Quest for the best: Effects of errorless and active encoding on word re-learning in semantic dementia
Bibliographic record
Abstract
Semantic dementia is a neurocognitive disorder characterised by a steady and progressive loss of semantic knowledge in the presence of relatively preserved other cognitive skills. Recent treatment studies have proven that language rehabilitation aimed at anomia in semantic dementia can be successful. The objective of this study was to examine the separate and interactive effects of errorless vs. errorful and active vs. passive learning approaches to anomia and their effects on naming and comprehension of treated items, as well as maintenance and generalisation of treatment gains. Seven participants with semantic dementia re-learned two sets of words (one for which participants retained auditory comprehension, and one for which they did not) in each of four different treatment methods based on those approaches. Errorless learning proved more successful than errorful learning in restoring lexical representations in all but one participant while there was no interaction between effects of errorless and active approaches on treatment success. Maintenance of treatment gains showed an advantage for errorless learning at one but not three months post-treatment, although all overall gains were maintained to a significant degree at both time points. Effects of both treatment and maintenance were stronger for items for which participants showed preserved auditory comprehension. The results are discussed in a framework of progressive language disorders and applicability of errorless methods to language rehabilitation in semantic dementia.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".