Laboratory method to induce state boredom increases impulsive choice in people who use cocaine and controls
Bibliographic record
Abstract
Background: Impulsive choice is associated with both cocaine use and relapse. Little is known about the influence of transient states on impulsive choice in people who use cocaine (PWUC).Objective: This study investigated the direct effects of induced boredom on impulsive choice (i.e., temporal discounting) in PWUC relative to well-matched community controls.Methods: Forty-one PWUC (≥1× cocaine use in past 3 months; 7 females) and 38 demographically matched controls (5 females) underwent two experimental conditions in counterbalanced order. Temporal discounting was assessed immediately after a standardized boredom induction task (peg-turning) and a self-selected video watched for the same duration (non-boredom). Subjective mood state and perceived task characteristics were assessed at baseline, during experimental manipulations, and after the choice task.Results: PWUC and controls were well matched on sex, age, and socioeconomic status. Groups were also similar in reported use of drugs other than cocaine, except for recent cigarette and alcohol use (PWUC > controls). As expected, peg-turning increased boredom in the sample overall, with higher boredom reported during peg-turning than the video (p < .001, η2p = .20). Participants overall exhibited greater impulsive choice after boredom than non-boredom (p = .028, η2p = .07), with no preferential effects in PWUC (p > .05, BF01 = 2.9).Conclusion: Experimentally induced boredom increased state impulsivity irrespective of cocaine use status – in PWUC and carefully matched controls – suggesting a broad link between boredom and impulsive choice. This is the first study to show that transient boredom directly increases impulsive choice. Data support a viable laboratory method to further parse the effects of boredom on impulsive choice.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".