Emotional turbulence during simulation training: Unraveling emotion dynamics and performance accuracy using simulations for pilot training
Bibliographic record
Abstract
Affective responses, such as stress and emotions, are discussed as factors influencing human error in aviation; however, aviation research tends to explore affect as a subordinate element of cognition.Yet, more and more research is demonstrating that affective responses have a considerable effect on performance accuracy.Thus, the purpose of this dissertation is to examine the impact of pilots' affective responses on their flying performance accuracy in the context of training using simulations.The investigation started by synthesizing findings of previous research exploring affect and performance when using flight simulations, in a systematic literature review.Based on the findings of the literature review and previous research in education and psychology, two empirical studies were conducted aiming to understand the influence of emotions dynamics in flying performance.Emotions dynamics refer to the patterns and regularities characterizing fluctuations in emotions over time.Thus, the first study examines changes in performance and emotions dynamics (i.e., frequency, intensity, and variability of emotional responses) across training phases and difficulty levels in a flight simulation.The second empirical study builds on the previous one by examining the relationship between emotional variability and flying performance moderated by pilot trainees' perceived control and value over the task.The findings of this dissertation provide strong evidence that trainees' performance accuracy when performing simulated flying tasks is connected to their emotional responses.Particularly, findings from the empirical studies suggest that emotion dynamics might have adaptive functions for improving flying accuracy.Results demonstrate that affect and performance are dynamically impacted by training phases, difficulty levels, and subjective perceptions over the task.This dissertation concludes with a discussion of theoretical, methodological, and practical contributions, limitations, and future directions.I would like to thank my dissertation committee members, Dr. Jason Harley and Dr. Adam Dubé for their thoughtful advice in my comprehensive examination, research proposal, and this dissertation, their feedback helped improve this work.Thanks as well for sharing knowledge and advice to become a better researcher.Thanks to previous members of Advanced Technologies for Learning in Authentic Settings for their academic, professional, and personal advice as well as current members for their company and feedback during this doctoral journey
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".