Set Players to Stun: Inducing Basic Psychological Need Frustration in a Casual Video Game
Bibliographic record
Abstract
Abstract This study investigates the concept of psychological need frustration within the context of video games. We explore the potential of discrete in-game events, specifically the usage of widely popular ‘stun’ mechanics, to induce feelings of need frustration in players. We designed, developed and experimentally tested a bespoke video game with four conditions: No Stuns, Avoidable Stuns, Unavoidable Stuns and Layered Stuns (a combination of avoidable and unavoidable stuns). Our findings show that Unavoidable Stuns lead to statistically significantly greater autonomy need frustration. This finding has important implications for games research, as psychological need frustration is linked to negative effects on player engagement and wellbeing. Our results also highlight that a variety of stun mechanics can undermine psychological need satisfaction. Taken together, this work makes a meaningful contribution to HCI and games literature, showcasing that game mechanics can be designed in a way that undermines psychological needs. Research Highlights A study was conducted to investigate psychological needs frustration in a video game. Autonomy need frustration scores were higher in the presence of unavoidable stuns This study provides early evidence that psychological need frustration can be induced
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".