More than a feeling: Measurement of sonic user experience and psychophysiology in a first-person shooter game
Bibliographic record
Abstract
The combination of psychophysiological and psychometric methods provides reliable measurements of affective user experience (UX). Understanding the nature of affective UX in interactive entertainment, especially with a focus on sonic stimuli, is an ongoing research challenge. In the empirical study reported here, participants played a fast-paced, immersive first-person shooter (FPS) game modification, in which sound (on/off) and music (on/off) were manipulated, while psychophysiological recordings of electrodermal activity (EDA) and facial muscle activity (EMG) were recorded in addition to a Game Experience Questionnaire (GEQ). Results indicate no main or interaction effects of sound or music on EMG and EDA. However, a significant main effect of sound on all GEQ dimensions (immersion, tension, competence, flow, negative affect, positive affect, and challenge) was found. In addition, an interaction effect of sound and music on GEQ dimension tension and flow indicates an important relationship of sound and music for gameplay experience. Additionally, we report the results of a correlation between GEQ dimensions and EMG/EDA activity. We conclude subjective measures could advance our understanding of sonic UX in digital games, while affective tonic (i.e., long-term psychophysiological) measures of sonic UX in digital games did not yield statistically significant results. One approach for future affective psychophysiological measures of sonic UX could be experiments investigating phasic (i.e., event-related) psychophysiological measures of sonic gameplay elements in digital games. This could improve our general understanding of sonic UX beyond affective gaming evaluation.
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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 | 0.000 | 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 | 0.000 | 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) | 0.002 | 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".