Measuring the slot machine zone with attentional dual tasks and respiratory sinus arrhythmia.
Bibliographic record
Abstract
Recent accounts of problematic electronic gaming machine (EGM) gambling have suggested attentional pathology among at-risk players. A putative slot machine zone is characterized by an intense immersion during game play, causing a neglect of outside events and competing goals. Prior studies of EGM immersion have relied heavily upon retrospective self-report scales. Here, the authors attempt to identify behavioral and psychophysiological correlates of the immersion experience. In samples of undergraduate students and experienced EGM users from the community, they tested 2 potential behavioral measures of immersion during EGM use: peripheral target detection and probe-caught mind wandering. During the EGM play sessions, electrocardiogram data were collected for analysis of respiratory sinus arrhythmia (RSA), a measure of calming self-regulation governed by the parasympathetic nervous system. Subjective measures of immersion during the EGM play session were consistently related to risk of problem gambling. Problem gambling score, in turn, significantly predicted decrements in peripheral target detection among experienced EGM users. Both samples showed robust RSA decreases during EGM play, indicating parasympathetic withdrawal, but neither immersion nor gambling risk were related to this change. This study identifies peripheral attention as a candidate for quantifying game immersion and its links with risk of problem gambling, with implications for responsible gambling interventions at both the game and venue levels. (PsycINFO Database Record
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".