Learning, Retention and Generalization of a Mirror Tracing Skill in Alzheimer's Disease
Bibliographic record
Abstract
The present study examined the ability of 12 patients with probable Alzheimer's disease (AD) and 12 age- and education-matched normal control (NC) subjects to learn and retain the visuomotor skills necessary to efficiently trace a pattern (e.g., a 4- or 6-pointed star) seen only in mirror-reversed view. Those AD (N=6) and NC (N=7) subjects who were able to initially perform the basic mirror tracing task did not differ significantly in initial level of performance, learning over trials, retention of the skill over a 30-min delay interval, and generalization of the skill to a new figure or to the opposite direction of tracing. The AD patients who were unable to initially perform the mirror tracing task were significantly worse than those who could perform the task on several neuropsychological measures sensitive to deficits in problem solving and executive functions, but not on tests of global cognitive decline, memory, language, or visuoperceptual functioning. These results indicate that acquisition and retention of a complex visuomotor skill can proceed normally in the early stages of AD in those individuals who can initially perform the basic task, and that inability to perform the basic task may be related to the frontal lobe dysfunction that is often prominent in the disorder.
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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".