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Record W4403443507 · doi:10.1145/3677070

Exploring the Role of Action Mechanics in Game-Based Stress Recovery

2024· article· en· W4403443507 on OpenAlexaff
Rafael Alves Heinze, Regan L. Mandryk, Madison Klarkowski

Bibliographic record

VenueProceedings of the ACM on Human-Computer Interaction · 2024
Typearticle
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsUniversity of VictoriaUniversity of Saskatchewan
Fundersnot available
KeywordsAction (physics)Stress (linguistics)Cognitive scienceMechanicsPsychologyEpistemologyComputer sciencePhilosophyPhysicsLinguistics

Abstract

fetched live from OpenAlex

Digital games can provide effective recovery from stress, with players turning to a variety of genres-including those with game mechanics that can be considered stressors themselves, i.e., action mechanics. We examine whether action mechanics undermine or facilitate game-based recovery by exposing participants (n=60) to a stress induction, and then having them play a roguelike game in one of three conditions: Combat-Required, Combat-Optional, and Combat-Free. We assess experience through self-report and observed physiological responses. Our findings suggest that gameplay-irrespective of action mechanic intensity-supports the recovery process through the pathways of experienced psychological detachment, control, dominance, and pleasure. Additionally, action mechanics were perceived by participants as particularly promising for mastery recovery experiences-but undermine the recovery pathways of relaxation, as corroborated by experienced arousal and subjective stress. Physiological measures corroborate subjective self-report. We contend that video games featuring action mechanics represent a promising strategy for stress recovery, and may be particularly beneficial in the re-assertion of mastery.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.825
Threshold uncertainty score0.355

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.276
GPT teacher head0.442
Teacher spread0.166 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2024
Admission routes1
Has abstractyes

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