Investigating the Effect of Information Display Type on Performance and Experience in Virtual Reality First-Person Shooter Video Games
Bibliographic record
Abstract
For decades, the first-person shooter (FPS) has remained one of the most popular genres in the video game industry.Since the inception of the FPS genre in the 1990s, many archetypal design strategies have emerged for conveying important information to players during gameplay, including their aiming trajectory, remaining ammunition, and current health level.These traditional design strategies are well-understood by developers and players alike.However, the arrival and rising popularity of consumer-grade virtual reality (VR) gaming platforms has introduced an entirely new way to experience interactive entertainment, which necessitates a reevaluation of traditional information display strategies for all applications, including FPS games.To this end, I conducted two studies to investigate how different information display strategies may impact key objective user performance metrics and subjective experience quality in VR FPS games.My first study compared a selection of aiming, ammo, and health information displays used in isolation within separate gameplay scenarios.My second study compared three different combined information displays each consisting of an aiming display, ammo display, and health display during a realistic FPS gameplay scenario.The first study revealed major performance gains to aiming tasks when participants were presented with an on-screen aiming display, as well as a broad preference for information displays which exist as physical elements within the game world and information display elements which are closely co-located within the player's focal space.The second study revealed that objective gameplay performance was largely unaffected by the information display method, but major differences in reported subjective experience quality were identified.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".