The experience of videogame tilt, moods, and well-being in daily gamers: An ecological momentary assessment.
Bibliographic record
Abstract
People often play videogames to unwind from stress or to improve their skills and outperform opposing players. However, like in sports, underperforming can give rise to profoundly negative affective experiences. Gamers describe the phenomenon of tilt as a compounding cycle of performance failures and negative emotions. Playing videogames for mood-repair and competitive-gratification motives has been found to predict tilt frequency cross-sectionally (Bonk & Tamminen, 2022), but no research has yet examined gamers’ day-to-day experiences of tilt and emotions. The purpose of this study was to examine the motivational antecedents, emotional correlates, and psychosocial costs of experiencing tilt in gamers’ daily lives using ecological momentary assessment. Gamers (N = 82) were recruited online and completed up to 7 daily surveys for 7 days (nresponses = 749). Generalized estimating equations with exchangeable correlation structures were used to account for the clustered and non-normal nature of the data. Playing videogames for competitive gratification motives was found to be a stronger predictor of tilt intensity (b = .20, p < .001) than mood management motives (b = .11, p = .017). Of all assessed in-game emotions, tilt intensity was most strongly associated with experiencing anger (b = .69, p < .001). Higher tilt intensity in a gaming session also predicted more negative post-game mood valence (b = -.12, p = .008) and lower post-game general well-being (b = -.005, p = .015). Though playing videogames is generally enjoyable, these findings highlight the potential psychosocial consequences when frustration and failures interact giving rise to tilt.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".