Effects of Audiovisual Cues on Game Immersion during Simulated Slot Machine Gambling
Bibliographic record
Abstract
Modern slot machines can create immersive experiences for gamblers. Design features, including audiovisual cues, may influence these experiences, potentially interacting with personal risk factors for disordered gambling. According to the incentive salience hypothesis of addiction, reward-paired audiovisual cues strongly motivate behavior. This study manipulated audiovisual cues during simulated slot machine gambling to test the effects of varying intensities on self-reported immersion. Undergraduate students (n = 156) played a realistic slot machine simulation within an authentic cabinet. They experienced three intensities of audiovisual cues: Minus, Intermediate, and Plus. Participants completed self-report questionnaires, including a retrospective game immersion questionnaire. The pre-registered hypothesis was partially supported: the Intermediate cue condition was associated with greater game immersion than the Minus condition (p < 0.05). Exploratory models revealed higher total scores on the depression, anxiety, and stress scale predicted greater immersion in the Intermediate condition (p < 0.001). A Cue x Gender interaction was driven by greater immersion in the Intermediate cue condition among women, but not men (p < 0.05). Problem gambling severity predicted greater immersion across all models (p < 0.001). Audiovisual cues influenced immersion in slot machine gambling, supporting regulatory attention to audiovisual features as an engineered product aspect. Contrary to predictions, immersion was highest at the intermediate not maximal level of stimulation. Gender and affective symptoms also impacted immersion, indicating personal risk factors in susceptibility.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".