Using treatment to improve the production of emotive adjectives in aphasia: a single-case study
Bibliographic record
Abstract
Background: Emotive adjectives are used in everyday conversations to express opinions and feelings and make evaluations (e.g., “interesting”, “intelligent”). It has been reported that people with aphasia have difficulty using emotive language and that they would like this to be targeted in therapy. However, the literature provides little guidance whether it is possible to improve production of emotive adjectives.Aims: Our aim was to test the hypothesis that a treatment technique that has been found to be effective in improving noun and verb retrieval (Repetition in the Presence of a Picture) would be effective in improving production of emotive adjectives.Methods & Procedures: This study involved GEC, a 66-year-old English-speaking man who presented with non-fluent aphasia including frequent word-finding difficulties and impaired production of emotive adjectives following a left-hemisphere stroke. Treatment was carried out using a single-subject multiple-baseline design consisting of two treatment periods each of 2 weeks preceded by four baseline measurements, with one within-treatment measurement and three post-treatment measurements (immediately, 1-week, and 11 weeks after the end of the treatment programme). The treatment comprised weekly meetings with the therapist and computer-presented, self-paced, home-practice using to treat 72 emotive adjectives (36 positive and 36 negative adjectives) associated with 24 pictures.Outcomes & Results: GEC’s ability to produce treated adjectives for treated pictures significantly improved. The effect was maintained for the positive items with maintenance for negative items close to significant. However, these item-specific effects of treatment did not generalise: No significant improvement was observed in producing new, untreated labels for the treated pictures. Nor was GEC able to use treated labels with pictures other than those with which they were treated. In addition, GEC’s performance in a connected speech task remained unchanged. These results indicate that the treatment effects were not only item-specific but also task-specific.Conclusions: This study provides the first demonstration that emotive adjective retrieval may be improved using a treatment method similar to that commonly used for treating nouns and verbs. This result was achieved after only 2 weeks of practice at home with a computer including sparse meetings with a therapist and targeting only single-word production of adjectives. While there was no evidence of generalisation across items or tasks, this study encourages further exploration of the topic. This should include replication across participants and the inclusion of more natural conversational tasks in the treatment to facilitate transfer.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".