loss of aircraft control was 2.4 times more prevalent in startle reports. These findings provide large-scale empirical evidence that fatigue, physiological vulnerability, and high workload significantly amplify the risk and severity of startle reactions in operational aviation, supporting the development of targeted Crew Resource Management interventions and evidence-based training protocols.
Keywords: Startle Effect, Surprise, Human Factors, Aviation Safety, ASRS, Pilot Performance, Fatigue, Crew Resource Management
DOI: 10.54941/ahfe1007844
Cite this paper
More from this volume
- Characteristics of Changes in Body Composition Measurements Among Japanese Alpine Skiers
- The Role of Fatigue Risk Management Systems (FRMS) in the Implementation of Human -AI teaming in the Aviation Ecosystem.
- Human Factors Analysis and Classification System (HFACS) Applications in Transportation Human Factors: Review Study
- Implementation of human teaming in aviation industry: The Turkish Airlines case study
- Training Challenges in Human -AI Teaming in Aviation
- Implementation of Human - AI teaming in the Single Pilot Operations Era.
- The role of workforce planning in the implementation of Human - AI Teaming in Transportation
- The Role of Safety Management Systems (SMS) in the implementation of Human - AI teaming in Aviation Ecosystem.
- Assessing Signal Detection Performance Under Operational Fatigue in Air Traffic Controllers
- Action-Oriented Pilot Training
- The Gold and the Failed Results of Artificial Intelligence in Aviation
- Cognitive reinforcement for aircrew coordination with autonomous collaborative platforms in next-generation fighters


AHFE Open Access