Target following performance in the presence of latency, jitter, and signal dropouts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper we describe how human target following performance changes in the presence of latency, latency variations, and signal dropouts. Many modern games and game systems allow for networked, remote participation. In such networks latency, variations and dropouts are commonly encountered factors. Our user study reveals that all of the investigated factors decrease tracking performance. The errors increase very quickly for latencies of over 110 ms, for latency jitters above 40 ms, and for dropout rates of more than 10 %. The effects of target velocity on errors are close to linear, and transverse errors are smaller than longitudinal ones. The results can be used to better quantify the effects of different factors on moving objects in interactive scenarios. They also aid the designers in selecting target sizes and velocities, as well as in adjusting smoothing, prediction and compensation algorithms.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it