Hexed by Pressure: How Action-State Orientation Explains Propensity to Choke in Super Hexagon
Bibliographic record
Abstract
Many videogames require players to perform under pressure; however, not all players respond equivalently to pressure: why are some players more likely to tilt (lose control during play) or choke (perform poorly relative to their ability) whereas others seem to thrive under pressure? Given the importance of both emotion regulation in tilting and optimal arousal in achieving optimal performance, we propose that individual differences in ability to down-regulate negative affect under stress--known as failure-related action-state orientation (fASO)--could explain propensity to choke under pressure. We conducted an online between-subjects experiment (N=144) in which we measured baseline performance in Super Hexagon (day 1), then exposed participants to a stress induction (i.e., PASAT-C) or had them play a low-intensity bubble-popping game before playing again (day 2). Under stress, players higher in fASO performed better relative to their baseline in terms of average time alive and stalled progress; whereas, without stress, players lower in fASO performed better on both measures. Traits reflective of proposed explanations for choking (i.e., reinvestment, attentional control) did not influence performance under pressure. The ability to down-regulate negative affect and overcome setbacks is a useful theoretical lens to explore why some players choke under pressure, whereas others thrive.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".