Using deliberate mind-wandering to escape negative mood states: Implications for gambling to escape
Bibliographic record
Abstract
BACKGROUND AND AIMS: Slot machines are a pervasive form of gambling in North America. Some gamblers describe entering "the slot machine zone"-a complete immersion into slots play to the exclusion of all else. METHODS: We assessed 111 gamblers for mindfulness (using the Mindful Attention Awareness Scale (MAAS)), gambling problems (using the Problem Gambling Severity Index (PGSI)), depressive symptoms (using the Depression, Anxiety, and Stress Scale), and boredom proneness (using the Boredom Proneness Scale). In a counterbalanced order, participants played a slot machine simulator and completed an auditory vigilance task. During each task, participants were interrupted with thought probes to assess whether they were: on-task, spontaneously mind-wandering, or deliberately mind-wandering. After completing each task, we retrospectively assessed flow and affect. Compared to the more exciting slots play, we propose that gamblers may use deliberate mind-wandering as a maladaptive means to regulate affect during a repetitive vigilance task. RESULTS: Our key results were that gamblers reported greater negative affect following the vigilance task (when compared to slots) and greater positive affect following slots play (when compared to the vigilance task). We also found that those who scored higher in problem gambling were more likely to use deliberate mind-wandering as a means to cope with negative affect during the vigilance task. Using hierarchical multiple regression, we found that the number of "deliberately mind-wandering" responses accounted for unique variance when predicting problem gambling severity (over and above depression, mindfulness, and boredom proneness). CONCLUSIONS: These assessments highlight a potential coping mechanism used by problem gamblers in order to deal with negative affect.
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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 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".