Playing with Emotions: A Systematic Review Examining Emotions and Emotion Regulation in Esports Performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The massive growth of esports has vitalized the need to study human performance in competitive video gaming. The pressure of competitive play elicits a range of emotional experiences, which can affect players during and beyond a gaming session. In this work, we review the state of the literature concerning the role emotions play in esports performance as well as highlight coping strategies players use to regulate emotions during competitive play. We review the findings of N=32 peer-reviewed articles pertaining to emotions and esports, finding that the emotional experiences elicited by competitive play affect esports performance. In response, players attempt to regulate their emotions to maintain performance; however, efforts to do so vary, as they currently lack effective coping strategies. Lastly, we review the potential of technical interventions in esports training for improving emotion regulation among players. Our findings support knowledge development in esports, and present avenues towards promoting the emotional wellbeing of competitive gamers.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it