Treatment of perseveration speech in a young adult with Autism Spectrum Disorder
Bibliographic record
Abstract
This study was implemented to evaluate a treatment approach to reduce perseverative speech in a 21 year old adult with Autism Spectrum Disorder (ASD). This study is a brief replication of previous literature that shows differential reinforcement (DR) and extinction can be used to increase appropriate speech and decrease perseverative speech. This also intends to extend the previous literature that shows DR and extinction can be used to increase appropriate speech selected by the listener, and decrease perseverative speech. To promote appropriate speech, a turn-taking format was used during sessions while DR of non-perseverative speech and DR of on-topic speech was implemented. The turn taking sessions were presented in a multiple schedule with a discriminative stimulus that signaled the contingencies, and who was to choose the topic. Both treatments reduced perseverative speech, but only DR of on-topic speech increased appropriate turn taking during conversations. There was a rapid increasing trend of appropriate speech contingent on reinforcement, and a rapidly decreasing trend of perseverative speech. The results show that perseverative speech is sensitive to contingent attention as reinforcement, and DR and extinction can markedly reduce this speech. Discipline: Psychology Honours Faculty Mentor: Miranda Macauley
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".