The Relative Importance of Suboperations of Prospective Memory
Bibliographic record
Abstract
An event-based and a time-based prospective memory (PM) task, a script generation task, several working memory tasks, an incidental retrospective memory task, and a screen clock were implemented on the computer in one integrated procedure lasting between one and two hours. The procedure was designed to simulate four working days and four nights for a white-collar employee. Sixty-eight normal participants completed the task. Time-based prospective memory (self-injecting and going to bed at preordained times of day) shared unique variance with clock checking, but hardly at all with incidental retrospective memory. On the other hand, event-based prospective memory (answering a faint telephone cue as quickly as possible) shared unique variance with incidental retrospective memory of formally task irrelevant context and less with clock checking. The latter correlational dissociation of event-based versus time-based PM by retrospective memory reached significance, inspiring the idea that administrative versus clerical work might each impose its own type of PM demands. In both types of PM, low-level abilities (use of external aids and incidental encoding of context, respectively) seem to be critical for good performance, more so than for high-order executive functions. Our software is offered to the readership to explicitate these findings further or for other research pursuits.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".