Test–retest consistency of Virtual Week: A task to investigate prospective memory
Bibliographic record
Abstract
The present study reports test-retest consistency of Virtual Week, a well-known measure of prospective memory (PM) performance. PM is the memory associated with carrying out actions at a specific moment in the future. Patients with neurological disorders as well as healthy older adults often report PM dysfunctions that affect their everyday living. In Experiment 1, 19 younger and 20 older adults undertook the standard version of Virtual Week (version A). Older adults showed lower performance compared to younger participants. However, the discrepancy between groups was eliminated at retest. Experiment 2 was conducted to investigate if remembering of PM content determined the improvement observed in older adults at retest in Experiment 1. To this end we created a parallel version (version B) in which we varied the content of the PM actions. Fifty older adults were assigned to one of the two experimental conditions: Version A at test and version B at retest or vice versa (25 participants in each condition). Results showed no group differences in PM performance between version A and version B; moreover, no effect of test-retest was found. The study confirmed that Virtual Week is a reliable measure of PM performance and also provided a new parallel version that can be useful in clinical setting.
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".