Decreasing Stereotypy Using NCR and DRO With Functionally Matched Stimulation
Bibliographic record
Abstract
We conducted a series of studies on multiple forms of repetitive behavior displayed by four children with autism spectrum disorder. Study 1 showed that each participant's highest probability repetitive behavior persisted in the absence of social consequences, thereby meeting the functional definition of stereotypy. Study 2 showed that preferred, structurally matched stimulation decreased each participant's targeted (highest probability) stereotypy, as well as their non-targeted (lower probability) stereotypy. Study 3 showed that for three participants, non-contingent access to preferred stimulation decreased immediate and, to some extent, subsequent engagement in targeted and non-targeted stereotypy. For the fourth participant, non-contingent access to preferred stimulation decreased immediate engagement in the targeted stereotypy, but increased subsequent engagement in non-targeted stereotypy; this subsequent increase was attenuated by reducing the duration of access to the preferred stimulus. Study 4 showed that a trial-based differential reinforcement of other behavior (DRO) procedure systematically increased the period of time for which the targeted stereotypy was not displayed for three of three participants. In addition, results showed that the participants' non-targeted stereotypy either decreased or was unchanged when DRO was provided for the targeted stereotypy.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".