Delay and probability discounting in the context of gambling function and expectancies
Bibliographic record
Abstract
The current study investigated the relationship between two forms of discounting (delay and probability) and two measures of factors that may maintain gambling behavior (behavioral contingencies and expectancies). Participants (272 undergraduates) completed discounting questions for scenarios of gaining or losing $1,000 or $100,000 with uncertain or delayed outcomes. They also filled out the South Oaks Gambling Screen, the Gambling Functional Assessment -Revised, and the Gambling Expectancies Questionnaire. Results showed that gambling for positive reinforcement was consistently the best predictor of discounting, suggesting that the function of gambling behavior may be a better predictor of discounting than are the emotional expectancies of gambling. However, the direction of the relationship was inconsistent, with function negatively predicting discounting of both uncertain gains and losses. No consistent relationship was found between discounting and gambling for negative reinforcement or emotional expectancies. Results were generally the same when non-gamblers were excluded from the analyses. The results suggest that studying gambling function may be an informative pursuit.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".