Dark Flow, Depression and Multiline Slot Machine Play
Bibliographic record
Abstract
Multiline slot machines allow for a unique outcome type referred to as a loss disguised as a win (LDW). An LDW occurs when a player gains credits on a spin, but fewer credits than their original wager (e.g. 15-cent gain on a 20-cent wager). These outcomes alter the gambler's play experience by providing frequent, albeit smaller, credit gains throughout a playing session that are in fact net losses. Despite this negative overall value, research has shown that players physiologically respond to LDWs as if they are wins, not losses. These outcomes also create a "smoother" experience for the player that seems to promote a highly absorbing, flow-like state that we have called "dark flow". Past research has indicated that there may be a relationship between problem gambling status and dark flow, as well as between dark flow, depression, and gambling expectancies. In this study, we sought to further understand these relationships, while examining the influence of LDWs on game preference in the context of single versus multiline slots play. We used a realistic slot machine simulator equipped with a force transducer to measure how hard players pressed the spin button following different outcomes. This measure of arousal showed that LDWs were treated similarly to small wins. Participants overwhelmingly preferred the multiline game and experienced more positive affect while playing it, compared to the single-line game. Problem gambling severity index scores were related to dark flow in both games, but this relationship was stronger for the multiline game. Additionally, depression symptomatology and dark flow were strongly correlated in the multiline game, with significant relationships between depression and gambling expectancy, and gambling expectancy and dark flow ratings also emerging.
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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".