Replication Data for: "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 The ratio of dwell time on the game’s credit window relative to the game’s reels was positively associated with immersion (t(51) = 1.68, p = .049 one-tailed, R2 = .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 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 | 0.036 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.089 | 0.020 |
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".