A novel test of implicit memory; an eye tracking study
Bibliographic record
Abstract
Novelty preference in visual scanning behaviour is used to test implicit memory in patients with Alzheimer’s disease (AD). During the test, subjects are presented with slides that include both novel images and images that were seen before (repeated images). Slides are presented sequentially and the number of slides between the first and second presentations of repeated images is varied to mask the purpose of the test. The normalised average glance duration (N-AGD) on repeated images (the bias towards novelty) was used to measure novelty preference. Data from 10 young controls showed that the bias towards novelty is reduced as the number of slides between the first and second presentations of repeated images is increased. A group of 17 patients with AD showed no significant bias towards novelty while a group of 21 age matched controls do exhibit such bias (t(20) = 6.16, p < 0.001). The data suggest that patients with AD have no preference to novel images and support the idea that AD affects implicit memory. The receiver operator characteristics of the bias towards novelty showed that patients with AD and age-matched controls can be differentiated with both high sensitivity and high specificity.
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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".