How Low Should We Go? Understanding the Perception of Latency While Inking
Bibliographic record
Abstract
Recent advances in hardware have enabled researchers to study the perception of latency. Thus far, latency research has utilized simple touch and stylus-based tasks that do not represent inking activities found in the real world. In this work, we report on two studies that utilized writing and sketching tasks to understand the limits of human perception. Our studies revealed that latency perception while inking is worse (~50 milliseconds) than perception while performing non-inking tasks reported previously (~2–7 milliseconds). We also determined that latency perception is not based on the distance from the stylus’ nib to the ink, but rather on the presence of a visual referent such as the hand or stylus. The prior and current work has informed the Latency Perception Model, a framework upon which latency knowledge and the underlying mechanisms of perception can be understood and further explored.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.052 | 0.001 |
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; both teacher heads agree on what is shown here.
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".