Zoned in or zoned out? Investigating immersion in slot machine gambling using mobile eye‐tracking
Bibliographic record
Abstract
BACKGROUND AND AIMS: Immersion during slot machine gambling has been linked to disordered gambling. Current conceptualizations of immersion (namely dissociation, flow and the machine zone) make contrasting predictions as to whether gamblers are captivated by the game per se ('zoned in') or motivated by the escape that immersion provides ('zoned out'). We examined whether selected eye-movement metrics can distinguish between these predictions. DESIGN AND SETTING: Pre-registered, correlational analysis in a laboratory setting. Participants gambled on a genuine slot machine for 20 minutes while wearing eye-tracking glasses. PARTICIPANTS: Fifty-three adult slot machine gamblers who were not high-risk problem gamblers. MEASUREMENTS: We examined self-reported immersion during the gambling session and eye movements at different areas of the slot machine screen (the reels, the credit window, etc.). We further explored these variables' relationships with saccade count and amplitude. FINDINGS: = 0.05). Follow-up analyses described event-related changes in these patterns following different spin outcomes. CONCLUSIONS: Immersion while gambling on a slot machine appears to be associated with active scanning of the game and a focus on the game's credit window. These results are more consistent with a 'zoned in' account of immersion aligned with flow theory than a 'zoned out' account based on escape.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".