Prospective Memory Tasks in Aviation: Effects of Age and Working Memory.
Bibliographic record
Abstract
Prospective memory is the ability to remember to perform acts in the future. Prospective memory is essential in the aviation domain because it supports a range of tasks including remembering to complete critical radio communications. A wide variety of literature reports that in the laboratory younger adults outperform older adults on many prospective memory tasks. In naturalistic settings however, older adults perform as well as or better than younger adults. It is suggested that lower working memory load from on-going background tasks, context cues and the habitual nature of the tasks are reasons for the improved performance by older adults in naturalistic settings. We tested this notion using a Cessna 172 aircraft simulator to examine radio communication task completion rates of 45 pilots (16 older and 29 younger participants). Individual measures of working memory were also collected. In contrast to the trends reported in the literature, we found that older pilots had significantly lower communication task completion rates than younger pilots in both the low and high working memory workload conditions. A multiple regression model identified age and working memory scores as the strongest individual predictors of prospective memory task performance in the low workload condition and working memory and recent pilot-in-command hours as significant predictors of performance in the high workload condition. Our results suggest that, even in a low workload condition, a naturalistic aviation context did not afford advantages to older pilots and that prospective memory task performance appears associated with age and working memory function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".