Research reading 084 / Actors / 2022
Conceptual Models of Startle, Surprise and Automation Bias Analyzed Through Recent Aviation Accident Reports
Source authors: Karen Cristiane Gardini; Fernando Holanda Chiavelli; José Ricardo Silva Scarpari; Donizeti de Andrade
Originally published in Journal of Aerospace Technology and Management · 2022
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Conceptual Models of Startle, Surprise and Automation Bias Analyzed Through Recent Aviation Accident Reports
Karen Cristiane Gardini; Fernando Holanda Chiavelli; José Ricardo Silva Scarpari; Donizeti de Andrade
The guide and its evidence limits are journal annotations, not quotations from the original researchers. The complete original paper is not currently in this agent’s searchable context.
A3 Safety, uncertainty and startle
Why read this for flight training?
Applies models of startle, surprise and automation bias to accident reports. Useful for building reflective scenarios around how understanding breaks down.
How to interpret the evidence
Retrospective conceptual analysis cannot establish the causal effectiveness of a training intervention.
Screening basis: Publisher description screened. This annotation identifies relevance and limits; it does not replace appraisal of the original methods and findings.
Cite the original article
Karen Cristiane Gardini; Fernando Holanda Chiavelli; José Ricardo Silva Scarpari; Donizeti de Andrade (2022). Conceptual Models of Startle, Surprise and Automation Bias Analyzed Through Recent Aviation Accident Reports. Journal of Aerospace Technology and Management, 14, e3022. https://doi.org/10.1590/jatm.v14.1282
Publication record
The original paper retains its published authors, venue and DOI. This journal’s reading guide was prepared with AI assistance under the editorial direction of Dr. Freedom Cheteni. The source authors are not credited as authors of this annotation. No new peer review or DOI is claimed for the guide.
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