Experimental manipulations of behavioral economic demand for addictive commodities: a meta‐analysis
Bibliographic record
Abstract
Abstract Background and Aims Reinforcing value, an index of motivation for a drug, is commonly measured using behavioral economic purchase tasks. State‐oriented purchase tasks are sensitive to phasic manipulations, but with heterogeneous methods and findings. The aim of this meta‐analysis was to characterize the literature examining manipulations of reinforcing value, as measured by purchase tasks and multiple‐choice procedures, to inform etiological models and treatment approaches Methods A random‐effects meta‐analysis of published findings in peer‐reviewed articles. Following the Preferred Reporting Items for Systematic Reviews and Meta‐Analysis (PRISMA) protocol, studies were gathered through searches in PsycINFO and PubMed/MEDLINE (published 22 May 2018). Searches returned 34 unique studies (aggregate sample n = 2402; average sample size = 68.94) yielding 126 effect sizes. Measurements included change (i.e. Cohen's d) in six behavioral economic indices (intensity, breakpoint, Omax, Pmax, elasticity, cross‐over point) in relation to six experimental manipulations (cue exposure, stress/negative affect, reinforcer magnitude, pharmacotherapy, behavioral interventions, opportunity cost). Results Cue exposure (d range = 0.25–0.44, all Ps < 0.05) and reinforcer magnitude [d = 0.60; 95% confidence interval (CI) = 0.18, 1.01; P < 0.005] manipulations resulted in significant increases in behavioral economic demand across studies. Stress/negative affect manipulations also resulted in a small, significant increase in Omax (d = 0.18; 95% CI = 0.01, 0.34; P = 0.03); all other effect sizes for negative affect/stress were non‐significant, albeit similar in size (d range = 0.14–0.18). In contrast, pharmacotherapy (d range = −0.37 to −0.49; Ps < 0.04), behavioral intervention (d = −0.36 to −1.13) and external contingency (d = −1.42; CI = −2.30, −0.54; P = 0.002) manipulations resulted in a significant decrease in intensity. Moderators (substance type) explained some of the heterogeneity in findings across meta‐analyses. Conclusions In behavioral economic studies, purchase tasks and multiple‐choice procedures appear to provide indices that are sensitive to manipulations found to influence motivation to consume addictive substances in field experiments.
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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 | 0.021 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.035 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".