Replication Data for: Behavioral analysis of habit formation in modern slot machine gambling
Bibliographic record
Abstract
Study Abstract: Habit formation is a key process in contemporary models of addictive behaviours, but has received limited attention in the context of gambling and problem gambling. Methods for examining habit formation and expression in relation to gambling are also lacking. In this study, 60 participants with no prior slot machine experience attended three sessions spaced 6 to 8 days apart, during which they played a short 200-spin session on a realistic simulation of a modern multi-line slot machine. Behavioral data were analyzed to characterize habit formation within and between sessions. Fixed-effects regressions, integrating trial- and session-level effects, assessed predictors of gambling speed (spin initiation latencies) and betting rigidity (the likelihood of switching the bet amount), as two putative markers of habit formation. Participants gambled faster and showed less variability in betting strategy as they accumulated experience in the number of trials and sessions gambled. Simultaneously, as the number of sessions gambled increased, participants showed a more pronounced tendency to slow their betting after larger wins (i.e., the post-reinforcement pause increased from session 1 to session 3). Our methods provide a basis for future research to examine habits in the context of slot machine gambling.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.035 |
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".