Winning isn’t everything: The impact of optimally challenging Smartphone games on flow, game preference and individuals gaming to escape aversive bored states
Bibliographic record
Abstract
Recently there has been concern surrounding the relation between flow and the development of problematic gaming among players who game to escape noxious mood states. There is a scarcity of research examining how this relation might extend to smartphone games. Here we assessed whether gaming to escape is characterized by heightened boredom proneness and depressive symptomology in everyday life in addition to negative consequences related to smartphone gaming. We also assessed whether escape players preferentially experience flow, positive affect and effectively less boredom than non-escape players. We also measured whether escape players had enhanced arousal and urge during actual gameplay. To compare the in-game experiences between escape and non-escape players, we characterized gaming to escape as the upper tercile of all escape scores in our sample (n = 20), and non-escape players as the lower tercile of escape scores (n = 20). As expected we showed that gaming to escape was associated with boredom proneness in everyday life, which was in itself correlated with depressive symptomology. During gameplay, those who game to escape boredom demonstrated heightened flow and positive affect compared to non-escape players. State boredom scores however were comparable between the two groups. Importantly, those who game to escape demonstrated greater arousal and urge-to-play following gameplay than non-escape players – but only for optimally challenging games. Findings converge to suggest that bored escape players may seek flow and its consequent positive affect for relief from states of hypo-arousal and monotony through optimally challenging games.
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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 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".