Abstract
The human factor in aviation is a complex and multidimensional construct, incorporating different levels of analysis. At the core of this complexity, cognitive and psychological variables occur in dynamic interaction. A model of aerospace neuropsychology is proposed in order to investigate the interaction between psychological variables and neurocognitive errors in the 100 most fatal civil aviation accidents. Recurring psychological and cognitive themes described in the accidents were subjected to multidimensional scaling. Five conceptual clusters pertaining to individual pilot characteristics, sociotechnical cockpit performance, organizational/operational effect, training-level of involvement and decision making-overestimation, were identified. Psychological and cognitive interaction is apparent throughout the accidents’ human factor space, while individual characteristics are located within a dense area visually separated from sociotechnical and human-machine interaction features. Results are discussed in light of the need to review current aeromedical examination procedures under a scientific accurate and ad hoc methodology.
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Notes
- 1.
Best poster award at the 5th European Congress of Aerospace Medicine (ECAM 2016) in Oslo, Norway.
- 2.
Prospective memory, computation, multitasking, use of checklists, alertness, comprehension, navigational errors, procedural steps, planning, spatial disorientation, flawed assumptions.
- 3.
Impulsivity, invulnerability, aggressiveness, macho attitude, excessive reaction, obedience to authority, over-reliance on automation, performance under stress, sleep deprivation, fatigue, work conditions, real-life stressors, psychopathology.
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Nidos, A., Stavrakis Kontostavlos, S., Roussos, P., Mylonas, K. (2018). Aerospace Neuropsychology: Exploring the Construct of Psychological and Cognitive Interaction in the 100 Most Fatal Civil Aviation Accidents Through Multidimensional Scaling. In: Baldwin, C. (eds) Advances in Neuroergonomics and Cognitive Engineering. AHFE 2017. Advances in Intelligent Systems and Computing, vol 586. Springer, Cham. https://doi.org/10.1007/978-3-319-60642-2_22
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