The Benefits of Errorless Learning for Serial Reaction Time Performance in Alzheimer's Disease
Bibliographic record
Abstract
Identifying the conditions favoring new procedural skill learning in Alzheimer's disease (AD) could be important for patients' autonomy. It has been suggested that error elimination is beneficial during skill learning, but no study has explored the advantage of this method in sequential learning situations. In this study, we examined the acquisition of a 6-element perceptual-motor sequence by AD patients and healthy older adults (control group). We compared the impact of two preliminary sequence learning conditions (Errorless versus Errorful) on Serial Reaction Time performance at two different points in the learning process. A significant difference in reaction times for the learned sequence and a new sequence was observed in both conditions in healthy older participants; in AD patients, the difference was significant only in the errorless condition. The learning effect was greater in the errorless than the errorful condition in both groups. However, while the errorless advantage was found at two different times in the learning process in the AD group, in the control group this advantage was observed only at the halfway point. These results support the hypothesis that errorless learning allows for faster automation of a procedure than errorful learning in both AD and healthy older subjects.
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 | 0.004 |
| 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".