Associations Between Delay Discounting and Risk‐Related Behaviors, Traits, Attitudes, and Outcomes
Bibliographic record
Abstract
Abstract Delay discounting—preference for immediate, smaller rewards over distal, larger rewards—has been argued to be part of the “generality of deviance”, which describes the co‐occurrence of various forms of impulsive and risky behaviors among individuals. Some studies have linked laboratory‐measured delay discounting to behaviors, traits, attitudes, and outcomes associated with risk, but these associations have been inconsistent. Furthermore, many of these studies have been conducted with exclusively undergraduate samples, or in samples offering low statistical power. In a large community sample (n = 328) diverse in age and socioeconomic status, we examined associations between two measures of behavioral delay discounting (single‐shot and canonical k‐parameter estimation) and behavioral risk‐taking, personality traits associated with risk, domain‐specific risk attitudes, gambling and problem gambling, antisocial behavior, and criminal outcomes. In addition, we explored whether a novel response time latency measure of delay discounting explained variance in these risk‐related outcomes. Results indicated that behavioral delay discounting was consistently associated with all variables related to impulse control: high trait impulsivity, low trait self‐control, risk‐averse attitudes toward financial investment, risk‐prone attitudes toward gambling and health/safety risks, gambling and problem gambling, antisocial conduct, and criminal outcomes. Latency‐measured delay discounting was inconsistently associated with behavioral delay discounting and risk‐related measures. Together, results suggest that delay discounting is associated with poor impulse control consistent with a generality of deviance account. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".