Separate and combined effects of operant <scp>ABA</scp> renewal mitigation strategies
Bibliographic record
Abstract
Due to the undesirable effects of operant renewal for behavioral interventions, recent research has advocated for the advancement of renewal mitigation strategies. One strategy includes the use of extinction cues, which are stimuli used to establish discriminative control over responding in the second context that are subsequently transferred to the initial context. A second strategy involves context fading, which refers to progressively increasing the similarity between the second context and the initial context. The current study evaluated the separate and combined effects of these techniques using a preclinical human laboratory arrangement. Participants were exposed to the extinction cue strategy, the context fading strategy, both strategies, or neither strategy during a three-phase ABA renewal procedure using differential reinforcement of an alternative response combined with extinction. The results indicated that context fading or combining context fading with an extinction cue was effective at mitigating renewal. The use of an extinction cue alone reduced renewal relative to the control group, but this difference was not statistically significant. The results are discussed in terms of methodological and theoretical differences across strategies as well as implications for future research on renewal mitigation strategies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".