The NASA MATB-II Predicts Prospective Memory Performance During Complex Simulated Flight
Bibliographic record
Abstract
Prospective memory is essential for flight, where failures can result in incorrect flight control settings, leading to loss of life and equipment. Furthermore, prospective memory is highly-sensitive to pilot age, cognition, and experience. This research reports on the relation of the NASA Multi-Attribute Test Battery-II (MATB-II) to prospective memory during simulated VFR flight (N=51). Prospective memory was indexed with specialized radio calls that were associated with non-focal visual cues. Linear regression models examined the relative association of MATB-II variables to prospective memory in low and high workloads. System monitoring, psychomotor tracking, and resource management, generally at higher difficulty levels, were the variables most predictive of prospective memory, r2 =0.41. Pilot experience improved the model in the high workload condition. Estimating risk for prospective memory failures via multitasking ability, with a focus on monitoring tasks, may inform cognitive assessment approaches to enhance aviation safety.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".