Investigating Flow State and Cardiac Pre-ejection Period During Electronic Gaming Machine Use
Bibliographic record
Abstract
Flow activities (e.g. sports and gaming) have been associated with positive affect and prolonged engagement. In the gambling field, modern electronic gaming machines (EGMs, including modern slot machines) have drawn concern as a potentially flow-inducing activity that may be associated with gambling-related harms. Current research has heavily relied on self-reported flow, and further insights may be afforded by physiological methods. We present data from three separate experiments in which self-reported gambling flow and cardiac pre-ejection period (PEP; a measure of sympathetic nervous system arousal) were examined. Male undergraduate participants gambled on a genuine EGM in a laboratory setting for a period of at least 15 min, and completed the Flow subscale of the game experience questionnaire (GEQ). Aggregated data were analyzed using multilevel regression. Although EGM gambling was not associated with significant changes in PEP across participants, we found that self-reported flow states were associated with significant decreases in PEP during the first five minutes of EGM use. Thus, participants who experienced flow showed a greater sympathetic nervous system response to the onset of gambling. Though these effects were consistent in experiments 1 and 2, in experiment 3 the effect was inverted during the same time window. We conclude that flow during EGM gambling appears to be associated with early changes in sympathetic nervous system activity, but stress that more research is needed to characterize boundary conditions and moderating factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 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".