Errorless learning of computer-generated words in a patient with semantic dementia
Bibliographic record
Abstract
This study explores the effectiveness and feasibility of an errorless learning approach administered via a computer-based treatment for anomia to CS, an individual with semantic dementia. Using a multiple baseline across behaviours design, we explored treatment specific effects, maintenance and generalisation of gains derived from the MossTalk Words((R)) therapy programme. CS was treated on three lists of words, each containing items for which CS retained some semantic knowledge and some for which he did not. CS was tested immediately after therapy, and one and three months later. Improved naming was maintained on all lists at all testing intervals. In addition, among those words for which CS retained some semantic knowledge, he maintained the ability to name all practised words, but only half of the not practised words. This study underscored the feasibility of computer-based treatments for anomia in progressive disorders, demonstrated the effectiveness of an errorless approach in semantic dementia in re-training lost words, and provided justification for training words that patients still have in their daily vocabulary. The results are discussed in relation to other treatment studies in progressive aphasia and in the context of factors necessary for therapeutic success 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".