Above-Real-Time Training (ARTT) Improves Transfer to a Simulated Flight Control Task
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to measure the effects of above-real-time-training (ARTT) speed and screen resolution on a simulated flight control task. BACKGROUND: ARTT has been shown to improve transfer to the criterion task in some military simulation experiments. We tested training speed and screen resolution in a project, sponsored by Defence Research and Development Canada, to develop components for prototype air mission simulators. METHOD: For this study, 54 participants used a single-screen PC-based flight simulation program to learn to chase and catch an F-18A fighter jet with another F-18A while controlling the chase aircraft with a throttle and side-stick controller. Screen resolution was varied between participants, and training speed was varied factorially across two sessions within participants. Pretest and posttest trials were at high resolution and criterion (900 knots) speed. RESULTS: Posttest performance was best with high screen resolution training and when one ARTT training session was followed by a session of criterion speed training. CONCLUSION: ARTT followed by criterion training improves performance on a visual-motor coordination task. We think that ARTT influences known facilitators of transfer, including similarity to the criterion task and contextual interference. APPLICATION: Use high-screen resolution, start with ARTT, and finish with criterion speed training when preparing a mission simulation.
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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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".