Previous errorless sequence-learning promotes subsequent SRT performance in patients with Alzheimer's Disease
Bibliographic record
Abstract
Motor-learning capacities are known to be relatively preserved in Alzheimer's disease (AD), which is crucial in the context of the patient’s autonomy (e.g., Rouleau et al., 2002). However, it is important to determine the most appropriate techniques for such learning. In AD, implicit or procedural rehabilitation techniques would be more effective to train new skills than explicit or declarative learning methods (van Halteren-van Tilborg, 2007). Maxwell et al. (2001) showed that reducing errors during motor learning minimizes the building of declarative knowledge and would allow implicit knowledge accumulation. If errorless learning induces the formation of an implicit knowledge, this technique appears to be adapted to the learning of a perceptual-motor skill in patients with impaired controlled processes. Very few studies have investigated errorless learning in procedural learning situations, even though some data suggest that errorless learning would be efficient for learning instrumental activities of daily living (e.g., Thivierge et al., 2008). In this study we examined the acquisition of a new perceptual-motor skill in 12 patients with AD and 12 healthy older adults. We compared the impact of two preliminary sequence learning conditions (errorless vs. errorful) on a serial reaction time (SRT) performance. In SRT, the subject must react as quickly as possible to the appearance of a target on a screen by pressing the key corresponding to the position of the stimulus. The effectiveness of learning is demonstrated by a reaction time improvement when the target follows a repeating sequence. For patients with AD, results confirm that the advantage provided by prior learning occurs only in the errorless condition whereas both learning modes improve SRT performance in healthy participants. In conclusion, these results confirm that the errorless learning promotes the development of implicit knowledge and appears to be an effective method for procedural learning in Alzheimer's disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".