Affect and Performance in Simulated Flying Tasks: A Systematic Literature Review
Bibliographic record
Abstract
Objective This systematic review explores the relationship between affect and performance during flight simulations in the context of training using simulated flying tasks.Background A goal of human factors is to understand pilots’ psychological processing to reduce human error. Psychological insights have mainly focused on cognition, whereas recent approaches suggest that affective responses (stress and emotions) can impact performance accuracy. This is the first literature review exploring affect and performance in simulated flying tasks.Method Following the PRISMA framework, a systematic review was conducted, resulting in 29 articles meeting the inclusion criteria.Results Studies were grouped in themes according to the manner they contributed to our objective. Findings revealed that: 1) affect and flying performance are continuous processes that need to use continuous and noninvasive measurements; 2) negative-activating affect (stress and anxiety) tend to be detrimental for flight performance; 3) pilot–copilot and pilot–instructor interactions induce affect in simulated flight; 4) unusual flying scenarios induce negative and activating affective responses and decrease performance accuracy; 5) interventions are successful for pilots to manage performance and affect.Conclusion This systematic review demonstrates the relationships between affect and performance. The main connection is that unpredicted and intense affective reactions can distract pilots, resulting in errors. Simulations can induce authentic affective reactions, allowing pilot trainees to familiarize themselves with their reactions, and regulate themselves, to achieve successful performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".