Emotions, Moods, and Videogames: Exploring the Affective Experience of Gamers
Bibliographic record
Abstract
This dissertation had as a purpose to explore the affective experience of people who play videogames. Across three studies, participants’ experiences were examined at two levels of inquiry: more specific in-game emotional experiences, and more general relationships between videogame playing behaviours, daily moods, and psychosocial functioning. Study 1 (n = 595) cross-sectionally investigated the relationship between emotion regulation flexibility – the ability to read a situation, implement an emotion regulation strategy appropriate strategy, and adapt that strategy if it proves to be ineffective – in relation to dysregulated gaming severity. The findings suggested feedback responsiveness may play the largest protective role in buffering against problematic videogame playing behaviours. Study 2 sought to qualitatively explore gamers’ experiences of tilt (i.e., a cyclical phenomenon where performance failures cause negative emotions leading to even poorer performance) and ragequitting (i.e., an abrupt early removal of oneself from competition fueled by frustration). In semi-structured interviews, participants (n = 30) described tilt as cycle wherein performance failures prompt negative emotions which cloud judgements leading to poorer decisions and more performance failures. They also described how cycles of tilt can lead to ragequitting if unresolved. Study 3 examined the relationship between daily moods and videogame playing behaviours using experience sampling (n = 97; nassessments = 3032). Though participants’ moods tended to improve from pre- to post-videogame timepoints on average, and people higher in trait dysregulated gaming severity tended to report more mood-repair motives for gaming, moods at pre-videogame timepoints were not found to be significantly lower than at other non-gaming related timepoints. Taken together, the research that comprises this dissertation contributes to a better understanding of the psychology of those who play videogames and the relationships between gaming, moods, and emotions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".