Computer-based Deceptive Game Design in Commercial Virtual Reality Games: A Preliminary Investigation
Bibliographic record
Abstract
As Virtual Reality (VR) games become more popular, it is crucial to understand how deceptive game design patterns manifest and impact player experiences in this emerging medium. Our study sheds light on the presence and effects of manipulative design techniques in commercial VR games compared to a traditional computer game. We conducted an autoethnography study and developed a VR Deceptive Game Design Assessment Guide based on a critical literature review. Using our guide, we compared how deceptive patterns in a popular computer game are different from two commercial VR titles. While VR’s technological constraints, such as battery life and limited temporal manipulation, VR’s unique sensory immersion amplified the impact of emotional and sensory deception. Current VR games showed similar but evolved forms of deceptive design compared to the computer game. We forecast more sophisticated player manipulation as VR technology advances. Our findings contribute to a better understanding of how deceptive game design persists and escalates in VR. We highlight the urgent need to develop ethical design guidelines for the rapidly advancing VR games industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".