Discounting of delayed monetary and cannabis rewards in a crowdsourced sample of adults.
Bibliographic record
Abstract
Excessive delayed reward discounting (DD) is observed across many addictive disorders. However, research on DD among cannabis users is limited, with even less research on discounting of cannabis rewards. This study examined monetary and cannabis reward discounting among cannabis and noncannabis users. A large sample of adults (N = 2,857) recruited from an online crowdsourcing platform was assessed on demographics and DD of monetary ($10, $100) and cannabis (10 g) rewards. Analyses of variance were used to evaluate magnitude and commodity effects. Hierarchical multiple regression models were run to assess whether cannabis use frequency was associated with discounting rates for monetary and cannabis rewards. A magnitude effect was found for the monetary rewards where $10 was discounted more steeply compared to $100 (p < .0001). A commodity effect was found where discounting was higher for the 10g cannabis reward compared to monetary rewards (ps < .05). Regression models controlling for demographics and other substance use indicated severity of cannabis problems significantly predicted discounting of $100 (β = .045, p < .05) and 10 g of cannabis (β = .088, p < .05). Cannabis use frequency was not significantly associated with any DD measures after controlling for other substance use (ps > .05). These results suggest the association between cannabis use and DD is complex and generally small in magnitude. This study adds to the literature on DD and cannabis use and suggests the need for further studies to determine the extent to which cannabis use impacts DD, both chronically and acutely. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.002 | 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 teacher head, 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".