Assessing target acquisition and tracking performance for complex moving targets in the presence of latency and jitter
Bibliographic record
Abstract
Many modern games and game systems allow for networked remote participation. In such networks latency variability is a commonly encountered factor, but there is still little information available to designers about how human performance changes in the presence of delay. To add to our understanding of performance thresholds for mouse-based tasks that are common in real-time games, we carried out a study of human target acquisition and target tracking in the presence of latency and jitter (variance in latency), for various target velocities and trajectories. Our study indicates critical thresholds at which human performance decreases in the presence of delay. Target acquisition accuracy drops very quickly for latencies over 50 ms and for high velocities. Tracking error, however, is only slightly affected by latency, with deterioration starting at around 110 ms. The effects of latency and target velocity on errors are close to linear, and transverse error is usually smaller than longitudinal error. These results help to quantify the effects of delay on closely-coupled interactive tasks in networked games and real-time groupware systems. They also aid designers in determining when it is critical to improve system parameters and when to apply prediction and delay-compensation algorithms to improve quality of interaction.
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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 | 0.003 |
| 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 | 0.000 | 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".