A review of investigations of operant renewal with human participants: Implications for theory and practice
Bibliographic record
Abstract
Operant renewal is the recurrence of a previously eliminated target behavior as a function of changing stimulus contexts. Renewal as a model of treatment relapse in humans suggests that a change in stimulus conditions or context is sufficient to produce relapse of a previously eliminated maladaptive behavior. The extent to which general findings from operant renewal studies involving nonhuman animal subjects are supported by relapse studies involving human participants is unknown. We conducted a systematic review of studies demonstrating or mitigating operant renewal in human participants in peer-reviewed studies found in PsycINFO, ERIC, PubMed, and Scopus between 1980 and 2019. We identified 12 studies involving 61 participants and 93 cases of operant renewal. We coded descriptive data on participant and study characteristics and calculated summary statistics. Results indicated that the renewal effect was a robust phenomenon, supported by demonstrations in both clinical and human-laboratory studies, and across a variety of variables and experimental preparations. However, there were relatively few studies involving human participants that attempted to reduce or eliminate renewal of clinically meaningful behavior. We discuss variables relevant for studying renewal in socially meaningful contexts, practical limitations of observing the renewal effect in real-world settings, implications for theoretical models of renewal, and identify barriers to methodology unique to human participants. We provide directions for future research related to implementing and translating nonhuman animal studies of renewal to applied settings.
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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 | 0.013 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".