Post-reinforcement pauses during slot machine gambling are moderated by immersion
Bibliographic record
Abstract
The post-reinforcement pause (PRP) is an operant effect in which response latencies increase on trials following the receipt and consumption of reward. Human studies demonstrate analogous effects in electronic gambling machines that utilise random ratio reinforcement schedules. We sought to identify moderators of the human PRP effect, hypothesising that the magnitude of gamblers’ PRPs is moderated by the type of reinforcing outcome (genuine wins vs. losses-disguised-as-wins [LDWs] vs. free-spin bonus features) and individuals’ level of gambling immersion , a cognitive state linked to problem gambling. Experienced slot machine users ( N = 53) played a real slot machine for 20 min. The dependent variable was defined as the time delay in the initiation of each bet (“Spin Initiation Latency”; SIL). Using 80% of trials, a linear model was fit regressing SIL on the independent variables (outcome type, immersion, and outcome-by-immersion interaction), and a larger group of covariates (participant ID, trial number, winnings, etc.) selected using double-robust LASSO-regularised regression. The previously unseen 20% of cases were used to validate the model. Positively reinforcing outcome types (wins, LDWs, bonus spins) showed significantly larger SILs than losses, indicating a PRP effect. Immersion did not predict response latencies, but win-by-immersion and LDW-by-immersion interactions indicated that pauses were greater among more immersed participants. The small number of free-spin bonus features showed similar trends that were not statistically significant. These results indicate that gamblers immersed in play remained sensitive to in-game reinforcement (contrary to a prevailing account), and provide guidance for researchers bridging laboratory research and real-world behaviour.
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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 | 0.009 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".