Quantifying and Mitigating the Negative Effects of Local Latencies on Aiming in 3D Shooter Games
Bibliographic record
Abstract
Real-time games such as first-person shooters (FPS) are sensitive to even small amounts of lag. The effects of net-work latency have been studied, but less is known about local latency, the lag caused by input devices and displays. While local latency is important to gamers, we do not know how it affects aiming performance and whether we can reduce its negative effects. To explore these issues, we tested local latency in a variety of real-world gaming scenarios and carried out a controlled study focusing on targeting and tracking activities in an FPS game with varying degrees of local latency. In addition, we tested the ability of a lag compensation technique (based on aim assistance) to mitigate the negative effects. Our study found local latencies in the real-world range from 23 to 243 ms which cause significant and substantial degradation in performance (even for latencies as low as 41 ms). The study also showed that our compensation technique worked extremely well, reducing the problems caused by lag in the case of targeting, and removing the problem altogether in the case of tracking. Our work shows that local latency is a real and substantial problem -- but games can mitigate the problem with appropriate compensation methods.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".