Characterizing and Modeling the Effects of Local Latency on Game Performance and Experience
Bibliographic record
Abstract
Studies have shown that local latency -- delays between an input action and the resulting change to the display -- can negatively affect gameplay. However, these studies report several different thresholds (from 50 to 500ms) where local latency causes problems, and there is still little understanding of the relationship between the temporal requirements of a game and the effects of local latency. To help designers determine how lag will affect their games, we designed two studies that focus on specific atoms of interaction in simple games, and characterize both gameplay performance and experience under increasing local latency. We use the data from the first study to develop a simple predictive model of performance based on the amount of lag and the speed of the game. We used the model to predict performance in the second study, and our predictions were accurate, particularly for faster games and higher levels of lag. Our work provides a new analysis of how local latency affects games, which explains why some game atoms will be sensitive to latency, and which can allow predictive modeling of when playability will suffer due to lag, even without extensive playtesting.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".